Ambulatory Recordings Research Articles

Hypertension in Dialysis Patients: Diagnostic Approaches and Evaluation of Epidemiology.

Whereas hypertension is an established cardiovascular risk factor in the general population, the contribution of increased blood pressure (BP) to the huge burden of cardiovascular morbidity and mortality in patients receiving dialysis continues to be debated. In a large part, this controversy is attributable to particular difficulties in the accurate diagnosis of hypertension. The reverse epidemiology of hypertension in dialysis patients is based on evidence from large cohort studies showing that routine predialysis or postdialysis BP measurements exhibit a U-shaped or J-shaped association with cardiovascular or all-cause mortality. However, substantial evidence supports the notion that home or ambulatory BP measurements are superior to dialysis-unit BP recordings in diagnosing hypertension, in detecting evidence of target-organ damage and in prognosticating the all-cause death risk. In the first part of this article, we explore the accuracy of different methods of BP measurement in diagnosing hypertension among patients on dialysis. In the second part, we describe how the epidemiology of hypertension is modified when the assessment of BP is based on dialysis-unit versus home or ambulatory recordings.

A flexible adhesive surface electrode array capable of cervical electroneurography during a sequential autonomic stress challenge

This study introduces a flexible, adhesive-integrated electrode array that was developed to enable non-invasive monitoring of cervical nerve activity. The device uses silver-silver chloride as the electrode material of choice and combines it with an electrode array consisting of a customized biopotential data acquisition unit and integrated graphical user interface (GUI) for visualization of real-time monitoring. Preliminary testing demonstrated this electrode design can achieve a high signal to noise ratio during cervical neural recordings. To demonstrate the capability of the surface electrodes to detect changes in cervical neuronal activity, the cold-pressor test (CPT) and a timed respiratory challenge were employed as stressors to the autonomic nervous system. This sensor system recording, a new technique, was termed Cervical Electroneurography (CEN). By applying a custom spike sorting algorithm to the electrode measurements, neural activity was classified in two ways: (1) pre-to-post CPT, and (2) during a timed respiratory challenge. Unique to this work: (1) rostral to caudal channel position-specific (cephalad to caudal) firing patterns and (2) cross challenge biotype-specific change in average CEN firing, were observed with both CPT and the timed respiratory challenge. Future work is planned to develop an ambulatory CEN recording device that could provide immediate notification of autonomic nervous system activity changes that might indicate autonomic dysregulation in healthy subjects and clinical disease states.

Evaluation of Heart Rate Variability in Children with Congenital Heart Diseases before and after Surgical Repair

Background: Surgical repair of congenital heart diseases (CHD) affects heart rate variability (HRV) in different ways. It leads to varying degrees of cardiac autonomic derangement, clinically detectable as depression of HRV parameters. The aim of this work was to evaluate HRV in children with CHDs before and after surgical repair to detect the effect of CHDs (acyanotic or cyanotic) and cardiac surgery on cardiac autonomic function.
 Methods: This observational case-control study enrolled thirty cases with CHD. Cases were divided in two equal groups: group1 included children with CHDs (not repaired) before surgical intervention and group 2 included children after surgical repair of CHDs. Another fifteen healthy children, matched for age and sex, were enrolled as a control group. All children in this study were subjected to full history taking, clinical evaluation, Holter monitor, twenty-four-hour ambulatory electrocardiographic recordings and the seven time-domain indices of HRV.
 Results: There was significant increase of HR in patients before surgical repair as compared to patients after surgical repair as compared to control group. There was significant decrease of PNN50, r MMSD, SDNN index and SDANN of children before surgical repair as compared to children after surgical repair as compared to controls. There was a negative significant correlation between HR and age before surgery, after surgery there was a negative significant correlation between HR and age, pNN50, rMMSD, SDNN index and SDANN, in control there was negative significant correlation with age, SDNN index, SDANN and SDNN.
 Conclusion: HRV parameters increased significantly in children with CHDs after surgical repair as compared to these before surgical repair denoting effect of cardiac surgery on cardiac autonomic function.

Actigraphy in studies on insomnia: Worth the effort?

In the past decades, actigraphy has emerged as a promising, cost-effective, and easy-to-use tool for ambulatory sleep recording. Polysomnography (PSG) validation studies showed that actigraphic sleep estimates fare relatively well in healthy sleepers. Additionally, round-the-clock actigraphy recording has been used to study circadian rhythms in various populations. To this date, however, there is little evidence that the diagnosis, monitoring, or treatment of insomnia can significantly benefit from actigraphy recordings. Using a case-control design, we therefore critically examined whether mean or within-subject variability of actigraphy sleep estimates or circadian patterns add to the understanding of sleep complaints in insomnia. We acquired actigraphy recordings and sleep diaries of 37 controls and 167 patients with varying degrees of insomnia severity for up to 9 consecutive days in their home environment. Additionally, the participants spent one night in the laboratory, where actigraphy was recorded alongside PSG to check whether sleep, in principle, is well estimated. Despite moderate to strong agreement between actigraphy and PSG sleep scoring in the laboratory, ambulatory actigraphic estimates of average sleep and circadian rhythm variables failed to successfully differentiate patients with insomnia from controls in the home environment. Only total sleep time differed between the groups. Additionally, within-subject variability of sleep efficiency and wake after sleep onset was higher in patients. Insomnia research may therefore benefit from shifting attention from average sleep variables to day-to-day variability or from the development of non-motor home-assessed indicators of sleep quality.

Paintable Carbon Nanotube Coating-Based Textronics for Sustained Holter-Type Electrocardiography.

A growing population suffering from or at high risk of developing cardiovascular diseases can benefit from rapid, precise, and readily available diagnostics. Textronics is an interdisciplinary approach for designing and manufacturing high-performance flexible electronics integrated with textiles for various applications, with electrocardiography (ECG) being the most convenient and most frequently used diagnostic technique for textronic solutions. The key challenges that still exist for textronics include expedient manufacturing, adaptation to human subjects, sustained operational stability for Holter-type data acquisition, reproducibility, and compatibility with existing solutions. The present study demonstrates conveniently paintable ECG electroconductive coatings on T-shirts woven from polyester or 70% polyamide and 30% polyester. The up to 600-μm-thick coatings encompass working electrodes of low resistivity 60 Ω sq-1 sheathed in the insulated pathways-conjugable with a wireless, multichannel ECG recorder. Long (800 μm) multiwalled carbon nanotubes, with scalable reproducibility and purity (18 g per round of synthesis), constituted the electroactive components and were embedded into a commercially available screen-printing acrylic base. The resulting paint had a viscosity of 0.75 Pa·s at 56 s-1 and 25 °C and was conveniently applied using a paintbrush, making this technique accessible to manufacturers. The amplified and nondigitally processed ECG signals were recorded under dry-skin conditions using a certified ECG recorder. The system enabled the collection of ECG signals from two channels, allowing the acquisition of cardiac electrical activity on six ECG leads with quality at par with medical diagnostics. Importantly, the Holter-type ECG allowed ambulatory recording for >24 h under various activities (sitting, sleeping, walking, and running) in three male participants. The ECG signal was stable for >5 cycles of washing, a level of stability not reported yet previously. The developed ECG-textronic application possesses acceptable and reproducible characteristics, making this technology a suitable candidate for further testing in clinical trials.

A prediction model for ventricular tachycardia events using 24h ambulatory ECG

Abstract Background Ventricular tachycardia (VT) is a potentially lethal condition that occurs intermittently. The aim of this study was to derive a risk prediction model for VT episodes detected on ≤30 day mobile cardiac telemetry using a 24 hour ambulatory ECG recording. Methods We included patients who were monitored for 2–30 full days in the USA using a full-disclosure mobile cardiac telemetry device in 2017. Patients with a VT episode ≥10 beats duration (VT≥10 beats) on the first full recording day were excluded. Arrhythmias were algorithmically detected and manually verified. A LASSO model was derived for the outcome of a VT≥10 beats detected on days 2–30. Potential predictors included age, sex, and ECG data from the first 24h: heart rate (max, min and mean), premature atrial and ventricular complexes occurring as singles, couplets, triplets, and runs ≥4 beats as well as the fastest rate for each event. The population was split into equal random training and testing samples. Results In a population of 19,789 patients (mean age 65.3, 43.4% men), and during a median recording time of 18 days there were 1,511 patients with at least one VT≥10 beats. The LASSO model had good discrimination in the testing sample, ROC-statistic 0.7586, 95% CI 0.7398–0.7774 (Figure 1a). A model excluding age and gender had similar discrimination (ROC 0.7528, 95% CI 0.7339–0.7717). In the testing sample the model was well calibrated (Figure 1b). In the top quintile more than one in five patients had a VT≥10 beats, enough to warrant extended monitoring. Conclusion A risk score based on variables easily derived from a standard 24h ECG can be used to predict high risk of VT episodes ≥10 beats within 30 days. In the top quintile VT events ≥10 beats were ten times more common than in the bottom quintile. Funding Acknowledgement Type of funding sources: Foundation. Main funding source(s): Hjärt-LungfondenSwedish Society for Medical Research

