BackgroundThe use of point-of-care (POC) tests prior to the COVID-19 pandemic was relatively infrequent outside of the healthcare context. Little is known about how public opinions regarding POC tests have changed during the pandemic.MethodsWe redeployed a validated survey to uncompensated volunteers to assess preferences for point-of-care testing (POCT) benefits and concerns between June and September 2022. We received a total of 292 completed surveys. Linear regression analysis was used to compare differences in survey average response scores (ARSs) from 2020-2022.ResultsRespondent ARSs indicated agreement for all 16 POCT benefits in 2022. Of 14 POCT concerns, there were only two statements that respondents agreed with most frequently, which were that “Insurance might not cover the costs of the POC test” (ARS 0.9, +/- 1.0) and “POC tests might not provide a definitive result” ( ARS 0.1, +/- 1.0). Additionally, when comparing survey responses from 2020-2022, we observed 8 significant trends for POCT harms and benefits.ConclusionsThe public’s opinion on POC tests has become more favorable over time. However, concerns regarding the affordability and reliability of POCT results persist. We suggest that stakeholders address these concerns by developing accurate POC tests that continue to improve care and facilitate access to healthcare for all.
BACKGROUND Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation. OBJECTIVE This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm. METHODS The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th - 75th percentile: 15 -37). RESULTS During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02 -9.72; P = . 045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74 -9.06; P=. 001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74% -98%]) and 55% specificity (95% CI [51% -60%]), while BiV % < 96% predicted alerts with 74% sensitivity (95% CI [55% - 88%]) and 81% specificity (95% CI [77% -85%]). CONCLUSION A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.
BackgroundFatal Coronary Heart Disease (FCHD) is often described as sudden cardiac death (affects >4 million people/year), where coronary artery disease is the only identified condition. Electrocardiographic-Artificial Intelligence (ECG-AI) models for FCHD risk prediction using ECG data from wearable devices could enable wider screening/monitoring efforts.ObjectivesTo develop a single-lead ECG-based deep learning model for FCHD risk prediction and assess concordance between clinical and Apple Watch ECGs.MethodsA FCHD single-lead (‘Lead I’ from 12-Lead ECGs) ECG-AI model was developed using 167,662 ECGs (50,132 patients) from the University of Tennessee Health Sciences Center. 80% of the data (five-fold cross-validation) was used for training and 20% as a holdout. Cox Proportional Hazard (CPH) models incorporating ECG-AI predictions with age, sex and race were also developed. The models were tested on paired clinical single-lead and Apple Watch ECGs from 243 St. Jude Life Cohort participants. The correlation and concordance of the predictions were assessed using Pearson’s correlation(R), Spearman’s correlation(ρ) and Cohen’s Kappa.ResultsThe ECG-AI and CPH models resulted in AUCs=0.76 and 0.79, respectively, on the 20% holdout and AUC=0.85 and 0.87 on the AHWFB external validation data. There was moderate-strong positive correlation between predictions (R=0.74, ρ=0.67 and Kappa=0.58) when tested on the 243 paired ECGs. The clinical (Lead I) and Apple Watch predictions led to the same low/high risk FCHD classification for 99% of the participants. CPH prediction correlation resulted in a R=0.81, ρ=0.76 and Kappa=0.78.ConclusionRisk of FCHD can be predicted from single-lead ECGs obtained from wearable devices and are statistically concordant with Lead I of a 12-lead ECG.
BACKGROUND Cardiomyopathy is a leading cause of pregnancy related mortality and the number one cause of death in the late postpartum period. Delay in diagnosis is associated with severe adverse outcomes. OBJECTIVE To evaluate the performance of an artificial Intelligence-enhanced electrocardiogram (AI-ECG) and AI-enabled digital stethoscope to detect left ventricular systolic dysfunction in an obstetric population. METHODS We conducted a single-arm prospective study of pregnant and postpartum women enrolled at 3 sites between October 28, 2021, and October 27, 2022. Study participants completed standard 12-lead ECG, digital stethoscope ECG and phonocardiogram recordings, and a transthoracic echocardiogram within 24 hours. Diagnostic performance was evaluated using the area under the curve (AUC). RESULTS One hundred women were included in the final analysis. The median age was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, and 21% as Hispanic. Five percent and 6% had left ventricular ejection fraction (LVEF) < 45% and < 50%, respectively. The AI-ECG model had near-perfect classification performance (AUC: 1.0, 100% sensitivity; 99%-100% specificity) for detection of cardiomyopathy both LVEF categories. The AI-enabled digital stethoscope had an AUC of 0.98 (95% CI: 0.95, 1.00) and 0.97 (95% CI: 0.93, 1.00), for detection of LVEF < 45% and < 50%, respectively, with 100% sensitivity and 90% specificity. CONCLUSION We demonstrate an AI-ECG and AI-enabled digital stethoscope were effective for detecting cardiac dysfunction in an obstetric population. Larger studies, including an evaluation the impact of screening on clinical outcomes, are essential next steps.
