Estimating prognosis for traumatic brain injury patients in a low-resource setting: how do providers compare to the CRASH risk calculator?

Autor:	Elahi C; 2Duke Global Health Institute.; 6Paul L. Foster School of Medicine, El Paso, Texas; and., Williamson T; 1Duke University Division of Global Neurosurgery and Neurology.; 3Department of Neurosurgery, Duke University Hospital., Spears CA; 4Duke University School of Medicine, Durham, North Carolina., Williams S; 2Duke Global Health Institute., Nambi Najjuma J; 5Mbarara University of Science and Technology, Mbarara Regional Referral Hospital, Mbarara, Uganda., Staton CA; 1Duke University Division of Global Neurosurgery and Neurology.; 2Duke Global Health Institute.; 7Duke Surgery, Division of Emergency Medicine, Durham, North Carolina., Nickenig Vissoci JR; 1Duke University Division of Global Neurosurgery and Neurology.; 2Duke Global Health Institute., Fuller A; 1Duke University Division of Global Neurosurgery and Neurology.; 2Duke Global Health Institute.; 3Department of Neurosurgery, Duke University Hospital.; 4Duke University School of Medicine, Durham, North Carolina., Kitya D; 5Mbarara University of Science and Technology, Mbarara Regional Referral Hospital, Mbarara, Uganda., Haglund MM; 1Duke University Division of Global Neurosurgery and Neurology.; 2Duke Global Health Institute.; 3Department of Neurosurgery, Duke University Hospital.; 4Duke University School of Medicine, Durham, North Carolina.
Jazyk:	angličtina
Zdroj:	Journal of neurosurgery [J Neurosurg] 2020 Apr 03; Vol. 134 (3), pp. 1285-1293. Date of Electronic Publication: 2020 Apr 03 (Print Publication: 2021).
DOI:	10.3171/2020.2.JNS192512
Abstrakt:	Objective: Traumatic brain injury (TBI), a burgeoning global health concern, is one condition that could benefit from prognostic modeling. Risk stratification of TBI patients on presentation to a health facility can support the prudent use of limited resources. The CRASH (Corticosteroid Randomisation After Significant Head Injury) model is a well-established prognostic model developed to augment complex decision-making. The authors' current study objective was to better understand in-hospital decision-making for TBI patients and determine whether data from the CRASH risk calculator influenced provider assessment of prognosis. Methods: The authors performed a choice experiment using a simulated TBI case. All participant doctors received the same case, which included a patient history, vitals, and physical examination findings. Half the participants also received the CRASH risk score. Participants were asked to estimate the patient prognosis and decide the best next treatment step. The authors recruited a convenience sample of 28 doctors involved in TBI care at both a regional and a national referral hospital in Uganda. Results: For the simulated case, the CRASH risk scores for 14-day mortality and an unfavorable outcome at 6 months were 51.4% (95% CI 42.8%, 59.8%) and 89.8% (95% CI 86.0%, 92.6%), respectively. Overall, participants were overoptimistic when estimating the patient prognosis. Risk estimates by doctors provided with the CRASH risk score were closer to that score than estimates made by doctors in the control group; this effect was more pronounced for inexperienced doctors. Surgery was selected as the best next step by 86% of respondents. Conclusions: This study was a novel assessment of a TBI prognostic model's influence on provider estimation of risk in a low-resource setting. Exposure to CRASH risk score data reduced overoptimistic prognostication by doctors, particularly among inexperienced providers.
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu