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Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older

BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study...

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Autores principales: El Hechi, Majed, Gebran, Anthony, Bouardi, Hamza Tazi, Maurer, Lydia R., El Moheb, Mohamad, Zhuo, Daisy, Dunn, Jack, Bertsimas, Dimitris, Velmahos, George C., Kaafarani, Haytham M.A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131296/
https://www.ncbi.nlm.nih.gov/pubmed/34955288
http://dx.doi.org/10.1016/j.surg.2021.11.016
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author El Hechi, Majed
Gebran, Anthony
Bouardi, Hamza Tazi
Maurer, Lydia R.
El Moheb, Mohamad
Zhuo, Daisy
Dunn, Jack
Bertsimas, Dimitris
Velmahos, George C.
Kaafarani, Haytham M.A.
author_facet El Hechi, Majed
Gebran, Anthony
Bouardi, Hamza Tazi
Maurer, Lydia R.
El Moheb, Mohamad
Zhuo, Daisy
Dunn, Jack
Bertsimas, Dimitris
Velmahos, George C.
Kaafarani, Haytham M.A.
author_sort El Hechi, Majed
collection PubMed
description BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. METHODS: All patients aged 65 years and older in the American College of Surgeons–Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 260,505 patients were included. Median age was 77 (71–84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). CONCLUSION: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care.
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spelling pubmed-91312962022-05-25 Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older El Hechi, Majed Gebran, Anthony Bouardi, Hamza Tazi Maurer, Lydia R. El Moheb, Mohamad Zhuo, Daisy Dunn, Jack Bertsimas, Dimitris Velmahos, George C. Kaafarani, Haytham M.A. Surgery Trauma/Critical Care BACKGROUND: The Trauma Outcomes Predictor tool was recently derived using a machine learning methodology called optimal classification trees and validated for prediction of outcomes in trauma patients. The Trauma Outcomes Predictor is available as an interactive smartphone application. In this study, we sought to assess the performance of the Trauma Outcomes Predictor in the elderly trauma patient. METHODS: All patients aged 65 years and older in the American College of Surgeons–Trauma Quality Improvement Program 2017 database were included. The performance of the Trauma Outcomes Predictor in predicting in-hospital mortality and combined and specific morbidity based on incidence of 9 specific in-hospital complications was assessed using the c-statistic methodology, with planned subanalyses for patients 65 to 74, 75 to 84, and 85+ years. RESULTS: A total of 260,505 patients were included. Median age was 77 (71–84) years, 57% were women, and 98.8% had a blunt mechanism of injury. The Trauma Outcomes Predictor accurately predicted mortality in all patients, with excellent performance for penetrating trauma (c-statistic: 0.92) and good performance for blunt trauma (c-statistic: 0.83). Its best performance was in patients 65 to 74 years (c-statistic: blunt 0.86, penetrating 0.93). Among blunt trauma patients, the Trauma Outcomes Predictor had the best discrimination for predicting acute respiratory distress syndrome (c-statistic 0.75) and cardiac arrest requiring cardiopulmonary resuscitation (c-statistic 0.75). Among penetrating trauma patients, the Trauma Outcomes Predictor had the best discrimination for deep and organ space surgical site infections (c-statistics 0.95 and 0.84, respectively). CONCLUSION: The Trauma Outcomes Predictor is a novel, interpretable, and highly accurate predictor of in-hospital mortality in the elderly trauma patient up to age 85 years. The Trauma Outcomes Predictor could prove useful for bedside counseling of elderly patients and their families and for benchmarking the quality of geriatric trauma care. Elsevier Inc. 2022-06 2021-12-23 /pmc/articles/PMC9131296/ /pubmed/34955288 http://dx.doi.org/10.1016/j.surg.2021.11.016 Text en © 2021 Elsevier Inc. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Trauma/Critical Care
El Hechi, Majed
Gebran, Anthony
Bouardi, Hamza Tazi
Maurer, Lydia R.
El Moheb, Mohamad
Zhuo, Daisy
Dunn, Jack
Bertsimas, Dimitris
Velmahos, George C.
Kaafarani, Haytham M.A.
Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title_full Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title_fullStr Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title_full_unstemmed Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title_short Validation of the artificial intelligence–based trauma outcomes predictor (TOP) in patients 65 years and older
title_sort validation of the artificial intelligence–based trauma outcomes predictor (top) in patients 65 years and older
topic Trauma/Critical Care
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9131296/
https://www.ncbi.nlm.nih.gov/pubmed/34955288
http://dx.doi.org/10.1016/j.surg.2021.11.016
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