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Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

ABSTRACT: Aim: Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. Methods: PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF detection were evaluated for each time-frame. AF alarms were generated to simulate continuous AF monitoring. Sensitivities, specificities, and positive predictive values (PPVs) of the alarms were evaluated. User experiences of PPG and ECG recordings were assessed. The study was registered in the Clinical Trials database (NCT03507335). Results: The quality of PPG signal was better during night-time than in daytime (67.3 ± 22.4% vs. 30.5 ± 19.4%, p < 0.001). The 30-min time-frame yielded the highest F1-score (0.9536), identifying AF correctly in 72/76 AF patients (sensitivity 94.7%), only 3/97 SR patients receiving a false AF diagnosis (specificity 96.9%). The sensitivity and PPV of the simulated AF alarms were 78.2 and 97.2% at night, and 49.3 and 97.0% during the daytime. 82% of patients were willing to use the device at home. Conclusion: PPG wrist-band provided reliable AF identification both during daytime and night-time. The PPG data's quality was better at night. The positive user experience suggests that wearable PPG devices could be feasible for continuous rhythm monitoring.

SUBMITTER: Valiaho ES

PROVIDER: S-EPMC8764282 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Väliaho Eemu-Samuli ES Lipponen Jukka A JA Kuoppa Pekka P Martikainen Tero J TJ Jäntti Helena H Rissanen Tuomas T TT Castrén Maaret M Halonen Jari J Tarvainen Mika P MP Laitinen Tiina M TM Laitinen Tomi P TP Santala Onni E OE Rantula Olli O Naukkarinen Noora S NS Hartikainen Juha E K JEK

Frontiers in physiology 20220104

<b>Aim:</b> Atrial fibrillation (AF) detection is challenging because it is often asymptomatic and paroxysmal. We evaluated continuous photoplethysmogram (PPG) for signal quality and detection of AF. <b>Methods:</b> PPGs were recorded using a wrist-band device in 173 patients (76 AF, 97 sinus rhythm, SR) for 24 h. Simultaneously recorded 3-lead ambulatory ECG served as control. The recordings were split into 10-, 20-, 30-, and 60-min time-frames. The sensitivity, specificity, and F1-score of AF ...[more]

PMID: 35058796

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Project description:Atrial fibrillation is often asymptomatic and intermittent making its detection challenging. A photoplethysmography (PPG) provides a promising option for atrial fibrillation detection. However, the shapes of pulse waves vary in atrial fibrillation decreasing pulse and atrial fibrillation detection accuracy. This study evaluated ten robust photoplethysmography features for detection of atrial fibrillation. The study was a national multi-center clinical study in Finland and the data were combined from two broader research projects (NCT03721601, URL: https://clinicaltrials.gov/ct2/show/NCT03721601 and NCT03753139, URL: https://clinicaltrials.gov/ct2/show/NCT03753139). A photoplethysmography signal was recorded with a wrist band. Five pulse interval variability, four amplitude features and a novel autocorrelation-based morphology feature were calculated and evaluated independently as predictors of atrial fibrillation. A multivariate predictor model including only the most significant features was established. The models were 10-fold cross-validated. 359 patients were included in the study (atrial fibrillation n = 169, sinus rhythm n = 190). The autocorrelation univariate predictor model detected atrial fibrillation with the highest area under receiver operating characteristic curve (AUC) value of 0.982 (sensitivity 95.1%, specificity 93.7%). Autocorrelation was also the most significant individual feature (p < 0.00001) in the multivariate predictor model, detecting atrial fibrillation with AUC of 0.993 (sensitivity 96.4%, specificity 96.3%). Our results demonstrated that the autocorrelation independently detects atrial fibrillation reliably without the need of pulse detection. Combining pulse wave morphology-based features such as autocorrelation with information from pulse-interval variability it is possible to detect atrial fibrillation with high accuracy with a commercial wrist band. Photoplethysmography wrist bands accompanied with atrial fibrillation detection algorithms utilizing autocorrelation could provide a computationally very effective and reliable wearable monitoring method in screening of atrial fibrillation.

Project description:ImportancePostoperative atrial fibrillation (POAF) occurring after cardiac surgery is associated with adverse outcomes. Whether POAF persists beyond discharge is not well defined.ObjectiveTo determine whether continuous cardiac rhythm monitoring enhances detection of POAF among cardiac surgical patients during the first 30 days after hospital discharge compared with usual care.Design, setting, and participantsThis study is an investigator-initiated, open-label, multicenter, randomized clinical trial conducted at 10 Canadian centers. Enrollment spanned from March 2017 to March 2020, with follow-up through September 11, 2020. As a result of the COVID-19 pandemic, enrollment stopped on July 17, 2020, at which point 85% of the proposed sample size was enrolled. Cardiac surgical patients with CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, prior stroke or transient ischemic attack, vascular disease, age 65-74 years, female sex) score greater than or equal to 4 or greater than or equal to 2 with risk factors for POAF, no history of preoperative AF, and POAF lasting less than 24 hours during hospitalization were enrolled.InterventionsThe intervention group underwent continuous cardiac rhythm monitoring with wearable, patch-based monitors for 30 days after randomization. Monitoring was not mandated in the usual care group within 30 days after randomization.Main outcomes and measuresThe primary outcome was cumulative AF and/or atrial flutter lasting 6 minutes or longer detected by continuous cardiac rhythm monitoring or by a 12-lead electrocardiogram within 30 days of randomization. Prespecified secondary outcomes included cumulative AF lasting 6 hours or longer and 24 hours or longer within 30 days of randomization, death, myocardial infarction, ischemic stroke, non-central nervous system thromboembolism, major bleeding, and oral anticoagulation prescription.ResultsOf the 336 patients randomized (163 patients in the intervention group and 173 patients in the usual care group; mean [SD] age, 67.4 [8.1] years; 73 women [21.7%]; median [interquartile range] CHA2DS2-VASc score, 4.0 [3.0-4.0] points), 307 (91.4%) completed the trial. In the intent-to-treat analysis, the primary end point occurred in 32 patients (19.6%) in the intervention group vs 3 patients (1.7%) in the usual care group (absolute difference, 17.9%; 95% CI, 11.5%-24.3%; P < .001). AF lasting 6 hours or longer was detected in 14 patients (8.6%) in the intervention group vs 0 patients in the usual care group (absolute difference, 8.6%; 95% CI, 4.3%-12.9%; P < .001).Conclusions and relevanceIn post-cardiac surgical patients at high risk of stroke, no preoperative AF history, and AF lasting less than 24 hours during hospitalization, continuous monitoring revealed a significant increase in the rate of POAF after discharge that would otherwise not be detected by usual care. Studies are needed to examine whether these patients will benefit from oral anticoagulation therapy.Trial registrationClinicalTrials.gov Identifier: NCT02793895.

