Abstract
Background
Post-COVID-19 pulmonary fibrosis is a significant long-term respiratory morbidity affecting patients’ respiratory health. This exploratory study aims to investigate the incidence, clinical characteristics, and acute-phase risk factors for pulmonary fibrosis in COVID-19 patients. Additionally, it evaluates pulmonary function and chest CT outcomes to provide clinical evidence for the early identification of high-risk patients and the prevention of post-COVID-19 pulmonary fibrosis.
Methods
We retrospectively analyzed 595 patients hospitalized for COVID-19 from January 2022 to July 2023. Patients were divided into fibrosis and nonfibrosis groups on the basis of imaging changes. Baseline data, including demographics, disease severity, laboratory indicators, and chest imaging characteristics, were collected. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for pulmonary fibrosis. Pulmonary function and chest CT follow-ups were conducted for the fibrosis group. The data were processed via SPSS 26.0, with P < 0.05 considered statistically significant.
Results
The incidence of pulmonary fibrosis was 4.37%, with 2.08% in moderate cases and 8.22% in severe cases. Significant differences were found between the fibrosis and nonfibrosis groups in sex; disease severity; NLR; ALB and LDH levels; and percentages of lung reticular lesions, consolidations, and GGOs (P < 0.05). Multivariate analysis revealed LDH (OR = 1.004, 95% CI 1.000–1.007, P = 0.035), ALB (OR = 0.871, 95% CI 0.778–0.974, P = 0.015), lung reticular lesion volume (OR = 1.116, 95% CI 1.040–1.199, P = 0.002), and lung consolidation volume (OR = 1.131, 95% CI 1.012–1.264, P = 0.030) as independent risk factors. The follow-up results revealed significant improvements in pulmonary function, specifically in the FVC%, FEV1%, and DLCO%, but not in the FEV1/FVC. Quantitative chest CT analysis revealed significant differences in lung reticular lesions, consolidation, and GGO volumes but no significant difference in honeycomb volume.
Conclusions
The incidence of pulmonary fibrosis post-COVID-19 increases with disease severity. LDH, ALB, lung reticular lesions, and consolidation volume are independent risk factors for patients with fibrosis.
Supplementary Information
The online version contains supplementary material available at 10.1186/s40001-024-02197-5.
Keywords: COVID-19, Post-COVID-19 pulmonary fibrosis, Incidence, Clinical characteristics, Risk factors
Introduction
Coronavirus disease 2019 (COVID-19) is caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [1]. As of 31 March 2024, over 774 million confirmed cases and more than seven million deaths have been reported globally [2]. Although COVID-19 affects multiple organs, SARS-CoV-2 primarily targets the respiratory system [3]. Angiotensin-converting enzyme 2 (ACE2) is the main cellular receptor for SARS-CoV-2 and an essential component of the renin–angiotensin system (RAS) [4, 5]. ACE2 converts angiotensin II (Ang II) to the heptapeptide Ang1-7. Following SARS-CoV-2 infection, RAS activity can be disrupted, including the downregulation of ACE2, reduced expression of Ang1-7, and diminished protection against pulmonary fibrosis during the acute phase of the disease. Concurrently, there is an increase in Ang II production, upregulation of transforming growth factor (TGF)-β1 expression, and mediation of collagen deposition, all contributing to the promotion of pulmonary fibrosis [6]. Accumulating evidence indicates that COVID-19 has subacute and long-term effects, which are collectively known as post-acute sequelae of SARS-CoV-2 infection (PASC) or “long COVID” [7].
One of the most significant long-term respiratory morbidities impacting patients’ respiratory health is post-COVID-19 pulmonary fibrosis [8–10]. Post-COVID-19 pulmonary fibrosis can result from acute respiratory distress syndrome (ARDS) and pneumonia during acute COVID-19 infection [11–13]. This condition leads to persistent respiratory symptoms such as fatigue, cough, and dyspnea. In severe cases, it can culminate in respiratory failure and poses a life-threatening risk [14].
Post-COVID-19 pulmonary fibrosis, as defined by Tanni et al. [15] and others in the China expert consensus, is characterized by fibrotic changes related to functional impairment. These changes are mainly manifested as radiological features [16], including reticular shadows, traction bronchiectasis, parenchymal bands, structural distortion, and honeycombing. Pulmonary fibrosis can lead to a decline in lung function and a reduced quality of life [17]. The pathogenesis of post-COVID-19 pulmonary fibrosis involves complex interactions between virus-induced lung injury, the immune response, and subsequent fibrotic processes [18]. Risk factors for developing post-COVID-19 pulmonary fibrosis include older age, preexisting comorbidities, and the severity of the initial infection, particularly the need for mechanical ventilation [19].
