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An app‐based clinical trial enrolment process can contribute to duplicated records, carrying data management implications. Our objective was to identify duplicated records in real time in the Apple Heart Study (AHS). We leveraged personal identifiable information (PII) to develop a dissimilarity score (DS) using the Damerau–Levenshtein distance. For computational efficiency, we focused on four types of records at the highest risk of duplication. We used the receiver operating curve (ROC) and resampling methods to derive and validate a decision rule to classify duplicated records. We identified 16,398 (4%) duplicated participants, resulting in 419,297 unique participants out of a total of 438,435 possible. Our decision rule yielded a high positive predictive value (96%) with negligible impact on the trial's original findings. Our findings provide principled solutions for future digital trials. When establishing deduplication procedures for digital trials, we recommend collecting device identifiers in addition to participant identifiers; collecting and ensuring secure access to PII; conducting a pilot study to identify reasons for duplicated records; establishing an initial deduplication algorithm that can be refined; creating a data quality plan that informs refinement; and embedding the initial deduplication algorithm in the enrolment platform to ensure unique enrolment and linkage to previous records. [ABSTRACT FROM AUTHOR]

The last half century has seen extraordinary advances in the field of cardiology, including innovations in medications, diagnostic modalities, and therapeutics. Even so, cardiovascular disease remains the leading cause of morbidity and mortality globally, with suboptimal quality of care, inconsistent health outcomes, and unsustainable costs. It is clear that cardiovascular medicine must undergo a digital transformation to enhance the delivery of quality care and to improve outcomes. To meet this need, the American College of Cardiology developed an innovation program focused on the digital transformation of cardiovascular care, with the goal of improving heart health for individuals and populations. [ABSTRACT FROM AUTHOR]

Aims The prognosis of patients with MINOCA (myocardial infarction with non-obstructive coronary arteries) is poorly understood. We examined major adverse cardiac events (MACE) defined as all-cause mortality, re-hospitalization for acute myocardial infarction (AMI), heart failure (HF), or stroke 12-months post-AMI in patients with MINOCA versus AMI patients with obstructive coronary artery disease (MICAD). Methods and results Multicentre, observational cohort study of patients with AMI (≥65 years) from the National Cardiovascular Data Registry CathPCI Registry (July 2009–December 2013) who underwent coronary angiography with linkage to the Centers for Medicare and Medicaid (CMS) claims data. Patients were classified as MICAD or MINOCA by the presence or absence of an epicardial vessel with ≥50% stenosis. The primary endpoint was MACE at 12 months, and secondary endpoints included the components of MACE over 12 months. Among 286 780 AMI admissions (276 522 unique patients), 16 849 (5.9%) had MINOCA. The 12-month rates of MACE (18.7% vs. 27.6%), mortality (12.3% vs. 16.7%), and re-hospitalization for AMI (1.3% vs. 6.1%) and HF (5.9% vs. 9.3%) were significantly lower for MINOCA vs. MICAD patients (P < 0.001), but was similar between MINOCA and MICAD patients for re-hospitalization for stroke (1.6% vs. 1.4%, P  = 0.128). Following risk-adjustment, MINOCA patients had a 43% lower risk of MACE over 12 months (hazard ratio = 0.57, 95% confidence interval 0.55–0.59), in comparison to MICAD patients. This pattern was similar for adjusted risks of the MACE components. Conclusion This study confirms an unfavourable prognosis in elderly patients with MINOCA undergoing coronary angiography, with one in five patients with MINOCA suffering a major adverse event over 12 months. Open in new tab Download slide Open in new tab Download slide [ABSTRACT FROM AUTHOR]