Short-term blood pressure variability and outcomes in non-dialysis chronic kidney disease.

BackgroundBlood pressure variability (BPV) is associated with cardiovascular and all-cause mortality, and has been demonstrated in dialysis patients, but has been poorly studied and remains controversial in non-dialysis chronic kidney disease (CKD) patients. We investigated the effect of short-term BPV on prognosis in this population.MethodsA total of 245 stage 1–4 CKD patients with 24-h ambulatory blood pressure recordings were recruited. BPV was evaluated by standard deviation, coefficient of variation, and variation independent of the mean, respectively. All subjects were followed up to the composite end-point event or until January 15, 2020. Patients were divided into two groups based on 24-h median variation independent of the mean, and demographics, laboratory indicators and echocardiogram results were compared. Logistic regression was used to analyze the risk factors for increased BPV. Multivariate Cox regression and Kaplan-Meier survival analysis were used to explore the relationship between BPV and renal prognosis and major cardiovascular events.ResultsThe mean age was 42.07 ± 12.66 years, with 141 males (57.55%). Multivariate Logistic regression analysis showed that high BMI (OR 1.110, P = 0.017), hyperkalemia (OR 2.227, P = 0.040), increased left ventricular end-diastolic diameter (OR 1.103, P = 0.010) and hypertension (OR 2.525, P = 0.002) were independent risk factors for high BPV. Kaplan-Meier survival analysis showed that renal and cardiovascular outcomes were better in the low BPV group than in the high BPV group (P = 0.006; P = 0.002). After adjusting for age, sex and traditional kidney related risk factors, BPV were not independently associated with renal outcomes. High BPV (HR 4.662, P = 0.017) was the main independent risk factor for major cardiovascular events in CKD.ConclusionsIn non-dialysis CKD, short-term BPV was associated with major cardiovascular disease but not renal progression. BMI, hypertension, potassium balance, and left ventricular end-diastolic diameter influenced short-term BPV.

Behavioral sleep medicine-The need for harmonization of clinical best practice outcome measures in children and adolescents with intellectual or developmental disabilities and restless sleep.

In behavioral medicine, sleep disorders, insomnia in particular, may be considered comorbidities and precipitating factors to intellectual or developmental disabilities (IDD). Nevertheless, sleep alterations have often been neglected in favor of daytime features and symptoms, albeit simple behavioral nighttime observations may disclose hypermotor features that characterize restless sleep. The root of most hypermotor restlessness is linked to central iron deficiency. The latter is often exacerbated by vitamin D deficiency (VDD), which interferes with both dopaminergic and serotonergic mechanisms. In this way, an imbalance affecting daytime behavior and mood is created. Several sleep-related motor disorders such as bruxism, periodic and aperiodic leg movements, Restless Legs Syndrome (RLS), and Restless Sleep Disorder (RSD) are commonly seen in Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD). However, they are rarely diagnosed and often overlooked in affected children and adolescents. As a result, not only are these disorders not adequately addressed therapeutically, but their symptoms may be worsened by the side-effects of drugs used to contain disruptive daytime behavior, such as antipsychotics and antidepressants. In children with IDDs, obesity, inactivity and metabolic effects of antipsychotics often lead to Sleep Disordered Breathing (SDB), which is currently understood as an inflammatory state leading to “hyperactive” lethargy and further alterations of the hypoxic chain and vitamin D levels. Endorsing simple routine blood tests, including inflammatory markers such as C-reactive protein, ferritin, transferrin, and vitamin D levels, may favorably complement caregiver observations and ambulatory sleep recordings, leading to a sleep disorder diagnosis and consequent therapy. In fact, the treatment of SDB, RLS, and RSD has been copiously demonstrated to favorably impact vigilance, behavior, social competence, and academic skills in healthy and, to a greater extent, in IDD children. Thus, consulting and deliberating the root causes of functional and categorical diagnoses within a clinical framework may engender a more precise diagnosis and further benefit pediatric daytime and nighttime management of hyperactive behaviors.