Recent studies have highlighted the importance of early identification and management of PC19-PF to improve patient outcomes [20]. Imaging techniques such as high-resolution computed tomography (HRCT) play crucial roles in diagnosing and monitoring the progression of pulmonary fibrosis [21]. Additionally, pulmonary function tests (PFTs) are essential for assessing the extent of functional impairment [22].
Given the potential significant long-term health impacts of COVID-19, enhancing our understanding of post-COVID-19 pulmonary fibrosis is crucial. Therefore, this study aimed to investigate the impact of acute-phase COVID-19-related indicators on pulmonary fibrosis and identify its independent risk factors. Additionally, by conducting a follow-up chest CT scan and PFTs one year after the initial infection, we aimed to evaluate the progression of pulmonary fibrosis. This study aimed to provide clinical evidence for identifying risk factors and understanding the outcomes of fibrosis.
Materials and methods
Study design and participants
We retrospectively analyzed the clinical data of hospitalized patients with COVID-19 who were treated at Ningbo No.2 Hospital from December 2022 to July 2023. According to the Diagnosis and Treatment Protocol for COVID‐19 patients (Tentative 10th Version) [23], the diagnostic criteria are as follows: (a) clinical manifestations associated with SARS-CoV-2 infection; (b) positive nucleic acid test for SARS-CoV-2; and (c) positive antigen test for SARS-CoV-2. Exclusion criteria: (1) patients who did not meet the inclusion criteria; (2) patients with documented interstitial lung disease (ILD) or fibrotic lung conditions; (3) patients in the active phase of neurological or rheumatological disease; (4) patients currently receiving treatment for malignant tumors; (5) severe abnormalities in vital organ functions, such as the heart, liver, or kidney; (6) patients with new severe trauma, surgical history, or other infectious diseases; (7) patients who were hospitalized for less than one day; (8) patients who died during treatment; (9) patients who did not return for a follow-up chest HRCT or PFTs after discharge; (10) patients who lacked relevant laboratory indicators. On the basis of the inclusion and exclusion criteria and by randomly selecting fibrosis group patients at a 1:5 ratio to match the nonfibrosis group, a total of 156 patients were included in this study. Among them, 26 were assigned to the fibrosis group, and 130 were assigned to the nonfibrosis group (as depicted in Fig. 1).
Fig. 1.
Flowchart for patient screening
Information sources
The following data were collected from two groups of patients diagnosed with COVID-19: 1. basic information: name, age, sex, BMI. 2. Disease information: severity of COVID-19, history of underlying disease, and smoking history. 3. The laboratory indicators used were as follows: white blood cell count (WBC), red blood cell count (RBC), platelet count (PLT), neutrophil count (NE), lymphocyte count (Lym), neutrophil-to-lymphocyte ratio (NLR), serum creatinine (SCr), C-reactive protein (CRP), lactate dehydrogenase (LDH), serum albumin (ALB), fasting blood glucose (FBG), d-dimer (d–d), international normalized ratio (INR), creatine kinase (CK), alanine aminotransferase (ALT), procalcitonin (PCT), troponin (Tn), interleukin 2 (IL-2), interleukin 4 (IL-4), interleukin 6 (IL-6), interleukin 10 (IL-10), tumor necrosis factor alpha (TNF-α), and interferon gamma (IFN-γ). Blood tests were performed at the time of COVID-19 diagnosis. 4. Chest HRCT imaging data: all data were obtained from a 64-slice CT scanner (SOMATOM Force, Siemens) with a slice thickness of 1.25 mm. Baseline chest CT scans were obtained at the time of COVID-19 diagnosis. Follow-up chest CT scans were reexamined at 1 and 12 months after the diagnosis to evaluate the progression or regression of pulmonary fibrosis. 5. Pulmonary function: forced vital capacity (FVC % predicted), forced expiratory volume in 1 s (FEV1% predicted), FEV1/FVC % predicted, and diffusing capacity of the lungs for carbon monoxide (DLCO % predicted). Patients were followed up at 1 and 12 months after the diagnosis of COVID-19 to monitor changes in respiratory function over time.
