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

ObjectivesTo 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.DesignRetrospective cohort analysis.SettingData 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).ParticipantsData from 1519 patients with persistent S-AKI were extracted from the MIMIC-IV database.Primary outcomeAll-cause in-hospital death from persistent S-AKI.ResultsMultiple 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.ConclusionsThis 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.