Utility of a smartphone application in assessing palmar circulation prior to radial artery harvesting for coronary artery bypass grafting: rationale and design of the randomised CAPITAL iRADIAL-CABG trial.

INTRODUCTION: There is emerging evidence supporting the use of the radial artery (RA) as a preferred secondary conduit for coronary artery bypass grafting (CABG) as it is associated with higher rates of graft patency at 5 years when compared with saphenous vein grafts (SVG). The modified Allen's test (MAT) is traditionally regarded as the standard of care in the assessment of ulnar artery (UA) patency prior to RA harvesting. Unfortunately, due to high false-positive rates, a substantial number of pre-CABG patients are found to have an abnormal MAT despite normal UA patency, resulting in inappropriate exclusion from RA harvesting. The SVG is generally used in its place when this occurs, resulting in potentially lower rates of long-term graft patency. METHODS AND ANALYSIS: The CAPITAL iRADIAL-CABG trial is currently enrolling participants 18 years of age or older undergoing CABG for whom the treating physician is considering the use of an RA conduit. Eligible patients will be randomised in a 1:1 fashion to MAT or smartphone-based photoplethysmography application assessment to assess collateral palmar circulation prior to RA harvesting. The primary outcome of the trial is the use of the RA as a conduit during CABG. The primary safety outcome is postoperative palmar ischaemia as determined by clinical assessment or requirement of vascular intervention. Secondary outcomes include vascular complications, early graft failure, need for rescue percutaneous coronary intervention during the index hospitalisation and a composite cardiovascular outcome of myocardial infarction, stroke and cardiovascular death prior to discharge from hospital. A total of 236 participants are planned to be recruited. ETHICS AND DISSEMINATION: The study was approved by the Ottawa Heart Science Network Research Ethics Board (approval number 20180865-01H). The study results will be disseminated via conference presentations and peer-reviewed publications. TRIAL REGISTRATION NUMBER: NCT03810729.

A digital biomarker of diabetes from smartphone-based vascular signals.

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the 'primary cohort'), which we then validated in a separate cohort of 7,806 individuals (the 'contemporary cohort') and a cohort of 181 prospectively enrolled individuals from three clinics (the 'clinic cohort'). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750-0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723-0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes.

Best practices for analyzing large-scale health data from wearables and smartphone apps.

Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the "wild", and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.

Real-world heart rate norms in the Health eHeart study.

Emerging technology allows patients to measure and record their heart rate (HR) remotely by photoplethysmography (PPG) using smart devices like smartphones. However, the validity and expected distribution of such measurements are unclear, making it difficult for physicians to help patients interpret real-world, remote and on-demand HR measurements. Our goal was to validate HR-PPG, measured using a smartphone app, against HR-electrocardiogram (ECG) measurements and describe out-of-clinic, real-world, HR-PPG values according to age, demographics, body mass index, physical activity level, and disease. To validate the measurements, we obtained simultaneous HR-PPG and HR-ECG in 50 consecutive patients at our cardiology clinic. We then used data from participants enrolled in the Health eHeart cohort between 1 April 2014 and 30 April 2018 to derive real-world norms of HR-PPG according to demographics and medical conditions. HR-PPG and HR-ECG were highly correlated (Intraclass correlation = 0.90). A total of 66,788 Health eHeart Study participants contributed 3,144,332 HR-PPG measurements. The mean real-world HR was 79.1 bpm ± 14.5. The 95th percentile of real-world HR was ≤110 in individuals aged 18-45, ≤100 in those aged 45-60 and ≤95 bpm in individuals older than 60 years old. In multivariable linear regression, the number of medical conditions, female gender, increasing body mass index, and being Hispanic was associated with an increased HR, whereas increasing age was associated with a reduced HR. Our study provides the largest real-world norms for remotely obtained, real-world HR according to various strata and they may help physicians interpret and engage with patients presenting such data.

Accuracy and Usability of a Self-Administered 6-Minute Walk Test Smartphone Application.

BACKGROUND: The 6-minute walk test (6MWT) independently predicts congestive heart failure severity, death, and heart failure hospitalizations, but must be administered in clinic by qualified staff on a premeasured course. As part of the Health eHeart Study, we sought to develop and validate a self-administered 6MWT mobile application (SA-6MWTapp) for independent use at home by patients. METHODS AND RESULTS: We performed a validation study of an SA-6MWTapp in 103 participants. In phase 1 (n=52), we developed a distance-estimation algorithm for the SA-6MWTapp by comparing step counts from an Actigraph and measured distance on a premeasured 6MWT course with step counts and estimated distance obtained simultaneously from our SA-6MWTapp (best estimation algorithm, r=0.89 [95% confidence interval 0.78-0.99]). In phase 2, 32 participants (including those with congestive heart failure and pulmonary hypertension) used the SA-6MWTapp independently in clinic, and the distance estimated by the SA-6MWTapp was compared with the measured distance (r=0.83 [95% confidence interval 0.79-0.92]). In phase 3, 19 patients with congestive heart failure and pulmonary hypertension consecutively enrolled from clinic performed 3.2±1 SA-6MWTapp tests per week at home over 2 weeks. Distances estimated from the SA-6MWTapp during home 6MWTs were highly repeatable (coefficient of variation =4.6%) and correlated with in-clinic-measured distance (r=0.88 [95% confidence interval 0.87-0.89]). Usability surveys performed during the second (in-clinic) and third (at-home) phases demonstrated that the SA-6MWTapp was simple and easy to use independently. CONCLUSIONS: An SA-6MWTapp is easy to use and yields accurate repeatable measurements in the clinic and at home.