Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach

Abstract

Background: Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death.

Objective: The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI.

Methods: We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test.

Results: The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function.

Conclusions: According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.

Keywords: acute kidney injury; heart failure; phenogrouping; risk stratification; unsupervised machine learning.

Figure 1

Preprocessing procedure of the PLAGH data set. CKD: chronic kidney disease; eGFR: estimate glomerular filtration rate; HF: heart failure; PLAGH: PLA General Hospital.

Figure 2

Kaplan-Meier curves for AKI and in-hospital mortality in the development (PLAGH) and external validation (MIMIC-III) data sets. AKI: acute kidney injury; MIMIC: Medical Information Mart for Intensive Care; PLAGH: PLA General Hospital.

Figure 3

Hazard ratios of of top-ranked 10 discriminative features for (a) acute kidney injury and (b) in-hospital mortality from the PLA General Hospital data set. AST: aspartate aminotransferase; eGFR: estimated glomerular filtration rate; NT-pro-BNP: N-terminal probrain natriuretic peptide. *Anemia was defined as hemoglobin <135 g/L for men and hemoglobin <120 g/L for women. All units of variables in this figure are same as the units in Table 2.

Figure 4

Subgroup analysis of the generated phenogroup index for (a) acute kidney injury and (b) in-hospital mortality. AF: atrial fibrillation; CHD: chronic heart disease; HF: heart failure; LVEF: left ventricular ejection fraction. *Anemia was defined as hemoglobin <135 g/L for men and hemoglobin <120 g/L for women. All units of variables in this figure are same as the units in Table 2.