Shah, Nilay D.

Introduction: For patients with atrial fibrillation (AF), novel oral anticoagulants (NOACs) provide effective alternatives to warfarin. However, a better understanding is needed of how the introduction of NOAC therapy has improved use of any oral anticoagulation (OAC) for patients with AF. Methods: Within the NCDR PINNACLE registry, we identified 447,770 patients comprising 1,428,660 outpatient encounters with non-valvular AF and CHADS-VASc >1 between April 1, 2008 and September 30, 2014. We examined use of any OAC, any NOAC, and of dabigatran, rivaroxaban, and apixaban individually over time. Multivariable logistic regression was performed to identify factors associated with any OAC and NOAC use at the end of the study period, including CHADS-VASc score, age, weight, CAD, PAD, dyslipidemia, stroke/TIA, heart failure, gender, hypertension, and diabetes. Results: Use of any OAC increased from 52.1 to 61.2% during the study period among patients without a documented contraindication (p for trend <0.01) (Figure). Although warfarin use decreased from 52.1% to 36.8% (p for trend <0.01), use of NOACs increased from 0% to 24.4% (p for trend <0.01). Rivaroxaban was the most frequently prescribed NOAC at the end of the study period (12.1%), followed by dabigatran (6.6%), and apixaban (5.7%). Patient factors associated with use of OAC and NOACs are presented in the Table. Conclusions: In a large U.S. outpatient registry, introduction of NOACs into clinical practice has improved the use of any OAC for AF patients at high risk for stroke. However, more than 1 in 3 high risk patients without a documented contraindication were not treated with any OAC.

Background: While implantation of left ventricular assist devices (LVAD) improves survival and quality of life in patients with advanced heart failure, very little is known about healthcare resource utilization, including hospital readmissions, post-LVAD. Methods: Administrative claims data from a large, national US health plan were used to identify privately insured and Medicare Advantage patients that received an LVAD (ICD-9-CM code 37.66) and survived to hospital discharge from January 1, 2006 through 31 August 31, 2013, and had at least six months coverage pre-LVAD. The individuals covered by this health plan, about 88 million in years 2003 to 2013, are geographically dispersed across the United States, with greatest representation in the South and Midwest census regions. Reasons for subsequent readmissions were categorized according to the AHRQ Clinical Classifications Software and risk factors for readmission analyzed using multiple failure Cox regression analysis. Patients were censored at the time of heart transplantation or end of coverage through November 30, 2013. Results: In total, 857 patients received an LVAD, of whom 693 (81%) survived to hospital discharge without undergoing heart transplantation (mean age 56 years, 77% male). After a median follow-up of 0.69 (interquartile range [IQR]: 0.31, 1.50) years, 1,659 readmissions occurred among 509 patients. Over this period, patients spent a median of 10.5 days (IQR: 0 -40 days) per followup year hospitalized. The number of readmissions per patient varied widely (0 to 20 per person), with 184 (26.6%) patients experiencing no readmissions, while 112 (16.2%) had 5 or more readmissions during follow-up. The most common reasons for readmission were device complications (291 readmissions in 155 patients), heart failure (246 readmissions in 135 patients), gastrointestinal bleeding (90 readmissions in 52 patients), and arrhythmia (88 readmissions in 55 patients). Readmission risk was higher in patients who had longer initial length of stay and more comorbid conditions. Discussion: There is tremendous variability in the readmission burden experienced post-LVAD, with some patients experiencing no readmissions while others are repeatedly hospitalized. LVAD therapy introduces a unique set of problems that often prompt readmission including device complications and gastrointestinal bleeding.

Research ObjectiveThroughout the spring of 2020, stay‐at‐home orders were imposed to curb the spread of the coronavirus disease 2019 (COVID‐19) in the United States. However, there is limited data and evidence on their effectiveness, especially in rural areas as compared to urban areas. Therefore, we examined the association between stay‐at‐home order implementation and the incidence of COVID‐19 cases in rural and urban counties.Study DesignWe conducted an interrupted time series analysis using a mixed effects zero‐inflated Poisson model with random intercept by county and standardized by population, which examined the association between stay‐at‐home orders and 14‐day lagged county‐level counts of daily new COVID‐19 cases in rural vs. urban counties between 1/22/2020 and 6/10/2020. To further bolster our findings, the association between stay‐at‐home orders and mobility in rural vs. urban counties was assessed using Google Community Mobility Reports.Population StudiedWe examined the COVID‐19 daily incident and stay‐at‐home orders for all 3142 United States counties. Each county was categorized as urban or rural using the 2013 county‐level Rural–Urban Continuum Codes per federal standards. There are 1976 rural and 1166 urban counties in the United States.Principal FindingsAs of June 10, 2020, there were 1,786,886 cases of COVID‐19 in the U.S., of which 9.0% (N = 161,452) were in rural counties. During the study period, 1854 (93.8%) rural counties and 1075 (92.2%) urban counties were covered by a stay‐at‐home order. Stay‐at‐home orders were implemented later (median March 30 vs. March 28) and were shorter (median 35 vs. 54 days) in rural than urban counties. Indoor mobility was, on average, 2.6–6.9% higher in rural than urban counties during and after stay‐at‐home orders. Compared to the pre‐stay‐at‐home baseline period, the number of new COVID‐19 cases increased under stay‐at‐home by IRR 1.60 (95% CI, 1.57–1.64) in rural and 1.36 (95% CI, 1.30–1.42) in urban counties. For each day under stay‐at‐home orders, the number of new cases changed by a factor of 0.982 (95% CI, 0.981–0.982) in rural and 0.952 (95% CI, 0.951–0.953) in urban counties. Each day after stay‐at‐home orders expired, the number of new cases changed by a factor of 0.995 (95% CI, 0.994–0.995) in rural and 0.997 (95% CI, 0.995–0.999) in urban counties.ConclusionsStay‐at‐home orders decreased mobility and slowed the spread of COVID‐19 less effectively in rural than urban counties.Implications for Policy or PracticeAs states are considering implementing a new round of stay‐at‐home orders, our findings reveal several ways to improve the implementation, enforcement, and adherence. First, they should be implemented earlier and maintained longer for optimal effectiveness. There was marked heterogeneity in the timing and duration of stay‐at‐home orders and our data reinforces the need for multi‐jurisdictional, ideally federal, infection control mandates. Any new stay‐at‐home orders should be gradually implemented to avoid the pre‐stay‐at‐home surge of mobility and subsequent spike in COVID‐19 cases. To better encourage and facilitate compliance, leaders at all levels need to use scientific evidence to advocate for the importance of stay‐at‐home orders, set personal examples, and develop employment, housing, educational, and healthcare assistance for the most vulnerable.Primary Funding SourceNational Institutes of Health.

Research ObjectiveAn artificial intelligence (AI) algorithm based on ECG was integrated into clinical workflows to identify patients with a high likelihood of low ejection (EF), a condition that is underdiagnosed but treatable. The trial aimed to assess whether the AI‐powered clinical decision support enabled early diagnosis of low EF.Study DesignA pragmatic, cluster randomized controlled trial (RCT) was conducted in 45 primary care practices, including an academic medical center as well as community and rural clinics across Minnesota and Wisconsin. A total of 120 primary care teams, including 358 clinicians, were randomized to either intervention (access to AI results with positive results indicating a high likelihood of low EF; 181 clinicians) or control (usual care; 177 clinicians). The primary endpoint was a new diagnosis of EF≤50% within 90 days. A positive deviance qualitative study and a survey were used to assess implementation strategies.Population StudiedIn total, 22,641 adult patients (N = 11,573 intervention; N = 11,068 control) with ECG performed as part of routine care between 8/5/2019 and 3/31/2020 were included. Patients were excluded if they had known EF≤50% or a history of documented heart failure (HF) identified by clinical notes abstracted by natural language processing.Principal FindingsThe mean age of patients was 60.5 ± 17.6 years; 53.9% were women. Positive AI results were found in 6.0% of patients in both arms. Clinicians in the intervention group obtained more echocardiograms for patients with positive results (38.1% control vs. 49.6% intervention, p < 0.001), but in the overall cohort, the echocardiogram utilization was similar (18.2% vs. 19.2%, p = 0.17). The intervention increased the diagnosis of low EF in the overall cohort (1.6% vs. 2.1%, odds ratio [OR] 1.32 [1.01–1.61], p = 0.007) and among patients with positive results (14.5% vs. 19.5% OR 1.43 [1.08–1.91], p = 0.01). Results using different thresholds for low EF were consistent with the primary result. Among patients with positive results, the intervention increased echocardiography more significantly in women vs. men (p for interaction 0.02), in patients without prior echocardiogram vs. those with prior echocardiogram (p for interaction 0.04), and in patients whose ECGs were ordered in outpatient clinics (p for interaction 0.02).ConclusionsAn AI algorithm run on existing ECGs enabled the early diagnosis of low EF in a large cohort of patients managed in routine primary care practices. Because ECG is a low‐cost test frequently performed for a variety of purposes, the algorithm could improve early diagnosis and treatment of a condition that is often asymptomatic but has effective treatments, and thus, reduce the disease burden in broad populations.Implications for Policy or PracticeThe current study is one of the first RCTs that demonstrated the value of AI in routine practice. Low EF confers an increased risk of HF progression and mortality. Although there are effective therapies, low EF is undiagnosed, leading to missed opportunities for treatment. EF can be readily determined by echocardiography, but the imagining test is too resource‐intensive to screen unselected populations. The AI applied to the existing ECGs could identify high‐risk patients for further assessment.Primary Funding SourceMayo Clinic.

