Integrating Anti-SARS-CoV-2 Spike Protein IgG, IL-6, and NK Cell Percentage with Routine Laboratory Markers for COVID-19 Severity Assessment: Development and Validation of the CORIS Nomogram

Abstract

Background: Rapid assessment of coronavirus disease 2019 (COVID-19) severity at hospital admission is essential for clinical triage and efficient healthcare resource allocation. However, prediction models developed using data from the early phases of the pandemic may exhibit reduced performance owing to the continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants, and existing nomograms rarely integrate routine laboratory parameters with immune-related biomarkers in a systematic manner. Methods: We retrospectively included 774 patients with laboratory-confirmed COVID-19 admitted to Beijing Ditan Hospital between January 2022 and December 2023, and randomly assigned them at a 7:3 ratio to a training set (n = 543) and a validation set (n = 231). Variables consistently selected by three approaches—multivariable logistic regression, least absolute shrinkage and selection operator (LASSO) regression, and recursive feature elimination (RFE)—were defined as core predictors and used to construct the COVID Routine-Immune Score (CORIS) nomogram. Model performance was assessed in terms of discrimination, calibration, and clinical net benefit. The prespecified outcome was non-severe versus severe/critical status determined at hospital admission. CORIS was designed for admission severity discrimination and was not intended to diagnose SARS-CoV-2 infection or predict post-admission deterioration, survival, or thrombotic events. Results: Age, lactate dehydrogenase (LDH), anti-SARS-CoV-2 spike protein immunoglobulin G (IgG), interleukin-6 (IL-6), and NK cell percentage (NK-pct) were consistently identified by all three feature-selection methods. The CORIS nomogram achieved an area under the receiver operating characteristic curve (AUC) of 0.914 (95% CI, 0.884–0.940) in the training set and 0.918 (95% CI, 0.868–0.960) in the validation set, with good calibration (Hosmer–Lemeshow p = 0.322 and 0.659, respectively). In the validation set, the CORIS nomogram significantly outperformed a lymphocyte subset-based model (AUC, 0.811; DeLong p < 0.001) and a routine laboratory parameter-based model (AUC, 0.839; DeLong p = 0.003). Conclusions: The CORIS nomogram integrates predictors spanning four dimensions—demographic susceptibility, tissue injury, systemic inflammation, and immune status—to differentiate non-severe from severe or critical COVID-19. The model demonstrated favorable discrimination and calibration, and may provide an objective and practical tool for severity assessment at hospital admission.