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BMC Geriatrics Jun 2024Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to...
INTRODUCTION
Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning.
METHODS
The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, β = beta coefficient, CI = confidence intervals).
RESULTS
Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (β = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: β = 4.60, 95% CI 1.38-7.83, p = 0.005).
CONCLUSIONS
PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.
Topics: Humans; Male; Aged; Female; Exercise; Frailty; Aged, 80 and over; Pacemaker, Artificial; Defibrillators, Implantable; Middle Aged; Accelerometry; Walking Speed; Frail Elderly; Remote Sensing Technology
PubMed: 38886679
DOI: 10.1186/s12877-024-05083-1 -
The Canadian Journal of Cardiology Jun 2024The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic... (Review)
Review
The potential of artificial intelligence (AI) in medicine lies in its ability to enhance clinicians' capacity to analyze medical images, thereby improving diagnostic precision and accuracy, thus enhancing current tests. However, the integration of AI within healthcare is fraught with difficulties. Heterogeneity among healthcare system applications, reliance on proprietary closed-source software, and rising cyber-security threats pose significant challenges. Moreover, prior to their deployment in clinical settings, AI models must demonstrate their effectiveness across a wide range of scenarios and must be validated by prospective studies, but doing so requires testing in an environment mirroring the clinical workflow which is difficult to achieve without dedicated software. Finally, the use of AI techniques in healthcare raises significant legal and ethical issues, such as the protection of patient privacy, the prevention of bias, and the monitoring of the device's safety and effectiveness for regulatory compliance. This review describes challenges to AI integration in healthcare and provides guidelines on how to move forward. We describe an open-source solution that we developed which integrates AI models into the Picture Archives Communication System (PACS), called PACS-AI. This approach aims to increase the evaluation of AI models by facilitating their integration and validation with existing medical imaging databases. PACS-AI may overcome many current barriers to AI deployment and offers a pathway towards responsible, fair, and effective deployment of AI models in healthcare. Additionally, we propose a list of criteria and guidelines that AI researchers should adopt when publishing a medical AI model, to enhance standardization and reproducibility.
PubMed: 38885787
DOI: 10.1016/j.cjca.2024.05.025 -
JMIR MHealth and UHealth Jun 2024Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older...
Digital Phenotyping of Geriatric Depression Using a Community-Based Digital Mental Health Monitoring Platform for Socially Vulnerable Older Adults and Their Community Caregivers: 6-Week Living Lab Single-Arm Pilot Study.
BACKGROUND
Despite the increasing need for digital services to support geriatric mental health, the development and implementation of digital mental health care systems for older adults have been hindered by a lack of studies involving socially vulnerable older adult users and their caregivers in natural living environments.
OBJECTIVE
This study aims to determine whether digital sensing data on heart rate variability, sleep quality, and physical activity can predict same-day or next-day depressive symptoms among socially vulnerable older adults in their everyday living environments. In addition, this study tested the feasibility of a digital mental health monitoring platform designed to inform older adult users and their community caregivers about day-to-day changes in the health status of older adults.
METHODS
A single-arm, nonrandomized living lab pilot study was conducted with socially vulnerable older adults (n=25), their community caregivers (n=16), and a managerial social worker over a 6-week period during and after the COVID-19 pandemic. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted verbal conversations with a mobile chatbot. Digital biomarkers for depression, including heart rate variability, sleep, and physical activity, were measured using a wearable sensor (Fitbit Sense) that was worn continuously, except during charging times. Daily individualized feedback, using traffic signal signs, on the health status of older adult users regarding stress, sleep, physical activity, and health emergency status was displayed on a mobile app for the users and on a web application for their community caregivers. Multilevel modeling was used to examine whether the digital biomarkers predicted same-day or next-day depressive symptoms. Study staff conducted pre- and postsurveys in person at the homes of older adult users to monitor changes in depressive symptoms, sleep quality, and system usability.
RESULTS
Among the 31 older adult participants, 25 provided data for the living lab and 24 provided data for the pre-post test analysis. The multilevel modeling results showed that increases in daily sleep fragmentation (P=.003) and sleep efficiency (P=.001) compared with one's average were associated with an increased risk of daily depressive symptoms in older adults. The pre-post test results indicated improvements in depressive symptoms (P=.048) and sleep quality (P=.02), but not in the system usability (P=.18).
