Circulation-Cardiovascular Quality and Outcomes最新文献

Background: The BE ACTIVE trial (Behavioral Economic Approaches to Increase Physical Activity Among Patients with Elevated Risk for Cardiovascular Disease) documented the effectiveness, compared with an attention control arm that received daily text messages, of gamification, financial incentives, or gamification+financial incentives to increase steps/day. Increases in daily step count are associated with longer life expectancy, but understanding the cost-effectiveness of these interventions is essential for payers and other stakeholders seeking to implement findings.

Methods: We built a probabilistic Markov model to compare intervention costs with lifetime estimates of life-years and quality-adjusted life-years for 2 sets of comparisons: (1) each behavioral intervention versus attention control, and (2) each trial arm, including attention control, versus no intervention. Since the durability of changes in steps/day post-intervention is unknown, we modeled optimistic, intermediate, and pessimistic scenarios.

Results: Over the 12-month intervention, per-participant cost to deliver attention control was $878, gamification $938, financial incentives $1534, and gamification+financial incentives $1712. Compared with attention control, gamification's cost-effectiveness ranged from $261 (95% CI, 259-263) per life-year gained if mean steps/day during the last 18 weeks of follow-up are maintained (optimistic), to $30 550 (95% CI, 30 503-30 597) per life-year if steps/day continue to decline at the rate observed during the full 26-week follow-up (pessimistic). Gamification+financial incentives cost <$50 000/life-year only under the optimistic and intermediate scenarios. Financial incentives was dominated by gamification and gamification+financial incentives. When all 4 trial arms, including attention control, were compared with no intervention, gamification again cost <$50 000/life-year across all durability scenarios.

Conclusions: Across a range of scenarios about the durability of increases in steps/day post-intervention, gamification consistently cost <$50 000 per life-year gained, the threshold for high value interventions set by American College of Cardiology/American Heart Association guidelines. Gamification+financial incentives was high-value except in the pessimistic scenario. Financial incentives was dominated.

Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03911141.

Background: As cardiovascular disease (CVD) is the leading cause of noncancer mortality in colorectal or gastric cancer patients, it is essential to identify patients at increased CVD risk. Coronary artery calcium (CAC) is an established predictor of atherosclerotic CVD; however, its application is limited in this population. This study evaluates the association between automated CAC scoring using chest computed tomography and atherosclerotic CVD risk in colorectal or gastric cancer patients.

Methods: A retrospective cohort study was conducted using electronic health records linked to claims data of colorectal or gastric cancer patients who underwent non-ECG-gated chest computed tomography at 2 tertiary hospitals in South Korea between 2011 and 2019. CAC was automatically quantified using deep learning software and used to classify patients into 4 groups (CAC=0, 0400). The primary outcome was major adverse cardiovascular events (myocardial infarction, stroke, or cardiovascular mortality), and assessed using the multivariable Fine and Gray subdistribution hazard model. A meta-analysis was performed to calculate pooled subdistribution hazard ratios.

Results: A total of 3153 patients were included in this study (36.5% women; 36.3% CAC=0; 38.1% 0400). The mean follow-up period was 4.1 years. The incidence rate of MACE was 5.28, 8.03, 9.99, and 29.14 per 1000 person-years in CAC=0, 0400. Compared with CAC=0, the risk of MACE was not significantly different in patients with 0400 had 2.33 (95% CI, 1.24-4.39) times higher risk of MACE compared with those with CAC=0.

Conclusions: CAC>400 was associated with an increased risk of MACE compared with CAC=0 among colorectal or gastric cancer patients. CAC quantified on routine chest computed tomography scans provides prognostic information for atherosclerotic CVD risk in this population.

Background: The accuracy of contemporary administrative claims codes to discriminate between different phenotypes of peripheral artery disease is not well defined. We aimed to validate a predefined set of International Classification of Diseases, Tenth Revision, codes used to distinguish between claudication and chronic limb-threatening ischemia (CLTI) and to optimize their diagnostic accuracy using a supervised machine-learning approach.

Methods: We included all patients who underwent a peripheral vascular intervention for claudication or CLTI in the US Medicare-matched VQI-VISION (Vascular Quality Initiative Vascular Implant Surveillance and Interventional Outcomes Network) registry database between January 2016 and December 2019. Gold standard claudication and CLTI diagnoses were determined using VQI (Vascular Quality Initiative) registry data. These diagnoses were compared with a predetermined set of International Classification of Diseases, Tenth Revision, codes in the Medicare-matched data set. We used traditional logistic regression modeling and 6 machine-learning models to distinguish claudication from CLTI. We evaluated the sensitivity, specificity, total agreement, and area under the curve for all models, implementing grid search cross-validation to boost machine-learning model performance.

Results: Of 54 180 patients who underwent a peripheral vascular intervention (mean age, 71.9±10.0 years; 41.0% female; 74.2 non-Hispanic White), 20 769 (38.3%) had claudication and 33 411 (61.7%) had CLTI per gold standard registry definitions. The predefined set of International Classification of Diseases, Tenth Revision, codes had high sensitivity (80.9%), specificity (81.9%), and total agreement (81.3%) for distinguishing claudication versus CLTI. Traditional logistic regression improved sensitivity to 96.2%, but with a substantial drop in specificity (41.8%) and an area under the curve of 0.785. Of the machine-learning models, gradient boosting classifier performed the best (area under the curve, 0.892), improving sensitivity to 88.6% and total agreement to 84.2% with minimal drop in specificity (77.1%).

Conclusions: International Classification of Diseases, Tenth Revision, codes can be used to discriminate between claudication and CLTI in claims data. Our defined set of claims codes can be used by investigators to accurately distinguish between these 2 peripheral artery disease phenotypes.