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Background: Despite the increased use of mechanical thrombectomy (MT) in recent years, there remains a lack of research on in-hospital mortality rates following the procedure, the primary factors influencing these rates, and the potential for predicting them. This study aimed to utilize interpretable machine learning (ML) to help clarify these uncertainties. Methods: This retrospective study involved patients with anterior circulation large vessel occlusion (LVO)-related ischemic stroke who underwent MT. The patient division was made into two groups: (I) the in-hospital death group, referred to as miserable outcome, and (II) the in-hospital survival group, or favorable outcome. Python 3.10.9 was utilized to develop the machine learning models, which consisted of two types based on input features: (I) the Pre-MT model, incorporating baseline features, and (II) the Post-MT model, which included both baseline and MT-related features. After a feature selection process, the models were trained, internally evaluated, and tested, after which interpretation frameworks were employed to clarify the decision-making processes. Results: This study included 602 patients with a median age of 76 years (interquartile range (IQR) 65–83), out of which 54% (n = 328) were female, and 22% (n = 133) had miserable outcomes. Selected baseline features were age, baseline National Institutes of Health Stroke Scale (NIHSS) value, neutrophil-to-lymphocyte ratio (NLR), international normalized ratio (INR), the type of the affected vessel ('Vessel type'), peripheral arterial disease (PAD), baseline glycemia, and premorbid modified Rankin scale (pre-mRS). The highest odds ratio of 4.504 was observed with the presence of peripheral arterial disease (95% confidence interval (CI), 2.120–9.569). The Pre-MT model achieved an area under the curve (AUC) value of around 79% utilizing these features, and the interpretable framework discovered the baseline NIHSS value as the most influential factor. In the second data set, selected features were the same, excluding pre-mRS and including puncture-to-procedure-end time (PET) and onset-to-puncture time (OPT). The AUC value of the Post-MT model was around 84% with age being the highest-ranked feature. Conclusions: This study demonstrates the moderate to strong effectiveness of interpretable machine learning models in predicting in-hospital mortality following mechanical thrombectomy for ischemic stroke, with AUCs of 0.792 for the Pre-MT model and 0.837 for the Post-MT model. Key predictors included patient age, baseline NIHSS, NLR, INR, occluded vessel type, PAD, baseline glycemia, pre-mRS, PET, and OPT. These findings provide valuable insights into risk factors and could improve post-procedural patient management. [ABSTRACT FROM AUTHOR]

Background: as a relatively high number of ST-segment elevation myocardial infarction (STEMI) patients develop major adverse cardiovascular events (MACE) following percutaneous coronary intervention (PCI), our aim was to determine the significance, and possible predictive value of QRS complex width and ST-segment elevation. Methods: our patient sample included 200 PCI-treated STEMI patients, which were divided into two groups based on the following duration of symptoms: (I) less than 6 h, and (II) 6 to 12 h. For every patient, an ECG was performed at six different time points, patients were followed for up to six years for the occurrence of MACE. Results: the mean age was 60.6 ± 11.39 years, and 142 (71%) were male. The 6–12 h group had significantly wider QRS complex, higher ST-segment elevation, lower prevalence of ST-segment resolution as well as MACE prevalence (p < 0.05). ECG parameters, QRS width, and magnitude of ST-segment elevation were proved to be independent significant predictors of MACE in all measured time points (p < 0.05). Even after controlling for biomarkers of myocardial injury, these ECG parameters remained statistically significant predictors of MACE (p < 0.05). Conclusion: our study highlights that wider QRS complex and a more pronounced ST-segment elevation are associated with longer total ischemic time and higher risk of long-term MACE. [ABSTRACT FROM AUTHOR]