Cardiovascular digital health journal最新文献

Background

Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations.

Objective

The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population.

Methods

Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant’s cardiac health.

Results

A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices.

Conclusion

Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.

Background

Heart-healthy diets are important in the prevention and treatment of hypertension (HTN), including among pregnant women. Yet, the barriers, facilitators, and beliefs/preferences regarding healthy eating are not well described in this population.

Objective

To identify barriers and facilitators to healthy diet, examine the prevalence of food insecurity, and determine interest in specific healthy diet interventions.

Methods

Pregnant women, aged 18–50 years (N = 38), diagnosed with HTN, hypertensive disorders in pregnancy (HDP), or risk factors for HDP, were recruited from a large academic medical center in central Massachusetts between June 2020 and June 2022. Participants completed an electronic survey using a 5-point Likert scale (strongly disagree to strongly agree).

Results

The mean age of participants was 31.6 years (SD 5.5) and 35.1% identified as Hispanic. Finances and time were major barriers to a healthy diet, reported by 42.1% and 28.9% of participants, respectively. Participants reported that their partners and families were supportive of healthy eating and preparing meals at home, though 30.0% of those with children considered their children’s diet a barrier to preparing healthy meals. Additionally, 40.5% of the sample were considered food insecure. Everyone agreed that healthy diet was important for maternal and fetal health, and the most popular interventions were healthy ingredient grocery deliveries (89.4%) and meal deliveries (84.2%).

Conclusion

Time and cost emerged as major challenges to healthy eating in these pregnant women. Such barriers, facilitators, and preferences can aid in intervention development and policy-level changes to mitigate obstacles to healthy eating in this vulnerable patient population.

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Background

The Apple Watch (AW) is the first commercially available wearable device with built-in electrocardiogram (ECG) electrodes to perform a single-lead ECG to detect atrial fibrillation (AF).

Methods

Patients with AF who were scheduled for electrical cardioversion (ECV) were included in this study. The AW ECGs were obtained pre-ECV and post-ECV. In case of an unclassified recording, the AW ECG was obtained up to 3 times. The 12-lead ECG was used as the reference standard. Sensitivity, specificity, and kappa coefficient were calculated.

Results

In total, 74 patients were included. Mean age was 67.1 ± 12.3 years and 20.3% were female. In total 65 AF and 64 sinus rhythm measurements were obtained. The first measurement with the AW showed a sensitivity of 93.5% and specificity of 100% (κ = 0.94). A second measurement resulted in a sensitivity of 94.6% and specificity of 100% (κ = 0.95). A third measurement resulted in a sensitivity of 93% and a specificity of 96.5% (κ = 0.90). Adjudication of unclassified recordings by a physician reduced the total unclassified recordings from 27.9% to 1.6%, but also reduced the accuracy. The kappa coefficient for unclassified single-lead ECGs was 0.58.

Conclusion

The single-lead ECG of the AW shows a high accuracy for identifying AF in a clinical setting. Repeating the recording once decreases the total of unclassified recordings; however, a third recording resulted in a lower accuracy and the occurrence of false-positive measurements. Unclassified results of the AW can be reduced by physicians’ interpretation of the single-lead ECG; however, the interrater agreement is only moderate.

Background

Sarcoidosis with cardiac involvement, although rare, has a worse prognosis than sarcoidosis involving other organ systems.

Objective

We used a large dataset to train machine learning models to predict in-hospital mortality among sarcoidosis patients admitted with heart failure (HF).

Method

Utilizing the National Inpatient Sample, we identified 4659 patients hospitalized with a primary diagnosis of HF. In this cohort, we identified patients with a secondary diagnosis of sarcoidosis using International Statistical Classification of Disease, Tenth Revision (ICD-10) codes. Patients were separated into a training group and a testing group in a 7:3 ratio. Least absolute shrinkage and selection operator regression was used to select variables to prevent model overfitting or underfitting. For machine learning models, logistic regression, random forest, and XGBoosting were applied in the training group. Parameters in each of the models were tuned using the GridSearchCV function. After training, all models were further validated in the testing group. Models were then evaluated using the area under curve (AUC) score, sensitivity, and specificity.

Results

A total of 2.3% of sarcoidosis patients died in HF admission. Our machine learning model analysis found the RF model to have the highest AUC score and sensitivity. Feature analysis found that comorbid arrhythmias and fluid electrolyte disorders were the strongest factors in predicting in-hospital mortality.

Conclusion

Machine learning methods can be useful in identifying predictors of in-hospital mortality in a given dataset.

