AUTHOR=Chen Lei , Xu Haiting , He Jianqing , Zhang Chunlei , Maas Andrew I. R. , Nieboer Daan , Raj Rahul , Sun Hong , Wang Yuhai TITLE=Performance of the IMPACT and Helsinki models for predicting 6-month outcomes in a cohort of patients with traumatic brain injury undergoing cranial surgery JOURNAL=Frontiers in Neurology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.1031865 DOI=10.3389/fneur.2022.1031865 ISSN=1664-2295 ABSTRACT=Background and aim

Prediction models for patients with traumatic brain injury (TBI) require generalizability and should apply to different settings. We aimed to validate the IMPACT and Helsinki prognostic models in patients with TBI who underwent cranial surgery in a Chinese center.

Methods

This validation study included 607 surgical patients with moderate to severe TBI (Glasgow Coma Scale [GCS] score ≤12) who were consecutively admitted to the Neurotrauma Center of People's Liberation Army (PLANC), China, between 2009 and 2021. The IMPACT models (core, extended and lab) and the Helsinki CT clinical model were used to estimate 6-month mortality and unfavorable outcomes. To assess performance, we studied discrimination and calibration.

Results

In the PLANC database, the observed 6-month mortality rate was 28%, and the 6-month unfavorable outcome was 52%. Significant differences in case mix existed between the PLANC cohort and the development populations for the IMPACT and, to a lesser extent, for the Helsinki models. Discrimination of the IMPACT and Helsinki models was excellent, with most AUC values ≥0.80. The highest values were found for the IMPACT lab model (AUC 0.87) and the Helsinki CT clinical model (AUC 0.86) for the prediction of unfavorable outcomes. Overestimation was found for all models, but the degree of miscalibration was lower in the Helsinki CT clinical model.

Conclusion

In our population of surgical TBI patients, the IMPACT and Helsinki CT clinical models demonstrated good performance, with excellent discrimination but suboptimal calibration. The good discrimination confirms the validity of the predictors, but the poorer calibration suggests a need to recalibrate the models to specific settings.