Effects of a 12-week, seated, virtual, home-based tele-exercise programme compared with a prerecorded video-based exercise programme in people with chronic neurological impairments: protocol for a randomised controlled trial

Introduction
Exercise is vital to staying well and preventing secondary complications in people with chronic neurological impairments (CNI). Appropriate exercise is often inaccessible to this population. The purpose of the study is to investigate the effects of a seated, virtual exercise programme on heart rate, recovery, fatigue, pain, motivation, enjoyment and quality of life in people with CNI.

Methods and analysis
Individuals with CNI will be screened for eligibility, and 60 participants will be randomised 1:1 into either a live or prerecorded group. There is no geographical limitation to where participants reside, since participation is virtual. The study will be coordinated by one site in White Plains, New York, USA. The live group will exercise with an instructor via Zoom while the prerecorded group will exercise at their chosen time using prerecorded videos, 3x/week for 12 weeks. Primary outcome measures: change in heart rate during exercise/recovery. Secondary outcome measures: fatigue, motivation, level of pain and exertion, physical well-being, enjoyment of physical activity, motivation and quality of life. Outcomes will be assessed at baseline, midpoint, end of study and 1-month poststudy. Adverse events, medication changes and physical activity will be tracked throughout. Within-group and between-group comparisons will be performed by using analysis of covariance and regression.

Ethics and dissemination
BRANY IRB approval: 22 September 2020, protocol #20-08-388-512. All participants will provide written informed consent. Results will be disseminated through presentations, publications and ClinicalTrials.gov.

Trial registration number
NCT04564495.

Leggi
Gennaio 2023

Impact of additional community services provision on dementia caregiver burden: an interrupted time-series analysis of 12-year interRAI assessments in Hong Kong

Objectives
To evaluate the impact of providing additional dementia caregiver support services on caregiver burden.

Design
Interrupted time-series analysis using territory-wide panel data.

Settings
All public-funded district elderly community centres in Hong Kong (HK).

Participants
Primary caregivers for older adults (age over 65 years) living with dementia assessed through International Residential Assessment in HK between 1 October 2004 and 31 September 2016. Paid caregivers were excluded.

Interventions
In April 2014, US$280 million was allocated to provide additional psychological support, education and respite care for dementia caregivers in HK.

Main outcome measures
Caregiver burden was measured by two age-standardised rates: (1) caregivers in emotional distress; and (2) caregivers with long care time in a week (more than 20 hours a week). We fitted the two time-series into Autoregressive Integrated Moving Average models to evaluate intervention impacts, with follow-up analyses to consider a 6-month transition period of policy implementation. Segmented linear regressions and Holt-Winter exponential smoothening models were used as sensitivity analyses.

Results
36 689 dementia caregivers were included in this study, of which 14.4% caregivers were distress and 31.9% were long-hours caregivers after the policy intervention in April 2014. Providing additional caregiver service significantly reduced standardised rates of caregivers in distress (β (95% CI)=–3.93 (–7.85 to –0.01), p

Leggi
Novembre 2022

Spatial distribution, determinants and trends of full vaccination coverage in children aged 12-59 months in Peru: A subanalysis of the Peruvian Demographic and Health Survey

Objective
To assess the spatial distribution, trends and determinants of crude full vaccination coverage (FVC) in children aged 12–59 months between 2010 and 2019 in Peru.

Design, setting and analysis
A cross-sectional study based on the secondary data analysis of the 2010 and 2019 Peruvian Demographic and Health Surveys (DHSs) was conducted. Logit based multivariate decomposition analysis was employed to identify factors contributing to differences in FVC between 2010 and 2019. The spatial distribution of FVC in 2019 was evaluated through spatial autocorrelation (Global Moran’s I), ordinary kriging interpolation (Gaussian process regression) and Bernoulli-based purely spatial scan statistic.

Outcome measure
FVC, as crude coverage, was defined as having completely received BCG; three doses of diphtheria, pertussis, and tetanus, and polio vaccines; and measles vaccine by 12 months of age.

Participants
A total of 5 751 and 14 144 children aged 12–59 months from 2010 and 2019 DHSs, respectively, were included.

