Publications by authors named "David D McManus"

242 Publications

Accelerometer-derived physical activity and risk of atrial fibrillation.

Eur Heart J 2021 May 25. Epub 2021 May 25.

Cardiovascular Disease Initiative, Broad Institute of Harvard University and the Massachusetts Institute of Technology, Cambridge, MA, USA.

Aims: Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity.

Methods And Results: We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75-0.89; incidence 3.5/1000 person-years, 95% CI 3.3-3.8 vs. 6.5/1000 person-years, 95% CI 6.1-6.8] and stroke (HR 0.76, 95% CI 0.64-0.90; incidence 1.0/1000 person-years, 95% CI 0.9-1.1 vs. 1.8/1000 person-years, 95% CI 1.6-2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r = 0.16, 95% CI 0.16-0.17). Self-reported activity was not associated with incident AF or stroke.

Conclusions: Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds.
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http://dx.doi.org/10.1093/eurheartj/ehab250DOI Listing
May 2021

Prevalence of Frailty and Associations with Oral Anticoagulant Prescribing in Atrial Fibrillation.

J Gen Intern Med 2021 May 4. Epub 2021 May 4.

Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.

Background: Frailty is often cited as a factor influencing oral anticoagulation (OAC) prescription in patients with non-valvular atrial fibrillation (NVAF). We sought to determine the prevalence of frailty and its association with OAC prescription in older veterans with NVAF.

Methods: We used ICD-9 codes in Veterans Affairs (VA) records and Medicare claims data to identify patients with NVAF and CHADSVASC ≥2 receiving care between February 2010 and September 2015. We examined rates of OAC prescription, further stratified by direct oral anticoagulant (DOAC) or vitamin K antagonist (VKA). Participants were characterized into 3 categories: non-frail, pre-frail, and frail based on a validated 30-item EHR-derived frailty index. We examined relations between frailty and OAC receipt; and frailty and type of OAC prescribed in regression models adjusted for factors related to OAC prescription.

Results: Of 308,664 veterans with NVAF and a CHADSVASC score ≥2, 121,839 (39%) were prescribed OAC (73% VKA). The mean age was 77.7 (9.6) years; CHADSVASC and ATRIA scores were 4.6 (1.6) and 5.0 (2.9) respectively. Approximately a third (38%) were frail, another third (32%) were pre-frail, and the remainder were not frail. Veterans prescribed OAC were younger, had higher bleeding risk, and were less likely to be frail than participants not receiving OAC (all p's<0.001). After adjustment for factors associated with OAC use, pre-frail (OR: 0.89, 95% CI: 0.87-0.91) and frail (OR: 0.66, 95% CI: 0.64-0.68) veterans were significantly less likely to be prescribed OAC than non-frail veterans. Of those prescribed OAC, pre-frail (OR:1.27, 95% CI: 1.22-1.31) and frail (OR: 1.75, 95% CI: 1.67-1.83) veterans were significantly more likely than non-frail veterans to be prescribed a DOAC than a VKA.

Conclusions: There are high rates of frailty among older veterans with NVAF. Frailty using an EHR-derived index is associated with decreased OAC prescription.
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http://dx.doi.org/10.1007/s11606-021-06834-1DOI Listing
May 2021

Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study.

Am Heart J 2021 Apr 15;238:16-26. Epub 2021 Apr 15.

Harvard Medical School, Boston, MA; Biostatistics Center, Massachusetts General Hospital, Boston, MA.

Background: Early detection of atrial fibrillation or flutter (AF) may enable prevention of downstream morbidity. Consumer wrist-worn wearable technology is capable of detecting AF by identifying irregular pulse waveforms using photoplethysmography (PPG). The validity of PPG-based software algorithms for AF detection requires prospective assessment.

Methods: The Fitbit Heart Study (NCT04380415) is a single-arm remote clinical trial examining the validity of a novel PPG-based software algorithm for detecting AF. The proprietary Fitbit algorithm examines pulse waveform intervals during analyzable periods in which participants are sufficiently stationary. Fitbit consumers with compatible wrist-worn trackers or smartwatches were invited to participate. Enrollment began May 6, 2020 and as of October 1, 2020, 455,699 participants enrolled. Participants in whom an irregular heart rhythm was detected were invited to attend a telehealth visit and eligible participants were then mailed a one-week single lead electrocardiographic (ECG) patch monitor. The primary study objective is to assess the positive predictive value of an irregular heart rhythm detection for AF during the ECG patch monitor period. Additional objectives will examine the validity of irregular pulse tachograms during subsequent heart rhythm detections, self-reported AF diagnoses and treatments, and relations between irregular heart rhythm detections and AF episode duration and time spent in AF.

Conclusions: The Fitbit Heart Study is a large-scale remote clinical trial comprising a unique software algorithm for detection of AF. The study results will provide critical insights into the use of consumer wearable technology for AF detection, and for characterizing the nature of AF episodes detected using consumer-based PPG technology.
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http://dx.doi.org/10.1016/j.ahj.2021.04.003DOI Listing
April 2021

Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection.

medRxiv 2021 Mar 22. Epub 2021 Mar 22.

What Is Already Known About This Topic?: Diagnostic tests and sample types for SARS-CoV-2 vary in sensitivity across the infection period.

What Is Added By This Report?: We show that both RTqPCR (from nasal swab and saliva) and the Quidel SARS Sofia FIA rapid antigen tests peak in sensitivity during the period in which live virus can be detected in nasal swabs, but that the sensitivity of RTqPCR tests rises more rapidly in the pre-infectious period. We also use empirical data to estimate the sensitivities of RTqPCR and antigen tests as a function of testing frequency.

What Are The Implications For Public Health Practice?: RTqPCR tests will be more effective than rapid antigen tests at identifying infected individuals prior to or early during the infectious period and thus for minimizing forward transmission (provided results reporting is timely). All modalities, including rapid antigen tests, showed >94% sensitivity to detect infection if used at least twice per week. Regular surveillance/screening using rapid antigen tests 2-3 times per week can be an effective strategy to achieve high sensitivity (>95%) for identifying infected individuals.
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http://dx.doi.org/10.1101/2021.03.19.21253964DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8010751PMC
March 2021

Prognostic value of geriatric conditions for death and bleeding in older patients with atrial fibrillation.

