Evidence map›Paper›PMID 39653977›Full record

ArticleNeurocritical care2025

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

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 and 4 more

Abstract read
PubMed Publisher
In one paragraph

Article in Neurocritical care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Carlos A AudeDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.ORCID 0000-0002-1253-1998
Vikas N VattipallyDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Oishika DasDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Kathleen R RanDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Ganiat A GiwaDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Jordina Rincon-TorroellaDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Risheng XuDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
James P ByrneDivision of Acute Care Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Susanne MuehlschlegelDivision of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine, Neurology, and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Jose I SuarezDivision of Neurosciences Critical Care, Departments of Anesthesiology and Critical Care Medicine, Neurology, and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Debraj MukherjeeDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Judy HuangDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.
Tej D AzadDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA. tazad1@jhmi.edu.
Chetan BettegowdaDepartment of Neurosurgery, Johns Hopkins University School of Medicine, 1800 Orleans Street, Baltimore, 21287, MD, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTimely 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.

methodsData 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.

resultsThree 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.

conclusionsThis 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.

Indexed as

Brain Injuries, TraumaticHealthcare DisparitiesMachine LearningNeurosurgical ProceduresPalliative CareReferral and ConsultationTime-to-TreatmentAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedAge factorsCluster analysisCritical careDecision support techniquesHealth care disparitiesMachine learningNeurosurgeryPalliative carePrognosisQuality of health careRace factorsSex factorsTraumatic brain injury

Identifiers

PMID39653977

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.