Background: The current acute kidney injury (AKI) risk prediction model for patients undergoing percutaneous coronary intervention (PCI) from the American College of Cardiology (ACC) National Cardiovascular Data Registry (NCDR) employed regression techniques. This study aimed to evaluate whether models using machine learning techniques could significantly improve AKI risk prediction after PCI.Methods and Findings: We used the same cohort and candidate variables used to develop the current NCDR CathPCI Registry AKI model, including 947,091 patients who underwent PCI procedures between June 1, 2009, and June 30, 2011. The mean age of these patients was 64.8 years, and 32.8% were women, with a total of 69,826 (7.4%) AKI events. We replicated the current AKI model as the baseline model and compared it with a series of new models. Temporal validation was performed using data from 970,869 patients undergoing PCIs between July 1, 2016, and March 31, 2017, with a mean age of 65.7 years; 31.9% were women, and 72,954 (7.5%) had AKI events. Each model was derived by implementing one of two strategies for preprocessing candidate variables (preselecting and transforming candidate variables or using all candidate variables in their original forms), one of three variable-selection methods (stepwise backward selection, lasso regularization, or permutation-based selection), and one of two methods to model the relationship between variables and outcome (logistic regression or gradient descent boosting). The cohort was divided into different training (70%) and test (30%) sets using 100 different random splits, and the performance of the models was evaluated internally in the test sets. The best model, according to the internal evaluation, was derived by using all available candidate variables in their original form, permutation-based variable selection, and gradient descent boosting. Compared with the baseline model that uses 11 variables, the best model used 13 variables and achieved a significantly better area under the receiver operating characteristic curve (AUC) of 0.752 (95% confidence interval [CI] 0.749-0.754) versus 0.711 (95% CI 0.708-0.714), a significantly better Brier score of 0.0617 (95% CI 0.0615-0.0618) versus 0.0636 (95% CI 0.0634-0.0638), and a better calibration slope of observed versus predicted rate of 1.008 (95% CI 0.988-1.028) versus 1.036 (95% CI 1.015-1.056). The best model also had a significantly wider predictive range (25.3% versus 21.6%, p < 0.001) and was more accurate in stratifying AKI risk for patients. Evaluated on a more contemporary CathPCI cohort (July 1, 2015-March 31, 2017), the best model consistently achieved significantly better performance than the baseline model in AUC (0.785 versus 0.753), Brier score (0.0610 versus 0.0627), calibration slope (1.003 versus 1.062), and predictive range (29.4% versus 26.2%). The current study does not address implementation for risk calculation at the point of care, and potential challenges include the availability and accessibility of the predictors.Conclusions: Machine learning techniques and data-driven approaches resulted in improved prediction of AKI risk after PCI. The results support the potential of these techniques for improving risk prediction models and identification of patients who may benefit from risk-mitigation strategies. [ABSTRACT FROM AUTHOR]

Importance: Little contemporary information is available about comparative performance between Veterans Affairs (VA) and non-VA hospitals, particularly related to mortality and readmission rates, 2 important outcomes of care.Objective: To assess and compare mortality and readmission rates among men in VA and non-VA hospitals.Design, Setting, and Participants: Cross-sectional analysis involving male Medicare fee-for-service beneficiaries aged 65 years or older hospitalized between 2010 and 2013 in VA and non-VA acute care hospitals for acute myocardial infarction (AMI), heart failure (HF), or pneumonia using the Medicare Standard Analytic Files and Enrollment Database together with VA administrative claims data. To avoid confounding geographic effects