Machine Learning Reveals Demographic Disparities in Palliative Care Timing Among Patients With Traumatic Brain Injury Receiving Neurosurgical Consultation.

IF 3.1 3区 医学 Q2 CLINICAL NEUROLOGY
Carlos A Aude, Vikas N Vattipally, Oishika Das, Kathleen R Ran, Ganiat A Giwa, Jordina Rincon-Torroella, Risheng Xu, James P Byrne, Susanne Muehlschlegel, Jose I Suarez, Debraj Mukherjee, Judy Huang, Tej D Azad, Chetan Bettegowda
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引用次数: 0

Abstract

Background: Timely palliative care (PC) consultations offer demonstrable benefits for patients with traumatic brain injury (TBI), yet their implementation remains inconsistent. This study employs machine learning methods to identify distinct patient phenotypes and elucidate the primary drivers of PC consultation timing variability in TBI management, aiming to uncover disparities and inform more equitable care strategies.

Methods: Data on admission, hospital course, and outcomes were collected for a cohort of 232 patients with TBI who received both PC consultations and neurosurgical consultations during the same hospitalization. Patient phenotypes were uncovered using principal component analysis and K-means clustering; time-to-PC consultation for each phenotype was subsequently compared by Kaplan-Meier analysis. An extreme gradient boosting model with Shapley Additive Explanations identified key factors influencing PC consultation timing.

Results: Three distinct patient clusters emerged: cluster A (n = 86), comprising older adult White women (median 87 years) with mild TBI, received the earliest PC consultations (median 2.5 days); cluster B (n = 108), older adult White men (median 81 years) with mild TBI, experienced delayed PC consultations (median 5.0 days); and cluster C (n = 38), middle-aged (median: 46.5 years), severely injured, non-White patients, had the latest PC consultations (median 9.0 days). The clusters did not differ by discharge disposition (p = 0.4) or inpatient mortality (p > 0.9); however, Kaplan-Meier analysis revealed a significant difference in time-to-PC consultation (p < 0.001), despite no differences in time-to-mortality (p = 0.18). Shapley Additive Explanations analysis of the extreme gradient boosting model identified age, sex, and race as the most influential drivers of PC consultation timing.

Conclusions: This study unveils crucial disparities in PC consultation timing for patients with TBI, primarily driven by demographic factors rather than clinical presentation or injury characteristics. The identification of distinct patient phenotypes and quantification of factors influencing PC consultation timing provide a foundation for developing for standardized protocols and decision support tools to ensure timely and equitable palliative care access for patients with TBI.

机器学习揭示了接受神经外科会诊的脑外伤患者在姑息治疗时机选择上的人口统计学差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neurocritical Care
Neurocritical Care 医学-临床神经学
CiteScore
7.40
自引率
8.60%
发文量
221
审稿时长
4-8 weeks
期刊介绍: Neurocritical Care is a peer reviewed scientific publication whose major goal is to disseminate new knowledge on all aspects of acute neurological care. It is directed towards neurosurgeons, neuro-intensivists, neurologists, anesthesiologists, emergency physicians, and critical care nurses treating patients with urgent neurologic disorders. These are conditions that may potentially evolve rapidly and could need immediate medical or surgical intervention. Neurocritical Care provides a comprehensive overview of current developments in intensive care neurology, neurosurgery and neuroanesthesia and includes information about new therapeutic avenues and technological innovations. Neurocritical Care is the official journal of the Neurocritical Care Society.
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