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.

Artificial Intelligence in Heart Failure and Acute Kidney Injury: Emerging Concepts and Controversial Dimensions

“…More advanced machine-learning techniques, such as large language models (LLMs) and chatbots, have emerged recently [24]. Chatbots are AI systems designed to simulate human conversation audibly or with text-based tools [25][26][27]. Integrating these AI and machinelearning approaches into the study of HF and AKI has yielded encouraging outcomes.…”

“…Furthermore, while in-hospital mortality was observed to be higher in the high-risk group, the specific causes of death were not reported. Hong et al [26] highlighted the significant potential of unsupervised machine learning in patient risk stratification. Their innovative approach shed new light on identifying high-risk HF patients, underscoring the critical role of data-driven phenotyping in risk assessment.…”

“…Hong et al [26] utilized an unsupervised machine-learning approach to identify HF patients with normal renal function susceptible to AKI. Using an unsupervised clustering algorithm on data from 5,075 hospitalizations, they discerned 2 patient clusters of “high-risk” and “low-risk” groups (Fig.…”

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning

“…Several studies have been conducted on the use of artificial intelligence (AI) in ESRD and CKD. [20,[25][26][27][28][29] However, these studies mostly focused on CKD prevention or identification. [21,[30][31][32][33] For mortality prediction in ESRD patients, most studies have focused on populations with only kidney transplant therapy or hemodialysis.…”

Analysis of the clinical features and risk factors of kidney injury in patients with chronic heart failure—a retrospective cohort study

“…Correct clinical decision making relies on early risk assessment and diagnosis ( 10 ). Thus, it is clinically important to properly assess the risk factors for kidney injury in patients with CHF in an appropriate clinical context ( 11 ). Previous studies have shown that vascular disease, thiazide diuretic use, baseline blood urea nitrogen >9 mmol/L ( 12 ), elevated sCr on admission, a poor New York Heart Association (NYHA) classification of cardiac function ( 13 ), a medical history of chronic kidney disease (CKD), spironolactone exposure, and higher doses of loop diuretics ( 14 ) are independent risk factors for kidney injury in patients with CHF.…”