• World Neurosurg · Nov 2024

    Prediction Model for Unfavorable Outcome in Primary Decompressive Craniectomy for Isolated Moderate to Severe Traumatic Brain Injury in India -A Prospective Observational Study.

    • Kirandeep Kaur, Nidhi Bidyut Panda, Shalvi Mahajan, Narender Kaloria, Venkata Ganesh, and M Karthigeyan.
    • Department of Anesthesia and Intensive Care, Division of Neuroanesthesia, Post Graduate Institute of Medical Education and Research, Chandigarh, India.
    • World Neurosurg. 2024 Nov 22.

    ObjectiveTraumatic brain injury (TBI) prediction models have gained significant attention in recent years because of their potential to aid in clinical decision making. Existing models, such as Corticosteroid Randomization after Significant Head Injury (CRASH) and International Mission for Prognosis and Analysis of Clinical Trials (IMPACT), are currently losing external validity and performance, probably because of their diverse inclusion criteria and changes in treatment modalities over the years. There is a lack of models that predict outcomes strictly pertaining to primary decompression after TBI. In this study, we aimed to develop an easy-to-use prediction model for predicting the risk of poor functional outcomes at 3 months after hospital discharge in adult patients who had undergone primary decompressive craniectomy (DC) for isolated moderate-to-severe TBI.MethodsWe conducted a prospective observational study at our tertiary care hospital. We trained and tested multiple prognostic logistic regression models with ten-fold cross validation to choose the model with the lowest Akaike Information Criterion, high sensitivity, and positive predictive value (PPV). Using the final model, we generated a nomogram to predict the risk of having a Glasgow outcome scale-extended (GOSE) 1-4 at three months after hospital discharge.ResultsA total of 215 patients were included in this study. Variables with an absolute standardized difference > 0·25 when grouped by GOSE 1-4/5-8 at three months were included in multivariable modelling. The model of choice had an accuracy of 87·91% (95% CI of 82·78-91·95%), a sensitivity of 84·42%, specificity of 89·86%, PPV of 82·28% (72·06-89·96%), NPV of 91·18% (85·09-95·36%), LR+ of 8·32 (5·02-13·80) and LR- of 0·17 (0·10-0·29).ConclusionOur study provides a ready-to-use prognostic nomogram derived from prospective data that can predict the risk of having a GOSE of 1-4 at three months following primary DC with high sensitivity, PPV, and low LR-.Copyright © 2024. Published by Elsevier Inc.

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