Key result
ACEF-QFR score outperforms other risk models in predicting 2-year patient-oriented composite endpoints.
Why the study?
The study sought to establish a scoring system combining the ACEF score and quantitative blood flow ratio (QFR) to improve long-term risk prediction in patients undergoing PCI.
Does the ACEF-QFR scoring system improve long-term risk prediction for 2-year adverse cardiac events in CAD patients after PCI compared to other commonly used risk scores?
Cohort (n=1,805)
Does the ACEF-QFR scoring system improve long-term risk prediction for 2-year adverse cardiac events in CAD patients after PCI compared to other commonly used risk scores?
The ACEF-QFR scoring system, integrating clinical and coronary lesion-based functional assessment, improves long-term risk prediction for adverse cardiac events in patients undergoing PCI.
Answers are grounded in this paper and cite their sources.
ACEF-QFR may aid post-PCI risk stratification in CAD; leaves open prospective validation before clinical adoption.
OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio (QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention (PCI). METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263 consecutive cases of CAD patients after PCI in PANDA III trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort. RESULTS In both the Random Forest Model and the DeepSurv Model, age, renal function (creatinine), cardiac function (LVEF) and post-PCI coronary physiological index (QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age (years)/EF (%) + 1 (if creatinine ≥ 2.0 mg/dL) + 1 (if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination (C-statistic = 0.651; 95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration (Hosmer–Lemeshow χ² = 7.070; P = 0.529) for predicting 2-year patient-oriented composite endpoint (POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis (adjusted HR = 1.89; 95% CI: 1.18–3.04; log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group. CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables (ACEF-QFR) was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.
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Wu et al. (2024) conducted a cohort in Coronary artery disease (CAD) after percutaneous coronary intervention (PCI) (n=1,805). ACEF-QFR scoring system vs. Post-PCI physiological index and other commonly used risk scores was evaluated on 2-year patient-oriented composite endpoint (POCE) (C-statistic 0.651, 95% CI 0.611-0.691, p=<0.05).
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