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Virology Journal logoLink to Virology Journal
. 2021 Nov 18;18:225. doi: 10.1186/s12985-021-01699-6

Predictors of the chest CT score in COVID-19 patients: a cross-sectional study

Niloofar Ayoobi Yazdi 1,#, Abdolkarim Haji Ghadery 2,#, Seyed Ahmad Seyedalinaghi 3, Fatemeh Jafari 4, Sirous Jafari 4, Malihe Hasannezad 4, Hamid Emadi Koochak 4, Mohammadreza Salehi 4, Seyed Ali Dehghan Manshadi 4, Mohsen Meidani 4, Mahboubeh Hajiabdolbaghi 4, Zahra Ahmadinejad 4, Hossein Khalili 5, Mohammad-Mehdi Mehrabinejad 2,✉,#, Ladan Abbasian 4,✉,#
PMCID: PMC8600490  PMID: 34794467

Abstract

Background

Since the COVID-19 outbreak, pulmonary involvement was one of the most significant concerns in assessing patients. In the current study, we evaluated patient’s signs, symptoms, and laboratory data on the first visit to predict the severity of pulmonary involvement and their outcome regarding their initial findings.

Methods

All referred patients to the COVID-19 clinic of a tertiary referral university hospital were evaluated from April to August 2020. Four hundred seventy-eight COVID-19 patients with positive real-time reverse-transcriptase-polymerase chain reaction (RT-PCR) or highly suggestive symptoms with computed tomography (CT) imaging results with typical findings of COVID-19 were enrolled in the study. The clinical features, initial laboratory, CT findings, and short-term outcomes (ICU admission, mortality, length of hospitalization, and recovery time) were recorded. In addition, the severity of pulmonary involvement was assessed using a semi-quantitative scoring system (0–25).

Results

Among 478 participants in this study, 353 (73.6%) were admitted to the hospital, and 42 (8.7%) patients were admitted to the ICU. Myalgia (60.4%), fever (59.4%), and dyspnea (57.9%) were the most common symptoms of participants at the first visit. A review of chest CT scans showed that Ground Glass Opacity (GGO) (58.5%) and consolidation (20.7%) were the most patterns of lung lesions. Among initial clinical and laboratory findings, anosmia (P = 0.01), respiratory rate (RR) with a cut point of 25 (P = 0.001), C-reactive protein (CRP) with a cut point of 90 (P = 0.002), white Blood Cell (WBC) with a cut point of 10,000 (P = 0.009), and SpO2 with a cut point of 93 (P = 0.04) was associated with higher chest CT score. Lung involvement and consolidation lesions on chest CT scans were also associated with a more extended hospitalization and recovery period.

Conclusions

Initial assessment of COVID-19 patients, including symptoms, vital signs, and routine laboratory tests, can predict the severity of lung involvement and unfavorable outcomes.

Keywords: COVID-19, Computed tomography, Outcome, Recovery, Chest CT score

Background

The coronavirus disease (COVID-19), caused by a novel coronavirus named severe acute respiratory syndrome coronavirus2 (SARS-CoV-2), has spread to 223 countries with more than 186 million confirmed cases and more than 4 million deaths (World Health Organization) [1]. SARS-CoV-2 shares similarities in disease dynamics, transmission route, and cell entry receptors, angiotensin-converting enzyme 2 (ACE2) with severe acute respiratory syndrome coronavirus (SARS-CoV) [24].

Person-to-person transmission of SARS-CoV-2 occurs with respiratory droplets from an infected person to others. Viral shedding may occur 1–2 days before the onset of symptoms and may continue for 1–2 weeks in mild to moderate cases or go beyond two weeks in severe cases[5, 6]. Symptoms usually appear between 2 and 14 days after exposure; The common initial symptoms of COVID-19 are fever, cough, fatigue, and dyspnea [7, 8], with more specific symptoms, including loss of taste and smell [6]. SARS-CoV-2 targets multiple organs, including respiratory, cardiac, and renal systems causing pneumonia and respiratory failure in patients [9, 10]. In addition, systemic inflammatory response syndrome and cytokine storm contribute to multi-organ failure and coagulopathy in critical patients with COVID-19 [11, 12].

