Second Department of Interior, Lichuan County People's Hospital of Jiangxi Province, Fuzhou, China.
Department of Cardiovascular Medicine, Suizhou Hospital, Hubei Medicine University, Suizhou, China.
Medicine (Baltimore). 2024 Nov 1;103(44):e40044. doi: 10.1097/MD.0000000000040044.
In this study, risk factors for coronary slow flow (CSF) patients were examined, and a clinical prediction model was created. This study involved 573 patients who underwent coronary angiography at our hospital because of chest pain from January 2020 to April 2022. They were divided into CSF group (249 cases) and noncoronary slow flow (NCF) group (324 cases) according to the coronary blood flow results. According to a 7:3 ratio, the patients were categorized into a training group consisting of 402 cases and a validation group consisting of 171 cases. The outcome was assessed by employing multiple logistic regression analysis to examine the factors that influenced it. The model's recognizability was assessed by calculating the consistency index and plotting the receiver operating characteristic curve. Its consistency was assessed by calibration curve, decision curve, and Hosmer-Lemeshow testing goodness-of-fit. The multivariate model included factors such as male, BMI, smoking, diabetes, ursolic acid, and high-density lipoprotein cholesterol. The model validation showed that the consistency index was 0.714, and the external validation set had a consistency index of 0.741. The areas under the curve for the training and external validation sets were respectively 0.730 (95% CI: 0.681-0.779) and 0.770 (95%CI: 0.699-0.841). Nomogram calibration curves indicated intense calibration, and the results of the Hosmer-Lemeshow goodness-of-fit test indicated that χ² = 1.118, P = .572. The nomogram combining various risk factors can be used for individualized predictions of CSF patients and then facilitate prompt and specific treatment.
在这项研究中，我们检查了冠状动脉慢血流（CSF）患者的危险因素，并建立了一个临床预测模型。这项研究纳入了 2020 年 1 月至 2022 年 4 月因胸痛在我院行冠状动脉造影的 573 例患者。根据冠状动脉血流结果，将其分为 CSF 组（249 例）和非冠状动脉慢血流（NCF）组（324 例）。按照 7:3 的比例将患者分为训练组（402 例）和验证组（171 例）。采用多因素 logistic 回归分析评估其影响因素，评估结果。采用一致性指数和绘制受试者工作特征曲线评估模型的可识别性。采用校准曲线、决策曲线和 Hosmer-Lemeshow 检验拟合优度评估模型的一致性。多变量模型包括男性、BMI、吸烟、糖尿病、熊果酸和高密度脂蛋白胆固醇等因素。模型验证显示，一致性指数为 0.714，外部验证集的一致性指数为 0.741。训练集和外部验证集的曲线下面积分别为 0.730（95%CI：0.681-0.779）和 0.770（95%CI：0.699-0.841）。列线图校准曲线表明校准度较高，Hosmer-Lemeshow 拟合优度检验结果表明 χ²=1.118，P=0.572。结合多种危险因素的列线图可用于 CSF 患者的个体化预测，从而有助于及时、特异性治疗。
