Machine learning-based risk prediction of acute kidney disease and hospital mortality in older patients

Abstract

Introduction: Acute kidney injury (AKI) is a prevalent complication in older people, elevating the risks of acute kidney disease (AKD) and mortality. AKD reflects the adverse events developing after AKI. We aimed to develop and validate machine learning models for predicting the occurrence of AKD, AKI and mortality in older patients.

Methods: We retrospectively reviewed the medical records of older patients (aged 65 years and above). To explore the trajectory of kidney dysfunction, patients were categorized into four groups: no kidney disease, AKI recovery, AKD without AKI, or AKD with AKI. We developed eight machine learning models to predict AKD, AKI, and mortality. The best-performing model was identified based on the area under the receiver operating characteristic curve (AUC) and interpreted using the Shapley additive explanations (SHAP) method.

Results: A total of 22,005 patients were finally included in our study. Among them, 4,434 patients (20.15%) developed AKD, 4,000 (18.18%) occurred AKI, and 866 (3.94%) patients deceased. Light gradient boosting machine (LGBM) outperformed in predicting AKD, AKI, and mortality, and the final lite models with 15 features had AUC values of 0.760, 0.767, and 0.927, respectively. The SHAP method revealed that AKI stage, albumin, lactate dehydrogenase, aspirin and coronary heart disease were the top 5 predictors of AKD. An online prediction website for AKD and mortality was developed based on the final models.

Discussion: The LGBM models provide a valuable tool for early prediction of AKD, AKI, and mortality in older patients, facilitating timely interventions. This study highlights the potential of machine learning in improving older adult care, with the developed online tool offering practical utility for healthcare professionals. Further research should aim at external validation and integration of these models into clinical practice.

Keywords: acute kidney disease; hospital mortality; machine learning; older people; risk prediction.

Figure 1

Performance of eight ML models for different outcomes with all features. (A) The ROC curve of AKD. (B) The ROC curve of AKI. (C) The ROC curve of mortality.

Figure 2

Importance matrix plot and SHAP summary plot of the final lite LGBM model. (A) The importance ranking of the first 15 features of the LGBM model. (B) The SHAP summary plot demonstrates the general importance of each feature in LGBM model. The color bar on the right indicates the relative value of a feature in each case. Red dots indicate high values and blue dots indicate low values. The violin graph lining up on the midline is the aggregation of dots representing each case in the train set. The distance between the upper and lower margin of the violin graph represents the amount of the cases that end up with the same SHAP values offered by this feature. SHAP force plots of 4 examples of patients. Categorical features including AKI stage, CHD, Omeprazole and β-lactam antibiotics were represented by 0 and 1, while “0” means “No” and “1” means “Yes.” *ALB, albumin; LDH, lactate dehydrogenase, CHD, coronary heart disease; CK, creatine kinase; Cys, cystatin C; GGT, gamma-glutamyl transferase; Scr, serum creatinine, CCB, calcium channel blocker; RBC, red blood cell count.

Figure 3

SHAP dependence plots demonstrate the distribution of SHAP output value of a single feature. The colors on the dependence plot correspond to another feature that could potentially interact with the feature being analyzed. (A) The relationship between Cys and AKI stage SHAP values, with the color bar indicating various levels of AKI stage. (B) The relationship between Cys and Scr SHAP values, where the color bar represents different levels of Scr. (C) The relationship between Scr and AKI stage SHAP values, with the color bar also denoting distinct AKI stage levels. (D) The relationship between Scr and ALB SHAP values, with the color bar reflecting varying ALB levels. *ALB, albumin; Cys, cystatin C; Scr, serum creatinine.

Figure 4

Force plots of the final lite LGBM model. (A,B) Show the examples of patients predicted to have AKD. (C,D) Show the examples of patients predicted to be non-AKD. The features shown in red represent a higher risk of AKD, while the features shown in blue represent a lower risk. The plots help physicians identify the main features in the model that have high decision power at the individual level. Categorical features including AKI stage, CHD, Omeprazole and β-lactam antibiotics were represented by 0 and 1, while “0” means “No” and “1” means “Yes.” *ALB, albumin; LDH, lactate dehydrogenase, CHD, coronary heart disease; CK, creatine kinase; Cys, cystatin C; GGT, gamma-glutamyl transferase; Scr, serum creatinine, CCB, calcium channel blocker; RBC, red blood cell count.