Device agnostic AI-based analysis of ambulatory ECG recordings

Deep Convolutional Neural Networks (DCNNs) have been shown to provide improved performance over traditional heuristic algorithms for the detection of arrhythmias from ambulatory ECG recordings. However, these DCNNs have primarily been trained and tested on device-specific databases with standardized electrode positions and uniform sampling frequencies. This work explores the possibility of training a DCNN for Atrial Fibrillation (AF) detection on a database of single‑lead ECG rhythm strips extracted from resting 12‑lead ECGs. We then test the performance of the DCNN on recordings from ambulatory ECG devices with different recording leads and sampling frequencies.We developed an extensive proprietary resting 12‑lead ECG dataset of 549,211 patients. This dataset was randomly split into a training set of 494,289 patients and a testing set of the remaining 54,922 patients. We trained a 34-layer convolutional DCNN to detect AF and other arrhythmias on this dataset. The DCNN was then validated on two Physionet databases commonly used to benchmark automated ECG algorithms (1) MIT-BIH Arrhythmia Database and (2) MIT-BIH Atrial Fibrillation Database. Validation was performed following the EC57 guidelines, with performance assessed by gross episode and duration sensitivity and positive predictive value (PPV). Finally, validation was also performed on a selection of rhythm strips from an ambulatory ECG patch that a committee of board-certified cardiologists annotated.On MIT-BIH, The DCNN achieved a sensitivity of 100% and 84% PPV in detecting episodes of AF. and 100% sensitivity and 94% PPV in quantifying AF episode duration. On AFDB, The DCNN achieved a sensitivity of 94% and PPV of 98% in detecting episodes of AF, and 98% sensitivity and 100% PPV in quantifying AF episode duration. On the patch database, the DCNN demonstrated performance that was closely comparable to that of a cardiologist.The results indicate that DCNN models can learn features that generalize between resting 12‑lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single‑lead ECG recordings and enabling a range of clinical applications.

On the fractal geometry of different heart rhythms

In this study, we explore the possibility that ECG recordings belong to class of multifractal process for which a large number of scaling exponents are required to characterize their scaling structures. We use the BIDMC Congestive Heart Failure database including long term ECG recordings from 11 men, aged 22 to 71, and 4 women, aged 54 to 63 with severe congestive heart failure and the MIT-BIH Arrhythmia database that contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. We compare these two chronic heart diseases with the control people in the MIT-BIH Normal Sinus Rhythm database which includes 18 long-term ECG recordings of 5 men, aged 26 to 45, and 13 women, aged 20 to 50 without significant arrhythmia. The vibration analysis such as power spectral densities (PSD) analysis has been performed for differentiating the time series. Multifractal dynamics of heartbeat interval signals have been assessed by multifractal spectrum analysis to differentiate normal signals with arrhythmia and severe congestive heart failure patients. We apply Higuchi algorithm to find the fractal complexity of each cardiac rhythm and then compare the signals for different time intervals. According to our analysis, we investigate that neither the power spectral densities nor the Higuchi algorithm to find the fractal dimension alone were sufficient to separate different classes of patients and healthy people. However, when multifractal analysis and scaling exponent were used as a classifier, the three classes were well separated. In addition, multifractal analysis revealed that we have a narrow range of exponents for arrhythmia and congestive heart failure subjects and as a result, a clear loss of multifractality for them. It was of great significance to show that we have a narrower range of exponents for arrhythmia subject compared to congestive heart failure subject which is useful to recognize these two classes of patients with heart disease. Our findings provide a comprehensive framework for diagnostic and classifying different patients with cardiac disease such as arrhythmia and congestive heart failure and differentiate them with normal people without heart disease which is crucial in finding the best diagnostic and controlling strategy in fight against chronic heart disease.

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

AimsIt is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. MethodsIn this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. ResultsThe model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. ConclusionsThis algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

Home Blood Pressure monitoring: An economical, reliable, and convenient Tool over ambulatory Blood Pressure monitoring in Patients on Dialysis.

Hypertension and cardiovascular disease (CVD) is the leading cause of morbidity and mortality in patients on dialysis. Blood pressure (BP) control is of paramount importance in reducing morbidity and mortality in this high-risk population, although there is no consensus on target BP. Ambulatory BP recording is considered gold standard in estimating the BP among patients on dialysis. But, ambulatory BP recording is cumbersome, not economical, and not easily available to Indian patients. Therefore, an easier and more convenient method has to be implemented to monitor the BP. Some studies demonstrated that home BP recordings may be promising in making a more accurate diagnosis of hypertension in hemodialysis patients. However, there is paucity of research that compares home-based BP monitoring with ambulatory BP recordings in patients on hemodialysis. The present study was thus planned to examine the hypothesis that out-of-dialysis unit BP measurement in the form of home-based measurement of BP is as efficacious as ambulatory BP monitoring (ABPM) in evaluating hypertension among patients on hemodialysis. To assess the accuracy of home-based BP monitoring in comparison to ABPM among chronic kidney disease (CKD) patients on hemodialysis. To assess the ability of home-based BP monitoring to detect hypertension among CKD patients on hemodialysis. This was a prospective observational study carried out in the Department of Medicine in a tertiary care hospital. The total duration of the study was 24 months. Fifty-two CKD patients on hemodialysis fulfilling the eligibility criteria were taken up for the study after informed consent. Blood pressure was measured using a standardized BP measuring equipment at home, thrice a day for 3 days in the interdialysis period. Also, all these patients were subjected to 24 hours of ABPM in the interdialysis period. Home-based BP monitoring records are then compared with the one-time ABPM records. All statistical calculations were done using computer programs Microsoft Excel 2007 (Microsoft Corporation, NY, USA) and SPSS (Statistical Product and Service Solutions; SPSS Inc., Chicago, Illinois, USA) version 21. In our study, the mean awake, asleep, and average ABPM readings of systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 141.69/81.33, 139.39/80.04, and 141.23/80.67 mm Hg, respectively, while the mean SBP and DBP recorded on home-based measurements were 143.6 and 82.69 mm Hg, respectively. All the subjects showing mean SBP ≥140 mm Hg as per ABPM had home-based readings of above 140 mm Hg while 23 out of 27 patients (85.2%) with mean SBP &lt;140 mm Hg as per ABPM had similar observation by home-based monitoring (kappa 0.847; p&lt;0.01). All the subjects showing mean DBP ≥90 mm Hg as per ABPM had home-based readings of above 90 mm Hg while 42 out of 44 patients (95.5%) with mean DBP &lt;90 mm Hg as per ABPM had similar observation by home-based monitoring (kappa 0.866; p&lt;0.01). Our study shows that there is no difference between BP readings as observed by ABPM and home-based BP monitoring. Also, home-based BP monitoring can detect hypertension as effectively as ABPM among patients on hemodialysis, thereby making home-based BP monitoring a safe and reliable method of BP measurement in clinical practice.

Optimum models of hospice at home services for end-of-life care in England: a realist-informed mixed-methods evaluation

Background Many people prefer to die at home when the time comes. Hospice at home services aim to support patients to achieve this. A range of hospice at home services exist; some services have been evaluated, but there has been limited evidence synthesis. Objectives The main objective was to find out what models of hospice at home services work best, for whom and in what circumstances. Other objectives supported this aim, including an analysis of the health economic costs of hospice at home models. Design The study was an overarching, non-interventional, realist evaluation comprising three phases. Phase 1 was a survey of hospice at home services. Phase 2 involved 12 case studies, grouped into four models on the basis of size and 24 hours per day, 7 days per week (24/7), operations, from which quantitative and health economics data were gathered. Qualitative interview data from bereaved carers, commissioners and providers were analysed to generate context–mechanism–outcome configurations. Phase 3 comprised stakeholder consensus meetings. Setting Hospice at home services across England. Participants A total of 70 hospice at home managers responded to the survey. A total of 339 patient and family/informal carer dyads were recruited; 85 hospice at home providers and commissioners were interviewed. A total of 88 stakeholders participated in consensus meetings. Main outcome measures The quality of dying and death of patients was assessed by bereaved carers (using the Quality of Dying and Death questionnaire). A patient’s use of services was collected using the Ambulatory and Home Care Record. Results Hospice at home services varied; two-thirds were mainly charitably funded, and not all operated 24/7. Most patients (77%) had cancer. Hospice at home services overall provided care that was likely to deliver ‘a good death’, and 73% of patients died in their preferred place. Six context–mechanism–outcome configurations captured factors relevant to providing optimum hospice at home services: (1) sustainability (of the hospice at home service); (2) volunteers (use of, in the hospice at home service); (3) integration and co-ordination (with the wider health and social care system); (4) marketing and referral (of the hospice at home service); (5) knowledge, skills and ethos (of hospice at home staff); and (6) support directed at the carer at home. Key markers of a good service included staff who had time to care, providing hands-on care; staff whose knowledge and behaviour promoted supportive relationships and confidence through the process of dying; and services attending to the needs of the informal carer. Areas of potential improvement for most hospice at home services were the use of volunteers in hospice at home, and bereavement care. Limitations The study had the following limitations – heterogeneity of hospice at home services, variations in numbers and patient clinical statuses at recruitment, a low Quality of Dying and Death questionnaire response rate, and missing data. Only patients with an informal carer involved on a daily basis were eligible for the study. Conclusions Hospice at home services delivered high-quality care and a ‘good death’, with the majority of patients dying in their stated preferred place. Hospice at home providers can improve their impact by focusing on the features identified that deliver the best patient outcomes. Commissioners can facilitate patient preference and reduce the number of hospital deaths by working with hospice at home services to secure their financial sustainability and increase the numbers and range of patients admitted to hospice at home services. Future research Future research should explore the use of volunteers in the hospice at home setting and evaluate approaches to bereavement support. Funding This project was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme and will be published in full in Health and Social Care Delivery Research; Vol. 10, No. 24. See the NIHR Journals Library website for further project information.

Water-soluble adhesive for stable long-term ambulatory EEG recordings

ObjectiveConventional methods used to adhere EEG electrodes are often uncomfortable. Here, we present a polymer-based water-soluble EEG adhesive that can be maintained for up to 6 days. The primary outcome measure of this study is the median electrode impedance at day 6. MethodsImpedance measurements for 841 EEG recordings using a 21 channel 10–20 configuration were remotely logged daily for 6 days after connection. A novel electrode adhesive was used to attach EEG electrodes. Patients were instructed to maintain their electrodes on day 4. ResultsMedian electrode impedances were significantly below 10kOhms for each day of recording, with a median value on day 6 of 4.18kOhms. Impedance values were significantly lower on day 5 than on day 4, demonstrating that the maintenance process can reduce impedance. Except for day 4–5, the median impedance increased each day. No significant difference was found on the first or final day between clinics or residences from areas of different geographic remoteness. ConclusionsEEG is able to be recorded in patients homes for 6 days with acceptable impedance and no significant effect of regionality or patients age. SignificanceTo the best of our knowledge, this is the first report in the literature of impedance data from long-term ambulatory EEG studies.

Device agnostic AI based analysis of ambulatory ECG recordings

Device agnostic AI based analysis of ambulatory ECG recordings

CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset.

ECG is a non-invasive tool for arrhythmia detection. In recent years, wearable ECG-based ambulatory arrhythmia monitoring has gained increasing attention. However, arrhythmia detection algorithms trained on existing public arrhythmia databases show higher FPR when applied to such ambulatory ECG recordings. It is primarily because the existing public databases are relatively clean as they are recorded using clinical-grade ECG devices in controlled clinical environments. They may not represent the signal quality and artifacts present in ambulatory patient-operated ECG. To help build and evaluate arrhythmia detection algorithms that can work on wearable ECG from free-living conditions, we present the design and development of the CACHET-CADB, a multi-site contextualized ECG database from free-living conditions. The CACHET-CADB is subpart of the REAFEL study, which aims at reaching the frail elderly patient to optimize the diagnosis of atrial fibrillation. In contrast to the existing databases, along with the ECG, CACHET-CADB also provides the continuous recording of patients' contextual data such as activities, body positions, movement accelerations, symptoms, stress level, and sleep quality. These contextual data can aid in improving the machine/deep learning-based automated arrhythmia detection algorithms on patient-operated wearable ECG. Currently, CACHET-CADB has 259 days of contextualized ECG recordings from 24 patients and 1,602 manually annotated 10 s heart-rhythm samples. The length of the ECG records in the CACHET-CADB varies from 24 h to 3 weeks. The patient's ambulatory context information (activities, movement acceleration, body position, etc.) is extracted for every 10 s interval cumulatively. From the analysis, nearly 11% of the ECG data in the database is found to be noisy. A software toolkit for the use of the CACHET-CADB is also provided.