Quantitative assessment of chest imaging
The chest imaging DICOM data were transferred to the image processing analysis software YZY CCIP (Yizhiyuan Health Technology Co., Ltd., Hangzhou, Zhejiang, China). YZY CCIP developed a lung segmentation method using the U-Net deep learning architecture [24, 25] for various lung disease phenotypes [26]. In summary, the software was utilized to identify and delineate areas of normal lung tissue and lung disease phenotypes (emphysema, honeycombing, reticular structures, ground-glass opacity, consolidation) on chest CT images. This process yielded volume data and the percentage of total lung volume for these regions. The distribution of these areas was then quantified across the entire lung, left and right lungs, and individual lobes, ultimately providing volume data and percentages of total lung volume for normal lung tissue or lung disease phenotypes (as shown in Additional file 1–2).
Disease severity classification
Disease severity classification and Murray score calculation were performed as previously reported [27]. The severity of COVID-19 was graded according to the China National Health Commission Guidelines for the Diagnosis and Treatment of SARS-CoV-2 infection. Laboratory-confirmed patients with fever, respiratory manifestations and radiological findings indicative of pneumonia were considered moderate cases. Laboratory-confirmed patients with any of the following conditions were considered to have severe COVID-19: (a) respiratory distress (respiration rate ≥ 30/min; (b) resting oxygen saturation ≤ 93%, and (c) arterial oxygen partial pressure (PaO2)/fraction of inspired oxygen (FiO2) ≤ 300 mmHg (1 mmHg = 0.133 kPa); (d) pulmonary lesions progress by more than 50% within 24–48 h. Laboratory-confirmed patients with any of the following conditions, such as (a) respiratory failure requiring mechanical ventilation, (b) shock, and (c) failure of other organs requiring intensive care unit (ICU) admission.
Statistical analysis
All the data were analyzed via SPSS statistical software version 26.0. For normally or approximately normally distributed data, the results are expressed as the means ± standard deviations, and differences between two groups were compared via independent sample t tests. For skewed data, the results are presented as M (Q1, Q3), and comparisons between two groups were made via the Mann‒Whitney U test. Categorical data are expressed as cases (%), and comparisons between groups were conducted via the χ2 test. Variables with statistical significance in the univariate analysis were included in the multivariate logistic regression analysis to identify independent risk factors for post-COVID-19 pulmonary fibrosis. A P value of < 0.05 was considered to indicate statistical significance.
Results
Patient demographics and incidence of post-COVID-19 pulmonary fibrosis
Among the 1,571 hospitalized COVID-19 patients, 952 (60.6%) were male, and 619 (39.4%) were female. In terms of disease severity, there were no mild cases, 1004 moderate cases, 511 severe cases, and 56 critical cases (as depicted in Fig. 2).
Fig. 2.
Distribution of disease severity among enrolled patients
After applying the exclusion criteria, 595 patients remained, comprising 377 moderate cases, 201 severe cases, and 17 critical cases. Within the fibrosis group, there were 8 moderate cases, 18 severe cases, and no critical cases. The overall incidence of post-COVID-19 pulmonary fibrosis was 4.37%, with an incidence of 2.08% in moderate cases and 8.22% in severe cases.
Comparative analysis of clinical features and comorbidities
In this study, as shown in Table 1, the proportion of males in the fibrosis group was significantly greater than that in the nonfibrosis group (84.6% vs. 56.9%, P = 0.008). Additionally, the proportion of severe cases in the fibrosis group was significantly greater than that in the nonfibrosis group (69.2% vs. 26.2%, P < 0.001). However, there were no statistically significant differences between the two groups in terms of age, BMI, smoking history, length of hospital stay, diabetes, hypertension, cardiovascular and cerebrovascular diseases, renal insufficiency, the presence of two or more underlying diseases, vaccinated patients, anti-viral treatment, or non-invasive mechanical ventilation (P > 0.05).
Table 1.
