Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study

Carlos Roberto Ribeiro Carvalho; Celina Almeida Lamas; Rodrigo Caruso Chate; João Marcos Salge; Marcio Valente Yamada Sawamura; André L P de Albuquerque; Carlos Toufen Junior; Daniel Mario Lima; Michelle Louvaes Garcia; Paula Gobi Scudeller; Cesar Higa Nomura; Marco Antonio Gutierrez; Bruno Guedes Baldi; HCFMUSP Covid-19 Study Group

doi:10.1371/journal.pone.0280567

. 2023 Jan 20;18(1):e0280567. doi: 10.1371/journal.pone.0280567

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study

Carlos Roberto Ribeiro Carvalho ^1,^*, Celina Almeida Lamas ¹, Rodrigo Caruso Chate ², João Marcos Salge ¹, Marcio Valente Yamada Sawamura ², André L P de Albuquerque ¹, Carlos Toufen Junior ¹, Daniel Mario Lima ³, Michelle Louvaes Garcia ¹, Paula Gobi Scudeller ¹, Cesar Higa Nomura ², Marco Antonio Gutierrez ³, Bruno Guedes Baldi ¹; HCFMUSP Covid-19 Study Group^¶

Editor: Yu Ru Kou⁴

¹Pulmonary Division, Heart Institute (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), Sao Paulo, SP, Brazil

²Radiology Institute (InRad), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), Sao Paulo, SP, Brazil

³Informatics Division, Heart Institute (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), Sao Paulo, SP, Brazil

⁴Hualien Tzu Chi Hospital, TAIWAN

Competing Interests: The authors have declared that no competing interests exist.

¶ Membership of the HCFMUSP Covid-19 Study Group is provided in the Acknowledgments.

^✉

* E-mail: carlos.carvalho@hc.fm.usp.br

Roles

Carlos Roberto Ribeiro Carvalho: Conceptualization, Data curation, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – review & editing

Celina Almeida Lamas: Data curation, Formal analysis, Visualization, Writing – original draft

Rodrigo Caruso Chate: Data curation, Investigation, Validation, Visualization, Writing – review & editing

João Marcos Salge: Data curation, Investigation, Validation, Visualization, Writing – review & editing

Marcio Valente Yamada Sawamura: Data curation, Investigation, Validation, Visualization, Writing – review & editing

André L P de Albuquerque: Data curation, Validation, Visualization, Writing – review & editing

Carlos Toufen Junior: Validation, Visualization, Writing – review & editing

Daniel Mario Lima: Formal analysis

Michelle Louvaes Garcia: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Paula Gobi Scudeller: Conceptualization, Investigation, Project administration, Supervision, Writing – review & editing

Cesar Higa Nomura: Validation, Visualization, Writing – review & editing

Marco Antonio Gutierrez: Data curation, Formal analysis, Funding acquisition, Methodology, Visualization, Writing – review & editing

Bruno Guedes Baldi: Validation, Visualization, Writing – review & editing

Yu Ru Kou: Editor

PMCID: PMC9858876 PMID: 36662879

Abstract

Background

Coronavirus disease (COVID-19) survivors exhibit multisystemic alterations after hospitalization. Little is known about long-term imaging and pulmonary function of hospitalized patients intensive care unit (ICU) who survive COVID-19. We aimed to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and identify risk factors associated with chest computed tomography (CT) lesion severity.

Methods

A prospective cohort study of COVID-19 patients admitted to a tertiary hospital ICU in Brazil (March-August/2020), and followed-up six-twelve months after hospital admission. Initial assessment included: modified Medical Research Council dyspnea scale, SpO₂ evaluation, forced vital capacity, and chest X-Ray. Patients with alterations in at least one of these examinations were eligible for CT and pulmonary function tests (PFTs) approximately 16 months after hospital admission. Primary outcome: CT lesion severity (fibrotic-like or non-fibrotic-like). Baseline clinical variables were used to build a machine learning model (ML) to predict the severity of CT lesion.

Results

In total, 326 patients (72%) were eligible for CT and PFTs. COVID-19 CT lesions were identified in 81.8% of patients, and half of them showed mild restrictive lung impairment and impaired lung diffusion capacity. Patients with COVID-19 CT findings were stratified into two categories of lesion severity: non-fibrotic-like (50.8%-ground-glass opacities/reticulations) and fibrotic-like (49.2%-traction bronchiectasis/architectural distortion). No association between CT feature severity and altered lung diffusion or functional restrictive/obstructive patterns was found. The ML detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity(sensitivity,0.78±0.02;specificity,0.79±0.01;F1-score,0.78±0.02;positive predictive rate,0.78±0.02; accuracy,0.78±0.02; and area under the curve,0.83±0.01).

Conclusion

ICU hospitalization due to COVID-19 led to respiratory system alterations six-twelve months after hospital admission. Male sex and critical disease acute phase, characterized by a longer ICU and IMV period, and need for tracheostomy and vasoactive drugs, were risk factors for severe CT lesions six-twelve months after hospital admission.

1. Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide since the end of 2019. SARS-CoV-2 infection triggered the Coronavirus Disease 2019 (COVID-19) pandemic, which has caused more than six million deaths globally to date [1]. Scientists worldwide have made efforts to clarify the clinical consequences and prognosis of the acute phase of COVID-19 [2–4]. However, studies assessing the long-term consequences of this disease, especially considering the recovery of critically ill patients in the intensive care unit (ICU), are still scarce. The World Health Organization (WHO) has drawn attention to long COVID, which is the persistence of disease-related symptoms for more than three months after recovery [5–7]. Frequent manifestations of long COVID include dyspnea, fatigue, fever, myalgia, headache, and fibrotic-like lung abnormalities [5, 7–9].

A recent cohort study by the United States Department of Veterans Affairs showed that hospitalized ICU patients had a higher risk of death and pulmonary disease than non-ICU patients six months after COVID-19 infection [10]. Our previous study showed that 76.5% of patients who had recovered from COVID-19 still had at least one abnormality on chest computed tomography (CT) six months after hospital admission, which was more frequent in ICU than in ward patients [11]. Thus far, most cohort studies on recovered ICU COVID-19 patients have focused on long-term symptoms than pulmonary assessments. In view of this, a recent Dutch cohort study on 246 patients one year after COVID-19 ICU treatment demonstrated that 74% of patients still reported physical symptoms, 26% reported mental symptoms, and 16% reported cognitive symptoms. Moreover, a larger study that evaluated 390 patients six months after recovery from COVID-19 in China was restricted to only 4% of ICU patients [12]. Most of those ICU patients required invasive mechanical ventilation (IMV), had more comorbidities, and worse lung function, therefore, further studies are needed for a better insight.

It is well known that ICU hospitalization has inherent risk factors that could lead to future problems even in the non-COVID-19 context [13]. A study of survivors of other diseases caused by viruses of the SARS family showed that pulmonary sequelae can persist for up to 15 years [14], in addition to being correlated with a longer duration and severity of the acute phase of the disease, and consequently, the need for ICU hospitalization [15]. The post-recovery effects included reduced diffusion capacity for carbon monoxide (DLCO), restrictive pattern in pulmonary function tests (PFTs), and ground glass opacities on computed tomography (CT) scan [16, 17].

These previous results reinforce the well-known assumption that ICU hospitalization represents a risk factor for development of lung abnormalities in the long term and provide clues that ICU hospitalization due to COVID-19 could lead to chronic lung CT damage, which should be investigated. Thus, it is essential to provide insights regarding ICU hospitalization that could influence the persistence of respiratory system alterations, considering that the COVID-19 pandemic is ongoing and the possibility of ICU hospitalization still exists. Therefore, we aimed to evaluate the respiratory outcomes of a consecutive large cohort of patients admitted to ICUs for COVID-19, focusing on the assessment of lung imaging and PFTs, and to determine the risk factors in the acute phase of the disease that could be predictors of chronic lung injury.

2. Materials and methods

2.1. Study design and participants

This was a prospective cohort study of COVID-19 patients admitted to the ICUs of Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), São Paulo, Brazil, from March 30 to August 31^st, 2020. HCFMUSP was a reference center for the treatment of critically ill COVID-19 patients in Brazil, with 300 ICU beds, and had adopted an institutional treatment protocol. The protocol included a protective ventilation strategy (tidal volume < 8 ml/kg and plateau pressure < 30 cm H₂O), specific pharmacological treatment, thrombosis prophylaxis, and sedation [18]. Six to twelve months after hospital admission, patients aged 18 years who had RT-PCR-confirmed SARS-CoV-2 infection during hospitalization were consecutively invited to participate in the study.

This study was part of a large protocol previously described [19] and was approved by the Research Ethics Committee of our institution (No. 31942020.0.000.0068). Written informed consent was obtained from all the patients. HCFMUSP electronic medical records were assessed for the retrospective collection of patients’ hospitalization clinical data, during the acute phase of the disease, such as comorbidities, symptoms, smoking history, length of ICU stay, and IMV parameters during the first 24 h of mechanical ventilation. All clinical data were cataloged in a structured form using REDCap software (https://www.redcapbrasil.com.br/).

2.2. Follow-up protocol

The follow-up visit procedures have been previously described [19]. Patients underwent a face-to-face general evaluation during the follow-up that included anthropometric examination, and an initial pulmonary assessment, including the modified Medical Research Council (mMRC) dyspnea scale, oxygen saturation (SpO₂) measured by pulse oximetry at rest and after the 1-min sit and stand test, spirometry, and chest X-ray (CXR) [19]. The protocols used to perform these tests have been described previously [11, 19]. The results of CXR images were evaluated as: normal/with findings not related to COVID-19 (cardiomegaly and pulmonary nodules, for instance) or findings probably related to COVID-19 (bilateral linear and/or reticular opacities, especially peripheral opacities) [11]. Two thoracic radiologists who were blinded to the particulars of the study evaluated the chest CXR images independently. Disagreements were resolved through consensus.

Based on the general evaluation results, patients who met at least one of the following criteria were enrolled to undergo chest CT and PFTs during a second complementary face-to-face evaluation: (a) mMRC≥2; (b) resting SpO₂ ≤ 90% and/or a decrease in SpO₂ of ≥ 4% during the 1-min sit and stand test; (c) CXR findings probably related to COVID-19; and (d) forced vital capacity (FVC) < lower limit of normal (LLN) [11, 19].

