Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning
Abstract Background Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient’s 180-day GIB hospitalization risk. Methods An eXtreme Gradient Boosting (XGBoost) and logistic regression model were...
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BMC
2024-10-01
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| Series: | BMC Nephrology |
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| Online Access: | https://doi.org/10.1186/s12882-024-03809-2 |
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| author | John W. Larkin Suman Lama Sheetal Chaudhuri Joanna Willetts Anke C. Winter Yue Jiao Manuela Stauss-Grabo Len A. Usvyat Jeffrey L. Hymes Franklin W. Maddux David C. Wheeler Peter Stenvinkel Jürgen Floege on behalf of the INSPIRE Core Group |
| author_facet | John W. Larkin Suman Lama Sheetal Chaudhuri Joanna Willetts Anke C. Winter Yue Jiao Manuela Stauss-Grabo Len A. Usvyat Jeffrey L. Hymes Franklin W. Maddux David C. Wheeler Peter Stenvinkel Jürgen Floege on behalf of the INSPIRE Core Group |
| author_sort | John W. Larkin |
| collection | DOAJ |
| description | Abstract Background Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient’s 180-day GIB hospitalization risk. Methods An eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017–2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data. Results Incidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regression showed high importance for parathyroid hormone (PTH) levels. Conclusions Machine learning can be considered for early detection of GIB event risk in HD. XGBoost outperforms logistic regression, yet both appear suitable. External and prospective validation of these models is needed. Association between bone mineral metabolism markers and GIB events was unexpected and warrants investigation. Trial registration This retrospective analysis of real-world data was not a prospective clinical trial and registration is not applicable. Graphical Abstract |
| format | Article |
| id | doaj-art-f4272cea88a24d7fa5d86c3b4f57a0ee |
| institution | OA Journals |
| issn | 1471-2369 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | BMC |
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| series | BMC Nephrology |
| spelling | doaj-art-f4272cea88a24d7fa5d86c3b4f57a0ee2025-08-20T02:17:37ZengBMCBMC Nephrology1471-23692024-10-0125111610.1186/s12882-024-03809-2Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learningJohn W. Larkin0Suman Lama1Sheetal Chaudhuri2Joanna Willetts3Anke C. Winter4Yue Jiao5Manuela Stauss-Grabo6Len A. Usvyat7Jeffrey L. Hymes8Franklin W. Maddux9David C. Wheeler10Peter Stenvinkel11Jürgen Floege12on behalf of the INSPIRE Core GroupFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care, Global Medical OfficeFresenius Medical Care AG, Global Medical OfficeUniversity College LondonDept of Renal Medicine, Karolinska University HospitalDivisions of Nephrology and Cardiology, University Hospital RWTH AachenAbstract Background Gastrointestinal bleeding (GIB) is a clinical challenge in kidney failure. INSPIRE group assessed if machine learning could determine a hemodialysis (HD) patient’s 180-day GIB hospitalization risk. Methods An eXtreme Gradient Boosting (XGBoost) and logistic regression model were developed using an HD dataset in United States (2017–2020). Patient data was randomly split (50% training, 30% validation, and 20% testing). HD treatments ≤ 180 days before GIB hospitalization were classified as positive observations; others were negative. Models considered 1,303 exposures/covariates. Performance was measured using unseen testing data. Results Incidence of 180-day GIB hospitalization was 1.18% in HD population (n = 451,579), and 1.12% in testing dataset (n = 38,853). XGBoost showed area under the receiver operating curve (AUROC) = 0.74 (95% confidence interval (CI) 0.72, 0.76) versus logistic regression showed AUROC = 0.68 (95% CI 0.66, 0.71). Sensitivity and specificity were 65.3% (60.9, 69.7) and 68.0% (67.6, 68.5) for XGBoost versus 68.9% (64.7, 73.0) and 57.0% (56.5, 57.5) for logistic regression, respectively. Associations in exposures were consistent for many factors. Both models showed GIB hospitalization risk was associated with older age, disturbances in anemia/iron indices, recent all-cause hospitalizations, and bone mineral metabolism markers. XGBoost showed high importance on outcome prediction for serum 25 hydroxy (25OH) vitamin D levels, while logistic regression showed high importance for parathyroid hormone (PTH) levels. Conclusions Machine learning can be considered for early detection of GIB event risk in HD. XGBoost outperforms logistic regression, yet both appear suitable. External and prospective validation of these models is needed. Association between bone mineral metabolism markers and GIB events was unexpected and warrants investigation. Trial registration This retrospective analysis of real-world data was not a prospective clinical trial and registration is not applicable. Graphical Abstracthttps://doi.org/10.1186/s12882-024-03809-2BleedingGastrointestinalHospitalizationKidney FailurePredictive Modeling |
| spellingShingle | John W. Larkin Suman Lama Sheetal Chaudhuri Joanna Willetts Anke C. Winter Yue Jiao Manuela Stauss-Grabo Len A. Usvyat Jeffrey L. Hymes Franklin W. Maddux David C. Wheeler Peter Stenvinkel Jürgen Floege on behalf of the INSPIRE Core Group Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning BMC Nephrology Bleeding Gastrointestinal Hospitalization Kidney Failure Predictive Modeling |
| title | Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| title_full | Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| title_fullStr | Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| title_full_unstemmed | Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| title_short | Prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| title_sort | prediction of gastrointestinal bleeding hospitalization risk in hemodialysis using machine learning |
| topic | Bleeding Gastrointestinal Hospitalization Kidney Failure Predictive Modeling |
| url | https://doi.org/10.1186/s12882-024-03809-2 |
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