Comparison of the features of the subjects after admission
| Fibrosis group (n = 26) | Nonfibrosis group (n = 130) | P value | |
|---|---|---|---|
| Male (%) | 22/26 (84.6%) | 74/130 (56.9%) | 0.008* |
| Age (years) | 70.077 ± 11.218 | 71.246 ± 14.523 | 0.669 |
| BMI (kg/m2) | 23.3 (21.2, 24.7) | 22.9 (20.8, 24.2) | 0.426 |
| Severe COVID-19 (%) | 18 (69.2%) | 34 (26.2%) | <0.001* |
| History of smoking (%) | 6 (23.1%) | 21 (16.2%) | 0.394 |
| Hospital stay (days) | 11.0 (7.7, 16.5) | 10.0 (7.0, 14.3) | 0.444 |
| Diabetes (%) | 7 (26.9%) | 31 (23.8%) | 0.739 |
| Hypertension (%) | 13 (50.0%) | 52 (40.0%) | 0.345 |
| Cardiovascular and cerebrovascular diseases (%) | 9 (34.6%) | 28 (21.5%） | 0.152 |
| Renal insufficiency (%) | 6 (23.1%) | 17 (13.1%) | 0.189 |
| The presence of two or more underlying diseases (%) | 19 (73.1%) | 105 (80.8%) | 0.375 |
| Vaccinated patients (%) | 24 (92.3%) | 121 (93.1%) | 0.890 |
| Anti-viral treatment(%) | 25 (96.2%) | 113 (86.9%) | 0.179 |
| Non-invasive mechanical ventilation (%) | 7 (27.0%) | 19 (15.0%) | 0.124 |
Comparative analysis of serological markers
As shown in Table 2, compared with patients in the nonfibrosis group, patients in the fibrosis group presented significant differences in the following serological indicators: NLR (median: 8.11% compared with 5.67%, P = 0.021) and LDH (median: 319.5 U/L compared with 235.5 U/L, P < 0.001). Additionally, the serum ALB concentration was significantly lower in the fibrosis group (median: 30.858 g/L vs. 35.177 g/L, P < 0.001). However, there were no statistically significant differences between the two groups in terms of white blood cell count, red blood cell count, platelet count, neutrophil count, C-reactive protein level, or d-dimer level (P > 0.05).
Table 2.
Comparison of the features of the subjects after admission
| Fibrosis group (n = 26) | Nonfibrosis group (n = 130) | P value | |
|---|---|---|---|
| White blood cell count (× 109/L) | 6.0 (3.7, 8.4) | 6.0 (3.7, 8.2) | 0.971 |
| Red blood cell count (× 1012/L) | 3.671 ± 0.792 | 3.810 ± 0.714 | 0.377 |
| Platelet count (× 109/L) | 200.692 ± 89.337 | 195.394 ± 90.218 | 0.785 |
| Neutrophil count (× 109/L) | 4.8 (2.4, 8.7) | 6.0 (3.7, 7.7) | 0.312 |
| Lymphocyte count (× 109/L) | 0.85 (0.58, 1.20) | 0.70 (0.40, 1.10) | 0.080 |
| Neutrophil-to-lymphocyte ratio (%) | 8.11 (4.47, 14.4) | 5.67 (3.00, 10.00) | 0.021* |
| Serum creatinine (μmol/L) | 68.7 (59.6, 116.1) | 65.6 (49.4, 89.8) | 0.072 |
| C-reactive protein (mg/L) | 54.19 (9.56, 102.83) | 29.05 (4.96, 81.08) | 0.153 |
| Lactate dehydrogenase (U/L) | 319.5 (257.0, 400.8) | 235.5 (187.8, 293.5) | < 0.001* |
| Albumin (g/L) | 30.858 ± 5.576 | 35.177 ± 4.994 | 0.001* |
| Fasting blood glucose (mmol/L) | 7.57 (5.77, 11.80) | 7.07 (5.39, 9.45) | 0.163 |
| d-dimer levels (μmol/L) | 519.0 (339.0, 1590.5) | 416.5 (191.5, 1008.3) | 0.124 |
| International normalized ratio (%) | 1.06 (0.99, 1.18) | 1.04 (0.98, 1.13) | 0.392 |
| Creatine kinase (U/L) | 93.00 (42.75, 200.75) | 58.50 (38.50, 11.50) | 0.112 |
| Alanine aminotransferase | 26.5 (17.3, 57.3) | 26.0 (14.0, 50.8) | 0.426 |
| Procalcitonin (U/L) | 0.12 (0.07, 0.27) | 0.09 (0.06, 0.18) | 0.296 |
| Troponin (μg/L) | 0.016 (0.006, 0.027) | 0.007 (0.004, 0.026) | 0.189 |
| Interleukin-2 (pg/mL) | 1.82 (1.19, 2.72) | 1.93 (1.30, 2.70) | 0.754 |
| Interleukin-4 (pg/mL) | 0.828 ± 0.496 | 0.942 ± 0.665 | 0.475 |
| Interleukin-6 (pg/mL) | 9.95 (4.36, 19.72) | 10.88 (6.02, 23.40) | 0.357 |
| Interleukin-10 (pg/mL) | 4.37 (2.50, 4.94) | 4.61 (2.97, 6.56) | 0.284 |
| Tumor necrosis factor alpha-α (pg/mL) | 1.212 ± 0.389 | 1.301 ± 0.536 | 0.488 |
| Interferon-γ (pg/mL) | 1.79 (1.01, 2.29) | 1.63 (1.19, 2.24) | 0.719 |
Qualitative analysis of chest CT data from COVID-19 patients
We collected chest CT scans from each patient at the onset of illness and evaluated them via YZY CCIP image analysis software. The results indicated that ground-glass opacities, pulmonary parenchymal bands, irregular interfaces, and reticular patterns were the most common CT findings in COVID-19 patients (as depicted in Fig. 3).
