Abstract Background Heart failure represents a common end-stage syndrome for many adults with congenital heart disease (ACHD). These patients, however, have been excluded from most heart ...transplantation research. It is not known how current criteria, derived from non-ACHD populations, used to determine priority at the time of transplant listing, impact the outcomes for ACHD patients listed for heart transplantation. Objectives The goal of this study was to investigate outcomes of ACHD in comparison to non-ACHD patients while listed for heart transplantation. Methods We conducted a retrospective study using the Scientific Registry of Transplant Recipients on patients ≥18 years of age listed in the United States between 1999 and 2014. The probability of mortality or delisting due to clinical worsening was estimated using cumulative incidence functions, where transplantation was a competing event. Results Among 1,290 ACHD and 38,557 non-ACHD patients listed, 237 ACHD and 6,377 non-ACHD patients died or were delisted due to clinical worsening. Death or delisting for clinical worsening was more likely for ACHD patients initially listed as status 1A (24% ACHD vs. 17% non-ACHD after 180 days; p < 0.001). There were no significant differences between ACHD and non-ACHD patients listed as status 1B or 2. In multivariable analysis, factors associated with death or delisting due to clinical worsening within 1 year in ACHD included: estimated glomerular filtration rate <60 ml/min/1.73 m2 (hazard ratio HR: 1.4; 95% confidence interval CI: 1.0 to 1.9; p = 0.043); albumin <3.2 g/dl (HR: 2.0; 95% CI: 1.3 to 2.9; p <0.001); and hospitalization at the time of listing, whether in the intensive care unit (HR: 2.3; 95% CI: 1.6 to 3.5; p < 0.001) or not (HR: 1.9; 95% CI: 1.2 to 3.0; p = 0.006) relative to outpatients. Conclusions Wait-list mortality or delisting due to worsening clinical status is disproportionately common for ACHD patients listed as status 1A. An allocation system that takes into account the distinctive aspects of ACHD patients may help better care for this growing population.
The Health Resources and Services Administration (HRSA), Federal Office of Rural Health Policy (FORHP) funded the Evidence-Based Tele-Emergency Network Grant Program (EB TNGP) to serve the dual ...purpose of providing telehealth services in rural emergency departments (teleED) and systematically collecting data to inform the telehealth evidence base. This provided a unique opportunity to examine trends across multiple teleED networks and examine heterogeneity in processes and outcomes.
Six health systems received funding from HRSA under the EB TNGP to implement teleED services and they did so to 65 hospitals (91% rural) in 11 states. Three of the grantees provided teleED services to a general patient population while the remaining three grantees provided teleED services to specialized patient populations (i.e., stroke, behavioral health, critically ill children). Over a 26-month period (November 1, 2015 -December 31, 2017), each grantee submitted patient-level data for all their teleED encounters on a uniform set of measures to the data coordinating center. The six grantees reported a total of 4,324 teleED visits and 99.86% were technically successful. The teleED patients were predominantly adult, White, not Latinx, and covered by Medicare or private insurance. Across grantees, 7% of teleED patients needed resuscitation services, 58% were rated as emergent, and 30% were rated as urgent. Across grantees, 44.2% of teleED patients were transferred to another inpatient facility, 26.0% had a routine discharge, and 24.5% were admitted to the local inpatient facility. For the three grantees who served a general patient population, the most frequent presenting complaints for which teleED was activated were chest pain (25.7%), injury or trauma (17.1%), stroke symptoms (9.9%), mental/behavioral health (9.8%), and cardiac arrest (9.5%). The teleED consultation began before the local clinician exam in 37.8% of patients for the grantees who served a general patient population, but in only 1.9% of patients for the grantees who provided specialized services.
Grantees used teleED services for a representative rural population with urgent or emergent symptoms largely resulting in transfer to a distant hospital or inpatient admission locally. TeleED was often available as the first point of contact before a local provider examination. This finding points to the important role of teleED in improving access for rural ED patients.
Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in ...pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007-2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009-2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman's risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.
This study investigates outcomes from two federal grant programs: the Evidence-Based Tele-Behavioral Health Network Program (EB THNP) funded from September 2018 to August 2021 and the Substance Abuse ...Treatment Telehealth Network Grant Program (SAT TNGP) funded from September 2017 to August 2020. As part of the health services implementation program, the aims of this study were to evaluate outcomes in patient symptoms of depression and anxiety across the programs' 17 grantees and 95 associated sites, with each grantee having data from telehealth patients and from an in-person comparison group.
The research design is a prospective multi-site observational study. Each grantee provided data on a nonrandomized convenience sample of telehealth patients and an in-person comparison group from sites with similar rural characteristics and during the same time period. Patient characteristics were collected at treatment initiation, and clinical outcome measures were collected at baseline and monthly. The validated clinical outcome measure instruments included the Patient Health Questionnaire-9 (PHQ-9) for depression symptoms and the Generalized Anxiety Disorder-7 (GAD-7) scale for anxiety-related symptoms. Linear mixed models, with grantee as the random effect, were used to determine the association of behavioral health delivery (telehealth versus in-person) on the one-month change in PHQ-9 and GAD-7 while adjusting for covariates.
Across a total of 1,514 patients, one-month change scores were improved indicating that PHQ-9 and GAD-7 scores decreased from baseline to the one-month follow-up at similar rates in both the in-person and telehealth groups. Reduction in scores averaged 2.8 for the telehealth treatment group and 2.9 for the in-person treatment group in the PHQ-9 subsample and 2.0 for the telehealth treatment group and 2.4 for the in-person treatment group in the GAD-7 subsample. There was no statistically significant association between the modality of care (telehealth treatment group versus in-person comparison group) and the one-month change scores for either PHQ-9 or GAD-7. Individuals with higher baseline scores demonstrated the greatest decrease in scores for both measures. Upon adjusting for baseline scores and grantee program, patient demographics were not found to be significantly associated with change in anxiety or depression symptoms.
In our very large pragmatic study comparing behavioral health treatment delivered to a population of patients in rural, underserved communities, we found no clinical or statistical differences in improvements in depression or anxiety symptoms as measured by the PHQ-9 and GAD-7 between patients treated via telehealth or in-person.
Health care professions trainees and clinicians who perceive ambiguous situations as sources of threat (low tolerance for ambiguity TFA) experience greater risk for mental health disorders and ...professional burnout. Physical therapists likely encounter substantial ambiguity because of the biopsychosocial nature of their main therapeutic strategies. The purpose of this study was to identify student traits and experiences within the learning environment that differentiate students with high and low TFA for medicine and physical therapy (PT), and to identify areas of interprofessional overlap and distinction.
Graduation Questionnaire survey data from graduating PT (n = 2,727) and medical students (n = 33,159) from the 2019-2020 and 2020-2021 academic years were sorted according to student TFA score, and respondents in the highest and lowest TFA quartiles were retained for analysis. Difference-in-differences analysis was used to reduce the number of potential explanatory factors to a parimonious subset that was put into linear regression models. Inferential statistics were applied to all significant factors identified from the linear regression models.
For both professions, higher TFA was generally associated with more positive ratings of the learning environment (student-faculty interactions, faculty professionalism, satisfaction with career choice), lower experiences of exhaustion and disengagement (the 2 axes of academic burnout), and higher scores for the empathy domain of perspective taking. Uniquely for medical students, low TFA was associated with lower empathy scores and a lower degree of interest in working with underserved individuals.
Findings suggest that for both professions, high TFA corresponded with better ratings of the educational experience and with traits that are advantageous for patient-centered practice and occupational resilience. Interventions to cultivate TFA among health care trainees may be an important way to meet the growing demand for humanistic health care professionals who are prepared to meet society's complex needs.