The protocol used to perform the chest CT was previously described [11, 19]. Two thoracic radiologists who were blinded to the particulars of the study evaluated the chest CT images independently. Disagreements were resolved through consensus. Patients with COVID-19 CT findings were stratified into two categories according to lesion severity: with fibrotic-like changes (presence of traction bronchiectasis and architectural distortion) and without fibrotic-like changes (ground-glass opacities and reticulations) (modified from Han et al. [20]. The extent of lung involvement in these groups was quantified according to the following scores for each pulmonary lobe: 0, none; 1, <5%; 2, 5–25%; 3, 26–50%; 4, 51–75%; and 5, >75%. The total score was the sum of the scores of the five lobes, ranging from 0 to 25 [11].

PFTs were performed according to the recommendations of the American Thoracic Society [21]. The following parameters were determined: total lung capacity (TLC), forced vital capacity (FVC), forced expiratory volume in 1 s (FEV₁), FEV₁/FVC ratio and DLCO. A restrictive pattern, an obstructive pattern, and impaired diffusion capacity were defined as TLC < LLN, FEV₁/FVC ratio < LLN, and DLCO < LLN, respectively [22–24].

2.3. Data analysis

The D’agostino-Pearson test was used to determine the variables normality. Normally and nonnormally distributed data were expressed as the mean and standard deviations or median and interquartile range, respectively. The Student’s t-test and MannWhitney U test were used to compare normally and non-normally distributed continuous variables, respectively. Numbers with percentages were used to describe the categorical variables, and were compared using the X2 test. The following software was used to perform the analysis: Excel 2016; Python 3.8.11; extension packages: Pandas 1.0.1; Numpy 1.19.5; Scipy 1.5.4; Scikit-Learn 0.24.0.

A Machine Learning model (ML) was developed to predict lesion severity after six to twelve months from ICU admission for COVID-19, based on baseline clinical variables. The ML prediction model was based on XGBoost, which makes use of a type of gradient boosting, where multiple decision tree models are trained in succession, each tending to improve performance. The variables collected at baseline with p<0.05 between two categories of CT lesion severity (without fibrotic-like changes and with fibrotic-like changes) were used as input variable into the ML model: sex (%), ICU length of stay (days), tracheostomy (%), duration of IMV (days) and the use of vasoactive drug (%). The ML analysis outcome was the prediction of lesion severity on CT images six to twelve months from ICU admission for COVID-19, based on baseline clinical variables. A three-fold cross-validation strategy was adopted for the training and validation sets. The ML prediction model performance was evaluated by the following metrics: sensitivity, specificity, F1-score, positive predictive rate, accuracy and area under the curve (AUC). The ML model is detailed in the S1 Appendix.

3. Results

Among the 2,290 patients hospitalized in the ICUs, 1,032 met the inclusion criteria and were eligible for this study. Fig 1 shows the flow of study participant selection.

Of the 1,032 eligible patients, a total of 453 (43.9%) underwent face-to-face general evaluation (52.54% men; median age 56.8, IQR 44.8–65.4) and were included in the study. The median time between hospital admission and general evaluation was 219 days (IQR 206–291). Hypertension (58%) was the most frequent comorbidity, and 39.1% of the patients had history of smoking. The median duration of ICU hospitalization was 10 days (IQR 6–18). In addition, 64% of these patients required IMV, with a median Simplified Acute Physiology Score 3 (SAPS3) of 57 (IQR 47–68). (Table 1 and S1 Table)

Table 1. Baseline demographic and clinical characteristics of enrolled patients that underwent the general evaluation.

	All Patients (N = 453)	Patients with pulmonary involvement (N = 326)	Patients without pulmonary involvement (N = 127)	p-value
Demographics
Age, median (IQR, n)—yr	56.8 (44.8–65.4, n = 453)	58.4 (45.5–66.2, n = 326)	51.1 (42.3–63.4, n = 127)	0.004
Male, % (n/N)	52.5 (238/453)	49.7 (162/326)	59.8 (76/127)	0.059
BMI, median (IQR, n)—kg/m²	28 (24.4–34, n = 418)	28 (24–33.6, n = 304)	28 (24.8–34.6, n = 114)	0.268
Characteristics in ICU
ICU lenght of stay, median (IQR, n)—d	10 (6–18, n = 453)	11 (6–19.7, n = 326)	8 (4–14, n = 127)	<0.001
SAPS 3 at admission, median (IQR, n)	57 (47–68, n = 427)	56 (47–68, n = 312)	58 (46.5–68, n = 115)	0.466
D Dimer 72h, median (IQR, n)—ng/ml	1555 (846.7–4328.2, n = 428)	1595 (890–4608, n = 307)	1403 (797–3799, n = 121)	0.083
CRP 72h, median (IQR, n)—mg/l	146.8 (73.6–252.3, n = 437)	144.8 (73.4–252.2, n = 313)	165 (87.2–252.4, n = 124)	0.137
Dialysis, % (n/N)	17.9 (81/453)	17.8 (58/326)	18.1 (23/127)	0.822
Tracheostomy, % (n/N)	7.5 (34/453)	9.51 (31/326)	2.4 (3/127)	0.009
VAD, % (n/N)	35.3 (160/453)	35.6 (116/326)	34.6 (44/127)	0.913
IMV during hospitalization, % (n/N)	64 (290/453)	65 (212/326)	61.4 (78/127)	0.514
Duration of IMV, median (IQR, n)—d	8 (5–13, n = 264)	9 (6–14, n = 190)	7 (5–11, n = 74)	0.004
IMV at first 24 hours
Tidal Volume, median (IQR, n) -ml/kg	6.1 (5.9–6.9, n = 250)	6.1 (5.9–6.8, n = 180)	6.1 (5.9–7, n = 70)	0.377
Minute Volume, median (IQR, n)—l/min	12 (10–15, n = 199)	12 (10–15, n = 147)	11 (10–14, n = 52)	0.006
Compliance, median (IQR, n)—mlcmH₂O−1	31.8 (24.5–41, n = 204)	30 (23.7–38.7, n = 150)	37.4 (28.2–44.3, n = 54)	0.013
Respiratory rate, median (IQR, n)—rpm	30 (26–35, n = 220)	30 (26–35, n = 161)	30 (27.5–35, n = 59)	0.273
PEEP, median (IQR, n)—cmH₂O	10 (8–12, n = 220)	10 (8–12, n = 161)	10 (8–10, n = 59)	0.134
Plateau pressure, median (IQR, n)—cmH₂O	22 (19–25.5, n = 199)	23 (20–26, n = 147)	21.5 (19–24, n = 52)	0.119
Driving pressure, median (IQR, n)—cmH₂O	12 (10–15, n = 227)	12 (10–14, n = 59)	12 (10–15, n = 168)	0.137
PaO₂/FIO_2, median (IQR, n)—%	160 (124–211.5, n = 220)	155 (120–210, n = 161)	172 (137.5–212.5, n = 59)	0.3
Compliance ≥ 40, % (n/N)	27.6 (63/228)	22.6 (38/168)	41.7 (25/60)	0.007
Plateau pressure ≥ 28, % (n/N)	12.4 (28/226)	13.2 (22/167)	10.2 (6/59)	0.65
Driving pressure ≥ 15, % (n/N)	26.9 (61/227)	28.6 (48/168)	22 (13/59)	0.395
PaO₂/FIO₂ ≤ 150, % (n/N)	47.4 (121/255)	50 (93/186)	40.6 (28/69)	0.205

Open in a new tab

Values are presented as median [IQR, n] or % (n/N). Abbreviations: BMI, body mass index; CRP, c-reactive protein; d, days; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; SAPS3, Simplified Acute Physiology Score 3; VAD, vasoactive drugs; yr, years.

The general evaluation results showed that 72% of the patients (326 of 453) had at least one sign of pulmonary involvement. CXRs with COVID-19-related features were observed in 40.6% of patients (174 of 428 patients); FVC under the LLN was observed in 38.9% of patients (167 of 429 patients); mMRC score greater than two was observed in 29.1% of patients (131 of 450 patients), and altered oximetry was observed in 10.4% of patients (44 of 424 patients). Patients with pulmonary involvement were older, had a longer duration of ICU hospitalization, and required IMV for a longer period than those without pulmonary involvement. In addition, patients with pulmonary involvement had a slightly higher minute volume and lower compliance in the first 24h of IMV than those without pulmonary involvement. (Table 1 and S1 Table)

The 326 patients who had at least one sign of pulmonary involvement on general evaluation were enrolled in a complementary evaluation with CT and PFTs (Fig 1). The median interval between general and complementary evaluations was 43 days (IQR 28–57). Chest CT and PFTs were completed in 74.5% and 65.6% of the selected patients, respectively. The demographic and clinical characteristics of the patients stratified by the completion or absence of chest CT or PFTs are described in S2 and S3 Tables, respectively.

The CT and PFT results are presented in Table 2. At least one abnormal CT feature was found in 85% of the patients. The most common abnormalities were ground-glass opacities, parenchymal bands, reticulations, traction bronchiectasis and architectural distortions. Functional results showed that half of the patients had a restrictive pattern and reduced DLCO, almost 40% had low FVC and FEV₁, and 5% had an obstructive pattern.

Table 2. Chest CT and PFTs among COVID-19 survivors at the follow-up.

Examinations	Results at the follow-up
Chest CT	Total (N = 243)
At least one abnormal CT feature, % (n)	85 (206)
Ground-glass opacities, % (n)	81 (196)
Parenchymal bands, % (n)	71 (173)
Reticulations, % (n)	61 (149)
Traction bronchiectasis, % (n)	38 (93)
Architectural distortion, % (n)	31 (75)
Bronchial wall thickening, % (n)	26 (63)
Perilobular opacities, % (n)	20 (49)
Mosaic attenuation pattern, % (n)	20 (48)
Consolidations, % (n)	1.2 (3)
Pneumatocele, % (n)	1.2 (2)
Honeycombing, % (n)	0
PFTs	Total (N = 214)
FVC, mean ± SD—% of predicted	80.8 ± 14.5
FVC < LLN, % (n)	45 (97)
FEV_1, mean ± SD—% of predicted	85.5 ± (73–95)
FEV₁ < LLN, % (n)	34.1 (73)
FEV₁/FVC, median (IQR)	0.84 (0.79–0.87)
FEV₁/FVC < LLN, % (n) (Obstructive Pattern)	6 (13)
TLC, mean ± SD (n)—% of predicted	83.1 ± 12.3
TLC < LLN, % (n) (Restrictive Pattern)	50 (108)
VR, median (IQR, n)—% of predicted	79 (69–93, n = 213)
VR/CPT, median (IQR, n)	0.33 (0.28–0.39, n = 213)
DLCO, mean ± SD (n)—% of predicted	77.4 ± 19.1
DLCO < LLN, % (n)	48 (103)

Open in a new tab

Values are presented as % (n), median [IQR] or mean ± SD. Abbreviations: CT, computed tomography; DLCO, diffusion capacity for carbon monoxide; FEV₁, forced expiratory volume in 1 s; FVC, forced vital capacity; LLN, lower limit of normal; PFTs, pulmonary function tests; TLC, total lung capacity.

