Addition of eptifibatide and manual thrombus aspiration to ticagrelor does not improve long-term survival after STEMI treated with primary PCI.

Background: Current guidelines recommend that glycoprotein IIb/IIIa inhibitor (GPI) and manual aspiration thrombectomy should not be routinely used in patients with ST-segment elevation myocardial infarction (STEMI) treated by primary percutaneous coronary intervention (pPCI), although there is a lack of dedicated studies. The aim of this study was to examine the impact of combined usage of a potent P2Y12 inhibitor, GPI, and manual aspiration thrombectomy on long-term survival after STEMI. Methods: All STEMI patients treated by pPCI in a tertiary center who have been included prospectively in the local PCI registry between January 2016 and December 2022 were analyzed in this study. Patients were excluded if they required oral anticoagulation or bridging between clopidogrel or ticagrelor during hospitalization. Results: A total of 1,210 patients were included in the present study, with a median follow-up of 2.78 (1.00-4.88) years. Ticagrelor significantly reduced all-cause and cardiovascular-cause mortality [HR = 0.27 (0.21-0.34), p < 0.0001 and HR = 0.23 (0.17-0.30), p < 0.0001, respectively]. Eptifibatide significantly reduced all-cause and cardiovascular-cause mortality [HR = 0.72 (0.57-0.92), p = 0.002, and HR = 0.68 (0.52-0.89), p = 0.001, respectively]. Manual thrombus aspiration had no significant effect on both all-cause and cardiovascular-cause mortality. In multivariate Cox regression, all-cause mortality was reduced by ticagrelor, while eptifibatide or manual thrombus aspiration had no significant effect. However, cardiovascular-cause mortality was reduced by both ticagrelor and eptifibatide, while manual thrombus aspiration had no significant effect. Conclusion: Ticagrelor consistently reduced cardiovascular and all-cause mortality, while eptifibatide reduced only cardiovascular mortality. Manual thrombus aspiration provided no long-term benefit. Our findings support the current guideline recommendation that GPI and manual aspiration thrombectomy should not be routinely used in treatment of STEMI with pPCI.

Significance Associated with Phenotype Score Aids in Variant Prioritization for Exome Sequencing Analysis.

Several in silico annotation-based methods have been developed to prioritize variants in exome sequencing analysis. This study introduced a novel metric Significance Associated with Phenotypes (SAP) score, which generates a statistical score by comparing an individual's observed phenotypes against existing gene-phenotype associations. To evaluate the SAP score, a retrospective analysis was performed on 219 exomes. Among them, 82 family-based and 35 singleton exomes had at least one disease-causing variant that explained the patient's clinical features. SAP scores were calculated, and the rank of the disease-causing variant was compared with a known method, Exomiser. Using the SAP score, the known causative variant was ranked in the top 10 retained variants for 94% (77 of 82) of the family-based exomes and in first place for 73% of these cases. For singleton exomes, the SAP score analysis ranked the known pathogenic variants within the top 10 for 80% (28 of 35) of cases. The SAP score, which is independent of detected variants, demonstrates comparable performance with Exomiser, which considers both phenotype and variant-level evidence simultaneously. Among 102 cases with negative results or variants of uncertain significance, SAP score analysis revealed two cases with a potential new diagnosis based on rank. The SAP score, a phenotypic quantitative metric, can be used in conjunction with standard variant filtration and annotation to enhance variant prioritization in exome analysis.

Structured Program for Developing the Psychomotor Skills of Institutionalized Children with Special Educational Needs.

Tailoring motor activities to the unique needs of children with special educational requirements has shown considerable efficacy. Our study aimed to develop a structured program specifically designed to enhance psychomotor abilities, focusing on balance and motor-cognitive skills among 28 students (aged 12-14) from two institutional centers in Romania. The program spanned 36 weeks, with biweekly 30 min sessions. Psychomotor skills were assessed through tests measuring balance, speed of movements, and upper limb motor laterality. Initial and final data were collected for evaluation. A statistical analysis, employing the Kolmogorov-Smirnov and Wilcoxon Z tests, compared the assessments. The results indicated significant improvements in movement speed, with a notable increase in stimulus identification (averaging from approximately 13 to 14) (p < 0.05) and reduced processing time (decreasing from about 28.7 to 28 s) (p < 0.05). However, while the structured program demonstrated substantial enhancements in specific motor and cognitive-motor skills, it did not yield significant changes in dynamic balance, maintaining values close to 0.9 (p > 0.05) for open-eyed balance and 0.88 (p > 0.05) for closed-eyed balance. Additionally, an analysis of the processing speed in pulses per second showcased a marginal decline, from approximately 0.46 to 0.45, revealing notable disparities between the initial and final measurements (p < 0.05).

Prediction of 3-year all-cause and cardiovascular cause mortality in a prospective percutaneous coronary intervention registry: Machine learning model outperforms conventional clinical risk scores.

BACKGROUND AND AIMS: Machine learning (ML) models have been proposed as a prognostic clinical tool and superiority over clinical risk scores is yet to be established. Our aim was to analyse the performance of predicting 3-year all-cause- and cardiovascular cause mortality using ML techniques and compare it with clinical scores in a percutaneous coronary intervention (PCI) population. METHODS: An all-comers patient population treated by PCI in a tertiary cardiovascular centre that have been included prospectively in the local registry between January 2016-December 2017 was analysed. The ML model was trained to predict 3-year mortality and prediction performance was compared with that of GRACE, ACEF, SYNTAX II 2020 and TIMI scores. RESULTS: A total number of 2242 patients were included with 12.1% and 14.9% 3-year cardiovascular and -all-cause mortality, respectively. The area under receiver operator characteristic curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.886 vs. 0.797, 0.792, 0.757 and 0.696 for 3-year cardiovascular- and 0.854 vs. 0.762, 0.764, 0.730 and 0.691 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). Similarly, the area under precision-recall curve for the ML model was higher than that of GRACE, ACEF, SYNTAX II and TIMI scores: 0.729 vs. 0.474, 0.469, 0.365 and 0.389 for 3-year cardiovascular- and 0.718 vs. 0.483, 0.466, 0.388 and 0.395 for 3-year all-cause mortality prediction, respectively (all p ≤ 0.001). CONCLUSION: The ML model was superior in predicting 3-year cardiovascular- and all-cause mortality when compared to clinical scores in a prospective PCI registry.