Dataset Information

Use of a Bluetooth tablet-based technology to improve outcomes in lung transplantation: A pilot study.

ABSTRACT: The impact of remote patient monitoring platforms to support the postoperative care of solid organ transplant recipients is evolving. In an observational pilot study, 28 lung transplant recipients were enrolled in a novel postdischarge home monitoring program and compared to 28 matched controls during a 2-year period. Primary endpoints included hospital readmissions and total days readmitted. Secondary endpoints were survival and inflation-adjusted hospital readmission charges. In univariate analyses, monitoring was associated with reduced readmissions (incidence rate ratio [IRR]: 0.56; 95% confidence interval [CI]: 0.41-0.76; P < .001), days readmitted (IRR: 0.46; 95% CI: 0.42-0.51; P < .001), and hospital charges (IRR: 0.52; 95% CI: 0.51-0.54; P < .001). Multivariate analyses also showed that remote monitoring was associated with lower incidence of readmission (IRR: 0.38; 95% CI: 0.23-0.63; P < .001), days readmitted (IRR: 0.14; 95% CI: 0.05-0.37; P < .001), and readmission charges (IRR: 0.11; 95% CI: 0.03-0.46; P = .002). There were 2 deaths among monitored patients compared to 6 for controls; however, this difference was not significant. This pilot study in lung transplant recipients suggests that supplementing postdischarge care with remote monitoring may be useful in preventing readmissions, reducing subsequent inpatient days, and controlling hospital charges. A multicenter, randomized control trial should be conducted to validate these findings.

SUBMITTER: Schenkel FA

PROVIDER: S-EPMC7754459 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Use of a Bluetooth tablet-based technology to improve outcomes in lung transplantation: A pilot study.

Schenkel Felicia A FA Barr Mark L ML McCloskey Chris C CC Possemato Tammie T O'Conner Jeremy J Sadeghi Roya R Bembi Maria M Duong Marian M Patel Jaynita J Hackmann Amy E AE Ganesh Sivagini S

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons 20200713 12

The impact of remote patient monitoring platforms to support the postoperative care of solid organ transplant recipients is evolving. In an observational pilot study, 28 lung transplant recipients were enrolled in a novel postdischarge home monitoring program and compared to 28 matched controls during a 2-year period. Primary endpoints included hospital readmissions and total days readmitted. Secondary endpoints were survival and inflation-adjusted hospital readmission charges. In univariate ana ...[more]

PMID: 32558226

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Project description:BackgroundMachine learning has been used to develop predictive models to support clinicians in making better and more reliable decisions. The high volume of collected data in the lung transplant process makes it possible to extract hidden patterns by applying machine learning methods. Our study aims to investigate the application of machine learning methods in lung transplantation.MethodA systematic search was conducted in five electronic databases from January 2000 to June 2022. Then, the title, abstracts, and full text of extracted articles were screened based on the PRISMA checklist. Then, eligible articles were selected according to inclusion criteria. The information regarding developed models was extracted from reviewed articles using a data extraction sheet.ResultsSearches yielded 414 citations. Of them, 136 studies were excluded after the title and abstract screening. Finally, 16 articles were determined as eligible studies that met our inclusion criteria. The objectives of eligible articles are classified into eight main categories. The applied machine learning methods include the Support vector machine (SVM) (n = 5, 31.25%) technique, logistic regression (n = 4, 25%), Random Forests (RF) (n = 4, 25%), Bayesian network (BN) (n = 3, 18.75%), linear regression (LR) (n = 3, 18.75%), Decision Tree (DT) (n = 3, 18.75%), neural networks (n = 3, 18.75%), Markov Model (n = 1, 6.25%), KNN (n = 1, 6.25%), K-means (n = 1, 6.25%), Gradient Boosting trees (XGBoost) (n = 1, 6.25%), and Convolutional Neural Network (CNN) (n = 1, 6.25%). Most studies (n = 11) employed more than one machine learning technique or combination of different techniques to make their models. The data obtained from pulmonary function tests were the most used as input variables in predictive model development. Most studies (n = 10) used only post-transplant patient information to develop their models. Also, UNOS was recognized as the most desirable data source in the reviewed articles. In most cases, clinicians succeeded to predict acute diseases incidence after lung transplantation (n = 4) or estimate survival rate (n = 4) by developing machine learning models.ConclusionThe outcomes of these developed prediction models could aid clinicians to make better and more reliable decisions by extracting new knowledge from the huge volume of lung transplantation data.