Computed tomography (CT) is the most sensitive tool for diagnosing COVID-19, and several radiological patterns are seen in different phases of disease [13, 14]. Among different patterns of chest CT scan, ground-glass opacities (GGO) and mixed GGO with consolidation is reported as the most common patterns in COVID-19 patients [15]. Although definite diagnosis relies on real-time reverse-transcriptase-polymerase chain reaction (RT-PCR) [16], chest CT is a valuable modality to measure the extent of lung involvement and propose a treatment plan.

Initial assessment of COVID-19 patients is essential for further management. In this study, we assessed the patient’s signs, symptoms, laboratory tests, and imaging findings to identify which initial clinical and laboratory findings may predict the severity of lung involvement and, accordingly, short-term outcome.

Method

Study design and participants

The study protocol and consent notes were reviewed and approved by the ethics board of our institute (approval code: TUMS.VCR.REC.1399.138). This study is a cross-sectional, observational, single-center study conducted between April and August 2020 at a tertiary medical center. All referred patients to the COVID-19 clinic of Imam Khomeini Hospital, university hospital, were evaluated for eligibility of participation in the study. Patients with mild, moderate, or severe clinical symptoms suggestive of COVID-19 who had positive RT-PCR (226, 47.2%) confirming COVID-19 or suggestive chest CT scan (252, 52.8%) were included after signing a written informed consent form. Patients with history of previous lung diseases excluded from study. Four physicians did the patient examination and data registration, and five medical residents followed up with the patients through telephone calls. Both hospitalized (after discharge) and outpatient participants were followed weekly through telephone calls until symptoms resolved. Treatment regimens were based on the latest version of the national protocol of COVID-19.Hydroxychloroquine was used as the primary therapeutic option in the outpatient setting; hydroxychloroquine, lopinavir/ritonavir (Kaletra), atazanavir (nonboosted with ritonavir or cobicistat) were administered for hospitalized patients, and in case of severe hypoxemia (not attaining SpO2 of > 88% with reservoir mask), corticosteroid was given.

Data collection

Following clinical data were collected in the specific forms for each patient, including: (a) demographics information: (age and sex); (b) vital signs: temperature (T-Celsius), oxygen saturation (SpO2), respiratory rate (RR per minute), and pulse rate (PR per minute); (c) symptoms: myalgia, generalized weakness, fever, chills, headache, chest pain, dyspnea, sore throat, cough, sputum, loss of appetite, loss of taste, anosmia; (d) comorbidities: hypertension (HTN), diabetes mellitus (DM); (e) laboratory data: white blood cell (WBC-cell/mm3), lymphocyte count (cell/mm3), platelet(cell/mm3), C-reactive protein (CRP-mg/L), erythrocyte sedimentation rate (ESR-mm/hr), and lactate dehydrogenase (units/L); (f) admission status: inpatient or outpatient; (g) Intensive care unit (ICU) admission; (h) death; (i) radiologic findings (will be mentioned in following section); (j) length of hospitalization (day) and recovery time (day). The recovery time was defined as the patient's subjective statement indicating no symptoms other than his/her baseline.

Chest CT protocols and interpretation

All chest CT scans were performed on either lightspeed 64-detector CT (GE Healthcare) or the 16-slice (Siemens SOMATOM Emotion) MDCT scanner with patients in the supine position at full inspiration breath-hold. The leading scanning parameters were as follows: 120 kVp tube voltage; 50–150 mAs tube current; 0.75 s tube rotation time; 0.5–0.75 s gantry rotation time; 2–3-mm section thickness; and 0.6–2 mm beam collimation.

