{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,13]],"date-time":"2026-11-13T17:47:34Z","timestamp":1794592054801,"version":"4.9.1"},"reference-count":28,"publisher":"American Society for Microbiology","issue":"5","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72274153"],"award-info":[{"award-number":["72274153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Innovation Capacity Support Plan of Shaanxi Province","award":["2024ZCKJXX-085"],"award-info":[{"award-number":["2024ZCKJXX-085"]}]},{"name":"the Key Research and Development Program of Shaanxi Province","award":["2023-YBSF-243"],"award-info":[{"award-number":["2023-YBSF-243"]}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Microbiol Spectr"],"published-print":{"date-parts":[[2025,5,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      Predicting vancomycin exposure is essential for optimizing dosing regimens in sepsis patients. While population pharmacokinetic (PPK) models are commonly used, their performance is limited. Machine learning (ML) models offer advantages over PPK models, but it remains unclear which model\u2014PPK, Bayesian, ML, or hybrid PPK-ML\u2014is best for predicting vancomycin exposure across different clinical scenarios in sepsis patients. This study compares the performance of these models in predicting the 24 hour area under the blood concentration curve (AUC\n                      <jats:sub>24<\/jats:sub>\n                      ) to support precision dosing in sepsis care. Data from sepsis patients treated with intravenous vancomycin were sourced from the MIMIC-IV database. The data set was split into training and testing sets, and four models\u2014PPK, Bayesian, ML, and hybrid\u2014were developed. In the testing set, AUC\n                      <jats:sub>24<\/jats:sub>\n                      was predicted using all models, and performance was evaluated using mean absolute error, mean squared error, root mean squared error, mean absolute percentage error (MAPE), and R\u00b2. A total of 4,059 patients were included. In the absence of vancomycin concentration data, the hybrid model outperformed both PPK and Bayesian models, with MAPE improvements of 58% and 17%, respectively. When vancomycin concentration data were available, the Bayesian model demonstrated the best performance (MAPE: 13.37% vs 68.17%, 34.17%, and 28.52% for PPK, Random Forest, and hybrid models). The hybrid model is recommended to predict AUC\n                      <jats:sub>24<\/jats:sub>\n                      when concentration data were unavailable, while the Bayesian model should be used when concentrations were available, offering robust strategies for precise vancomycin dosing in sepsis patients.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>IMPORTANCE<\/jats:title>\n                    <jats:p>\n                      This study evaluates and compares the performance of four models\u2014PPK, Bayesian, ML, and hybrid PPK-ML\u2014in predicting vancomycin exposure (AUC\n                      <jats:sub>24<\/jats:sub>\n                      ) in sepsis patients using real-world data from the MIMIC-IV database. These results underscore the importance of selecting appropriate models based on the availability of concentration data, providing valuable guidance for precision dosing strategies in sepsis care. This work contributes to advancing personalized vancomycin therapy, optimizing dosing regimens, and improving clinical outcomes in sepsis patients.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1128\/spectrum.00499-25","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T09:20:02Z","timestamp":1743412802000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":17,"title":["Machine learning and population pharmacokinetics: a hybrid approach for optimizing vancomycin therapy in sepsis patients"],"prefix":"10.1128","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6799-0334","authenticated-orcid":false,"given":"Keyu","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuhui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Wei","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sinan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The Second Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijia","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The Second Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5619-0788","authenticated-orcid":true,"given":"Yalin","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The First Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6511-1495","authenticated-orcid":true,"given":"Yan","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pharmacy, The Second Affiliated Hospital of Xi\u2019an Jiaotong University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13054-023-04527-1"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0285717B3"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1093\/chromsci\/bmae017"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiac.2023.10.025"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jiph.2022.04.007"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13318-021-00681-5"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmi.2020.11.008"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/jcpt.13474"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.1993"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41580-021-00407-0"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1167\/tvst.9.2.14"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jconrel.2022.11.014"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2022.942129"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1002\/jcph.2156"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01899-x"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.0287"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.2119"},{"key":"e_1_3_3_19_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.01903"},{"key":"e_1_3_3_20_2","volume-title":"Proceedings of the 31st international conference on neural information processing systems long beach","author":"Lundberg SM","year":"2017","unstructured":"Lundberg SM, Lee S-I. 2017. A unified approach to interpreting model predictions. Proceedings of the 31st international conference on neural information processing systems long beach. 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