RWD66 Leveraging Laboratory Results from Multiple Data Sources to Re-Assess a Possible Association between Serum Uric Acid and Schizophrenia in Real World Practice Settings

The relationship between serum uric acid (UA) and schizophrenia has been extensively investigated but remains unclear. Analysis of this relationship in RWD requires sufficient sample size and integrating multiple data sources. We leveraged laboratory results from a national supplier (Quest Diagnostics) to enhance clinical records in IQVIA’s Ambulatory Electronic Medical Records (AEMR).

Clinical utility of a video/audio-based epilepsy monitoring system Nelli

ObjectiveThe aim of this study was to evaluate the clinical utility of a semi-automated hybrid video/audio-based epilepsy monitoring system (Nelli®) in a home setting. MethodsIn this retrospective study, 104 consecutive patients underwent Nelli-registration for an average of 29 days at their home. The seizure-related data obtained from the registration were assessed to investigate the utility of the Nelli-registration regarding clinical decision-making. ResultsOf 104 patients, Nelli® hybrid system was able to recognize clinically relevant events in 83 (80%) patients: epileptic seizures in 67 (65%) and nonepileptic events in 16 (15%). A total of 2767 epileptic seizures of different seizure types were captured and identified. These seizures included not only tonic-clonic seizures but also other complex or simple motor seizures.For the outcomes regarding clinical decision-making, a need for a new therapeutic intervention was recognized in 54 (51.9%) patients based on the number and severity of seizures captured by Nelli-registration. In 12 (11.5%) patients, the need to change the treatment plan was excluded because no evidence of suspected epileptic seizures was found. Nelli-registration aided in confirming the therapeutic efficacy of modifications of antiseizure medications (ASMs) or neuromodulation therapies in 13 (12.5%) patients. Nelli-registration enabled to determine the change in seizure classification and facilitated to reach clear diagnostic conclusions in 11 (10.6%) patients. In 14 (13.5%) patients, there was no change in clinical outcome, as Nelli-registration was unable to infer any clinical decision either due to inconclusive results or lack of typical events.Seizures detected during Nelli-registration aided in decision-making for therapeutic interventions in 71 (68%) patients. Altogether, 44 (42%) patients had adjustment of ASMs, and in 9 (9%) patients, Nelli-registrations led to the change in the settings of vagus nerve stimulation (VNS) or deep brain stimulation (DBS) treatment. Additionally, 18 (17%) patients were referred to presurgical evaluation or established a baseline seizure frequency before surgical implantation for neuromodulation treatment with VNS or DBS, while 33 (32%) patients had no change in therapy. Nine patients (8.7%) were referred to video-EEG monitoring (VEM), as Nelli-recorded events highlighted the need for presurgical evaluation in 6 patients or further diagnostic evaluation in 3 patients. ConclusionThis study confirms the clinical utility of the video/audio monitoring system Nelli® in home settings. Home monitoring with Nelli® hybrid system provides a new alternative for the assessment of frequency and type of epileptic seizures as well as for a recognition of nonepileptic events. Thus, Nelli-registration can facilitate the optimization of seizure monitoring and management in clinical practice, complementing existing methods such as VEM and ambulatory EEG recordings.

1112-P: Prevalence of Testing for Blood Glucose among Adults with Overweight or Obesity in the U.S.

