Faculty Bibliography

Introduction/UNASSIGNED:Virtual urgent care (VUC) provides real-time evaluation, triage, and treatment of low-acuity medical problems; however, VUC physicians have varying levels of telemedicine training. We created a workplace-based experiential onboarding program that deployed standardized patients (SPs) into a VUC clinic to evaluate and deliver feedback to independently practicing physicians, providing quality assurance and identifying areas for improvement. Methods/UNASSIGNED:SPs evaluated communication, disease-specific, and telemedicine skills by observing behaviors. We surveyed participants to evaluate the program. Results/UNASSIGNED:= 34%) well done-highlighting specific behaviors most ripe for improvement. All queried participants indicated that this simulation improved communication and telemedicine skills. Discussion/UNASSIGNED:This workplace-based experiential onboarding program uncovered knowledge gaps within telemedicine skills and patient education domains. Identification of these gaps can help drive new virtual care curricula.

BACKGROUND:Guidelines recommend moderate to high-intensity statins and antithrombotic agents in patients with atherosclerotic cardiovascular disease (ASCVD). However, guideline-directed medical therapy (GDMT) remains suboptimal. METHODS:In this quality initiative, best practice alerts (BPA) in the electronic health record (EHR) were utilized to alert providers to prescribe to GDMT upon hospital discharge in ASCVD patients. Rates of GDMT were compared for 5 months pre- and post-BPA implementation. Multivariable regression was used to identify predictors of GDMT. RESULTS:In 5985 pre- and 5568 post-BPA patients, the average age was 69.1 ± 12.8 years and 58.5% were male. There was a 4.0% increase in statin use from 67.3% to 71.3% and a 3.1% increase in antithrombotic use from 75.3% to 78.4% in the post-BPA cohort. CONCLUSIONS:This simple EHR-based initiative was associated with a modest increase in ASCVD patients being discharged on GDMT. Leveraging clinical decision support tools provides an opportunity to influence provider behavior and improve care for ASCVD patients, and warrants further investigation.

Predictive models may be particularly beneficial to clinicians when they face uncertainty and seek to develop a mental model of disease progression, but we know little about the post-implementation effects of predictive models on clinicians' experience of their work. Combining survey and interview methods, we found that providers using a predictive algorithm reported being significantly less uncertain and better able to anticipate, plan and prepare for patient discharge than non-users. The tool helped hospitalists form and develop confidence in their mental models of a novel disease (Covid-19). Yet providers' attention to the predictive tool declined as their confidence in their own mental models grew. Predictive algorithms that not only offer data but also provide feedback on decisions, thus supporting providers' motivation for continuous learning, hold promise for more sustained provider attention and cognition augmentation.

Introduction:The known markers of insulin resistance in obese children are well studied. However, they require serial measurements and complicated calculations. The objective is to study IGFBP-1 and its relation with other known risk measures. Materials and Methods:The study included 98 New York City school students of diverse ethnic/racial backgrounds (57 males and 41 females), 11-15 years of age. Subjects were enrolled in a cross-sectional study, and anthropometric measures were collected. They underwent fasting intravenous glucose tolerance tests (IVGTT), and glucose, insulin, lipids, IGFBP-1, adiponectin and inflammatory markers were collected. Results:The subjects were stratified into 3 groups based upon the BMI Z-score. Out of all the subjects, 65.3% were in the group with a BMI Z-score <1 SDS, 16.3% subjects were in the group with a BMI Z-score of 1 to 2 SDS, and 18.4% of the subjects were in the group with a BMI Z-score of more than 2 SDS. The group with a BMI Z-score of more than 2 SDS had increased waist circumference (WC), body fat, increased fasting insulin, and triglycerides (TG). This group had decreased levels of adiponectin and HDL and low IGFBP-1 as compared to the group with BMI <1 SDS. The group with a BMI Z-score of 1 to 2 SDS had a decreased level of IGFBP-1 as compared to the group with a BMI Z-score less than 1 SDS. IGFBP-1 inversely correlated with age, WC, BMI, body fat, TG, and insulin levels. IGFBP-1 positively correlated with adiponectin and HDL levels. Conclusion:IGFBP-1 in children can identify the presence of insulin resistance in the group with BMI 1 to 2 SDS, even before the known markers of insulin resistance such as elevated triglycerides and even before decreased HDL and adiponectin levels are identified.

