Cardiometabolic-Kidney Indices and Machine Learning Model for Predicting All-Cause Mortality in Patients with
Yi Lu1, Junfeng Ge2, Lin Zhu3
1The First Affiliated Hospital, Hengyang Medical School, The Health Management Center, University of South China, Hengyang, China, 13974610213@163.com.
Insights
Cardiovascular-kidney-metabolic (CKM) syndrome prognosis is linked to specific biomarkers. Machine learning models, particularly XGBoost, effectively predict mortality risk in CKM patients using these integrated indices.
Area of Science:
- Cardiology
- Nephrology
- Metabolic Diseases
- Biomarkers
- Machine Learning
- Prognosis
Background:
- Cardiovascular-kidney-metabolic (CKM) syndrome poses significant clinical challenges.
- Integrated cardiometabolic-kidney biomarkers for prognosis in CKM syndrome are understudied.
- This research addresses the need for improved prognostic tools in CKM patients.
Purpose of the Study:
- To evaluate the prognostic associations of integrated cardiometabolic-kidney biomarkers.
- To develop and validate machine learning (ML) models for predicting mortality in CKM patients.
- To explore the role of inflammation in mediating biomarker-mortality relationships.
Main Methods:
- Analysis of NHANES data (1999-2018) and death records for 10,616 CKM patients (stage 0-3).
- Assessment of cardiometabolic index (CMI), atherogenic index of plasma (AIP), estimated glomerular filtration rate (eGFR), and urinary albumin-creatinine ratio (uACR).
- Utilized survival analysis (Kaplan-Meier, Cox regression, restricted cubic splines) and seven ML models, including XGBoost, for prediction and validation.
Main Results:
- Elevated CMI, AIP, uACR, and reduced eGFR independently predicted mortality (p < 0.05).
- Nonlinear associations were observed for CMI, eGFR, and uACR.
- The XGBoost model demonstrated superior performance (AUC = 0.852), with significant improvements in risk reclassification (NRI = 15.8%, IDI = 3.4%).
Conclusions:
- Integrated cardiometabolic-kidney biomarkers are significant prognostic indicators in CKM syndrome.
- The interplay between heart, kidney, and metabolism is crucial for patient outcomes.
- Combining accessible biomarkers with the XGBoost model offers a promising approach for enhanced CKM patient risk stratification.
Introduction:
Cardiovascular-kidney-metabolic (CKM) syndrome significantly impacts clinical outcomes, though evidence linking integrated cardiometabolic-kidney biomarkers to prognosis remains sparse. This study evaluated prognostic associations of these biomarkers and developed machine learning (ML)-based mortality prediction models for CKM patients.
Methods:
Using NHANES data (1999-2018) and death records from 10,616 stage 0-3 CKM patients, we analyzed cardiometabolic-kidney indices: cardiometabolic index (CMI), atherogenic index of plasma (AIP), estimated glomerular filtration rate (eGFR), and urinary albumin-creatinine ratio (uACR). Survival analysis incorporated the Kaplan-Meier curves, Cox regression, and restricted cubic splines to evaluate nonlinear associations. Risk reclassification was quantified via net reclassification index (NRI) and integrated discrimination improvement (IDI). Optimal mortality thresholds were determined using survival cut-point analysis, and inflammation's mediating role was explored. Seven ML models were trained, with performance assessed by area under the receiver operating characteristic curve (AUC-ROC), Brier score, and net clinical benefit.
Results:
Over a median 96-month follow-up, 847 deaths occurred. Elevated CMI, AIP, and uACR, along with reduced eGFR, independently predicted mortality (all p < 0.05), with nonlinear trends for CMI, eGFR, and uACR (p-nonlinearity < 0.05). High-risk thresholds for these indices increased mortality risk by 1.19-1.91-fold. Combining all indices improved risk stratification (NRI = 15.8%, IDI = 3.4%). Inflammation mediated 1.1-5.0% of biomarker-mortality associations. Among ML models, XGBoost achieved optimal performance (AUC = 0.852, 95% CI: 0.829-0.877), with Brier score of 0.063 (95% CI: 0.056-0.069) and provided clinical net benefits across risk thresholds from 0 to 0.6.
Conclusion:
Cardiometabolic-kidney indices significantly associated with prognosis in CKM patients, highlighting the importance of heart-kidney-metabolism crosstalk. Combining easily accessible biomarkers with the XGBoost model may facilitate risk stratification.
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