Background
Recent international health events have led to an increased proliferation of remotely delivered health interventions. Even with the pandemic seemingly coming under control, the experiences of the past year have fueled a growth in ideas and technology for increasing the scope of remote care delivery. Unfortunately, clinicians and health systems will have difficulty with the adoption and implementation of these interventions if ongoing and future clinical trials fail to report necessary details about execution, platforms, and infrastructure related to these interventions. The purpose was to develop guidance for reporting of telehealth interventions.
Methods
A working group from the US Pain Management Collaboratory developed guidance for complete reporting of telehealth interventions. The process went through 5-step process from conception to final checklist development with input for many stakeholders, to include all 11 primary investigators with trials in the Collaboratory.
Results
An extension focused on unique considerations relevant to telehealth interventions was developed for the Template for the Intervention Description and Replication (TIDieR) checklist.
Conclusion
The Telehealth Intervention guideline encourages use of the Template for the Intervention Description and Replication (TIDieR) checklist as a valuable tool (TIDieR-Telehealth) to improve the quality of research through a reporting guide of relevant interventions that will help maximize reproducibility and implementation.
The COVID-19 pandemic has slowed research progress, with particularly disruptive effects on investigations of addressing urgent public health challenges, such as chronic pain. The National Institutes of Health (NIH) Department of Defense (DoD) Department of Veterans Affairs (VA) Pain Management Collaboratory (PMC) supports 11 large-scale, multisite, embedded pragmatic clinical trials (PCTs) in military and veteran health systems. The PMC rapidly developed and enacted a plan to address key issues in response to the COVID-19 pandemic. The PMC tracked and collaborated in developing plans for addressing COVID-19 impacts across multiple domains and characterized the impact of COVID-19 on PCT operations, including delays in recruitment and revisions of study protocols. A harmonized participant questionnaire will facilitate later meta-analyses and cross-study comparisons of the impact of COVID-19 across all 11 PCTs. The pandemic has affected intervention delivery, outcomes, regulatory and ethics issues, participant recruitment, and study design. The PMC took concrete steps to ensure scientific rigor while encouraging flexibility in the PCTs, while paying close attention to minimizing the burden on research participants, investigators, and clinical care teams. Sudden changes in the delivery of pain management interventions will probably alter treatment effects measured via PMC PCTs. Through the use of harmonized instruments and surveys, we are capturing these changes and plan to monitor the impact on research practices, as well as on health outcomes. Analyses of patient-reported measures over time will inform potential relationships between chronic pain, mental health, and various socioeconomic stressors common among Americans during the COVID-19 pandemic.
Virtual delivery encompasses all the ways providers interact synchronously or asynchronously with patients using technology, including videoconferencing, audio-only communication, mobile apps, and remote health monitoring. 24 There was prepandemic interest in virtual delivery as a strategy to overcome the pain management evidence-practice gap, but implementation was sluggish. 17 Thus, although the pivot to virtual delivery has been disruptive, it presents unique opportunities to address key questions through ongoing pragmatic research. The National Institutes of Health (NIH)-Department of Defense (DoD)-Department of Veterans Affairs (VA) Pain Management Collaboratory (PMC) supports 11 large, multisite pragmatic clinical trials in military and VA health systems. 45 The PMC pragmatic trials examine a spectrum of NPTs. COVID-19 disruptions have posed a variety of challenges and opportunities for these studies. 13 Pragmatic studies evaluate the effects of treatment provided under usual clinical circumstances. 11,79 Relative to research evaluating treatment efficacy under controlled conditions, pragmatic studies permit greater flexibility in several domains, including Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
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Background: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservative interventions for spinal pain conditions, including chiropractic care, have been associated with lower healthcare costs and improvements in pain status in different clinical populations, including veterans. Little is currently known about predicting healthcare service utilization in the domain of conservative interventions for spinal pain conditions, including the frequency of use of chiropractic services. The purpose of this retrospective cohort study was to explore the use of supervised machine learning approaches to predicting one-year chiropractic service utilization by veterans receiving VA chiropractic care. Methods: We included 19,946 veterans who entered the Musculoskeletal Diagnosis Cohort between October 1, 2003 and September 30, 2013 and utilized VA chiropractic services within one year of cohort entry. The primary outcome was one-year chiropractic service utilization following index chiropractic visit, split into quartiles represented by the following classes: 1 visit, 2 to 3 visits, 4 to 6 visits, and 7 or greater visits. We compared the performance of four multiclass classification algorithms (gradient boosted classifier, stochastic gradient descent classifier, support vector classifier, and artificial neural network) in predicting visit quartile using 158 sociodemographic and clinical features. Results: The selected algorithms demonstrated poor prediction capabilities. Subset accuracy was 42.1% for the gradient boosted classifier, 38.6% for the stochastic gradient descent classifier, 41.4% for the support vector classifier, and 40.3% for the artificial neural network. The micro-averaged area under the precision-recall curve for each oneversus-rest classifier was 0.43 for the gradient boosted classifier, 0.38 for the stochastic gradient descent classifier, 0.43 for the support vector classifier, and 0.42 for the artificial neural network. Performance of each model yielded only a small positive shift in prediction probability (approximately 15%) compared to naïve classification.
OBJECTIVE:To examine whether infants exposed to tocolytics are at increased risk for selected adverse clinical and hospitalization outcomes.
STUDY DESIGN:We conducted a population-based cohort study of women with preterm labor, in Washington State from 1989 to 2001 (N ¼ 79,679), using linked hospitalization records. Relative risks for infant outcomes were estimated using multivariate logistic regression.
RESULTS:Adjusted risk estimates for infants exposed to tocolysis were greater for respiratory distress (RR ¼ 1.5, 95% CI 1.4 to 1.6), intubation (RR ¼ 1.4, 95% CI 1.2 to 1.5), and bacterial infection (RR ¼ 1.6, 95% CI 1.4 to 1.8). Exposed infants were also more likely to have birth hospitalizations >2 days (RR ¼ 1.4, 95% CI 1.3 to 1.4), require transfer (RR ¼ 1.5, 95% CI 1.3 to 1.8), have increased hospital costs (RR ¼ 2.3, 95% CI 2.2 to 2.4), and require readmisssion within the first year of life (RR ¼ 1.2, 95% CI 1.1 to 1.3).
CONCLUSION:Infants exposed to tocolytics appeared to have relatively poorer hospitalization and clinical outcomes; significant benefits were not observed.
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