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Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury.

ABSTRACT: Gold standard prognostic models for long-term outcome in patients with severe traumatic brain injury (TBI) use admission characteristics and are considered useful in some areas but not for clinical practice. In this study, we aimed to build prognostic models for 6-month Glasgow Outcome Score (GOS) in patients with severe TBI, combining baseline characteristics with physiological, treatment, and injury severity data collected during the first 24?h after injury. We used a training dataset of 472 TBI subjects and several data mining algorithms to predict the long-term neurological outcome. Performance of these algorithms was assessed in an independent (test) sample of 158 subjects. The least absolute shrinkage and selection operator (LASSO) led to the highest prediction accuracy (area under the receiving operating characteristic curve?=?0.86) in the test set. The most important post-baseline predictor of GOS was the best motor Glasgow Coma Scale (GCS) recorded in the first day post-injury. The LASSO model containing the best motor GCS and baseline variables as predictors outperformed a model with baseline data only. TBI patient physiology of the first day-post-injury did not have a major contribution to patient prognosis six months after injury. In conclusion, 6-month GOS in patients with TBI can be predicted with good accuracy by the end of the first day post-injury, using hospital admission data and information on the best motor GCS achieved during those first 24?h post-injury. Passed the first day after injury, important physiological predictors could emerge from landmark analyses, leading to prediction models of higher accuracy than the one proposed in the current research.

SUBMITTER: Rubin ML

PROVIDER: S-EPMC6661910 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury.

Rubin M Laura ML Yamal Jose-Miguel JM Chan Wenyaw W Robertson Claudia S CS

Journal of neurotrauma 20190423 16

Gold standard prognostic models for long-term outcome in patients with severe traumatic brain injury (TBI) use admission characteristics and are considered useful in some areas but not for clinical practice. In this study, we aimed to build prognostic models for 6-month Glasgow Outcome Score (GOS) in patients with severe TBI, combining baseline characteristics with physiological, treatment, and injury severity data collected during the first 24 h after injury. We used a training dataset of 472 T ...[more]

PMID: 30860434

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Project description:INTRODUCTION: The aim of this study was to evaluate the usefulness of the APACHE II (Acute Physiology and Chronic Health Evaluation II), SAPS II (Simplified Acute Physiology Score II) and SOFA (Sequential Organ Failure Assessment) scores compared to simpler models based on age and Glasgow Coma Scale (GCS) in predicting long-term outcome of patients with moderate-to-severe traumatic brain injury (TBI) treated in the intensive care unit (ICU). METHODS: A national ICU database was screened for eligible TBI patients (age over 15 years, GCS 3-13) admitted in 2003-2012. Logistic regression was used for customization of APACHE II, SAPS II and SOFA score-based models for six-month mortality prediction. These models were compared to an adjusted SOFA-based model (including age) and a reference model (age and GCS). Internal validation was performed by a randomized split-sample technique. Prognostic performance was determined by assessing discrimination, calibration and precision. RESULTS: In total, 1,625 patients were included. The overall six-month mortality was 33%. The APACHE II and SAPS II-based models showed good discrimination (area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.75 to 0.82; and 0.80, 95% CI 0.77 to 0.83, respectively), calibration (P?>?0.05) and precision (Brier score 0.166 to 0.167). The SOFA-based model showed poor discrimination (AUC 0.68, 95% CI 0.64 to 0.72) and precision (Brier score 0.201) but good calibration (P?>?0.05). The AUC of the SOFA-based model was significantly improved after the insertion of age and GCS (?AUC +0.11, P?<?0.001). The performance of the reference model was comparable to the APACHE II and SAPS II in terms of discrimination (AUC 0.77; compared to APACHE II, ?AUC -0.02, P?=?0.425; compared to SAPS II, ?AUC -0.03, P?=?0.218), calibration (P?>?0.05) and precision (Brier score 0.181). CONCLUSIONS: A simple prognostic model, based only on age and GCS, displayed a fairly good prognostic performance in predicting six-month mortality of ICU-treated patients with TBI. The use of the more complex scoring systems APACHE II, SAPS II and SOFA added little to the prognostic performance.

