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.

BACKGROUND & AIMS:Vibration-controlled transient elastography (VCTE) is a non-invasive tool for detecting hepatic steatosis and fibrosis in patients who have not received liver transplants. We aimed to evaluate the diagnostic performance of VCTE in detection of hepatic steatosis and fibrosis in patients who have undergone liver transplantation. METHODS:We performed a prospective study of 99 liver transplant recipients assessed by VCTE using a standard protocol. Controlled attenuation parameter cutoff values for pairwise steatosis grade and liver stiffness measurements (LSM) and cutoff values for pairwise fibrosis stage were determined using cross-validated area under the receiver operating characteristics (AUROC) curve analyses. We calculated sensitivity (fixed at 90%) and specificity (fixed at 90%) values. RESULTS:A controlled attenuation parameter cutoff value of 270 dB/m detected any hepatic steatosis with an AUROC of 0.88 (95% CI, 0.78-0.93). VCTE detected steatosis grades 2-3 vs 0-1 with an AUROC of 0.94 (95% CI, 0.89-0.99) and steatosis grade 3 vs 0-2 was similar and AUROC of 0.89 (95% CI, 0.83-0.96). When we used an LSM cutoff value of 10.5 kPa, VCTE identified patients with advanced fibrosis (fibrosis stages ‡ 3) with an AUROC of 0.94 (95% CI, 0.88-0.99). At fixed sensitivity, the cutoff LSM value of 10.5k Pa excluded advanced fibrosis with a negative predictive value of 0.99. At fixed specificity, the cutoff LSM value of 16.9 kPa detected advanced fibrosis with a sensitivity of 0.86, a positive predictive value (PPV) of 0.40, and a negative predictive value of 0.99. CONCLUSIONS:VCTE accurately detects hepatic steatosis and fibrosis in recipients of liver transplants. This non-invasive method might be used to identify patients in need of confirmatory liver biopsy analysis.

Objective: To build decision tree prediction models for long-term employment outcomes of individuals after moderate to severe closed traumatic brain injury (TBI) and assess model accuracy in an independent sample. Setting: TBI Model Systems Centers. Participants: TBI Model Systems National Database participants injured between January 1997 and January 2017 with moderate to severe closed TBI. Sample sizes were 7867 (year 1 postinjury), 6783 (year 2 postinjury), and 4927 (year 5 postinjury). Design: Cross-sectional analyses using flexible classification tree methodology and validation using an independent subset of TBI Model Systems National Database participants. Main Measures: Competitive employment at 1, 2, and 5 years postinjury. Results: In the final employment prediction models, posttraumatic amnesia duration was the most important predictor of employment in each outcome year. Additional variables consistently contributing were age, preinjury education, productivity, and occupational category. Generally, individuals spending fewer days in posttraumatic amnesia, who were competitively employed preinjury, and more highly educated had better outcomes. Predictability in test data sets ranged from a C-statistic of 0.72 (year 5; confidence interval: 0.68-0.76) to 0.77 (year 1; confidence interval: 0.74-0.80). Conclusion: An easy-to-use decision tree tool was created to provide prognostic information on long-term competitive employment outcomes in individuals with moderate to severe closed TBI. Length of posttraumatic amnesia, a clinical marker of injury severity, and preinjury education and employment status were the most important predictors.

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