Fig. 3.
Qualitative comparative analysis of chest CT data. A HRCT scans of a patient with acute COVID-19 in the nonfibrosis group. B Qualitative analysis of the chest CT image suggested a yellow area for the pulmonary disease phenotype when the glass area was ground. C HRCT scans of one acute COVID-19 patient in the fibrosis group. D Qualitative analysis of the chest CT image suggested that yellow areas are areas with a ground-glass lung disease phenotype. The pink area is the consolidation area. The orange area is the reticulate area
Quantitative chest CT analysis: comparison of pulmonary disease phenotypes
The quantitative structured data analysis, performed via YZY CCIP image analysis software, revealed that, as indicated in Table 3, the fibrosis group presented a greater proportion of lung consolidation volume (median: 1.756% compared with 0.030%, P < 0.001), ground-glass opacity volume (median: 4.206% compared with 0.442%, P = 0.006), and reticular pattern volume (median: 6.664% compared with 1.015%, P < 0.001) on chest HRCT than did the nonfibrosis group. However, there was no statistically significant difference between the two groups in terms of the proportion of honeycombing volume (P > 0.05).
Table 3.
Comparison of quantitative lung imaging results
| Fibrosis group (n = 26) | Nonfibrosis group (n = 130) | P value | |
|---|---|---|---|
| Proportion of lung consolidation volume (%) | 1.756 (0.150, 3.485) | 0.030 (0.006, 0.106) | < 0.001* |
| Proportion of honeycombing volume (%) | 0.006 (0.002, 0.217) | 0.002 (0.003, 0.246) | 0.083 |
| Proportion of ground-glass opacity volume (%) | 4.206 (0.317, 14.606) | 0.442 (0.460, 3.119) | 0.006* |
| Proportion of reticular pattern volume (%) | 6.664 (2.442, 12.912) | 1.015 (0.174, 3.656) | < 0.001* |
Multivariate logistic regression: independent risk factors for post-COVID-19 pulmonary fibrosis
After the general data and clinical features of the two groups were compared, variables with statistically significant differences (P < 0.05) were selected as independent variables. The presence of pulmonary fibrosis was designated the dependent variable, with fibrosis coded as 1 and nonfibrosis coded as 2. The independent variables included the NLR, ALB level, LDH level, proportion of total lung reticular lesion volume, total lung consolidation volume and total lung ground-glass opacity volume.
Multivariate logistic regression analysis was conducted to identify independent risk factors for post-COVID-19 pulmonary fibrosis. As shown in Table 4, the total lung reticular lesion volume (OR: 1.116, 95% CI 1.040–1.199), total lung consolidation volume (OR: 1.313, 95% CI 1.012–1.264), and LDH level (OR: 1.004, 95% CI 1.000–1.007) were identified as independent risk factors for post-COVID-19 pulmonary fibrosis. There was a negative correlation between post-COVID-19 pulmonary fibrosis and the serum ALB concentration (OR: 0.871, 95% CI 0.778–0.974).
Table 4.
