Elise A Biesboer, Courtney J Pokrzywa, Basil S Karam, Benjamin Chen, Aniko Szabo, Bi Qing Teng, Matthew D Bernard, Andrew Bernard, Sharfuddin Chowdhury, Al-Hasher E Hayudini, Michal A Radomski, Stephanie Doris, Brian K Yorkgitis, Jennifer Mull, Benjamin W Weston, Mark R Hemmila, Christopher J Tignanelli, Marc A de Moya, Rachel S Morris
{"title":"前瞻性验证医院分诊预测模型以减少误诊：EAST 多中心研究。","authors":"Elise A Biesboer, Courtney J Pokrzywa, Basil S Karam, Benjamin Chen, Aniko Szabo, Bi Qing Teng, Matthew D Bernard, Andrew Bernard, Sharfuddin Chowdhury, Al-Hasher E Hayudini, Michal A Radomski, Stephanie Doris, Brian K Yorkgitis, Jennifer Mull, Benjamin W Weston, Mark R Hemmila, Christopher J Tignanelli, Marc A de Moya, Rachel S Morris","doi":"10.1136/tsaco-2023-001280","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA.</p><p><strong>Methods: </strong>The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury.</p><p><strong>Results: </strong>14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%.</p><p><strong>Conclusion: </strong>The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage.</p><p><strong>Level of evidence: </strong>2.</p>","PeriodicalId":23307,"journal":{"name":"Trauma Surgery & Acute Care Open","volume":"9 1","pages":"e001280"},"PeriodicalIF":2.1000,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086287/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prospective validation of a hospital triage predictive model to decrease undertriage: an EAST multicenter study.\",\"authors\":\"Elise A Biesboer, Courtney J Pokrzywa, Basil S Karam, Benjamin Chen, Aniko Szabo, Bi Qing Teng, Matthew D Bernard, Andrew Bernard, Sharfuddin Chowdhury, Al-Hasher E Hayudini, Michal A Radomski, Stephanie Doris, Brian K Yorkgitis, Jennifer Mull, Benjamin W Weston, Mark R Hemmila, Christopher J Tignanelli, Marc A de Moya, Rachel S Morris\",\"doi\":\"10.1136/tsaco-2023-001280\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA.</p><p><strong>Methods: </strong>The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury.</p><p><strong>Results: </strong>14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%.</p><p><strong>Conclusion: </strong>The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage.</p><p><strong>Level of evidence: </strong>2.</p>\",\"PeriodicalId\":23307,\"journal\":{\"name\":\"Trauma Surgery & Acute Care Open\",\"volume\":\"9 1\",\"pages\":\"e001280\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-05-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11086287/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trauma Surgery & Acute Care Open\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1136/tsaco-2023-001280\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"CRITICAL CARE MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trauma Surgery & Acute Care Open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1136/tsaco-2023-001280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"CRITICAL CARE MEDICINE","Score":null,"Total":0}
Prospective validation of a hospital triage predictive model to decrease undertriage: an EAST multicenter study.
Background: Tiered trauma team activation (TTA) allows systems to optimally allocate resources to an injured patient. Target undertriage and overtriage rates of <5% and <35% are difficult for centers to achieve, and performance variability exists. The objective of this study was to optimize and externally validate a previously developed hospital trauma triage prediction model to predict the need for emergent intervention in 6 hours (NEI-6), an indicator of need for a full TTA.
Methods: The model was previously developed and internally validated using data from 31 US trauma centers. Data were collected prospectively at five sites using a mobile application which hosted the NEI-6 model. A weighted multiple logistic regression model was used to retrain and optimize the model using the original data set and a portion of data from one of the prospective sites. The remaining data from the five sites were designated for external validation. The area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC) were used to assess the validation cohort. Subanalyses were performed for age, race, and mechanism of injury.
Results: 14 421 patients were included in the training data set and 2476 patients in the external validation data set across five sites. On validation, the model had an overall undertriage rate of 9.1% and overtriage rate of 53.7%, with an AUROC of 0.80 and an AUPRC of 0.63. Blunt injury had an undertriage rate of 8.8%, whereas penetrating injury had 31.2%. For those aged ≥65, the undertriage rate was 8.4%, and for Black or African American patients the undertriage rate was 7.7%.
Conclusion: The optimized and externally validated NEI-6 model approaches the recommended undertriage and overtriage rates while significantly reducing variability of TTA across centers for blunt trauma patients. The model performs well for populations that traditionally have high rates of undertriage.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