Int J Cardiol Heart Vasc 2021 Apr 4;33:100739. Epub 2021 Mar 4.

Cardiology Division, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA.

Background: Geriatric conditions, such as frailty and cognitive impairment, are prevalent in older patients with atrial fibrillation (AF). We examined the prognostic value of geriatric conditions for predicting 1-year mortality and bleeding events in these patients.

Methods: SAGE (Systematic Assessment of Geriatric Elements)-AF study is a multicenter cohort study which enrolled individuals (mean age 75 years, 48% women, 86% taking oral anticoagulation) 65 years and older with AF and CHADS -VASc score of 2 or higher from clinics in Massachusetts and Georgia, USA between 2016 and 2018. A six-component geriatric assessment included validated measures of frailty, cognitive function, social support, depressive symptoms, vision, and hearing was performed at baseline. Study endpoints included all-cause mortality and clinically relevant bleeding.

Results: At 1 year, 1,097 (96.5%) individuals attended the follow up visit, 44 (3.9%) had died, and 56 (5.1%) had clinically relevant bleeding. After adjustment for demographic and clinical factors, social isolation (odds ratio [OR] 1.69, 95% confidence interval [CI]: 1.01-2.84), depression (OR 1.94, 95% CI: 1.28-2.95) and frailty (OR 2.55, 95% CI: 1.55-4.19) were significantly associated with the composite endpoint of death or clinically relevant bleeding. After multivariable adjustment, depression (OR 1.79, 95% CI 1.09-2.93) and frailty (OR 2.83, 95% CI 1.55-5.17) were significantly associated with clinically relevant bleeding.

Conclusions:  Social isolation, depression, and frailty were prognostic of dying or experiencing clinically relevant bleeding during the coming year in older men and women with AF. Assessing geriatric impairments merits consideration in the care of these patients.
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http://dx.doi.org/10.1016/j.ijcha.2021.100739DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935705PMC
April 2021

Evaluation of a Diabetes Remote Monitoring Program Facilitated by Connected Glucose Meters for Patients With Poorly Controlled Type 2 Diabetes: Randomized Crossover Trial.

JMIR Diabetes 2021 Mar 11;6(1):e25574. Epub 2021 Mar 11.

Division of Diabetes, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, United States.

Background: Patients with poorly controlled type 2 diabetes (T2D) experience increased morbidity, increased mortality, and higher cost of care. Self-monitoring of blood glucose (SMBG) is a critical component of diabetes self-management with established diabetes outcome benefits. Technological advancements in blood glucose meters, including cellular-connected devices that automatically upload SMBG data to secure cloud-based databases, allow for improved sharing and monitoring of SMBG data. Real-time monitoring of SMBG data presents opportunities to provide timely support to patients that is responsive to abnormal SMBG recordings. Such diabetes remote monitoring programs can provide patients with poorly controlled T2D additional support needed to improve critical outcomes.

Objective: To evaluate 6 months of a diabetes remote monitoring program facilitated by cellular-connected glucose meter, access to a diabetes coach, and support responsive to abnormal blood glucose recordings greater than 400 mg/dL or below 50 mg/dL in adults with poorly controlled T2D.

Methods: Patients (N=119) receiving care at a diabetes center of excellence participated in a two-arm, 12-month randomized crossover study. The intervention included a cellular-connected glucose meter and phone-based diabetes coaching provided by Livongo Health. The coach answered questions, assisted in goal setting, and provided support in response to abnormal glucose levels. One group received the intervention for 6 months before returning to usual care (IV/UC). The other group received usual care before enrolling in the intervention (UC/IV) for 6 months. Change in hemoglobin A (HbA) was the primary outcome, and change in treatment satisfaction was the secondary outcome.

Results: Improvements in mean HbA were seen in both groups during the first 6 months (IV/UC -1.1%, SD 1.5 vs UC/IV -0.8%, SD 1.5; P<.001). After crossover, there was no significant change in HbA in IV/UC (mean HbA change +0.2, SD 1.7, P=.41); however, those in UC/IV showed further improvement (mean HbA change -0.4%, SD 1.0, P=.008). A mixed-effects model showed no significant treatment effect (IV vs UC) over 12 months (P=.06). However, participants with higher baseline HbA and those in the first time period experienced greater improvements in HbA. Both groups reported similar improvements in treatment satisfaction throughout the study.

Conclusions: Patients enrolled in the diabetes remote monitoring program intervention experienced improvements in HbA and treatment satisfaction similar to usual care at a specialty diabetes center. Future studies on diabetes remote monitoring programs should incorporate scheduled coaching components and involve family members and caregivers.

Trial Registration: ClinicalTrials.gov NCT03124043; https://clinicaltrials.gov/ct2/show/NCT03124043.
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http://dx.doi.org/10.2196/25574DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7995078PMC
March 2021

Dietary Habits and Medications to Control Hypertension among Women of Child-bearing Age in the United States from 2001-2016.

Am J Hypertens 2021 Mar 8. Epub 2021 Mar 8.

Division of General Medicine, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts.

Background: Hypertension in pregnancy is a leading cause of maternal morbidity and mortality in the United States. Although the Dietary Approaches to Stop Hypertension (DASH) diet is recommended for all adults with hypertension, rates of DASH adherence and anti-hypertensive medications use in women of child-bearing age is unknown.

Objectives: To determine DASH adherence and anti-hypertensive medication use in women of child-bearing age.

Methods: In the National Health and Nutrition Examination Surveys from 2001-2016, we estimated DASH adherence among women of child-bearing age (20-50 years). We derived a DASH score (0-9) based on 9 nutrients, with DASH adherence defined as DASH score ≥4.5. Hypertension was defined by blood pressure (BP) ≥130/80 mm Hg or anti-hypertensive medication use. DASH scores were compared across BP categories and anti-hypertensive medication use was categorized.