AbstractObjectiveTo describe physicians’ variation in de‐adopting concurrent statin and fibrate therapy for type 2 diabetic patients following a reversal in clinical evidence.Data SourcesWe analyzed 2007‐2015 claims data from OptumLabs® Data Warehouse, a longitudinal, real‐world data asset with de‐identified administrative claims and electronic health record data.Study DesignWe modeled fibrate use among Medicare Advantage and commercially insured type 2 diabetic statin users before and after the publication of the ACCORD lipid trial, which found statins and fibrates were no more effective than statins alone in reducing cardiovascular events among type 2 diabetic patients. We modeled fibrate use trends with physician random effects and physician characteristics such as age and specialty.Data ExtractionWe identified patient‐year‐quarters with one year of continuous insurance enrollment, type 2 diabetes diagnoses, and fibrate use. We designated the physician most responsible for patients’ diabetes care based on evaluation and management visits and prescriptions of glucose‐lowering drugs.Principal FindingsFibrate use increased by 0.12 percentage points per quarter among commercial patients (95% CI, 0.10 to 0.14) and 0.17 percentage points per quarter among Medicare Advantage patients (95% CI, 0.13 to 0.20) before the trial and then decreased by 0.16 percentage points per quarter among commercial patients (95% CI, −0.18 to −0.15) and 0.05 percentage points per quarter among Medicare Advantage patients (95% CI, −0.06 to −0.03) after the trial. However, 45% of physicians treating commercial patients and 48% of physicians treating Medicare Advantage patients had positive trends in prescribing following the trial. Physicians’ characteristics did not explain their variation (pseudo R2 = 0.000).ConclusionOn average, physicians decreased fibrate prescribing following the ACCORD lipid trial. However, many physicians increased prescribing following the trial. Observable physician characteristics did not explain variations in prescribing. Future research should examine whether physicians vary similarly in other de‐adoption settings.

Background: This paper assesses obesity- and smoking-related incremental healthcare costs for the employees and dependents of a large U.S. employer. Objectives: Unlike previous studies, this study evaluates the distributional effects of obesity and smoking on healthcare cost distribution using a recently developed econometric framework: the unconditional quantile regression (UQR). Methods: Results were compared with the traditional conditional quantile regression (CQR), and the generalized linear modeling (GLM) framework that is commonly used for modeling healthcare cost. Results: The study found strong evidence of association of healthcare costs with obesity and smoking. More importantly, the study found that these effects are substantially higher in the upper quantiles of the healthcare cost distribution than in the lower quantiles. The insights on the heterogeneity of impacts of obesity and smoking on healthcare costs would not have been captured by traditional mean-based approaches. The study also found that UQR impact estimates were substantially different from CQR impact estimates in the upper quantiles of the cost distribution. Conclusions: These results suggest the potential role that smoking cessation and weight management programs can play in arresting the growth in healthcare costs. Specifically, given the finding that obesity and smoking have markedly higher impacts on high-cost patients, such programs appear to have significant cost saving potential if targeted toward high-cost patients.

Introduction There is a considerable implementation gap in managing early stage chronic kidney disease (CKD) in primary care despite the high prevalence and risk for increased morbidity and mortality associated with CKD. This systematic review aims to synthesise the evidence of efficacy of implementation interventions aimed at primary care practitioners (PCPs) to improve CKD identification and management. We further aim to describe the interventions’ behavioural change components. Methods and analysis We will conduct a systematic review of studies from 2000 to October 2017 that evaluate implementation interventions targeting PCPs and which include at least one clinically meaningful CKD outcome. We will search several electronic data bases and conduct reference mining of related systematic reviews and publications. An interdisciplinary team will independently and in duplicate, screen publications, extract data and assess the risk of bias. Clinical outcomes will include all clinically meaningful medical management outcomes relevant to CKD management in primary care such as blood pressure, chronic heart disease and diabetes target achievements. Quantitative evidence synthesis will be performed, where possible. Planned subgroup analyses include by (1) study design, (2) length of follow-up, (3) type of intervention, (4) type of implementation strategy, (5) whether a behavioural or implementation theory was used to guide study, (6) baseline CKD severity, (7) patient minority status, (8) study location and (9) academic setting or not. Ethics and dissemination Approval by research ethics board is not required since the review will only include published and publicly accessible data. Review findings will inform a future trial of an intervention to promote uptake of CKD diagnosis and treatment guidelines in our primary care setting and the development of complementary tools to support its successful adoption and implementation. We will publish our findings in a peer-reviewed journal and develop accessible summaries of the results. PROSPERO registration number CRD42018102441.