Background The COVID-19 pandemic highlighted the importance of telemedicine in health care. However, video telemedicine requires adequate broadband internet speeds. As video-based telemedicine grows, ...variations in broadband access must be accurately measured and characterized. Objective This study aims to compare the Federal Communications Commission (FCC) and Microsoft US broadband use data sources to measure county-level broadband access among veterans receiving mental health care from the Veterans Health Administration (VHA). Methods Retrospective observational cohort study using administrative data to identify mental health visits from January 1, 2019, to December 31, 2020, among 1161 VHA mental health clinics. The exposure is county-level broadband percentages calculated as the percentage of the county population with access to adequate broadband speeds (ie, download >25 megabits per second) as measured by the FCC and Microsoft. All veterans receiving VHA mental health services during the study period were included and categorized based on their use of video mental health visits. Broadband access was compared between and within data sources, stratified by video versus no video telemedicine use. Results Over the 2-year study period, 1,474,024 veterans with VHA mental health visits were identified. Average broadband percentages varied by source (FCC mean 91.3%, SD 12.5% vs Microsoft mean 48.2%, SD 18.1%; P<.001). Within each data source, broadband percentages generally increased from 2019 to 2020. Adjusted regression analyses estimated the change after pandemic onset versus before the pandemic in quarterly county-based mental health visit counts at prespecified broadband percentages. Using FCC model estimates, given all other covariates are constant and assuming an FCC percentage set at 70%, the incidence rate ratio (IRR) of county-level quarterly mental video visits during the COVID-19 pandemic was 6.81 times (95% CI 6.49-7.13) the rate before the pandemic. In comparison, the model using Microsoft data exhibited a stronger association (IRR 7.28; 95% CI 6.78-7.81). This relationship held across all broadband access levels assessed. Conclusions This study found FCC broadband data estimated higher and less variable county-level broadband percentages compared to those estimated using Microsoft data. Regardless of the data source, veterans without mental health video visits lived in counties with lower broadband access, highlighting the need for accurate broadband speeds to prioritize infrastructure and intervention development based on the greatest community-level impacts. Future work should link broadband access to differences in clinical outcomes.
To investigate the role of participant level of effort (LoE) on outcome in post-acute brain injury rehabilitation with the hypothesis that greater effort is associated with more positive outcomes.
...Observational cohort study.
Comprehensive integrated rehabilitation program for brain injury within a skilled nursing facility.
Consecutive admissions with acquired brain injury (N=101).
Individualized interdisciplinary brain injury rehabilitation; therapist rating of participant LoE with Acquired Brain Injury LoE Scale (ABI-LoES) during physical therapy, occupational therapy, and speech and language pathology sessions.
Mayo-Portland Adaptability Inventory, fourth edition (MPAI-4); Supervision Rating Scale (SRS).
Linear regression showed that discharge MPAI-4 Total T scores were significantly associated with mean ABI-LoES rating, admission MPAI-4 Total T scores, age at admission, and days from injury but not with standard deviation of ABI-LoES rating, sex, injury type, length of stay, or treatment before or during the COVID-19 pandemic. Discharge SRS scores were significantly associated with mean ABI-LoES rating, admission SRS scores, and age. A 1-unit increase in mean ABI-LoES rating was associated with 5.1-unit lower discharge MPAI-4 Total T scores and 1.5 lower discharge SRS scores, after controlling for other variables. Logistic regression showed that the odds of achieving a minimal clinically important difference on the MPAI-4 were 8.34 times higher with each 1-unit increase in mean ABI-LoES rating after controlling for other variables. Admission MPAI-4 was negatively associated with mean ABI-LoES rating (β=-0.07, t=-8.85, P<.0001).
After controlling for nonmodifiable variables, average ABI-LoES rating is positively associated with outcome. Initial level of disability is negatively associated with mean ABI-LoES rating.
Background
The high prevalence of chronic diseases, including congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD), and diabetes mellitus (DM), accounts for a large burden of ...cost and poor health outcomes in US hospitals, and home telehealth (HT) monitoring has been proposed to improve outcomes.
