Department of Hyperbaric Oxygen, The First Medical Centre of Chinese People's Liberation Army (PLA) General Hospital, Beijing, China.
Graduate School, Chinese People's Liberation Army Medical School, Beijing, China.
Front Endocrinol (Lausanne). 2024 Mar 4;15:1337284. doi: 10.3389/fendo.2024.1337284. eCollection 2024.
Coronary slow flow (CSF) has gained significance as a chronic coronary artery disease, but few studies have integrated both biological and anatomical factors for CSF assessment. This study aimed to develop and validate a simple-to-use nomogram for predicting CSF risk by combining biological and anatomical factors.
In this retrospective case-control study, 1042 patients (614 CSF cases and 428 controls) were randomly assigned to the development and validation cohorts at a 7:3 ratio. Potential predictive factors were identified using least absolute shrinkage and selection operator regression and subsequently utilized in multivariate logistic regression to construct the nomogram. Validation of the nomogram was assessed by discrimination and calibration.
N-terminal pro brain natriuretic peptide, high density lipoprotein cholesterol, hemoglobin, left anterior descending artery diameter, left circumflex artery diameter, and right coronary artery diameter were independent predictors of CSF. The model displayed high discrimination in the development and validation cohorts (C-index 0.771, 95% CI: 0.737-0.805 and 0.805, 95% CI: 0.757-0.853, respectively). The calibration curves for both cohorts showed close alignment between predicted and actual risk estimates, demonstrating improved model calibration. Decision curve analysis suggested high clinical utility for the predictive nomogram.
The constructed nomogram accurately and individually predicts the risk of CSF for patients with suspected CSF and may be considered for use in clinical care.
冠状动脉慢血流（CSF）已成为慢性冠状动脉疾病的重要表现，但很少有研究综合考虑生物学和解剖学因素来评估 CSF。本研究旨在通过结合生物学和解剖学因素，开发并验证一种简单易用的 CSF 风险预测列线图。
这是一项回顾性病例对照研究，1042 名患者（614 例 CSF 病例和 428 例对照）以 7:3 的比例随机分配到开发和验证队列中。使用最小绝对收缩和选择算子回归确定潜在的预测因素，然后将这些因素用于多变量逻辑回归中，构建列线图。通过判别和校准评估列线图的验证。
N 末端脑利钠肽前体、高密度脂蛋白胆固醇、血红蛋白、左前降支直径、左旋支直径和右冠状动脉直径是 CSF 的独立预测因素。该模型在开发和验证队列中具有较高的判别能力（C 指数分别为 0.771（95%CI：0.737-0.805）和 0.805（95%CI：0.757-0.853））。两个队列的校准曲线均显示预测风险与实际风险估计之间的紧密一致性，表明模型校准得到改善。决策曲线分析表明，预测列线图具有较高的临床实用性。
所构建的列线图可准确地个体化预测疑似 CSF 患者的 CSF 风险，可考虑用于临床护理。