Multivariate logistic regression analysis
| Risk factors | P value | OR-value | 95%CI |
|---|---|---|---|
| NLR | 0.323 | 1.024 | 0.977–1.072 |
| ALB | 0.015 | 0.871 | 0.778–0.974 |
| LDH | 0.035 | 1.004 | 1.000–1.007 |
| Proportion of reticular pattern volume | 0.002 | 1.116 | 1.040–1.199 |
| Proportion of lung consolidation volume | 0.030 | 1.131 | 1.012–1.264 |
| Proportion of ground-glass opacity volume | 0.511 | 0.983 | 0.934–1.034 |
Baseline and 1-year follow-up: quantitative chest CT and pulmonary function tests in fibrosis patients
Through a 12-month follow-up of lung function and chest CT scans in patients with post-COVID-19 pulmonary fibrosis, as shown in Table 5, we observed statistically significant improvements in FVC% (median: 78.892 vs. 80.376, P = 0.16), FEV1% (median: 80.7 vs. 81.3, P = 0.002), and DLCO% (median: 75.960 vs. 81.960, P < 0.001) compared with baseline data (1 month post-onset). However, there was no statistically significant difference in FEV1/FVC %.
Table 5.
Baseline and one-year follow-up results
| COVID-19 patients | Follow-up time after onset | P value | |
|---|---|---|---|
| One month | One year | ||
| Pulmonary function tests | |||
| FVC % predicted | 78.892 ± 7.300 | 80.376 ± 7.536 | 0.16* |
| FEV1% predicted | 80.7 (73.0, 82.9) | 81.3 (76.6, 84.6) | 0.002* |
| FEV1/FVC% predicted | 77.752 ± 8.180 | 77.324 ± 8.264 | 0.78 |
| DLCO % predicted | 75.960 ± 7.257 | 81.960 ± 9.391 | 0.001* |
| Lung imaging quantitative analysis | |||
| Proportion of lung consolidation volume(%) | 1.850 (0.175, 3.922) | 0.061 (0.005, 0.429) | 0.001* |
| Proportion of honeycombing volume(%) | 0.006 (0.001, 0.295) | 0.045 (0.001, 0.209) | 0.563 |
| Proportion of ground-glass opacity volume(%) | 4.461 (0.313, 16.577) | 1.243 (0.113, 2.738) | 0.002* |
| Proportion of reticular pattern volume(%) | 5.908 (2.393, 12.012) | 3.122 (0.504, 6.488) | 0.004* |
Quantitative analysis of chest CT images revealed significant differences in the proportion of lung consolidation volume (median: 1.850% compared with 0.061%, P < 0.001), ground-glass opacity volume (median: 4.461% compared with 1.234%, P = 0.002), and reticular pattern volume (median: 5.908% compared with 3.122%, P = 0.004) between the two groups. Conversely, the proportion of honeycomb volume did not significantly differ.
Discussion
With the COVID-19 pandemic, the incidence of post-COVID-19 pulmonary fibrosis has increased, leading to persistent respiratory symptoms that significantly impact patients’ quality of life and can be life-threatening in severe cases. Identifying risk factors for post-COVID-19 pulmonary fibrosis is crucial for understanding the disease. This study retrospectively analyzed data from COVID-19 patients to explore the incidence rate, clinical characteristics, risk factors, and outcomes of post-COVID-19 pulmonary fibrosis.
The reported incidence rates of post-COVID-19 pulmonary fibrosis vary significantly among studies. Zou et al. [28] reported that 84% of COVID-19 patients had ground-glass opacities at discharge, with 30% and 36% showing reticular and honeycomb patterns, respectively. Bocchino et al. [29] followed 84 nonintubated patients with high-resolution chest CT for up to 12 months and reported 50% fibrotic-like changes at 3 months, 42% at 6 months, and 5% at 12 months. Groff et al. [30] reported that only 7% of COVID-19 patients developed pulmonary fibrosis.
In our study, the incidence of pulmonary fibrosis was 4.37%, with 2.08% for mild cases and 8.22% for severe cases. The overall low incidence may be attributed to our exclusion criteria, which included patients receiving treatment for malignant tumors, those with active rheumatic immune diseases, or those with a history of pulmonary fibrosis. However, it is evident that the incidence of pulmonary fibrosis increases with the severity of COVID-19. Therefore, it is crucial to reduce the severity of the disease. Additionally, many recent studies have highlighted the role of genetic factors in COVID-19 outcomes, emphasizing the importance of considering genetic susceptibility when assessing the risk of post-COVID-19 conditions [31, 32], which warrants further exploration in the future.
Research by Alrajhi [33] suggested that male sex may be a potential risk factor for post-COVID-19 pulmonary fibrosis. Studies by the Chinese Research Hospital Association’s Professional Committee on Respiratory Diseases indicate that older age and disease severity are independent risk factors [34]. Our study revealed significant differences between the fibrosis and nonfibrosis groups in terms of sex and disease severity, which is consistent with previous findings. However, owing to the limited sample size, these factors were not included in the final analysis. Notably, the lack of a significant difference in age between the groups may be due to the predominance of elderly patients in both groups, which affects the age-related statistical results.