The visual chest CT interpretation was performed using a single, experienced (10 years) thoracic radiologist. The radiologist was blinded to clinical and laboratory data of participants and reviewed on both lung and mediastinal window settings. The presence of CT features including (a) predominant pattern of lesions: ground-glass opacification (GGO), consolidation, or mixed GGO and consolidation; (b) dominant distribution of lesions: central, peripheral, diffuse, or peri- broncho vascular; (c) shape of lesions: round, elongated, wedged, or confluent; (d) additional findings: crazy paving pattern, reverse-halo sign, interlobular septal thickening, linear opacities combined, air bronchogram sign, tree in bud, adjacent pleural thickening, pleural effusion, pericardial effusion, lymphadenopathy, and pulmonary emphysema, was reported as defined in Fleischner Society Glossary of terms for Thoracic Imaging [17].

To quantify the extension of lung lesions, a scoring system as follows was used: each of the five lobes of lungs visually scored from 0 to 5 (0, no involvement; 1, < 5% involvement; 2, 5–25% involvement; 3, 26–49% involvement; 4, 50–75% involvement; 5, > 75% involvement). Then, the total chest CT score was calculated by the sum of each lob's scores, ranging from 0 to 25 [18].

Statistical analysis

The statistical analysis was performed using SPSS version 16 (SPSS Inc. Chicago, IL). Continuous variables were presented as mean (standard deviation), and categorical variables as frequency and percentages. Variables were tested for normality using Kolmogorov–Smirnov test. Normally distributed continuous variables were analyzed using the independent sample t-test; otherwise, the Mann–Whitney U test was used. A Chi-square test was employed for nominal variables. P values less than 0.05 were considered statistically significant.

Results

Participant’s characteristic

A total of 478 participants were recruited in the current study with convenient sampling. Of those, 353 (73.6%) participants were admitted to the hospital, and among hospitalized participants, 42 (8.7%) were admitted to ICU. The patient's mean age was 53.92 (15.4), and 267 (55.8%) was male. The most common complaints of patients were myalgia (60.4%), fever (59.4%), dyspnea (57.9%), chills (49.5%), and generalized weakness (45.3%). Demographics, initial signs, symptoms, and laboratory data of all participants are listed (Table 1).

Table 1.

Detail of demographic data and baseline signs, symptoms, and laboratory findings of all studied participants

Variables All patients N = 478
Demographics
Age (years) 53.92(15.4)
 < 39 90 (18.8%)
 39–49 102 (21.3%)
 50–59 104 (21.7%)
 60–69 98 (20.5%)
 ≥ 70 84 (17.5%)
Gender (male) 267 (55.8%)
Symptoms on the first visit
Myalgia 289 (60.4%)
Generalized weakness 217 (45.3%)
Fever 284 (59.4%)
Chills 237 (49.5%)
Headache 134 (28%)
Chest pain 126 (26.3%)
Dyspnea 277 (57.9%)
Sore throat 80 (16.7%)
Cough 334 (69.8%)
Sputum 15 (3.1%)
Loss of appetite 194 (40.5%)
Loss of taste 48 (10%)
Anosmia 60 (12.5%)
Vital signs on first visit
Temperature (°C)
 ≤ 37.2 212 (44.3%)
 ≥ 37.3 266 (55.7%)
O2 saturation %
 ≥ 93 276 (57.7%)
 < 93 202 (42.3%)
Respiratory rate (breaths/minute)
 < 25 389 (81.3%)
 ≥ 25 89 (18.7%)
Initial laboratory findings
White blood cell (WBC) (cell/ mm3)
 < 4000 51 (10.6%)
 4000–9999 364 (76.3%)
 ≥ 10,000 63 (13.1%)
Lymphocyte count (cell/ mm3)
 ≤ 1000 103 (21.5%)
 > 1000 375 (78.5%)
Platelet (cell/mm3)
 < 150,000 82 (17.1%)
 ≥ 150,000 396 (82.9%)
C-reactive protein (CRP) (mg/L)
 ≤ 90 17 (3.5%)
 ≥ 91 461 (96.5%)
Erythrocyte sedimentation rate (ESR) (mm/h)
 ≤ 60 13 (2.7%)
 ≥ 61 465 (97.3%)
Lactate dehydrogenase (LDH) (units/L)
 < 480 65 (13.5%)
 ≥ 480 413 (86.5%)
Underlying disease
 DM 132 (27.6%)
 HTN 136 (28.4%)