Project description:Cross-sectional approaches to outcome assessment may not adequately capture heterogeneity in recovery after traumatic brain injury (TBI). Using latent class mixed models (LCMM), a data-driven analytic that identifies groups of patients with similar trajectories, we identified distinct 6 month functional recovery trajectories in a large cohort (n = 1046) of adults 18-70 years of age with complicated mild to severe TBI who participated in the Citicoline Brain Injury Treatment Trial (COBRIT). We used multinomial logistic fixed effect models and backward elimination, forward selection, and forward stepwise selection with several stopping rules to explore baseline predictors of functional recovery trajectory. Based on statistical and clinical considerations, the seven-class model was deemed superior. Visualization of these seven functional recovery trajectories revealed that each trajectory class started at one of three recovery levels at 1 month, which, for ease of reference we labeled groups A-C: Group A, good recovery (two classes; A1 and A2); Group B, moderate disability (two classes; B1 and B2); and Group C, severe disability (three classes; C1, C2, and C3). By 6 months, these three groups experienced dramatically divergent trajectories. Group A experienced stable good recovery (A1, n = 115) or dramatic decline (A2, n = 4); Group B experienced rapid complete recovery (B1, n = 71) or gradual recovery (B2, n = 742); Group C experienced dramatic rapid recovery (C1, n = 12), no recovery (C2, n = 91), or death (C3, n = 11). Trajectory class membership was not predicted by citicoline treatment (p = 0.57). The models identified demographic, pre-injury, and injury-related predictors of functional recovery trajectory, including: age, race, education, pre-injury employment, pre-injury diabetes, pre-injury psychiatric disorder, site, Glasgow Coma Scale (GCS) score, post-traumatic amnesia, TBI mechanism, major extracranial injury, hemoglobin, and acute computed tomographic (CT) findings. GCS was the most consistently selected predictor across all models. All models also selected at least one demographic or pre-injury medical predictor. LCMM successfully identified dramatically divergent, clinically meaningful 6 month recovery trajectories with utility to inform clinical trial design.

Project description:BackgroundTraumatic brain injury (TBI) is a leading cause of death and disability. A reliable prediction of outcome on admission is of great clinical relevance. We aimed to develop prognostic models with readily available traditional and novel predictors.Methods and findingsProspectively collected individual patient data were analyzed from 11 studies. We considered predictors available at admission in logistic regression models to predict mortality and unfavorable outcome according to the Glasgow Outcome Scale at 6 mo after injury. Prognostic models were developed in 8,509 patients with severe or moderate TBI, with cross-validation by omission of each of the 11 studies in turn. External validation was on 6,681 patients from the recent Medical Research Council Corticosteroid Randomisation after Significant Head Injury (MRC CRASH) trial. We found that the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model that combined age, motor score, and pupillary reactivity had an area under the receiver operating characteristic curve (AUC) between 0.66 and 0.84 at cross-validation. This performance could be improved (AUC increased by approximately 0.05) by considering CT characteristics, secondary insults (hypotension and hypoxia), and laboratory parameters (glucose and hemoglobin). External validation confirmed that the discriminative ability of the model was adequate (AUC 0.80). Outcomes were systematically worse than predicted, but less so in 1,588 patients who were from high-income countries in the CRASH trial.ConclusionsPrognostic models using baseline characteristics provide adequate discrimination between patients with good and poor 6 mo outcomes after TBI, especially if CT and laboratory findings are considered in addition to traditional predictors. The model predictions may support clinical practice and research, including the design and analysis of randomized controlled trials.

Project description:Polytrauma and traumatic brain injury (TBI) frequently co-occur and outcomes are routinely measured by the Glasgow Outcome Scale-Extended (GOSE). Polytrauma may confound GOSE measurement of TBI-specific outcomes. Adult patients with TBI from the prospective Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study had presented to a Level 1 trauma center after injury, received head computed tomography (CT) within 24 h, and completed the GOSE at 3 months and 6 months post-injury. Polytrauma was defined as an Abbreviated Injury Score (AIS) ≥3 in any extracranial region. Univariate regressions were performed using known GOSE clinical cutoffs. Multi-variable regressions were performed for the 3- and 6-month GOSE, controlling for known demographic and injury predictors. Of 361 subjects (age 44.9 ± 18.9 years, 69.8% male), 69 (19.1%) suffered polytrauma. By Glasgow Coma Scale (GCS) assessment, 80.1% had mild, 5.8% moderate, and 14.1% severe TBI. On univariate logistic regression, polytrauma was associated with increased odds of moderate disability or worse (GOSE ≤6; 3 month odds ratio [OR] = 2.57 [95% confidence interval (CI): 1.50-4.41; 6 month OR = 1.70 [95% CI: 1.01-2.88]) and death/severe disability (GOSE ≤4; 3 month OR = 3.80 [95% CI: 2.03-7.11]; 6 month OR = 3.33 [95% CI: 1.71-6.46]). Compared with patients with isolated TBI, more polytrauma patients experienced a decline in GOSE from 3 to 6 months (37.7 vs. 24.7%), and fewer improved (11.6 vs. 22.6%). Polytrauma was associated with greater univariate ordinal odds for poorer GOSE (3 month OR = 2.79 [95% CI: 1.73-4.49]; 6 month OR = 1.73 [95% CI: 1.07-2.79]), which was conserved on multi-variable ordinal regression (3 month OR = 3.05 [95% CI: 1.76-5.26]; 6 month OR = 2.04 [95% CI: 1.18-3.42]). Patients with TBI with polytrauma are at greater risk for 3- and 6-month disability compared with those with isolated TBI. Methodological improvements in assessing TBI-specific disability, versus disability attributable to all systemic injuries, will generate better TBI outcomes assessment tools.

Dataset Information

Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury.

Publications

Prognosis of Six-Month Glasgow Outcome Scale in Severe Traumatic Brain Injury Using Hospital Admission Characteristics, Injury Severity Characteristics, and Physiological Monitoring during the First Day Post-Injury.

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