{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T13:55:20Z","timestamp":1790690120821,"version":"4.1.0"},"reference-count":19,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100022944","name":"Suzhou Gusu Medical Youth Talent","doi-asserted-by":"publisher","award":["GSWS2023098"],"award-info":[{"award-number":["GSWS2023098"]}],"id":[{"id":"10.13039\/100022944","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ther Apher Dial"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Peritonitis is a serious complication of peritoneal dialysis (PD), in which insufficient control of antibacterial drug concentrations poses a significant risk for poor outcomes. Predicting antibacterial drug concentrations is crucial in clinical practice. The limitations imposed by compartment models have presented a considerable challenge.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we employed machine learning as model\u2010free methods to circumvent the constraints of compartment models. We collected data from 68 observations from 38 patients with peritoneal dialysis\u2010related peritonitis who were treated with vancomycin from the EHR system. This data included information about drug administration, demographic details, and experimental indicators as predictors. We constructed models using Genetic Adaptive Supporting Vector Regression (GA\u2010SVR), KNN\u2010regression, GBM, XGBoost, and a stacking ensemble model. Additionally, we used RMSE loss and partial\u2010dependence profiles to elucidate the effects of these predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>GA\u2010SVAR outperformed other large\u2010scale models. In 10\u2010fold cross\u2010validation, the RMSE ratio and R\u2010squared values for direct concentration prediction were 23.5% and 0.633, respectively. The ROC AUC for predicting concentrations below 15 and exceeding 20\u2009\u03bcg\/mL were 0.890 and 0.948, respectively. Notably, the most influential predictors included times of drug administration and weight. These predictors were also influenced by residual kidney function.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>To assist in controlling vancomycin concentrations for patients with PD\u2010related peritonitis in clinical practice, we developed GA\u2010SVR and a corresponding explainer model. Our study improves the controlling of vancomycin in clinical settings by enhancing our understanding of vancomycin concentration in patients with PD\u2010related peritonitis.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1744-9987.14188","type":"journal-article","created":{"date-parts":[[2024,7,22]],"date-time":"2024-07-22T02:29:28Z","timestamp":1721615368000},"page":"106-113","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning\u2010based prediction of vancomycin concentration after abdominal administration in patients with peritoneal dialysis\u2010related peritonitis"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8252-3675","authenticated-orcid":false,"given":"Bo","family":"Lv","sequence":"first","affiliation":[{"name":"Department of Pharmacy The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxiu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pharmacy The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Nephrology The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Nephrology The Second Affiliated Hospital of Soochow University  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiming","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Pharmacy The Second Affiliated Hospital of Soochow University  Suzhou 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Dialysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/1744-9987.14188","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,3]],"date-time":"2025-01-03T00:11:15Z","timestamp":1735863075000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/1744-9987.14188"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,21]]},"references-count":19,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["10.1111\/1744-9987.14188"],"URL":"https:\/\/doi.org\/10.1111\/1744-9987.14188","archive":["Portico"],"relation":{},"ISSN":["1744-9979","1744-9987"],"issn-type":[{"value":"1744-9979","type":"print"},{"value":"1744-9987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,21]]},"assertion":[{"value":"2024-04-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication 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