For adults with T1D using multiple insulin injections and still experiencing suboptimal glycemic control, CGM is cost-effective at the willingness-to-pay threshold of $100,000 per QALY, with improved glucose control and reductions in nonsevere hypoglycemia.
Objective: Development of a risk-stratification model to predict severe Covid-19 related illness, using only presenting symptoms, comorbidities and demographic data. Materials and methods: We performed a case-control study with cases being those with severe disease, defined as ICU admission, mechanical ventilation, death or discharge to hospice, and controls being those with non-severe disease. Predictor variables included patient demographics, symptoms and past medical history. Participants were 556 patients with laboratory confirmed Covid-19 and were included consecutively after presenting to the emergency department at a tertiary care center from March 1, 2020 to April 21, 2020 Results: Most common symptoms included cough (82%), dyspnea (75%), and fever/chills (77%), with 96% reporting at least one of these. Multivariable logistic regression analysis found that increasing age (adjusted odds ratio [OR], 1.05; 95% confidence interval [CI], 1.03-1.06), dyspnea (OR, 2.56; 95% CI: 1.51-4.33), male sex (OR, 1.70; 95% CI: 1.10-2.64), immunocompromised status (OR, 2.22; 95% CI: 1.17-4.16) and CKD (OR, 1.76; 95% CI: 1.01-3.06) were significant predictors of severe Covid-19 infection. Hyperlipidemia was found to be negatively associated with severe disease (OR, 0.54; 95% CI: 0.33-0.90). A predictive equation based on these variables demonstrated fair ability to discriminate severe vs non-severe outcomes using only this historical information (AUC: 0.76). Conclusions: Severe Covid-19 illness can be predicted using data that could be obtained from a remote screening. With validation, this model could possibly be used for remote triage to prioritize evaluation based on susceptibility to severe disease while avoiding unnecessary waiting room exposure. Published by Elsevier Inc. 1.2. Importance Given that most patients presenting with symptoms concerning for Covid-19 infection have ultimately been negative [8], there is a concern
PURPOSE The US Preventive Services Task Force recommends screening for depression in the general adult population. Although screening questionnaires for depression and anxiety exist in primary care settings, electronic health tools such as computerized adaptive tests based on item response theory can advance screening practices. This study evaluated the validity of the Computerized Adaptive Test for Mental Health (CAT-MH) for screening for major depressive disorder (MDD) and assessing MDD and anxiety severity among adult primary care patients. METHODS We approached 402 English-speaking adults for participation from a primary care clinic, of whom 271 adults (71% female, 65% black) participated. Participants completed modules from the CAT-MH (Computerized Adaptive Diagnostic Test for MDD, CAT-Depression Inventory, CAT-Anxiety Inventory); brief paper questionnaires (9-item Patient Health Questionnaire [PHQ-9], 2-item Patient Health Questionnaire [PHQ-2], Generalized Anxiety Disorder 7-item Scale [GAD-7]); and a reference-standard interview, the Structured Clinical Interview for DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition) Diagnoses. RESULTS On the basis of the interview, 31 participants met criteria for MDD and 29 met criteria for GAD. The diagnostic accuracy of the Computerized Adaptive Diagnostic Test for MDD (area under curve [AUC] = 0.85) was similar to that of the PHQ-9 (AUC = 0.84) and higher than that of the PHQ-2 (AUC = 0.76) for MDD screening. Using the interview as the reference standard, the accuracy of the CAT-Anxiety Inventory (AUC = 0.93) was similar to that of the GAD-7 (AUC = 0.97) for assessing anxiety severity. The patient-preferred screening method was assessment via tablet/computer with audio. CONCLUSIONS Computerized adaptive testing could be a valid and efficient patient-centered screening strategy for depression and anxiety screening in primary care settings.
Summary
Soluble human leukocyte antigen-G (sHLA-G), an immunomodulatory molecule associated with suppression of inflammation, is elevated in the airways of asthmatic patients with a low inflammatory endotype as evidenced by low airway eosinophils and low exhaled nitric oxide.
Background:
The economic impact of both continuous glucose monitoring (CGM) and
insulin pumps (continuous subcutaneous insulin infusion (CSII)) in Type 1
diabetes (T1D) have been evaluated separately. However, the
cost-effectiveness of adding CSII to existing CGM users has not yet been
assessed.
Objective:
To evaluate the societal cost-effectiveness of CSII versus continuing
multiple daily injections (MDI) in adults with T1D already using CGM.
Methods:
In the second phase of the DIAMOND trial, 75 adults using CGM were
randomized to either CGM+CSII or CGM+MDI (control) and surveyed at baseline
and 28 weeks. We performed within-trial and lifetime cost-effectiveness
analyses (CEAs) and estimated lifetime costs and quality-adjusted life years
(QALYs) via a modified Sheffield T1D model.
Results:
Within the trial, the CGM+CSII group had a significant reduction in
quality of life from baseline (−0.02 ± 0.05 difference in
difference (DiD)), compared to controls. Total per-person 28-week costs were
$8,272 (CGM+CSII) vs $5,623 (CGM+MDI); the difference in costs was primarily
attributable to pump use ($2,644). Pump users reduced insulin intake
(−12.8 units DiD), but increased use of daily number of test strips
(+1.2 DiD). Pump users also increased time with glucose in range
70–180 mg/dL, but had higher HbA1c (+0.13 DiD) and more non-severe
hypoglycemic events. In the lifetime CEA, CGM+CSII would increase total
costs by $112,045 DiD, decrease QALYs by 0.71, and life expectancy by 0.48
years.
Conclusions:
Based on this single trial, initiating an insulin pump in adults with
T1D already using CGM was associated with higher costs and reduced quality
of life. Additional evidence regarding the clinical effects of adopting
combinations of new technologies from trials and real-world populations are
needed to confirm these findings.
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