Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study.
Background: 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)-b...
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2025
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| _version_ | 1849927630085685248 |
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| author | figshare admin karger (2628495) |
| author2 | Lu Y. (3116760) Ge J. (4135477) Zhu L. (2945931) Wang L. (3116751) Wu J. (3278256) Dong F. (4153438) Deng J. (4043975) |
| author2_role | author author author author author author author |
| author_facet | figshare admin karger (2628495) Lu Y. (3116760) Ge J. (4135477) Zhu L. (2945931) Wang L. (3116751) Wu J. (3278256) Dong F. (4153438) Deng J. (4043975) |
| author_role | author |
| dc.creator.none.fl_str_mv | figshare admin karger (2628495) Lu Y. (3116760) Ge J. (4135477) Zhu L. (2945931) Wang L. (3116751) Wu J. (3278256) Dong F. (4153438) Deng J. (4043975) |
| dc.date.none.fl_str_mv | 2025-11-25T16:55:11Z |
| dc.identifier.none.fl_str_mv | 10.6084/m9.figshare.30712841.v1 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Supplementary_Material_for_Cardiometabolic-kidney_indices_and_machine_learning_model_for_predicting_all-cause_mortality_in_patients_with_cardiovascular-kidney-metabolic_syndrome_a_longitudinal_cohort_study_/30712841 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Medicine |
| dc.title.none.fl_str_mv | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | Background: 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 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 cutpoint analysis, and inflammation's mediating role was explored. Seven ML models were trained, with performance assessed by 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 |
| eu_rights_str_mv | openAccess |
| id | Manara_ba602342b2ec3152bf083ceb87a6bc54 |
| identifier_str_mv | 10.6084/m9.figshare.30712841.v1 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30712841 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study.figshare admin karger (2628495)Lu Y. (3116760)Ge J. (4135477)Zhu L. (2945931)Wang L. (3116751)Wu J. (3278256)Dong F. (4153438)Deng J. (4043975)MedicineMedicineBackground: 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 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 cutpoint analysis, and inflammation's mediating role was explored. Seven ML models were trained, with performance assessed by 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 stratification2025-11-25T16:55:11ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.6084/m9.figshare.30712841.v1https://figshare.com/articles/dataset/Supplementary_Material_for_Cardiometabolic-kidney_indices_and_machine_learning_model_for_predicting_all-cause_mortality_in_patients_with_cardiovascular-kidney-metabolic_syndrome_a_longitudinal_cohort_study_/30712841CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/307128412025-11-25T16:55:11Z |
| spellingShingle | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. figshare admin karger (2628495) Medicine Medicine |
| status_str | publishedVersion |
| title | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| title_full | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| title_fullStr | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| title_full_unstemmed | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| title_short | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| title_sort | Supplementary Material for: Cardiometabolic-kidney indices and machine learning model for predicting all-cause mortality in patients with cardiovascular-kidney-metabolic syndrome: a longitudinal cohort study. |
| topic | Medicine Medicine |