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Published on: January 28, 2020
Development and validation of a model integrating clinical and coronary lesion-based functional assessment for
Shao-Yu Wu1,2, Rui Zhang1,2, Sheng Yuan1,2
1State Key Laboratory of Cardiovascular Disease, Beijing, China.
Insights
A new ACEF-QFR score combining clinical factors and quantitative blood flow ratio (QFR) improves long-term risk prediction for patients after percutaneous coronary intervention (PCI). This validated score offers superior discrimination for adverse cardiac events.
Area of Science:
- Cardiovascular Medicine
- Interventional Cardiology
- Biostatistics and Health Informatics
Background:
- Accurate long-term risk prediction is crucial for patients undergoing percutaneous coronary intervention (PCI).
- Existing scoring systems may not fully capture the complex interplay of clinical and physiological factors influencing post-PCI outcomes.
- Quantitative blood flow ratio (QFR) offers a functional assessment of coronary lesions, but its integration into risk prediction models needs further exploration.
Purpose of the Study:
- To develop and validate a novel scoring system, the ACEF-QFR score, by integrating the ACEF score with quantitative blood flow ratio (QFR).
- To enhance the long-term risk prediction of adverse cardiac events in patients following PCI.
- To compare the performance of the ACEF-QFR score against existing risk prediction tools and the post-PCI QFR alone.
Main Methods:
- A population-based cohort study utilizing machine learning models to analyze 46 features from patient clinical and coronary lesion characteristics.
- Development of the ACEF-QFR scoring system using a dataset of 1263 patients with coronary artery disease (CAD) after PCI from the PANDA III trial.
- Validation of the ACEF-QFR score on an independent cohort of 542 patients, employing Random Forest and DeepSurv models for predictive factor identification.
Main Results:
- Age, renal function (creatinine), cardiac function (left ventricular ejection fraction - LVEF), and post-PCI QFR were identified as significant predictors of 2-year adverse cardiac events.
- The ACEF-QFR score, calculated as age/EF + creatinine adjustment + QFR adjustment, demonstrated superior discrimination (C-statistic = 0.651) and excellent calibration (Hosmer-Lemeshow χ² = 7.070, P = 0.529) for predicting 2-year patient-oriented composite endpoints (POCE).
- The score's prognostic value was confirmed by multivariable Cox regression and Kaplan-Meier analysis, showing significantly better prediction than existing scores.
Conclusions:
- The developed ACEF-QFR scoring system effectively combines clinical and coronary lesion-based functional variables for improved prognostic prediction in PCI patients.
- The ACEF-QFR score offers significantly enhanced predictive ability compared to the post-PCI physiological index and other conventional risk scores.
- This novel score holds promise for refining risk stratification and guiding clinical decision-making in patients undergoing PCI.
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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