{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:49:25Z","timestamp":1785890965689,"version":"3.56.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T00:00:00Z","timestamp":1780099200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:00:00Z","timestamp":1785888000000},"content-version":"vor","delay-in-days":67,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010101","name":"Taichung Veterans General Hospital","doi-asserted-by":"publisher","award":["TCVGH-1073601B, TCVGH-1083602B and TCVGH-1123601B"],"award-info":[{"award-number":["TCVGH-1073601B, TCVGH-1083602B and TCVGH-1123601B"]}],"id":[{"id":"10.13039\/501100010101","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004391","name":"China Medical University Hospital","doi-asserted-by":"publisher","award":["DMR-114-110"],"award-info":[{"award-number":["DMR-114-110"]}],"id":[{"id":"10.13039\/501100004391","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Infect Dis"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients with advanced chronic kidney disease (CKD), defined as having an estimated glomerular filtration rate (eGFR)\u2009&lt;\u200945\u00a0ml\/min\/1.73\u00a0m\u00b2, are vulnerable to infections leading to hospitalization. Predicting such events using routine clinical data may facilitate early intervention. This study aimed to develop a machine-learning models to predict infection-related hospitalization within 30 days of a nephrology visit.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a single center retrospective cohort study of patients in the pre-ESRD pay-for-performance program at Taichung Veterans General Hospital (2010\u20132022). The primary outcome was infection-related hospitalization within 30 days of the index visit; qualifying admissions required intravenous antibiotic use. We excluded visits from patients\u2009&lt;\u200918 years and those with &lt;\u20092 nephrology visits in the prior year. Predictor variables included longitudinal laboratory parameters (means and variances within the prior 3 months), comorbidities, and recent medication use. Five machine learning models (XGBoost, random forest, logistic regression, support vector machine, and k-nearest neighbor) were trained and evaluated, with feature importance assessed using Shapley Additive explanations (SHAP).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The analysis included 11,502 index visits from 11,018 patients, with 2,195 infection-related hospitalizations. Among tested models, the XGBoost algorithm achieved the highest predictive performance (AUROC 85.5%, sensitivity 71.3%, specificity 84.8%). The top ten predictors, as ranked by SHAP importance, were recent use of diuretics, serum albumin, Charlson comorbidity index (CCI) excluding renal disease, mean eGFR, corticosteroid use, albumin variance, age, eGFR variance, mean potassium, and mean uric acid. SHAP dependence plots were used to illustrate nonlinear relationships and approximate thresholds associated with increased risk of infection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Machine learning models using routinely available outpatient data can effectively predict short-term infection-related hospitalization in advanced CKD patients. This approach has the potential to inform individualized surveillance and early intervention strategies in nephrology care.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial number<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12879-026-13308-4","type":"journal-article","created":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T15:45:08Z","timestamp":1780155908000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine-learning prediction of 30-day infection-related hospitalization in advanced CKD"],"prefix":"10.1186","volume":"26","author":[{"given":"Mu-Chi","family":"Chung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tung-Min","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min-Shian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ya-Wen","family":"Chuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Ju","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng-Hsu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chi-Jung","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,30]]},"reference":[{"issue":"1","key":"13308_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.kisu.2021.11.003","volume":"12","author":"Kovesdy","year":"2022","unstructured":"Kovesdy CP. Epidemiology of chronic kidney disease: an update 2022. Kidney Int Suppl (2011). 2022;12(1):7\u201311. https:\/\/doi.org\/10.1016\/j.kisu.2021.11.003.","journal-title":"Kidney Int Suppl (2011)"},{"issue":"11","key":"13308_CR2","doi-asserted-by":"publisher","first-page":"1157","DOI":"10.1161\/CIRCULATIONAHA.120.050686","volume":"143","author":"J Jankowski","year":"2021","unstructured":"Jankowski J, Floege J, Fliser D, Bohm M, Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(11):1157\u201372. https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.120.050686.","journal-title":"Circulation"},{"issue":"3","key":"13308_CR3","doi-asserted-by":"publisher","first-page":"534","DOI":"10.23876\/j.krcp.23.263","volume":"44","author":"YW Park","year":"2025","unstructured":"Park YW, Hwang J, Kim M, Kim SM, Jeong Y, Kang M, et al. Hospitalization among adults with chronic kidney disease: results from the KoreaN cohort study for Outcomes in patients With Chronic Kidney Disease (KNOW-CKD) study. Kidney Res Clin Pract. 2025;44(3):534\u201343. https:\/\/doi.org\/10.23876\/j.krcp.23.263.","journal-title":"Kidney Res Clin Pract"},{"issue":"6","key":"13308_CR4","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1053\/j.ajkd.2016.09.018","volume":"69","author":"J Ishigami","year":"2017","unstructured":"Ishigami J, Grams ME, Chang AR, Carrero JJ, Coresh J, Matsushita K. CKD and Risk for hospitalization with infection: the Atherosclerosis Risk in Communities (ARIC) study. 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This study was conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived by the IRB due to the retrospective nature of the study and the use of de-identified data.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"1429"}}