Development and internal validation of prehospital prediction models for identifying intracerebral haemorrhage in suspected stroke patients.

Abstract

Introduction: Distinguishing patients with intracerebral haemorrhage (ICH) from other suspected stroke cases in the prehospital setting is crucial for determining the appropriate level of care and minimising the onset-to-treatment time, thereby potentially improving outcomes. Therefore, we developed prehospital prediction models to identify patients with ICH among suspected stroke cases.

Methods: Data were obtained from the Field Administration of Stroke Therapy-Magnesium prehospital stroke trial, where paramedics evaluated multiple variables in suspected stroke cases within the first 2 hours from the last known well time. A total of 19 candidate predictors were included to minimise overfitting and were subsequently refined through the backward exclusion of non-significant predictors. We used logistic regression and eXtreme Gradient Boosting (XGBoost) models to evaluate the performance of the predictors. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), confusion matrix metrics and calibration measures. Additionally, models were internally validated and corrected for optimism through bootstrapping. Furthermore, a nomogram was built to facilitate paramedics in estimating the probability of ICH.

Results: We analysed 1649 suspected stroke cases, of which 373 (23%) were finally diagnosed with ICH. From the 19 candidate predictors, 9 were identified as independently associated with ICH (p<0.05). Male sex, arm weakness, worsening neurological status and high systolic blood pressure were positively associated with ICH. Conversely, a history of hyperlipidaemia, atrial fibrillation, coronary artery disease, ischaemic stroke and improving neurological status were associated with other diagnoses. Both logistic regression and XGBoost demonstrated good calibration and predictive performance, with optimism-corrected sensitivities ranging from 47% to 49%, specificities from 89% to 90% and AUCs from 0.796 to 0.801.

Conclusions: Our models demonstrate good predictive performance in distinguishing patients with ICH from other diagnoses, making them potentially useful tools for prehospital ICH management.