BackgroundDespite near global availability of remote monitoring (RM) in patients with cardiac implantable electronic devices (CIED), there is a high geographical variability in the uptake and use of RM. The underlying reasons for this geographic disparity remain largely unknown.ObjectivesTo study the determinants of worldwide RM utilization and identify locoregional barriers of RM uptake.MethodsAn international survey was administered to all CIED clinic personnel using the Heart Rhythm Society global network collecting demographic information, as well as information on the use of RM, the organization of the CIED clinic, and details on local reimbursement and clinic funding. The most complete response from each center was included in the current analysis. Stepwise forward multivariate linear regression was performed to identify determinants of the percentage of patients with a CIED on RM.ResultsA total of 302 response from 47 different countries were included, 61.3% by physicians and 62.3% from hospital-based CIED clinics. The median percentage of CIED patients on RM was 80% (IQR 40-90). Predictors of RM use were gross national income per capita (0.76% per 1000 USD, 95% CI 0.72 - 1.00, p<0.001), office-based clinics (7.48%, 95% CI 1.53 - 13.44, p=0.014), and presence of clinic funding (per patient payment model 7.90% (95% CI 0.63 - 15.17, p=0.033); global budget 3.56% (95% CI -6.14 - 13.25, p=0.471)).ConclusionThe high variability in RM utilization can partly be explained by economic and structural barriers that may warrant specific efforts by all stakeholders to increase RM utilization.
Background During the COVID-19 pandemic, telemedicine was advocated and rapidly scaled up worldwide. However, little is known about for whom this type of care is acceptable. Objective To examine which patient characteristics (demographic, medical, psychosocial) are associated with telehealth care satisfaction, attitude toward telehealth, and preference regarding telehealth over time in a cardiac patient population. Methods In total, 317 patients were recruited at the Elisabeth-TweeSteden Hospital in The Netherlands. All patients who had received telehealth care (telephone and video) in the previous 2 months were approached for participation. Baseline, 3-month, and 6-month questionnaires were administered online. A 3-step latent class analysis was conducted to identify trajectories of telehealth use over time and the possible association of the found trajectories with external variables. Results Five trajectories (classes) were identified for satisfaction with telehealth and 4 for attitude toward telehealth. Patients with higher distress, lower physical and mental health, higher scores on pessimism, and negative affectivity were more likely to be less satisfied. Patients with no partner, more comorbidities, higher distress, lower physical and mental health, and higher scores on pessimism were more likely to hold a negative attitude toward telehealth. For the future application of telehealth, marital status, comorbidities, digital health literacy, and pessimism were significantly related. Conclusion Results show that patients' profiles should be considered when offering telehealth care and that the "one size fits all" approach does not apply. Results can inform clinical practice on how to better implement remote health care in the future while considering a personalized approach.
