Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea.

Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome, not readily available and represents a well-known roadblock to managing this huge societal burden. Assessment of neuromuscular function involved in the upper airway using electromyography (EMG) has shown potential to characterize and diagnose sleep apnea, while the development of transmembranous electromyography (tmEMG), a painless surface probe, has made this opportunity practical and highly feasible.

A machine learning framework to adjust for learning effects in medical device safety evaluation.

Objectives: Traditional methods for medical device post-market surveillance often fail to accurately account for operator learning effects, leading to biased assessments of device safety. These methods struggle with non-linearity, complex learning curves, and time-varying covariates, such as physician experience. To address these limitations, we sought to develop a machine learning (ML) framework to detect and adjust for operator learning effects.

Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases.

Introduction: Drug-induced acute kidney injury (DI-AKI) is a frequent adverse event. The identification of DI-AKI is challenged by competing etiologies, clinical heterogeneity among patients, and a lack of accurate diagnostic tools. Our research aims to describe the clinical characteristics and predictive variables of DI-AKI.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

Background: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships.

Development of a national Department of Veterans Affairs mortality risk prediction model among patients with cirrhosis.

Objective: Cirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models.

Rate of Risk Factors for and Interventions to Reduce Hospital Readmission in Patients With Inflammatory Bowel Diseases.

Background & Aims: We investigated 30- and 90-day rates and causes of, risk factors for, and interventions to reduce hospital readmission in patients who received medical treatment for inflammatory bowel diseases (IBD).

Methods: We performed a systematic search of publications through July 1, 2018 for studies of rates of hospital readmission and associated causes and risk factors in patients who received medical treatments for IBD. Our final analysis included 17 cohort studies (6324 patients) of hospitalized adults with IBD who had received medical treatment, along with reported readmission rates with detailed chart review.

A clinical risk prediction model to identify patients with hepatorenal syndrome at hospital admission.

Background: Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis and early detection of evolving HRS may provide opportunities for early intervention. We developed a HRS risk model to assist early recognition of inpatient HRS.

Methods: We analysed a retrospective cohort of patients hospitalised from among 122 medical centres in the US Department of Veterans Affairs between 1 January 2005 and 31 December 2013.

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

Background: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis.

Objective: Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow.

Methods: Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information technologists to create user interface designs.

Consumer Health Informatics Adoption among Underserved Populations: Thinking beyond the Digital Divide.

Objectives:  Underserved populations can benefit from consumer health informatics (CHI) that promotes self-management at a lower cost. However, prior literature suggested that the digital divide and low motivation constituted barriers to CHI adoption. Despite increased Internet use, underserved populations continue to show slow CHI uptake.

Application of contextual design methods to inform targeted clinical decision support interventions in sub-specialty care environments.

Background & Objectives: In healthcare, the routine use of evidence-based specialty care management plans is mixed. Targeted computerized clinical decision support (CCDS) interventions can improve physician adherence, but adoption depends on CCDS' 'fit' within clinical work. We analyzed clinical work in outpatient and inpatient settings as a basis for developing guidelines for optimizing CCDS design.

Development of an automated phenotyping algorithm for hepatorenal syndrome.

Objective: Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification.

Materials And Methods: A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013.

Motor threshold in transcranial magnetic stimulation: the impact of white matter fiber orientation and skull-to-cortex distance.

The electrophysiology of transcranial magnetic stimulation (TMS) of motor cortex is not well understood. In this study, we investigate several structural parameters of the corticospinal tract and their relation to the TMS motor threshold (MT) in 17 subjects, with and without schizophrenia. We obtained structural and diffusion tensor MRI scans and measured the fractional anisotropy and principal diffusion direction for regions of interest in the corticospinal tract.

Infections associated with severe malnutrition among hospitalised children in East Africa.

Severe protein-energy malnutrition (PEM) predisposes affected children to various infections, which either worsens their nutritional status or causes malnutrition, hence complicating their management and outcome. This study was carried out to determine the infections associated with severe malnutrition among children admitted at Kilifi District Hospital (KDH) in Kenya and Muhimbili National Hospital (MNH) in Dar es Salaam, Tanzania. Data was collected from hospital register books and online system database.

Emotion facilitates action: a transcranial magnetic stimulation study of motor cortex excitability during picture viewing.

Emotional stimuli capture attention, receive increased perceptual processing resources, and alter peripheral reflexes. In the present study, we examined whether emotional stimuli would modulate the magnitude of the motor evoked potential (MEP) elicited in the abductor pollicus brevis muscle by transcranial magnetic stimulation (TMS) delivered to the motor cortex. The electromyogram (EMG) was recorded from 16 participants while they viewed six blocks of pleasant, neutral, and unpleasant images; 36 TMS pulses at increasing intensities were delivered during each block.

Serial vagus nerve stimulation functional MRI in treatment-resistant depression.

Vagus nerve stimulation (VNS) therapy has shown antidepressant effects in open acute and long-term studies of treatment-resistant major depression. Mechanisms of action are not fully understood, although clinical data suggest slower onset therapeutic benefit than conventional psychotropic interventions. We set out to map brain systems activated by VNS and to identify serial brain functional correlates of antidepressant treatment and symptomatic response.

Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: a computer simulation evaluation of best methods.

Objectives: Resting motor threshold is the basic unit of dosing in transcranial magnetic stimulation (TMS) research and practice. There is little consensus on how best to estimate resting motor threshold with TMS, and only a few tools and resources are readily available to TMS researchers. The current study investigates the accuracy and efficiency of 5 different approaches to motor threshold assessment for TMS research and practice applications.

Decreased cortical response to verbal working memory following sleep deprivation.

Study Objective: To investigate the cerebral hemodynamic response to verbal working memory following sleep deprivation.

Design: Subjects were scheduled for 3 functional magnetic resonance imaging scanning visits: an initial screening day (screening state), after a normal night of sleep (rested state), and after 30 hours of sleep deprivation (sleep-deprivation state). Subjects performed the Sternberg working memory task alternated with a control task during an approximate 13-minute functional magnetic resonance imaging scan.

The maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using parameter estimation by sequential testing is faster than conventional methods with similar precision.

Background: The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses.