Real-world abdominal aorta aneurysm screening patterns among patients with new or worsening of symptomatic peripheral artery disease.

BACKGROUND: Patients with peripheral artery disease (PAD) have an increased risk of abdominal aortic aneurysms (AAA), but it remains unclear whether practitioners are screening patients for AAA as part of routine PAD management. METHODS: The Patient-centered Outcomes Related to Treatment Practices in Peripheral Arterial Disease (PORTRAIT) Registry is an international prospective registry of patients with new or worsening PAD symptoms presenting to 16 specialty centers in the United States, Netherlands, and Australia, from June 2011 to December 2015. Patients were stratified by AAA screening or AAA positivity. An adjusted median odds ratio was calculated for AAA screening rates across sites. RESULTS: Of the 1275 patients in the study, 871 (68%) were screened for AAA, with 53 (6.1%) having AAA. AAA screening rates did not differ significantly by country (p = 0.36), but there was a large variation across sites for documentation of AAA screening with an adjusted median odds ratio 12.0 (95% CI 4.7-93.1), with AAA screening rates ranging from 7% to 100% across vascular specialty centers. CONCLUSIONS: Among patients with PAD in a multicenter registry, over two-thirds were screened for AAA, with 6% having documented aneurysms. A large variation was seen across clinical sites, suggesting efforts are needed to increase awareness for guideline implementation and establish new benefit-risk evidence inclusive of high-risk populations such as patients with PAD.

Patient characteristics and survival outcomes of cardiac arrest in the cardiac catheterization laboratory: Insights from get with the Guidelines®-Resuscitation registry.

BACKGROUND: Characteristics and outcomes of patients with in-hospital cardiac arrest (IHCA) in the cardiac catheterization laboratory (CCL) have not been well-described. Thus, we compared the outcomes of patients with an IHCA in the CCL versus those in the intensive care unit (ICU) and operating rooms (OR). METHODS: Within the American Heart Association's Get With the Guidelines®-Resuscitation registry, we identified patients ≥ 18 years old with IHCA in the CCL, ICU, or OR between 2000-2019. Using hierarchical multivariable logistic regression, we compared rates of survival to discharge for patients with IHCA in the CCL versus ICU and OR. RESULTS: Across 428 hospitals, 193,950 patients had IHCA, of whom 6865, 181,905 and 5180 were in the CCL, ICU and OR, respectively. Overall, 2614 (38.1%) patients with IHCA in the CCL survived to discharge, whereas 30,830 (16.9%) and 2096 (40.5%) survived to discharge from the ICU and OR, respectively. After adjustment, patients with IHCA in CCL were more likely to survive to discharge as compared to those with IHCA in the ICU (odds ratio, 1.37 [95%CI: 1.29-1.46], p < 0.001). In contrast, those who had IHCA in the CCL were less likely to survive to discharge as compared to patients with IHCA in the OR (odds ratio, 0.81 [95%CI: 0.69-0.94], p = 0.006). CONCLUSION: IHCA in the CCL is not uncommon and has a lower survival rate when compared with IHCA in the OR. The reasons for this difference deserve further study given that cardiac arrest in both settings is witnessed and response time should be similar.

Prospective evaluation of the effect of smartphone electrocardiogram usage on anticoagulant medication compliance.

PURPOSE: Compliance with anticoagulation treatment for atrial fibrillation is highly variable. Smartphone electrocardiograms that allow patients to have greater insight into their arrhythmia burden may improve anticoagulant compliance. METHODS: Patients were enrolled if they had atrial fibrillation with a CHA2DS2-VASc score of 2 or more, were eligible for anticoagulation and had a smartphone. Participants were randomly assigned to receive a smartphone electrocardiogram (AliveCor Kardia) to record their electrocardiograms 5 times/week or to the control group. All patients received 6 months of anticoagulant (apixaban) dispensed as 1-month pre-loaded pill boxes. RESULTS: A total of 100 patients were enrolled from July 2017 to August 2019, but 5 patients in the monitor arm and 1 in the control arm withdrew prematurely. The monitor and control groups did not differ in age, gender, CHA2DS2-VASc score, or comorbidities. Median medication compliance was 99.7%, with nonsignificantly greater compliance in the monitor group (100%) than in the control group (99.7%) (p-value = 0.247). There was also no significant difference between missing any dose and use/nonuse of the smartphone monitor (48.9% vs. 55.1%; p-value = 0.692). Mean monitor compliance was 86.8% ± 14.0% with an average of 4.34 recorded electrocardiograms per week. Monitor group patients with perfect medication compliance had significantly higher monitor compliance than those patients who missed doses (median 95.3% vs 86.7%; p-value = 0.02). CONCLUSIONS: In a study population with higher-than-expected medication compliance, the use of smartphone electrocardiogram did not demonstrate an improvement in medication compliance as compared to usual care. Greater monitor compliance was associated with greater medication compliance. CLINICAL TRIAL REGISTRATION: BOAT-OAR ClinicalTrials.gov number, NCT03515083.

Association of Disease-Specific Health Status With Long-Term Survival in Peripheral Artery Disease.