Objective
To measure the association between HT initiation and 12-month inpatient hospitalizations, emergency department (ED) visits, and mortality in veterans with CHF, COPD, or DM.
Design
Comparative effectiveness matched cohort study.
Patients
Veterans aged 65 years and older treated for CHF, COPD, or DM.
Main Measures
We matched veterans initiating HT with veterans with similar demographics who did not use HT (1:3). Our outcome measures included a 12-month risk of inpatient hospitalization, ED visits, and all-cause mortality.
Key Results
A total of 139,790 veterans with CHF, 65,966 with COPD, and 192,633 with DM were included in this study. In the year after HT initiation, the risk of hospitalization was not different in those with CHF (adjusted odds ratio aOR 1.01, 95% confidence interval 95%CI 0.98–1.05) or DM (aOR 1.00, 95%CI 0.97–1.03), but it was higher in those with COPD (aOR 1.15, 95%CI 1.09–1.21). The risk of ED visits was higher among HT users with CHF (aOR 1.09, 95%CI 1.05–1.13), COPD (1.24, 95%CI 1.18–1.31), and DM (aOR 1.03, 95%CI 1.00–1.06). All-cause 12-month mortality was lower in those initiating HT monitoring with CHF (aOR 0.70, 95%CI 0.67–0.73) and DM (aOR 0.79, 95%CI 0.75–0.83), but higher in COPD (aOR 1.08, 95%CI 1.00–1.16).
Conclusions
The initiation of HT was associated with increased ED visits, no change in hospitalizations, and lower all-cause mortality in patients with CHF or DM, while those with COPD had both higher healthcare utilization and all-cause mortality.
A common model selection approach is to select the best model, according to some criterion, from among the collection of models defined by all possible subsets of the explanatory variables. ...Identifying an optimal subset has proven to be a challenging problem, both statistically and computationally. Our model selection procedure allows the researcher to nominate, a priori, the probability at which models containing false or spurious variables will be selected from among all possible subsets. The procedure determines whether inclusion of each candidate variable results in a sufficiently improved fitting term - and is hence named the SIFT procedure. Two variants are proposed: a naive method based on a set of restrictive assumptions and an empirical permutation-based method. Properties of these methods are investigated within the standard linear modeling framework and performance is evaluated against other model selection techniques. The SIFT procedure behaves as designed - asymptotically selecting variables that characterize the underlying data generating mechanism, while limiting selection of spurious variables to the desired level. The SIFT methodology offers researchers a promising new approach to model selection, providing the ability to control the probability of selecting a model that includes spurious variables to a level based on the context of the application.
Despite evidence of volume-outcome relationships for cancer surgery, treatment at low-volume hospitals remains common. Our objective was to evaluate whether individuals actively involved in selecting ...their cancer surgeon were more likely to go to hospitals recognized for quality cancer care.
Individuals diagnosed with breast, prostate and colorectal cancer in 2015 completed online surveys in 2017–2018. Participants were categorized as “directed” to a surgeon (relied on referral) or “active” (sought additional information), and hospitals were categorized by NCI-designation, CoC accreditation, and academic affiliation.
Of 299 participants, 42% were active. Individuals with breast cancer were more active (aOR = 2.46,95%CI:1.32–4.59). Active participants had nonsignificantly higher odds of surgery at NCI-designated facilities (aOR = 2.04,95%CI:0.95–4.38), or academic centers (aOR = 1.51,95%CI:0.86–2.64).
While most participants were directed to their cancer surgeon, active participants tended to select NCI-designated/academic hospitals. Although centralization of cancer care would require altering referral patterns, decision-support resources may help patients make informed choices.
•Most cancer survivors were directed (e.g. referred) to a cancer surgeon.•A minority of participants were actively involved in selecting their cancer surgeon.•Breast cancer patients were more likely to be “active” in surgery decision-making.•“Active” patients may be more likely to get surgery at a large or accredited hospital.
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