Literature DB >> 26652051

External Validation of the International Mission for Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury: Prognostic Models for Traumatic Brain Injury on the Study of the Neuroprotective Activity of Progesterone in Severe Traumatic Brain Injuries Trial.

Hong Sun1, Hester F Lingsma2, Ewout W Steyerberg2, Andrew I R Maas1.   

Abstract

Prediction models for patients with traumatic brain injury (TBI) are important for multiple reasons, including case-mix adjustment, trial design, and benchmarking for quality-of-care evaluation. Models should be generalizable and therefore require regular external validation. We aimed to validate the International Mission for Prognosis and Analysis of Clinical Trials in TBI (IMPACT) prognostic models for moderate and severe TBI in a recent randomized controlled trial. We studied 1124 patients enrolled in the multi-center randomized placebo-controlled Study of the Neuroprotective Activity of Progesterone in Severe Traumatic Brain Injuries (SyNAPSe) trial that evaluated the efficacy of progesterone in TBI. Treatment and placebo groups were combined for analysis. We evaluated the predictive performance of the three prognostic models (core, extended, and lab) from the IMPACT study with regard to discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (comparison of observed to predicted risks). Substantial differences were found in case-mix and outcome distribution between IMPACT and SyNAPSe. In line with the more homogeneous case-mix of a clinical trial, the discriminative performance was reasonable. For the core model, an AUC of 0.677 and 0.684 was obtained for 6-month mortality and unfavorable outcome, respectively. Performance was slightly better for the extended model (0.693 and 0.705) and for the lab model (0.689 and 0.711, respectively). For calibration, we found overestimation of mortality, especially at higher risk predictions, and underestimation of unfavorable outcome, especially at lower risk predictions. This pattern of miscalibration was consistent across all three models. In a contemporary trial setting, the IMPACT models have reasonable discrimination if enrollment restrictions apply. Observed changes in outcome distribution necessitate updating of previously developed prognostic models.

Entities:  

Keywords:  case-mix; external validation; outcome; prognostic model; traumatic brain injury

Mesh:

Substances:

Year:  2016        PMID: 26652051     DOI: 10.1089/neu.2015.4164

Source DB:  PubMed          Journal:  J Neurotrauma        ISSN: 0897-7151            Impact factor:   5.269


  10 in total

1.  Multicenter Validation of the Survival After Acute Civilian Penetrating Brain Injuries (SPIN) Score.

Authors:  Abdul Ghani Mikati; Julie Flahive; Muhammad W Khan; Aditya Vedantam; Shankar Gopinath; Mina F Nordness; Claudia Robertson; Mayur B Patel; Kevin N Sheth; Susanne Muehlschlegel
Journal:  Neurosurgery       Date:  2019-11-01       Impact factor: 4.654

Review 2.  Prognostic Models in Severe Traumatic Brain Injury: A Systematic Review and Meta-analysis.

Authors:  Rita de Cássia Almeida Vieira; Juliana Cristina Pereira Silveira; Wellingson Silva Paiva; Daniel Vieira de Oliveira; Camila Pedroso Estevam de Souza; Eduesley Santana-Santos; Regina Marcia Cardoso de Sousa
Journal:  Neurocrit Care       Date:  2022-08-09       Impact factor: 3.532

3.  Goals-of-care decision aid for critically ill patients with TBI: Development and feasibility testing.

Authors:  Susanne Muehlschlegel; David Y Hwang; Julie Flahive; Thomas Quinn; Christopher Lee; Jesse Moskowitz; Kelsey Goostrey; Kelsey Jones; Jolanta J Pach; Andrea K Knies; Lori Shutter; Robert Goldberg; Kathleen M Mazor
Journal:  Neurology       Date:  2020-06-17       Impact factor: 9.910

4.  The aggressiveness of neurotrauma practitioners and the influence of the IMPACT prognostic calculator.

Authors:  Joshua Letsinger; Casey Rommel; Ryan Hirschi; Raminder Nirula; Gregory W J Hawryluk
Journal:  PLoS One       Date:  2017-08-23       Impact factor: 3.240

5.  Impact of correlation of predictors on discrimination of risk models in development and external populations.

Authors:  Suman Kundu; Madhu Mazumdar; Bart Ferket
Journal:  BMC Med Res Methodol       Date:  2017-04-19       Impact factor: 4.615

6.  Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

Authors:  Cheng-Shyuan Rau; Pao-Jen Kuo; Peng-Chen Chien; Chun-Ying Huang; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  PLoS One       Date:  2018-11-09       Impact factor: 3.240

Review 7.  Blood Biomarkers for Traumatic Brain Injury: A Quantitative Assessment of Diagnostic and Prognostic Accuracy.

Authors:  Zoe S Gan; Sherman C Stein; Randel Swanson; Shaobo Guan; Lizette Garcia; Devanshi Mehta; Douglas H Smith
Journal:  Front Neurol       Date:  2019-04-26       Impact factor: 4.003

8.  Gap Analysis Regarding Prognostication in Neurocritical Care: A Joint Statement from the German Neurocritical Care Society and the Neurocritical Care Society.

Authors:  Katja E Wartenberg; David Y Hwang; Karl Georg Haeusler; Susanne Muehlschlegel; Oliver W Sakowitz; Dominik Madžar; Hajo M Hamer; Alejandro A Rabinstein; David M Greer; J Claude Hemphill; Juergen Meixensberger; Panayiotis N Varelas
Journal:  Neurocrit Care       Date:  2019-10       Impact factor: 3.210

9.  External validation of the TRISS, CRASH, and IMPACT prognostic models in severe traumatic brain injury in Japan.

Authors:  Yukihiro Maeda; Rie Ichikawa; Jimpei Misawa; Akiko Shibuya; Teruyoshi Hishiki; Takeshi Maeda; Atsuo Yoshino; Yoshiaki Kondo
Journal:  PLoS One       Date:  2019-08-26       Impact factor: 3.240

10.  Prediction of Mortality in Patients with Isolated Traumatic Subarachnoid Hemorrhage Using a Decision Tree Classifier: A Retrospective Analysis Based on a Trauma Registry System.

Authors:  Cheng-Shyuan Rau; Shao-Chun Wu; Peng-Chen Chien; Pao-Jen Kuo; Yi-Chun Chen; Hsiao-Yun Hsieh; Ching-Hua Hsieh
Journal:  Int J Environ Res Public Health       Date:  2017-11-22       Impact factor: 3.390

  10 in total

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