Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention.

BACKGROUND: The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI). METHODS: The data were obtained retrospectively from 854 STEMI patients who underwent pPCI. MLA was applied to predict the potential NR phenomenon and confirm the in-hospital mortality. A random sampling method was used to split the data into the training (66.7%) and testing (33.3%) sets. The final results were an average of 10 repeated procedures. The area under the curve (AUC) and the associated 95% confidence intervals (CIs) of the receiver operator characteristic were measured. RESULTS: A random forest algorithm (RAN) had optimal discrimination for the NR phenomenon with an AUC of 0.7891 (95% CI: 0.7093-0.8688) compared with 0.6437 (95% CI: 0.5506-0.7368) for the decision tree (CTREE), 0.7488 (95% CI: 0.6613-0.8363) for the support vector machine (SVM), and 0.681 (95% CI: 0.5767-0.7854) for the neural network algorithm (NNET). The optimal RAN AUC for in-hospital mortality was 0.9273 (95% CI: 0.8819-0.9728), for SVM, 0.8935 (95% CI: 0.826-0.9611); NNET, 0.7756 (95% CI: 0.6559-0.8952); and CTREE, 0.7885 (95% CI: 0.6738-0.9033). CONCLUSIONS: The MLA had a relatively higher performance when evaluating the NR risk and in-hospital mortality in patients with STEMI who underwent pPCI and could be utilized in clinical decision making.

Latest clinical evidence about the effect of PCSK9 monoclonal antibodies in patients with familial hypercholesterolaemia: an updated meta-analysis.

INTRODUCTION: Familial hypercholesterolaemia (FH) is the most common autosomal genetic disease of cholesterol metabolism disorder. Proprotein convertase subtilisin/kexin type 9 (PCSK9) monoclonal antibody (mAb) is a new target lipid-regulating drug related to cholesterol metabolism that has been developed in recent years. The reported rate of reduction varies widely, and comprehensive assessments of efficacy and safety are lacking. Therefore, we conducted this study to investigate the clinical effect of PCSK9 mAbs in patients with familial hypercholesterolaemia to provide a theoretical reference for clinical practice. MATERIAL AND METHODS: We analysed the clinical data of patients, including the percentage change in LDL-C and the incidence rates of treatment-emergent adverse events (TEAEs) and serious adverse events (SAEs), from selected articles. Weighted mean differences (WMDs), risk ratios (RRs), and 95% confidence intervals (95% CIs) were calculated to compare the endpoints. RESULTS: The results showed that, compared with placebo, the PCSK9 mAb reduced the percentage change in LDL-C in FH patients (WMD = -45.52, 95% CI: -49.70 to -41.34, I2 = 99.6%). In addition, there was no significant difference between the experimental and placebo groups in the incidence of TEAEs (RR = 1.03, 95% CI: 0.97 to 1.10, I2 = 19.1%) and SAEs (RR = 1.02, 95% CI: 0.72 to 1.44, I2 = 0.0%). CONCLUSIONS: Overall, PSCK9 mAbs are an effective and safe method of LDL-C reduction in patients with FH.