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Published on: September 22, 2020
Prediction of 1-Year Major Adverse Cardiovascular Events in Chronic Limb Threatening Ischemia
Sreekanth Vemulapalli1, Gheorghe Doros2, E Hope Weissler3
1Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Duke Clinical Research Institute, Durham, North Carolina, USA.
Insights
A new risk model predicts major adverse cardiovascular events (MACE) in patients with chronic limb threatening ischemia (CLTI). The model, derived from the BEST-CLI trial, highlights nonmodifiable factors and the benefit of statin therapy for CLTI patients.
Area of Science:
- Cardiovascular Medicine
- Vascular Surgery
- Clinical Epidemiology
Background:
- Patients with chronic limb threatening ischemia (CLTI) face significant risks of major adverse cardiovascular events (MACE).
- Existing risk stratification tools for CLTI patients are limited.
- Effective prediction of MACE is crucial for managing CLTI.
Purpose of the Study:
- To develop and validate a predictive model for 1-year MACE in patients with CLTI.
- To identify key risk factors for MACE in this population.
- To assess the model's performance in an independent cohort.
Main Methods:
- A multivariable prediction model for 1-year MACE was developed using data from the BEST-CLI trial (80% derivation cohort).
- The model was validated internally and externally using the BEST Registry.
- Participants were randomly split, and external validation included data from a subset of BEST-CLI trial sites.
Main Results:
- The 1-year cumulative incidence of MACE was 17.33% in the study population.
- Key predictors of MACE included prior coronary artery disease, congestive heart failure, and advanced chronic kidney disease.
- Statin use demonstrated a protective effect (HR: 0.7), while the mode of revascularization did not significantly influence MACE.
- The model showed good discrimination (C-statistic = 0.669) and calibration (calibration slope = 0.884) in the derivation cohort and acceptable performance in external validation.
Conclusions:
- The derived BEST-CLI MACE model effectively predicts 1-year MACE in CLTI patients.
- The model's accuracy is not affected by the chosen revascularization strategy.
- The findings underscore the importance of statin therapy and nonmodifiable risk factors in CLTI management.
- Further research is needed to evaluate the model's utility in guiding clinical decisions for CLTI patients.
Background:
Patients with chronic limb threatening ischemia (CLTI) are at risk for major adverse cardiovascular events (MACE), yet few tools exist for risk stratification.
Objectives:
The purpose of this study was to derive and validate a CLTI MACE risk prediction model.
Methods:
Participants in the BEST-CLI (Best Endovascular vs. Best Surgical Therapy for Patients with Critical Limb Ischemia) trial were randomly split 80%/20% into derivation and validation cohorts. A parsimonious multivariable model to predict 1-year MACE was developed. External validation was performed in the observational BEST Registry with 1-year outcomes conducted at a subset of 40 BEST-CLI trial sites.
Results:
Among 1,780 patients, the average age was 67.2 years, 28.3% were female, and 20.2% were Black. Hypertension (87.1%), hyperlipidemia (73.9%), diabetes (69.2%), and coronary artery disease (44.9%) were prevalent. The 1-year cumulative incidence of MACE was 17.33% (95% CI: 15.59%-19.24%). Overall model discrimination (C-statistic = 0.669) and calibration (calibration slope = 0.884) were good. Model performance was driven by nonmodifiable risk factors: prior coronary artery disease (HR: 2.22 [95% CI: 1.72-2.89]; P < 0.0001), congestive heart failure (HR: 1.93 [95% CI: 1.35-2.75]; P = 0.0003), stage 3 or greater chronic kidney disease (HR: 1.46 [95% CI: 1.14-1.87]; P = 0.0025). Statin use was protective (HR: 0.7 [95% CI: 0.54-0.91]; P = 0.007) but mode of revascularization was not. External validation in the BEST Registry yielded a C-statistic of 0.66 and a calibration slope of 0.78.
Conclusions:
The BEST-CLI MACE model predicts 1-year MACE in CLTI, is not influenced by mode of revascularization, and highlights the importance of statin therapy. Further work is needed to determine if this model may be helpful in guiding therapeutic decision-making in CLTI patients.
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