Project description:BackgroundAccurate immunosuppression is of critical importance in preventing rejection, while avoiding toxicity following lung transplantation. The mainstay immunosuppressants are calcineurin inhibitors, which require regular monitoring due to interactions with other medications and diet. Adherence to immunosuppression and patient knowledge is vital and can be improved through patient education. Education using tablet-computers was investigated.ObjectiveTo compare tablet-PC education and conventional education in improving immunosuppression trough levels in target range 6 months after a single education. Secondary parameters were ratio of immunosuppression level measurements divided by per protocol recommended measurements, time and patient satisfaction regarding education.DesignSingle-centre, open labelled randomised controlled trial.ParticipantsPatients >6 months after lung-transplantation with <50% of calcineurin inhibitor trough levels in target range.InterventionTablet-pc education versus personal, nurse-led education.MeasurementsCalcineurin inhibitor levels in target range 6 months after education, level variability, interval adherence, knowledge and adherence was studied. As outcome parameter, renal function was measured and adverse events registered.ResultsSixty-four patients were 1:1 randomised for either intervention. Levels of immunosuppression 6 months after education were equal (tablet-PC 58% vs. conventional 48%, p = 0.27), both groups improved in achieving a CNI trough level within target range by either education method (delta tablet-PC 29% vs. conventional 20%). In all patients, level variability decreased (-20.4%), whereas interval adherence remained unchanged. Knowledge about immunosuppression improved by 7% and compliance tests demonstrated universal improvements with no significant difference between groups.ConclusionEducation is a simple, effective tool in improving adherence to immunosuppression. Tablet-PC education was non-inferior to conventional education.Trial registrationClinicalTrials.gov NCT01398488 http://clinicaltrials.gov/ct2/show/NCT01398488? term=gottlieb+tablet+pc+education&rank=1.

Project description:BACKGROUND:Children need access to high quality mental health care. Effective treatments now exist for a wide range of mental health conditions. However, these interventions are delivered with variable effectiveness in traditional mental health service settings. Innovative solutions are needed to improve treatment delivery quality and effectiveness. OBJECTIVE:The aim of this study was to develop a scalable, sustainable technology-based approach to improve the quality of care in child mental health treatment. METHODS:A tablet-based resource was developed with input from mental health training experts, mental health providers, and patients. A series of qualitative data collection phases (ie, expert interviews, patient and provider focus groups, usability testing) guided the initial concept and design of the resource, and then its refinement. The result was an iPad-based "e-workbook" designed to improve child engagement and provider fidelity in implementation of a best-practice treatment. We are currently conducting a small scale randomized controlled trial to evaluate the feasibility of e-workbook facilitated child mental health treatment with 10 providers and 20 families recruited from 4 local community-based mental health clinics. RESULTS:Usability and focus group testing yielded a number of strong, favorable reactions from providers and families. Recommendations for refining the e-workbook also were provided, and these guided several improvements to the resource prior to initiating the feasibility trial, which is currently underway. CONCLUSIONS:This study aimed to develop and preliminarily evaluate a tablet-based application to improve provider fidelity and child engagement in child mental health treatment. If successful, this approach may serve as a key step toward making best-practice treatment more accessible to children and families. As various technologies continue to increase in popularity worldwide and within the health care field more specifically, it is essential to rigorously test the usability, feasibility, acceptability, and effectiveness of novel health technology solutions. It is also essential to ensure that patients and providers drive decision making that supports the development of these resources to ensure that they can be seamlessly integrated into practice. TRIAL REGISTRATION:Clinicaltrials.gov NCT01915160; https://clinicaltrials.gov/ct2/show/NCT01915160 (Archived by WebCite at http://www.webcitation.org/6cPIiQDpu).

Project description:BACKGROUND:Advancements in treatment have contributed to increased survivorship among children with sickle cell disease (SCD). Increased transition readiness, encompassing disease knowledge and self-management skills before transfer to adult care, is necessary to ensure optimal health outcomes. The Sickle Cell Transition E-Learning Program (STEP) is a public, Web-based, 6-module tool designed to increase transition readiness for youth with SCD. OBJECTIVE:The objective of our study was to investigate the participation rate of youth with SCD in STEP and its association with transition readiness. METHODS:This was a single-center, Institution Review Board-approved, retrospective cohort review. A total of 183 youths with SCD, aged between 12 and 15 years, were offered STEP as an adjunct to in-clinic disease education sessions. Participation rate (number of patients who used at least one STEP module divided by those approached) was calculated. The association among the number of STEP modules completed, disease knowledge, and self-management was explored. RESULTS:Overall, 53 of the 183 approached adolescents completed at least one STEP module, yielding a participation rate in STEP of 29.0%. Of the 53 participants, 37 and 39 adolescents had disease knowledge and self-management confidence rating available, respectively. A positive correlation (r=0.47) was found between the number of STEP modules completed and disease knowledge scores (P=.003). No association was found between the number of modules completed and self-management confidence ratings. Disease knowledge scores were significantly higher among participants who completed ?3 STEP modules compared with those who completed <3 STEP modules (U=149.00; P=.007). CONCLUSIONS:Improvement in disease knowledge in adolescence is critical to ensure the youth's ability to self-care during the period of transition to adult care. Despite low participation, the cumulative exposure to the STEP program suggested greater promotion of disease knowledge among adolescents with SCD before transfer to adult care.