INTRODUCTION Unmanaged hypertension in pregnancy is the second most common cause of direct maternal death and disproportionately affects women in rural areas. While telehealth technologies have worked to reduce barriers to healthcare, lack of internet access has created new challenges. Cellular -enabled remote patient monitoring devices provide an alternative option for those without access to internet. OBJECTIVE This study aimed to assess maternal and neonatal clinical outcomes and patient acceptability of an integrated model of cellular -enabled remote patient monitoring devices for blood pressure supported by a 24/7 nurse call center. METHODS In a mixed -methods study, 20 women with hypertension during pregnancy were given a cellular -enabled BodyTrace blood pressure cuff. Participants ' blood pressures were continuously monitored by a nurse call center. Participants completed a baseline survey, post -survey, and semi -structured interview after 8 weeks of device use. RESULTS Participants reported a signi fi cant decrease in perceived stress after device use ( P = . 0004), high satisfaction with device us ability (mean = 78.38, SD = 13.68), and high intention to continue device use (mean = 9.05, SD = 1.96). Relatively low hospitalization and emergency department rates was observed (mean = 0.35, SD = 0.59; mean = 0.75, SD = 0.91). Participant -perceived bene fi ts of device use included convenience, perceived better care owing to increased monitoring, and patient empowerment. Perceived disadvantages included higher blood pressure readings compared to clinical readings and excessive calls from call center. CONCLUSION Remote patient monitoring for women whose pregnancies are complicated by hypertension can reduce barriers and improve health outcomes for women living in rural and lowhealth -resource areas.
Postoperative atrial fibrillation (POAF) after noncardiac surgery comprises around 13% of all new AF diagnoses in the community and has been associated with increased risk of subsequent stroke and transient ischemic attack compared to those without a history of AF.1-3 However, the management of POAF after non-cardiac surgery, including the indications and approaches to ambulatory rhythm monitoring and oral anticoagulation (OAC) for stroke prophylaxis, remains uncertain. Recent data have also demonstrated that AF tends to recur in about one third of patients with POAF within the first year of the index POAF episode4.
Background Remote monitoring devices for atrial fibrillation are known to positively contribute to the diagnostic process and therapy compliance. However, automatic algorithms within devices show varying sensitivity and specificity, so manual double-checking of electrocardiographic (ECG) recordings remains necessary. Objective The purpose of this study was to investigate the validity of the KardiaMobile algorithm within the Dutch telemonitoring program (HartWacht). Methods This retrospective study determined the diagnostic accuracy of the algorithm using assessments by a telemonitoring team as reference. The sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and F1 scores were determined. Results A total of 2298 patients (59.5% female; median age 57 ± 15 years) recorded 86,816 ECGs between April 2019 and January 2021. The algorithm showed sensitivity of 0.956, specificity 0.985, PPV 0.996, NPV 0.847, and F1 score 0.976 for the detection of sinus rhythm. A total of 29 false-positive outcomes remained uncorrected within the same patients. The algorithm showed sensitivity of 0.989, specificity 0.953, PPV 0.835, NPV 0.997, and F1 score 0.906 for detection of atrial fibrillation. A total of 2 false-negative outcomes remained uncorrected. Conclusion Our research showed high validity of the algorithm for the detection of both sinus rhythm and, to a lesser extent, atrial fibrillation. This finding suggests that the algorithm could function as a standalone instrument particularly for detection of sinus rhythm.
Key Findings•Patients scheduled for electrophysiology (EP) study and possible catheter ablation undergoing a preprocedure telehealth visit with the EP provider can provide the patient and family increased satisfaction and overall knowledge regarding their procedure.•Patients were overall satisfied with the use of the KardiaMobile 6L (AliveCor Inc, Mountain View, CA) device and application.•EP physicians were satisfied with the quality of the KardiaMobile 6L electrocardiogram (ECG) data.•Patients who exhibit subtle pre-excitation may require a standard 12-lead ECG tracing postablation to access for recurrence. •Patients scheduled for electrophysiology (EP) study and possible catheter ablation undergoing a preprocedure telehealth visit with the EP provider can provide the patient and family increased satisfaction and overall knowledge regarding their procedure.•Patients were overall satisfied with the use of the KardiaMobile 6L (AliveCor Inc, Mountain View, CA) device and application.•EP physicians were satisfied with the quality of the KardiaMobile 6L electrocardiogram (ECG) data.•Patients who exhibit subtle pre-excitation may require a standard 12-lead ECG tracing postablation to access for recurrence.
