Development and validation of a model predicting mild stroke severity on admission using electronic health record data.

OBJECTIVE: Initial stroke severity is a potent modifier of stroke outcomes but this information is difficult to obtain from electronic health record (EHR) data. This limits the ability to risk-adjust for evaluations of stroke care and outcomes at a population level. The purpose of this analysis was to develop and validate a predictive model of initial stroke severity using EHR data elements. METHODS: This observational cohort included individuals admitted to a US Department of Veterans Affairs hospital with an ischemic stroke. We extracted 65 independent predictors from the EHR. The primary analysis modeled mild (NIHSS score 0-3) versus moderate/severe stroke (NIHSS score ≥4) using multiple logistic regression. Model validation included: (1) splitting the cohort into derivation (65%) and validation (35%) samples and (2) evaluating how the predicted stroke severity performed in regard to 30-day mortality risk stratification. RESULTS: The sample comprised 15,346 individuals with ischemic stroke (n = 10,000 derivation; n = 5,346 validation). The final model included 15 variables and correctly classified 70.4% derivation sample patients and 69.4% validation sample patients. The areas under the curve (AUC) were 0.76 (derivation) and 0.76 (validation). In the validation sample, the model performed similarly to the observed NIHSS in terms of the association with 30-day mortality (AUC: 0.72 observed NIHSS, 0.70 predicted NIHSS). CONCLUSIONS: EHR data can be used to construct a surrogate measure of initial stroke severity. Further research is needed to better differentiate moderate and severe strokes, enhance stroke severity classification, and how to incorporate these measures in evaluations of stroke care and outcomes.

Impact of the coronavirus disease-2019 pandemic on Veterans Health Administration Sleep Services.

Objectives: To understand the impact of the coronavirus disease-2019 pandemic on sleep services within the United States Department of Veterans Affairs using separate surveys from "pre-COVID" and pandemic periods. Methods: Data from a pre-pandemic survey (September to November 2019) were combined with data from a pandemic-period survey (August to November 2020) to Veterans Affairs sleep medicine providers about their local sleep services within 140 Veterans Affairs facilities). Results: A total of 67 (47.9%) facilities responded to the pandemic online survey. In-lab diagnostic and titration sleep studies were stopped at 91.1% of facilities during the pandemic; 76.5% of facilities resumed diagnostic studies and 60.8% resumed titration studies by the time of the second survey. Half of the facilities suspended home sleep testing; all facilities resumed these services. In-person positive airway pressure clinics were stopped at 76.3% of facilities; 46.7% resumed these clinics. Video telehealth was either available or in development at 86.6% of facilities and was considered a lasting addition to sleep services. Coronavirus disease-2019 transmission precautions occurred at high rates. Sleep personnel experienced high levels of stress, anxiety, fear, and burnout because of the pandemic and in response to unexpected changes in sleep medicine care delivery. Conclusions: Sleep medicine services within the Veterans Affairs evolved during the pandemic with many key services being interrupted, including in-lab studies and in-person positive airway pressure clinics. Expansion and initiation of telehealth sleep services occurred commonly. The pandemic adversely affected sleep medicine personnel as they sought to maintain access to care.

The impact of the COVID-19 pandemic on blood pressure control after a stroke or transient ischemic attack among patients at VA medical centers.

OBJECTIVE: To study factors associated with systolic blood pressure(SBP) control for patients post-discharge from an ischemic stroke or transient ischemic attack(TIA) during the early months of the COVID-19 pandemic compared to pre-pandemic periods within the Veterans Health Administration(VHA). MATERIALS AND METHODS: We analyzed retrospective data from patients discharged from Emergency Departments or inpatient admissions after an ischemic stroke or TIA. Cohorts consisted of 2,816 patients during March-September 2020 and 11,900 during the same months in 2017-2019. Outcomes included primary care or neurology clinic visits, recorded blood pressure readings and average blood pressure control in the 90-days post-discharge. Random effect logit models were used to compare clinical characteristics of the cohorts and relationships between patient characteristics and outcomes. RESULTS: The majority (73%) of patients with recorded readings during the COVID-19 period had a mean post-discharge SBP within goal (<140 mmHg); this was slightly lower than the pre-COVID-19 period (78%; p=0.001). Only 38% of the COVID-19 cohort had a recorded SBP in the 90-days post-discharge compared with 83% of patients during the pre-pandemic period (p=0.001). During the pandemic period, 29% did not have follow-up primary care or neurologist visits, and 33% had a phone or video visit without a recorded SBP reading. CONCLUSIONS: Patients with an acute cerebrovascular event during the initial COVID-19 period were less likely to have outpatient visits or blood pressure measurements than during the pre-pandemic period; patients with uncontrolled SBP should be targeted for follow-up hypertension management.

Heterogeneity in COVID-19 patient volume, characteristics and outcomes across US Department of Veterans Affairs facilities: an observational cohort study.

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.

Acceptability of a complex team-based quality improvement intervention for transient ischemic attack: a mixed-methods study.

BACKGROUND: The Protocol-guided Rapid Evaluation of Veterans Experiencing New Transient Neurologic Symptoms (PREVENT) program was a complex quality improvement (QI) intervention targeting transient ischemic attack (TIA) evidence-based care. The aim of this study was to evaluate program acceptability among the QI teams and factors associated with degrees of acceptability. METHODS: QI teams from six Veterans Administration facilities participated in active implementation for a one-year period. We employed a mixed methods study to evaluate program acceptability. Multiple data sources were collected over implementation phases and triangulated for this evaluation. First, we conducted 30 onsite, semi-structured interviews during active implementation with 35 participants at 6 months; 27 interviews with 28 participants at 12 months; and 19 participants during program sustainment. Second, we conducted debriefing meetings after onsite visits and monthly virtual collaborative calls. All interviews and debriefings were audiotaped, transcribed, and de-identified. De-identified files were qualitatively coded and analyzed for common themes and acceptability patterns. We conducted mixed-methods matrix analyses comparing acceptability by satisfaction ratings and by the Theoretical Framework of Acceptability (TFA). RESULTS: Overall, the QI teams reported the PREVENT program was acceptable. The clinical champions reported high acceptability of the PREVENT program. At pre-implementation phase, reviewing quality data, team brainstorming solutions and development of action plans were rated as most useful during the team kickoff meetings. Program acceptability perceptions varied over time across active implementation and after teams accomplished actions plans and moved into sustainment. We observed team acceptability growth over a year of active implementation in concert with the QI team's self-efficacy to improve quality of care. Guided by the TFA, the QI teams' acceptability was represented by the respective seven components of the multifaceted acceptability construct. CONCLUSIONS: Program acceptability varied by time, by champion role on QI team, by team self-efficacy, and by perceived effectiveness to improve quality of care aligned with the TFA. A complex quality improvement program that fostered flexibility in local adaptation and supported users with access to data, resources, and implementation strategies was deemed acceptable and appropriate by front-line clinicians implementing practice changes in a large, national healthcare organization. TRIAL REGISTRATION: clinicaltrials.gov : NCT02769338 .

Association of Intensive Care Unit Patient Load and Demand With Mortality Rates in US Department of Veterans Affairs Hospitals During the COVID-19 Pandemic.

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.