Double cross-validation of the booklet category test in detecting malingered traumatic brain injury

1996 ◽  
Vol 10 (1) ◽  
pp. 104-116 ◽  
Author(s):  
Wendy N. Tenhula ◽  
Jerry J. Sweet
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cross Validation ◽  
Category Test ◽  
Booklet Category Test ◽  
Double Cross

Double Cross-Validation and Improved Sensitivity of the Rapid Screen of Mild Traumatic Brain Injury

2004 ◽  
Vol 26 (5) ◽  
pp. 628-644 ◽  
Author(s):  
Veronica Eileen De Monte ◽  
Gina Malke Geffen ◽  
Christopher Randall May ◽  
Ken McFarland
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Cross Validation ◽  
Double Cross

scholarly journals Comprehensive analysis of the predictors of neurobehavioral symptom reporting in veterans

Neurology ◽  
2018 ◽  
Vol 91 (8) ◽  
pp. e732-e745 ◽  
Author(s):  
Ryan J. Andrews ◽  
Jennifer R. Fonda ◽  
Laura K. Levin ◽  
Regina E. McGlinchey ◽  
William P. Milberg
Keyword(s):  
Traumatic Brain Injury ◽  
Posttraumatic Stress Disorder ◽  
Brain Injury ◽  
Posttraumatic Stress ◽  
Cross Validation ◽  
Stress Disorder ◽  
Predictor Variables ◽  
Behavioral Measures ◽  
Behavioral Conditions

ObjectiveThis study assessed the strength of military-related concussion-, psychological-, and behavioral-related measures to predict neurobehavioral symptom (NBS) reporting in order to help clarify the extent to which persistent NBS reflect lingering effects of concussion vs other psychological/behavioral factors among veterans.MethodsBaseline analysis included 351 consecutively enrolled veterans in the Translational Research Center for Traumatic Brain Injury and Stress Disorders longitudinal cohort study. One hundred eighty-six returned for a follow-up evaluation averaging 24 months post baseline. The Neurobehavioral Symptom Inventory (NSI) was used to measure NBS reporting. Predictor variables included diagnosis of military-related mild traumatic brain injury (M-mTBI), psychological measures, including posttraumatic stress disorder, mood, anxiety, and substance abuse disorders, and behavioral measures, including self-reported current pain and sleep impairment. Hierarchical and multivariable regression analyses examined the relationships between the predictor variables and NSI scores. The k-fold cross-validation assessed generalizability and validity of the regressions.ResultsBaseline analysis revealed that psychological and behavioral conditions independently accounted for 42.5% of variance in the NSI total score compared to 1.5% for M-mTBI after controlling for psychological and behavioral conditions. Prospective analysis revealed that M-mTBI at baseline did not significantly predict NSI score at follow-up, while psychological and behavioral measures at baseline independently accounted for 24.5% of NSI variance. Posttraumatic stress disorder was the most consistent predictor. Cross-validation analyses supported generalizability of the results.ConclusionsPsychological and behavioral-related measures are strong predictors of persistent NBS reporting in veterans, while M-mTBI is negligible. NBS more likely reflect influential comorbidities as opposed to brain injury, per se.

Patient-specific thresholds of intracranial pressure in severe traumatic brain injury

2014 ◽  
Vol 120 (4) ◽  
pp. 893-900 ◽  
Author(s):  
Christos Lazaridis ◽  
Stacia M. DeSantis ◽  
Peter Smielewski ◽  
David K. Menon ◽  
Peter Hutchinson ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Intracranial Pressure ◽  
Severe Traumatic Brain Injury ◽  
Continuous Monitoring ◽  
Cross Validation ◽  
Area Under The Curve ◽  
Reactivity Index ◽  
Patient Specific ◽  
Pressure Reactivity

Object Based on continuous monitoring of the pressure reactivity index (PRx), the authors defined individualized intracranial pressure (ICP) thresholds by graphing the relationship between ICP and PRx. These investigators hypothesized that an “ICP dose” based on individually assessed ICP thresholds would correlate more closely with the 6-month outcome when compared with ICP doses derived by the recommended universal thresholds of 20 and 25 mm Hg. Methods This study was a retrospective analysis of prospectively collected data from 327 patients with severe traumatic brain injury. Results Individualized thresholds were visually identified from graphs of PRx versus ICP; PRx > 0.2 was the cutoff. Intracranial pressure doses were then computed as the cumulative area under the curve above the defined thresholds in graphing ICP versus time. The term “Dose 20” (D20) was used to refer to an ICP threshold of 20 mm Hg; the markers D25 and DPRx were calculated similarly. Separate logistic regression models were fit with death as the outcome and each dose as the predictor, both alone and adjusted for covariates. The discriminative ability of each dose for mortality was assessed by receiver operating characteristic AUC analysis in which 5-fold cross-validation was used. A clearly identifiable PRx-based threshold was possible in 224 patients (68%). The DPRx (AUC 0.81, 95% CI 0.74–0.87) was found to have the highest area under the curve (AUC) over both D20 (0.75, 95% CI 0.68–0.81) and D25 (0.77, 95% CI 0.70–0.83); in the cross-validation model, DPRx remained the best discriminator of mortality (DPRx: AUC 0.77 [95% CI 0.68–0.89]; D20: 0.72 [95% CI 0.66–0.81]; and D25: 0.65 [95% CI 0.56–0.73]). Conclusions The authors explored the importance of different ICP thresholds for outcome by calculating patient-specific ICP doses based on the continuous monitoring of cerebrovascular pressure reactivity. They found that these individualized doses of intracranial hypertension were stronger predictors of death than doses derived from the universal thresholds of 20 and 25 mm Hg. The PRx could offer a method that can be directed toward individualizing the ICP threshold.

scholarly journals Machine learning-based dynamic mortality prediction after traumatic brain injury

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Rahul Raj ◽  
Teemu Luostarinen ◽  
Eetu Pursiainen ◽  
Jussi P. Posti ◽  
Riikka S. K. Takala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Cross Validation ◽  
Characteristic Curve ◽  
Mortality Prediction ◽  
Internal Validation ◽  
Middle Income ◽  
Middle Income Countries ◽  
Validation Technique

AbstractOur aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified cross-validation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm’s area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60–0.74) on day 1 to 0.81 (95% CI 0.75–0.87) on day 5. The ICP-MAP-CPP-GCS algorithm’s AUC increased from 0.72 (95% CI 0.64–0.78) on day 1 to 0.84 (95% CI 0.78–0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middle-income countries.

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