Results: Of the 7782 women, the mean age (SE) was 32.8 (0.2) years, 21.4% were non-Hispanic Black, and 20.3% had hypertension. The mean DASH score was 2.11 (0.06) for women with self-reported hypertension and 2.40 (0.03) for women with normal BP (P<0.001). DASH adherence was prevalent in 6.5% of women with self-reported hypertension compared to 10.1% of women with normal BP (P<0.05). Self-reported hypertension is predominantly managed with medications (84.8%), while DASH-adherence has not improved in these women from 2001-2016. Moreover, 39.5% of US women of child-bearing age are taking medications contraindicated in pregnancy.

Conclusions: Given the benefits of optimized BP during pregnancy, this study highlights the critical need to improve DASH adherence and guide prescribing among women of child-bearing age.
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http://dx.doi.org/10.1093/ajh/hpab041DOI Listing
March 2021

Comparative Effectiveness of Heart Rate Control Medications for the Treatment of Sepsis-Associated Atrial Fibrillation.

Chest 2021 Apr 24;159(4):1452-1459. Epub 2020 Oct 24.

Pulmonary Center, Department of Medicine, Boston University School of Medicine, Boston University School of Public Health, Boston, MA.

Background: Atrial fibrillation (AF) with rapid ventricular response frequently complicates the management of critically ill patients with sepsis and may necessitate the initiation of medication to avoid hemodynamic compromise. However, the optimal medication to achieve rate control for AF with rapid ventricular response in sepsis is unclear.

Research Question: What is the comparative effectiveness of frequently used AF medications (β-blockers, calcium channel blockers, amiodarone, and digoxin) on heart rate (HR) reduction among critically ill patients with sepsis and AF with rapid ventricular response?

Study Design And Methods: We conducted a multicenter retrospective cohort study among patients with sepsis and AF with rapid ventricular response (HR > 110 beats/min). We compared the rate control effectiveness of β-blockers to calcium channel blockers, amiodarone, and digoxin using multivariate-adjusted, time-varying exposures in competing risk models (for death and addition of another AF medication), adjusting for fixed and time-varying confounders.

Results: Among 666 included patients, 50.6% initially received amiodarone, 10.1% received a β-blocker, 33.8% received a calcium channel blocker, and 5.6% received digoxin. The adjusted hazard ratio for HR of < 110 beats/min by 1 h was 0.50 (95% CI, 0.34-0.74) for amiodarone vs β-blocker, 0.37 (95% CI, 0.18-0.77) for digoxin vs β-blocker, and 0.75 (95% CI, 0.51-1.11) for calcium channel blocker vs β-blocker. By 6 h, the adjusted hazard ratio for HR < 110 beats/min was 0.67 (95% CI, 0.47-0.97) for amiodarone vs β-blocker, 0.60 (95% CI, 0.36-1.004) for digoxin vs β-blocker, and 1.03 (95% CI, 0.71-1.49) for calcium channel blocker vs β-blocker.

Interpretation: In a large cohort of patients with sepsis and AF with rapid ventricular response, a β-blocker treatment strategy was associated with improved HR control at 1 h, but generally similar HR control at 6 h compared with amiodarone, calcium channel blocker, or digoxin.
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http://dx.doi.org/10.1016/j.chest.2020.10.049DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8039002PMC
April 2021

Development and Validation of an Automated Algorithm to Detect Atrial Fibrillation Within Stored Intensive Care Unit Continuous Electrocardiographic Data: Observational Study.

JMIR Cardio 2021 Feb 15;5(1):e18840. Epub 2021 Feb 15.

University of Massachusetts Medical School, Worcester, MA, United States.

Background: Atrial fibrillation (AF) is the most common arrhythmia during critical illness, representing a sepsis-defining cardiac dysfunction associated with adverse outcomes. Large burdens of premature beats and noisy signal during sepsis may pose unique challenges to automated AF detection.

Objective: The objective of this study is to develop and validate an automated algorithm to accurately identify AF within electronic health care data among critically ill patients with sepsis.

Methods: This is a retrospective cohort study of patients hospitalized with sepsis identified from Medical Information Mart for Intensive Care (MIMIC III) electronic health data with linked electrocardiographic (ECG) telemetry waveforms. Within 3 separate cohorts of 50 patients, we iteratively developed and validated an automated algorithm that identifies ECG signals, removes noise, and identifies irregular rhythm and premature beats in order to identify AF. We compared the automated algorithm to current methods of AF identification in large databases, including ICD-9 (International Classification of Diseases, 9th edition) codes and hourly nurse annotation of heart rhythm. Methods of AF identification were tested against gold-standard manual ECG review.

Results: AF detection algorithms that did not differentiate AF from premature atrial and ventricular beats performed modestly, with 76% (95% CI 61%-87%) accuracy. Performance improved (P=.02) with the addition of premature beat detection (validation set accuracy: 94% [95% CI 83%-99%]). Median time between automated and manual detection of AF onset was 30 minutes (25th-75th percentile 0-208 minutes). The accuracy of ICD-9 codes (68%; P=.002 vs automated algorithm) and nurse charting (80%; P=.02 vs algorithm) was lower than that of the automated algorithm.

Conclusions: An automated algorithm using telemetry ECG data can feasibly and accurately detect AF among critically ill patients with sepsis, and represents an improvement in AF detection within large databases.
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http://dx.doi.org/10.2196/18840DOI Listing
February 2021

Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program.

Nature 2021 02 10;590(7845):290-299. Epub 2021 Feb 10.

The Broad Institute of MIT and Harvard, Cambridge, MA, USA.