Background/Purpose Remission and low disease activity (LDA) are associated with decreased flares, damage, and mortality. However, little is known about the impact of disease activity states (DAS) on health care costs. We determined the independent impact of different definitions of remission and LDA on direct and indirect costs (DC, IC) in a multicentre, multiethnic inception cohort. Methods Patients fulfilling revised ACR classification criteria for SLE from 33 centres in 11 countries were enrolled within 15 months of diagnosis and assessed annually. Patients with >=2 annual assessments were included. Five mutually independent DAS were defined: 1) Remission off-treatment: clinical (c) SLEDAI-2K=0, without prednisone or immunosuppressants 2) Remission on-treatment: cSLEDAI-2K=0, prednisone <=5mg/d and/or maintenance immunosuppressants 3) LDA-Toronto Cohort (TC): cSLEDAI-2K<=2, without prednisone or immunosuppressants 4) Modified Lupus LDA State (mLLDAS): SLEDAI-2K<=4, no activity in major organs/systems, no new disease activity, prednisone <=7.5mg/d and/or maintenance immunosuppressants 5) Active: all remaining assessments Antimalarials were permitted in all DAS. At each assessment, patients were stratified into 1 DAS; if >1 definition was fulfilled per assessment, the patient was stratified into the most stringent. The proportion of time patients were in a specific DAS at each assessment since cohort entry was determined. At each assessment, annual DC and IC were based on health resource use and lost workforce/non-workforce productivity over the preceding year. Resource use was costed using 2021 Canadian prices and lost productivity using Statistics Canada age-and-sex-matched wages. To examine the association between the proportion of time in a specific DAS at each assessment since cohort entry and annual DC and IC, multivariable random-effects linear regression modelling was used. Potential covariates included age at diagnosis, disease duration, sex, race/ethnicity, education, region, smoking, and alcohol use. Results 1631 patients (88.7% female, 48.9% White, mean age at diagnosis 34.5) were followed for a mean of 7.7 (SD 4.7) years (table 1, Panel A). Across 12,281 assessments, 49.3% were classified as active (table 1, Panel B). Patients spending <25% vs 75-100% of their time since cohort entry in an active DAS had lower annual DC and IC (DC $4042 vs $9101, difference -$5060, 95%CI -$5983, -$4136; IC $21,922 vs $32,049, difference -$10,127, 95% -$16,754, -$3499) (table 2, Panel B&C). In multivariable models, remission and LDA (per 25% increase in time spent in specified DAS vs active) were associated with lower annual DC and IC: remission off-treatment (DC -$1296, 95%CI -$1800, -$792; IC -$3353, 95%CI -$5382, -$1323), remission on-treatment (DC -$987, 95%CI -$1550, -$424; IC -$3508, 95%CI -$5761, -$1256), LDA-TC (DC -$1037, 95%CI -$1853, -$222; IC -$3229, 95%CI -$5681, -$778) and mLLDAS (DC -$1307, 95%CI -$2194, -$420; IC -$3822, 95%CI -$6309, $-1334) (table 3, Model B). There were no differences in costs between remission and LDA. Conclusions Remission and LDA are associated with lower costs, likely mediated through the known association of these DAS with more favourable clinical outcomes

MAIN OBJECTIVE:There is limited information on how patient outcomes have changed during the COVID-19 pandemic. This study characterizes changes in mortality, intubation, and ICU admission rates during the first 20 months of the pandemic. STUDY DESIGN AND METHODS:University of Wisconsin researchers collected and harmonized electronic health record data from 1.1 million COVID-19 patients across 21 United States health systems from February 2020 through September 2021. The analysis comprised data from 104,590 adult hospitalized COVID-19 patients. Inclusion criteria for the analysis were: (1) age 18 years or older; (2) COVID-19 ICD-10 diagnosis during hospitalization and/or a positive COVID-19 PCR test in a 14-day window (+/- 7 days of hospital admission); and (3) health system contact prior to COVID-19 hospitalization. Outcomes assessed were: (1) mortality (primary), (2) endotracheal intubation, and (3) ICU admission. RESULTS AND SIGNIFICANCE:The 104,590 hospitalized participants had a mean age of 61.7 years and were 50.4% female, 24% Black, and 56.8% White. Overall risk-standardized mortality (adjusted for age, sex, race, ethnicity, body mass index, insurance status and medical comorbidities) declined from 16% of hospitalized COVID-19 patients (95% CI: 16% to 17%) early in the pandemic (February-April 2020) to 9% (CI: 9% to 10%) later (July-September 2021). Among subpopulations, males (vs. females), those on Medicare (vs. those on commercial insurance), the severely obese (vs. normal weight), and those aged 60 and older (vs. younger individuals) had especially high mortality rates both early and late in the pandemic. ICU admission and intubation rates also declined across these 20 months. CONCLUSIONS:Mortality, intubation, and ICU admission rates improved markedly over the first 20 months of the pandemic among adult hospitalized COVID-19 patients although gains varied by subpopulation. These data provide important information on the course of COVID-19 and identify hospitalized patient groups at heightened risk for negative outcomes. TRIAL REGISTRATION:ClinicalTrials.gov Identifier: NCT04506528 (https://clinicaltrials.gov/ct2/show/NCT04506528).