Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning
Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patie...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Karger Publishers
2023-06-01
|
| Series: | Kidney Diseases |
| Subjects: | |
| Online Access: | https://beta.karger.com/Article/FullText/531619 |
| _version_ | 1797776573571530752 |
|---|---|
| author | Daqing Hong Huan Chang Xin He Ya Zhan Rongsheng Tong Xingwei Wu Guisen Li |
| author_facet | Daqing Hong Huan Chang Xin He Ya Zhan Rongsheng Tong Xingwei Wu Guisen Li |
| author_sort | Daqing Hong |
| collection | DOAJ |
| description | Introduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People’s Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results: A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811–0.813), 0.748 (95% CI, 0.747–0.749), and 0.743 (95% CI, 0.742–0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions. |
| first_indexed | 2024-03-12T22:52:47Z |
| format | Article |
| id | doaj.art-2d1a88b6c16e47998067e0b404623519 |
| institution | Directory Open Access Journal |
| issn | 2296-9381 2296-9357 |
| language | English |
| last_indexed | 2024-03-12T22:52:47Z |
| publishDate | 2023-06-01 |
| publisher | Karger Publishers |
| record_format | Article |
| series | Kidney Diseases |
| spelling | doaj.art-2d1a88b6c16e47998067e0b4046235192023-07-20T07:26:13ZengKarger PublishersKidney Diseases2296-93812296-93572023-06-011110.1159/000531619531619Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine LearningDaqing Hong0Huan Chang1Xin He2Ya Zhan3Rongsheng Tong4Xingwei Wu5Guisen Li6https://orcid.org/0000-0003-1970-9979Department of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Pharmacy, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaDepartment of Nephrology, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, ChinaIntroduction: Intradialytic hypotension (IDH) is prevalent and associated with high hospitalization and mortality rates. The purpose of this study was to explore the risk factors for IDH and use artificial intelligence to establish an early alert system before hemodialysis sessions to identify patients at high risk of IDH. Materials and Methods: We obtained data on 314,534 hemodialysis sessions conducted at Sichuan Provincial People’s Hospital from the renal disease treatment information system. IDH was defined as a systolic blood pressure drop ≥20 mm Hg, a mean arterial pressure drop ≥10 mm Hg during dialysis, or the occurrence of clinical hypotensive events requiring nursing intervention. After pre-processing, the data were randomly divided into training (80%) and testing (20%) sets. Four interpolation methods, three feature selection methods, and 18 machine learning algorithms were used to construct predictive models. The area under the receiver operating characteristic curve (AUC) was the main indicator for evaluating the performance of the models, while Shapley Additive ExPlanation was used to explain the contribution of each variable to the best predictive model. Results: A total of 3,906 patients and 314,534 dialysis sessions were included, of which 142,237 cases showed IDH (incidence rate, 45.2%). Nineteen parameters were identified through artificial intelligence feature screening. They included age, pre-dialysis weight, dry weight, pre-dialysis blood pressure, heart rate, prescribed ultrafiltration, blood cell counts (neutrophil, lymphocyte, monocyte, eosinophil, lymphocyte, and platelet counts), hematocrit, serum calcium, creatinine, urea, glucose, and uric acid. Random forest, gradient boosting, and logistic regression were the three best models, and the AUCs were 0.812 (95% confidence interval [CI], 0.811–0.813), 0.748 (95% CI, 0.747–0.749), and 0.743 (95% CI, 0.742–0.744), respectively. Conclusion: Our dialysis software-based artificial intelligence alert system can be used to predict IDH occurrence, enabling the initiation of relevant interventions.https://beta.karger.com/Article/FullText/531619intradialytic hypotensionhemodialysisalert systemartificial intelligencemachine learning |
| spellingShingle | Daqing Hong Huan Chang Xin He Ya Zhan Rongsheng Tong Xingwei Wu Guisen Li Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning Kidney Diseases intradialytic hypotension hemodialysis alert system artificial intelligence machine learning |
| title | Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning |
| title_full | Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning |
| title_fullStr | Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning |
| title_full_unstemmed | Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning |
| title_short | Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning |
| title_sort | construction of an early alert system for intradialytic hypotension before initiating hemodialysis based on machine learning |
| topic | intradialytic hypotension hemodialysis alert system artificial intelligence machine learning |
| url | https://beta.karger.com/Article/FullText/531619 |
| work_keys_str_mv | AT daqinghong constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT huanchang constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT xinhe constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT yazhan constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT rongshengtong constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT xingweiwu constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning AT guisenli constructionofanearlyalertsystemforintradialytichypotensionbeforeinitiatinghemodialysisbasedonmachinelearning |