Development and validation of a nomogram for predicting in-hospital mortality of elderly patients with persistent sepsis-associated acute kidney injury in intensive care units: a retrospective cohort study using the MIMIC-IV database.

ABSTRACT

OBJECTIVES: To identify the clinical risk factors that influence in-hospital mortality in elderly patients with persistent sepsis-associated acute kidney injury (S-AKI) and to establish and validate a nomogram to predict in-hospital mortality. DESIGN: Retrospective cohort analysis. SETTING: Data from critically ill patients at a US centre between 2008 and 2021 were extracted from the Medical Information Mart for Intensive Care (MIMIC)-IV database (V.1.0). PARTICIPANTS: Data from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database. PRIMARY OUTCOME: All-cause in-hospital death from persistent S-AKI. RESULTS: Multiple logistic regression revealed that gender (OR 0.63, 95% CI 0.45-0.88), cancer (2.5, 1.69-3.71), respiratory rate (1.06, 1.01-1.12), AKI stage (2.01, 1.24-3.24), blood urea nitrogen (1.01, 1.01-1.02), Glasgow Coma Scale score (0.75, 0.70-0.81), mechanical ventilation (1.57, 1.01-2.46) and continuous renal replacement therapy within 48 hours (9.97, 3.39-33.9) were independent risk factors for mortality from persistent S-AKI. The consistency indices of the prediction and the validation cohorts were 0.780 (95% CI 0.75-0.82) and 0.80 (95% CI 0.75-0.85), respectively. The model's calibration plot suggested excellent consistency between the predicted and actual probabilities. CONCLUSIONS: This study's prediction model demonstrated good discrimination and calibration abilities to predict in-hospital mortality of elderly patients with persistent S-AKI, although it warrants further external validation to verify its accuracy and applicability.