Results
FVC increased from 53.62% (95% CI 51.75% to 55.49%) in 2010 to 75.86% (95% CI 74.84% to 76.85%) in 2019. Most of the increase (70.39%) was attributable to differences in coefficients effects. Family size, visit of health workers in the last 12 months, age of the mother at first delivery, place of delivery and antenatal care follow-up were all significantly associated with the increase. The trend of FVC was non-linear and increased by 2.22% annually between 2010 and 2019. FVC distribution was heterogeneous at intradepartmental and interdepartmental level. Seven high-risk clusters of incomplete coverage were identified.

Conclusions
Although FVC has increased in Peru, it still remains below the recommended threshold. The increase of FVC was mainly attributed to the change in the effects of the characteristics of the population. There was high heterogeneity across Peruvian regions with the presence of high-risk clusters. Interventions must be redirected to reduce these geographical disparities.

Leggi
Novembre 2022

Abstract 14804: Are Mobile Cardiac Outpatient Monitors Reliable to Monitor Qtc in Comparison to 12 Lead ECG

Circulation, Volume 146, Issue Suppl_1, Page A14804-A14804, November 8, 2022. Introduction:Use of mobile cardiac outpatient monitor (MCOT) increased during the COVID-19 pandemic as a substitute for telemetry and monitoring of arrythmias during loading of antiarrhythmic drugs (AAD). However, data comparing difference of QTc interval between a MCOT, and 12 lead ECG is scare.Hypothesis:To assess the accuracy of mobile cardiac outpatient monitor in comparison to 12 lead ECG for QTc monitoringMethods:We prospectively evaluated 24 patients at our institution who received IV sotalol as single day loading dose for initiation of oral sotalol therapy for atrial fibrillation/atrial flutter (AF/AFL). All patients were discharged 6 hours after the IV loading dose with a MCOT for 3 days. All patients had a 12 lead ECG within 12-18 hours of the baseline line MCOT transmission. Variation in heart rate and QTc was assessed.Results:A total of 24 patients were included in the study. The mean age was 65+7.3 years, 80% of patients were men. The mean difference between the QTc interval measured on 12 lead ECG and MCOT was 5.1+6 milliseconds [450+33 (EKG) – 445+39 (MCOT)], p=0.92. The mean heart rate difference between the two modalities was also not significant, p=0.726 [ 70.4+19 (EKG) -72+11.8 (MCOT), ΔHR=1.6+7.2 beats per minute].Conclusions:MCOT can be considered as a reliable alternate to 12 lead ECG for monitoring of QTc in patients receiving AAD.

Leggi
Ottobre 2022

Abstract 15229: An Artificial Intelligence Deep Neural Network Analysis of the 12-lead Electrocardiogram Distinguishes Patients With Congenital Long Qt Syndrome From Patients With Acquired Qt Prolongation

Circulation, Volume 146, Issue Suppl_1, Page A15229-A15229, November 8, 2022. Introduction:Over 100 FDA-approved medications, electrolyte perturbations, and many disease states can prolong the QTc beyond its 99thpercentile value resulting in acquired QT prolongation. In contrast, approximately 1 in 2000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation secondary to genetic defects in the heart.Hypothesis:An artificial intelligence (AI) deep neural network (DNN) can distinguish patients with LQTS from those with acquired QT prolongation.Methods:The study cohort included all patients with LQTS evaluated in the Windland Smith Rice Genetic Heart Rhythm Clinic and controls from Mayo Clinic’s ECG data vault comprising over 2.5 million patients. For the AI-DNN model, every patient/control with ≥ 1 ECG above age- and sex- specific 99thpercentile values for QTc [ > 460 ms for all patients (male/female) < 13 years of age, or > 470 ms for men and > 480 ms for women above this age] was included. An AI-DNN involving a multi-layerconvolutionalneuralnetwork (CNN) was developed to classify patients. LQTS patients were age- and sex- matched to controls at 1:5 ratio.Results:Among the 1,599 patients with genetically confirmed LQTS, 808 had ≥ 1 ECG with QTc above the threshold (2,987 ECGs) compared to 361,069/2.5M controls (14% of Mayo Clinic patients getting an ECG, ‘presumed negative’; 989,313 ECGs). Following age- and sex- matching and splitting, 3,309 (training), 411 (validation) and 887 (testing) control ECGs were used. This model distinguished LQTS from those with acquired QT prolongation with an AUC of 0.896 (accuracy 85%, sensitivity 77%, specificity 88%, PPV 0.58, NPV 0.94). After exclusion of patients with a wide QRS ( >150 ms) or pacemaker, the model remained successful in distinguishing the two groups (AUC 0.85, accuracy 78%, sensitivity 78%, specificity 87%, PPV 59%, NPV 94%).Conclusions:For patients with a QTc exceeding its 99thpercentile values, this novel AI-DNN functions as a LQTS mutation detector being able to identify patients with abnormal QT prolongation secondary to a LQTS-causative mutation rather than acquired QT prolongation with a >50% positive predictive value. This algorithm may facilitate screening for this potentially lethal, yet highly treatable, genetic heart disease.

