Admission Laboratory-Based Prediction of Six-Month Mortality After COVID-19 Hospitalization: Model Development, Bootstrap Internal Validation, and Single-Center Temporal Validation
Background and Objectives: Early identification of patients at high risk of death after COVID-19 hospitalization may support monitoring, follow-up planning and resource allocation. We aimed to develop and internally validate a parsimonious admission laboratory-based model for six-month all-cause mortality and derive a simplified risk score. Materials and Methods: This retrospective single-center cohort included 1827 consecutive adults hospitalized with COVID-19 between 3 March and 6 November 2020. The primary model was developed in 1559 patients with complete predictor data after data-quality review. Prespecified predictors were age, albumin, neutrophil-to-lymphocyte ratio, blood urea nitrogen, lactate dehydrogenase, troponin I and sodium. Model performance was assessed using discrimination and calibration measures, 500-sample bootstrap internal validation and a chronological split-sample assessment in later admissions from the same center; this was internal–temporal rather than external validation. The simplified score was derived exclusively in the chronological development cohort and applied unchanged to the later cohort. Results: Among 1827 eligible patients, 437 died within six months (23.9%). The primary complete-case modeling cohort included 1559 patients, of whom 386 died. Older age, lower albumin and higher neutrophil-to-lymphocyte ratio, blood urea nitrogen, lactate dehydrogenase, troponin I and sodium were independently associated with mortality. The model achieved an apparent AUC of 0.936 (95% CI 0.919–0.954) and an optimism-corrected AUC of 0.933. The later same-center cohort yielded an AUC of 0.908 (95% CI 0.871–0.939). The development-derived simplified score had an AUC of 0.927 (95% CI 0.909–0.943) in the development cohort and 0.916 (95% CI 0.885–0.944) when applied unchanged to the later cohort. Conclusions: A model combining age with six routinely available admission laboratory variables showed strong discrimination for six-month all-cause mortality after COVID-19 hospitalization and retained good discrimination in single-center temporal assessment. However, calibration drift was observed, and the model and simplified score should currently be considered supportive risk-stratification frameworks rather than deployable stand-alone clinical calculators. External validation, contemporary recalibration and prospective assessment of clinical utility are required before implementation.