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Predicting outcome after traumatic brain injury: development of prognostic scores based on the IMPACT and the APACHE II.

ABSTRACT: Prediction models are important tools for heterogeneity adjustment in clinical trials and for the evaluation of quality of delivered care to patients with traumatic brain injury (TBI). We sought to improve the predictive performance of the IMPACT (International Mission for Prognosis and Analysis of Clinical Trials) prognostic model by combining it with the APACHE II (Acute Physiology and Chronic Health Evaluation II) for 6-month outcome prediction in patients with TBI treated in the intensive care unit. A total of 890 patients with TBI admitted to a large urban level 1 trauma center in 2009-2012 comprised the study population. The IMPACT and the APACHE II scores were combined using binary logistic regression. A randomized, split-sample technique with secondary bootstrapping was used for model development and internal validation. Model performance was assessed by discrimination (by area under the curve [AUC]), calibration, precision, and net reclassification improvement (NRI). Overall 6-month mortality was 22% and unfavorable neurological outcome 47%. The predictive power of the new combined IMPACT-APACHE II models was significantly superior, compared to the original IMPACT models (AUC, 0.81-0.82 vs. 0.84-0.85; p<0.05) for 6-month mortality prediction, but not for unfavorable outcome prediction (AUC, 0.81-0.82 vs. 0.83; p>0.05). However, NRI showed a significant improvement in risk stratification of patients with unfavorable outcome by the IMPACT-APACHE II models, compared to the original models (NRI, 5.4-23.2%; p<0.05). Internal validation using split-sample and resample bootstrap techniques yielded equivalent results, indicating low grade of overestimation. Our findings show that by combining the APACHE II with the IMPACT, improved 6-month outcome predictive performance is achieved. This may be applicable for heterogeneity adjustment in forthcoming TBI studies.

SUBMITTER: Raj R

PROVIDER: S-EPMC4179932 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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:For patients surviving serious traumatic brain injury (TBI), families and other stakeholders often desire information on long-term functional prognosis, but accurate and easy-to-use clinical tools are lacking. We aimed to build utilitarian decision trees from commonly collected clinical variables to predict Glasgow Outcome Scale (GOS) functional levels at 1, 2, and 5 years after moderate-to-severe closed TBI. Flexible classification tree statistical modeling was used on prospectively collected data from the TBI-Model Systems (TBIMS) inception cohort study. Enrollments occurred at 17 designated, or previously designated, TBIMS inpatient rehabilitation facilities. Analysis included all participants with nonpenetrating TBI injured between January 1997 and January 2017. Sample sizes were 10,125 (year-1), 8,821 (year-2), and 6,165 (year-5) after cross-sectional exclusions (death, vegetative state, insufficient post-injury time, and unavailable outcome). In our final models, post-traumatic amnesia (PTA) duration consistently dominated branching hierarchy and was the lone injury characteristic significantly contributing to GOS predictability. Lower-order variables that added predictability were age, pre-morbid education, productivity, and occupational category. Generally, patient outcomes improved with shorter PTA, younger age, greater pre-morbid productivity, and higher pre-morbid vocational or educational achievement. Across all prognostic groups, the best and worst good recovery rates were 65.7% and 10.9%, respectively, and the best and worst severe disability rates were 3.9% and 64.1%. Predictability in test data sets ranged from C-statistic of 0.691 (year-1; confidence interval [CI], 0.675, 0.711) to 0.731 (year-2; CI, 0.724, 0.738). In conclusion, we developed a clinically useful tool to provide prognostic information on long-term functional outcomes for adult survivors of moderate and severe closed TBI. Predictive accuracy for GOS level was demonstrated in an independent test sample. Length of PTA, a clinical marker of injury severity, was by far the most critical outcome determinant.

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.

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Predicting outcome after traumatic brain injury: development of prognostic scores based on the IMPACT and the APACHE II.

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