Background

Early self-detection of atrial fibrillation (AF) can help delay and/or prevent significant associated complications, including embolic stroke and heart failure. We developed a facial video technology, videoplethysmography (VPG), to detect AF based on the analysis of facial pulsatile signals.

Objective

The purpose of this study was to evaluate the accuracy of a video-based technology to detect AF on a smartphone and to test the performance of the technology in AF patients across the whole spectrum of skin complexion and under various recording conditions.

Methods

The performance of video-based monitoring depends on a set of factors such as the angle and the distance between the camera and the patient’s face, the strength of illumination, and the patient’s skin tone. We conducted a clinical study involving 60 subjects with a confirmed diagnosis of AF. A continuous electrocardiogram was used as the gold standard for cardiac rhythm annotation. The VPG technology was fine-tuned on a smartphone for the first 15 subjects. Validation recordings were then done using 7053 measurements collected from the remaining 45 subjects.

Results

The VPG technology detected the presence of AF using the video camera from a common smartphone with sensitivity and specificity ≥90%. The ambient level of illumination needs to be ≥100 lux for the technology to deliver consistent performance across all skin tones.

Conclusion

We demonstrated that facial video-based detection of AF provides accurate outpatient cardiac monitoring including high pulse rate accuracy and medical-grade performance for AF detection.

Background

Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures.

Objective

To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction.

Methods

Unlike conventional data-driven “black box” ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the “black box” at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias and highlight the important elements and features.

Results

Experimental results on the benchmark Cleveland IHD dataset showed that the proposed clinical knowledge–enhanced ML pipeline overperformed state-of-the-art data-driven ML models, using even fewer features. Our model based on one-hot feature encoding and support vector machine achieved the best accuracy of 94.4% and sensitivity 95% by using only 7 discriminative attributes.

Conclusion

We share insights and discuss the effectiveness of incorporating clinical input in machine learning to improve model performance, as well as addressing some practical issues such as data bias and interpretability. We hope this preliminary study on engaging clinical expertise to explore the “black box” would improve the trustworthiness of AI and its potential wider uptake in the medical field.

Background

An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging owing to the low disease prevalence and potentially high false-positive rates.

Objective

Identify clinical characteristics associated with true- and false-positive HCM AI-ECG results to improve its clinical application.

Methods

We reviewed the records of the 200 patients with highest HCM AI-ECG scores in January 2021 at our institution. Logistic regression was used to create a clinical variable–based “Candidacy for HCM Detection (HCM-DETECT)” score, differentiating true-positive from false-positive AI-ECG results. We validated the HCM-DETECT score in an independent cohort of 200 patients with the highest AI-ECG scores from January 2022.

Results

In the 2021 cohort (median age 71 [interquartile range 58–80] years, 48% female), the rates of true-positive, false-positive, and indeterminate AI-ECG results for HCM detection were 36%, 48%, and 16%, respectively. In the 2022 cohort, the rates were 26%, 47%, and 27%, respectively. The HCM-DETECT score included age, coronary artery disease, prior pacemaker, and prior cardiac valve surgery, and had an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.73–0.87) for differentiating true- vs false-positive AI results. When the 2022 cohort was limited to HCM detection candidates identified with the HCM-DETECT score, the false-positive AI-ECG rate was reduced from 47% to 13.5%.

Conclusion

Application of a clinical score (HCM-DETECT) in tandem with an AI-ECG model improved HCM detection yield, reducing the false-positive rate of AI-ECG more than 3-fold.

Coronary artery disease and heart failure are leading causes of morbidly and mortality, resulting in a substantial economic burden globally. Guidelines from the European Society of Cardiology and American Heart Association place adherence to medication and healthy lifestyle behaviors at the core of cardiovascular disease primary and secondary prevention strategies. The growing collective burden of cardiovascular disease is likely to eventually outgrow the available resources allocated for traditional care provision, such as nurse-led outreach services. Novel strategies are required to address this growing need. Worldwide, more than 6.5 billion people own smartphones and opportunities to deliver healthcare digitally for patients with cardiac conditions are expanding exponentially. Multiple randomized controlled trials have now demonstrated that various modes of noninvasive digital health technology, including teleconsultations, smartphone applications (apps), wearables, remote monitoring, and predictive analytics can influence patient behaviors in both the primary and secondary prevention of coronary artery disease and prevention and management of heart failure. The purpose of this narrative review is to critically analyze pivotal trials and discuss examples of successfully deployed mobile digital technology in the prevention of heart failure hospitalizations, and in the primary and secondary prevention of coronary artery disease.