Browsing by Author "Dysangco, Andrew"

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    Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic
    (AMA, 2021) Bravata, Dawn M.; Perkins, Anthony J.; Myers, Laura J.; Arling, Greg; Zhang, Ying; Zillich, Alan J.; Reese, Lindsey; Dysangco, Andrew; Agarwal, Rajiv; Myers, Jennifer; Austin, Charles; Sexson, Ali; Leonard, Samuel J.; Dev, Sharmistha; Keyhani, Salomeh; Medicine, School of Medicine
    Importance Although strain on hospital capacity has been associated with increased mortality in nonpandemic settings, studies are needed to examine the association between coronavirus disease 2019 (COVID-19) critical care capacity and mortality. Objective To examine whether COVID-19 mortality was associated with COVID-19 intensive care unit (ICU) strain. Design, Setting, and Participants This cohort study was conducted among veterans with COVID-19, as confirmed by polymerase chain reaction or antigen testing in the laboratory from March through August 2020, cared for at any Department of Veterans Affairs (VA) hospital with 10 or more patients with COVID-19 in the ICU. The follow-up period was through November 2020. Data were analyzed from March to November 2020. Exposures Receiving treatment for COVID-19 in the ICU during a period of increased COVID-19 ICU load, with load defined as mean number of patients with COVID-19 in the ICU during the patient’s hospital stay divided by the number of ICU beds at that facility, or increased COVID-19 ICU demand, with demand defined as mean number of patients with COVID-19 in the ICU during the patient’s stay divided by the maximum number of patients with COVID-19 in the ICU. Main Outcomes and Measures All-cause mortality was recorded through 30 days after discharge from the hospital. Results Among 8516 patients with COVID-19 admitted to 88 VA hospitals, 8014 (94.1%) were men and mean (SD) age was 67.9 (14.2) years. Mortality varied over time, with 218 of 954 patients (22.9%) dying in March, 399 of 1594 patients (25.0%) dying in April, 143 of 920 patients (15.5%) dying in May, 179 of 1314 patients (13.6%) dying in June, 297 of 2373 patients (12.5%) dying in July, and 174 of 1361 (12.8%) patients dying in August (P < .001). Patients with COVID-19 who were treated in the ICU during periods of increased COVID-19 ICU demand had increased risk of mortality compared with patients treated during periods of low COVID-19 ICU demand (ie, demand of ≤25%); the adjusted hazard ratio for all-cause mortality was 0.99 (95% CI, 0.81-1.22; P = .93) for patients treated when COVID-19 ICU demand was more than 25% to 50%, 1.19 (95% CI, 0.95-1.48; P = .13) when COVID-19 ICU demand was more than 50% to 75%, and 1.94 (95% CI, 1.46-2.59; P < .001) when COVID-19 ICU demand was more than 75% to 100%. No association between COVID-19 ICU demand and mortality was observed for patients with COVID-19 not in the ICU. The association between COVID-19 ICU load and mortality was not consistent over time (ie, early vs late in the pandemic). Conclusions and Relevance This cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased COVID-19 ICU mortality. Tracking COVID-19 ICU demand may be useful to hospital administrators and health officials as they coordinate COVID-19 admissions across hospitals to optimize outcomes for patients with this illness.
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    Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study
    (BMJ, 2021) Bravata, Dawn M.; Myers, Laura J.; Perkins, Anthony J.; Keyhani, Salomeh; Zhang, Ying; Zillich, Alan J.; Dysangco, Andrew; Lindsey, Reese; Sharmitha, Dev; Myers, Jennifer; Austin, Charles; Sexson, Ali; Arling, Greg; Medicine, School of Medicine
    Objective Studies describe COVID-19 patient characteristics and outcomes across populations, but reports of variation across healthcare facilities are lacking. The objectives were to examine differences in COVID-19 patient volume and mortality across facilities, and understand whether facility variation in mortality was due primarily to differences in patient versus facility characteristics. Design Observational cohort study with multilevel mixed effects logistic regression modelling. Setting The Veterans Health Administration (VA) is the largest healthcare system in the USA. Participants Patients with COVID-19. Main outcome All-cause mortality within 45 days after COVID-19 testing (March–May, follow-up through 16 July 2020). Results Among 13 510 patients with COVID-19, 3942 (29.2%) were admitted (2266/3942 (57.5%) ward; 1676/3942 (42.5%) intensive care unit (ICU)) and 679/3942 (17.2%) received mechanical ventilation. Marked heterogeneity was observed across facilities in median age (range: 34.3–83.9 years; facility mean: 64.7, SD 7.2 years); patient volume (range: 1–737 at 160 facilities; facility median: 48.5, IQR 14–105.5); hospital admissions (range: 1–286 at 133 facilities; facility median: 11, IQR 1–26.5); ICU caseload (range: 1–85 at 115 facilities; facility median: 4, IQR 0–12); and mechanical ventilation (range: 1–53 at 90 facilities; facility median: 1, IQR 0–5). Heterogeneity was also observed in facility mortality for all patients with COVID-19 (range: 0%–29.7%; facility median: 8.9%, IQR 2.4%–13.7%); inpatients (range: 0%–100%; facility median: 18.0%, IQR 5.6%–28.6%); ICU patients (range: 0%–100%; facility median: 28.6%, IQR 14.3%–50.0%); and mechanical ventilator patients (range: 0%–100%; facility median: 52.7%, IQR 33.3%–80.6%). The majority of variation in facility mortality was attributable to differences in patient characteristics (eg, age). Conclusions Marked heterogeneity in COVID-19 patient volume, characteristics and mortality were observed across VA facilities nationwide. Differences in patient characteristics accounted for the majority of explained variation in mortality across sites. Variation in unadjusted COVID-19 mortality across facilities or nations should be considered with caution.