All patients who had both chest CT and lung function results (n = 214) were stratified into two categories according to the tomographic features: lung without COVID-19-related findings (normal chest CT or with CT abnormalities prior to COVID-19) (N = 39) and lung with COVID-19-related findings (N = 175) (Fig 1). Patients with lung CT with COVID-19-related findings had a higher SAPS3, increased need for IMV and tracheostomy during hospitalization, and a longer ICU hospitalization and IMV period than patients with lung CT without COVID-19-related findings. In addition, patients with lung abnormalities associated with COVID-19 had higher FVC, FEV1, and FEV1/FVC than patients with lung abnormalities without COVID-19-related findings. (S4 Table)

Patients who had lung CT with COVID-19-related findings were subdivided into two further groups according to lesion severity: without fibrotic-like changes and with fibrotic-like changes (Fig 1). Patients with fibrotic-like changes were older, had a longer duration of ICU hospitalization, more frequently needed IMV and tracheostomy, had a higher CT score, FVC, FEV_1, and FEV₁/FVC, and a lower VR than those without fibrotic-like changes. (Table 3 and S5 Table)

Table 3. Demographic and clinical characteristics of patients with completed chest CT and lung function results stratified by chest CT lesion severity.

	Without Fibrotic-Like Changes (N = 89)	With Fibrotic-Like Changes (N = 86)	p-value
Demographics
Age, median (IQR, n)—yr	52.7 (43–62.3, n = 89)	60.7 (52.5–67.2, n = 86)	0.016
Male, % (n/N)	49.4 (44/89)	51.2 (44/86)	0.88
BMI, median (IQR, n)—kg/m²	32.1 (28.3–35.6, n = 89)	31 (28.3–35.5, n = 86)	1
Characteristics in ICU
ICU lenght of stay, median (IQR, n)—d	9 (6–13, n = 89)	19.5 (11–35.5, n = 86)	<0.001
SAPS 3 at admission, mean ± SD (n)	58.6 ± 14.4 (n = 88)	60.2 ± 14.2 (n = 83)	0.717
D Dimer 72h, median (IQR, n)—ng/ml	1518 (846–4023, n = 85)	1929 (1073.5–4500.2, n = 80)	0.851
CRP 72h, median (IQR, n)—mg/l	163 (73.4–277.8, n = 86)	156.8 (82.6–240.3, n = 81)	1
Dialysis, % (n/N)	16.8 (15/89)	22.1 (19/86)	1
Tracheostomy, % (n/N)	3.4 (3/89)	18.6 (16/86)	0.003
VAD, % (n/N)	27 (24/89)	45.3 (39/86)	0.037
IMV during hospitalization, % (n/N)	64 (57/89)	81.4 (70/86)	0.034
Duration of IMV, median (IQR, n)—d	8 (6–11, n = 51)	12 (7–19, n = 61)	0.003
IMV at first 24 hours
Tidal Volume, median (IQR, n) -ml/kg	6.2 (6–6.8, n = 50)	6 (5.9–6.7, n = 58)	0.676
Minute Volume, median (IQR, n) -l/min	11.5 (9.3–13, n = 50)	10.5 (9–12, n = 61)	0.529
Compliance, median (IQR, n)—mlcmH₂O−1	30 (23.7–43.9, n = 47)	29.4 (24.6–38, n = 53)	1
Respiratory rate, median (IQR, n)—rpm	28.5 (24.2–35.7, n = 50)	30 (26–35, n = 61)	1
PEEP, mean ± SD (n)—cmH₂O	9.7 ± 2.2 (n = 49)	10.1 ± 2.2 (n = 61)	0.511
Plateau pressure, median (IQR, n)- cmH₂O	23.5 (18.7–26, n = 44)	22.5 (20–25, n = 54)	1
Driving pressure, median (IQR, n)—cmH₂O	13 (10–16, n = 45)	12 (10–14, n = 54)	1
PaO₂/FIO_2, median (IQR, n)—%	154 (108–217, n = 49)	142 (113–171, n = 61)	0.494
Compliance ≥ 40, % (n/N)	27.7 (13/47)	22.6 (12/53)	0.646
Plateau pressure ≥ 28, % (n/N)	13.6 (6/44)	11.1 (6/54)	0.763
Driving pressure ≥ 15, % (n/N)	35.6 (16/45)	24.1 (13/54)	0.806
PaO₂/FIO₂ ≤ 150, % (n/N)	49 (24/49)	55.7 (34/61)	0.565
Time between hospital admission and the CT follow-up, mean ± SD (n)—d	531.6 ± 10.3 (n = 89)	541.9 ± 10.3 (n = 86)	0.483
CT Score at the follow-up, mean ± SD (n)	7.2 ± 4.7 (n = 89)	14.25 ± 4.6 (n = 86)	<0.001
PFTs at the follow-up
FVC, median (IQR, n)—% of predicted	80 (73–90, n = 89)	86 (73–93, n = 86)	0.126
FVC < LLN, % (n/N)	50.5 (45/89)	34 (30/86)	0.046
FEV_1, mean ± SD (n)—% of predicted	83.6 ± 15,4 (n = 89)	88.4 ± 16 (n = 86)	0.040
FEV₁ < LLN, % (n/N)	36 (32/89)	25.6 (22/86)	0.144
FEV₁/FVC, mean ± SD (n)	0.81 (0.74–0.85, n = 89)	0.85 (0.81–0.87, n = 86)	0.003
FEV₁/FVC < LLN, % (n) (Obstructive Pattern)	3.4 (3/89)	2.3 (2/86)	1
TLC, median (IQR, n)—% of predicted	82 (76–89, n = 89)	82 (74–91, n = 86)	1
TLC < LLN, % (n/N) (Restrictive Pattern)	51.7 (46/89)	50 (43/86)	0.88
VR, mean ± SD (n)—% of predicted	80 (71–93, n = 89)	73.5 (64.2–87, n = 86)	0.006
VR/CPT, median (IQR, n)	33.8 ± 8.2 (n = 89)	32.8 ± 7.3 (n = 86)	0.414
DLCO, median (IQR, n)—% of predicted	81.5 (72.2–93, n = 86)	76.5 (62.7–85.2, n = 84)	0.057
DLCO < LLN, % (n/N)	43.5 (37/85)	52.4 (44/84)	0.848

Open in a new tab

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; DLCO, diffusion capacity for carbon monoxide; FEV₁, forced expiratory volume in 1 s; FVC, forced vital capacity; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; PFTs, pulmonary function tests; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; TLC, total lung capacity; VAD, vasoactive drugs; yr, years.

The ML model showed that the hospitalization variables selected (sex, ICU length of stay, tracheostomy, duration of IMV, and use of vasoactive drugs) could be predictors of CT lesion severity six to twelve months after ICU admission for COVID-19. The observed performance metrics of the ML prediction model, expressed in terms of mean ± standard deviation and 95% Confidence Interval (CI), were as follows: sensitivity, 0.78 ± 0.02 (95% CI [0.76, 0.79]); specificity, 0.79 ± 0.01 (95% CI [0.78, 0.8]); F1-score, 0.78 ± 0.02 (95% CI [0.76, 0.8]); positive predictive rate, 0.78 ± 0.02 (95% CI [0.76, 0.8]); accuracy, 0.78 ± 0.02 (95% CI [0.76, 0.8]); and AUC, 0.83 ± 0.01 (95% CI [0.82, 0.83]). (S1 Appendix)

4. Discussion

To our knowledge, this is the largest prospective cohort study of ICU hospitalized COVID-19 survivors to date that has focused on face-to-face assessment of pulmonary alterations. The use of a protective MV protocol to treat ICU hospitalized COVID-19 patients led to increased patient survival, allowing more patients to be followed up [18]. Our results show that 82% of patients remain with COVID-19-related lung CT sequelae for up to twelve months of follow-up. These long-term imaging alterations were associated with restrictive lung impairment and impaired diffusion capacity in 50% of enrolled patients. However, even in those with fibrotic-like changes, the impairment in PFTs was mild. Additionally, male sex, ICU length of stay, duration of IMV, and need for tracheostomy and vasoactive drugs during hospitalization were predictors of CT lesion severity six to twelve months after ICU admission for COVID-19.

The general evaluation showed that COVID-19 ICU patients with initial pulmonary involvement at follow-up had low respiratory compliance, despite no significant impairment in oxygenation. Worse respiratory mechanics in acute respiratory distress syndrome (ARDS) non-COVID were previously associated with impaired production of pulmonary collagen and independently associated with tomographic and physiological abnormalities after ARDS [25]. This suggests that the reduction in respiratory mechanics, in addition to indicating greater severity of acute lung injury, may increase the risk of pulmonary impairment, perhaps due to the high difficulty associated with adjusting protective ventilatory parameters in these patients. However, this difference was slight, and this hypothesis should be confirmed in future studies.

We found that half of the patients with lung CT with COVID-19 abnormalities had restrictive lung impairment and impaired diffusion capacity. Bellan et al. [26] evaluated a cohort of 200 patients one year after COVID-19 discharge. Their results showed that a high chest CT severity score is one of the most relevant factors associated with low DLCO, as this functional impairment may be secondary to the extent of the pulmonary parenchymal lesions. Additionally, these authors showed that the percentage of patients with impaired DLCO showed no functional improvement from 4 to 12 months of follow-up after COVID-19 hospital discharge, which reinforces the hypothesis of chronic functional impairment.

Follow-up studies have demonstrated that lower DLCO is more frequent than lower TLC in survivors of COVID or ARDS [12, 17, 26–28]. Hui et al. [28] evaluated the PFT outcomes of patients 1 year after hospitalization for ARDS showing a post-discharge frequency of 24% of patients with impaired DLCO and only 5% of patients with low TLC. Notably, they also observed that all PFT predicted values (%) were lower in ICU patients than in ward patients. The incidence of restrictive patterns was higher in our study, which could be a consequence of the critical acute phase of the disease in our population, considering that the other studies evaluated a mixed population of non-ICU and few ICU patients.

