Combination of Chest Computed Tomography Value and Clinical Laboratory Data for the Prognostic Risk Evaluation of Patients with COVID-19

Objective: This study aims to investigate the independent prognostic factors of patients with coronavirus disease 2019 (COVID-19) and thereafter construct a related prognostic model. Methods: The subjects were screened following the COVID-19 diagnostic criteria. The independent prognostic factors were selected based on the indicators, including medical history, clinical manifestation, laboratory tests, imaging examination and clinical prognosis. Subsequently, we constructed a nomogram model to predict short-term prognosis. Results: Clinical information was obtained from 393 COVID-19 patients admitted to Zhongshan Hospital at Xiamen University between December 2022 and January 2023. The independent risk factors determined by Cox multivariate regression analysis included gender (OR: 0.355, 95% CI: 0.16 similar to 0.745), age (OR: 3.938, 95% CI: 1.221 similar to 15.9), pectoral muscle index (PMI, OR: 4.985, 95% CI: 2.336 similar to 11.443), pneumonia severity score (PSS, OR: 6.486, 95% CI: 2.082 similar to 21.416) and lactate dehydrogenase (LDH, OR: 3.857, 95% CI: 1.571 similar to 10.266). A short-term prognostic nomogram was developed based on the five independent risk factors above. The area under the receiver operating characteristic (ROC) curve (AUC) of the nomogram model was 0.857. The calibration curve confirmed the outcomes of the prognostic model, which exhibited excellent consistency with the actual results. Conclusion: In summary, gender, age, pectoral muscle index, pneumonia severity score, and lactate dehydrogenase are all independent risk factors for COVID-19 mortality. Thus, the nomogram based on the above indicators can predict the risk of mortality in COVID-19 patients. This may have the potential of being clinical application in prognostic evaluation of COVID-19.