Search

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.

The purpose of this project was to improve dysphagia-screening processes in a tertiary Veterans Affairs Medical Center. The dysphagia-screening tool was redesigned on the basis of frontline clinician feedback, clinical guidelines, user satisfaction, and multidisciplinary expertise. The revised tool triggered a speech-language consult for positive screens and demonstrated higher scores in user satisfaction and task efficiency. Systems redesign processes were effective for redesigning the tool and implementing practice changes with clinicians involved in dysphagia screening.

Background: The Centers for Medicare & Medicaid Services (CMS) and Veterans Health Administration (VA) will report 30-day stroke readmission rates as a measure of hospital quality. A national debate on whether social risk factors should be included in models developed for hospital profiling is ongoing., Objective: To compare a CMS-based model of 30-day readmission with a more comprehensive model that includes measures of social risk (such as homelessness) or clinical factors (such as stroke severity and functional status)., Design: Data from a retrospective cohort study were used to develop a CMS-based 30-day readmission model that included age and comorbid conditions based on codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (model 1). This model was then compared with one that included administrative social risk factors (model 2). Finally, the CMS model (model 1) was compared with a model that included social risk and clinical factors from chart review (model 3). These 3 models were used to rank hospitals by 30-day risk-standardized readmission rates and examine facility rankings among the models., Setting: Hospitals in the VA., Participants: Patients hospitalized with stroke in 2007., Measurements: 30-day readmission rates., Results: The 30-day readmission rate was 12.8%. The c-statistics for the 3 models were 0.636, 0.646, and 0.661, respectively. All hospitals were classified as performing "as expected" using all 3 models (that is, performance did not differ from the VA national average); therefore, the addition of detailed clinical information or social risk factors did not alter assessment of facility performance., Limitation: A predominantly male veteran cohort limits the generalizability of these findings., Conclusion: In the VA, more comprehensive models that included social risk and clinical factors did not affect hospital comparisons based on 30-day readmission rates., Primary Funding Source: U.S. Department of Veterans Affairs.

Purpose: Differences in stroke care quality for patients in rural and urban locations have been suggested, but whether differences exist across Veteran Administration Medical Centers (VAMCs) is unknown. This study examines whether rural-urban disparities exist in inpatient quality among veterans with acute ischemic stroke., Methods: In this retrospective study, inpatient stroke care quality was assessed in a national sample of veterans with acute ischemic stroke using 14 quality indicators (QIs). Rural-Urban Commuting Areas codes defined each VAMC's rural-urban status. A hierarchical linear model assessed the rural-urban differences across the 14 QIs, adjusting for patient and facility characteristics, and clustering within VAMCs., Findings: Among 128 VAMCs, 18 (14.1%) were classified as rural VAMCs and admitted 284 (7.3%) of the 3,889 ischemic stroke patients. Rural VAMCs had statistically significantly lower unadjusted rates on 6 QIs: Deep vein thrombosis (DVT) prophylaxis, antithrombotic at discharge, antithrombotic at day 2, lipid management, smoking cessation counseling, and National Institutes of Health Stroke Scale completion, but they had higher rates of stroke education, functional assessment, and fall risk assessment. After adjustment, differences in 2 QIs remained significant-patients treated in rural VAMCs were less likely to receive DVT prophylaxis, but more likely to have documented functional assessment., Conclusions: After adjustment for key demographic, clinical, and facility-level characteristics, there does not appear to be a systematic difference in inpatient stroke quality between rural and urban VAMCs. Future research should seek to understand the few differences in care found that could serve as targets for future quality improvement interventions., (Published 2013. This article is a U.S. Government work and is in the public domain in the USA.)

Purpose: To determine whether radiology reports describe clinically significant carotid arterial stenosis in a consistent format that is actionable by ordering clinicians., Materials and Methods: This study was HIPAA compliant. Informed consent was waived. Institutional review board approval was obtained for this retrospective chart review, which included radiology reports of carotid artery imaging for patients hospitalized with ischemic stroke at 127 Veterans Affairs medical centers in 2006-2007. "Clinically significant results" were defined as results with at least 50% stenosis or at least moderate stenosis, excluding complete occlusion. How often clinically significant results were reported as an exact percentage stenosis (such as 60%), range (such as 50%-69%), or category (such as moderate) was determined. Among results reported as a range, how often the range bracketed clinical thresholds of 50% and 70% (typically used to determine appropriateness of carotid arterial revascularization) was determined., Results: Among 2675 patients, there were 6618 carotid imaging results, of which 1015 (15%) were considered clinically significant. Among 695 clinically significant results at ultrasonography (US), 348 (50%) were described as a range, and another 314 (45%) were reported as an exact percentage stenosis. Among the 348 clinically significant US results reported as a range, 259 (74%) bracketed the thresholds of 50% or 70%. For magnetic resonance angiographic results, 48% (106 of 221) qualitatively described clinically significant results as a category, 38% (84 of 221) as an exact percentage stenosis, and 14% (31 of 221) as a range., Conclusion: In this national health care system, the manner in which clinically significant carotid arterial stenosis was reported varied widely., (RSNA, 2012)