A recent meta-analysis of parenchymal lung abnormalities following hospitalization for COVID-19 assessed follow-up studies within twelve months and showed that fibrotic sequelae were estimated in 29% of patients, which is consistent with our findings [29]. Ground-glass opacities and reticulation were considered non-fibrotic lesions because of the belief that resolution during follow-up was possible, whereas traction bronchiectasis and/or architectural distortion were classified as fibrotic-like changes since they are typically considered more definitive changes [20, 26]. Our findings demonstrated that CT lesion severity did not point to a higher functional limitation, since all CT patterns were associated with mild functional impairment. Fortini et al. [30] evaluated a cohort of patients one year after COVID-19 discharge and, showed that functional improvement was not associated with complete tomographic resolution. In addition, Han et al. [31] did not observe differences in PFTs in a cohort of Chinese patients stratified by the presence or absence of fibrotic interstitial lung abnormalities at the one year COVID-19 follow-up. These results indicate that structural recovery may be slower than functional improvement after COVID-19. Therefore, anatomical sequelae seem to have little functional repercussions, indicating that respiratory evaluation after COVID-19 infection should focus more on functional and clinical evaluation rather than imaging.

Our study also revealed that patients with fibrotic-like changes had a more severe acute phase characterized by longer hospital stay and greater need for IMV and tracheostomy than patients without fibrotic-like changes up to twelve months after COVID-19 hospitalization. Thus, we used an ML to identify the predictors of CT lesion severity at follow-up. Our results showed that male sex, ICU length of stay, duration of IMV, need of tracheostomy, and use of vasoactive drugs are risk factors for CT lesion severity six to twelve months after COVID-19 ICU admission. Previous data reinforce our findings, demonstrating that male sex [32] and length of hospital stay [33] were associated with severe CT lesions one year after COVID-19 hospitalization. Invasive respiratory procedures, such as IMV and tracheostomy, have the inherent potential to induce structural and functional damages to the lung due to inadequate pressure or volume [34]. Additionally, both IMV and vasoactive drugs administered during ICU hospitalization have been associated with increased mortality and complications after discharge [35]. Other characteristics and factors identified during the acute phase of COVID-19 that are associated with a higher risk of development of fibrotic pulmonary lesions in the follow-up of COVID-19 patients in previous studies include a higher CT score of lung involvement, use of high-flow oxygen support, duration of mechanical ventilation, obesity, male sex, smoking, diabetes, and higher levels of C-reactive protein, lactate dehydrogenase, D-dimer, and fibrinogen [8, 20, 32, 36, 37]. Additionally, persistent dyspnea and myalgia and higher serum levels of Krebs von den Lungen 6 (KL-6) at follow-up were associated with a greater risk of occurrence of post-COVID pulmonary fibrosis [31, 37, 38].

Our study had several limitations. First, plethysmography was not performed in all patients who underwent chest CT, reducing (12%) the number of patients evaluated by both methods. Such an examination was not feasible in some cases due to patient limitations (tracheostomy, wheelchair use or intellectual difficulties). Second, we did not exclude patients with COPD. Nevertheless, the number of these patients was small (7%) and, data accuracy was not affected. Another limitation was the recruitment period: we enrolled patients six to twelve months after hospital admission. This recruitment period was selected because it occurred during the first wave of the pandemic when there were restrictions to control the virus, and fear drove people away from hospitals. However, the median follow-up time was approximately 7 months, and most patients enrolled were followed up on time without impacting data accuracy. In addition, the study design allowed mainly the most affected patients to reach the stage of performing chest CT. Thus, patients “without COVID-19-related lesion” included patients with normal chest CT and patients with pre-existing lesions unrelated to COVID-19. This fact contributed to these patients having a lower FVC and FEV1 than patients with COVID-19 lesions, not representing an ideal control group (without lesions) for comparison purposes. In addition, there is variability in the definition of long COVID fibrotic-like changes in the scientific literature. Although previous data have included reticular opacities as indicative of fibrosis, we understand that, at least in some patients, they may be relatively mild and only associated with ground-glass opacities, which could represent an inflammatory process in resolution, especially organizing pneumonia. Therefore, to increase the specificity of tomography as a method of detecting long COVID definitive fibrosis, our group decided to consider well-established imaging findings indicative of fibrosis (not only in long COVID scenarios, but also in idiopathic interstitial pneumonias), including traction bronchiectasis, architectural distortion, and honeycombing (which was not found in our cohort). Finally, this was a single-center study. However, HCFMUSP is the largest university hospital in our country and, has been designated as a reference hospital to treat COVID-19 patients. Thus, during the COVID-19 pandemic, heterogeneous groups of people of different ethnicities from all districts of the metropolitan region of São Paulo city (approximately 21 million inhabitants) were admitted to this hospital [18].

5. Conclusion

Our results show that ICU hospitalization due to COVID-19 led to chronic alterations characterized by imaging and functional abnormalities in the respiratory system that could persist for up to twelve months after hospital admission. The high frequency of lung lesions verified was particularly concerning, mainly because severe CT lesions were more frequent in older patients with more comorbidities, who are prone to infections and acute episodes of exacerbation. It could lead to a collapse in Brazil and the worldwide public health system, and it highlights the importance of a longer follow-up to monitor COVID-19 pulmonary consequences. We believe that monitoring these patients is one way to understand the effects of COVID-19 and to create opportunities to establish public policies that would help to relieve the public health system. Our study paves the way for future investigations focusing on practical options to mitigate the consequences of long COVID-19 and highlights the necessity of longer follow-up.

Supporting information

S1 Appendix. Supplemental machine learning model (ML) information’s.

Classification of computed tomography (CT) lung lesions. ML prediction model: training and validation. Results. Implementations notes. References.

(DOCX)

Click here for additional data file.^{(27.5KB, docx)}

S1 Table. Complementary baseline demographic and clinical characteristics of enrolled patients that underwent the general evaluation.

Values are presented as % (n/N). Abbreviations: COPD, chronic obstructive pulmonary disease; FIO₂, inspired fraction of oxygen; IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure.

(DOCX)

Click here for additional data file.^{(24.5KB, docx)}

S2 Table. Demographic and clinical characteristics of patients with signs of pulmonary involvement stratified by completion of chest computed tomography examination.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S3 Table. Demographic and clinical data of patients with signs of pulmonary involvement stratified by completion of lung function examination.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; PFTs, pulmonary function tests; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S4 Table. Demographic and clinical characteristics of patients stratified by the presence of COVID-19 CT findings.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; DLCO, diffusion capacity for carbon monoxide; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; PFTs, pulmonary function tests; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; TLC, total lung capacity; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(28.5KB, docx)}

S5 Table. Demographic and clinical characteristics of patients with completed chest CT and lung function results stratified by chest CT lesion severity.

(DOCX)

Click here for additional data file.^{(24.1KB, docx)}

Acknowledgments

We acknowledge the infrastructure support from the HCFMUSP COVID-19 task force (Antonio José Pereira, Elizabeth de Faria, Lucila Pedroso and Marcelo CA Ramos) during the baseline stage of in-hospital data collection and during the setting-up of the follow-up assessments. *Members of the HCFMUSP Covid-19 Study Group: Adriana L Araújo¹, Aluisio C Segurado², Amanda C Montal³, Anna Miethke-Morais³, Anna S Levin⁴, Beatriz Perondi³, Bruno F Guedes⁵, Carolina Carmo³, Carolina S Lázari⁶, Cassiano C Antonio⁷, Clarice Tanaka⁸, Claudia C Leite⁹, Cristiano Gomes¹⁰, Edivaldo M Utiyama¹¹, Emmanuel A Burdmann¹², Eloisa Bonfá³, Esper G Kallas², Ester Sabino¹³, Euripedes C Miguel¹⁴, Fabio R Pinna¹⁵, Geraldo F Busatto¹⁴, Giovanni G Cerri¹⁶, Heraldo P Souza¹⁷, Izabel Marcilio¹⁸, Izabel C Rios³, Jorge Hallak¹⁰, José Eduardo Krieger¹⁹, Juliana C Ferreira⁷, Julio F M Marchini²⁰, Larissa S Oliveira⁷, Leila Harima²¹, Linamara R Batisttella²², Luis Yu⁵, Luiz Henrique M Castro⁵, Marcelo C Rocha²³, Marcello M C Magri²⁴, Marcio Mancini²⁵, Maria Amélia de Jesus³, Maria Cassia J M Corrêa², Maria Cristina P B Francisco³, Maria Elizabeth Rossi³, Marjorie F Silva³, Marta Imamura²⁵, Maura S Oliveira²⁴, Nelson Gouveia³, Orestes V Forlenza¹⁴, Paulo A Lotufo⁶, Ricardo F Bento²⁷, Ricardo Nitrini⁵, Rodolfo F Damiano¹⁴, Roger Chammas²⁸, Rossana P Francisco²⁹, Solange R G Fusco³⁰, Tarcisio E P Barros-Filho³, Thais Mauad³¹, Thaís Guimarães³, Thiago Avelino-Silva³, Vilson C Junior³ and Wilson J Filho. Affiliations: 1 Diretoria Executiva dos LIMs, Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 2 Divisao/Departamento de Molestias Infecciosas e Parasitarias, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR. 3 Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR. 4 Departamento de Moléstias Infecciosas e Parasitárias do Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 5 Departamento de Neurologia, Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 6 Divisão de Laboratório Central do Hospital das Clínicas, da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 7 Pulmonary Division, Heart Institute (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), Sao Paulo, SP, Brazil. 8 Hospital das Clínicas da Faculdade de Medicina, University of São Paulo, Brazil; Department of Physiotherapy, Communication Science and Disorders, Occupational Therapy, University of São Paulo, Brazil. 9 Radiology Institute (InRad), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), Sao Paulo, SP, Brazil. 10 Division of Urology, Hospital das Clinicas, University of Sao Paulo Medical School, Sao Paulo, Brazil. 11 Division of General Surgery and Trauma, Department of Surgery, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo (HC/FMUSP), São Paulo, Brazil. 12 Laboratório de Investigação (LIM) 12, Serviço de Nefrologia, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 13 Universidade de São Paulo, Instituto de Medicina Tropical de São Paulo, Laboratório de Investigação Médica (LIM 46), São Paulo, São Paulo, Brazil. 14 Departamento e Instituto de Psiquiatria, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 15 Division of Otorhinolaryngology, University of São Paulo, Brazil. 16 Departamento de Radiologia, Faculdade de Medicina, LIM/44, Laboratório de Ressonância Magnética em Neurorradiologia Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, SP, Brasil. 17 Departamento de Clínica Médica, Disciplina de Emergências Clínicas, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 18 Epidemiological Surveillance Department, Hospital das Clinicas da Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil. 19 Laboratório de Genética e Cardiologia Molecular, Instituto do Coração (InCor), Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo (HCFMUSP), São Paulo, SP, Brazil. 20 Emergency Department, Hospital das Clı´nicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 21 Clinical Director’s Office, Hospital das Clínicas, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. 22 Departamento de Medicina Legal, Etica Medica e Medicina Social e do Trabalho, Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 23 Divisao de Clinica Cirurgica III, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR. 24 Division of Infectious Diseases, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. 25 Instituto de Medicina Física e de Reabilitação, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, Sao Paulo, Brazil. 25 Unidade de Obesidade e Síndrome Metabólica, Disciplina de Endocrinologia e Metabologia, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 26 Centro de Pesquisa Clínica e Epidemiológica, Hospital Universitário, Universidade de São Paulo, São Paulo, Brazil. 27 Divisão de Otorrinolaringologia, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 28 Centro de Investigação Translacional em Oncologia, Instituto do Câncer do Estado de São Paulo, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil. 29 Department of Obstetrics and Gynecology, University of Sao Paulo Medical School, Sao Paulo, Brazil. 30 Rheumatology Division, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil. 31 Departamento de Patologia, LIM/05- Laboratório de Poluição Atmosférica Experimental, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo HCFMUSP, São Paulo, SP, Brasil. 32 Division of Geriatrics, Department of Internal Medicine, Faculty of Medicine, University of São Paulo (USP), São Paulo, Brazil. Lead author email contact: geraldo.busatto@hc.fm.usp.br.