All variables are reported as N (%)

HTN: hypertension, DM: diabetes mellitus

Chest CT scan findings

The most common patterns of pulmonary involvement were GGO (58.5%) and consolidation 99 (20.7%) with peri-broncho-vascular (33.7%), peripheral (33%), and diffuse (32%) distribution, and the most common shape of lesions were confluent (47.2%). Detailed chest CT scan findings are presented (Table 2).

Table 2.

Radiological findings in all studied participants

Variables All patients N = 478
Pulmonary involvement scores*
 RUL Total Score 1.57 (1.75)
 RML Total Score 1.26 (1.71)
 RLL Total Score 1.91 (1.84)
 LUL Total Score 1.56 (1.72)
 LLL Total Score 1.79 (1.84)
 Total pulmonary involvement Score 8.11 (8.08)
Frequency of lobe involvement
 RUL 266 (55.5%)
 RML 225 (47%)
 RLL 304 (63.5%)
 LUL 274 (57.3%)
 LLL 287 (60%)
Laterality of lung involvement
 Unilateral 52 (10.8%)
 Bilateral 286 (59.8)
Pattern of lesions
 GGO 280 (58.5%)
 Consolidation 99 (20.7%)
 Mixed GGO and consolidation 78(16.3%)
Dominant distribution of lesions
 Peripheral 158 (33%)
 Central 6 (1.3%)
 Diffuse 153 (32%)
 Peri broncho vascular 161 (33.7%)
Shape of lesions
 Round 73 (15.2%)
 Elongated 82 (17.1%)
 Wedged 183 (38.2%)
 Confluent 226 (47.2%)
Additional findings
 Crazy paving pattern 87 (31%)
 Reverse-halo 14 (2.9%)
 Interlobular septal thickening 20 (4.1%)
 Linear opacities combined 81 (16.9%)
 Air bronchogram sign 114 (23.8%)
 Tree in bud 15 (3.1%)
 Adjacent pleura thickening 15 (3.1%)
 Pleural effusion 27 (5.6%)
  Unilateral 12 (2.5%)
  Bilateral 15 (3.1%)
 Pericardial effusion 10 (2%)
 Lymphadenopathy 7 (1.4%)
 Pulmonary emphysema 10 (2%)

RUL, right upper lobe; RML, right middle lobe; RLL, right lower lobe; LUL, left upper lobe; LLL, left lower lobe

*Pulmonary involvement scores are reported as mean (standard deviation); all other variables are reported as N (%)

Our results showed that the total chest CT score was significantly higher in patients with anosmia (mean of 12.46 ± 7.73 vs. 8.73 ± 8.33; P = 0.01), and had a significant positive association with RR with a cut point of 25 (mean of 8.37 ± 7.94 vs. 12.37 ± 9.23; P = 0.001), CRP with a cut point of 90 (mean of 7.82 ± 7.89 vs. 10.83 ± 8.67; P = 0.002), WBC with a cut point of 10,000 (mean of 8.70 ± 8.33 vs. 12.02 ± 8.17; P = 0.009) and negative association with SpO2 with a cut point of 93 (mean of 9.75 ± 8.54 vs. 7.76 ± 7.58; P = 0.04). Chest CT score was also associated with a higher risk of ICU admission (mean of 11.10 ± 9 vs. 7.71 ± 7.88; P = 0.003), longer hospital stay (mean of 9.08 ± 8.23 vs. 12 ± 9.21; P = 0.037) and recovery period (mean of 7.28 ± 8.02 vs. 9.29 ± 8.09; P = 0.009) (Table 3).

