Predictive Value of Pulmonary Involvement Score and Clinical Parameters for Severe COVID-19: A Nomogram-Based Approach in Emergency Department

Abstract

Background This study aims to evaluate the prognostic value of a pulmonary involvement (PI) score in COVID-19 patients, both independently and in combination with clinical and laboratory parameters, following the adjustment of the dynamic zeroing policy in China. Methods A total of 288 confirmed COVID-19 pneumonia patients (mild/moderate group, 155; severe group, 133) from the Emergence Department, Beijing Chaoyang Hospital, were enrolled in this study and allocated to the training and validation cohort. The PI score of the initial chest CT was evaluated using a semi-quantitative scoring system, and clinical and laboratory parameters were collected. Radiomics and combination predictive models were developed using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm and multivariate logistic regression. The models' performance for predicting severe COVID-19 was assessed by receiver operating characteristics curve (ROC) analysis and calibration curve. Results Compared with the mild/moderate patients, the severe patients had higher levels of C-reactive protein (CRP), D-dimer, procalcitonin (PCT), and brain natriuretic peptide (BNP), but lower blood oxygen saturation and vaccination rate (P < 0.05). The severe group had a higher incidence of consolidation, multi-lobe involvement, interlobular septal thickening, air bronchogram sign, and pleural effusion compared to the mild/moderate group (P < 0.05). Moreover, the PI total score of severe patients was 16.4 ± 3.8, significantly higher than 8.5 ± 3.8 of milder patients (P < 0.001). The developed predictive nomogram, which includes four clinical characteristics and one CT feature, exhibited good performance in predicting severe COVID-19 with an area under the ROC (AUC) of 0.98 (95% CI, 0.97-1.00) in the training dataset, and 0.97 (95% CI, 0.94-1.00) in the validation dataset. Conclusions The combination predictive model, including CT score, clinical factors, and laboratory data, shows favorable predictive efficacy for severe COVID-19, which could potentially aid clinicians in triaging emergency patients.