@article{1300231245a54e6dbd4292b061ccbc23,
title = "A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay",
abstract = "Introduction: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS). Methods: Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019. Trauma patients with one or fewer days LOS were excluded. Single layer and deep artificial neural networks were trained to identify patients in the top quartile of LOS and optimized on area under the receiver operator characteristic curve (AUROC). The predictive performance of the model was assessed on a separate test set using binary classification measures of accuracy, precision, and error. Results: 2953 admitted trauma patients with more than one-day LOS were included in this study. They were 70% male, 60% white, and averaged 47 years-old (SD: 21). 28% were penetrating trauma. Median length of stay was 5 days (IQR 3-9). For prediction of prolonged LOS, the deep neural network achieved an AUROC of 0.80 (95% CI: 0.786-0.814) specificity was 0.95, sensitivity was 0.32, with an overall accuracy of 0.79. Conclusion: Machine learning can predict, with excellent specificity, trauma patients who will have prolonged length of stay with only physiologic and demographic data available at the time of admission. These patients may benefit from additional resources with respect to disposition planning at the time of admission.",
keywords = "artificial neural networks, length of stay, machine-learning, predictive modeling, trauma surgery",
author = "Stonko, {David P.} and Weller, {Jennine H.} and {Gonzalez Salazar}, {Andres J.} and Hossam Abdou and Joseph Edwards and Jeremiah Hinson and Scott Levin and Byrne, {James P.} and Sakran, {Joseph V.} and Hicks, {Caitlin W.} and Haut, {Elliott R.} and Morrison, {Jonathan J.} and Kent, {Alistair J.}",
note = "Funding Information: Dr. Haut is/was primary investigator of contracts from PCORI entitled “Preventing Venous Thromboembolism: Empowering Patients and Enabling Patient-Centered Care via Health Information Technology” (CE-12-11-4489) and “Preventing Venous Thromboembolism (VTE): Engaging Patients to Reduce Preventable Harm from Missed/Refused Doses of VTE Prophylaxis” (DI-1603-34596). Dr. Haut is primary investigator of a grant from the AHRQ (1R01HS024547) entitled “Individualized Performance Feedback on Venous Thromboembolism Prevention Practice,” and is a co-investigator on a grant from the NIH/NHLBI (R21HL129028) entitled “Analysis of the Impact of Missed Doses of Venous Thromboembolism Prophylaxis.” Dr. Haut is supported by a contract from The Patient-Centered Outcomes Research Institute (PCORI), “A Randomized Pragmatic Trial Comparing the Complications and Safety of Blood Clot Prevention Medicines Used in Orthopedic Trauma Patients” (PCS-1511-32745). Dr. Haut is a paid consultant and speaker for the “Preventing Avoidable Venous Thromboembolism— Every Patient, Every Time” VHA/Vizient IMPERATIV{\textregistered} Advantage Performance Improvement Collaborative. Dr. Haut receives royalties from Lippincott, Williams, Wilkins for a book - “Avoiding Common ICU Errors.” Dr. Haut was the paid author of a paper commissioned by the National Academies of Medicine titled “Military Trauma Care{\textquoteright}s Learning Health System: The Importance of Data Driven Decision Making” which was used to support the report titled “A National Trauma Care System: Integrating Military and Civilian Trauma Systems to Achieve Zero Preventable Deaths After Injury.” Dr. Levin is a founder of StoCastic, a company that creates data-driven clinical decision support. Dr. Hinson is Chief Medical Officer for StoCastic. Both Drs. Levin and Hinson and the University own equity in the company. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. Drs. Levin and Hinson are also co-PIs of grants from AHRQ (R18HS026640-01 and R01 HS027793-01). David Stonko is an SEC registered investment advisor at Catalio Capital Management, LP, which is a biotechnology focused investment firm where he performs scientific diligence to support the firm{\textquoteright}s investment strategy. This firm is invested in several AI/ML focused companies, but none are related to this work. Publisher Copyright: {\textcopyright} The Author(s) 2022.",
year = "2023",
month = jun,
doi = "10.1177/15533506221139965",
language = "English",
volume = "30",
pages = "356--365",
journal = "Surgical Innovation",
issn = "1553-3506",
number = "3",
}