Development and validation of a model integrating clinical and coronary lesion-based functional assessment for longterm risk prediction in PCI patients.
| Autor: | Shao-Yu WU, Rui ZHANG, Sheng YUAN, Zhong-Xing CAI, Chang-Dong GUAN, Tong-Qiang ZOU, Li-Hua XIE, Ke-Fei DOU |
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| Předmět: |
RISK assessment
RANDOM forest algorithms KIDNEY function tests HUMAN services programs PREDICTION models CREATININE VENTRICULAR ejection fraction QUALITATIVE research RECEIVER operating characteristic curves T-test (Statistics) FUNCTIONAL assessment MAJOR adverse cardiovascular events FISHER exact test HEMODYNAMICS AGE distribution SYMPTOMS DESCRIPTIVE statistics CHI-squared test MULTIVARIATE analysis SURGICAL complications LONGITUDINAL method KAPLAN-Meier estimator LOG-rank test PERCUTANEOUS coronary intervention BLOOD flow measurement MATHEMATICAL models RESEARCH MACHINE learning CORONARY artery disease THEORY CONFIDENCE intervals CALIBRATION COMPARATIVE studies DATA analysis software PROPORTIONAL hazards models REGRESSION analysis DISEASE risk factors |
| Zdroj: | Journal of Geriatric Cardiology; Jan2024, Vol. 21 Issue 1, p44-63, 20p |
| Abstrakt: | 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. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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