Predictors of Long-Term Mortality in Patients with Stable Angina Pectoris and Coronary Slow Flow

Analysis of Risk Factors and Construction of a Predictive Model for Readmission in Patients with Coronary Slow Flow Phenomenon

Background

Coronary slow flow phenomenon (CSFP) is a phenomenon in which distal vascular perfusion is delayed on angiography, but coronary arteries are not significantly narrowed and there is no other organic cardiac disease. Patients with CSFP may be repeatedly readmitted to the hospital because of chest pain or other symptoms of precordial discomfort, and there is a risk of adverse events. In order to investigate the risk factors affecting the readmission of CSFP patients, a prediction model was constructed with the aim of identifying patients at risk of readmission at an early stage and providing a reference for further clinical intervention.

Methods

In this study, we collected clinical data from 397 CSFP patients between June 2021 and January 2023 in Xinjiang Medical University Hospital. Telephone follow-up clarified whether the patients were readmitted to the hospital. A predictive model for readmission of CSFP patients was constructed using multifactorial logistic regression. Nomogram was used to visualize the model and bootstrap was used to internally validate the model. ROC, DCA and Calibration curve were plotted to evaluate the calibration and discriminative ability of the column line graphs, respectively. Calibration and resolution of the column line graphs, respectively.

Results

A total of 34 of 397 CSFP patients experienced readmission. Smoking history, creatine kinase isoenzyme-MB, total cholesterol, and left ventricular ejection fraction were the predictors of readmission in patients with CSFP. The area under the curve of the Nomogram model was 0.87, which indicated that the model had good predictive ability and differentiation, and the DCA and Calibration curves also indicated that the model had good consistency and was clinically useful.

Conclusion

A readmission prediction model for patients with CSFP may facilitate early identification of patients at potential risk for readmission and timely interventional therapy to improve patient prognosis.

Coronary slow flow research: a bibliometric analysis

BACKGROUND

Studies on coronary slow flow are receiving increasing attention, but objective evaluations are still lacking. The purpose of this study was to visualize the current status and research hotspots of coronary slow flow through bibliometric analysis.

METHODS

All relevant publications on coronary slow flow from 2003 to 2022 were extracted from the Web of Science Core Collection database and analyzed by VOSviewer and CiteSpace visualization software. Year of publication, journal, country/region, institution, and first author of each paper, as well as research hotspots were identified.

RESULTS

A total of 913 publications were retrieved. The journal with the most publications was Coronary Artery Disease. The country/region with the most publications was Turkey, followed by China and the United States. The institution with the largest publication volume was Turkey Specialized Higher Education Research Hospital. The author with the largest publication volume was Chun-Yan Ma from China. Keyword analysis indicated that "treatment and prognosis", "pathogenesis and risk factors" and "diagnosis" were the clustering centers of coronary slow flow, and the research hotspots gradually changed with time, from pathogenesis to treatment and prognosis.

CONCLUSION

Future research will focus on the search for effective and non-invasive detection indicators and treatments of coronary slow flow. Collaboration needs to be enhanced between different institutions or countries/regions, which would improve clinical outcomes for patients with coronary slow flow.