Data Availability

All data files are available from the COVID-19 database Sharing/BR repository (FAPESP. COVID-19 DataSharing/ BR, 2021. Available: https://repositoriodatasharingfapesp.uspdigital.usp.br).

Funding Statement

The Funding Statement that should be included on the Funding Statement section of the online submission form is: “MAG acknowledge the Sao Paulo Research Foundation for financial support (grants number 16/17078-0 and 14/50889-7). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.WHO [Internet]. World Health Organization Coronavirus Disease (COVID-19) Dashboard;c2020 [cited 2020 September 25]. Available from: https://covid19.who.int/.
2.Huang Y, Tan C, Wu J, Chen M, Wang Z, Luo L, et al. Impact of coronavirus disease 2019 on pulmonary function in early convalescence phase. Respir Res. 2020;21(1):163. doi: 10.1186/s12931-020-01429-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Liu C, Ye L, Xia R, Zheng X, Yuan C, Wang Z, et al. Chest Computed Tomography and Clinical Follow-Up of Discharged Patients with COVID-19 in Wenzhou City, Zhejiang, China. Ann Am Thorac Soc. 2020;17(10):1231–7. doi: 10.1513/AnnalsATS.202004-324OC [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Xiong Q, Xu M, Li J, Liu Y, Zhang J, Xu Y, et al. Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-centre longitudinal study. Clin Microbiol Infect. 2021;27(1):89–95. doi: 10.1016/j.cmi.2020.09.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601–15. doi: 10.1038/s41591-021-01283-z [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo P, Cuapio A, et al. More Than 50 Long-Term Effects of COVID-19: A Systematic Review and Meta-Analysis. Res Sq. 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Sisó-Almirall A, Brito-Zerón P, Conangla Ferrín L, Kostov B, Moragas Moreno A, Mestres J, et al. Long Covid-19: Proposed Primary Care Clinical Guidelines for Diagnosis and Disease Management. Int J Environ Res Public Health. 2021;18(8). doi: 10.3390/ijerph18084350 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Tanni SE, Fabro AT, de Albuquerque A, Ferreira EVM, Verrastro CGY, Sawamura MVY, et al. Pulmonary fibrosis secondary to COVID-19: a narrative review. Expert Rev Respir Med. 2021;15(6):791–803. doi: 10.1080/17476348.2021.1916472 [DOI] [PubMed] [Google Scholar]
9.Bellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4(1):e2036142. doi: 10.1001/jamanetworkopen.2020.36142 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Al-Aly Z, Bowe B, Xie Y. Long COVID after breakthrough SARS-CoV-2 infection. Nat Med. 2022. doi: 10.1038/s41591-022-01840-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Carvalho CRR, Chate RC, Sawamura MVY, Garcia ML, Lamas CA, Cardenas DAC, et al. Chronic lung lesions in COVID-19 survivors: predictive clinical model. BMJ Open. 2022;12(6):e059110. doi: 10.1136/bmjopen-2021-059110 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397(10270):220–32. doi: 10.1016/S0140-6736(20)32656-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Madotto F, McNicholas B, Rezoagli E, Pham T, Laffey JG, Bellani G, et al. Death in hospital following ICU discharge: insights from the LUNG SAFE study. Crit Care. 2021;25(1):144. doi: 10.1186/s13054-021-03465-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Zhang P, Li J, Liu H, Han N, Ju J, Kou Y, et al. Long-term bone and lung consequences associated with hospital-acquired severe acute respiratory syndrome: a 15-year follow-up from a prospective cohort study. Bone Res. 2020;8:8. doi: 10.1038/s41413-020-0084-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Ketai L, Paul NS, Wong KT. Radiology of severe acute respiratory syndrome (SARS): the emerging pathologic-radiologic correlates of an emerging disease. J Thorac Imaging. 2006;21(4):276–83. doi: 10.1097/01.rti.0000213581.14225.f1 [DOI] [PubMed] [Google Scholar]
16.Toufen C, Costa EL, Hirota AS, Li HY, Amato MB, Carvalho CR. Follow-up after acute respiratory distress syndrome caused by influenza a (H1N1) virus infection. Clinics (Sao Paulo). 2011;66(6):933–7. doi: 10.1590/s1807-59322011000600002 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Herridge MS, Cheung AM, Tansey CM, Matte-Martyn A, Diaz-Granados N, Al-Saidi F, et al. One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med. 2003;348(8):683–93. doi: 10.1056/NEJMoa022450 [DOI] [PubMed] [Google Scholar]
18.Ferreira JC, Ho YL, Besen BAMP, Malbouisson LMS, Taniguchi LU, Mendes PV, et al. Protective ventilation and outcomes of critically ill patients with COVID-19: a cohort study. Ann Intensive Care. 2021;11(1):92. doi: 10.1186/s13613-021-00882-w [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Busatto GF, de Araújo AL, Duarte AJDS, Levin AS, Guedes BF, Kallas EG, et al. Post-acute sequelae of SARS-CoV-2 infection (PASC): a protocol for a multidisciplinary prospective observational evaluation of a cohort of patients surviving hospitalisation in Sao Paulo, Brazil. BMJ Open. 2021;11(6):e051706. doi: 10.1136/bmjopen-2021-051706 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Han X, Fan Y, Alwalid O, Li N, Jia X, Yuan M, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021;299(1):E177–E86. doi: 10.1148/radiol.2021203153 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Lung function testing: selection of reference values and interpretative strategies. American Thoracic Society. Am Rev Respir Dis. 1991;144(5):1202–18. doi: 10.1164/ajrccm/144.5.1202 [DOI] [PubMed] [Google Scholar]
22.Neder JA, Andreoni S, Castelo-Filho A, Nery LE. Reference values for lung function tests. I. Static volumes. Braz J Med Biol Res. 1999;32(6):703–17. doi: 10.1590/s0100-879x1999000600006 [DOI] [PubMed] [Google Scholar]
23.Pereira CA, Sato T, Rodrigues SC. New reference values for forced spirometry in white adults in Brazil. J Bras Pneumol. 2007;33(4):397–406. doi: 10.1590/s1806-37132007000400008 [DOI] [PubMed] [Google Scholar]
24.Neder JA, Andreoni S, Peres C, Nery LE. Reference values for lung function tests. III. Carbon monoxide diffusing capacity (transfer factor). Braz J Med Biol Res. 1999;32(6):729–37. doi: 10.1590/s0100-879x1999000600008 [DOI] [PubMed] [Google Scholar]
25.Toufen Junior C, De Santis Santiago RR, Hirota AS, Carvalho ARS, Gomes S, Amato MBP, et al. Driving pressure and long-term outcomes in moderate/severe acute respiratory distress syndrome. Ann Intensive Care. 2018;8(1):119. doi: 10.1186/s13613-018-0469-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Bellan M, Baricich A, Patrucco F, Zeppegno P, Gramaglia C, Balbo PE, et al. Long-term sequelae are highly prevalent one year after hospitalization for severe COVID-19. Sci Rep. 2021;11(1):22666. doi: 10.1038/s41598-021-01215-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Frija-Masson J, Debray MP, Boussouar S, Khalil A, Bancal C, Motiejunaite J, et al. Residual ground glass opacities three months after Covid-19 pneumonia correlate to alteration of respiratory function: The post Covid M3 study. Respir Med. 2021;184:106435. doi: 10.1016/j.rmed.2021.106435 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Hui DS, Joynt GM, Wong KT, Gomersall CD, Li TS, Antonio G, et al. Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors. Thorax. 2005;60(5):401–9. doi: 10.1136/thx.2004.030205 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Fabbri L, Moss S, Khan FA, Chi W, Xia J, Robinson K, et al. Parenchymal lung abnormalities following hospitalisation for COVID-19 and viral pneumonitis: a systematic review and meta-analysis. Thorax. 2022. doi: 10.1136/thoraxjnl-2021-218275 [DOI] [PubMed] [Google Scholar]
30.Fortini A, Rosso A, Cecchini P, Torrigiani A, Lo Forte A, Carrai P, et al. One-year evolution of DLCO changes and respiratory symptoms in patients with post COVID-19 respiratory syndrome. Infection. 2022;50(2):513–7. doi: 10.1007/s15010-022-01755-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Han X, Fan Y, Alwalid O, Zhang X, Jia X, Zheng Y, et al. Fibrotic Interstitial Lung Abnormalities at 1-year Follow-up CT after Severe COVID-19. Radiology. 2021:210972. doi: 10.1148/radiol.2021210972 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Luger AK, Sonnweber T, Gruber L, Schwabl C, Cima K, Tymoszuk P, et al. Chest CT of Lung Injury 1 Year after COVID-19 Pneumonia: The CovILD Study. Radiology. 2022:211670. doi: 10.1148/radiol.211670 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Wu X, Liu X, Zhou Y, Yu H, Li R, Zhan Q, et al. 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study. Lancet Respir Med. 2021;9(7):747–54. doi: 10.1016/S2213-2600(21)00174-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Keszler M. Mechanical ventilation strategies. Semin Fetal Neonatal Med. 2017;22(4):267–74. doi: 10.1016/j.siny.2017.06.003 [DOI] [PubMed] [Google Scholar]
35.Wolfe KS, Patel BK, MacKenzie EL, Giovanni SP, Pohlman AS, Churpek MM, et al. Impact of Vasoactive Medications on ICU-Acquired Weakness in Mechanically Ventilated Patients. Chest. 2018;154(4):781–7. doi: 10.1016/j.chest.2018.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Achkar M, Jamal O, Chaaban T. Post-COVID lung disease(s). Ann Thorac Med. 2022;17(3):137–44. doi: 10.4103/atm.atm_103_22 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Aul DR, Gates DJ, Draper DA, Dunleavy DA, Ruickbie DS, Meredith DH, et al. Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis. Respir Med. 2021;188:106602. doi: 10.1016/j.rmed.2021.106602 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.d’Alessandro M, Bergantini L, Cameli P, Curatola G, Remediani L, Bennett D, et al. Serial KL-6 measurements in COVID-19 patients. Intern Emerg Med. 2021;16(6):1541–5. doi: 10.1007/s11739-020-02614-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0280567.r001

Decision Letter 0

Yu Ru Kou

17 Oct 2022

PONE-D-22-26441Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: prospective cohort studyPLOS ONE

Dear Dr. Ribeiro Carvalho,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Both reviewers raised several concerns, especially regarding data interpretation. The authors should fully respond to their comments in the revised manuscript.

