Department of Cardiology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei 230000, Anhui, China.
Department of Cardiology, The Third Affiliated Hospital of Anhui Medical University (The First People’s Hospital of Hefei), Hefei 230000, Anhui, China.
Aging (Albany NY). 2024 May 10;16(9):8246-8259. doi: 10.18632/aging.205815.
To make predictions about the risk of MVA (Malignant Ventricular Arrhythmia) after primary PCI (Percutaneous Coronary Intervention) in patients with AMI (Acute Myocardial Infarction) through constructing and validating the Nomogram model.
311 AMI patients who suffered from emergency PCI in Hefei Second People's Hospital from January 2020 to May 2023 were selected as the training set; 253 patients suffering from the same symptom in Hefei First People's Hospital during the same period were selected as the validation set. Risk factors were further screened by means of multivariate logistic and stepwise regression. The nomogram model was constructed, and then validated by using C-index, ROC curve, decision curve and calibration curve.
Multivariate logistic analysis revealed that urea, systolic pressure, hypertension, Killip class II-IV, as well as LVEF (Left Ventricular Ejection Fraction) were all unrelated hazards for MVA after emergency PCI for AMI (P<0.05); a risk prediction nomogram model was constructed. The C-index was calculated to evaluate the predictive ability of the model. Result showed that the index of the training and the validation set was 0.783 (95% CI: 0.726-0.84) and 0.717 (95% CI: 0.65-0.784) respectively, which suggested that the model discriminated well. Meanwhile, other tools including ROC curve, calibration curve and decision curve also proved that this nomogram plays an effective role in forecasting the risk for MVA after PCI in AMI patients.
The study successfully built the nomogram model and made predictions for the development of MVA after PCI in AMI patients.
通过构建和验证列线图模型，对急性心肌梗死患者行直接经皮冠状动脉介入治疗（PCI）后恶性室性心律失常（MVA）的风险进行预测。
选择 2020 年 1 月至 2023 年 5 月在合肥市第二人民医院因急性心肌梗死行急诊 PCI 的 311 例患者作为训练集，选择同期在合肥市第一人民医院因相同症状行急诊 PCI 的 253 例患者作为验证集。采用多因素逻辑回归和逐步回归法进一步筛选风险因素，构建列线图模型，并通过 C 指数、ROC 曲线、决策曲线和校准曲线对其进行验证。
多因素逻辑回归分析显示，尿素、收缩压、高血压、Killip Ⅱ-Ⅳ级和左心室射血分数与急性心肌梗死患者急诊 PCI 后发生 MVA 无关（P<0.05）；构建了风险预测列线图模型。计算模型的 C 指数以评估其预测能力。结果显示，训练集和验证集的指数分别为 0.783（95%CI：0.726-0.84）和 0.717（95%CI：0.65-0.784），表明该模型具有良好的区分能力。同时，ROC 曲线、校准曲线和决策曲线等其他工具也证明了该列线图在预测急性心肌梗死患者 PCI 后 MVA 风险方面具有良好的预测作用。
本研究成功构建了列线图模型，可对急性心肌梗死患者 PCI 后 MVA 的发生进行预测。