CONCLUSIONS
The findings suggest that wearable sensors assessing sleep quality may be utilized to predict daily fluctuations in depressive symptoms among socially vulnerable older adults. The results also imply that receiving individualized health feedback and sharing it with community caregivers may help improve the mental health of older adults. However, additional in-person training may be necessary to enhance usability.
TRIAL REGISTRATION
ClinicalTrials.gov NCT06270121; https://clinicaltrials.gov/study/NCT06270121.
Topics: Humans; Pilot Projects; Aged; Male; Female; Depression; Caregivers; COVID-19; Aged, 80 and over; Middle Aged; Vulnerable Populations; Heart Rate; Telemedicine
PubMed: 38885033
DOI: 10.2196/55842 -
The Israel Medical Association Journal... Jun 2024Left ventricular assist devices (LVAD) are a staple element in contemporary treatment of advanced heart failure. LVAD surgeries are mostly done in heart transplantations...
BACKGROUND
Left ventricular assist devices (LVAD) are a staple element in contemporary treatment of advanced heart failure. LVAD surgeries are mostly done in heart transplantations centers, as a destination therapy or as a bridge to heart transplantation.
OBJECTIVES
To describe our step-by-step experience in establishing and implementing a new LVAD program in a non-heart transplant center. To give insight to our short- and long-term results of our first 25 LVAD patients.
METHODS
Preliminary steps included identifying the need for a new program and establishing the leading team. Next is defining protocols for pre-operative evaluation, operating room, post-operative management, and outpatient follow-up. The leading team needs to educate other relevant units in the hospital that will be involved in the care of these patients. It is essential to work in collaboration with a heart transplant center from the very beginning. Patient selection is of major importance especially in the early experience. Initially "low risk" patients should be enrolled.
RESULTS
We describe our first 25 LVAD patients. Our first five patients all survived beyond 2 years, with no major complications. Overall, there was one operative death due to massive GI bleeding. There were four late deaths due to septic events.
CONCLUSIONS
Establishing a new LVAD program can be successful also with small- and medium-size programs. With careful and meticulous planning LVAD implantation can be extended to more centers thus offering an excellent solution for advanced heart failure patients.
Topics: Humans; Heart-Assist Devices; Heart Failure; Male; Middle Aged; Female; Adult; Patient Selection; Program Development; Treatment Outcome
PubMed: 38884307
DOI: No ID Found -
Frontiers in Cardiovascular Medicine 2024In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve...
OBJECTIVES
In recent years, the use of artificial intelligence (AI) models to generate individualised risk assessments and predict patient outcomes post-Transcatheter Aortic Valve Implantation (TAVI) has been a topic of increasing relevance in literature. This study aims to evaluate the predictive accuracy of AI algorithms in forecasting post-TAVI mortality as compared to traditional risk scores.
METHODS
Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Systematic Reviews (PRISMA) standard, a systematic review was carried out. We searched four databases in total-PubMed, Medline, Embase, and Cochrane-from 19 June 2023-24 June, 2023.
RESULTS
From 2,239 identified records, 1,504 duplicates were removed, 735 manuscripts were screened, and 10 studies were included in our review. Our pooled analysis of 5 studies and 9,398 patients revealed a significantly higher mean area under curve (AUC) associated with AI mortality predictions than traditional score predictions (MD: -0.16, CI: -0.22 to -0.10, < 0.00001). Subgroup analyses of 30-day mortality (MD: -0.08, CI: -0.13 to -0.03, = 0.001) and 1-year mortality (MD: -0.18, CI: -0.27 to -0.10, < 0.0001) also showed significantly higher mean AUC with AI predictions than traditional score predictions. Pooled mean AUC of all 10 studies and 22,933 patients was 0.79 [0.73, 0.85].
CONCLUSION
AI models have a higher predictive accuracy as compared to traditional risk scores in predicting post-TAVI mortality. Overall, this review demonstrates the potential of AI in achieving personalised risk assessment in TAVI patients.
