Yan Changshun, Guo Yankai, Cao Guiqiu
Department of Cardiology, Fifth Affiliated Hospital of Xinjiang Medical University, Urumqi, The Xinjiang Uygur Autonomous Region, People's Republic of China.
Department of Pacing Electrophysiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, The Xinjiang Uygur Autonomous Region, People's Republic of China.
Int J Gen Med. 2024 Mar 4;17:791-808. doi: 10.2147/IJGM.S444169. eCollection 2024.
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.
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.
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.
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.
冠状动脉慢血流现象（CSFP）是一种在血管造影时远端血管灌注延迟，但冠状动脉无明显狭窄且无其他器质性心脏病的现象。CSFP患者可能因胸痛或其他心前区不适症状而反复入院，存在不良事件风险。为了研究影响CSFP患者再次入院的危险因素，构建了一个预测模型，旨在早期识别有再次入院风险的患者，并为进一步的临床干预提供参考。
在本研究中，我们收集了2021年6月至2023年1月在新疆医科大学附属医院的397例CSFP患者的临床资料。通过电话随访明确患者是否再次入院。采用多因素逻辑回归构建CSFP患者再次入院的预测模型。使用列线图可视化模型，并使用自助法对模型进行内部验证。绘制ROC、DCA和校准曲线分别评估列线图的校准和鉴别能力。
397例CSFP患者中共有34例再次入院。吸烟史、肌酸激酶同工酶-MB、总胆固醇和左心室射血分数是CSFP患者再次入院的预测因素。列线图模型的曲线下面积为0.87，表明该模型具有良好的预测能力和区分度，DCA和校准曲线也表明该模型具有良好的一致性且在临床上有用。
CSFP患者再次入院预测模型可能有助于早期识别有再次入院潜在风险的患者，并及时进行介入治疗以改善患者预后。