Telemedicine, telehealth, e-Health, and other related terms refer to the exchange of medical information or medical care from one site to another through electronic communication between a patient and a health care provider. As telemedicine infrastructure has changed since the coronavirus disease 2019 (COVID-19) pandemic, this review provides an overview of telemedicine use and effectiveness in cardiology, with emphasis on coronary artery disease in the postpandemic context. Prepandemic studies tend to report statistically insignificant or modest improvements in cardiovascular disease outcome from telemedicine use to usual care. In contrast, postpandemic studies tend to report positive outcomes or comparable acceptance of telemedicine use to usual care. Today, telemedicine can effectively replace in person follow-up visits to produce comparable (but not necessarily superior) outcomes in cardiovascular disease management. A benefit of telemedicine is the potential reduction in follow-up time or time to intervention, which may lead to earlier detection and prevention of adverse events. Nonetheless, barriers remain to effective telemedicine implementation in the postpandemic context. Ensuring accessible and user-friendly telemedicine devices, maintaining adherence to remote rehabilitation procedures, and normalizing use of telemedicine in routine follow-up visits are examples. Current knowledge gaps include the true economic cost of telemedicine infrastructure, feasibility of use in specific cardiology contexts, and sex/gender differences in telemedicine use. Future telemedicine developments will need to address these concerns before acceptance of telemedicine as the new standard of care.
BACKGROUND Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of death globally, and early detection of high -risk individuals is essential for initiating timely interventions. The authors aimed to develop and validate a deep learning (DL) model to predict an individual 's elevated 10-year ASCVD risk score based on retinal images and limited demographic data. METHODS The study used 89,894 retinal fundus images from 44,176 UK Biobank participants (96% non-Hispanic White, 5% diabetic) to train and test the DL model. The DL model was developed using retinal images plus age, race/ethnicity, and sex at birth to predict an individual 's 10-year ASCVD risk score using the pooled cohort equation (PCE) as the ground truth. This model was then tested on the US EyePACS 10K dataset (5.8% non-Hispanic White, 99.9% diabetic), composed of 18,900 images from 8969 diabetic individuals. Elevated ASCVD risk was de fi ned as a PCE score of > 7.5%. RESULTS In the UK Biobank internal validation dataset, the DL model achieved an area under the receiver operating characteristic curve of 0.89, sensitivity 84%, and speci fi city 90%, for detecting individuals with elevated ASCVD risk scores. In the EyePACS 10K and with the addition of a regression-derived diabetes modi fi er, it achieved sensitivity 94%, speci fi city 72%, mean error -0.2%, and mean absolute error 3.1%. CONCLUSION This study demonstrates that DL models using retinal images can provide an additional approach to estimating ASCVD risk, as well as the value of applying DL models to different external datasets and opportunities about ASCVD risk assessment in patients living with diabetes.
Background:The availability of portable and wearable electrocardiographic (ECG) devices has increased secondary to technological development. Single-lead ECG recordings have been shown to reliably detect and characterize cardiac rhythms such as atrial fibrillation. Acquisition of precordial electrodes for full 12-lead ECG reconstruction from bipolar recordings is complicated by the absence of a body ground/Wilson central terminal electrode. The extent of difference between standard precordial leads and those from a wearable bipolar ECG recorder has not been characterized. Objective:The purpose of this study was to characterize the precordial ECG lead set from sequential bipolar recordings from an ECG ring wearable device. Methods:In 70 patients who wore an ECG device on a right-hand finger, sequential precordial leads (CR1-CR6) were obtained along with chest electrodes (V1-V6). During acquisition of the modified precordial lead CR6, a full standardized 12-lead ECG capture was obtained. Signal quality was assessed using automated analysis software, and correlation values between the ring-derived ECG precordial leads and standard ECG leads were compared with regard to QRS duration, QT width, and RR interval. Results:High concordance in the morphologies of precordial ECG leads obtained in a standard fashion and those recorded through an ECG ring was observed. Morphologic alignment improved with increasing laterality of the precordial lead with chest to right arm ring recording (CR5, CR6) compared with anterior chest leads to right arm (CR1, CR2). Segmental measurements of QRS duration and QT segment were well aligned and of high correlation. Conclusion:Wearable ring-based ECG technology is capable of high-fidelity recordings of precordial leads for nonsimultaneous reconstruction of complete ECG sets. These recordings correlate highly with surface-obtained QRS and QT duration measurements and have significant implications for clinical applications. Uninterpretable tracings were primarily due to electrode noise from poor electrode contact.