Table 3.

Demographic, clinical, laboratory findings, and outcomes of patients with COVID-19 based on the total chest CT scan score

Variables All patients = 478 Total CT score Mean ± SD P value
Demographic data
Age (years)
 < 39 90 (18.8%) 10.47 ± 8.02 0.54a
 39–49 102 (21.3%) 8.15 ± 8.19
 50–59 104 (21.7%) 9.96 ± 9.03
 60–69 98 (20.5%) 9.57 ± 8.23
 ≥ 70 84 (17.5%) 8.44 ± 8.04
Sex
 Male 267 (55.8%) 7.70 ± 7.84 0.193
 Female 211 (44.2%) 8.67 ± 8.37
Symptoms on first visit
Fever
 Yes 284 (59.4%) 9.06 ± 8.25 0.78
 No 194 (40.6%) 9.34 ± 8.53
Dyspnea
 Yes 277 (57.9%) 9.56 ± 8.58 0.20
 No 201 (42.1%) 8.38 ± 7.82
Sputum
 Yes 15 (3.1%) 9.64 ± 9.70 0.87
 No 463 (96.9%) 9.14 ± 8.30
Cough
 Yes 334 (69.8%) 9.10 ± 8.20 0.85
 No 144 (30.2%) 9.29 ± 8.68
Chest pain
 Yes 126 (26.3%) 9.42 ± 8.08 0.74
 No 352 (73.7) 9.07 ± 8.43
Anosmia
 Yes 60 (12.5%) 12.46 ± 7.73 0.01*
 No 418 (87.5%) 8.73 ± 8.33
Vital signs on first visit
Temperature (°C)
 ≤ 37.2 212 (44.3%) 9.00 ± 8.37 0.76
 ≥ 37.3 266 (55.7%) 9.28 ± 8.35
Respiratory rate(breath/minute)
 < 25 389 (81.3%) 8.37 ± 7.94 0.001*
 ≥ 25 89 (18.7%) 12.37 ± 9.23
SpO2%
 ≥ 93 276 (57.7%) 7.76 ± 7.58 0.04*
 < 93 202 (42.3%) 9.75 ± 8.54
Initial laboratory findings
WBC (cell/ mm3)
 ≤ 10,000 415 (86.9%) 8.70 ± 8.33 0.009*
 > 10,000 63 (13.1%) 12.02 ± 8.17
Lymphocyte count(cell/ mm3)
 ≤ 1000 103 (21.5%) 9.77 ± 8.88 0.42
 > 1000 375 (78.5%) 8.91 ± 8.07
CRP (mg/L)
 ≤ 90 17 (3.5%) 7.82 ± 7.89 0.002*
 91 461 (96.5%) 10.83 ± 8.67
ESR(mm/h)
 ≤ 60 13 (2.7%) 8.24 ± 8.45 0.10
 ≥ 61 465 (97.3%) 10.05 ± 8.54
LDH (units/L)
 < 480 65 (13.5%) 9.02 ± 7.92 0.12
 ≥ 480 413 (86.5%) 11.02 ± 8.64
Underlying disease
DM
 Yes 132 (27.6%) 8.54 ± 8.10 0.35
 No 346 (72.4%) 9.42 ± 8.45
HTN
 Yes 136 (28.4%) 9.11 ± 8.30 0.92
 No 342 (71.6%) 9.22 ± 8.36
Outcomes
ICU admission
 Yes 42 (8.7%) 11.10 ± 9 0.003*
 No 436 (92.2%) 7.71 ± 7.88
Recovery time (days)
 < 15 278 (58.1%) 7.28 ± 8.02 0.009*
 ≥ 15 200 (41.9%) 9.29 ± 8.09
Hospitalization time (days)
 < 15 437 (91.4%) 9.08 ± 8.23 0.037*
 ≥ 15 41 (8.6%) 12 ± 9.21
Death
 Yes 16 (3.3%) 8.51 ± 1.85 0.86
 No 462 (96.7%) 8.05 ± 0.37

