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Development and validation of a prediction model for people with mild chronic kidney disease in Japanese individuals
Takahiro Miki1, Toshiya Sakoda2, Kojiro Yamamoto2
1PREVENT Inc, Nagoya, Japan. miki.takahiro@prevent.co.jp.
Insights
A new prediction model helps identify Japanese individuals with mild chronic kidney disease (CKD) at high risk for heart disease, stroke, or kidney events, enabling earlier intervention.
Area of Science:
- Nephrology
- Cardiology
- Epidemiology
Background:
- Chronic kidney disease (CKD) presents significant health risks, often asymptomatic in early stages, and is linked to increased cardiovascular and kidney events.
- Early detection and proactive management are crucial for mitigating adverse outcomes in CKD patients.
Purpose of the Study:
- To develop and validate a prediction model for hospitalization due to ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events.
- To utilize readily available health check and prescription data for risk stratification in Japanese individuals with mild CKD.
Main Methods:
- A retrospective cohort study analyzed data from approximately 850,000 individuals (40,351 included) from the PREVENT Inc. database (April 2013-April 2023).
- Cox proportional hazard regression models were employed to derive and validate risk scores, incorporating traditional risk factors and CKD-specific variables.
- Model performance was evaluated using the concordance index (c-index) and 5-fold cross-validation.
Main Results:
- Key predictors for IHD/CVD hospitalization and major kidney events included age, sex, diabetes, hypertension, and lipid levels.
- Age was a significant risk factor for both cardiovascular and kidney events.
- The developed risk models showed predictive capabilities with mean c-indexes of 0.75 for IHD/CVD and 0.69 for major kidney events.
Conclusions:
- The developed prediction model serves as a practical tool for early identification of high-risk Japanese individuals with mild CKD.
- Timely interventions based on this model can improve patient outcomes and potentially reduce healthcare costs.
- Further clinical validation is recommended to confirm the model's utility in diverse populations.
Background:
Chronic kidney disease (CKD) poses significant health risks due to its asymptomatic nature in early stages and its association with increased cardiovascular and kidney events. Early detection and management are critical for improving outcomes.
Objective:
This study aimed to develop and validate a prediction model for hospitalization for ischemic heart disease (IHD) or cerebrovascular disease (CVD) and major kidney events in Japanese individuals with mild CKD using readily available health check and prescription data.
Methods:
A retrospective cohort study was conducted using data from approximately 850,000 individuals in the PREVENT Inc. database, collected between April 2013 and April 2023. Cox proportional hazard regression models were utilized to derive and validate risk scores for hospitalization for IHD/CVD and major kidney events, incorporating traditional risk factors and CKD-specific variables. Model performance was assessed using the concordance index (c-index) and 5-fold cross-validation.
Results:
A total of 40,351 individuals were included. Key predictors included age, sex, diabetes, hypertension, and lipid levels for hospitalization for IHD/CVD and major kidney events. Age significantly increased the risk score for both hospitalization for IHD/CVD and major kidney events. The baseline 5-year survival rates are 0.99 for hospitalization for IHD/CVD and major kidney events are 0.99. The developed risk models demonstrated predictive ability, with mean c-indexes of 0.75 for hospitalization for IHD/CVD and 0.69 for major kidney events.
Conclusions:
This prediction model offers a practical tool for early identification of Japanese individuals with mild CKD at risk for hospitalization for IHD/CVD and major kidney events, facilitating timely interventions to improve patient outcomes and reduce healthcare costs. The models stratified patients into risk categories, enabling identification of those at higher risk for adverse events. Further clinical validation is required.
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