Miao Xing, Chen Yongli, Qiu Xiaoxia, Wang Rehua
Department of Cardiology, Fujian Provincial Hospital, Fuzhou 350001, China.
Department of Cardiology, Shengli Clinical Medical College of Fujian Medical University, Fuzhou 350001, China.
J Cardiovasc Dev Dis. 2023 Sep 6;10(9):385. doi: 10.3390/jcdd10090385.
To construct and validate a nomogram for predicting depression after acute coronary stent implantation for risk assessment.
This study included 150 patients with acute coronary syndrome (ACS) who underwent stent implantation. Univariate analysis was performed to identify the predictors of postoperative depression among the 24 factors. Subsequently, multivariate logistic regression was performed to incorporate the significant predictors into the prediction model. The model was developed using the "rms" software package in R software, and internal validation was performed using the bootstrap method.
Of the 150 patients, 82 developed depressive symptoms after coronary stent implantation, resulting in an incidence of depression of 54.7%. Univariate analysis showed that sleep duration ≥7 h, baseline GAD-7 score, baseline PHQ-9 score, and postoperative GAD-7 score were associated with the occurrence of depression after stenting in ACS patients (all < 0.05). Multivariate logistic regression analysis revealed that major life events in the past year (OR = 2.783,95%CI: 1.121-6.907, = 0.027), GAD-7 score after operation (OR = 1.165, 95% CI: 1.275-2.097, = 0.000), and baseline PHQ-9 score (OR = 3.221, 95%CI: 2.065-5.023, = 0.000) were significant independent risk factors for ACS patients after stent implantation. Based on these results, a predictive nomogram was constructed. The model demonstrated good prediction ability, with an AUC of 0.857 (95% CI = 0.799-0.916). The correction curve showed a good correlation between the predicted results and the actual results (Brier score = 0.15). The decision curve analysis and prediction model curve had clinical practical value in the threshold probability range of 7 to 94%.
This nomogram can help to predict the incidence of depression and has good clinical application value. This trial is registered with ChiCTR2300071408.
构建并验证一种用于预测急性冠状动脉支架植入术后抑郁风险评估的列线图。
本研究纳入了150例行支架植入术的急性冠状动脉综合征(ACS)患者。对24个因素进行单因素分析以确定术后抑郁的预测因素。随后,进行多因素逻辑回归分析,将显著的预测因素纳入预测模型。该模型使用R软件中的“rms”软件包开发,并采用自助法进行内部验证。
150例患者中,82例在冠状动脉支架植入术后出现抑郁症状,抑郁发生率为54.7%。单因素分析显示,睡眠时间≥7小时、基线广泛性焦虑障碍7项(GAD-7)评分、基线患者健康问卷9项(PHQ-9)评分及术后GAD-7评分与ACS患者支架植入术后抑郁的发生相关(均P<0.05)。多因素逻辑回归分析显示,过去一年中的重大生活事件(比值比[OR]=2.783,95%置信区间[CI]:1.121-6.907,P=0.027)、术后GAD-7评分(OR=1.165,95%CI:1.275-2.097,P=0.000)和基线PHQ-9评分(OR=3.221,95%CI:2.065-5.023,P=0.000)是ACS患者支架植入术后显著的独立危险因素。基于这些结果,构建了预测列线图。该模型显示出良好的预测能力,曲线下面积(AUC)为0.857(95%CI=0.799-0.916)。校正曲线显示预测结果与实际结果之间具有良好的相关性(布里尔评分=0.15)。决策曲线分析和预测模型曲线在阈值概率范围为7%至94%时有临床实用价值。
该列线图有助于预测抑郁发生率,具有良好的临床应用价值。本试验已在中国临床试验注册中心注册,注册号为ChiCTR2300071408。