HTN, hypertension; DM, diabetes mellitus; SpO2, oxygen saturation; WBC, white blood cell; LDH, lactate dehydrogenase; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; ICU, intensive care unit

*Statistically significant (p value < 0.05)

aOne way ANOVA test is used; independent t-test or Mann–Whitney U test used for all other comparisons

Further analysis showed consolidation on chest CT scan had a significant association with initial lower SpO2 (P = 0.001), higher risk of ICU admission (P = 0.005), extended hospitalization (P = 0.003), and longer recovery time (P = 0.008) (Table 4).

Table 4.

Details of demographic and clinical data of patients with or without consolidation

Variables All patients N = 478 With consolidation N = 99 Without consolidation N = 379 P value
Demographic data
Age 53.92 (15.4) 51.96 (15.6) 54.62 (15.6) 0.14
Gender
 Male 267 (55.8%) 55 (11.5%) 212 (44.3%) 0.94a
 Female 211 (44.2%) 44 (20.8%) 167 (23.4%)
Clinical data
Temperature (°C) 37.49 (0.7) 37.56 (0.7) 37.47 (0.7) 0.29
Respiratory rate (breath /per minute) 21.65 (5.1) 22.18 (6) 21.41 (4.9) 0.19
SpO2 91.07 (5.4) 89.39 (6.4) 91.47 (5.3) 0.001*
WBC (cell/mm3) 7.74 (5.1) 7.88 (5.6) 7.29 (4.2) 0.32
Lymphocyte count(cell/mm3) 1372.8 (1341.3) 1375.5 (1876.5) 1279 (870) 0.56
LDH (U/L) 640.8 (302.4) 624.6 (266.4) 630.1 (302.4) 0.90
CRP (mg/L) 96.90 (76.2) 105.97 (69.6) 90.82 (77) 0.13
ESR (mm/h) 74.06 (32.2) 77.34 (33.3) 71.23 (31.2) 0.16
Underlying disease
 DM 132 (27.6%) 26 (5.4%) 106 (22.2%) 0.73a
 HTN 136 (28.4%) 23 (4.8%) 113 (23.6%) 0.18a
Recovery time (days) 15.48 (8.3) 17.46 (9) 14.95 (8.1) 0.008*
Hospitalization duration (days) 6.89 (7.2) 8.97 (9.4) 6.25 (6.2) 0.003*
ICU admission 42 (8.7%) 20 (20.2%) 22 (5.5%) 0.005*a
Death 16 (3.3%) 4 (0.8%) 12 (2.5%) 0.70a

HTN, hypertension; DM, diabetes mellitus; SpO2, oxygen saturation; WBC, white blood cell; LDH, lactate dehydrogenase; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; ICU, intensive care unit

*Statistically significant (p value < 0.05), reported as mean (standard deviation), all other variables reported as N (%)

aChi-squared test is used; independent t-test or Mann–Whitney U test used for all other comparisons

Discussion

As treatment protocols are based on the extent of lung involvement, assessing the severity of pulmonary involvement is crucial in determining the treatment plan for COVID-19 infected patients. Chest CT scan is not available everywhere, and its usage is sometimes limited, especially in pregnant patients. Therefore, physicians need prompt assessment according to patient’s initial signs and symptoms and routine laboratory tests for timely management. In the current study, we assessed clinical and laboratory findings on the first visit to predict the extent of lung involvement in COVID-19 patients. We found that patients with anosmia, lower SpO2 (< 93), and higher respiratory rate (> 25 per minute), WBC count (> 10,000 cells/mm3), CRP (> 90 mg/Liter) had higher total CT scores. Besides, consolidation opacities as the worst lung lesions were more commonly detected in patients with lower initial SpO2 or admitted to ICU. Moreover, patients with consolidation on their chest CT scan experienced extended hospitalization (≥ 15) and recovery period (≥ 15). A higher chest CT score was also associated with more extended hospitalization and recovery time.

