Elevated uric acid/albumin ratio as a predictor of poor coronary collateral circulation development in patients with non-ST segment elevation myocardial infarction.

BACKGROUND: Uric acid/albumin ratio (UAR) is a novel composite biomarker with superior predictive value for cardiovascular disease. OBJECTIVE: To investigate the relationship between UAR and coronary collateral circulation (CCC) in patients with non-ST segment elevation myocardial infarction (NSTEMI). METHODS: A total of 205 NSTEMI patients who underwent coronary arteriography with at least one major coronary stenosis, 95% were included. Patients were divided into two groups according to CCC development: poorly-developed CCC group (Rentrop 0-1) and well-developed CCC (Rentrop 2-3). Univariate analysis and logistic regression analysis were utilized to investigate the factors influencing adverse CCC formation in NSTEMI patients. The receiver operating characteristic (ROC) curve was plotted to evaluate the predictive value of UAR, C-reactive protein (CRP), uric acid, and albumin for patients with poorly developed CCC, and the area under the curve (AUC) was compared. RESULTS: The UAR values of NSTEMI patients were significantly higher in the poorly developed CCC group than those in the well-developed CCC group (10.19 [8.80-11.74] vs. 7.79 [6.28-9.55], p < .001). In the multiple logistic regression tests, UAR (odds ratio [OR]: 1.365, 95% confidence interval [CI]: 1.195-1.560, p < .001), CRP (OR: 1.149, 95% CI: 1.072-1.231, p < .001), and diabetes (OR: 2.924, 95% CI: 1.444-5.920, p = .003) were independent predictors of poorly developed CCC. The ROC curve analysis showed that the optimal cut-off value of UAR was 8.78 in predicting poorly developed CCC with a sensitivity of 76.8% and specificity of 62.4%, with the AUC of 0.737 (95% Cl: 0.668-0.805, p < .001). CONCLUSION: Elevated UAR may be an independent and effective biomarker for predicting poorly-developed CCC development in NSTEMI patients.

Predicting long-term prognosis after percutaneous coronary intervention in patients with new onset ST-elevation myocardial infarction: development and external validation of a nomogram model.

BACKGROUND: The triglyceride glucose (TyG) index is a well-established biomarker for insulin resistance (IR) that shows correlation with poor outcomes in patients with coronary artery disease. We aimed to integrate the TyG index with clinical data in a prediction nomogram for the long-term prognosis of new onset ST-elevation myocardial infarction (STEMI) following primary percutaneous coronary intervention (PCI) . METHODS: This retrospective study included new-onset STEMI patients admitted at two heart centers for emergency PCI from December 2015 to March 2018 in development and independent validation cohorts. Potential risk factors were screened applying least absolute shrinkage and selection operator (LASSO) regression. Multiple Cox regression was employed to identify independent risk factors for prediction nomogram construction. Nomogram performance was assessed based on receiver operating characteristic curve analysis, calibration curves, Harrell's C-index and decision curve analysis (DCA). RESULTS: In total, 404 patients were assigned to the development cohort and 169 to the independent validation cohort. The constructed nomogram included four clinical variables: age, diabetes mellitus, current smoking, and TyG index. The Harrell's C-index values for the nomogram were 0.772 (95% confidence interval [CI]: 0.721-0.823) in the development cohort and 0.736 (95%CI: 0.656-0.816) in the independent validation cohort. Significant correlation was found between the predicted and actual outcomes in both cohorts, indicating that the nomogram is well calibrated. DCA confirmed the clinical value of the development prediction nomogram. CONCLUSIONS: Our validated prediction nomogram based on the TyG index and electronic health records data was shown to provide accurate and reliable discrimination of new-onset STEMI patients at high- and low-risk for major adverse cardiac events at 2, 3 and 5 years following emergency PCI.

Corrigendum to "Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model".

[This corrects the article DOI: 10.1155/2022/5497134.].

Novel Insights into the Predictors of Obstructive Sleep Apnea Syndrome in Patients with Chronic Coronary Syndrome: Development of a Predicting Model.

Background: Obstructive sleep apnea syndrome (OSAS) is common in patients with chronic coronary syndrome (CCS); however, a predictive model of OSAS in patients with CCS remains rarely reported. The study aimed to construct a novel nomogram scoring system to predict OSAS comorbidity in patients with CCS. Methods: Consecutive CCS patients scheduled for sleep monitoring at our hospital from January 2019 to September 2020 were enrolled in the current study. Coronary CT angiography or coronary angiography was used for the diagnosis of CCS, and clinical characteristics of the patients were collected. Significant predictors for OSAS in patients with moderate/severe CCS were estimated via logistic regression analysis, and a clinical nomogram was constructed. A calibration plot, examining discrimination (Harrell's concordance index) and decision curve analysis (DCA), was applied to validate the nomogram's predictive performance. Internal validity of the predictive model was assessed using bootstrapping (1000 replications). Results: The nomograms were constructed based on available clinical variables from 527 patients which were significantly associated with moderate/severe OSAS in patients with CCS, including body mass index, impaired glucose tolerance, hypertension, diabetes mellitus, nonalcoholic fatty liver disease, and routine laboratory indices such as neutrophil to lymphocyte ratio, platelet-to-lymphocyte ratio, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The C-index (0.793) and AUC (0.771, 95% CI: 0.731-0.811) demonstrated a favorable discriminative ability of the nomogram. Moreover, calibration plots revealed consistency between moderate/severe OSAS predicted by the nomogram and validated by the results of sleep monitoring. Clinically, DCA showed that the nomogram had good discriminative ability to predict moderate/severe OSAS in patients with CCS. Conclusions: The risk nomogram constructed via the routinely available clinical variables in patients with CCS showed satisfying discriminative ability to predict comorbid moderate/severe OSAS, which may be useful for identification of high-risk patients with OSAS in patients with CCS.