REGISTRATION AND PROTOCOL
This systematic review and meta-analysis was registered under the International Prospective Register of Systematic Reviews (PROSPERO), under the registration name "All-Cause Mortality in Transcatheter Aortic Valve Replacement Assessed by Artificial Intelligence" and registration number CRD42023437705. A review protocol was not prepared. There were no amendments to the information provided at registration.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/, PROSPERO (CRD42023437705).
PubMed: 38883982
DOI: 10.3389/fcvm.2024.1343210 -
Frontiers in Psychiatry 2024Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible...
BACKGROUND
Elucidating the association between heart rate variability (HRV) metrics obtained through non-invasive methods and mental health symptoms could provide an accessible approach to mental health monitoring. This study explores the correlation between HRV, estimated using photoplethysmography (PPG) signals, and self-reported symptoms of depression and anxiety.
METHODS
A 4-week longitudinal study was conducted among 47 participants. Time-domain and frequency-domain HRV metrics were derived from PPG signals collected via smartwatches. Mental health symptoms were evaluated using the Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) at baseline, week 2, and week 4.
RESULTS
Among the investigated HRV metrics, RMSSD, SDNN, SDSD, LF, and the LF/HF ratio were significantly associated with the PHQ-9 score, although the number of significant correlations was relatively small. Furthermore, only SDNN, SDSD and LF showed significant correlations with the GAD-7 score. All HRV metrics showed negative correlations with self-reported clinical symptoms.
CONCLUSIONS
Our findings indicate the potential of PPG-derived HRV metrics in monitoring mental health, thereby providing a foundation for further research. Notably, parasympathetically biased HRV metrics showed weaker correlations with depression and anxiety scores. Future studies should validate these findings in clinically diagnosed patients.
PubMed: 38881544
DOI: 10.3389/fpsyt.2024.1371946 -
Circulation Jun 2024Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral...
BACKGROUND
Artificial intelligence, particularly deep learning (DL), has immense potential to improve the interpretation of transthoracic echocardiography (TTE). Mitral regurgitation (MR) is the most common valvular heart disease and presents unique challenges for DL, including the integration of multiple video-level assessments into a final study-level classification.
METHODS
A novel DL system was developed to intake complete TTEs, identify color MR Doppler videos, and determine MR severity on a 4-step ordinal scale (none/trace, mild, moderate, and severe) using the reading cardiologist as a reference standard. This DL system was tested in internal and external test sets with performance assessed by agreement with the reading cardiologist, weighted κ, and area under the receiver-operating characteristic curve for binary classification of both moderate or greater and severe MR. In addition to the primary 4-step model, a 6-step MR assessment model was studied with the addition of the intermediate MR classes of mild-moderate and moderate-severe with performance assessed by both exact agreement and ±1 step agreement with the clinical MR interpretation.
RESULTS
A total of 61 689 TTEs were split into train (n=43 811), validation (n=8891), and internal test (n=8987) sets with an additional external test set of 8208 TTEs. The model had high performance in MR classification in internal (exact accuracy, 82%; κ=0.84; area under the receiver-operating characteristic curve, 0.98 for moderate/severe MR) and external test sets (exact accuracy, 79%; κ=0.80; area under the receiver-operating characteristic curve, 0.98 for moderate or greater MR). Most (63% internal and 66% external) misclassification disagreements were between none/trace and mild MR. MR classification accuracy was slightly higher using multiple TTE views (accuracy, 82%) than with only apical 4-chamber views (accuracy, 80%). In subset analyses, the model was accurate in the classification of both primary and secondary MR with slightly lower performance in cases of eccentric MR. In the analysis of the 6-step classification system, the exact accuracy was 80% and 76% with a ±1 step agreement of 99% and 98% in the internal and external test set, respectively.
CONCLUSIONS
This end-to-end DL system can intake entire echocardiogram studies to accurately classify MR severity and may be useful in helping clinicians refine MR assessments.
PubMed: 38881496
DOI: 10.1161/CIRCULATIONAHA.124.068996 -
Journal of the American Heart... Jun 2024
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Journal of the American Heart... Jun 2024We aim to identify the distinct lesion patterns and regions associated with functional outcome and inflammation in patients with acute ischemic stroke, and investigate...
