Dataset Information

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

ABSTRACT: BACKGROUND:Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG). OBJECTIVE:We investigated the feasibility and accuracy of a mobile phone and smart band for AF detection using pulse data measured by PPG. METHODS:A total of 112 consecutive inpatients were recruited from the Chinese PLA General Hospital from March 15 to April 1, 2018. Participants were simultaneously tested with mobile phones (HUAWEI Mate 9, HUAWEI Honor 7X), smart bands (HUAWEI Band 2), and 12-lead ECG for 3 minutes. RESULTS:In all, 108 patients (56 with normal sinus rhythm, 52 with persistent AF) were enrolled in the final analysis after excluding four patients with unclear cardiac rhythms. The corresponding sensitivity and specificity of the smart band PPG were 95.36% (95% CI 92.00%-97.40%) and 99.70% (95% CI 98.08%-99.98%), respectively. The positive predictive value of the smart band PPG was 99.63% (95% CI 97.61%-99.98%), the negative predictive value was 96.24% (95% CI 93.50%-97.90%), and the accuracy was 97.72% (95% CI 96.11%-98.70%). Moreover, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of mobile phones with PPG for AF detection were over 94%. There was no significant difference after further statistical analysis of the results from the different smart devices compared with the gold-standard ECG (P>.99). CONCLUSIONS:The algorithm based on mobile phones and smart bands with PPG demonstrated good performance in detecting AF and may represent a convenient tool for AF detection in at-risk individuals, allowing widespread screening of AF in the population. TRIAL REGISTRATION:Chinese Clinical Trial Registry ChiCTR-OOC-17014138; http://www.chictr.org.cn/showproj.aspx?proj=24191 (Archived by WebCite at http://www.webcitation/76WXknvE6).

SUBMITTER: Fan YY

PROVIDER: S-EPMC6423467 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

Fan Yong-Yan YY Li Yan-Guang YG Li Jian J Cheng Wen-Kun WK Shan Zhao-Liang ZL Wang Yu-Tang YT Guo Yu-Tao YT

JMIR mHealth and uHealth 20190305 3

<h4>Background</h4>Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. The asymptomatic nature and paroxysmal frequency of AF lead to suboptimal early detection. A novel technology, photoplethysmography (PPG), has been developed for AF screening. However, there has been limited validation of mobile phone and smart band apps with PPG compared to 12-lead electrocardiograms (ECG).<h4>Objective</h4>We investigated the feasibility and accuracy of a mobile phone and smart band fo ...[more]

PMID: 30835243

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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:BACKGROUND:Continuous photoplethysmography (PPG) monitoring with a wearable device may aid the early detection of atrial fibrillation (AF). OBJECTIVE:We aimed to evaluate the diagnostic performance of a ring-type wearable device (CardioTracker, CART), which can detect AF using deep learning analysis of PPG signals. METHODS:Patients with persistent AF who underwent cardioversion were recruited prospectively. We recorded PPG signals at the finger with CART and a conventional pulse oximeter before and after cardioversion over a period of 15 min (each instrument). Cardiologists validated the PPG rhythms with simultaneous single-lead electrocardiography. The PPG data were transmitted to a smartphone wirelessly and analyzed with a deep learning algorithm. We also validated the deep learning algorithm in 20 healthy subjects with sinus rhythm (SR). RESULTS:In 100 study participants, CART generated a total of 13,038 30-s PPG samples (5850 for SR and 7188 for AF). Using the deep learning algorithm, the diagnostic accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 96.9%, 99.0%, 94.3%, 95.6%, and 98.7%, respectively. Although the diagnostic accuracy decreased with shorter sample lengths, the accuracy was maintained at 94.7% with 10-s measurements. For SR, the specificity decreased with higher variability of peak-to-peak intervals. However, for AF, CART maintained consistent sensitivity regardless of variability. Pulse rates had a lower impact on sensitivity than on specificity. The performance of CART was comparable to that of the conventional device when using a proper threshold. External validation showed that 94.99% (16,529/17,400) of the PPG samples from the control group were correctly identified with SR. CONCLUSIONS:A ring-type wearable device with deep learning analysis of PPG signals could accurately diagnose AF without relying on electrocardiography. With this device, continuous monitoring for AF may be promising in high-risk populations. TRIAL REGISTRATION:ClinicalTrials.gov NCT04023188; https://clinicaltrials.gov/ct2/show/NCT04023188.

Project description:ImportanceThe Mobile Health Technology for Improved Screening and Optimized Integrated Care in Atrial Fibrillation (mAFA-II) trial is a prospective cluster randomized trial that found a significant reduction in the composite clinical outcome of stroke or thromboembolism, all-cause death, and rehospitalization among patients with atrial fibrillation (AF) who used a mobile health (mHealth) technology that implemented the Atrial Fibrillation Better Care (ABC) pathway (ie, A, anticoagulation/avoid stroke; B, better symptom control; and C, cardiovascular disease and comorbidity management) compared with those receiving usual care. Multimorbidity (defined as ≥2 chronic long-term conditions) is common in older patients with AF, but the impact of integrated or holistic care (based on the ABC pathway) on clinical outcomes in this population is uncertain.ObjectiveTo evaluate whether implementation of the integrated ABC pathway, supported by mHealth technology, would reduce AF-related adverse events in patients with multimorbidity.Design, setting, and participantsThis prespecified ancillary analysis of data from the extended follow-up of the mAFA II trial was conducted between June 2018 and April 2021. Adult patients with AF were included in the analysis if they had at least 2 comorbidities. Participants were enrolled across 40 centers in China.InterventionIntegrated care supported by mHealth technology (mAFA intervention) vs usual care.Main outcomes and measuresThe main outcome was the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization. Cox proportional hazard modeling was performed for adverse outcomes after adjusting for cluster effect and baseline risk factors.ResultsOf 1890 patients, 833 (mean [SD] age, 72.0 [12.0] years; 278 [33.4%] women) with multimorbidity were allocated to the intervention group (ABC pathway), with a mean (SD) follow-up of 419 (257) days, and 1057 patients (mean [SD] age, 72.8 [13.0] years; 443 [41.9%] women) with multimorbidity were allocated to usual care, with a mean (SD) follow-up of 457 (154) days. Compared with usual care, the composite outcome of stroke or thromboembolism, all-cause death, and rehospitalization was significantly reduced in the intervention group (hazard ratio [HR], 0.37; 95% CI, 0.26-0.53; P < .001), as were rehospitalizations alone (HR, 0.42; 95% CI, 0.27-0.64; P < .001). For the C criterion of the ABC pathway, rates of acute coronary syndrome, heart failure, and uncontrolled blood pressure during follow-up were lower in the intervention group than the usual care group (27 patients [3.2%] vs 145 patients [13.7%]; HR, 0.29; 95% CI, 0.19-0.45; P < .001). Subgroup analyses by age, prior stroke, and sex demonstrated consistently lower HRs for the primary composite outcome and rehospitalization for patients with AF allocated to the intervention group compared with patients receiving usual care.Conclusions and relevanceIn this study, mHealth technology-based integrated care that facilitated the implementation of the ABC pathway reduced meaningful clinical adverse events in older patients with AF and multimorbidity vs usual care.Trial registrationWHO International Clinical Trials Registry Platform (ICTRP) Registration number: ChiCTR-OOC-17014138.

Dataset Information

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

Publications

Diagnostic Performance of a Smart Device With Photoplethysmography Technology for Atrial Fibrillation Detection: Pilot Study (Pre-mAFA II Registry).

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