The Naples score (NS), which is a composite of cardiovascular adverse event predictors including neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, albumin, and total cholesterol, has emerged as a prognostic risk score in cancer patients. We aimed to investigate the predictive value of NS for long-term mortality in ST-segment elevation myocardial infarction patients (STEMI). A total of 1889 STEMI patients were enrolled in this study. The median duration of the study was 43 months (IQR: 32–78). Patients were divided into 2 groups according to NS as group 1 and group 2. We created 3 models as a baseline model, model 1 (baseline + NS in continuous), and model 2 (baseline + NS as categorical). Group 2 patients had higher long-term mortality rates than group 1 patients. The NS was independently associated with long-term mortality and adding NS to a baseline model improved the model performance for prediction and discrimination of long-term mortality. Decision curve analysis demonstrated that model 1 had a better net benefit probability for detecting mortality compared with the baseline model. NS had the highest contributive significant effect in the prediction model. An easily accessible and calculable NS might be used for risk stratification of long-term mortality in STEMI patients undergoing primary percutaneous coronary intervention.

The aim of this study was to examine the Intermountain Risk Score (IMRS) for short- and long-term mortality in ST elevation myocardial infarction (STEMI) patients and compare it with the well-known risk scores, such as the Thrombolysis in Myocardial Infarction (TIMI) and the Global Registry of Acute Coronary Events (GRACE). In this retrospective and cross-sectional study, 1057 consecutive patients with STEMI were evaluated. The end-points of the study were short- and long-term mortality. The overall mortality rate was 16% (n = 170 patients). The IMRS was significantly higher in STEMI patients who did not survive compared with those who survived. According to multivariable COX proportional regression analysis, the IMRS was independently related to both short- (HR: 1.482, 95% CI: 1.325–1.675, p < .001) and long-term mortality (HR: 1.915, 95% CI: 1.711–2.180, p < .001). The comparison of receiver operating characteristic curves revealed that the IMRS had non–inferior predictive capability for short- and long-term mortality than the TIMI and GRACE risk scores. To the best of our knowledge, this is the first study to show that the IMRS can predict short- and long-term prognosis of patients with STEMI. Further, the IMRS’ predictive value for overall mortality was non-inferior compared with TIMI and GRACE scores.

The goal of this investigation was to explore the relationship between serum uric acid/albumin ratio (UAR) and no-reflow (NR) in ST elevation myocardial infarction (STEMI) patients (n = 838) who underwent primary percutaneous coronary intervention (pPCI). Angiographic NR was defined as thrombolysis in myocardial infarction (TIMI) flows 0, 1, and 2 in the absence of coronary spasm or dissection. NR developed in 91 (10.9%) STEMI patients. Patients with NR had higher UAR and according to multivariable logistic regression models, a high UAR was an independent risk factor for NR. The area under the curve (AUC) value of the UAR was .760 (95%CI: .720-.801) in a receiver-operating characteristics curve (ROC) assessment. Notably, the UAR AUC value was greater than that of its components: albumin (AUC: .642) and serum uric acid (AUC: .637) ( P < .05 for both comparisons). The optimum UAR value in detecting NR in STEMI patients was >1.21 with a sensitivity of 82% and a specificity of 67%. This was the first study to report that the UAR was independently associated with NR in STEMI patients who underwent pPCI.

Acute coronary syndrome patients should be closely followed-up to maintain optimal adherence to medical treatments and to reduce adverse events. Digital health interventions might provide improved outcomes for patient care by providing closer follow- up, compared to standard care. Thus, in this meta-analysis, we aimed to evaluate the effect of digital health interventions on follow-up in acute coronary syndrome patients. We searched medical databases to obtain all relevant studies comparing digital health interventions with standard care in acute coronary syndrome patients. After reviewing all eligible studies, a meta-analysis was conducted with the remaining 11 randomized controlled studies and 2 non-randomized controlled studies. A modified Jadad scale and Newcastle-Ottawa scale were used to assess the quality of the publications for randomized controlled studies and non-randomized controlled studies, respectively. This meta-analysis consisted of 7657 patients. The all-cause mortality rate was 49% lower in the digital health intervention cases, compared to those who received standard care [relative risk (RR) = 0.51 (0.37; 0.70), P <.01]. There was a significant decrease in systolic blood pressure in the digital health interventions group, compared to the standard care group [mean difference = -5.28 (-9.47; -1.08), P =.01]. The rate of nonadherence to anti-aggregant drugs was 69% lower in the digital health interventions than in the standard care group [RR = 0.31 (0.20; 0.46), P <.01]. Also, nonadherence rates for statin and beta-blockers were lower in the digital health interventions group. The risk of rehospitalization was observed to be 55% less in the digital health interventions patients, compared to the standard care group [RR = 0.45 (0.30; 0.67), P <.01]. Digital health interventions can be effective in follow-up for secondary prevention in acute coronary syndrome patients.

Background: Currently, recommended pre-operative risk assessment models including the revised cardiac risk index (RCRI) are not very effective in predicting postoperative myocardial damage after non-elective surgery, especially for elderly patients. Aims: This study aimed to create a new risk prediction model to assess myocardial injury after non-cardiac surgery (MINS) in elderly patients and compare it with the RCRI, a well-known pre-operative risk prediction model. Materials and Methods: This retrospective study included 370 elderly patients who were over 65 years of age and had non-elective surgery in a tertiary hospital. Each patient underwent detailed physical evaluations before the surgery. The study cohort was divided into two groups: patients who had MINS and those who did not. Results: In total, 13% (48 out of 370 patients) of the patients developed MINS. Multivariable analysis revealed that creatinine, lymphocyte, aortic regurgitation (moderate-severe), stroke, hemoglobin, ejection fraction, and D-dimer were independent determinants of MINS. By using these parameters, a model called “CLASHED” was developed to predict postoperative MINS. The ROC analysis comparison demonstrated that the new risk prediction model was significantly superior to the RCRI in predicting MINS in elderly patients undergoing non-elective surgery (AUC: 0.788 vs. AUC: 0.611, p < 0.05). Conclusions: Our study shows that the new risk preoperative model successfully predicts MINS in elderly patients undergoing non-elective surgery. In addition, this new model is found to be superior to the RCRI in predicting MINS.

Inflammation plays an important role in the initiation of postoperative atrial fibrillation (PoAF) in individuals undergoing cardiac surgery, Thus, this study aimed to investigate the predictive value of the systemic immune inflammation index (SII) to develop PoAF in such patients. In total, 391 consecutive patients undergoing an isolated coronary artery bypass grafting (CABG) were retrospectively analyzed. PoAF was defined according to the current guideline. The SII is determined using the following equation: neutrophil (N) × platelet (P) ÷ lymphocyte (L). The incidence of PoAF in the present study was 24% (n=97 cases). Multivariate logistic regression analysis revealed that the SII was an independent predictor of PoAF (Odds ratio: 1.002 95% confidence interval: (1.001-1.002), p807.8 with 60.8% sensitivity and 80.9% specificity [area under the curve (AUC): 0.7107]. The AUC value of SII in detecting PoAF was much greater than the AUC values of both the neutrophil to lymphocyte ratio (NLR) and the platelet to lymphocyte ratio (PLR) (AUC: 0.6740 and AUC: 0.6426, respectively). This study revealed that SII was an independent predictor of PoAF in patients who were operated on for isolated CABG. Additionally, SII had a better discriminative ability for PoAF compared to either NLR or PLR among these cases.