Browsing by Author "Arling, Greg"
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Item 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 MedicineImportance 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.Item Barriers and facilitators to provide quality TIA care in the Veterans Healthcare Administration(American Academy of Neurology, 2017-12-12) Damush, Teresa M.; Miech, Edward J.; Sico, Jason J.; Phipps, Michael S.; Arling, Greg; Ferguson, Jared; Austin, Charles; Myers, Laura; Baye, Fitsum; Luckhurst, Cherie; Keating, Ava B.; Moran, Eileen; Bravata, Dawn M.; Medicine, School of MedicineObjective: To identify key barriers and facilitators to the delivery of guideline-based care of patients with TIA in the national Veterans Health Administration (VHA). Methods: We conducted a cross-sectional, observational study of 70 audiotaped interviews of multidisciplinary clinical staff involved in TIA care at 14 VHA hospitals. We de-identified and analyzed all transcribed interviews. We identified emergent themes and patterns of barriers to providing TIA care and of facilitators applied to overcome these barriers. Results: Identified barriers to providing timely acute and follow-up TIA care included difficulties accessing brain imaging, a constantly rotating pool of housestaff, lack of care coordination, resource constraints, and inadequate staff education. Key informants revealed that both stroke nurse coordinators and system-level factors facilitated the provision of TIA care. Few facilities had specific TIA protocols. However, stroke nurse coordinators often expanded upon their role to include TIA. They facilitated TIA care by (1) coordinating patient care across services, communicating across service lines, and educating clinical staff about facility policies and evidence-based practices; (2) tracking individual patients from emergency departments to inpatient settings and to discharge for timely follow-up care; (3) providing and referring TIA patients to risk factor management programs; and (4) performing regular audit and feedback of quality performance data. System-level facilitators included clinical service leadership engagement and use of electronic tools for continuous care across services. Conclusions: The local organization within a health care facility may be targeted to cultivate internal facilitators and a systemic infrastructure to provide evidence-based TIA care.Item Care Trajectories of Veterans in the Twelve Months following Hospitalization for Acute Ischemic Stroke(AHA, 2015-10) Arling, Greg; Ofner, Susan; Reeves, Mathew J.; Myers, Laura J.; Williams, Linda S.; Daggy, Joanne K.; Phipps, Michael S.; Chumbler, Neale R.; Bravata, Dawn M.; Department of Neurology, IU School of MedicineBackground—Recovery after a stroke varies greatly between individuals and is reflected by wide variation in the use of institutional and home care services. This study sought to classify veterans according to their care trajectories in the 12 months after hospitalization for ischemic stroke. Methods and Results—The sample consisted of 3811 veterans hospitalized for ischemic stroke in Veterans Health Administration facilities in 2007. Three outcomes—nursing home care, home care, and mortality—were modeled jointly >12 months using latent class growth analysis. Data on Veterans’ care use and cost came from the Veterans Administration and Medicare. Covariates included stroke severity (National Institutes of Health Stroke Scale), functional status (functional independence measure score), age, marital status, chronic conditions, and prestroke ambulation. Five care trajectories were identified: 49% of Veterans had Rapid Recovery with little or no use of care; 15% had a Steady Recovery with initially high nursing home or home care that tapered off; 9% had Long-Term Home Care; 13% had Long-Term Nursing Home Care; and 14% had an Unstable trajectory with multiple transitions between long-term and acute care settings. Care use was greatest for individuals with more severe strokes, lower functioning at hospital discharge, and older age. Average annual costs were highest for individuals with the Long-Term Nursing Home trajectory ($63 082), closely followed by individuals with the Unstable trajectory ($58 720). Individual with the Rapid Recovery trajectory had the lowest costs ($9271). Conclusions—Care trajectories after stroke were associated with stroke severity and functional dependency and they had a dramatic impact on subsequent costs.Item Comparison of Risk Factor Control in the Year After Discharge for Ischemic Stroke Versus Acute Myocardial Infarction(American Heart Association, 2018-02) Bravata, Dawn M.; Daggy, Joanne; Brosch, Jared; Sico, Jason J.; Baye, Fitsum; Myers, Laura J.; Roumie, Christianne L.; Cheng, Eric; Coffing, Jessica; Arling, Greg; Medicine, School of MedicineBACKGROUND AND PURPOSE: The Veterans Health Administration has engaged in quality improvement to improve vascular risk factor control. We