Hiroyuki Ohbe, Yuta Yokokawa, Tetsuya Sato, Daisuke Kudo, Shigeki Kushimoto
{"title":"开发和验证创伤患者出院时新出现功能障碍的早期预测模型。","authors":"Hiroyuki Ohbe, Yuta Yokokawa, Tetsuya Sato, Daisuke Kudo, Shigeki Kushimoto","doi":"10.1097/TA.0000000000004420","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Early identification of individuals at risk of functional impairment after trauma is crucial for the timely clinical decision-making and intervention to improve reintegration into the society. This study aimed to develop and validate models for predicting new-onset functional impairment after trauma using predictors that are routinely collected within 2 days of hospital admission.</p><p><strong>Methods: </strong>In this multicenter retrospective cohort study of acute care hospitals in Japan, we identified adult patients with trauma with independence in carrying out activities of daily living before hospitalization, treated in the intensive or high-dependency care unit, and survived for at least 2 days between April 2008 and September 2023. The primary outcome was functional impairment defined as Barthel Index ≤60 at hospital discharge. In the internal validation data set (between April 2008 and August 2022), using the routinely collected 129 candidate predictors within 2 days of admission, we trained and tuned the four conventional and machine learning models with repeated random subsampling cross-validation. We measured the performance of these models in the temporal validation data set (between September 2022 and September 2023). We also computed the importance of each predictor variable in our model.</p><p><strong>Results: </strong>We identified 8,529 eligible patients. Functional impairment at discharge was observed in 41% of the patients (n = 3,506/8,529). In the temporal validation data set, all four models showed moderate discrimination ability, with areas under the curve above 0.79, and extreme gradient boosting showing the best performance (0.83). In the variable importance analyses, age was the most important predictor, followed by consciousness, severity score, cervical spinal cord injury, mild dementia, and serum albumin level at admission.</p><p><strong>Conclusion: </strong>We successfully developed early prediction models for patients with trauma with new-onset functional impairment at discharge that achieved high predictive performance using routinely collected data within 2 days of hospital admission.</p><p><strong>Level of evidence: </strong>Prognostic and Epidemiological; Level III.</p>","PeriodicalId":17453,"journal":{"name":"Journal of Trauma and Acute Care Surgery","volume":" ","pages":"167-178"},"PeriodicalIF":2.9000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Development and validation of early prediction models for new-onset functional impairment of patients with trauma at hospital discharge.\",\"authors\":\"Hiroyuki Ohbe, Yuta Yokokawa, Tetsuya Sato, Daisuke Kudo, Shigeki Kushimoto\",\"doi\":\"10.1097/TA.0000000000004420\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Early identification of individuals at risk of functional impairment after trauma is crucial for the timely clinical decision-making and intervention to improve reintegration into the society. This study aimed to develop and validate models for predicting new-onset functional impairment after trauma using predictors that are routinely collected within 2 days of hospital admission.</p><p><strong>Methods: </strong>In this multicenter retrospective cohort study of acute care hospitals in Japan, we identified adult patients with trauma with independence in carrying out activities of daily living before hospitalization, treated in the intensive or high-dependency care unit, and survived for at least 2 days between April 2008 and September 2023. The primary outcome was functional impairment defined as Barthel Index ≤60 at hospital discharge. In the internal validation data set (between April 2008 and August 2022), using the routinely collected 129 candidate predictors within 2 days of admission, we trained and tuned the four conventional and machine learning models with repeated random subsampling cross-validation. We measured the performance of these models in the temporal validation data set (between September 2022 and September 2023). We also computed the importance of each predictor variable in our model.</p><p><strong>Results: </strong>We identified 8,529 eligible patients. Functional impairment at discharge was observed in 41% of the patients (n = 3,506/8,529). In the temporal validation data set, all four models showed moderate discrimination ability, with areas under the curve above 0.79, and extreme gradient boosting showing the best performance (0.83). In the variable importance analyses, age was the most important predictor, followed by consciousness, severity score, cervical spinal cord injury, mild dementia, and serum albumin level at admission.</p><p><strong>Conclusion: </strong>We successfully developed early prediction models for patients with trauma with new-onset functional impairment at discharge that achieved high predictive performance using routinely collected data within 2 days of hospital admission.</p><p><strong>Level of evidence: </strong>Prognostic and Epidemiological; Level III.</p>\",\"PeriodicalId\":17453,\"journal\":{\"name\":\"Journal of Trauma and Acute Care Surgery\",\"volume\":\" \",\"pages\":\"167-178\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Trauma and Acute Care Surgery\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/TA.0000000000004420\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/30 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Trauma and Acute Care Surgery","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/TA.0000000000004420","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/30 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
Development and validation of early prediction models for new-onset functional impairment of patients with trauma at hospital discharge.
Background: Early identification of individuals at risk of functional impairment after trauma is crucial for the timely clinical decision-making and intervention to improve reintegration into the society. This study aimed to develop and validate models for predicting new-onset functional impairment after trauma using predictors that are routinely collected within 2 days of hospital admission.
Methods: In this multicenter retrospective cohort study of acute care hospitals in Japan, we identified adult patients with trauma with independence in carrying out activities of daily living before hospitalization, treated in the intensive or high-dependency care unit, and survived for at least 2 days between April 2008 and September 2023. The primary outcome was functional impairment defined as Barthel Index ≤60 at hospital discharge. In the internal validation data set (between April 2008 and August 2022), using the routinely collected 129 candidate predictors within 2 days of admission, we trained and tuned the four conventional and machine learning models with repeated random subsampling cross-validation. We measured the performance of these models in the temporal validation data set (between September 2022 and September 2023). We also computed the importance of each predictor variable in our model.
Results: We identified 8,529 eligible patients. Functional impairment at discharge was observed in 41% of the patients (n = 3,506/8,529). In the temporal validation data set, all four models showed moderate discrimination ability, with areas under the curve above 0.79, and extreme gradient boosting showing the best performance (0.83). In the variable importance analyses, age was the most important predictor, followed by consciousness, severity score, cervical spinal cord injury, mild dementia, and serum albumin level at admission.
Conclusion: We successfully developed early prediction models for patients with trauma with new-onset functional impairment at discharge that achieved high predictive performance using routinely collected data within 2 days of hospital admission.
Level of evidence: Prognostic and Epidemiological; Level III.
期刊介绍:
The Journal of Trauma and Acute Care Surgery® is designed to provide the scientific basis to optimize care of the severely injured and critically ill surgical patient. Thus, the Journal has a high priority for basic and translation research to fulfill this objectives. Additionally, the Journal is enthusiastic to publish randomized prospective clinical studies to establish care predicated on a mechanistic foundation. Finally, the Journal is seeking systematic reviews, guidelines and algorithms that incorporate the best evidence available.