{"title":"一般情况较差的住院患者血液透析期间危及生命的并发症的机器学习预测。","authors":"Naotaka Kato, Takeshi Goto, Tomoyuki Ohira, Hirotaka Kinoshita, Kugo Kurokawa, Kouhei Naganuma, Chikako Ohminato, Junko Ogasawara, Shingo Hatakeyama, Yoshihiro Sasaki, Kazuyoshi Hirota, Chikara Ohyama","doi":"10.1111/aor.70008","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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-risk 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 h after treatment in high-risk inpatients at acute care hospitals.</p><p><strong>Methods: </strong>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 logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information).</p><p><strong>Results: </strong>Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD 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-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations.</p><p><strong>Conclusions: </strong>Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.</p>","PeriodicalId":8450,"journal":{"name":"Artificial organs","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine Learning-Based Prediction of Life-Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions.\",\"authors\":\"Naotaka Kato, Takeshi Goto, Tomoyuki Ohira, Hirotaka Kinoshita, Kugo Kurokawa, Kouhei Naganuma, Chikako Ohminato, Junko Ogasawara, Shingo Hatakeyama, Yoshihiro Sasaki, Kazuyoshi Hirota, Chikara Ohyama\",\"doi\":\"10.1111/aor.70008\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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-risk 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 h after treatment in high-risk inpatients at acute care hospitals.</p><p><strong>Methods: </strong>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 logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information).</p><p><strong>Results: </strong>Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD 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-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations.</p><p><strong>Conclusions: </strong>Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.</p>\",\"PeriodicalId\":8450,\"journal\":{\"name\":\"Artificial organs\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Artificial organs\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1111/aor.70008\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Artificial organs","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1111/aor.70008","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
Machine Learning-Based Prediction of Life-Threatening Complications During Hemodialysis in Hospitalized Patients With Poor General Conditions.
Background: 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-risk 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 h after treatment in high-risk inpatients at acute care hospitals.
Methods: 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 logistic regression model was constructed using backward stepwise selection from 51 patient characteristics (demographic data, clinical parameters, laboratory data, and HD-related information).
Results: Among the 739 patients, 17 (2.3%) experienced sudden events. The model identified 23 pre-HD 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-HD heart rate, lower serum albumin levels, and higher C-reactive protein concentrations.
Conclusions: Our model enables the early identification of high-risk inpatients receiving hemodialysis using pre-dialysis data, thereby supporting timely clinical interventions, optimized resource allocation, and improved patient safety.
期刊介绍:
Artificial Organs is the official peer reviewed journal of The International Federation for Artificial Organs (Members of the Federation are: The American Society for Artificial Internal Organs, The European Society for Artificial Organs, and The Japanese Society for Artificial Organs), The International Faculty for Artificial Organs, the International Society for Rotary Blood Pumps, The International Society for Pediatric Mechanical Cardiopulmonary Support, and the Vienna International Workshop on Functional Electrical Stimulation. Artificial Organs publishes original research articles dealing with developments in artificial organs applications and treatment modalities and their clinical applications worldwide. Membership in the Societies listed above is not a prerequisite for publication. Articles are published without charge to the author except for color figures and excess page charges as noted.