{"title":"预测在智利COVID-19大流行期间髋部骨折的老年患者延长住院时间:一项人工神经网络研究。","authors":"Claudio Diaz-Ledezma, Rodrigo Mardones","doi":"10.1177/15563316221120582","DOIUrl":null,"url":null,"abstract":"<p><p><i>Background:</i> Prolonged length of stay (LOS) after a hip fracture is associated with increased mortality. <i>Purpose:</i> We sought to create a model to predict prolonged LOS in elderly Chilean patients with hip fractures managed during the COVID-19 pandemic. <i>Methods:</i> Employing an official database, we created an artificial neural network (ANN), a computational model corresponding to a subset of machine learning, to predict prolonged LOS (≥14 days) among 2686 hip fracture patients managed in 43 Chilean public hospitals during 2020. We identified 18 clinically relevant variables as potential predictors; 80% of the sample was used to train the ANN and 20% was used to test it. The performance of the ANN was evaluated via measuring its discrimination power through the area under the curve of the receiver operating characteristic curve (AUC-ROC). <i>Results:</i> Of the 2686 patients, 820 (30.2%) had prolonged LOS. In the training sample (2,125 cases), the ANN correctly classified 1,532 cases (72.09%; AUC-ROC: 0.745). In the test sample (561 cases), the ANN correctly classified 401 cases (71.48%; AUC-ROC: 0.742). The most relevant variables to predict prolonged LOS were the patient's admitting hospital (relative importance [RI]: 0.11), the patient's geographical health service providing health care (RI: 0.11), and the patient's surgery being conducted within 2 days of admission (RI: 0.10). <i>Conclusions:</i> Using national-level big data, we developed an ANN that predicted with fair accuracy prolonged LOS in elderly Chilean patients with hip fractures during the COVID-19 pandemic. The main predictors of a prolonged LOS were unrelated to the patient's individual health and concerned administrative and organizational factors.</p>","PeriodicalId":35357,"journal":{"name":"Hss Journal","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2023-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434193/pdf/10.1177_15563316221120582.pdf","citationCount":"1","resultStr":"{\"title\":\"Predicting Prolonged Hospital Stays in Elderly Patients With Hip Fractures Managed During the COVID-19 Pandemic in Chile: An Artificial Neural Networks Study.\",\"authors\":\"Claudio Diaz-Ledezma, Rodrigo Mardones\",\"doi\":\"10.1177/15563316221120582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><i>Background:</i> Prolonged length of stay (LOS) after a hip fracture is associated with increased mortality. <i>Purpose:</i> We sought to create a model to predict prolonged LOS in elderly Chilean patients with hip fractures managed during the COVID-19 pandemic. <i>Methods:</i> Employing an official database, we created an artificial neural network (ANN), a computational model corresponding to a subset of machine learning, to predict prolonged LOS (≥14 days) among 2686 hip fracture patients managed in 43 Chilean public hospitals during 2020. We identified 18 clinically relevant variables as potential predictors; 80% of the sample was used to train the ANN and 20% was used to test it. The performance of the ANN was evaluated via measuring its discrimination power through the area under the curve of the receiver operating characteristic curve (AUC-ROC). <i>Results:</i> Of the 2686 patients, 820 (30.2%) had prolonged LOS. In the training sample (2,125 cases), the ANN correctly classified 1,532 cases (72.09%; AUC-ROC: 0.745). In the test sample (561 cases), the ANN correctly classified 401 cases (71.48%; AUC-ROC: 0.742). The most relevant variables to predict prolonged LOS were the patient's admitting hospital (relative importance [RI]: 0.11), the patient's geographical health service providing health care (RI: 0.11), and the patient's surgery being conducted within 2 days of admission (RI: 0.10). <i>Conclusions:</i> Using national-level big data, we developed an ANN that predicted with fair accuracy prolonged LOS in elderly Chilean patients with hip fractures during the COVID-19 pandemic. The main predictors of a prolonged LOS were unrelated to the patient's individual health and concerned administrative and organizational factors.</p>\",\"PeriodicalId\":35357,\"journal\":{\"name\":\"Hss Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9434193/pdf/10.1177_15563316221120582.pdf\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Hss Journal\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/15563316221120582\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ORTHOPEDICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Hss Journal","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/15563316221120582","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ORTHOPEDICS","Score":null,"Total":0}
Predicting Prolonged Hospital Stays in Elderly Patients With Hip Fractures Managed During the COVID-19 Pandemic in Chile: An Artificial Neural Networks Study.
Background: Prolonged length of stay (LOS) after a hip fracture is associated with increased mortality. Purpose: We sought to create a model to predict prolonged LOS in elderly Chilean patients with hip fractures managed during the COVID-19 pandemic. Methods: Employing an official database, we created an artificial neural network (ANN), a computational model corresponding to a subset of machine learning, to predict prolonged LOS (≥14 days) among 2686 hip fracture patients managed in 43 Chilean public hospitals during 2020. We identified 18 clinically relevant variables as potential predictors; 80% of the sample was used to train the ANN and 20% was used to test it. The performance of the ANN was evaluated via measuring its discrimination power through the area under the curve of the receiver operating characteristic curve (AUC-ROC). Results: Of the 2686 patients, 820 (30.2%) had prolonged LOS. In the training sample (2,125 cases), the ANN correctly classified 1,532 cases (72.09%; AUC-ROC: 0.745). In the test sample (561 cases), the ANN correctly classified 401 cases (71.48%; AUC-ROC: 0.742). The most relevant variables to predict prolonged LOS were the patient's admitting hospital (relative importance [RI]: 0.11), the patient's geographical health service providing health care (RI: 0.11), and the patient's surgery being conducted within 2 days of admission (RI: 0.10). Conclusions: Using national-level big data, we developed an ANN that predicted with fair accuracy prolonged LOS in elderly Chilean patients with hip fractures during the COVID-19 pandemic. The main predictors of a prolonged LOS were unrelated to the patient's individual health and concerned administrative and organizational factors.
期刊介绍:
The HSS Journal is the Musculoskeletal Journal of Hospital for Special Surgery. The aim of the HSS Journal is to promote cutting edge research, clinical pathways, and state-of-the-art techniques that inform and facilitate the continuing education of the orthopaedic and musculoskeletal communities. HSS Journal publishes articles that offer contributions to the advancement of the knowledge of musculoskeletal diseases and encourages submission of manuscripts from all musculoskeletal disciplines.