Objective: We determined whether site of care explains a previously identified racial disparity in carotid artery imaging., Methods: In this retrospective cohort study, data were obtained from a chart review of veterans hospitalized with ischemic stroke at 127 Veterans Administration hospitals in 2007. Extensive exclusion criteria were applied to obtain a sample who should have received carotid artery imaging. Minority-serving hospitals were defined as the top 10% of hospitals ranked by the proportion of stroke patients who were black. Population level multivariate logistic regression models with adjustment for correlation of patients in hospitals were used to calculate predictive probabilities of carotid artery imaging by race and minority-service hospital status. Bootstrapping was used to obtain 95% confidence intervals (CIs)., Results: The sample consisted of 1,534 white patients and 628 black patients. Nearly 40% of all black patients were admitted to 1 of 13 minority-serving hospitals. No racial disparity in receipt of carotid artery imaging was detected within nonminority serving hospitals. However, the predicted probability of receiving carotid artery imaging for white patients at nonminority-serving hospitals (89.7%, 95% CI [87.3%, 92.1%]) was significantly higher than both white patients (78.0% [68.3%, 87.8%] and black patients (70.5% [59.3%, 81.6%]) at minority-serving hospitals., Conclusions: Underuse of carotid artery imaging occurred most often among patients hospitalized at minority-serving hospitals. Further work is required to explore why site of care is a mechanism for racial disparities in this clinically important diagnostic test.

Background: The Centers for Medicare and Medicaid Services is considering developing a 30-day ischemic stroke hospital-level mortality model using administrative data. We examined whether inclusion of the National Institutes of Health Stroke Scale (NIHSS), a measure of stroke severity not included in administrative data, would alter 30-day mortality rates in the Veterans Health Administration., Methods and Results: A total of 2562 veterans admitted with ischemic stroke to 64 Veterans Health Administration Hospitals in the fiscal year 2007 were included. First, we examined the distribution of unadjusted mortality rates across the Veterans Health Administration. Second, we estimated 30-day all-cause, risk standardized mortality rates (RSMRs) for each hospital by adjusting for age, sex, and comorbid conditions using hierarchical models with and without the inclusion of the NIHSS. Finally, we examined whether adjustment for the NIHSS significantly changed RSMRs for each hospital compared with other hospitals. The median unadjusted mortality rate was 3.6%. The RSMR interquartile range without the NIHSS ranged from 5.1% to 5.6%. Adjustment with the NIHSS did not change the RSMR interquartile range (5.1%-5.6%). Among veterans ≥65 years, the RSMR interquartile range without the NIHSS ranged from 9.2% to 10.3%. With adjustment for the NIHSS, the RSMR interquartile range changed from 9.4% to 10.0%. The plot of 30-day RSMRs estimated with and without the inclusion of the NIHSS in the model demonstrated overlapping 95% confidence intervals across all hospitals, with no hospital significantly below or above the mean-unadjusted 30-day mortality rate. The 30-day mortality measure did not discriminate well among hospitals., Conclusions: The impact of the NIHSS on RSMRs was limited. The small number of stroke admissions and the narrow range of 30-day stroke mortality rates at the facility level in the Veterans Health Administration cast doubt on the value of using 30-day RSMRs as a means of identifying outlier hospitals based on their stroke care quality.

Background: Reporting of quality indicators (QIs) in Veterans Health Administration Medical Centers is complicated by estimation error caused by 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 13 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 interfacility 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, National Institutes of Health 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.

Background and Purpose: Quality of care delivered in the inpatient and ambulatory settings may be correlated within an integrated health system such as the Veterans Health Administration. We examined the correlation between stroke care quality at hospital discharge and within 6 months postdischarge., Methods: We conducted a cross-sectional hospital-level correlation analyses of chart-abstracted data for 3467 veterans discharged alive after an acute ischemic stroke from 108 Veterans Health Administration medical centers and 2380 veterans with postdischarge follow-up within 6 months in fiscal year 2007. Four risk-standardized processes of care represented discharge care quality: prescription of antithrombotic and antilipidmic therapy, anticoagulation for atrial fibrillation, and tobacco cessation counseling along with a composite measure of defect-free care. Five risk-standardized intermediate outcomes represented postdischarge care quality: achievement of blood pressure, low-density lipoprotein, international normalized ratio, and glycosylated hemoglobin target levels, and delivery of appropriate treatment for poststroke depression along with a composite measure of achieved outcomes., Results: Median risk-standardized composite rate of defect-free care at discharge was 79%. Median risk-standardized postdischarge rates of achieving goal were 56% for blood pressure, 36% for low-density lipoprotein, 41% for international normalized ratio, 40% for glycosylated hemoglobin, and 39% for depression management and the median risk-standardized composite 6-month outcome rate was 44%. The hospital composite rate of defect-free care at discharge was correlated with meeting the low-density lipoprotein goal (r=0.31; P=0.007) and depression management (r=0.27; P=0.03) goal but was not correlated with blood pressure, international normalized ratio, glycosylated hemoglobin goals, nor with the composite measure of achieved postdischarge outcomes (probability values >0.13)., Conclusions: Hospital discharge care quality was not consistently correlated with ambulatory care quality.