Deep Learning–Derived Quantitative HRCT Parameters at Initial Examination Predict Adverse Outcomes and Reinfection in COVID-19 Pneumonia

Objective: To determine whether deep-learning–derived quantitative HRCT parameters, combined with clinical characteristics, can predict in-hospital adverse outcomes and long-term reinfection in patients with COVID-19 pneumonia. Methods: We retrospectively analyzed 236 RT-PCR–confirmed patients who underwent HRCT between November 2022 and January 2023 at the Affiliated Hospital of North Sichuan Medical College. Pulmonary inflammatory regions were automatically segmented, and quantitative metrics—including opacity score, lesion volume and percentage, high-attenuation lesion volume and percentage, and mean total-lung attenuation—were extracted using an artificial-intelligence pneumonia analysis prototype (Siemens Healthineers, Erlangen, Germany). Optimal thresholds derived from receiver-operating-characteristic curves for the composite endpoint of intensive-care-unit admission or all-cause death were applied to stratify patients. Cox proportional-hazards models were used to identify independent predictors of adverse outcomes and subsequent SARS-CoV-2 reinfection. Results: Adverse outcomes occurred in 16.1% of patients. Higher opacity scores, larger lesion burdens, greater proportions of high-attenuation opacities, and higher mean lung attenuation were all associated with poorer outcomes (all P < 0.05). After adjusting for age and chronic obstructive pulmonary disease, an opacity score ≥ 5.5 (HR = 3.02), lesion percentage ≥ 18.85% (HR = 2.33), and mean attenuation ≥ −662.4 HU (HR = 2.20) remained independent predictors. During a median follow-up of 603 days, opacity score ≥ 5.5 (HR = 5.32), high-attenuation volume ≥ 140.37 ml (HR = 3.81), and high-attenuation percentage ≥ 4.94% (HR = 3.39) independently predicted reinfection (all P ≤ 0.027). Conclusions: Deep-learning–based quantitative HRCT metrics provide incremental prognostic information for risk stratification of both acute adverse outcomes and long-term reinfection among COVID-19 patient.