with health care system effects, we studied VA and non-VA hospitals within the same metropolitan statistical area (MSA).Exposures: Hospitalization in a VA or non-VA hospital in MSAs that contained at least 1 VA and non-VA hospital.Main Outcomes and Measures: For each condition, 30-day risk-standardized mortality rates and risk-standardized readmission rates for VA and non-VA hospitals. Mean aggregated within-MSA differences in mortality and readmission rates were also assessed.Results: We studied 104 VA and 1513 non-VA hospitals, with each condition-outcome analysis cohort for VA and non-VA hospitals containing at least 7900 patients (men; ≥65 years), in 92 MSAs. Mortality rates were lower in VA hospitals than non-VA hospitals for AMI (13.5% vs 13.7%, P = .02; -0.2 percentage-point difference) and HF (11.4% vs 11.9%, P = .008; -0.5 percentage-point difference), but higher for pneumonia (12.6% vs 12.2%, P = .045; 0.4 percentage-point difference). In contrast, readmission rates were higher in VA hospitals for all 3 conditions (AMI, 17.8% vs 17.2%, 0.6 percentage-point difference; HF, 24.7% vs 23.5%, 1.2 percentage-point difference; pneumonia, 19.4% vs 18.7%, 0.7 percentage-point difference, all P < .001). In within-MSA comparisons, VA hospitals had lower mortality rates for AMI (percentage-point difference, -0.22; 95% CI, -0.40 to -0.04) and HF (-0.63; 95% CI, -0.95 to -0.31), and mortality rates for pneumonia were not significantly different (-0.03; 95% CI, -0.46 to 0.40); however, VA hospitals had higher readmission rates for AMI (0.62; 95% CI, 0.48 to 0.75), HF (0.97; 95% CI, 0.59 to 1.34), or pneumonia (0.66; 95% CI, 0.41 to 0.91).Conclusions and Relevance: Among older men with AMI, HF, or pneumonia, hospitalization at VA hospitals, compared with hospitalization at non-VA hospitals, was associated with lower 30-day risk-standardized all-cause mortality rates for AMI and HF, and higher 30-day risk-standardized all-cause readmission rates for all 3 conditions, both nationally and within similar geographic areas, although absolute differences between these outcomes at VA and non-VA hospitals were small. [ABSTRACT FROM AUTHOR]

IMPORTANCE. Introducing new medical devices into routine practice raises concerns because patients and outcomes may differ from those in randomized trials. OBJECTIVE. To update the previous report of 30-day outcomes and present 1-year outcomes following transcatheter aortic valve replacement (TAVR) in the United States. DESIGN, SETTING, AND PARTICIPANTS Data from the Society of Thoracic Surgeons/American College of Cardiology (STS/ACC) Transcatheter Valve Therapies Registry were linked with patient-specific Centers for Medicare & Medicaid Services (CMS) administrative claims data. At 299 US hospitals, 12182 patients linked with CMS data underwent TAVR procedures performed from November 2011 through June 30,2013, and the end of the follow-up period was June 30,2014. EXPOSURE. Transcatheter aortic valve replacement. MAIN OUTCOMES AND MEASURES. One-year outcomes including mortality, stroke, and rehospitalization were evaluated using multivariate modeling. RESULTS. The median age of patients was 84 years and 52% were women, with a median STS Predicted Risk of Operative Mortality (STS PROM) score of 7.1%. Following the TAVR procedure, 59.8% were discharged to home and the 30-day mortality was 7.0% (95% Cl, 6.5%-7.4%) (n = 847 deaths). In the first year after TAVR, patients were alive and out of the hospital for a median of 353 days (interquartile range, 312-359 days); 24.4% (n = 2074) of survivors were rehospitalized once and 12.5% (n = 1525) were rehospitalized twice. By 1 year, the overall mortality rate was 23.7% (95% Cl, 22.8%-24.5%) (n = 2450 deaths), the stroke rate was 4.1% (95% Cl, 3.7%-4.5%) (n = 455 stroke events), and the rate of the composite outcome of mortality and stroke was 26.0% (25.1%-26.8%) (n = 2719 events). Characteristics significantly associated with 1-year mortality included advanced age (hazard ratio [HR] for a 95 vs <75 years, 1.61 [95% Cl, 1.24-2.09]; HR for 85-94 years vs <75 years, 1.35 [95% Cl, 1.18-1.55]; and HR for 75-84 years vs <75 years, 1.23 [95% Cl, 1.08-1.41]), male sex (HR, 1.21; 95% Cl, 1.12-1.31), end-stage renal disease (HR, 1.66; 95% Cl, 1.41-1.95), severe chronic obstructive pulmonary disease (HR, 1.39; 95% Cl, 1.25-1.55), nontransfemoral access (HR, 1.37; 95% Cl, 1.27-1.48), STS PROM score greater than 15% vs less than 8% (HR, 1.82; 95% Cl, 1.60-2.06), and preoperative atrial fibrillation/flutter (HR, 1.37; 95% Cl, 1.27-1.48). Compared with men, women had a higher risk of stroke (HR, 1.40; 95% Cl, 1.15-1.71). CONCLUSIONS. AND RELEVANCE Among patients undergoing TAVR in US clinical practice, at 1-year follow-up, overall mortality was 23.7%, the stroke rate was 4.1%, and the rate of the composite outcome of death and stroke was 26.0%. These findings should be helpful in discussions with patients undergoing TAVR. [ABSTRACT FROM AUTHOR]

IMPORTANCE Little is known about cardiac adverse events among patients with nonobstructive coronary artery disease (CAD). OBJECTIVE To compare myocardial infarction (Ml) and mortality rates between patients with nonobstructive CAD, obstructive CAD, and no apparent CAD in a national cohort. DESIGN. SETTING, AND PARTICIPANTS Retrospective cohort study of all US veterans undergoing elective coronary angiography for CAD between October 2007 and September 2012 in the Veterans Affairs health care system. Patients with prior CAD events were excluded. EXPOSURES Angiographic CAD extent, defined by degree (no apparent CAD: no stenosis >20%; nonobstructive CAD: ≥1 stenosis ≥ 20% but no stenosis ≥ 70%; obstructive CAD: any stenosis ≥ 70% or left main [LM] stenosis ≥ 50%) and distribution (1,2, or 3 vessel). MAIN OUTCOMES AND MEASURES The primary outcome was 1-year hospitalization for nonfatal Ml after the index angiography. Secondary outcomes included 1-year all-cause mortality and combined 1-year Ml and mortality. RESULTS Among 37 674 patients, 8384 patients (22.3%) had nonobstructive CAD and 20 899 patients (55.4%) had obstructive CAD. Within 1 year, 845 patients died and 385 were rehospitalized for Ml. Among patients with no apparent CAD, the 1-year Ml rate was 0.11% (n = 8,95% Cl, 0.10%-0.20%) and increased progressively by 1-vessel nonobstructive CAD, 0.24% (n = 10, 95% Cl, 0.10%-0.40%); 2-vessel nonobstructive CAD, 0.56% (n = 13, 95% Cl, 0.30%-1.00%); 3-vessel nonobstructive CAD, 0.59% (n = 6,95% Cl, 0.30%-1.30%); 1-vessel obstructive CAD, 1.18% (n = 101, 95% Cl, 1.00%-1.40%); 2-vessel obstructive CAD, 2.18% (n = 110, 95% Cl, 1.80%-2.60%); and 3-vessel or LM obstructive CAD, 2.47% (n = 137, 95% Cl, 2.10%-2.90%). After adjustment, 1-year Ml rates increased with increasing CAD extent. Relative to patients with no apparent CAD, patients with 1-vessel nonobstructive CAD had a hazard ratio (HR) for 1-year Ml of 2.0 (95% Cl, 0.8-5.1); 2-vessel nonobstructive HR, 4.6 (95% Cl, 2.0-10.5); 3-vessel nonobstructive HR, 4.5 (95% Cl, 1.6-12.5); 1-vessel obstructive HR, 9.0 (95% Cl, 4.2-19.0); 2-vessel obstructive HR, 16.5 (95% Cl, 8.1-33.7); and 3-vessel or LM obstructive HR, 19.5 (95% Cl, 9.9-38.2). One-year mortality rates were associated with increasing CAD extent, ranging from 1.38% among patients without apparent CAD to 4.30% with 3-vessel or LM obstructive CAD. After risk adjustment, there was no significant association between 1- or 2-vessel nonobstructive CAD and mortality, but there were significant associations with mortality for 3-vessel nonobstructive CAD (HR, 1.6; 95% Cl, 1.1-2.5), 1-vessel obstructive CAD (HR, 1.9; 95% Cl, 1.4-2.6), 2-vessel obstructive CAD (HR, 2.8; 95% Cl, 2.1-3.7), and 3-vessel or LM obstructive CAD (HR, 3.4; 95% Cl, 2.6-4.4). Similar associations were noted with the combined outcome. CONCLUSIONS AND RELEVANCE In this cohort of patients undergoing elective coronary angiography, nonobstructive CAD, compared with no apparent CAD, was associated with a significantly greater 1-year risk o f Ml and all-cause mortality. These findings suggest clinical importance of nonobstructive CAD and warrant further investigation of interventions to improve outcomes among these patients. [ABSTRACT FROM AUTHOR]