BACKGROUND Multiple smart devices capable of automatically detecting atrial fibrillation (AF) based on single -lead electrocardiograms (SL -ECG) are presently available. The rate of inconclusive tracings by manufacturers' algorithms is currently too high to be clinically useful. METHOD This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. We assessed the clinical value of applying a smart device artificial intelligence (AI) -based algorithm for detecting AF from 4 commercially available smart devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, and Samsung Galaxy Watch3). Patients underwent a nearly simultaneous 12 -lead ECG and 4 smart device SL-ECGs. The novel AI algorithm (PulseAI, Belfast, United Kingdom) was compared with each manufacturer's algorithm. RESULTS We enrolled 206 patients (31% female, median age 64 years). AF was present in 60 patients (29%). Sensitivity and specificity for the detection of AF by the novel AI algorithm vs manufacturer algorithm were 88% vs 81% (P = .34) and 97% vs 77% (P < .001) for the AliveCor KardiaMobile, 86% vs 81% (P = .45) and 95% vs 83% (P < .001) for the Apple Watch 6, 91% vs 67% (P < .01) and 94% vs 82% (P < .001) for the Fitbit Sense, and 86% vs 82% (P = .63) and 94% vs 80% (P < .001) for the Samsung Galaxy Watch3, respectively. In addition, the proportion of SL-ECGs with an inconclusive diagnosis (1.2%) was significantly lower for all smart devices using the AI -based algorithm compared to manufacturer's algorithms (14%-17%), P < .001. CONCLUSION A novel AI algorithm reduced the rate of inconclusive SL -ECG diagnosis massively while maintaining sensitivity and improving the specificity compared to the manufacturers' algorithms.
Background:Cardiac arrhythmias are a common health problem. Both common and rare genetic risk factors exist for cardiac arrhythmias. Cardiac amyloidosis is a rare disease that may manifest various arrhythmias. Few large-scale whole exome sequencing studies elucidating the contribution of rare variations to arrhythmias have been published.Objective:To access gene collapsing analysis of rare variations for different types of cardiac arrhythmias in UK Biobank. Identified genes were analyzed in silico for probability to form amyloid fibrils.Methods:We used 2 published UK Biobank portals (https://azphewas.com/ and https://app.genebass.org/) to access gene collapsing analysis of rare variations for different types of cardiac arrhythmias. Diagnosis of arrhythmia was based on the International Classification of Diseases, 10th Revision (ICD-10) codes: conduction disorders (I44, I45), paroxysmal tachycardia (I47), atrial fibrillation (I48), and other arrhythmias (I49).Results:Rare variations in 5 genes were linked to conduction disorders (SCN5A, LMNA, SMAD6, HSPB9, TMEM95). The TTN gene was associated with both paroxysmal tachycardia and other arrhythmias. Atrial fibrillation was associated with rare variations in 8 genes (TTN, RPL3L, KLF1, TET2, NME3, KDM5B, PKP2, PMVK). Two of the genes linked to heart conduction disorders were potential amyloid-forming proteins (HSPB9, TMEM95), while none of the 8 genes linked to other types of arrhythmias were potential amyloid-forming proteins.Conclusion:Rare variations in 13 genes were associated with arrhythmias in the UK Biobank. Two of the heart conduction disorder-linked genes are potential amyloid-forming candidates. Amyloid formation may be an underestimated cause of heart conduction disorders.
Background:For comprehensive electrocardiogram (ECG) synthesis, a recent promising approach has been based on a heart model with physical and chemical cardiac parameters. However, the problem is that such approach requires a high-cost and limited environment using supercomputers owing to the massive computation.Objective:The purpose of this study is to develop an efficient method for synthesizing 12-lead ECG signals from cardiac parameters.Methods:The proposed method is based on a variational autoencoder (VAE). The encoder and decoder of the VAE are conditioned by the cardiac parameters so that it can model the relationship between the ECG signals and the cardiac parameters. The training data are produced by a comprehensive, finite element method (FEM)-based heart simulator. New ECG signals can then be synthesized by inputting the cardiac parameters into the trained VAE decoder without relying on enormous computational resources. We used 2 metrics to evaluate the quality of ECG signals synthesized by the proposed model.Results:Experimental results showed that the proposed model synthesized adequate ECG signals while preserving empirically important feature points and the overall signal shapes. We also explored the optimal model by varying the number of layers and the size of latent variables in the proposed model that balances the model complexity and the simulation accuracy.Conclusion:The proposed method has the potential to become an alternative to computationally expensive FEM-based heart simulators. It is able to synthesize ECGs from various cardiac parameters within seconds on a personal laptop computer.