Background While peripheral artery disease (PAD) is associated with increased cardiovascular morbidity with mortality remaining high and challenging to predict, accurate understanding of serial PAD-specific health status around the time of diagnosis may prognosticate long-term mortality risk. Methods and Results Patients with new or worsening PAD symptoms enrolled in the PORTRAIT Registry across 10 US sites from 2011 to 2015 were included. Health status was assessed by the Peripheral Artery Questionnaire (PAQ) Summary score at baseline, 3-month, and change from baseline to 3-month follow-up. Kaplan-Meier using 3-month landmark and hierarchical Cox regression models were constructed to assess the association of the PAQ with 5-year all-cause mortality. Of the 711 patients (mean age 68.8±9.6 years, 40.9% female, 72.7% white; mean PAQ 47.5±22.0 and 65.9±25.0 at baseline and 3-month, respectively), 141 (19.8%) died over a median follow-up of 4.1 years. In unadjusted models, baseline (HR, 0.90 per-10-point increment; 95% CI, 0.84-0.97; P=0.008), 3-month (HR [95% CI], 0.87 [0.82-0.93]; P<0.001) and change in PAQ (HR [95% CI], 0.92 [0.85-0.99]; P=0.021) were each associated with mortality. In fully adjusted models including combination of scores, 3-month PAQ was more strongly associated with mortality than either baseline (3-month HR [95% CI], 0.85 [0.78-0.92]; P<0.001; C-statistic, 0.77) or change (3-month HR [95% CI], 0.79 [0.72-0.87]; P<0.001). Conclusions PAD-specific health status is independently associated with 5-year survival in patients with new or worsening PAD symptoms, with the most recent assessment being most prognostic. Future work is needed to better understand how this information can be used proactively to optimize care.

A risk-adjustment model for patients presenting to hospitals with out-of-hospital cardiac arrest and ST-elevation myocardial infarction.

BACKGROUND: Patients with ST-elevation myocardial infarction (STEMI) complicated by an out-of-hospital-cardiac-arrest (OHCA) may vary widely in their probability of dying. Large variation in mortality may have implications for current national efforts to benchmark operator and hospital mortality rates for coronary angiography. We aimed to build a risk-adjustment model of in-hospital mortality among OHCA survivors with concurrent STEMI. METHODS: Within the Cardiac Arrest Registry to Enhance Survival (CARES), we included adults with OHCA and STEMI who underwent emergent angiography within 2 hours of hospital arrival between January 2013 and December 2019. Using multivariable logistic regression to adjust for patient and cardiac arrest factors, we developed a risk-adjustment model for in-hospital mortality and examined variation in patients' predicted mortality. RESULTS: Of 2,999 patients (mean age 61.2 ± 12.0, 23.1% female, 64.6% white), 996 (33.2%) died during their hospitalization. The final risk-adjustment model included higher age (OR per 10-year increase, 1.50 [95% CI: 1.39-1.63]), unwitnessed OHCA (OR, 2.51 [1.99-3.16]), initial non-shockable rhythm [OR, 5.66 [4.52-7.13]), lack of sustained pulse for > 20 minutes (OR, 2.52 [1.88-3.36]), and longer resuscitation time (increased with each 10-minute interval) (c-statistic = 0.804 with excellent calibration). There was large variability in predicted mortality: median, 25.2%, inter-quartile-range: 14.0% to 47.8%, 10th-90th percentile: 8.2 % to 74.1%. CONCLUSIONS: In a large national registry, we identified 5 key predictors for mortality in patients with STEMI and OHCA and found wide variability in mortality risk. Our findings suggest that current national benchmarking efforts for coronary angiography, which simply adjusts for the presence of OHCA, may not adequately capture patient case-mix severity.

Risk Adjustment Model for Preserved Health Status in Patients With Heart Failure and Reduced Ejection Fraction: The CHAMP-HF Registry.

BACKGROUND: Health status outcomes are increasingly being promoted as measures of health care quality, given their importance to patients. In heart failure (HF), an American College of Cardiology/American Heart Association Task Force proposed using the proportion of patients with preserved health status as a quality measure but not as a performance measure because risk adjustment methods were not available. METHODS: We built risk adjustment models for alive with preserved health status and for preserved health status alone in a prospective registry of outpatients with HF with reduced ejection fraction across 146 US centers between December 2015 and October 2017. Preserved health status was defined as not having a ≥5-point decrease in the Kansas City Cardiomyopathy Questionnaire Overall Summary score at 1 year. Using only patient-level characteristics, hierarchical multivariable logistic regression models were developed for 1-year outcomes and validated using data from 1 to 2 years. We examined model calibration, discrimination, and variability in sites' unadjusted and adjusted rates. RESULTS: Among 3932 participants (median age [interquartile range] 68 years [59-75], 29.7% female, 75.4% White), 2703 (68.7%) were alive with preserved health status, 902 (22.9%) were alive without preserved health status, and 327 (8.3%) had died by 1 year. The final risk adjustment model for alive with preserved health status included baseline Kansas City Cardiomyopathy Questionnaire Overall Summary, age, race, employment status, annual income, body mass index, depression, atrial fibrillation, renal function, number of hospitalizations in the past 1 year, and duration of HF (optimism-corrected C statistic=0.62 with excellent calibration). Similar results were observed when deaths were ignored. The risk standardized proportion of patients alive with preserved health status across the 146 sites ranged from 62% at the 10th percentile to 75% at the 90th percentile. Variability across sites was modest and changed minimally with risk adjustment. CONCLUSIONS: Through leveraging data from a large, outpatient, observational registry, we identified key factors to risk adjust sites' proportions of patients with preserved health status. These data lay the foundation for building quality measures that quantify treatment outcomes from patients' perspectives.