Predictors of severe lung involvement

In line with previous studies, our results showed that higher initial WBC and CRP are associated with more severe cases of COVID-19 patients, as WBC > 1000 cell/mm3 and CRP > 90 mg/L were associated with higher chest CT scores. Similar to our findings, Salvatore et al. reported hospitalized and critical COVID-19 patients had higher CRP, leukocyte count, neutrophils, LDH, D-dimer, and troponin [19]. Zhang et al. also reported that chest CT score positively associated with WBC count, CRP, ESR, procalcitonin, and abnormal coagulation function [20].

Among several clinical symptoms on the first visit, just anosmia was associated with extended pulmonary involvement. A literature review did not show any previous report of extended lung involvement in patients with anosmia, and our study is the first to report this correlation. Association of anosmia and pulmonary involvement can be justified by the roll ofchemokines, as previous reports defined CXCL10 contribution in both cytokine storm of COVID-19 patients causing acute respiratory distress syndrome (ARDS) and demyelination process of the olfactory nerve causing anosmia [21].

Several clinical signs of patients on the first visit, including higher RR and lower SpO2, were associated with higher total chest CT scores. Lower SpO2 was also associated with consolidation opacities as the most severe lesion of COVID-19 in chest CT scan. In line with our findings, Kunwei Li et al. also showed that patients with RR ≥ 30 times/min, and SpO2 ≤ 93% as categorized in severe type patients, had significantly higher total chest CT scores than common type patients [22]. Another study also confirmed that severe/critical patients with respiratory rate ≥ 30 times/min and SpO2 of 93% or less in a resting state had higher total CT scores than ordinary COVID-19 patients [23]. Aalinezhad [24] and Osman[25] et al. also reported higher chest CT score is inversely associated with O2 saturation. Furthermore, a multicenter cohort study demonstrated that consolidation in upper lungs on the initial chest CT of COVID-19 patients was associated with increased odds of adverse endpoints, including SpO2 < 93% and partial arterial pressure of oxygen less than 60 mm Hg on room air [26].

Chest CT predictors of unfavorable outcomes

We found that ICU admission, more extended hospitalization, and recovery period were associated with higher total CT scores and consolidation opacities in chest CT of these patients. Similarly, a previous study showed a higher pulmonary score ≥ of 8 accompanied by age ≥ 53, and SpO2 ≤ 91 predicts ICU admission and mortality [27]. Deepak Nagra et al. also reported a higher lung opacification score is a reliable predictor of ICU admission for COVID-19 patients [28]. Another study on baseline chest CT scans and clinical and laboratory data of 72 patients admitted with COVID-19 pneumonia showed lung severity score >  was associated with a significantly lower recovery rate and discharge and extended hospitalization in patients admitted for COVID-19 pneumonia [29]. Similar to our findings, Ahlstrand et al. previously reported that chest CT score at hospital admission correlates closely with hospital length of stay and ICU admission [30]. CT severity score combined with age and history of at least one underlying disease had 79.7% sensitivity and 65.5% specificity in predicting the adverse outcomes in a previous study [31].

Initial assessment of patients in the clinic determines the treatment plan, so we assessed lung involvement based on initial signs, symptoms, and laboratory tests. Taken together, we found that patients with anosmia, higher respiratory rate, WBC count, or CRP, and lower SpO2 had extended pulmonary involvement of COVID-19 pneumonia, which can cause adverse outcomes, including more extended hospitalization and recovery period. Hence, those patients should be prioritized for greater attention and intensive care.

Limitations

As recovery time was defined as a subjective statement of patients, this could have resulted in a conclusion error; inviting the patients to the clinic for evaluation could have been more precise to confirm recovery. In addition, in this study, we assessed just the presence of symptoms, not their severity; we recommend further studies assessing the severity of symptoms and their correlation with lung involvement or adverse outcomes.