Background:Remote monitoring (RM) of cardiac implantable electronic device (CIED) patients is now considered standard of care. However, a fundamental requirement of RM is continuous connectivity between the patient's implanted device and the CIED manufacturer's central server. This study examined the rate of RM disconnections in CIED recipients and the impact of short message service (SMS) to facilitate reconnections.Methods:Using a platform that collects RM data from CIED manufacturers, we retrospectively examined the disconnection and reconnection events in 6085 patients from 20 medical centers. Each medical center reported their usual practice regarding RM disconnections, which consisted of either an automatic SMS from the platform to patients who were disconnected for 2 weeks or the standard of care (SC) of a phone call to patients.Results:During a 1-year period, 43% of patients had at least 1 disconnection. Half of these patients experienced multiple disconnections. The use of SMS reduced the time to reconnection by 43% in comparison to SC. The median time to reconnect a disconnected patient was 11.0 [3.2, 29.0] days for SC vs 6.3 [1.3, 22.0] days for SMS (P < .0001). Furthermore, there was a high rate of reconnections within the first 48 hours of the SMS message, which was nearly double that in the SC arm.Conclusion:This study demonstrates the feasibility of an automatic system to deliver an SMS to patients with a disconnected CIED to facilitate early reconnection to RM.
BackgroundCardiopulmonary resuscitation (CPR) quality significantly impacts patient outcomes during cardiac arrests. With advancements in healthcare technology, smartwatch-based CPR feedback devices have emerged as potential tools to enhance CPR delivery.ObjectiveThis study evaluated a novel smartwatch-based CPR feedback device in enhancing chest compression quality among healthcare professionals and lay rescuers.MethodsA single-center, open-label, randomized crossover study was conducted with 30 subjects categorized into three groups based on rescuer category. The Relay Response BLS smartwatch application was compared to a defibrillator-based feedback device (Zoll OneStep CPR Pads). Following an introduction to the technology, subjects performed chest compressions in three modules: baseline unaided, aided by the smartwatch-based feedback device, and aided by the defibrillator-based feedback device. Outcome measures included effectiveness, learnability, and usability.ResultsAcross all groups, the smartwatch-based device significantly improved mean compression depth effectiveness (68.4% vs. 29.7%; p < 0.05) and mean rate effectiveness (87.5% vs. 30.1%; p < 0.05), compared to unaided compressions. Compression variability was significantly reduced with the smartwatch-based device (coefficient of variation: 14.9% vs. 26.6%), indicating more consistent performance. 15/20 professional rescuers reached effective compressions utilizing the smartwatch-based device in an average 2.6 seconds. A usability questionnaire revealed strong preference for the smartwatch-based device over the defibrillator-based device.ConclusionThe smartwatch-based device enhances the quality of CPR delivery by keeping compressions within recommended ranges and reducing performance variability. Its user-friendliness and rapid learnability suggest potential for widespread adoption in both professional and lay rescuer scenarios, contributing positively to CPR training and real-life emergency responses.
Few innovations have the potential to reconfigure clinical care as thoroughly as digital medicine. One likely future of continuous physiological monitoring by novel devices—analyzed by artificial intelligence (AI)-guided algorithms and stored in secure repositories—to develop personal biometrics to guide care, improve risk stratifiers, and predict future events is both exciting and daunting. It is unclear how practitioners and patients will transition to that possible future, yet all stakeholders can at the present time directly influence how these technologies will develop and paths evolve.
BackgroundPatient monitoring devices are critical in alerting potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates.ObjectiveTo evaluate sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies.MethodsSix commonly-used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false positive rates were determined. Differences were assessed using Fisher’s exact tests (sensitivity) and Z-tests (false positive rates).ResultsB125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%, P<0.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, significantly lower (P<0.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by non-sustained VT.ConclusionWe found significant performance differences among multiparameter bedside ECG monitoring systems using previously-collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.