An estimated 13% of all U.S. adults aged ≥18 years have diabetes, and 34.5% meet criteria for having prediabetes. Screening adults at high risk for prediabetes and diabetes may allow earlier detection, diagnosis, and treatment, thereby improving health outcomes. The U.S. Preventive Services Task Force (USPSTF) recommends screening every three years for abnormal blood glucose in adults aged 40-70 years who have overweight or obesity (this recommendation was updated to adults aged 35-70 years in August of 2021) . Using IQVIA Ambulatory Electronic Medical Records (EMR) via the IQVIA E360 platform, we aimed to estimate the proportion of adults aged 40-70 years with overweight or obesity who received a test for blood glucose in a 3-year period from 2016-2019. We defined overweight or obesity based on body mass index (BMI of 25-29.9 kg/m2 and ≥30 kg/m2, respectively) calculated using measured height and weight. Receipt of a blood glucose test was defined as having a hemoglobin A1c or fasting plasma glucose measured, as recommended by USPSTF. Our analysis identified adults aged 40-70 years with overweight or obesity in 2016 (the index year) and without preexisting diabetes, defined using a modified SUPREME-DM algorithm for EMR data[1]. We included adults who were present in the dataset for at least two years before their index BMI in 2016 (to exclude those with prevalent diabetes) and three years after the index BMI, during which we examined the occurrence of blood glucose testing. Among 1,360,095 adults who had overweight or obesity, 33.4% received a test for blood glucose in 2016-2019. The testing rate was 28.7% among adults with overweight and 37.3% among adults with obesity. Our findings could help inform clinical and public health promotion efforts to improve screening for blood glucose levels among adults with overweight or obesity. Future studies may identify factors associated with receiving timely blood glucose screening. [1]https://pubmed.ncbi.nlm.nih.gov/25515167/ Disclosure Y.Chen: None. E.A.Lundeen: None. A.Koyama: None. L.Kompaniyets: None. L.J.Andes: None. S.R.Benoit: None. G.Imperatore: None. D.B.Rolka: None.

0730 Validation studies for scoring polysomnograms and home sleep apnea tests with artificial intelligence: Sleep stage probabilities (hypnodensity) derived from neurological or cardiorespiratory signals

Abstract Introduction There have been significant advances in machine learning in recent years. This means that powerful methods are now available for classification problems, such as scoring sleep stages from neurological or cardiorespiratory signals. In the present work, validation studies for both applications are presented. Methods To determine the 5 sleep stages from the neurological signals, 54 sleep-wake-related features were calculated and classified by a bidirectional long short-term memory (LSTM) network which had been trained on 1956 manual scorings of 588 PSGs from 294 subjects (supervised deep learning). To determine the 4 stages (wake, light sleep, deep sleep, REM) from cardiorespiratory signals, a convolutional neural network combined with LSTM layers was used for feature extraction and classification. This network had been trained on 685 PSGs from 391 subjects (Bakker et al. JCSM 2021). The networks obtained were validated in 428 PSGs with one and 10 PSGs with 12 manual scorings (neurological staging) as well as in 2 two datasets, each containing 296 ambulatory recordings (cardiorespiratory staging). Results Cohen’s kappa between autoscoring based on neurological signals and manual scoring was 0.74 (95%-confidence interval: 0.74-0.74) for the 428 PSGs. The intraclass correlation coefficient (ICC) for absolute agreement between autoscoring and manual scoring was for the AHI 0.97 (0.96-0.98), for the arousal index 0.79 (0.67-0.86) and for the PLMSI 0.91 (0.88-0.93). The ICC between the sleep stage probabilities derived from the 12 manual scorings and the artificial intelligence (AI) derived hypnodensity was 0.91 (0.91-0.91). Cohen’s kappa values for the cardiorespiratory sleep staging were 0.68 (0.68-0.68) and 0.64 (0.63-0.64) for the 2 datasets with 296 ambulatory recordings each. Conclusion All metrics from the PSG validation studies show substantial (Cohen’s kappa &amp;gt; 0.6) as well as good to excellent agreement (ICC &amp;gt; 0.75 or &amp;gt; 0.90) compared to manual scorings. As an added value of the AI-supported PSG evaluation, the probabilities of the sleep stages per epoch are determined (hypnodensity graph). The valid estimation of the sleep stages from cardiorespiratory signals by means of AI may result in improved clinical interpretation of home sleep apnea tests, which are increasingly used in the sleep-disordered breathing diagnostic pathway. Support (If Any)