BACKGROUND
We aim to identify the distinct lesion patterns and regions associated with functional outcome and inflammation in patients with acute ischemic stroke, and investigate whether the association between lesion patterns and functional outcome was mediated by inflammation.
METHODS AND RESULTS
We performed nonnegative matrix factorization to derived low-dimensional lesion patterns (atoms), and Bayesian linear regression models were applied to explore the associations of lesion patterns with inflammatory factors including high-sensitivity C-reactive protein and interleukin-6, as well as functional outcome (defined as modified Rankin Scale score at 3 months). The difference distribution mean and 95% highest probability density interval (HPDI) were calculated. Mediation analysis was used to examine the mediating effects of inflammation on the relationships between lesion patterns and functional outcome. Seven lesion patterns were derived from 5914 patients with acute ischemic stroke. Lesion patterns distributed in the cortical regions were associated with inflammatory response, including atom 1 (interleukin-6: mean, 0.113 [95% HPDI, 0.073-0.162]; high-sensitivity C-reactive protein: mean, 0.082 [95% HPDI, 0.038-0.123]) and atom 4 (interleukin-6: mean, 0.113 [95% HPDI, 0.071-0.167]; high-sensitivity C-reactive protein: mean, 0.108 [95% HPDI, 0.058-0.165]). These lesion patterns were also significantly associated with functional outcome (atom 1: mean, 1.958 [95% HPDI, 1.538-2.383]; atom 4: mean, 2.245 [95% HPDI, 1.773-2.741]). Mediation analysis suggested that interleukin-6 explained 15.34% and 7.47% in the association of atom 1 and atom 4 with functional outcome, respectively.
CONCLUSIONS
Certain lesion patterns that are associated with both inflammation and functional outcome of acute ischemic stroke, especially cortical infarction, may play a role in functional outcome through modulating inflammatory reactions.
Topics: Humans; Male; Female; Retrospective Studies; Aged; Prognosis; Inflammation; Ischemic Stroke; C-Reactive Protein; Middle Aged; Interleukin-6; Biomarkers; Bayes Theorem; Magnetic Resonance Imaging; Cerebral Infarction
PubMed: 38874064
DOI: 10.1161/JAHA.123.033616 -
Journal of the American Heart... Jun 2024There is limited evidence on the outcomes and safety of mechanical thrombectomy (MT) among patients with acute ischemic stroke (AIS) in the context of cardiac diseases.... (Review)
Review
There is limited evidence on the outcomes and safety of mechanical thrombectomy (MT) among patients with acute ischemic stroke (AIS) in the context of cardiac diseases. Our study reviews MT in AIS within the context of cardiac diseases, aiming to identify existing and emerging needs and gaps. PubMed and Scopus were searched until December 31, 2023, using a combination of cardiological diseases and "mechanical thrombectomy" or "endovascular treatment" as keywords. Study design included case reports/series, observational studies, randomized clinical trials, and meta-analyses/systematic reviews. We identified 943 articles, of which 130 were included in the review. Results were categorized according to the cardiac conditions. MT shows significant benefits in patients with atrial fibrillation (n=139) but lacks data for stroke occurring after percutaneous coronary intervention (n=2) or transcatheter aortic valve implantation (n=5). MT is beneficial in AIS attributable to infective endocarditis (n=34), although functional benefit may be limited. Controversy surrounds the functional outcomes and mortality of patients with AIS with heart failure undergoing MT (n=11). Despite technical challenges, MT appears feasible in aortic dissection cases (n=4), and in patients with left ventricular assist device or total artificial heart (n=10). Data on AIS attributable to congenital heart disease (n=4) primarily focus on pediatric cases requiring technical modifications. Treatment outcomes of MT in patients with cardiac tumors (n=8) vary because of clot consistency differences. After cardiac surgery stroke, MT may improve outcomes with early intervention (n=13). Available data outline the feasibility of MT in patients with AIS attributable to large-vessel occlusion in the context of cardiac diseases.
PubMed: 38874062
DOI: 10.1161/JAHA.124.034783