sought to examine blood pressure (<140/90 mm Hg), lipid (LDL [low-density lipoprotein] cholesterol <100 mg/dL), and glycemic control (hemoglobin A1c <9%), in the year post-hospitalization for acute ischemic stroke or acute myocardial infarction (AMI). METHODS: We identified patients who were hospitalized (fiscal year 2011) with ischemic stroke, AMI, congestive heart failure, transient ischemic attack, or pneumonia/chronic obstructive pulmonary disease. The primary analysis compared risk factor control after incident ischemic stroke versus AMI. Facilities were included if they cared for ≥25 ischemic stroke and ≥25 AMI patients. A generalized linear mixed model including patient- and facility-level covariates compared risk factor control across diagnoses. RESULTS: Forty thousand two hundred thirty patients were hospitalized (n=75 facilities): 2127 with incident ischemic stroke and 4169 with incident AMI. Fewer stroke patients achieved blood pressure control than AMI patients (64%; 95% confidence interval, 0.62-0.67 versus 77%; 95% confidence interval, 0.75-0.78; P<0.0001). After adjusting for patient and facility covariates, the odds of blood pressure control were still higher for AMI than ischemic stroke patients (odds ratio, 1.39; 95% confidence interval, 1.21-1.51). There were no statistical differences for AMI versus stroke patients in hyperlipidemia (P=0.534). Among patients with diabetes mellitus, the odds of glycemic control were lower for AMI than ischemic stroke patients (odds ratio, 0.72; 95% confidence interval, 0.54-0.96). CONCLUSIONS: Given that hypertension control is a cornerstone of stroke prevention, interventions to improve poststroke hypertension management are neededItem The Complexity of Determining Whether a Nursing Home Transfer Is Avoidable at Time of Transfer(Wiley, 2018-05) Unroe, Kathleen T.; Carnahan, Jennifer L.; Hickman, Susan E.; Sachs, Greg A.; Hass, Zachary; Arling, Greg; Medicine, School of MedicineObjectives To describe the relationship between nursing facility resident risk conditions and signs and symptoms at time of acute transfer and diagnosis of conditions associated with potentially avoidable acute transfers (pneumonia, urinary tract infection, congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) or asthma, dehydration, pressure sores). Design As part of a demonstration project to reduce potentially avoidable hospital transfers, Optimizing Patient Transfers, Impacting Medical Quality, Improving Symptoms: Transforming Institutional Care (OPTIMISTIC) project clinical staff collected data on residents who transferred to the emergency department (ED) or hospital. Cross‐tabulations were used to identify associations between risk conditions or symptoms and hospital diagnoses or death. Mixed‐effects logistic regression models were used to describe the significance of risk conditions, signs, or symptoms as predictors of potentially avoidable hospital diagnoses or death. Setting Indiana nursing facilities (N=19). Participants Long‐stay nursing facility residents (N=1,174), who experienced 1,931 acute transfers from November 2014 to July 2016. Measurements Participant symptoms, transfers, risk factors, and hospital diagnoses. Results We found that 44% of acute transfers were associated with 1 of 6 potentially avoidable diagnoses. Symptoms before transfer did not discriminate well among hospital diagnoses. Symptoms mapped into multiple diagnoses and most hospital diagnoses had multiple associated symptoms. For example, more than two‐thirds of acute transfers of residents with a history of CHF and COPD were for reasons other than exacerbations of those two conditions. Conclusion Although it is widely recognized that many transfers of nursing facility residents are potentially avoidable, determining “avoidability” at time of transfer is complex. Symptoms and risk conditions were only weakly predictive of hospital diagnoses.Item Correlation of inpatient and outpatient measures of stroke care quality within veterans health administration hospitals(2011-08) Ross, Joseph S.; Arling, Greg; Ofner, Susan; Roumie, Christianne L; Keyhani, Salomeh; Williams, Linda S.; Ordin, Diana L.; Bravata, Dawn M.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.Item Development and Validation of Electronic Quality Measures to Assess Care for Patients With Transient Ischemic Attack and Minor Ischemic Stroke(AHA, 2017) Bravata, Dawn M.; Myers, Laura J.; Cheng, Eric; Reeves, Mathew; Baye, Fitsum; Zhangsheng, Yu; Damush, Teresa; Miech, Edward J.; Sico, Jason; Phipps, Michael; Zillich, Alan; Johanning, Jason; Chaturvedi, Seemant; Austin, Curt; Ferguson, Jared; Maryfield, Bailey; Snow, Kathy; Ofner, Susan; Graham, Glenn; Rhude, Rachel; Williams, Linda S.; Arling, Greg; Medicine, School of MedicineBackground—Despite interest in using electronic health record (EHR) data to assess quality of care, the accuracy of such data is largely unknown. We sought to develop and validate transient ischemic