Phenotyping Depression After Mild Traumatic Brain Injury: Evaluating the Impact of Multiple Injury, Gender, and Injury Context

Kennedy, Eamonn; Ozmen, Mustafa; Bouldin, Erin D.; Panahi, Samin; Mobasher, Helal; Troyanskaya, Maya; Martindale, Sarah L.; Merritt, Victoria C.; O'Neil, Maya; Sponheim, Scott R.; Remigio-Baker, Rosemay A.; Presson, Angela; Swan, Alicia A.; Werner, J. Kent; Greene, Tom H.; Wilde, Elisabeth A.; Tate, David F.; Walker, William C.; Pugh, Mary Jo

doi:10.1089/neu.2023.0381

Cited by 5 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Building on recent efforts to identify TBI phenotypes, we extend these methods to patients with TBI receiving palliative care consultations, and additionally employ advanced machine learning techniques to elucidate the precise impact of each variable. 24 – 27 We hypothesize that identifying distinct clusters of patients based on admission characteristics will reveal significant disparities in the timing of initial PCCs. Additionally, we theorize that factors beyond injury and clinical details are responsible for producing these clusters and play a crucial role in the timing of PCC.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Identifies Variation in Timing of Palliative Care Consultations Among Traumatic Brain Injury Patients

Aude,

Vattipally,

Das

et al. 2024

Preprint

View full text Add to dashboard Cite

Background and Objective Timely palliative care involvement offers demonstrable benefits for traumatic brain injury (TBI) patients; however, palliative care consultations (PCCs) are used inconsistently during TBI management. This study aimed to employ advanced machine learning techniques to elucidate the primary drivers of PCC timing variability for TBI patients. Methods Data on admission, hospital course, and outcomes were collected for a cohort of 232 TBI patients who received both PCCs and neurosurgical consultations during the same hospitalization. Principal Component Analysis (PCA) and K-means clustering were used to identify patient phenotypes, which were then compared using Kaplan-Meier analysis. An extreme gradient boosting model (XGBoost) was employed to determine drivers of PCC timing, with model interpretation performed using SHapley Additive exPlanations (SHAP). Results Cluster A (n = 86) consisted mainly of older (median [IQR] = 87 [78, 94] years), White females with mild TBIs and demonstrated the shortest time-to-PCC (2.5 [1.0, 7.0] days). Cluster B (n = 108) also sustained mild TBIs but comprised moderately younger (81 [75, 86] years) married White males with later PCC (5.0 [3.0, 10.8] days). Cluster C (n = 38) represented much younger (46.5 [29.5, 59.8] years), more severely injured, non-White patients with the latest PCC initiation (9.0 [4.2, 17.0] days). The clusters did not differ by discharge disposition (p = 0.4) or frequency inpatient mortality (p > 0.9); however, Kaplan-Meier analysis revealed a significant difference in the time from admission to PCC (p < 0.001), despite no differences in time from admission to mortality (p = 0.18). SHAP analysis of the XGBoost model identified age, sex, and race as the most influential drivers of PCC timing. Conclusions This study highlights crucial disparities in PCC timing for TBI patients and underscores the need for targeted strategies to ensure timely and equitable palliative care integration for this vulnerable population.

show abstract

Section: Introductionmentioning

confidence: 99%