Construction and Validation of a Nomogram Predicting Depression Risk in Patients with Acute Coronary Syndrome...

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 p < 0.05). Multivariate logistic regression analysis revealed that major life events in the past year (OR = 2.783,95%CI: 1.121-6.907, p = 0.027), GAD-7 score after operation (OR = 1.165, 95% CI: 1.275-2.097, p = 0.000), and baseline PHQ-9 score (OR = 3.221, 95%CI: 2.065-5.023, p = 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.