This prospective, multicenter cohort study was approved under a waiver of informed consent by the Ethics Committees of both institutions (with ethics code IR.TUMS.VCR.REC.1399.319), and conducted in-line with the 1964 Declaration of Helsinki and Transparent Reporting of multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement (15).
Patient selection
We investigated consecutive hospitalized patients with COVID-19 who underwent a chest computed tomography (CT) scan between March 20 and April 30, 2020 (development cohort) in two early epicenters of the disease outbreak within the country (Tehran and Kashan university hospitals). Only those who had a positive reverse transcription-polymerase chain reaction test for SARS-CoV-2, using throat swabs, were included. Exclusion criteria were the presence of concomitant pulmonary diseases which interfered with the interpretation of chest CT scan (e.g. pulmonary edema due to other reasons, patients with positive blood or sputum culture caused by other infectious agents) and chest CT analysis confounders (e.g. blurred images, distorted images with beam hardening, and quantum mottle artifacts). Individuals with COVID-19 who were treated at home were not included. Between the above-mentioned dates, 184 patients were screened. After the exclusion of 12 patients because of CT scan violations, the total study sample for model development consisted of 172 patients.
An independent consecutive cohort of patients with similar inclusion and exclusion criteria to the development cohort, prospectively included in the study to externally validate the proposed model (validation cohort). This cohort consisted of 40 COVID-19 patients admitted to the Kashan university hospital between May 16 and June 2, 2020.
All patients were treated according to the interim guidance of WHO for COVID-19 (16). In case of respiratory failure requiring mechanical ventilation or other organ failures, the patients were admitted to the ICU. This criterion was similar to the previously published criterion for ICU admission of COVID-19 patients (17).
Data collection
The patients’ medical history and physical examination data, along with all the available laboratory findings were collected accordingly and double-checked by two independent authors to ensure accuracy.
All chest CT scans were performed with patient centered inside gantry in supine position, with raised arms at the first day of admission. No contrast medium was administered. All chest CT scans were performed using one of two 16 slice multidetector scanners (Toshiba Alexion, TSX-034A, Canon, Japan; Siemens Somatom Definition, Emotion 16, Syngo CT 2007E, Siemens Healthineers, Germany). CT parameters were according to the standard protocol: tube voltage, 120 kVp; tube current 40 to 50 mAS with automatic exposure control; Pitch factor 1-1.5; slice thickness, 1.0-3.0 mm; reconstruction interval, 1.0–3.0 mm; and a sharp reconstruction kernel (lung kernel).
Two radiologists (with 5 and 6 years of experience), blinded to the clinical and laboratory data, reviewed the chest CT images on a picture archiving and communication system independently and resolved discrepancies in consensus. The CT images were evaluated on lung (width, 1500 HU; level, -600 HU) and mediastinal (width, 400 HU; level, 40 HU) windows. The image description was based on a standardized structure, using the glossary of terms for thoracic imaging provided by Fleischner Society (18) and included the following items: involved lobes, axial distribution (i.e. outer one third of lung as peripheral, inner two third of lung as central, both peripheral and central), laterality of lesions (unilateral vs. bilateral), lesion density (i.e. pure ground glass opacity (GGO), pure consolidation, mixed), air-bronchogram (air-filled bronchi on an opaque (high-attenuation) airless background either within GGO or in consolidation), halo sign (nodule or mass like consolidation surrounded by GGO), reversed halo sign (a round area of GGO surrounded by a complete or incomplete rim of consolidation), cavitation, reticulation (intra- or interlobular septal thickening), parenchymal fibrotic bands, crazy paving pattern (inter and intralobular septal thickening within a GGO background), mosaic attenuation (alternative areas of differing attenuation with patchwork configuration), emphysematous changes (defined as paucity of vascular structures within lungs parenchyma and prominent anterior junction line), pleural thickening (measurement of pleural thickness> 2mm), pleural effusion, cardiomegaly (cardiothoracic ratio more than 50%), pericardial thickening (pericardial stripe thicker than 4mm), and lymphadenopathy. Finally, based on the parenchymal involvement, the CT severity score was calculated. Semi-quantitative CT severity score was assessed according to the extent of GGO, consolidation, and crazy-paving pattern at thin-section CT scan based on volumetric measurements. Each lung lobe (according to the anatomical structure of lungs defined by the Fleischner Society glossary of terms for thoracic imaging (18): left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe) was assigned a score based on a semi-quantitative criterion: score 0, no lobar involvement; score 1, <5% lobar involvement; score 2, 5-25% involvement; score 3, 25-50% involvement; score 4, 50-75% to involvement; and score 5, 75% or greater involvement. The summation of the scores was regarded as the CT severity score (scale of 0-25) (Figure 1) (19, 20).
Outcome definition
All patients were followed up for their entire hospital stay to assess the clinical deterioration defined as either in-hospital mortality or ICU admission, the main outcome under the study. The patients who required ICU care but due to the shortage of ICU beds, did not admitted to the ICU were also regarded as of deterioration group. The patients who died or required ICU care during hospitalization were compared with the group of patients who required only general admission and discharged without ICU admission.
Statistical analysis
Categorical variables were described as frequencies and percentages and continuous variables were presented as means ± standard deviations (SD). Univariate logistic regression analyses were performed to select the best predictors for making the multivariate model for predicting the clinical deterioration of COVID-19. The significance level in univariate logistic regression was defined at 0.2. The variables that were significantly associated with the clinical deterioration in univariate analysis were included in multivariate binary logistic regression, and their Z-Wald were also calculated. Then proportional to the Z scores, points were assigned to the predictor variables in the final scoring system. Additionally, in order to make a simplified scoring system, we transformed our continuous variables into dichotomous ones based on the most discriminative cut-off points using the receiver operating characteristic (ROC) curve. Based on the developed model, the score of each patient was calculated, and the ROC analyses were performed for both the whole model and the scoring system. Finally, with the selection of a cut-off value, patients were divided into two low- and high-risk groups with clinical deterioration rate of 10% and 60%, respectively.
The calibration of the model, that is its ability to predict the clinical deterioration corresponding to the observed proportion of the COVID-19 patients who died during the hospital stay or admitted to the ICU, was assessed by conducting the Hosmer-Lemeshow (HL) goodness-of-fit statistic. A P value of more than 0.05 indicated non-significant inconsistency between observed and predicted deterioration. The discrimination abilities of the model and the scoring system in the development and validation cohorts were assessed using the ROC curve analysis and the area under the ROC curve (AUC) and its corresponding 95% confidence interval (CI) calculation. Besides, the accuracy, sensitivity, specificity, and positive and negative likelihood ratio of the scoring system were calculated in both cohorts. All the analyses were performed in STATA v. 12.0 (STATA Corp., TX., USA).