How useful is the smartwatch ECG? Isakadze, Nino; Martin, Seth S.
Trends in cardiovascular medicine,
October 2020, 2020-10-00, 20201001, Volume:
30, Issue:
7
Journal Article
Peer reviewed
Open access
Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired ...with an app that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important questions regarding the utilization of such devices and their features in clinical practice arise. It is unclear how the data obtained from these devices can be optimally incorporated in patient care and what it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be determined. We discuss possible uses and challenges of Apple's (Apple Inc.) newly launched ECG feature and review an upcoming trial looking at clinical applications and outcomes using this technology. We also review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Diabetes mellitus is a disease that can be difficult to manage and requires high levels of health literacy and numeracy, self-monitoring and frequent contact with clinicians. If not optimally ...controlled, diabetes can lead to kidney failure, blindness and cardiovascular complications, which, in turn, contribute to increasing healthcare costs. Although not yet widely used, mobile health (mHealth) tools have enhanced diabetes management and prevention and are likely to play an increasing role with the growth of smartphone ownership and medical device innovations. Recent mHealth interventions targeting type 1 and type 2 diabetes are diverse in their goals and components, and include insulin management applications, wearable blood glucose meters, automated text messages, health diaries and virtual health coaching. In this paper, we review the modalities and components of various impactful interventions for insulin management, diabetes education, self-management and prevention. More work is needed to investigate how individual demographic, socioeconomic, behavioural and clinical characteristics contribute to patient engagement and the efficacy of mHealth tools for diabetes.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Patients with familial hypercholesterolemia, who have a severely increased low-density lipoprotein (LDL) cholesterol level from birth and are at high risk for premature cardiovascular disease, have ...inspired and contributed to major advances in lipid therapeutics. A notable example is the drug class targeting proprotein convertase subtilisin–kexin type 9 (PCSK9). Overactivity of PCSK9, which promotes LDL receptor degradation, was discovered to be a cause of familial hypercholesterolemia.
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The addition of a PCSK9 inhibitor to statin therapy can lower the LDL cholesterol level by 60% and reduce cardiovascular risk.
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Reduction of cardiovascular risk with PCSK9 inhibitors is correlated with absolute lowering of . . .
IMPORTANCE In clinical and research settings worldwide, low-density lipoprotein cholesterol (LDL-C) is typically estimated using the Friedewald equation. This equation assumes a fixed factor of 5 for ...the ratio of triglycerides to very low-density lipoprotein cholesterol (TG:VLDL-C); however, the actual TG:VLDL-C ratio varies significantly across the range of triglyceride and cholesterol levels. OBJECTIVE To derive and validate a more accurate method for LDL-C estimation from the standard lipid profile using an adjustable factor for the TG:VLDL-C ratio. DESIGN, SETTING, AND PARTICIPANTS We used a convenience sample of consecutive clinical lipid profiles obtained from 2009 through 2011 from 1 350 908 children, adolescents, and adults in the United States. Cholesterol concentrations were directly measured after vertical spin density-gradient ultracentrifugation, and triglycerides were directly measured. Lipid distributions closely matched the population-based National Health and Nutrition Examination Survey (NHANES). Samples were randomly assigned to derivation (n = 900 605) and validation (n = 450 303) data sets. MAIN OUTCOMES AND MEASURES Individual patient-level concordance in clinical practice guideline LDL-C risk classification using estimated vs directly measured LDL-C (LDL-CD). RESULTS In the derivation data set, the median TG:VLDL-C was 5.2 (IQR, 4.5-6.0). The triglyceride and non–high-density lipoprotein cholesterol (HDL-C) levels explained 65% of the variance in the TG:VLDL-C ratio. Based on strata of triglyceride and non–HDL-C values, a 180-cell table of median TG:VLDL-C values was derived and applied in the validation data set to estimate the novel LDL-C (LDL-CN). For patients with triglycerides lower than 400 mg/dL, overall concordance in guideline risk classification with LDL-CD was 91.7% (95% CI, 91.6%-91.8%) for LDL-CN vs 85.4% (95% CI, 85.3%-85.5%) for Friedewald LDL-C (LDL-CF) (P < .001). The greatest improvement in concordance occurred in classifying LDL-C lower than 70 mg/dL, especially in patients with high triglyceride levels. In patients with an estimated LDL-C lower than 70 mg/dL, LDL-CD was also lower than 70 mg/dL in 94.3% (95% CI, 93.9%-94.7%) for LDL-CN vs 79.9% (95% CI, 79.3%-80.4%) for LDL-CF in samples with triglyceride levels of 100 to 149 mg/dL; 92.4% (95% CI, 91.7%-93.1%) for LDL-CN vs 61.3% (95% CI, 60.3%-62.3%) for LDL-CF in samples with triglyceride levels of 150 to 199 mg/dL; and 84.0% (95% CI, 82.9%-85.1%) for LDL-CN vs 40.3% (95% CI, 39.4%-41.3%) for LDL-CF in samples with triglyceride levels of 200 to 399 mg/dL (P < .001 for each comparison). CONCLUSIONS AND RELEVANCE A novel method to estimate LDL-C using an adjustable factor for the TG:VLDL-C ratio provided more accurate guideline risk classification than the Friedewald equation. These findings require external validation, as well as assessment of their clinical importance. The implementation of these findings into clinical practice would be straightforward and at virtually no cost. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01698489
Abstract Objective To evaluate the effect of statins on short-term cognitive function and the long-term incidence of dementia. Patients and Methods A systematic search was performed of MEDLINE, ...EMBASE, and the Cochrane Central Register from their inception to April 25, 2013. Adults with no history of cognitive dysfunction treated with statins were included from high-quality randomized controlled trials and prospective cohort studies after formal bias assessment. Results Sixteen studies were included in qualitative synthesis and 11 in quantitative synthesis. Short-term trials did not show a consistent effect of statin therapy on cognitive end points. Digit Symbol Substitution Testing (a well-validated measure of cognitive function) was the most common short-term end point, with no significant differences in the mean change from baseline to follow-up between the statin and placebo groups (mean change, 1.65; 95% CI, –0.03 to 3.32; 296 total exposures in 3 trials). Long-term cognition studies included 23,443 patients with a mean exposure duration of 3 to 24.9 years. Three studies found no association between statin use and incident dementia, and 5 found a favorable effect. Pooled results revealed a 29% reduction in incident dementia in statin-treated patients (hazard ratio, 0.71; 95% CI, 0.61-0.82). Conclusion In patients without baseline cognitive dysfunction, short-term data are most compatible with no adverse effect of statins on cognition, and long-term data may support a beneficial role for statins in the prevention of dementia.
Objectives The aim of this study was to compare Friedewald-estimated and directly measured low-density lipoprotein cholesterol (LDL-C) values. Background LDL-C is routinely estimated by the ...Friedewald equation to guide treatment; however, compatibility with direct measurement has received relatively little scrutiny, especially at levels <70 mg/dl now targeted in high-risk patients. Methods We examined 1,340,614 U.S. adults who underwent lipid profiling by vertical spin density gradient ultracentrifugation (Atherotech, Birmingham, Alabama) from 2009 to 2011. Following standard practice, Friedewald LDL-C was not estimated if triglyceride levels were ≥400 mg/dl (n = 30,174), yielding 1,310,440 total patients and 191,333 patients with Friedewald LDL-C <70 mg/dl. Results Patients were 59 ± 15 years of age and 52% were women. Lipid distributions closely matched those in the National Health and Nutrition Examination Survey. A greater difference in the Friedewald-estimated versus directly measured LDL-C occurred at lower LDL-C and higher triglyceride levels. If the Friedewald-estimated LDL-C was <70 mg/dl, the median directly measured LDL-C was 9.0 mg/dl higher (5th to 95th percentiles, 1.8 to 15.4 mg/dl) when triglyceride levels were 150 to 199 mg/dl and 18.4 mg/dl higher (5th to 95th percentiles, 6.6 to 36.0 mg/dl) when triglyceride levels were 200 to 399 mg/dl. Of patients with a Friedewald-estimated LDL-C <70 mg/dl, 23% had a directly measured LDL-C ≥70 mg/dl (39% if triglyceride levels were concurrently 150 to 199 mg/dl; 59% if triglyceride levels were concurrently 200 to 399 mg/dl). Conclusions The Friedewald equation tends to underestimate LDL-C most when accuracy is most crucial. Especially if triglyceride levels are ≥150 mg/dl, Friedewald estimation commonly classifies LDL-C as <70 mg/dl despite directly measured levels ≥70 mg/dl, and therefore additional evaluation is warranted in high-risk patients.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Technological innovations reach deeply into our daily lives and an emerging trend supports the use of commercial smart wearable devices to manage health. In the era of remote, decentralized and ...increasingly personalized patient care, catalysed by the COVID-19 pandemic, the cardiovascular community must familiarize itself with the wearable technologies on the market and their wide range of clinical applications. In this Review, we highlight the basic engineering principles of common wearable sensors and where they can be error-prone. We also examine the role of these devices in the remote screening and diagnosis of common cardiovascular diseases, such as arrhythmias, and in the management of patients with established cardiovascular conditions, for example, heart failure. To date, challenges such as device accuracy, clinical validity, a lack of standardized regulatory policies and concerns for patient privacy are still hindering the widespread adoption of smart wearable technologies in clinical practice. We present several recommendations to navigate these challenges and propose a simple and practical 'ABCD' guide for clinicians, personalized to their specific practice needs, to accelerate the integration of these devices into the clinical workflow for optimal patient care.
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GEOZS, IJS, IMTLJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBMB, UL, UM, UPUK, ZAGLJ
Abstract Classically, the 3 pillars of atrial fibrillation (AF) management have included anticoagulation for prevention of thromboembolism, rhythm control, and rate control. In both prevention and ...management of AF, a growing body of evidence supports an increased role for comprehensive cardiac risk factor modification (RFM), herein defined as management of traditional modifiable cardiac risk factors, weight loss, and exercise. In this narrative review, we summarize the evidence demonstrating the importance of each facet of RFM in AF prevention and therapy. Additionally, we review emerging data on the importance of weight loss and cardiovascular exercise in prevention and management of AF.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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