Please submit your revised manuscript by Dec 01 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Yu Ru Kou, PhD

Academic Editor

PLOS ONE

Journal requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

3. One of the noted authors is a group or consortium HCFMUSP Covid-19 Study Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Title: Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: prospective cohort study

The authors conducted a prospective cohort study to investigate long-term consequences of COVID-19 on the respiratory system of patients discharged from hospital ICU and to identify risk factors associated with chest CT lesion severity. The authors declared that the ML accurately detected that male sex, ICU and invasive mechanic ventilation (IMV) period, tracheostomy and vasoactive drug need during hospitalization were predictors of CT lesion severity.

Comments:

1. In Abstract, Method, the follow-up period in the study was 6-12 months after hospital discharge. What is the reason for this prospective cohort study designed as an uncertain follow-up period? I suppose that the predictors for fibrotic change of CT checked at 6month may be different from those at 12month due to regression of image abnormalities in COVID-19 patients.

2. In Abstract, Method, the authors did not clearly define the primary outcome measure, CT severity, in this study.

3. In Abstract, Method, the authors did not mention the time point after discharge for performing chest CT and pulmonary function test.

4. In Abstract, Results, that authors mentioned that “An association between the CT feature severity and an impaired PFTs was not found.” However, in Table 3, there were statistical significances in some variables.

5. In Abstract, Conclusion, the authors mentioned “….. were risk factors to the development of severe CT lesion after discharge.”. How to define the development of CT lesion after discharge or during hospitalization? Does it mean that CT lesion got progressive after discharge?

6. In Methods, 2.2 follow-up protocol, the enrolled criteria for performing chest CT and pulmonary function test were different from the description in Abstract. Please reedit it in Abstract.

7. In Results, Table 3, the follow-up duration in each group was not showed here.

8. In Results, Table 3, the authors mentioned that “patients with fibrotic-like changes were older, had a greater duration of ICU hospitalization and need of IMV, were more tracheostomized, had a lower FVC and VR, and a higher FEV1 and FEV1/FVC than those with mild/moderate lesion”. What is the meaning for mild/moderate lesion? The authors did not define what is the severity graded via the CT findings in ICU hospitalized COVID-19 patient in Methods. The authors did not provide CT extent scores, so how to define the severity in fibrotic group (Traction bronchiectasis, but no honeycombing in the study population) greater than non-fibrotic group (GGO,…).

9. In Results, Table 3, what is the reason that the authors did not include the signs of pulmonary involvement (enrolled criteria) as the variables for statistical analysis to test whether the signs of pulmonary involvement were the predictors of CT severity in the study patients?

10. In Discussion, 2nd paragraph, the authors mentioned that “This fact suggests that the respiratory mechanics is possibly more relevant than gas exchange in determining COVID-19 late respiratory alterations.” The authors should have more explanation for this statement. Dose the result of this study support this finding? Moreover, the reference 25 and the conclusion that “these data triggered the idea that possible interventions that improve lung mechanics would be important to reduce these dysfunctions.” Have any evidence to support it and how to do?

11. In Discussion, paragraph 2-4, the authors had discussions about parameters of lung mechanics (e.g., DLCO, TLC) in COVID-19. However, pulmonary function parameters were not the clinical predictors for CT severity in ICU hospitalized COVID 19. in additional to respiratory parameters, the authors can make a more comprehensive literature review to point out the predictors or factor associated with fibrotic-like changes in COVID 19 patients.

Reviewer #2: This manuscript was designed to investigate the long-term respiratory effect, including pulmonary function and chest CT findings, caused by the SARS-CoV-2 infection. Although this research was a prospective study, it seems that patients’ groups and medical record were divided and collected retrospectively. Therefore, the including and excluding criteria were not defined very clear. Several questions need to be answered.

1. Among patients enrolled for Chest CT scan (N=243), how many patients have chronic lung disease, including fibrotic

disease (reticulation), traction bronchiectasis, architectural distortion or malignancy? Were these patients divided into

fibrotic-like or without fibrotic-like change group?

2. Why did authors define reticulation on chest CT scan into the without fibrotic-like change group?

3. Sometimes, we can see “both” fibrotic-like changes and ground-glass opacities in a “single” chest CT scan. Furthermore,

it is very difficult to define this patient into with or without fibrotic-like group. How many patients in this study

encountered this issue and need to be resolved by consensus?

4. In this study, a machine learning model was developed to precited CT lesion severity. Did the authors validate this model

in other cohort to confirm its reproducibility?

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Hsin-Kuo Ko

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Jan 20;18(1):e0280567. doi: 10.1371/journal.pone.0280567.r002

Author response to Decision Letter 0

15 Dec 2022

Comments to the Author

Editor comments

Editor: 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Answer: The manuscript meets all PLOS ONE’s style requirements

Editor: 2. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

Answer: The data availability statement was were specified. (pages 26, lines 558)

Editor: 3. One of the noted authors is a group or consortium HCFMUSP Covid-19 Study Group. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgments section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

Answer: The list of the individual authors and affiliations within the HCFMUSP Covid-19 Study Group, and the email contact of the lead author for this group were included in the acknowledgments section. (Pages 22, 23 and 24. Line 467)

Editor: 4. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Answer: The captions for your Supporting Information files were included in the end of the manuscript (page 22. Line 424)

Reviewers Comments

Reviewer 1

Reviewer: 1. In Abstract, Method, the follow-up period in the study was 6-12 months after hospital discharge. What is the reason for this prospective cohort study designed as an uncertain follow-up period? I suppose that the predictors for fibrotic change of CT checked at 6 month may be different from those at 12month due to regression of image abnormalities in COVID-19 patients.

Answer: The protocol of this study, described in detail in Busatto et al 2021, considered a 6-9 months follow-up. However, because it occurred during the first pandemic wave when there were restrictions to control the virus and the fear drove people away from hospitals, the recruitment period was extended. The median follow-up time was 219 days (IQR 206 - 291), approximately 7 months, and the minimum and maximum values of this interval were 161 (± 6 months) and 383 days (± 12 months), respectively. Thus, most patients enrolled were followed-up on time, not impacting data accuracy. Nevertheless, we appreciate and understand your concern about it and added this information as a study limitation (Page 20, 381)

Reviewer 1: 2. In Abstract, Method, the authors did not clearly define the primary outcome measure, CT severity, in this study.

Answer: We appreciate the reviewer’s suggestion. The primary outcome measure and CT severity were clearly defined at the Abstract, Method. (Page 2, line 36)

Reviewer 1: 3. In Abstract, Method, the authors did not mention the time point after discharge for performing chest CT and pulmonary function test.

Answer: We appreciate the reviewer’s suggestion. This information was added at the Abstract, Method. (Page 2, line 36)

Reviewer 1: 4. In Abstract, Results, that authors mentioned that “An association between the CT feature severity and an impaired PFTs was not found.” However, in Table 3, there were statistical significances in some variables.

Answer: We appreciate the reviewer’s suggestion and altered this information at the Abstract, Results (Page 2 line 44). Patients without fibrotic-like changes in the CT scan had lower impairment in pulmonary function tests than those with fibrotic-like tomographic changes.

Reviewer 1: 5. In Abstract, Conclusion, the authors mentioned “….. were risk factors to the development of severe CT lesion after discharge.”. How to define the development of CT lesion after discharge or during hospitalization? Does it mean that CT lesion got progressive after discharge?

Answer: We appreciate the reviewer’s suggestion and altered this information at the Abstract, Conclusion (Page 3, line 53). Our study comprehends a cross-sectional study that evaluated the CT lesion at a specific time-point after hospital admission for COVID-19 and did not have the intention to discuss the CT lesion longitudinal evolution. However, comparing the cases of patients that had a CT at hospital admission and a CT 6-12 months after hospital admission, we verified patients with total lesion regression, others that maintained the lesion pattern and others that developed fibrotic-like lesion thought this time. Unfortunately, few patients had a CT at hospital admission, making it impossible to include this information in the present study. In the future we will have data regarding CT lesion evolution, which will be further investigated in the next follow-up we will perform 2-years after the acute phase.

Reviewer 1: 6. In Methods, 2.2 follow-up protocol, the enrolled criteria for performing chest CT and pulmonary function test were different from the description in Abstract. Please reedit it in Abstract.

Answer: We appreciate the reviewer’s suggestion and altered this information at the Abstract, Methods. (Page 2, line 33)

Reviewer 1: 7. In Results, Table 3, the follow-up duration in each group was not showed here.

Answer: We appreciate the reviewer’s suggestion and added this information at the Results, Table 3. (Page 15)

Reviewer 1: 8. In Results, Table 3, the authors mentioned that “patients with fibrotic-like changes were older, had a greater duration of ICU hospitalization and need of IMV, were more tracheostomized, had a lower FVC and VR, and a higher FEV1 and FEV1/FVC than those with mild/moderate lesion”. What is the meaning for mild/moderate lesion? The authors did not define what is the severity graded via the CT findings in ICU hospitalized COVID-19 patient in Methods. The authors did not provide CT extent scores, so how to define the severity in fibrotic group (Traction bronchiectasis, but no honeycombing in the study population) greater than non-fibrotic group (GGO,…).

Answer: We appreciate the reviewer’s comments and substituted the term “mild/moderate lesion” by “without fibrotic-like changes” (Page 14, line 267). Also, we provided the CT extent score for these groups (without fibrotic-like changes and with fibrotic-like changes). (Methods - Page 8, lines 174 and table 3)

Reviewer 1: 9. In Results, Table 3, what is the reason that the authors did not include the signs of pulmonary involvement (enrolled criteria) as the variables for statistical analysis to test whether the signs of pulmonary involvement were the predictors of CT severity in the study patients?