Project description:(1) Background: Current vestibular rehabilitation therapy is an exercise-based approach aimed at promoting gaze stability, habituating symptoms, and improving balance and walking in patients with mild traumatic brain injury (mTBI). A major component of these exercises is the adaptation of the vestibulo-ocular reflex (VOR) and habituation training. Due to acute injury, the gain of the VOR is usually reduced, resulting in eye movement velocity that is less than head movement velocity. There is a higher chance for the success of the therapy program if the patient (a) understands the exercise procedure, (b) performs the exercises according to the prescribed regimen, (c) reports pre- and post-exercise symptoms and perceived difficulty, and (d) gets feedback on performance. (2) Methods: The development and laboratory evaluation of VestAid, an innovative, low-cost, tablet-based system that helps patients perform vestibulo-ocular reflex (VORx1) exercises correctly at home without therapist guidance, is presented. VestAid uses the tablet camera to automatically assess patient performance and compliance with exercise parameters. The system provides physical therapists (PTs) with near real-time, objective (head speed and gaze fixation compliance), and subjective (perceived difficulty and pre- and post- exercise symptoms) metrics through a web-based provider portal. The accuracy of the head-angle and eye-gaze compliance metrics was evaluated. The accuracy of estimated head angles calculated via VestAid's low-complexity algorithms was compared to the state-of-the-art deep-learning method on a public dataset. The accuracy of VestAid's metric evaluation during the VORx1 exercises was assessed in comparison to the output of an inertial measurement unit (IMU)-based system. (3) Results: There are low mean interpeak time errors (consistently below 0.1 s) across all speeds of the VORx1 exercise, as well as consistently matching numbers of identified peaks. The spatial comparison (after adjusting for the lag measured with the cross-correlation) between the VestAid and IMU-based systems also shows good matching, as shown by the low mean absolute head angle error, in which for all speeds, the mean is less than 10 degrees. (4) Conclusions: The accuracy of the system is sufficient to provide therapists with a good assessment of patient performance. While the VestAid system's head pose evaluation model may not be perfectly accurate as a result of the occluded facial features when the head moves further towards an extreme in pitch and yaw, the head speed measurements and associated compliance measures are sufficiently accurate for monitoring patients' VORx1 exercise compliance and general performance.

Project description:BackgroundThe number of fatal pedestrian injuries in the United States has steadily increased over the past decade. Multiple factors likely contribute to this trend, but the growth of pedestrians distracted by mobile devices is widely hypothesized to play a major role. Existing strategies to reduce distracted pedestrian behavior are few and mostly ineffective. The present study evaluated StreetBit, a mostly-passive primary prevention program to reduce distracted pedestrian behavior by alerting distracted pedestrians directly on their smartphone when they approach an intersection, reminding them to attend to traffic as they crossed.Methods385 individuals who regularly crossed a target street corner at an urban university downloaded StreetBit on their phones and participated in a crossover design study whereby the app was inactive for 3 weeks (baseline behavior phase), actively provided alerts for 3 weeks (intervention phase), and then was inactive again for 4 weeks (post-intervention phase). User distraction while crossing the intersection was collected electronically for a total of 34,923 street-crossing events throughout the 10-week study.ResultsIn crude (unadjusted) models, participant distraction was similar across all phases of the research; this result was maintained after adjusting for potential covariates as well as after conducting a sensitivity analysis limited to data from only week 3 of each study intervention phase. In a model stratified by phone/warning type and baseline distraction rates, Android phone users who received a warning that blocked the full screen and had a high baseline distraction rate (≥75% distracted crossings) had a 64% decreased odds of distraction during the alert phase (OR 0.36, 95% CI 0.25-0.51) and a 52% decreased odds of distraction during the post-intervention phase (OR 0.48, 95% CI 0.25-0.94). Users reported positive impressions about the StreetBit app in a post-intervention survey.DiscussionStreetBit, an innovative app designed to prevent distracted pedestrian behavior through a mostly-passive primary prevention strategy relying on intrusive reminders, proved effective among smartphone users who received a warning blocking the full screen and who were frequently distracted at baseline, but not among other users. The results appear to reflect the confluence of two influencing factors. First, due to software development limitations, visually-distracted Android users received a highly intrusive app warning that blocked their smartphone screen whereas iOS users received a less intrusive banner notification blocking a small upper portion of the screen. Second, most users were curious to see if the app was functioning properly, creating artificially-inflated estimates of distraction as users purposefully watched their phones when crossing. Thus, our results indicate promise for StreetBit as an effective intervention and warrant continued software development and empirical testing.

Dataset Information

Use of a Bluetooth tablet-based technology to improve outcomes in lung transplantation: A pilot study.

Publications

Use of a Bluetooth tablet-based technology to improve outcomes in lung transplantation: A pilot study.

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