The Trans-Omics for Precision Medicine (TOPMed) programme seeks to elucidate the genetic architecture and biology of heart, lung, blood and sleep disorders, with the ultimate goal of improving diagnosis, treatment and prevention of these diseases. The initial phases of the programme focused on whole-genome sequencing of individuals with rich phenotypic data and diverse backgrounds. Here we describe the TOPMed goals and design as well as the available resources and early insights obtained from the sequence data. The resources include a variant browser, a genotype imputation server, and genomic and phenotypic data that are available through dbGaP (Database of Genotypes and Phenotypes). In the first 53,831 TOPMed samples, we detected more than 400 million single-nucleotide and insertion or deletion variants after alignment with the reference genome. Additional previously undescribed variants were detected through assembly of unmapped reads and customized analysis in highly variable loci. Among the more than 400 million detected variants, 97% have frequencies of less than 1% and 46% are singletons that are present in only one individual (53% among unrelated individuals). These rare variants provide insights into mutational processes and recent human evolutionary history. The extensive catalogue of genetic variation in TOPMed studies provides unique opportunities for exploring the contributions of rare and noncoding sequence variants to phenotypic variation. Furthermore, combining TOPMed haplotypes with modern imputation methods improves the power and reach of genome-wide association studies to include variants down to a frequency of approximately 0.01%.
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http://dx.doi.org/10.1038/s41586-021-03205-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7875770PMC
February 2021

Research Priorities in Atrial Fibrillation Screening: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop.

Circulation 2021 Jan 25;143(4):372-388. Epub 2021 Jan 25.

Division of Cardiology and Duke Clinical Research Institute, Duke University Medical Center, Durham, NC (R.D.L., J.P.P., S.M.A.-K.).

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute's virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.
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http://dx.doi.org/10.1161/CIRCULATIONAHA.120.047633DOI Listing
January 2021

Adherence of Mobile App-Based Surveys and Comparison With Traditional Surveys: eCohort Study.

J Med Internet Res 2021 01 20;23(1):e24773. Epub 2021 Jan 20.

Section of General Internal Medicine, Department of Medicine, Boston University School of Medicine, Boston, MA, United States.

Background: eCohort studies offer an efficient approach for data collection. However, eCohort studies are challenged by volunteer bias and low adherence. We designed an eCohort embedded in the Framingham Heart Study (eFHS) to address these challenges and to compare the digital data to traditional data collection.

Objective: The aim of this study was to evaluate adherence of the eFHS app-based surveys deployed at baseline (time of enrollment in the eCohort) and every 3 months up to 1 year, and to compare baseline digital surveys with surveys collected at the research center.

Methods: We defined adherence rates as the proportion of participants who completed at least one survey at a given 3-month period and computed adherence rates for each 3-month period. To evaluate agreement, we compared several baseline measures obtained in the eFHS app survey to those obtained at the in-person research center exam using the concordance correlation coefficient (CCC).

Results: Among the 1948 eFHS participants (mean age 53, SD 9 years; 57% women), we found high adherence to baseline surveys (89%) and a decrease in adherence over time (58% at 3 months, 52% at 6 months, 41% at 9 months, and 40% at 12 months). eFHS participants who returned surveys were more likely to be women (adjusted odds ratio [aOR] 1.58, 95% CI 1.18-2.11) and less likely to be smokers (aOR 0.53, 95% CI 0.32-0.90). Compared to in-person exam data, we observed moderate agreement for baseline app-based surveys of the Physical Activity Index (mean difference 2.27, CCC=0.56), and high agreement for average drinks per week (mean difference 0.54, CCC=0.82) and depressive symptoms scores (mean difference 0.03, CCC=0.77).

Conclusions: We observed that eFHS participants had a high survey return at baseline and each 3-month survey period over the 12 months of follow up. We observed moderate to high agreement between digital and research center measures for several types of surveys, including physical activity, depressive symptoms, and alcohol use. Thus, this digital data collection mechanism is a promising tool to collect data related to cardiovascular disease and its risk factors.
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http://dx.doi.org/10.2196/24773DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857942PMC
January 2021

Association Between Frailty and Atrial Fibrillation in Older Adults: The Framingham Heart Study Offspring Cohort.

J Am Heart Assoc 2021 Jan 29;10(1):e018557. Epub 2020 Dec 29.

Department of Biostatistics Boston University School of Public Health Boston MA.

Background Frailty is associated bidirectionally with cardiovascular disease. However, the relations between frailty and atrial fibrillation (AF) have not been fully elucidated. Methods and Results Using the FHS (Framingham Heart Study) Offspring cohort, we sought to examine both the association between frailty (2005-2008) and incident AF through 2016 and the association between prevalent AF and frailty status (2011-2014). Frailty was defined using the Fried phenotype. Models adjusted for age, sex, and smoking. Cox proportional hazards models, adjusted for competing risk of death, assessed the association between prevalent frailty and incident AF. Logistic regression models assessed the association between prevalent AF and new-onset frailty. For the incident AF analysis, we included 2053 participants (56% women; mean age, 69.7±6.9 years). By Fried criteria, 1018 (50%) were robust, 903 (44%) were prefrail, and 132 (6%) were frail. In total, 306 incident cases of AF occurred during an average 9.2 (SD, 3.1) follow-up years. After adjustment, there was no statistically significant association between prevalent frailty status and incident AF (prefrail versus robust: hazard ratio [HR], 1.22 [95% CI, 0.95-1.55]; frail versus robust: HR, 0.92 [95% CI, 0.57-1.47]). At follow-up, there were 111 new cases of frailty. After adjustment, there was no statistically significant association between prevalent AF and new-onset frailty (odds ratio, 0.48 [95% CI, 0.17-1.36]). Conclusions Although a bidirectional association between frailty and cardiovascular disease has been suggested, we did not find evidence of an association between frailty and AF. Our findings may be limited by sample size and should be further explored in other populations.
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http://dx.doi.org/10.1161/JAHA.120.018557DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7955470PMC
January 2021

Geographic Variation in Anticoagulant Use and Resident, Nursing Home, and County Characteristics Associated With Treatment Among US Nursing Home Residents.

J Am Med Dir Assoc 2021 Jan;22(1):164-172.e9

Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.

Objectives: To quantify geographic variation in anticoagulant use and explore what resident, nursing home, and county characteristics were associated with anticoagulant use in a clinically complex population.

Design: A repeated cross-sectional design was used to estimate current oral anticoagulant use on December 31, 2014, 2015, and 2016.

Setting And Participants: Secondary data for United States nursing home residents during the period 2014-2016 were drawn from the Minimum Data Set 3.0 and Medicare Parts A and D. Nursing home residents (≥65 years) with a diagnosis of atrial fibrillation and ≥6 months of Medicare fee-for-service enrollment were eligible for inclusion. Residents in a coma or on hospice were excluded.

Methods: Multilevel logistic models evaluated the extent to which variation in anticoagulant use between counties could be explained by resident, nursing home, and county characteristics and state of residence. Proportional changes in cluster variation (PCVs), intraclass correlation coefficients (ICCs), and adjusted odds ratios (aORs) were estimated.

Results: Among 86,736 nursing home residents from 11,860 nursing homes and 1694 counties, 45% used oral anticoagulants. The odds of oral anticoagulant use were 18% higher in 2016 than 2014 (aOR: 1.18; 95% confidence interval: 1.14-1.22). Most states had counties in the highest (51.3-58.9%) and lowest (31.1%-41.4%) deciles of anticoagulant use. Compared with the null model, adjustment for resident characteristics explained one-third of the variation between counties (PCV: 34.8%). The full model explained 65.5% of between-county variation. Within-county correlation was a small proportion (ICC < 2.2%) of total variation.

Conclusions And Implications: In this older adult population at high risk for ischemic stroke, less than half of the residents received treatment with anticoagulants. Variation in treatment across counties was partially attributable to the characteristics of residents, nursing homes, and counties. Comparative evidence and refinement of predictive algorithms specific to the nursing home setting may be warranted.
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http://dx.doi.org/10.1016/j.jamda.2020.10.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8092949PMC
January 2021

Incident frailty and cognitive impairment by heart failure status in older patients with atrial fibrillation: the SAGE-AF study.

J Geriatr Cardiol 2020 Nov;17(11):653-658

Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA.

Background: Atrial fibrillation (AF) and heart failure (HF) frequently co-occur in older individuals. Among patients with AF, HF increases risks for stroke and death, but the associations between HF and incident cognition and physical impairment remain unknown. We aimed to examine the cross-sectional and prospective associations between HF, cognition, and frailty among older patients with AF.

Methods: The SAGE-AF (Systematic Assessment of Geriatric Elements in AF) study enrolled 1244 patients with AF (mean age 76 years, 48% women) from five practices in Massachusetts and Georgia. HF at baseline was identified from electronic health records using ICD-9/10 codes. At baseline and 1-year, frailty was assessed by Cardiovascular Health Survey score and cognition was assessed by the Montreal Cognitive Assessment.

Results: Patients with prevalent HF ( = 463, 37.2%) were older, less likely to be non-Hispanic white, had less education, and had greater cardiovascular comorbidity burden and higher CHADSVASC and HAS-BLED scores than patients without HF (all 's < 0.01). In multivariable adjusted regression models, HF (present absent) was associated with both prevalent frailty (adjusted odds ratio [aOR]: 2.38, 95% confidence interval [CI]: 1.64-3.46) and incident frailty at 1 year (aOR: 2.48, 95% CI: 1.37-4.51). HF was also independently associated with baseline cognitive impairment (aOR: 1.60, 95% CI: 1.22-2.11), but not with developing cognitive impairment at 1 year (aOR 1.04, 95%CI: 0.64-1.70).

Conclusions: Among ambulatory older patients with AF, the co-existence of HF identifies individuals with physical and cognitive impairments who are at higher short-term risk for becoming frail. Preventive strategies to this vulnerable subgroup merit consideration.
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http://dx.doi.org/10.11909/j.issn.1671-5411.2020.11.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7729184PMC
November 2020

Joint influences of obesity, diabetes, and hypertension on indices of ventricular remodeling: Findings from the community-based Framingham Heart Study.

PLoS One 2020 10;15(12):e0243199. Epub 2020 Dec 10.

Boston University's and National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts, United States of America.

Introduction: Obesity, hypertension, and diabetes are independently associated with cardiac remodeling and frequently co-cluster. The conjoint and separate influences of these conditions on cardiac remodeling have not been investigated.

Materials And Methods: We evaluated 5,741 Framingham Study participants (mean age 50 years, 55% women) who underwent echocardiographic measurements of left ventricular (LV) mass (LVM), LV ejection fraction (LVEF), global longitudinal strain (GLS), mitral E/e', left atrial end-systolic (peak) dimension (LASD) and emptying fraction (LAEF). We used multivariable generalized linear models to estimate the adjusted-least square means of these measures according to cross-classified categories of body mass index (BMI; normal, overweight and obese), hypertension (yes/no), and diabetes (yes/no).

Results: We observed statistically significant interactions of BMI category, hypertension, and diabetes with LVM, LVEF, GLS, and LAEF (p for all 3-way interactions <0.01). Overweight and obesity (compared to normal BMI), hypertension, and diabetes status were individually and conjointly associated with higher LVM and worse GLS (p<0.01 for all). We observed an increase of 34% for LVM and of 9% for GLS between individuals with a normal BMI and without hypertension or diabetes compared to obese individuals with hypertension and diabetes. Presence of hypertension was associated with higher LVEF, whereas people with diabetes had lower LVEF.

Conclusions: Obesity, hypertension, and diabetes interact synergistically to influence cardiac remodeling. These findings may explain the markedly heightened risk of heart failure and cardiovascular disease when these factors co-cluster.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243199PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7728232PMC
January 2021

Automated Electronic Phenotyping of Cardioembolic Stroke.

Stroke 2021 01 10;52(1):181-189. Epub 2020 Dec 10.

Cardiovascular Research Center and Cardiac Arrhythmia Service (W.G., S.K., A.T.T.L., L.X.H., S.A.L.), Massachusetts General Hospital, Boston.

Background And Purpose: Oral anticoagulation is generally indicated for cardioembolic strokes, but not for other stroke causes. Consequently, subtype classification of ischemic stroke is important for risk stratification and secondary prevention. Because manual classification of ischemic stroke is time-intensive, we assessed the accuracy of automated algorithms for performing cardioembolic stroke subtyping using an electronic health record (EHR) database.