LDH is an enzyme released into the bloodstream when cells are damaged or die [35], serving as a biomarker of tissue injury [36]. In COVID-19, elevated LDH levels reflect lung tissue damage and inflammation, both of which may be associated with the progression of lung fibrosis [37, 38]. Recent studies have identified elevated LDH levels four months post-COVID-19 as an independent risk factor for residual fibrotic lesions [39]. Our study confirms that LDH is an independent risk factor for post-COVID-19 lung fibrosis, although its long-term effects require further validation due to the limited follow-up time.
Our study also revealed that the serum ALB concentration is an independent risk factor for post-COVID-19 lung fibrosis, with a negative correlation. SARS-CoV-2 infection may lead to the breakdown of thrombocytes [40]. Serum ALB has anti-inflammatory, nutritional, and hemorheological properties that prevent platelet activation and aggregation [41, 42]. Malnutrition or hypercatabolism can lead to hypoalbuminemia, whereas systemic inflammation and increased cytokine release can inhibit albumin production [43, 44]. These findings suggest that severe inflammation is correlated with lower ALB levels, although further validation is needed due to the limited sample size.
Recent studies suggest that a quantified uninvolved lung volume of ≤ 80% at admission predicts fibrotic lesions six months later [45]. Our study revealed that higher proportions of total lung reticulation and consolidation volumes at admission significantly correlate with the development of lung fibrosis. These findings align with those of previous studies and highlight these radiological parameters as potential independent factors for post-COVID-19 lung fibrosis. Increases in these parameters are associated with decreases in pulmonary function, gas exchange impairment, and reduced quality of life. However, these parameters are not absolute predictive tools, as fibrosis development is influenced by multiple factors, including baseline health status, comorbidities, treatment response, and genetic predispositions. Future research should consider a comprehensive assessment of these parameters alongside other clinical features to increase the prediction accuracy.
Our study revealed significant improvements in lung function parameters, including FVC %, FEV1%, and DLCO %, over a 12-month follow-up period in patients with post-COVID-19 pulmonary fibrosis. These findings are consistent with previous studies that reported similar trends in lung function recovery post-COVID-19. However, the lack of a significant change in FEV1/FVC% suggests that the obstructive component of lung function may not be as prominently affected in these patients. This aligns with the findings of Han et al., who reported persistent fibrotic-like changes, such as architectural distortion and traction bronchiectasis, in a subset of patients at a 2-year follow-up [46].
Quantitative analysis of chest CT images revealed significant differences in the proportions of lung consolidation volume, ground-glass opacity volume, and reticular pattern volume between the baseline and follow-up scans. These changes indicate that the inflammatory and fibrotic processes induced by COVID-19 gradually improve over time [19]. Interestingly, the proportion of honeycomb volume did not significantly differ, possibly because honeycombing typically represents irreversible fibrotic changes, which may not be as prevalent in the initial stages of post-COVID-19 pulmonary fibrosis [47].
The incidence of post-COVID-19 lung fibrosis is positively correlated with the severity of the disease during the acute phase. Studies indicate that patients with post-COVID-19 lung fibrosis are predominantly male and critically ill, exhibiting acute-phase clinical manifestations such as a high NLR, elevated LDH levels, and low ALB levels. Radiologically, these patients often present with increased consolidation, ground‒glass opacities, and reticular lesions. Our study identified LDH and ALB levels, as well as the percentage of total lung reticulation and consolidation volume, as independent risk factors for post-COVID-19 lung fibrosis.
These findings have significant clinical implications for the management and follow-up of COVID-19 patients. Elevated LDH levels and low ALB levels can serve as early indicators for the risk of developing pulmonary fibrosis, enabling clinicians to identify high-risk patients and implement timely interventions. Regular monitoring of these biomarkers, along with quantitative CT imaging, can facilitate the early detection and management of fibrotic changes, potentially improving patient outcomes and reducing long-term morbidity. Furthermore, understanding the role of these parameters in predicting fibrosis can aid in the development of targeted therapies and personalized treatment regimens, ultimately enhancing the quality of life for patients recovering from COVID-19.
