Documented radiological and physiological anomalies among coronavirus disease 2019 survivors necessitate prompt recognition of residual pulmonary parenchymal abnormalities for effective management of chronic pulmonary consequences. This study aimed to devise a predictive model to identify patients at risk of such abnormalities post-COVID-19. Our prognostic model was derived from a dual-center retrospective cohort comprising 501 hospitalized COVID-19 cases from July 2022 to March 2023. Of these, 240 patients underwent Chest CT scans three months post-infection. A predictive model was developed using stepwise regression based on the Akaike Information Criterion, incorporating clinical and laboratory parameters. The model was trained and validated on a split dataset, revealing a 33.3% incidence of pulmonary abnormalities. It achieved strong discriminatory power in the training set (area under the curve: 0.885, 95% confidence interval 0.832–0.938), with excellent calibration and decision curve analysis suggesting substantial net benefits across various threshold settings. We have successfully developed a reliable prognostic tool, complemented by a user-friendly nomogram, to estimate the probability of residual pulmonary parenchymal abnormalities three months post-COVID-19 infection. This model, demonstrating high performance, holds promise for guiding clinical interventions and improving the management of COVID-19-related pulmonary sequela.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-024-79715-2.