{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:55:36Z","timestamp":1792176936264,"version":"4.4.0"},"reference-count":30,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T00:00:00Z","timestamp":1758326400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Artificial Organs"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Patients undergoing hemodialysis (HD) face a significantly elevated risk of cardiovascular mortality, with sudden events during treatment posing a critical threat to survival. These risks are particularly pronounced in high\u2010risk populations, such as patients recovering from cardiovascular surgery or those being treated for sepsis. Therefore, the development of effective preventive strategies is essential for improving patient outcomes. This study aimed to develop a machine learning model that uses pretreatment patient characteristics to predict sudden adverse events during HD and within 24\u2009h after treatment in high\u2010risk inpatients at acute care hospitals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>His retrospective study analyzed data from 739 patients who underwent HD at Hirosaki University Hospital between 2018 and 2021. Sudden events were defined as fatal arrhythmia, refractory intradialytic hypotension, or respiratory arrest. A\u00a0logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD\u2010related information).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre\u2010HD covariates and achieved an area under the receiver operating characteristic curve (AUC) of 0.889. Key covariates included emergency hospitalization (present in 71% of patients with sudden events), recent surgery (76%), shorter HD history, elevated pre\u2010HD heart rate, lower serum albumin levels, and higher C\u2010reactive protein concentrations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our model enables the early identification of high\u2010risk inpatients receiving hemodialysis using pre\u2010dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/aor.70008","type":"journal-article","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T07:50:53Z","timestamp":1758354653000},"page":"139-149","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning\u2010Based Prediction of Life\u2010Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions"],"prefix":"10.1111","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7038-2103","authenticated-orcid":false,"given":"Naotaka","family":"Kato","sequence":"first","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6975-8248","authenticated-orcid":false,"given":"Takeshi","family":"Goto","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomoyuki","family":"Ohira","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hirotaka","family":"Kinoshita","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology Hirosaki University Graduate School of Medicine  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kugo","family":"Kurokawa","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kouhei","family":"Naganuma","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chikako","family":"Ohminato","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junko","family":"Ogasawara","sequence":"additional","affiliation":[{"name":"Department of Clinical Engineering Hirosaki University School of Medicine and Hospital  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0026-4079","authenticated-orcid":false,"given":"Shingo","family":"Hatakeyama","sequence":"additional","affiliation":[{"name":"Department of Urology Hirosaki University Graduate School of Medicine  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yoshihiro","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics Hirosaki University Graduate School of Medicine  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kazuyoshi","family":"Hirota","sequence":"additional","affiliation":[{"name":"Departments of Perioperative Stress Management Hirosaki University Graduate School of Medicine  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chikara","family":"Ohyama","sequence":"additional","affiliation":[{"name":"Department of Advanced Transplant and Regenerative Medicine Hirosaki University Graduate School of Medicine  Hirosaki Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,20]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-022-00542-7"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.4009\/jsdt.57.543"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1046\/j.1523-1755.2001.00806.x"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/sj.ki.5000446"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2020.01.046"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.1093\/ckj\/sfaa078"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/00368504241308982"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1111\/nep.12561"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfad064"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2022.878858"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-024-80900-6"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfx037"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1159\/000531619"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/hdi.12138"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2021.01.018"},{"key":"e_1_2_12_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40620-021-01185-w"},{"issue":"2","key":"e_1_2_12_18_1","first-page":"211","article-title":"Blood Pressure and Mortality in Long\u2010Term Hemodialysis\u2014Time to Move Forward","volume":"30","author":"Georgianos P. I.","year":"2017","journal-title":"American Journal of Hypertension"},{"key":"e_1_2_12_19_1","doi-asserted-by":"publisher","DOI":"10.1161\/HYPERTENSIONAHA.114.04334"},{"key":"e_1_2_12_20_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39474-6"},{"key":"e_1_2_12_21_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfq507"},{"key":"e_1_2_12_22_1","doi-asserted-by":"publisher","DOI":"10.2188\/jea.JE20100180"},{"key":"e_1_2_12_23_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-23354-y"},{"key":"e_1_2_12_24_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-022-03020-1"},{"key":"e_1_2_12_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00380-020-01586-0"},{"key":"e_1_2_12_26_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm11092392"},{"key":"e_1_2_12_27_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12882-022-02725-7"},{"key":"e_1_2_12_28_1","doi-asserted-by":"publisher","DOI":"10.2215\/CJN.05070419"},{"key":"e_1_2_12_29_1","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfz182"},{"key":"e_1_2_12_30_1","doi-asserted-by":"crossref","unstructured":"T.ChenandC.Guestrin \u201cXGBoost: A Scalable Tree Boosting System \u201d(2016) In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13\u201317; San Francisco CA. New York: ACM; p. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"e_1_2_12_31_1","unstructured":"S. M.LundbergandS.\u2010I.Lee \u201cA Unified Approach to Interpreting Model Predictions \u201d(2017) In: Advances in Neural Information Processing Systems; 30."}],"container-title":["Artificial Organs"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/aor.70008","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/aor.70008","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/aor.70008","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T10:28:21Z","timestamp":1772965701000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/aor.70008"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,20]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["10.1111\/aor.70008"],"URL":"https:\/\/doi.org\/10.1111\/aor.70008","archive":["Portico"],"relation":{},"ISSN":["0160-564X","1525-1594"],"issn-type":[{"value":"0160-564X","type":"print"},{"value":"1525-1594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,20]]},"assertion":[{"value":"2025-06-04","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-08","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}