However, this study has several limitations that need to be considered in future research. First, the relatively small sample size may limit the generalizability of the results. Second, the timing of SARS-CoV-2 diagnosis may differ from the actual time of SARS-CoV-2 infection. Third, some patient data (such as smoking history, height, and weight) rely on self-reports, which can lead to inaccuracies or incomplete recall. Future studies should expand the sample size to increase the representativeness and reliability of the findings. Prospective studies are recommended to collect more comprehensive data, further validating and deepening our findings. In the future, standardized questionnaires could be used, and patient data cross-validated with medical records where possible, to ensure the accuracy and consistency of the information collected and reduce recall bias.
Conclusion
The incidence of post-COVID-19 pulmonary fibrosis is positively correlated with the severity of the acute phase of the disease. Our study identified elevated LDH levels, low ALB levels, and higher proportions of total lung reticulation and consolidation volumes as independent risk factors for post-COVID-19 lung fibrosis. These findings underscore the importance of early identification and intervention in high-risk patients to mitigate long-term respiratory morbidity. Regular monitoring of these biomarkers, along with quantitative CT imaging, can facilitate early detection and management of fibrotic changes, potentially improving patient outcomes and reducing long-term morbidity. Future research should focus on expanding sample sizes and employing prospective study designs to validate and deepen these findings.
Supplementary Information
Additional file 1: Comparison of chest CT images before and after processing using YZY CCIP image analysis software. A and C show chest HRCT images of post-COVID-19 pulmonary fibrosis, revealing visible mesh shadows and funicular shadow fibrosis changes. B and D serve as a “YZY CCIP” for the pulmonary disease phenotype area, outlining the image to identify the orange mesh shadow region
Additional file 2: YZY CCIP Chest CT Image Analysis Findings. A shows volume data of the whole lung and each lobe. B shows volume data of the pulmonary disease phenotype in the whole lung and each lobe. C shows the lung disease phenotype or the volume of normal lung tissue/volume to obtain its volume percentage data
Acknowledgements
We would like to thank all the investigators and patients participating in this study.
Abbreviations
- COVID-19
Coronavirus disease 2019
- PFTs
Pulmonary function tests
- WBC
White blood cell count
- RBC
Red blood cell count
- PLT
Platelet count
- NE
Neutrophil count
- LYM
Lymphocyte count
- NLR
Neutrophil-to-lymphocyte ratio
- SCR
Serum creatinine
- CRP
C-reactive protein
- LDH
Lactate dehydrogenase
- ALB
Serum albumin
- FBG
Fasting blood glucose
- d-d
D-dimer
- INR
International normalized ratio
- CK
Creatine kinase
- ALT
Alanine aminotransferase
- PCT
Procalcitonin
- TN
Troponin
- IL-2
Interleukin 2
- IL-4
Interleukin 4
- IL-6
Interleukin 6
- IL-10
Interleukin 10
- TNF-α
Tumor necrosis factor alpha
- IFN-γ
Interferon gamma
Author contributions
All the authors contributed substantially to this manuscript. All the authors read and approved the final manuscript. Qiong Wang and Ying Zhou contributed to the study concept and design, data interpretation, critical revision of the manuscript, and final approval of the manuscript. Fangxue Jing and Yingying Feng contributed to the acquisition of the data and the data analysis. Zhaoxing Dong contributed to the conception and design of the work.
Funding
This work was financially supported by Ningbo Clinical Research Center for Respiratory Disease (Grant No. 2022L004).
Data availability
No datasets were generated or analysed during the current study.
Declarations
Ethics approval and consent to participate
The study protocol was reviewed and approved by the Ethical Committee of the Research and Development Department of NingBo No. 2 Hospital, reference number PJ-NBEY-KY-2024-089-01. This study has been granted an exemption from requiring informed consent by the Research and Development Department from NingBo No. 2 Hospital.
Consent for publication
All authors agreed to publish this manuscript.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Ying Zhou: co-first author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Additional file 1: Comparison of chest CT images before and after processing using YZY CCIP image analysis software. A and C show chest HRCT images of post-COVID-19 pulmonary fibrosis, revealing visible mesh shadows and funicular shadow fibrosis changes. B and D serve as a “YZY CCIP” for the pulmonary disease phenotype area, outlining the image to identify the orange mesh shadow region
Additional file 2: YZY CCIP Chest CT Image Analysis Findings. A shows volume data of the whole lung and each lobe. B shows volume data of the pulmonary disease phenotype in the whole lung and each lobe. C shows the lung disease phenotype or the volume of normal lung tissue/volume to obtain its volume percentage data
Data Availability Statement
No datasets were generated or analysed during the current study.