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    HIV infection, antiretroviral therapy, and measures of endothelial function, inflammation, metabolism, and oxidative stress
    (PLOS, 2017-08-17) Dysangco, Andrew; Liu, Ziyue; Stein, James H.; Dubé, Michael P.; Gupta, Samir K.; Medicine, School of Medicine
    Background HIV-infected patients have an increased risk of cardiovascular disease (CVD). Impaired endothelial function is an early risk factor for CVD in the general population. It is presumed that HIV infection is associated with impaired endothelial function, but results have been inconsistent. Objectives Our objectives were to determine the relationships between HIV infection, virologic suppression with antiretroviral therapy (ART), in vivo measures of conduit artery and microvascular endothelial function, and circulating biomarkers of pathways associated with CVD. Methods We performed a cross-sectional analysis of three prospectively enrolled groups from a single center: 28 were HIV-infected and virologically-suppressed on a regimen of FTC/TDF/EFV (HIV+ART+), 44 were HIV-infected but not on ART (HIV+ART-), and 39 were HIV-uninfected healthy volunteers (HIV-) matched to the HIV+ART- group for age, sex, smoking status, and height. None had diabetes, uncontrolled hypertension, known CVD, or other pro-inflammatory condition. Flow mediated dilation (FMD), nitroglycerin-mediated dilation (NTGMD), reactive hyperemia velocity time integral (RHVTI), and FMD/RHVTI of the brachial artery were measured, as well as circulating biomarkers of systemic inflammation, metabolism, oxidative stress, and endothelial activation. Results No significant differences were found amongst the three groups in FMD (P = 0.46), NTGMD (P = 0.42), RHVTI (P = 0.17), and FMD/RHVTI (P = 0.22) in unadjusted comparisons. Adjusted ANOVA models which included brachial artery diameter, demographics, and conventional CVD risk factors did not appreciably change these findings. In pairwise comparisons, the HIV+ART- group had significantly higher soluble tumor necrosis factor receptor II, soluble CD163, β-2 microglobulin, interferon-γ- induced protein-10, tissue inhibitor of metalloproteinase-1, and vascular cell adhesion molecule-1 compared to the other two groups (all p<0.05). Correlates of endothelial function differed between study groups. Conclusion Although untreated HIV infection was associated with elevated levels of several biomarkers of inflammation and endothelial activation, we were unable to demonstrate differences in measures of conduit artery and microvascular endothelial function in this study population.
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    Using Audit and Feedback to Improve Antimicrobial Prescribing in Emergency Departments: A Multicenter Quasi-Experimental Study in the Veterans Health Administration
    (Oxford University Press, 2021-04-14) Livorsi, Daniel J.; Nair, Rajeshwari; Dysangco, Andrew; Aylward, Andrea; Alexander, Bruce; Smith, Matthew W.; Kouba, Sammantha; Perencevich, Eli N.; Medicine, School of Medicine
    Background: In this pilot trial, we evaluated whether audit-and-feedback was a feasible strategy to improve antimicrobial prescribing in emergency departments (EDs). Methods: We evaluated an audit-and-feedback intervention using a quasi-experimental interrupted time-series design at 2 intervention and 2 matched-control EDs; there was a 12-month baseline, 1-month implementation, and 11-month intervention period. At intervention sites, clinicians received (1) a single, one-on-one education about antimicrobial prescribing for common infections and (2) individualized feedback on total and condition-specific (uncomplicated acute respiratory infection [ARI]) antimicrobial use with peer-to-peer comparisons at baseline and every quarter. The primary outcome was the total antimicrobial-prescribing rate for all visits and was assessed using generalized linear models. In an exploratory analysis, we measured antimicrobial use for uncomplicated ARI visits and manually reviewed charts to assess guideline-concordant management for 6 common infections. Results: In the baseline and intervention periods, intervention sites had 28 016 and 23 164 visits compared to 33 077 and 28 835 at control sites. We enrolled 27 of 31 (87.1%) eligible clinicians; they acknowledged receipt of 33.3% of feedback e-mails. Intervention sites compared with control sites had no absolute reduction in their total antimicrobial rate (incidence rate ratio = 0.99; 95% confidence interval, 0.98-1.01). At intervention sites, antimicrobial use for uncomplicated ARIs decreased (68.6% to 42.4%; P < .01) and guideline-concordant management improved (52.1% to 72.5%; P < .01); these improvements were not seen at control sites. Conclusions: At intervention sites, total antimicrobial use did not decrease, but an exploratory analysis showed reduced antimicrobial prescribing for viral ARIs. Future studies should identify additional targets for condition-specific feedback while exploring ways to make electronic feedback more acceptable.