Conclusion

Extended lung involvement of COVID-19 pneumonia can be predicted in clinics with patient's initial symptoms, vital signs, and laboratory tests, including anosmia, low SpO2, high RR, WBC, and CRP. Therefore, these patients should be considered high-risk patients for further medical planning.

Acknowledgements

The authors would like to thank all participants in this study.

Abbreviations

COVID-19

The coronavirus disease

SARS-CoV-2

Severe acute respiratory syndrome coronavirus2

ACE2

Angiotensin-converting enzyme 2

SARS-CoV

Severe acute respiratory syndrome coronavirus

CT

Computed tomography

GGO

Ground glass opacity

RT-PCR

Real-time reverse-transcriptase-polymerase chain reaction

SpO2

Oxygen saturation

RR

Respiratory rate

PR

Pulse rate

HTN

Hypertension

DM

Diabetes mellitus

WBC

White blood cell

CRP

C-reactive protein

ESR

Erythrocyte sedimentation rate

LDH

Lactate dehydrogenase

ICU

Intensive care unit

ARDS

Acute respiratory distress syndrome

RUL

Right upper lobe

RML

Right middle lobe

RLL

Right lower lobe

LUL

Left upper lobe

LLL

Left lower lobe

Authors' contributions

NAY Conceptualized the project and developed the study methodology and also had the major role in investigation of radiological findings of patients and meanwhile Review & Editing the manuscript. AHG developed the study methodology and conducted formal analysis meanwhile participated in writing the original draft. SS participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. FJ participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. SJ participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. MH participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. HEK participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. MS participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. SADM participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. MM participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. MHajiabdolbaghi participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. ZA participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. HK participated in Investigation, Resources, Visualization, Validation, Review & Editing the manuscript. MMM developed the study methodology and conducted formal analysis meanwhile participated in writing the original draft. LA was Project administrative and supervisor and acquired the funding for project and meanwhile she participated in writing and editing the manuscript. All authors read and approved the final manuscript.

Funding

This work was supported by a grant from Tehran University of Medical Sciences (Grant No: 47147), the fund is used for laboratory and imaging costs of patients.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

Study protocols and patients consent forms were reviewed and approved by the ethics committee of Tehran University of Medical Sciences (approval code: TUMS.VCR.REC.1399.138). All patients signed an informed consent form before entering the study.

Consent for publication

Not applicable.

Competeing interest

The authors declare no competing interest.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Niloofar Ayoobi Yazdi and Abdolkarim Haji Ghadery are joint first authors

Ladan Abbasian and Mohammad-Mehdi Mehrabinejad are joint senior authors

Contributor Information

Niloofar Ayoobi Yazdi, Email: nayoobi@tums.ac.ir.

Abdolkarim Haji Ghadery, Email: ak-hajighadery@alumnus.tums.ac.ir.

Seyed Ahmad Seyedalinaghi, Email: A_alinaghi@sina.tums.ac.ir.

Fatemeh Jafari, Email: fatemejafari72@gmail.com.

Sirous Jafari, Email: jafarisi@tums.ac.ir.

Malihe Hasannezad, Email: mhasannezhad@sina.tums.ac.ir.

Hamid Emadi Koochak, Email: emadiham@tums.ac.ir.

Mohammadreza Salehi, Email: mr-salehi@sina.tums.ac.ir.

Seyed Ali Dehghan Manshadi, Email: a_dehghanm@sina.tums.ac.ir.

Mohsen Meidani, Email: meidani@med.mui.ac.ir.

Mahboubeh Hajiabdolbaghi, Email: hajiabdo@tums.ac.ir.

Zahra Ahmadinejad, Email: ahmadiz@tums.ac.ir.

Hossein Khalili, Email: khalilih@tums.ac.ir.

Mohammad-Mehdi Mehrabinejad, Email: mm-mehrabinejad@alumnus.tums.ac.ir.

Ladan Abbasian, Email: la-abbasian@sina.tums.ac.ir.

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Associated Data

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

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.


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