Methods: We adapted TOAST (Trial of ORG 10172 in Acute Stroke Treatment) features associated with cardioembolic stroke for derivation in the EHR. Using administrative codes and echocardiographic reports within Mass General Brigham Biobank (N=13 079), we iteratively developed EHR-based algorithms to define the TOAST cardioembolic stroke features, revising regular expression algorithms until achieving positive predictive value ≥80%. We compared several machine learning-based statistical algorithms for discriminating cardioembolic stroke using the feature algorithms applied to EHR data from 1598 patients with acute ischemic strokes from the Massachusetts General Hospital Ischemic Stroke Registry (2002-2010) with previously adjudicated TOAST and Causative Classification of Stroke subtypes.

Results: Regular expression-based feature extraction algorithms achieved a mean positive predictive value of 95% (range, 88%-100%) across 11 echocardiographic features. Among 1598 patients from the Massachusetts General Hospital Ischemic Stroke Registry, 1068 had any cardioembolic stroke feature within predefined time windows in proximity to the stroke event. Cardioembolic stroke tended to occur at an older age, with more TOAST-based comorbidities, and with atrial fibrillation (82.3%). The best model was a random forest with 92.2% accuracy and area under the receiver operating characteristic curve of 91.1% (95% CI, 87.5%-93.9%). Atrial fibrillation, age, dilated cardiomyopathy, congestive heart failure, patent foramen ovale, mitral annulus calcification, and recent myocardial infarction were the most discriminatory features.

Conclusions: Machine learning-based identification of cardioembolic stroke using EHR data is feasible. Future work is needed to improve the accuracy of automated cardioembolic stroke identification and assess generalizability of electronic phenotyping algorithms across clinical settings.
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http://dx.doi.org/10.1161/STROKEAHA.120.030663DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7769922PMC
January 2021

Premature Atrial and Ventricular Contraction Detection using Photoplethysmographic Data from a Smartwatch.

Sensors (Basel) 2020 Oct 5;20(19). Epub 2020 Oct 5.

Department of Biomedical Engineering, University of Connecticut, Storrs, CT 06269, USA.

We developed an algorithm to detect premature atrial contraction (PAC) and premature ventricular contraction (PVC) using photoplethysmographic (PPG) data acquired from a smartwatch. Our PAC/PVC detection algorithm is composed of a sequence of algorithms that are combined to discriminate various arrhythmias. A novel vector resemblance method is used to enhance the PAC/PVC detection results of the Poincaré plot method. The new PAC/PVC detection algorithm with our automated motion and noise artifact detection approach yielded a sensitivity of 86% for atrial fibrillation (AF) subjects while the overall sensitivity was 67% when normal sinus rhythm (NSR) subjects were also included. The specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy values for the combined data consisting of both NSR and AF subjects were 97%, 81%, 94% and 92%, respectively, for PAC/PVC detection combined with our automated motion and noise artifact detection approach. Moreover, when AF detection was compared with and without PAC/PVC, the sensitivity and specificity increased from 94.55% to 98.18% and from 95.75% to 97.90%, respectively. For additional independent testing data, we used two datasets: a smartwatch PPG dataset that was collected in our ongoing clinical study, and a pulse oximetry PPG dataset from the Medical Information Mart for Intensive Care III database. The PAC/PVC classification results of the independent testing on these two other datasets are all above 92% for sensitivity, specificity, PPV, NPV, and accuracy. The proposed combined approach to detect PAC and PVC can ultimately lead to better accuracy in AF detection. This is one of the first studies involving detection of PAC and PVC using PPG recordings from a smartwatch. The proposed method can potentially be of clinical importance as this enhanced capability can lead to fewer false positive detections of AF, especially for those NSR subjects with frequent episodes of PAC/PVC.
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http://dx.doi.org/10.3390/s20195683DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7582300PMC
October 2020

Association of Use of an Integrated Specialty Pharmacy With Total Medical Expenditures Among Members of an Accountable Care Organization.

JAMA Netw Open 2020 10 1;3(10):e2018772. Epub 2020 Oct 1.

Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester.

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http://dx.doi.org/10.1001/jamanetworkopen.2020.18772DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7539116PMC
October 2020

Digital Image Processing Features of Smartwatch Photoplethysmography for Cardiac Arrhythmia Detection.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:4071-4074

The aim of our work is to design an algorithm to detect premature atrial contraction (PAC), premature ventricular contraction (PVC), and atrial fibrillation (AF) among normal sinus rhythm (NSR) using smartwatch photoplethysmographic (PPG) data. Novel image processing features and two machine learning methods are used to enhance the PAC/PVC detection results of the Poincaré plot method. Compared with support vector machine (SVM) methods, the Random Forests (RF) method performs better. It yields a 10-fold cross validation (CV) averaged sensitivity, specificity, positive predicted value (PPV), negative predicted value (NPV), and accuracy for PAC/PVC labels of 63%, 98%, 83%, 94%, and 93%, respectively, and a 10-fold CV averaged sensitivity, specificity, PPV, NPV, and accuracy for AF subjects of 92%, 96%, 85%, 98%, and 95%, respectively. This is one of the first studies to derive image processing features from Poincaré plots to further enhance the accuracy of PAC/PVC detection using PPG recordings from a smartwatch.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176142DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7896754PMC
July 2020

Preliminary Results on Density Poincare Plot Based Atrial Fibrillation Detection from Premature Atrial/Ventricular Contractions

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2594-2597

Detection of Atrial fibrillation (AF) from premature atrial contraction (PAC) and premature ventricular contraction (PVC) is challenging as frequent occurrences of these ectopic beats can mimic the typical irregular patterns of AF. In this paper, we present a preliminary study of using density Poincare plot based machine learning method to detect AF from PAC/PVCs using electrocardiogram (ECG) recordings. First, we propose creation of this new density Poincare plot which is derived from the difference of the heart rate. Next, from this density Poincare plot, template correlation and discrete wavelet transform are used to extract suitable image-based features, which is followed by infinite latent feature selection algorithm to rank the features. Finally, classification of AF vs PAC/PVC is performed using K-Nearest Neighbor, discriminant analysis and support vector machine (SVM) classifiers. Our method is developed and validated using a subset of Medical Information Mart for Intensive Care (MIMIC) III database containing 8 AF and 8 PAC/PVC subjects. Both 10-fold and leave-one-subject-out cross validations are performed to show the robustness of our proposed method. During the 10-fold cross-validation, SVM achieved the best performance with 99.49% sensitivity, 94.51% specificity and 97.29% accuracy with the extracted features while for the leave-one-subject-out, the highest overall accuracy is 90.91%. Moreover, when compared with two state-of-the-art methods, the proposed algorithm achieves superior AF vs. PAC/PVC discrimination performance.Clinical Relevance-This preliminary study shows that with the help of density Poincare plot, AF can be separated from PAC/PVC with better accuracy.
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http://dx.doi.org/10.1109/EMBC44109.2020.9175216DOI Listing
July 2020

Self-reported risk of stroke and factors associated with underestimation of stroke risk among older adults with atrial fibrillation: the SAGE-AF study.

J Geriatr Cardiol 2020 Aug;17(8):502-509

Division of Cardiovascular Medicine, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA.

Background: Though engaging patients with atrial fibrillation (AF) in understanding their stroke risk is encouraged by guidelines, little is known regarding AF patients' perceived stroke risk or its relationship with oral anticoagulation (OAC) use. We aim to identify factors associated with underestimation of stroke risk among older patients with AF and relate this to OAC use.

Methods: Data are from the ongoing SAGE (Systematic Assessment of Geriatric Elements)-AF study, which included older patients (> 65 years) with non-valvular AF and a CHADS-VASc score of ≥ 2. Participants reported their perceived risk of having a stroke without OAC. We compared the perceived risk to CHADS-VASc predicted stroke risk and classified participants as "over" or "under" estimators, and identified factors associated with underestimation of risk using multiple logistic regression.

Results: The average CHADS-VASc score of 915 participants (average age: 75 years, 47% female, 86% white) was 4.3 ± 1.6, 43% of participants had discordant predicted and self-reported stroke risks. Among the 376 participants at highest risk (CHADS-VASc score ≥ 5), 46% of participants underestimated their risk. Older participants (≥ 85 years) were more likely and OAC treated patients less likely to underestimate their risk of developing a future stroke than respective comparison groups.

Conclusions: A significant proportion of study participants misperceived their stroke risk, mostly by overestimating. Almost half of participants at high risk of stroke underestimated their risk, with older patients more likely to do so. Patients on OAC were less likely to underestimate their risk, suggesting that successful efforts to educate patients about their stroke risk may influence treatment choices.
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http://dx.doi.org/10.11909/j.issn.1671-5411.2020.08.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475213PMC
August 2020

Survey of current perspectives on consumer-available digital health devices for detecting atrial fibrillation.

Cardiovasc Digit Health J 2020 Jul-Aug;1(1):21-29. Epub 2020 Aug 28.

Division of Cardiology, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.

Background: Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear.

Objective: To examine HCP perspectives on use of commercial technologies for AF detection and management.

Methods: We created an electronic survey for HCPs assessing practice demographics and perspectives on digital devices for AF detection and management. The survey was distributed electronically to all members of 3 heart rhythm professional societies.

Results: We received 1601 responses out of 73,563 e-mails sent, with 43.6% from cardiac electrophysiologists, 12.8% from fellows, and 11.6% from advanced practice practitioners. Most respondents (62.3%) reported having recommended patient use of a digital device for AF detection. Those who did not had concerns about their accuracy (29.6%), clinical utility of results (22.8%), and integration into electronic health records (19.8%). Results from a 30-second single-lead electrocardiogram were sufficient for 42.7% of HCPs to recommend oral anticoagulation for patients at high risk for stroke. Respondents wanted more data comparing the accuracy of digital devices to conventional devices for AF monitoring (64.9%). A quarter (27.3%) of HCPs had no reservations recommending digital devices for AF detection, and most (53.4%) wanted guidelines from their professional societies providing guidance on their optimal use.

Conclusion: Many HCPs have already integrated digital devices into their clinical practice. However, HCPs reported facing challenges when using digital technologies for AF detection, and professional society recommendations on their use are needed.
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http://dx.doi.org/10.1016/j.cvdhj.2020.06.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7452829PMC
August 2020

Letter from the Editor.

Authors:
David D McManus

Cardiovasc Digit Health J 2020 Jul-Aug;1(1). Epub 2020 Aug 28.

Professor and Vice-Chair, Department of Medicine, University of Massachusetts Medical School, Editor-in-Chief, Cardiovascular Digital Health Journal.

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http://dx.doi.org/10.1016/j.cvdhj.2020.08.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7452847PMC
August 2020

Multimorbidity, physical frailty, and self-rated health in older patients with atrial fibrillation.

BMC Geriatr 2020 09 11;20(1):343. Epub 2020 Sep 11.

Division of Cardiovascular Medicine, Department of Internal Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA, 01655, USA.

Background: Holistic care models emphasize management of comorbid conditions to improve patient-reported outcomes in treatment of atrial fibrillation (AF). We investigated relations between multimorbidity, physical frailty, and self-rated health (SRH) among older adults with AF.

Methods: Patients (n = 1235) with AF aged 65 years and older were recruited from five medical centers in Massachusetts and Georgia between 2015 and 2018. Ten previously diagnosed cardiometabolic and 8 non-cardiometabolic conditions were assessed from medical records. Physical Frailty was assessed with the Cardiovascular Health Study frailty scale. SRH was categorized as either "excellent/very good", "good", and "fair/poor". Separate multivariable ordinal logistic models were used to examine the associations between multimorbidity and SRH, physical frailty and SRH, and multimorbidity and physical frailty.

Results: Overall, 16% of participants rated their health as fair/poor and 14% were frail. Hypertension (90%), dyslipidemia (80%), and heart failure (37%) were the most prevalent cardiometabolic conditions. Arthritis (51%), anemia (31%), and cancer (30%), the most common non-cardiometabolic diseases. After multivariable adjustment, patients with higher multimorbidity were more likely to report poorer health status (Odds Ratio (OR): 2.15 [95% CI: 1.53-3.03], ≥ 8 vs 1-4; OR: 1.37 [95% CI: 1.02-1.83], 5-7 vs 1-4), as did those with more prevalent cardiometabolic and non-cardiometabolic conditions. Patients who were pre-frail (OR: 1.73 [95% CI: 1.30-2.30]) or frail (OR: 6.81 [95% CI: 4.34-10.68]) reported poorer health status. Higher multimorbidity was associated with worse frailty status.

Conclusions: Multimorbidity and physical frailty were common and related to SRH. Our findings suggest that holistic management approaches may influence SRH among older patients with AF.
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http://dx.doi.org/10.1186/s12877-020-01755-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7488548PMC
September 2020

Clinically Meaningful Change in Quality of Life and Associated Factors Among Older Patients With Atrial Fibrillation.

J Am Heart Assoc 2020 09 2;9(18):e016651. Epub 2020 Sep 2.

Division of Cardiovascular Medicine Department of Medicine University of Massachusetts Medical School Worcester MA.

Background Among older patients with atrial fibrillation, there are limited data examining clinically meaningful changes in quality of life (QoL). We examined the extent of, and factors associated with, clinically meaningful change in QoL over 1-year among older adults with atrial fibrillation. Methods and Results Patients from cardiology, electrophysiology, and primary care clinics in Massachusetts and Georgia were enrolled in a cohort study (2015-2018). The Atrial Fibrillation Effect on Quality-of-Life questionnaire was used to assess overall QoL and across 3 subscales: symptoms, daily activities, and treatment concern. Clinically meaningful change in QoL (ie, difference between 1-year and baseline QoL score) was categorized as either a decline (≤-5.0 points), no clinically meaningful change (-5.0 to +5.0 points), or an increase (≥+5.0 points). Ordinal logistic models were used to examine factors associated with QoL changes. Participants (n=1097) were on average 75 years old, 48% were women, and 87% White. Approximately 40% experienced a clinically meaningful increase in QoL and 1 in every 5 patients experienced a decline in QoL. After multivariable adjustment, women, non-Whites, those who reported depressive and anxiety symptoms, fair/poor self-rated health, low social support, heart failure, or diabetes mellitus experienced clinically meaningful declines in QoL. Conclusions These findings provide insights to the magnitude of, and factors associated with, clinically meaningful change in QoL among older patients with atrial fibrillation. Assessment of comorbidities and psychosocial factors may help identify patients at high risk for declining QoL and those who require additional surveillance to maximize important clinical and patient-centered outcomes.
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http://dx.doi.org/10.1161/JAHA.120.016651DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726984PMC
September 2020

Association of Habitual Physical Activity With Cardiovascular Disease Risk.

Circ Res 2020 10 26;127(10):1253-1260. Epub 2020 Aug 26.

Cardiology Division, Department of Medicine (D.D.M.), University of Massachusetts Medical School, Worcester.

Rationale: A sedentary lifestyle is associated with increased risk for cardiovascular disease (CVD). Smartwatches enable accurate daily activity monitoring for physical activity measurement and intervention. Few studies, however, have examined physical activity measures from smartwatches in relation to traditional risk factors associated with future risk for CVD.

Objective: To investigate the association of habitual physical activity measured by smartwatch with predicted CVD risk in adults.

Methods And Results: We enrolled consenting FHS (Framingham Heart Study) participants in an ongoing eFHS (electronic Framingham Heart Study) at the time of their FHS research center examination. We provided participants with a smartwatch (Apple Watch Series 0) and instructed them to wear it daily, which measured their habitual physical activity as the average daily step count. We estimated the 10-year predicted risk of CVD using the American College of Cardiology/American Heart Association 2013 pooled cohort risk equation. We estimated the association between physical activity and predicted risk of CVD using linear mixed effects models adjusting for age, sex, wear time, and familial structure. Our study included 903 eFHS participants (mean age 53±9 years, 61% women, 9% non-White) who wore the smartwatch ≥5 hours per day for ≥30 days. Median daily step count was similar among men (7202 with interquartile range 3619) and women (7260 with interquartile range 3068; =0.52). Average 10-year predicted CVD risk was 4.5% (interquartile range, 6.1%) for men and 1.2% (interquartile range, 2.2%) for women (=1.3×10). Every 1000 steps higher habitual physical activity was associated with 0.18% lower predicted CVD risk (=3.2×10). The association was attenuated but remained significant after further adjustment for body mass index (=0.01).

Conclusions: In this community-based sample of adults, higher daily physical activity measured by a study smartwatch was associated with lower predicted risk of CVD. Future research should examine the longitudinal association of prospectively measured daily activity and incident CVD.
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http://dx.doi.org/10.1161/CIRCRESAHA.120.317578DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7581630PMC
October 2020

Relations between plasma microRNAs, echocardiographic markers of atrial remodeling, and atrial fibrillation: Data from the Framingham Offspring study.

PLoS One 2020 19;15(8):e0236960. Epub 2020 Aug 19.

Division of Cardiology, Department of Medicine, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America.

Background: Circulating microRNAs may reflect or influence pathological cardiac remodeling and contribute to atrial fibrillation (AF).

Objective: The purpose of this study was to identify candidate plasma microRNAs that are associated with echocardiographic phenotypes of atrial remodeling, and incident and prevalent AF in a community-based cohort.

Methods: We analyzed left atrial function index (LAFI) of 1788 Framingham Offspring 8 participants. We quantified expression of 339 plasma microRNAs. We examined associations between microRNA levels with LAFI and prevalent and incident AF. We constructed pathway analysis of microRNAs' predicted gene targets to identify molecular processes involved in adverse atrial remodeling in AF.

Results: The mean age of the participants was 66 ± 9 years, and 54% were women. Five percent of participants had prevalent AF at the initial examination and 9% (n = 157) developed AF over a median 8.6 years of follow-up (IQR 8.1-9.2 years). Plasma microRNAs were associated with LAFI (N = 73, p<0.0001). Six of these plasma microRNAs were significantly associated with incident AF, including 4 also associated with prevalent AF (microRNAs 106b, 26a-5p, 484, 20a-5p). These microRNAs are predicted to regulate genes involved in cardiac hypertrophy, inflammation, and myocardial fibrosis.

Conclusions: Circulating microRNAs 106b, 26a-5p, 484, 20a-5p are associated with atrial remodeling and AF.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0236960PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7437902PMC
October 2020