Prognostication of Mortality and Long-Term Functional Outcomes Following Traumatic Brain Injury: Can We Do Better?

Bonds, Brandon W.; Dhanda, Amit; Wade, Christine; Massetti, Jennifer; Diaz, Carla; Stein, Deborah M.

doi:10.1089/neu.2014.3742

Cited by 21 publications

(13 citation statements)

References 21 publications

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“…However, acute severity of injury (CRASH) and the score for Cognitive Flexibility (TMTB-TMTA) were both moderately related to outcome on the adjustment scale, acute injury severity slightly stronger than Cognitive Flexibility (TMTB-TMTA) . The relationship between the CRASH index and the MPAI adjustment index indicates that CRASH not only can predict mortality but is also related to functional outcome, as have been seen in previous studies [ 28 ]. Impaired executive functions have also previously been demonstrated to affect functional outcome negatively, including social functions [ 14 ], and difficulties seem to increase with more severe forms of TBI [ 29 ].…”

Section: Discussionsupporting

confidence: 66%

Cognitive Reserve, Early Cognitive Screening, and Relationship to Long-Term Outcome after Severe Traumatic Brain Injury

Ekdahl

Godbolt

DeBoussard

et al. 2022

JCM

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The objective was to investigate the relationship between early global cognitive functioning using the Barrow Neurological Institute Screen for Higher Cerebral Functions (BNIS) and cognitive flexibility (Trail Making Test (TMT), TMT B-A), with long-term outcome assessed by the Mayo-Portland Adaptability Index (MPAI-4) in severe traumatic brain injury (sTBI) controlling for the influence of cognitive reserve, age, and injury severity. Of 114 patients aged 18–65 with acute Glasgow Coma Scale 3–8, 41 patients were able to complete (BNIS) at 3 months after injury and MPAI-4 5–8 years after injury. Of these, 33 patients also completed TMT at 3 months. Global cognition and cognitive flexibility correlated significantly with long-term outcome measured with MPAI-4 total score (rBNIS = 0.315; rTMT = 0.355). Global cognition correlated significantly with the participation subscale (r = 0.388), while cognitive flexibility correlated with the adjustment (r = 0.364) and ability (r = 0.364) subscales. Adjusting for cognitive reserve and acute injury severity did not alter these relationships. The effect size for education on BNIS and TMT scores was large (d ≈ 0.85). Early screenings with BNIS and TMT are related to long-term outcome after sTBI and seem to measure complementary aspects of outcome. As early as 3 months after sTBI, educational level influences the scores on neuropsychological screening instruments.

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Section: Discussionsupporting

confidence: 66%

Cognitive Reserve, Early Cognitive Screening, and Relationship to Long-Term Outcome after Severe Traumatic Brain Injury

Ekdahl

Godbolt

DeBoussard

et al. 2022

JCM

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“…The slope of the IMPACT Core+APTT model is close to 1 when predicting 6-month mortality, indicating that there is a strong consistency between the observed results and the predicted results. The possible reason is that there are individual differences in many determinants such as socioeconomic status, family support, or acceptance of treatment when predicting the long-term functional prognosis of patients with TBI [ 23 ], but the new combined prediction models still show satisfying results in distinguishing patients with the highest probability of unfavorable outcomes based on only the data available at the time of admission. It is guessed that the final functional prognosis may be seriously affected by the early injury.…”

Section: Discussionmentioning

confidence: 99%

Development and Verification of Prognostic Prediction Models for Patients with Brain Trauma Based on Coagulation Function Indexes

et al. 2022

Journal of Immunology Research

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Objective. To assess the effect of adding coagulation indices to the currently existing prognostic prediction models of traumatic brain injury (TBI) in the prediction of outcome. Methods. A total of 210 TBI patients from 2017 to 2019 and 131 TBI patients in 2020 were selected for development and internal verification of the new model. The primary outcomes include death at 14 days and Glasgow Outcome Score (GOS) at 6 months. The performance of each model is evaluated by means of discrimination (area under the curve (AUC)), calibration (Hosmer-Lemeshow (H-L) goodness-of-fit test), and precision (Brier score). Results. The IMPACT Core model showed better prediction ability than the CRASH Basic model. Adding one coagulation index at a time to the IMPACT Core model, the new combined models IMPACT Core+FIB and IMPACT Core+APTT are optimal for the 6-month unfavorable outcome and 6-month mortality, respectively (AUC, 0.830 and 0.878). The new models were built based on the regression coefficients of the models. Internal verification indicated that for the prediction of 6-month unfavorable outcome and 6-month mortality, both the IMPACT Core+FIB model and the IMPACT Core+APTT model show better discrimination (AUC, 0.823 vs. 0.818 and 0.853 vs. 0.837), better calibration (HL, p = 0.114 and p = 0.317 ) and higher precision (Brier score, 0.148 vs. 0.141 and 0.147 vs. 0.164), respectively, than the original models. Conclusion. Our research shows that the combination of the traumatic brain injury prognostic models and coagulation indices can improve the 6-month outcome prediction of patients with TBI.

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“…3 While neurosurgical interventions can minimize secondary brain injury, the heterogeneous nature of TBI requires complex clinical decision-making, and prognostic challenges further complicate the timely transition to end-of-life care. [4][5][6][7][8] Palliative care provides active, whole-person care to individuals nearing the end of life to improve the quality of life for patients, families, and caregivers. 9 As such, palliative care can play a vital role in clarifying and codifying care goals among patients with TBI.…”

Section: Introductionmentioning

confidence: 99%

“…Median (IQR); n (%)2 Kruskal-Wallis rank sum test; Pearson's Chi-squared test; Fisher's Exact Test3 Benjamini-Hochberg correction for multiple testing4 Signi cance Codes: [0, 0.001] '***' , (0.001, 0.01] '**', (0.01, 0.05] '*', (0.05, 1] ' '…”

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confidence: 99%

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

Aude,

Vattipally,

Das

et al. 2024

Preprint

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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.

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Prognostication of Mortality and Long-Term Functional Outcomes Following Traumatic Brain Injury: Can We Do Better?

Cited by 21 publications

References 21 publications

Cognitive Reserve, Early Cognitive Screening, and Relationship to Long-Term Outcome after Severe Traumatic Brain Injury

Cognitive Reserve, Early Cognitive Screening, and Relationship to Long-Term Outcome after Severe Traumatic Brain Injury

Development and Verification of Prognostic Prediction Models for Patients with Brain Trauma Based on Coagulation Function Indexes

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

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