Project description:BackgroundAtrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm.MethodsWe investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a random forest classifier and an expert-labeled training dataset of 2,532 recordings. The seven top-ranked algorithms are combined by using an optimized weighting approach.ResultsThe proposed fusion algorithm, when validated on a separate test dataset consisting of 4,644 recordings, resulted in an area under the receiver operating characteristic (ROC) curve of 0.99. The sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and F1-score of the proposed algorithm were 0.93, 0.97, 0.87, 0.99, and 0.90, respectively, which were all superior to any single algorithm or any previously published.ConclusionThis study demonstrates how a set of well-chosen independent algorithms and a voting mechanism to fuse the outputs of the algorithms, outperforms any single state-of-the-art algorithm for AFib detection. The proposed framework is a case study for the general notion of crowdsourcing between open-source algorithms in healthcare applications. The extension of this framework to similar applications may significantly save time, effort, and resources, by combining readily existing algorithms. It is also a step toward the democratization of artificial intelligence and its application in healthcare.

Project description:ImportanceOpportunistic screening for atrial fibrillation (AF) is recommended, and improved methods of early identification could allow for the initiation of appropriate therapies to prevent the adverse health outcomes associated with AF.ObjectiveTo determine the effect of a self-applied wearable electrocardiogram (ECG) patch in detecting AF and the clinical consequences associated with such a detection strategy.Design, setting, and participantsA direct-to-participant randomized clinical trial and prospective matched observational cohort study were conducted among members of a large national health plan. Recruitment began November 17, 2015, and was completed on October 4, 2016, and 1-year claims-based follow-up concluded in January 2018. For the clinical trial, 2659 individuals were randomized to active home-based monitoring to start immediately or delayed by 4 months. For the observational study, 2 deidentified age-, sex- and CHA2DS2-VASc-matched controls were selected for each actively monitored individual.InterventionsThe actively monitored cohort wore a self-applied continuous ECG monitoring patch at home during routine activities for up to 4 weeks, initiated either immediately after enrolling (n = 1364) or delayed for 4 months after enrollment (n = 1291).Main outcomes and measuresThe primary end point was the incidence of a new diagnosis of AF at 4 months among those randomized to immediate monitoring vs delayed monitoring. A secondary end point was new AF diagnosis at 1 year in the combined actively monitored groups vs matched observational controls. Other outcomes included new prescriptions for anticoagulants and health care utilization (outpatient cardiology visits, primary care visits, or AF-related emergency department visits and hospitalizations) at 1 year.ResultsThe randomized groups included 2659 participants (mean [SD] age, 72.4 [7.3] years; 38.6% women), of whom 1738 (65.4%) completed active monitoring. The observational study comprised 5214 (mean [SD] age, 73.7 [7.0] years; 40.5% women; median CHA2DS2-VASc score, 3.0), including 1738 actively monitored individuals from the randomized trial and 3476 matched controls. In the randomized study, new AF was identified by 4 months in 3.9% (53/1366) of the immediate group vs 0.9% (12/1293) in the delayed group (absolute difference, 3.0% [95% CI, 1.8%-4.1%]). At 1 year, AF was newly diagnosed in 109 monitored (6.7 per 100 person-years) and 81 unmonitored (2.6 per 100 person-years; difference, 4.1 [95% CI, 3.9-4.2]) individuals. Active monitoring was associated with increased initiation of anticoagulants (5.7 vs 3.7 per 100 person-years; difference, 2.0 [95% CI, 1.9-2.2]), outpatient cardiology visits (33.5 vs 26.0 per 100 person-years; difference, 7.5 [95% CI, 7.2-7.9), and primary care visits (83.5 vs 82.6 per 100 person-years; difference, 0.9 [95% CI, 0.4-1.5]). There was no difference in AF-related emergency department visits and hospitalizations (1.3 vs 1.4 per 100 person-years; difference, 0.1 [95% CI, -0.1 to 0]).Conclusions and relevanceAmong individuals at high risk for AF, immediate monitoring with a home-based wearable ECG sensor patch, compared with delayed monitoring, resulted in a higher rate of AF diagnosis after 4 months. Monitored individuals, compared with nonmonitored controls, had higher rates of AF diagnosis, greater initiation of anticoagulants, but also increased health care resource utilization at 1 year.Trial registrationClinicalTrials.gov Identifier: NCT02506244.

Dataset Information

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

Publications

Continuous 24-h Photoplethysmogram Monitoring Enables Detection of Atrial Fibrillation.

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