Answer: Our intention in this study was to identify risk factors in the acute phase of the disease that could be predictors of the CT alterations identified at the follow-up. Thus, the Machine Learning model (ML) was built considering the variables collected at baseline (during hospitalization) with p<0.05 between two categories of CT lesion severity (without fibrotic-like changes and with fibrotic-like changes). Thus, since the analysis performed to evaluate the signs of pulmonary involvement was performed 6-12 months after hospital admission, we considered that it would be not relevant to include them in the ML.

Reviewer 1: 10. In Discussion, 2nd paragraph, the authors mentioned that “This fact suggests that the respiratory mechanics is possibly more relevant than gas exchange in determining COVID-19 late respiratory alterations.” The authors should have more explanation for this statement. Dose the result of this study support this finding? Moreover, the reference 25 and the conclusion that “these data triggered the idea that possible interventions that improve lung mechanics would be important to reduce these dysfunctions.” Have any evidence to support it and how to do?

Answer: The results showed that low compliance was significantly associated with pulmonary involvement but not PaO2/FIO2. This data suggests that the reduction in respiratory mechanics, in addition to indicating greater severity of acute lung injury, may be associated with an additional risk of pulmonary impairment, perhaps due to the high difficulty associated with adjusting protective ventilatory parameters in these patients. We agree that this difference was slight and we modified the paragraph as suggested by the reviewer in order to emphasize that this is only a hypothesis and should be confirmed in further studies (Page 17, lines 301)

Reviewer 1: 11. In Discussion, paragraph 2-4, the authors had discussions about parameters of lung mechanics (e.g., DLCO, TLC) in COVID-19. However, pulmonary function parameters were not the clinical predictors for CT severity in ICU hospitalized COVID 19. in additional to respiratory parameters, the authors can make a more comprehensive literature review to point out the predictors or factor associated with fibrotic-like changes in COVID 19 patients.

Answer: Thanks for your comments. We expanded the discussion regarding other factors during the acute and long-term phases that are associated with an increased risk of developing fibrosing lung lesions in the follow-up of patients with COVID-19 (Page 19. Lines 365).

Reviewer 2

This manuscript was designed to investigate the long-term respiratory effect, including pulmonary function and chest CT findings, caused by the SARS-CoV-2 infection. Although this research was a prospective study, it seems that patients’ groups and medical record were divided and collected retrospectively. Therefore, the including and excluding criteria were not defined very clear. Several questions need to be answered.

Reviewer 2: 1. Among patients enrolled for Chest CT scan (N=243), how many patients have chronic lung disease, including fibrotic disease (reticulation), traction bronchiectasis, architectural distortion or malignancy? Were these patients divided into fibrotic-like or without fibrotic-like change group?

Answer: Patients that had at least one of these CT features (reticulation, traction bronchiectasis, architectural distortion or malignancy) previous to COVID-19 were included in the group of patients “Lung without COVID-19-related findings – Lung with CT features previous to COVID-19” and were not considered in the division into fibrotic-like or without fibrotic-like change group. The detailed description of these patients can be seen at the study flowchart (Figure 1).

Reviewer 2: 2. Why did authors define reticulation on chest CT scan into the without fibrotic-like change group?

Answer: Reviewing the literature to date, it is possible to notice that there is some variability regarding the tomographic criteria that have been used by different authors to define post-COVID fibrotic-like changes. Although some publications have included reticular opacities as indicative of fibrosis, we understand that, at least in some patients, they may be relatively mild and only associated with ground-glass opacities, which could represent an inflammatory process in resolution, especially organizing pneumonia (OP). Therefore, in order to increase the specificity of tomography as a method of detecting post-COVID definitive fibrosis, our group decided to consider more well-established imaging findings indicative of fibrosis in the literature (not only in the long-term COVID scenario, but also in idiopathic interstitial pneumonias), including traction bronchiectasis and architectural distortion (honeycombing, another classic tomographic finding of fibrosis, was not found in our cohort).

We understand that some patients with reticular opacities in our cohort, even when not associated with traction bronchiectasis or architectural distortion, may also have developed some degree of post-COVID fibrosis, which will be further investigated in the next follow-up we will perform after 2-years of the disease.

Reviewer 2: 3. Sometimes, we can see “both” fibrotic-like changes and ground-glass opacities in a “single” chest CT scan. Furthermore, it is very difficult to define this patient into with or without fibrotic-like group. How many patients in this study encountered this issue and need to be resolved by consensus?

Answer: The association of traction bronchiectasis and architectural distortion (the imaging characteristics considered to be more definitive of true fibrosis) with the other post-COVID tomographic findings (ie, ground-glass opacities, parenchymal bands, and reticulation) is frequent. In light of that, and as we decided to be more specific rather than sensitive, whenever traction bronchiectasis and/or architectural distortion were observed on CT scans, patients were categorized as belonging to the “fibrotic-like” group, regardless of the presence or absence of associated ground-glass opacities. Probably because our cohort was composed of patients with severe disease in the acute phase, the post-COVID tomographic findings indicative of “fibrotic-like” changes could be identified in the vast majority of those cases by the thoracic radiologists, allowing the categorization of patients into one of the two groups.

Reviewer 2: 4. In this study, a machine learning model was developed to precited CT lesion severity. Did the authors validate this model in other cohort to confirm its reproducibility?

Answer: We adopted a cross-validation strategy (Page 9 line 194, S1 Appendix). The cross-validation is a statistical method of evaluating and comparing machine learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model. In typical cross-validation strategy, the training and validation set must cross-over in successive rounds such that each data point has a chance of being validated against. The basic form of cross-validation is k-fold cross-validation. In k-fold cross-validation, the data is first partitioned into k equally (or nearly equally) sized segments or folds. Subsequently k iterations of training and validation are performed such that within each iteration a different fold of the data is held-out for validation while the remaining k − 1 folds are used for learning. In our experiment, we used 3 folds to distribute training and validation sets. (1)

1. Refaeilzadeh P, Tang L, Liu H. Cross-validation. Encyclopedia of Database Systems. Berlin Springer; 2009. p. 532-8.

PLoS One. doi: 10.1371/journal.pone.0280567.r003

Decision Letter 1

Yu Ru Kou

3 Jan 2023

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: prospective cohort study

PONE-D-22-26441R1

Dear Dr. Ribeiro Carvalho,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Yu Ru Kou, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have completely responded to my comments. No further issue about dual publication, research ethics or pulbilication ethics was found.

Reviewer #2: The manuscript was significantly improved after major revision and could be accepted in the present form.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Hsin-Kuo Ko

Reviewer #2: No

**********

PLoS One. doi: 10.1371/journal.pone.0280567.r004

Acceptance letter

Yu Ru Kou

10 Jan 2023

PONE-D-22-26441R1

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: prospective cohort study

Dear Dr. Ribeiro Carvalho:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Yu Ru Kou

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Appendix. Supplemental machine learning model (ML) information’s.

Classification of computed tomography (CT) lung lesions. ML prediction model: training and validation. Results. Implementations notes. References.

(DOCX)

Click here for additional data file.^{(27.5KB, docx)}

S1 Table. Complementary baseline demographic and clinical characteristics of enrolled patients that underwent the general evaluation.

(DOCX)

Click here for additional data file.^{(24.5KB, docx)}

S2 Table. Demographic and clinical characteristics of patients with signs of pulmonary involvement stratified by completion of chest computed tomography examination.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S3 Table. Demographic and clinical data of patients with signs of pulmonary involvement stratified by completion of lung function examination.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; PFTs, pulmonary function tests; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(26.7KB, docx)}

S4 Table. Demographic and clinical characteristics of patients stratified by the presence of COVID-19 CT findings.

Values are presented as median [IQR, n] or % (n/N) or mean ± SD (n). Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, c-reactive protein; d, days; DLCO, diffusion capacity for carbon monoxide; FIO₂, inspired fraction of oxygen; ICU, intensive care unit. IMV, invasive mechanical ventilation; PaO₂, arterial partial pressure of oxygen; PEEP, positive end-expiratory pressure; PFTs, pulmonary function tests; SAPS3, Simplified Acute Physiology Score 3; SD, standard deviation; TLC, total lung capacity; VAD, vasoactive drugs; yr, years.

(DOCX)

Click here for additional data file.^{(28.5KB, docx)}

S5 Table. Demographic and clinical characteristics of patients with completed chest CT and lung function results stratified by chest CT lesion severity.

(DOCX)

Click here for additional data file.^{(24.1KB, docx)}

Data Availability Statement

All data files are available from the COVID-19 database Sharing/BR repository (FAPESP. COVID-19 DataSharing/ BR, 2021. Available: https://repositoriodatasharingfapesp.uspdigital.usp.br).

[pone.0280567.ref001] 1.WHO [Internet]. World Health Organization Coronavirus Disease (COVID-19) Dashboard;c2020 [cited 2020 September 25]. Available from: https://covid19.who.int/.

