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A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study
BACKGROUND: In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predi...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391987/ https://www.ncbi.nlm.nih.gov/pubmed/37525240 http://dx.doi.org/10.1186/s12967-023-04387-x |
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author | Ma, Mengqing Wan, Xin Chen, Yuyang Lu, Zhichao Guo, Danning Kong, Huiping Pan, Binbin Zhang, Hao Chen, Dawei Xu, Dongxu Sun, Dong Lang, Hong Zhou, Changgao Li, Tao Cao, Changchun |
author_facet | Ma, Mengqing Wan, Xin Chen, Yuyang Lu, Zhichao Guo, Danning Kong, Huiping Pan, Binbin Zhang, Hao Chen, Dawei Xu, Dongxu Sun, Dong Lang, Hong Zhou, Changgao Li, Tao Cao, Changchun |
author_sort | Ma, Mengqing |
collection | PubMed |
description | BACKGROUND: In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients. METHODS: 3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used. RESULTS: In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777–0.853)) and external validation (AUC: 0.816 (95% CI 0.770–0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783–0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755–0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688–0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator. CONCLUSION: We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04387-x. |
format | Online Article Text |
id | pubmed-10391987 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103919872023-08-02 A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study Ma, Mengqing Wan, Xin Chen, Yuyang Lu, Zhichao Guo, Danning Kong, Huiping Pan, Binbin Zhang, Hao Chen, Dawei Xu, Dongxu Sun, Dong Lang, Hong Zhou, Changgao Li, Tao Cao, Changchun J Transl Med Research BACKGROUND: In patients undergoing percutaneous coronary intervention (PCI), contrast-induced acute kidney injury (CIAKI) is a frequent complication, especially in diabetics, and is connected with severe mortality and morbidity in the short and long term. Therefore, we aimed to develop a CIAKI predictive model for diabetic patients. METHODS: 3514 patients with diabetes from four hospitals were separated into three cohorts: training, internal validation, and external validation. We developed six machine learning (ML) algorithms models: random forest (RF), gradient-boosted decision trees (GBDT), logistic regression (LR), least absolute shrinkage and selection operator with LR, extreme gradient boosting trees (XGBT), and support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) of ML models was compared to the prior score model, and developed a brief CIAKI prediction model for diabetes (BCPMD). We also validated BCPMD model on the prospective cohort of 172 patients from one of the hospitals. To explain the prediction model, the shapley additive explanations (SHAP) approach was used. RESULTS: In the six ML models, XGBT performed best in the cohort of internal (AUC: 0.816 (95% CI 0.777–0.853)) and external validation (AUC: 0.816 (95% CI 0.770–0.861)), and we determined the top 15 important predictors in XGBT model as BCPMD model variables. The features of BCPMD included acute coronary syndromes (ACS), urine protein level, diuretics, left ventricular ejection fraction (LVEF) (%), hemoglobin (g/L), congestive heart failure (CHF), stable Angina, uric acid (umol/L), preoperative diastolic blood pressure (DBP) (mmHg), contrast volumes (mL), albumin (g/L), baseline creatinine (umol/L), vessels of coronary artery disease, glucose (mmol/L) and diabetes history (yrs). Then, we validated BCPMD in the cohort of internal validation (AUC: 0.819 (95% CI 0.783–0.855)), the cohort of external validation (AUC: 0.805 (95% CI 0.755–0.850)) and the cohort of prospective validation (AUC: 0.801 (95% CI 0.688–0.887)). SHAP was constructed to provide personalized interpretation for each patient. Our model also has been developed into an online web risk calculator. MissForest was used to handle the missing values of the calculator. CONCLUSION: We developed a novel risk calculator for CIAKI in diabetes based on the ML model, which can help clinicians achieve real-time prediction and explainable clinical decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04387-x. BioMed Central 2023-07-31 /pmc/articles/PMC10391987/ /pubmed/37525240 http://dx.doi.org/10.1186/s12967-023-04387-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ma, Mengqing Wan, Xin Chen, Yuyang Lu, Zhichao Guo, Danning Kong, Huiping Pan, Binbin Zhang, Hao Chen, Dawei Xu, Dongxu Sun, Dong Lang, Hong Zhou, Changgao Li, Tao Cao, Changchun A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title | A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title_full | A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title_fullStr | A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title_full_unstemmed | A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title_short | A novel explainable online calculator for contrast-induced AKI in diabetics: a multi-centre validation and prospective evaluation study |
title_sort | novel explainable online calculator for contrast-induced aki in diabetics: a multi-centre validation and prospective evaluation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10391987/ https://www.ncbi.nlm.nih.gov/pubmed/37525240 http://dx.doi.org/10.1186/s12967-023-04387-x |
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