Background: The aim of this study was to construct and external validate a nomogram for predicting cardiogenic shock acute kidney injury (CS-AKI) in patients in intensive care unit (ICU).
Methods: All patients diagnosed with CS from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) were included in this study. Least absolute shrinkage and selection operator (LASSO) regression and recursive feature elimination for support vector machine (SVM-RFE) were used to determine the overlapping clinical features associated with CS-AKI. The predictive nomogram was established based on the significant clinical parameters and externally verified in this study.
Results: LASSO regression and SVM-RFE demonstrated that Charlson Comorbidity Index (CCI), usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI in the training cohort. The predictive nomogram based on the eight parameters showed good predictive performance as calculated by C-index were 0.823 (95% confidence index, 95% CI 0.798– 0.849), 0.819 (95% CI 0.769– 0.849), and 0.733 (95% CI 0.704– 0.763) in the training set, in the internal validation set and in the external validation sets, respectively. Moreover, the nomogram exhibited not only encouraging calibration ability but also great clinical utility in the training set and in the validation sets.
Conclusion: CCI, usage of mechanical ventilation, SOFA score, white blood cell, albumin, eGFR, anion gap, and positive fluid balance were closely associated with CS-AKI. The predictive nomogram for CS-AKI manifested well-predictive ability for the identification of ICU patients with CS-AKI.
Keywords: cardiogenic shock, acute kidney injury, Medical Information Mart for Intensive Care, eICU Collaborative Research Database, intensive care unit, nomogram