August 1, 2021Clinical Interventions in AgingOpen Access

Development and Validation of a Risk Nomogram Model for Predicting Revascularization After Percutaneous Coronary Intervention in Patients with Acute Coronary Syndrome

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Why the study?

Postoperative revascularization remains a major problem after PCI for ACS, motivating the development of a predictive risk nomogram.

Does a 10-variable risk nomogram accurately predict the need for revascularization after PCI in patients with acute coronary syndrome?

Structured PICO

Does a 10-variable risk nomogram accurately predict the need for revascularization after PCI in patients with acute coronary syndrome?

Population

1083 patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI) (≥6 months) at a single center from June 2013 to December 2019.

Intervention

Risk nomogram model based on 10 variables (follow-up time, history of diabetes mellitus, admission serum creatinine, admission serum uric acid, admission lipoprotein-a, re-admission low density lipoprotein cholesterol, chronic total occlusion, multivessel disease, calcified lesions, and bifurcation lesions).

Outcome

Revascularization after PCIhard clinical

A newly developed 10-variable nomogram provides good predictive accuracy for the risk of revascularization after PCI in patients with ACS, potentially aiding in clinical risk stratification.

Implication

May aid post-PCI risk stratification in ACS; leaves open prospective validation before clinical adoption.

Abstract

OBJECTIVE: Percutaneous coronary intervention (PCI) is one of the most effective treatments for acute coronary syndrome (ACS). However, the need for postoperative revascularization remains a major problem in PCI. This study was to develop and validate a nomogram for prediction of revascularization after PCI in patients with ACS. METHODS: A retrospective observational study was conducted using data from 1083 patients who underwent PCI (≥6 months) at a single center from June 2013 to December 2019. They were divided into training (70%; n = 758) and validation (30%; n = 325) sets. Multivariate logistic regression analysis was used to establish a predictive model represented by a nomogram. The nomogram was developed and evaluated based on discrimination, calibration, and clinical efficacy using the concordance statistic (C-statistic), calibration plot and decision curve analysis (DCA), respectively. RESULTS: The nomogram was comprised of ten variables: follow-up time (odds ratio (OR): 1.01; 95% confidence interval (CI): 1.00-1.03), history of diabetes mellitus (OR: 1.83; 95% CI: 1.25-2.69), serum creatinine level on admission (OR: 0.99; 95% CI: 0.98-1.00), serum uric acid level on admission (OR: 1.005; 95% CI: 1.002-1.007), lipoprotein-a level on admission (OR: 1.0021; 95% CI: 1.0013-1.0029), low density lipoprotein cholesterol level on re-admission (OR: 1.33; 95% CI: 0.10-0.47), the presence of chronic total occlusion (OR: 3.30; 95% CI: 1.93-5.80), the presence of multivessel disease (OR: 4.48; 95% CI: 2.85-7.28), the presence of calcified lesions (OR: 1.63; 95% CI: 1.11-2.39), and the presence of bifurcation lesions (OR: 1.82; 95% CI: 1.20-2.77). The area under the receiver operating characteristic curve values for the training and validation sets were 0.765 (95% CI: 0.732-0.799) and 0.791 (95% CI: 0.742-0.830), respectively. The calibration plots showed good agreement between prediction and observation in both the training and validation sets. DCA also demonstrated that the nomogram was clinically useful. CONCLUSION: We developed an easy-to-use nomogram model to predict the risk of revascularization after PCI in patients with ACS. The nomogram may provide useful assessment of risk for subsequent treatment of ACS patients undergoing PCI.