attack and minor ischemic stroke electronic quality measures (eQMs) using EHR data. Methods and Results—A random sample of patients with transient ischemic attack or minor ischemic stroke, cared for in Veterans Health Administration facilities (fiscal year 2011), was identified. We constructed 31 eQMs based on existing quality measures. Chart review was the criterion standard for validating the eQMs. To evaluate eQMs in terms of eligibility, we calculated the proportion of patients who were genuinely not eligible to receive a process (based on chart review) and who were correctly identified as not eligible by the EHR data (specificity). To assess eQMs about classification of whether patients received a process, we calculated the proportion of patients who actually received the process (based on chart review) and who were classified correctly by the EHR data as passing (sensitivity). Seven hundred sixty-three patients were included. About eligibility, specificity varied from 25% (brain imaging; carotid imaging) to 99% (anticoagulation quality). About pass rates, sensitivity varied from 30% (antihypertensive class) to 100% (coronary risk assessment; international normalized ratio measured). The 16 eQMs with ≥70% specificity in eligibility and ≥70% sensitivity in pass rates included coronary risk assessment, international normalized ratio measured, HbA1c measurement, speech language pathology consultation, anticoagulation for atrial fibrillation, discharge on statin, lipid management, neurology consultation, Holter, deep vein thrombosis prophylaxis, oral hypoglycemic intensification, cholesterol medication intensification, antihypertensive intensification, antihypertensive class, carotid stenosis intervention, and substance abuse referral for alcohol. Conclusions—It is feasible to construct valid eQMs for processes of transient ischemic attack and minor ischemic stroke care. Healthcare systems with EHRs should consider using electronic data to evaluate care for their patients with transient ischemic attack and to complement and expand quality measurement programs currently focused on patients with stroke.Item Does Inclusion of Stroke Severity in a 30-day Mortality Model Change Standardized Mortality Rates at VA Hospitals?(2012-07) Keyhani, Salomeh; Cheng, Eric M.; Arling, Greg; Li, Xinli; Myers, Laura J.; Ofner, Susan; Williams, Linda S.; Phipps, Michael S.; Ordin, Diana L.; Bravata, Dawn M.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.Item Estimating and Reporting on the Quality of Inpatient Stroke Care by Veterans Health Administration Medical Centers(2012-01) Arling, Greg; Reeves, Mathew; Ross, Joseph S.; Williams, Linda S.; Keyhani, Salomeh; Chumbler, Neale R.; Phipps, Michael S.; Roumie, Christianne L; Myers, Laura J.; Salanitro, Amanda H; Ordin, Diana L.; Myers, Jennifer; Bravata, Dawn M.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.Item Have clinicians adopted the use of brain MRI for patients with TIA and minor stroke?(American Academy of Neurology, 2017-01-17) Chaturvedi, Seemant; Ofner, Susan; Baye, Fitsum; Myers, Laura J.; Phipps, Mike; Sico, Jason J.; Damush, Teresa; Miec, Edward; Reeves, Mat; Johanning, Jason; Williams, Linda S.; Arling, Greg; Cheng, Eric; Yu, Zhangsheng; Bravata, Dawn; Biostatistics, School of Public HealthBACKGROUND: Use of MRI with diffusion-weighted imaging (DWI) can identify infarcts in 30%-50% of patients with TIA. Previous guidelines have indicated that MRI-DWI is the preferred imaging modality for patients with TIA. We assessed the frequency of MRI utilization and predictors of MRI performance. METHODS: A review of TIA and minor stroke patients evaluated at Veterans Affairs hospitals was conducted with regard to medical history, use of diagnostic imaging within 2 days of presentation, and in-hospital care variables. Chart abstraction was performed in a subset of hospitals to assess clinical variables not available in the administrative data. RESULTS: A total of 7,889 patients with TIA/minor stroke were included. Overall, 6,694 patients (84.9%) had CT or MRI, with 3,396/6,694 (50.7%) having MRI. Variables that were associated with increased odds of CT performance were age >80 years, prior stroke, history of atrial fibrillation, heart failure, coronary artery disease, anxiety, and low hospital complexity, while blood pressure >140/90 mm Hg and high hospital complexity were associated with increased likelihood of MRI. Diplopia (87% had MRI, p = 0.03), neurologic consultation on the day of presentation (73% had MRI, p < 0.0001), and symptom duration of >6 hours (74% had MRI, p = 0.0009) were associated with MRI performance. CONCLUSIONS: Within a national health system, about 40% of patients with TIA/minor stroke had MRI performed within 2 days. Performance of MRI appeared to be influenced by several patient and facility-level variables, suggesting that there has been partial acceptance of the previous guideline that endorsed MRI for patients with TIA.