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Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients
Eiichiro Kanda1, Atsushi Suzuki2, Masaki Makino2
1Medical Science, Kawasaki Medical University, Okayama, Japan.
Insights
A new machine learning model effectively predicts the risk of chronic kidney disease (CKD) and heart failure (HF) in type 2 diabetes mellitus (T2DM) patients. This tool aids early identification and intervention for better patient outcomes.
Area of Science:
- Nephrology and Cardiology
- Artificial Intelligence in Medicine
- Diabetes Mellitus Research
Background:
- Type 2 diabetes mellitus (T2DM) frequently leads to chronic kidney disease (CKD) and heart failure (HF), increasing mortality risks.
- Current screening and risk assessment for CKD/HF in T2DM patients are insufficient for early detection.
- Identifying high-risk T2DM individuals is crucial for timely intervention and improved prognosis.
Purpose of the Study:
- To develop and validate a novel machine learning (ML) model for predicting the risk of developing CKD and/or HF (CKD/HF) in early-stage T2DM patients.
- To assess the model's performance in identifying T2DM patients at high risk for CKD/HF diagnosis and hospitalization.
- To establish an efficient clinical tool for risk stratification in T2DM management.
Main Methods:
- Utilized a retrospective cohort of 217,054 T2DM patients from a Japanese claims database.
- Developed and internally validated various ML models, selecting extreme gradient boosting for its superior performance.
- Externally validated the best-performing model using an independent dataset of 16,822 patients.
Main Results:
- The extreme gradient boosting model demonstrated strong predictive performance for CKD/HF diagnosis (AUC=0.718) and hospitalization (AUC=0.837) in external validation.
- Kaplan-Meier analysis confirmed significantly higher rates of CKD/HF diagnosis and hospitalization in patients identified as high-risk by the model.
- The model successfully predicted CKD/HF risk in T2DM patients with reasonable probability.
Conclusions:
- The developed ML model offers a reliable method for predicting CKD/HF risk in early-stage T2DM patients.
- Implementing this ML-based risk assessment can facilitate early diagnosis and targeted interventions.
- This approach holds potential for improving long-term outcomes and reducing mortality in T2DM patients with comorbidities.
Abstract:
Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan-Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.
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