[pone.0280567.ref002] 2.Huang Y, Tan C, Wu J, Chen M, Wang Z, Luo L, et al. Impact of coronavirus disease 2019 on pulmonary function in early convalescence phase. Respir Res. 2020;21(1):163. doi: 10.1186/s12931-020-01429-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref003] 3.Liu C, Ye L, Xia R, Zheng X, Yuan C, Wang Z, et al. Chest Computed Tomography and Clinical Follow-Up of Discharged Patients with COVID-19 in Wenzhou City, Zhejiang, China. Ann Am Thorac Soc. 2020;17(10):1231–7. doi: 10.1513/AnnalsATS.202004-324OC [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref004] 4.Xiong Q, Xu M, Li J, Liu Y, Zhang J, Xu Y, et al. Clinical sequelae of COVID-19 survivors in Wuhan, China: a single-centre longitudinal study. Clin Microbiol Infect. 2021;27(1):89–95. doi: 10.1016/j.cmi.2020.09.023 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref005] 5.Nalbandian A, Sehgal K, Gupta A, Madhavan MV, McGroder C, Stevens JS, et al. Post-acute COVID-19 syndrome. Nat Med. 2021;27(4):601–15. doi: 10.1038/s41591-021-01283-z [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref006] 6.Lopez-Leon S, Wegman-Ostrosky T, Perelman C, Sepulveda R, Rebolledo P, Cuapio A, et al. More Than 50 Long-Term Effects of COVID-19: A Systematic Review and Meta-Analysis. Res Sq. 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref007] 7.Sisó-Almirall A, Brito-Zerón P, Conangla Ferrín L, Kostov B, Moragas Moreno A, Mestres J, et al. Long Covid-19: Proposed Primary Care Clinical Guidelines for Diagnosis and Disease Management. Int J Environ Res Public Health. 2021;18(8). doi: 10.3390/ijerph18084350 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref008] 8.Tanni SE, Fabro AT, de Albuquerque A, Ferreira EVM, Verrastro CGY, Sawamura MVY, et al. Pulmonary fibrosis secondary to COVID-19: a narrative review. Expert Rev Respir Med. 2021;15(6):791–803. doi: 10.1080/17476348.2021.1916472 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref009] 9.Bellan M, Soddu D, Balbo PE, Baricich A, Zeppegno P, Avanzi GC, et al. Respiratory and Psychophysical Sequelae Among Patients With COVID-19 Four Months After Hospital Discharge. JAMA Netw Open. 2021;4(1):e2036142. doi: 10.1001/jamanetworkopen.2020.36142 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref010] 10.Al-Aly Z, Bowe B, Xie Y. Long COVID after breakthrough SARS-CoV-2 infection. Nat Med. 2022. doi: 10.1038/s41591-022-01840-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref011] 11.Carvalho CRR, Chate RC, Sawamura MVY, Garcia ML, Lamas CA, Cardenas DAC, et al. Chronic lung lesions in COVID-19 survivors: predictive clinical model. BMJ Open. 2022;12(6):e059110. doi: 10.1136/bmjopen-2021-059110 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref012] 12.Huang C, Huang L, Wang Y, Li X, Ren L, Gu X, et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. Lancet. 2021;397(10270):220–32. doi: 10.1016/S0140-6736(20)32656-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref013] 13.Madotto F, McNicholas B, Rezoagli E, Pham T, Laffey JG, Bellani G, et al. Death in hospital following ICU discharge: insights from the LUNG SAFE study. Crit Care. 2021;25(1):144. doi: 10.1186/s13054-021-03465-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref014] 14.Zhang P, Li J, Liu H, Han N, Ju J, Kou Y, et al. Long-term bone and lung consequences associated with hospital-acquired severe acute respiratory syndrome: a 15-year follow-up from a prospective cohort study. Bone Res. 2020;8:8. doi: 10.1038/s41413-020-0084-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref015] 15.Ketai L, Paul NS, Wong KT. Radiology of severe acute respiratory syndrome (SARS): the emerging pathologic-radiologic correlates of an emerging disease. J Thorac Imaging. 2006;21(4):276–83. doi: 10.1097/01.rti.0000213581.14225.f1 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref016] 16.Toufen C, Costa EL, Hirota AS, Li HY, Amato MB, Carvalho CR. Follow-up after acute respiratory distress syndrome caused by influenza a (H1N1) virus infection. Clinics (Sao Paulo). 2011;66(6):933–7. doi: 10.1590/s1807-59322011000600002 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref017] 17.Herridge MS, Cheung AM, Tansey CM, Matte-Martyn A, Diaz-Granados N, Al-Saidi F, et al. One-year outcomes in survivors of the acute respiratory distress syndrome. N Engl J Med. 2003;348(8):683–93. doi: 10.1056/NEJMoa022450 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref018] 18.Ferreira JC, Ho YL, Besen BAMP, Malbouisson LMS, Taniguchi LU, Mendes PV, et al. Protective ventilation and outcomes of critically ill patients with COVID-19: a cohort study. Ann Intensive Care. 2021;11(1):92. doi: 10.1186/s13613-021-00882-w [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref019] 19.Busatto GF, de Araújo AL, Duarte AJDS, Levin AS, Guedes BF, Kallas EG, et al. Post-acute sequelae of SARS-CoV-2 infection (PASC): a protocol for a multidisciplinary prospective observational evaluation of a cohort of patients surviving hospitalisation in Sao Paulo, Brazil. BMJ Open. 2021;11(6):e051706. doi: 10.1136/bmjopen-2021-051706 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref020] 20.Han X, Fan Y, Alwalid O, Li N, Jia X, Yuan M, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021;299(1):E177–E86. doi: 10.1148/radiol.2021203153 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref021] 21.Lung function testing: selection of reference values and interpretative strategies. American Thoracic Society. Am Rev Respir Dis. 1991;144(5):1202–18. doi: 10.1164/ajrccm/144.5.1202 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref022] 22.Neder JA, Andreoni S, Castelo-Filho A, Nery LE. Reference values for lung function tests. I. Static volumes. Braz J Med Biol Res. 1999;32(6):703–17. doi: 10.1590/s0100-879x1999000600006 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref023] 23.Pereira CA, Sato T, Rodrigues SC. New reference values for forced spirometry in white adults in Brazil. J Bras Pneumol. 2007;33(4):397–406. doi: 10.1590/s1806-37132007000400008 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref024] 24.Neder JA, Andreoni S, Peres C, Nery LE. Reference values for lung function tests. III. Carbon monoxide diffusing capacity (transfer factor). Braz J Med Biol Res. 1999;32(6):729–37. doi: 10.1590/s0100-879x1999000600008 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref025] 25.Toufen Junior C, De Santis Santiago RR, Hirota AS, Carvalho ARS, Gomes S, Amato MBP, et al. Driving pressure and long-term outcomes in moderate/severe acute respiratory distress syndrome. Ann Intensive Care. 2018;8(1):119. doi: 10.1186/s13613-018-0469-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref026] 26.Bellan M, Baricich A, Patrucco F, Zeppegno P, Gramaglia C, Balbo PE, et al. Long-term sequelae are highly prevalent one year after hospitalization for severe COVID-19. Sci Rep. 2021;11(1):22666. doi: 10.1038/s41598-021-01215-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref027] 27.Frija-Masson J, Debray MP, Boussouar S, Khalil A, Bancal C, Motiejunaite J, et al. Residual ground glass opacities three months after Covid-19 pneumonia correlate to alteration of respiratory function: The post Covid M3 study. Respir Med. 2021;184:106435. doi: 10.1016/j.rmed.2021.106435 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref028] 28.Hui DS, Joynt GM, Wong KT, Gomersall CD, Li TS, Antonio G, et al. Impact of severe acute respiratory syndrome (SARS) on pulmonary function, functional capacity and quality of life in a cohort of survivors. Thorax. 2005;60(5):401–9. doi: 10.1136/thx.2004.030205 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref029] 29.Fabbri L, Moss S, Khan FA, Chi W, Xia J, Robinson K, et al. Parenchymal lung abnormalities following hospitalisation for COVID-19 and viral pneumonitis: a systematic review and meta-analysis. Thorax. 2022. doi: 10.1136/thoraxjnl-2021-218275 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref030] 30.Fortini A, Rosso A, Cecchini P, Torrigiani A, Lo Forte A, Carrai P, et al. One-year evolution of DLCO changes and respiratory symptoms in patients with post COVID-19 respiratory syndrome. Infection. 2022;50(2):513–7. doi: 10.1007/s15010-022-01755-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref031] 31.Han X, Fan Y, Alwalid O, Zhang X, Jia X, Zheng Y, et al. Fibrotic Interstitial Lung Abnormalities at 1-year Follow-up CT after Severe COVID-19. Radiology. 2021:210972. doi: 10.1148/radiol.2021210972 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref032] 32.Luger AK, Sonnweber T, Gruber L, Schwabl C, Cima K, Tymoszuk P, et al. Chest CT of Lung Injury 1 Year after COVID-19 Pneumonia: The CovILD Study. Radiology. 2022:211670. doi: 10.1148/radiol.211670 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref033] 33.Wu X, Liu X, Zhou Y, Yu H, Li R, Zhan Q, et al. 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study. Lancet Respir Med. 2021;9(7):747–54. doi: 10.1016/S2213-2600(21)00174-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref034] 34.Keszler M. Mechanical ventilation strategies. Semin Fetal Neonatal Med. 2017;22(4):267–74. doi: 10.1016/j.siny.2017.06.003 [DOI] [PubMed] [Google Scholar]

[pone.0280567.ref035] 35.Wolfe KS, Patel BK, MacKenzie EL, Giovanni SP, Pohlman AS, Churpek MM, et al. Impact of Vasoactive Medications on ICU-Acquired Weakness in Mechanically Ventilated Patients. Chest. 2018;154(4):781–7. doi: 10.1016/j.chest.2018.07.016 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref036] 36.Achkar M, Jamal O, Chaaban T. Post-COVID lung disease(s). Ann Thorac Med. 2022;17(3):137–44. doi: 10.4103/atm.atm_103_22 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref037] 37.Aul DR, Gates DJ, Draper DA, Dunleavy DA, Ruickbie DS, Meredith DH, et al. Complications after discharge with COVID-19 infection and risk factors associated with development of post-COVID pulmonary fibrosis. Respir Med. 2021;188:106602. doi: 10.1016/j.rmed.2021.106602 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0280567.ref038] 38.d’Alessandro M, Bergantini L, Cameli P, Curatola G, Remediani L, Bennett D, et al. Serial KL-6 measurements in COVID-19 patients. Intern Emerg Med. 2021;16(6):1541–5. doi: 10.1007/s11739-020-02614-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Long-term respiratory follow-up of ICU hospitalized COVID-19 patients: Prospective cohort study

Carlos Roberto Ribeiro Carvalho

Celina Almeida Lamas

Rodrigo Caruso Chate

João Marcos Salge

Marcio Valente Yamada Sawamura

André L P de Albuquerque

Carlos Toufen Junior

Daniel Mario Lima

Michelle Louvaes Garcia

Paula Gobi Scudeller

Cesar Higa Nomura

Marco Antonio Gutierrez

Bruno Guedes Baldi

Roles

Abstract

Background

Methods

Results

Conclusion

1. Introduction

2. Materials and methods

2.1. Study design and participants

2.2. Follow-up protocol

2.3. Data analysis

3. Results

Fig 1. Study flow chart, showing how the COVID-19 survivors were selected to participate in this follow-up until the final numbers analyzed.

Table 1. Baseline demographic and clinical characteristics of enrolled patients that underwent the general evaluation.

Table 2. Chest CT and PFTs among COVID-19 survivors at the follow-up.

Table 3. Demographic and clinical characteristics of patients with completed chest CT and lung function results stratified by chest CT lesion severity.

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Yu Ru Kou

Roles

Author response to Decision Letter 0

Decision Letter 1

Yu Ru Kou

Roles

Acceptance letter

Yu Ru Kou

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases