Dataset Information

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

ABSTRACT: Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ?30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ? 7.4%) will be invited for a 12?lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.

SUBMITTER: Hill NR

PROVIDER: S-EPMC7571442 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Hill Nathan R NR Arden Chris C Beresford-Hulme Lee L Camm A John AJ Clifton David D Davies D Wyn DW Farooqui Usman U Gordon Jason J Groves Lara L Hurst Michael M Lawton Sarah S Lister Steven S Mallen Christian C Martin Anne-Celine AC McEwan Phil P Pollock Kevin G KG Rogers Jennifer J Sandler Belinda B Sugrue Daniel M DM Cohen Alexander T AT

Contemporary clinical trials 20201019

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, ...[more]

PMID: 33091585

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Project description:BackgroundDiabetes is an independent risk factor for atrial fibrillation (AF), which is associated with increases in mortality and morbidity, as well as a diminished quality of life. Renal involvement in diabetes is common, and since chronic kidney disease (CKD) shares several of the same putative mechanisms as AF, it may contribute to its increased risk in individuals with diabetes. The objective of this study is to identify the relationship between CKD and the rates of newly-diagnosed AF in individuals with diabetes taking part in a screening program using a self-applied wearable electrocardiogram (ECG) patch.Materials and methodsThe study included 608 individuals with a diagnosis of diabetes among 1738 total actively monitored participants in the prospective mHealth Screening to Prevent Strokes (mSToPS) trial. Participants, without a prior diagnosis of AF, wore an ECG patch for 2 weeks, twice, over a 4-months period and followed clinically through claims data for 1 year. Definitions of CKD included ICD-9 or ICD-10 chronic renal failure diagnostic codes, and the Health Profile Database algorithm. Individuals requiring dialysis were excluded from trial enrollment.ResultsNinety-six (15.8%) of study participants with diabetes also had a diagnosis of CKD. Over 12 months of follow-up, 19 new cases of AF were detected among the 608 participants. AF was newly diagnosed in 7.3% of participants with CKD and 2.3% in those without (P < 0.05) over 12 months of follow-up. In a univariate Cox proportional hazard regression analysis, the risk of incident AF was 3 times higher in individuals with CKD relative to those without CKD: hazard ratios (HR) 3.106 (95% CI 1.2-7.9). After adjusting for the effect of age, sex, and hypertension, the risk of incident AF was still significantly higher in those with CKD: HR 2.886 (95% CI 1.1-7.5).ConclusionAmong individuals with diabetes, CKD significantly increases the risk of incident AF. Identification of AF prior to clinical symptoms through active ECG screening could help to improve the clinical outcomes in individuals with CKD and diabetes.

Project description:BackgroundSome attempts have been made to detect atrial fibrillation (AF) with a wearable device equipped with photoelectric volumetric pulse wave technology, and it is expected to be applied under real clinical conditions.ObjectiveThis study is the second part of a 2-phase study aimed at developing a method for immediate detection of paroxysmal AF, using a wearable device with built-in photoplethysmography (PPG). The objective of this study is to develop an algorithm to immediately diagnose AF by an Apple Watch equipped with a PPG sensor that is worn by patients undergoing cardiac surgery and to use machine learning on the pulse data output from the device.MethodsA total of 80 patients who underwent cardiac surgery at a single institution between June 2020 and March 2021 were monitored for postoperative AF, using a telemetry-monitored electrocardiogram (ECG) and an Apple Watch. AF was diagnosed by qualified physicians from telemetry-monitored ECGs and 12-lead ECGs; a diagnostic algorithm was developed using machine learning on the pulse rate data output from the Apple Watch.ResultsOne of the 80 patients was excluded from the analysis due to redness caused by wearing the Apple Watch. Of 79 patients, 27 (34.2%) developed AF, and 199 events of AF including brief AF were observed. Of them, 18 events of AF lasting longer than 1 hour were observed, and cross-correlation analysis showed that pulse rate measured by Apple Watch was strongly correlated (cross-correlation functions [CCF]: 0.6-0.8) with 8 events and very strongly correlated (CCF>0.8) with 3 events. The diagnostic accuracy by machine learning was 0.9416 (sensitivity 0.909 and specificity 0.838 at the point closest to the top left) for the area under the receiver operating characteristic curve.ConclusionsWe were able to safely monitor pulse rate in patients who wore an Apple Watch after cardiac surgery. Although the pulse rate measured by the PPG sensor does not follow the heart rate recorded by telemetry-monitored ECGs in some parts, which may reduce the accuracy of AF diagnosis by machine learning, we have shown the possibility of clinical application of using only the pulse rate collected by the PPG sensor for the early detection of AF.

Project description:BackgroundAtrial fibrillation (AF) prevalence estimates vary and have been based on cohorts with clinically established or diagnosed disease. Undiagnosed AF prevalence estimates are less certain as they are based on nongeneralizable convenience samples.HypothesisBecause AF is often asymptomatic, it my remain undiagnosed until the development of complications such as stroke or heart failure. Consequently, the observed prevalence of diagnosed AF from the literature may underestimate total disease burden. We therefore sought to estimate the total prevalence of both diagnosed and undiagnosed AF.MethodsWe performed a retrospective cohort study from 2012 to 2017 using data from five US medical claims data sets. Undiagnosed AF prevalence was estimated based on the observed incidence of ischemic stroke, systemic embolism (SE), and AF incidence after a stroke/SE. The diagnosed AF cohort included AF patients between Q1 2014 and Q3 2015. The undiagnosed AF cohort were patients with assumed undiagnosed AF in the year before a stroke/SE and who were newly diagnosed with AF in the 3-month poststroke/SE. Stroke/SE incidence was calculated among all AF patients and the ratio of number of undiagnosed AF patients to stroke rate was created. Age- and sex-adjusted estimates were stratified by period of assumed undiagnosed AF before poststroke/SE AF diagnosis (1 or 2 years).ResultsThe estimated US prevalence of AF (diagnosed and undiagnosed) in Q3 2015 was 5 628 000 cases, of which 591 000 cases (11%) were undiagnosed. The assumed 2-year undiagnosed AF prevalence was 23% (1 531 000) of the total prevalent patients with AF (6 568 000). Undiagnosed (vs. diagnosed) AF patients were older and had higher CHA2DS2-VASc scores. Of undiagnosed AF, 93% had CHA2DS2-VASc ≥2 and met OAC criteria.ConclusionsThese contemporary estimates demonstrate the high prevalence of undiagnosed AF in the United States. Undiagnosed AF patients are composed of primarily elderly individuals who if diagnosed, would meet criteria for stroke prevention therapy.

Project description:BackgroundThe elderly multi-morbid patient is at high risk of adverse outcomes with COVID-19 complications, and in the general population, the development of incident AF is associated with worse outcomes in such patients. There is therefore the need to identify those patients with COVID-19 who are at highest risk of developing incident AF. We therefore investigated incident AF risks in a large prospective population of elderly patients with/without incident COVID-19 cases and baseline cardiovascular/non-cardiovascular multi-morbidities. We used two approaches: main effect modeling and secondly, a machine-learning (ML) approach, accounting for the complex dynamic relationships among comorbidity variables.MethodsWe studied a prospective elderly US cohort of 280,592 patients from medical databases in an 8-month investigation of with/without newly incident COVID19 cases. Incident AF outcomes were examined in relationship to diverse multi-morbid conditions, COVID-19 status and demographic variables, with ML accounting for the dynamic nature of changing multimorbidity risk factors.ResultsMulti-morbidity contributed to the onset of confirmed COVID-19 cases with cognitive impairment (OR 1.69; 95%CI 1.52-1.88), anemia (OR 1.41; 95%CI 1.32-1.50), diabetes mellitus (OR 1.35; 95%CI 1.27-1.44) and vascular disease (OR 1.30; 95%CI 1.21-1.39) having the highest associations. A main effect model (C-index value 0.718) showed that COVID-19 had the highest association with incident AF cases (OR 3.12; 95%CI 2.61-3.710, followed by congestive heart failure (1.72; 95%CI 1.50-1.96), then coronary artery disease (OR 1.43; 95%CI 1.27-1.60) and valvular disease (1.42; 95%CI 1.26-1.60). The ML algorithm demonstrated improved discriminatory validity incrementally over the statistical main effect model (training: C-index 0.729, 95%CI 0.718-0.740; validation: C-index 0.704, 95%CI 0.687-0.72). Calibration of the ML based formulation was satisfactory and better than the main-effect model. Decision curve analysis demonstrated that the clinical utility for the ML based formulation was better than the 'treat all' strategy and the main effect model.ConclusionCOVID-19 status has major implications for incident AF in a cohort with diverse cardiovascular/non-cardiovascular multi-morbidities. Our ML approach accounting for dynamic multimorbidity changes had good prediction for new onset AF amongst incident COVID19 cases.

Dataset Information

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Publications

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

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