Leggi
Ottobre 2022

Abstract 9374: Machine Learning on Automated Measurement of 12 Leads Electrocardiography Can Predict Appropriate Shock in Patients With Implantable Cardioverter Defibrillator

Circulation, Volume 146, Issue Suppl_1, Page A9374-A9374, November 8, 2022. Introduction:Prediction for appropriate shock in patients with implantable cardioverter defibrillator (ICD) is still challenging. We tried to predict the shock by machine learning (ML) model on automated microvolt-level measurement of 12-leads electrocardiography (ECG).Hypothesis:ML on 12-leads ECG can predict appropriate ICD shock.Methods:Consecutive 177 patients (61.5±14.4years, 141males, organic heart disease: 121cases) with ICD were enrolled. ECG was measured by ECAPs12c system (Nihon-Koden) at ICD implantation. Shock was defined as appropriate ICD shock and anti-tachycardia overdrive pacing. Statistic significant predictors were extracted by univariate Cox regression analysis. Because of many correlation/confounding/multicollinearity among the predictors, multivariate Cox was not performed, and machine learning (ML) predictive model was utilized to compare the importance of the predictors.Results:Fifty-six patients were treated by appropriate shock during the observation period (median during implantation to shock: 1.85years). Thirteen significant predictors were extracted, and T-axis showed the smallest P value of univariate Cox (P=0.0007). Random Forest Classifier model demonstrated high accuracy (0.740) and T-axis showed the most important role to build the model. Receiver operating characteristics (ROC) curve analysis indicated the cut-off value as 105 degree, and Kapan-Meier curve analysis demonstrated T-axis ≥105 degree group showed worse prognosis than T-axis >105 degree group.Conclusions:ML of microvolt-level measurement of 12-leads ECG potentially had high predictive value for appropriate shock of ICD, and T-axis played an essential role for the prediction.

Leggi
Ottobre 2022

Abstract 13312: Can the Extent of Late Gadolinium Enhancement on Cardiac Magnetic Resonance Be Predicted by the 12-Lead Electrocardiogram?

Circulation, Volume 146, Issue Suppl_1, Page A13312-A13312, November 8, 2022. Background:In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) is an in vivo marker of replacement fibrosis with a continuous relationship between amount of LGE and both risk for sudden death and development of endstage disease. Cost and accessibility can limit routine utilization of CMR. In contrast, electrocardiogram (ECG) is an inexpensive readily available test and identifies patients with myocardial scarring in ischemic heart disease. Its ability to identify presence and extent of LGE in HCM is unknown.Methods:1983 consecutive HCM patients were included; 62% male, average wall thickness 18 ± 3mm, and 36% with resting LV outflow tract obstruction ≥ 30mmHg. ECGs were analyzed for LV hypertrophy, T wave abnormalities, ST-segment depression or elevations, abnormal Q waves, conduction disease, atrial enlargement and QTc prolongation. Extent of LGE was quantified and expressed as a proportion of total LV myocardium.Results:822 (41%) had no LGE, while 1161 (59%) had LGE, including 687 (35%) with

Leggi
Ottobre 2022