Endogenous zinc protoporphyrin formation critically contributes to hemorrhagic stroke-induced brain damage

Stroke mortality prediction based on ensemble learning and the combination of structured and textual data

For severe cerebrovascular diseases such as stroke, the prediction of short-term mortality of patients has tremendous medical significance. In this study, we combined machine learning models Random Forest classifier (RF), Adaptive Boosting (AdaBoost), Extremely Randomised Trees (ExtraTree) classifier, XGBoost classifier, TabNet, and DistilBERT to construct a multi-level prediction model that used bioassay data and radiology text reports from haemorrhagic and ischaemic stroke patients to predict six-month mortality. The performances of the prediction models were measured using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), precision, recall, and F1-score. The prediction models were built with the use of data from 19,616 haemorrhagic stroke patients and 50,178 ischaemic stroke patients. Novel six-month mortality prediction models for these patients were developed, which enhanced the performance of the prediction models by combining laboratory test data, structured data, and textual radiology report data. The achieved performances were as follows: AUROC = 0.89, AUPRC = 0.70, precision = 0.52, recall = 0.78, and F1 score = 0.63 for haemorrhagic patients, and AUROC = 0.88, AUPRC = 0.54, precision = 0.34, recall = 0.80, and F1 score = 0.48 for ischaemic patients. Such models could be used for mortality risk assessment and early identification of high-risk stroke patients. This could contribute to more efficient utilisation of healthcare resources for stroke survivors.

Prior statin and short-term outcomes of primary intracerebral hemorrhage: From a large-scale nationwide longitudinal registry

Introduction

The relationship between statins and intracerebral hemorrhage outcomes is unclear.

Aim

We aimed to compare the in‐hospital mortality and evacuation of intracranial hematoma rates in patients with primary intracerebral hemorrhage between prior statin users and nonusers.

Results

The final study population included 66,263 patients. Multivariable logistics analyses showed that prior statin use was not associated with in‐hospital mortality for primary intracerebral hemorrhage (adjusted odd ratio 0.78, 95% CI 0.61–1.01), but reduced the proportion of patients undergoing evacuation of intracranial hematoma (adjusted odd ratio 0.70, 95% CI 0.61–0.82). Propensity score matching analyses yielded similar results.

Conclusion

Prior statin use was not associated with in‐hospital mortality but did reduce evacuation of intracranial hematoma rates.