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 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 Յ25%); 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 RELEVANCEThis cohort study found that although facilities augmented ICU capacity during the pandemic, strains on critical care capacity were associated with increased (continued) Key Points Question Is greater coronavirus disease 2019 (COVID-19) intensive care unit (ICU) strain associated with increased COVID-19 mortality? Findings In this cohort study of 8516 patients with COVID-19 admitted to 88 US Veterans Affairs hospitals, strains on critical care capacity were assoc...

BackgroundTransient ischemic attack (TIA) patients are at high risk of recurrent vascular events; timely management can reduce that risk by 70%. The Protocol-guided Rapid Evaluation of Veterans Experiencing New Transient Neurological Symptoms (PREVENT) developed, implemented, and evaluated a TIA quality improvement (QI) intervention aligned with Learning Healthcare System principles.MethodsThis stepped-wedge trial developed, implemented and evaluated a provider-facing, multi-component intervention to improve TIA care at six facilities. The unit of analysis was the medical center. The intervention was developed based on benchmarking data, staff interviews, literature, and electronic quality measures and included: performance data, clinical protocols, professional education, electronic health record tools, and QI support. The effectiveness outcome was the without-fail rate: the proportion of patients who receive all processes of care for which they are eligible among seven processes. The implementation outcomes were the number of implementation activities completed and final team organization level. The intervention effects on the without-fail rate were analyzed using generalized mixed-effects models with multilevel hierarchical random effects. Mixed methods were used to assess implementation, user satisfaction, and sustainability.DiscussionPREVENT advanced three aspects of a Learning Healthcare System. Learning from Data: teams examined and interacted with their performance data to explore hypotheses, plan QI activities, and evaluate change over time. Learning from Each Other: Teams participated in monthly virtual collaborative calls. Sharing Best Practices: Teams shared tools and best practices. The approach used to design and implement PREVENT may be generalizable to other clinical conditions where time-sensitive care spans clinical settings and medical disciplines.Trial registrationclinicaltrials.gov: NCT02769338 [May 11, 2016].

Quality improvement training was associated with early DVT improvement, but the effect was not sustained over time and was not seen with dysphagia screening. External quality improvement programmes may quickly boost performance but their effect may vary by indicator and may not sustain over time.

Background Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error due to small numbers of eligible patients per facility. We applied multilevel modeling and empirical Bayes (EB) estimation in addressing this issue in performance reporting of stroke care quality in the Medical Centers. Methods and Results We studied a retrospective cohort of 3812 veterans admitted to 106 Medical Centers with ischemic stroke during fiscal year 2007. The median number of study patients per facility was 34 (range: 12-105). Inpatient stroke care quality was measured with thirteen evidence-based QIs. Eligible patients could either pass or fail each indicator. Multilevel modeling of a patient’s pass/fail on individual QIs was used to produce facility-level EB estimated QI pass rates and confidence intervals. The EB estimation reduced inter-facility variation in QI rates. Small facilities and those with exceptionally high or low rates were most affected. We recommended 8 of the 13 QIs for performance reporting: dysphagia screening, NIH Stroke Scale documentation, early ambulation, fall risk assessment, pressure ulcer risk assessment, Functional Independence Measure documentation, lipid management, and deep vein thrombosis prophylaxis. These QIs displayed sufficient variation across facilities, had room for improvement, and identified sites with performance that was significantly above or below the population average. The remaining 5 QIs were not recommended because of too few eligible patients or high pass rates with little variation. Conclusions Considerations of statistical uncertainty should inform the choice of QIs and their application to performance reporting.

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Objective The Rapid Emergency Medicine Score (REMS) has not been widely studied for use in predicting outcomes of COVID‐19 patients encountered in the prehospital setting. This study aimed to determine whether the first prehospital REMS could predict emergency department and hospital dispositions for COVID‐19 patients transported by emergency medical services. Methods This retrospective study used linked prehospital and hospital records from the ESO Data Collaborative for all 911‐initiated transports of patients with hospital COVID‐19 diagnoses from July 1 to December 31, 2020. We calculated REMS with the first recorded prehospital values for each component. We calculated area under the receiver operating curve (AUROC) for emergency department (ED) mortality, ED discharge, hospital mortality, and hospital length of stay (LOS). We determined optimal REMS cut‐points using test characteristic curves. Results Among 13,830 included COVID‐19 patients, median REMS was 6 (interquartile range [IQR]: 5‐9). ED mortality was <1% (n = 80). REMS ≥9 predicted ED death (AUROC 0.79). One‐quarter of patients (n = 3,419) were discharged from the ED with an optimal REMS cut‐point of ≤5 (AUROC 0.72). Eighteen percent (n = 1,742) of admitted patients died. REMS ≥8 optimally predicted hospital mortality (AUROC 0.72). Median hospital LOS was 8.3 days (IQR: 4.1‐14.8 days). REMS ≥7 predicted hospitalizations ≥3 days (AUROC 0.62). Conclusion Initial prehospital REMS was modestly predictive of ED and hospital dispositions for patients with COVID‐19. Prediction was stronger for outcomes more proximate to the first set of emergency medical services (EMS) vital signs. These findings highlight the potential value of first prehospital REMS for risk stratification of individual patients and system surveillance for resource planning related to COVID‐19.