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Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …
Background: Stroke survivors have identified that home time is a high priority outcome, but there are limited data on factors associated with time at home following stroke as well as the degree to which home time varies among discharging hospitals. Methods: PROSPER is a PCORI-funded program designed with stroke survivors to evaluate post-stroke therapies and outcomes. We linked data for patients >65 years old who were enrolled in the GWTG-Stroke Registry to Medicare claims to ascertain home time, defined as time spent alive and out of a hospital or skilled nursing facility, at 90 days and 1 year after discharge for ischemic stroke. We used generalized mixed models with random effects to estimate adjusted mean home time for each hospital, accounting for patient characteristics. We then used linear regression to assess hospital factors associated with risk-adjusted home time. Results: From 2007-2011, 156,869 ischemic stroke patients at 1417 hospitals were linked to Medicare claims. Home time varied among hospitals, with overall unadjusted median home time (IQR) of 59.5 days (55.7-63.2) over the first 90 days and 270 days (256.0-281.1) over the first year. Hospital factors associated with more home time over 90 days included higher annual stroke volume; South, West, or Midwest geographic regions (vs. Northeast); and rural location (Table). Similar patterns were observed at 1 year. Compared with patients in the highest hospital home time quartile, patients in the lowest were older, more likely to be female, had more comorbidities, and had more severe strokes by NIHSS. Home time variation decreased after adjustment, with a median of 59.3 days (57.4-61.4) over 90 days and 270 days (266.3-274.2) over 1 year. Conclusions: In a population of older ischemic stroke survivors, home time after discharge varies by hospital stroke volume, severity case mix, and region. In adjusted analyses, hospital stroke volume and rural location were associated with more days at home following stroke.
Background: CT perfusion (CTP) imaging is an important tool for identifying candidates for mechanical thrombectomy (MT). This modality is increasingly available in regional facilities, but whether pre-transfer perfusion imaging studies may be relied upon for treatment decisions is unclear. We analyzed a cohort of stroke transfers to quantify changes in CTP imaging profiles over time. Methods: A cohort of confirmed ischemic stroke patients who received CTP before and after transfer to a large mid-western comprehensive stroke center (CSC) were identified. We compared perfusion mismatch (MM), core infarct volume (CIV), and favorability of imaging for mechanical thrombectomy (MT) candidacy (defined as the presence of LVO and MM/CIV ratio of >1.8) between studies. Regression models were used to examine predictors of CIV growth including hypoperfusion intensity ratio (HIR) and time between studies. Results: Over a 24-month period, 61 patients met inclusion criteria. Median age was 71 (IQR 60-80), 52% were female, median NIHSS was 14 (IQR 4-19), and most patients had occlusion of the internal carotid and/or middle cerebral arteries (77%). The median time from last known well (LKW) to initial CT was 148 minutes (IQR 71-620) and median time between imaging studies was 149 minutes (IQR 126-194). Median CIV growth was 0 mL (IQR 0-6) and 8% (5/61) had growth ≥25 mL. In linear regression, higher HIR predicted CIV growth (β=24.3, p=0.04), but time from LKW to presentation and time between imaging studies did not. Imaging studies taken at the regional and CSC sites demonstrated excellent agreement in classifying patients as candidates for MT (kappa 0.778 [95% CI: 0.611-0.944]). A favorable regional CTP profile was 93% sensitive for MT delivery at the CSC with a positive predictive value of 63%. Conclusion: In our cohort, CIV growth between facilities was minimal despite relatively long interfacility transport times and was predicted by the regional HIR. A favorable regional imaging profile demonstrated high agreement with the imaging result at the CSC and was strongly associated with MT treatment.
A survey of 1,965 equine colic cases was conducted from August 1985 to July 1986 at 10 equine referral centers located throughout the United States. The purpose of this study was to develop and validate a multivariable model for the need for surgery. Two-thirds of the cases were randomly selected for model development (1,336), whereas the remaining cases (629) were used only for subsequent validation of the model. If a lesion requiring surgical correction was found at either surgery or necropsy, the case for the horse was classified as surgical, otherwise the case was classified as medical. Only variables that were significant (P less than 0.05) in an initial bivariable screening procedure were considered in the model development. Because of the large number of missing values in the data set, only variables for which there were less than 400 missing values were considered in the multivariable analysis. A multivariable logistic regression model was constructed by use of a stepwise algorithm. The model used 640 cases and included variables: rectal findings, signs of abdominal pain, peripheral pulse strength, and abdominal sounds. The likelihood ratio for surgery was calculated for each horse in the validation data set, using the logistic regression equation. Using Bayes theorem, the posttest probability was calculated, using the likelihood ratio as the test odds and the prevalence of surgery cases (at each institution) as an estimate of the pretest odds. A Hosmer-Lemeshow goodness-of-fit chi 2 statistic indicated that the model fit the validation data set poorly, as demonstrated by the large chi 2 value of 26.7 (P less than 0.001).(ABSTRACT TRUNCATED AT 250 WORDS)
Previous research has shown that physical activity behaviors may be affected by season. Activity may be more sporadic and less intense in the colder months. However, the extent of this seasonality effect has not been quantified in population-based studies. PURPOSE: To determine the effect of season on self-reported leisure-time physical activity behaviors of Michigan adults. METHODS: Data were obtained from the 1996 Michigan Behavioral Risk Factor Survey (random-digit-dial telephone survey) that was conducted throughout the year. Survey respondents were considered active if they reported participating in at least one leisure-time physical activity during the past month. Complete information regarding type, frequency, and duration of up to two leisure-time activities was available on 2843 adults (1635 women and 1208 men). Four seasons were defined as Winter (January-March surveys; n = 677), Spring (April-June surveys; n = 759), Summer (July-September surveys; n = 760), and Fall (October-December surveys; n = 647). Total weekly leisure-time energy expenditure was quantified (kcalkg−1 week−1) from MET intensities, duration, and frequency of sessions per week. Seasonal differences were identified using ANOVA. RESULTS: Average (± SD) weekly leisure time energy expenditure was significantly greater (P < 0.001) during Spring (17.4 ± 19.0 kcalkg−1week−1) and Summer (17.4 ± 17.1 kcal kg−1week−1) compared to Winter (14.4 ± 16.6 kcal kg−1week−1) and Fall (15.0 ± 16.7 kcal kg−1week−1). Duration of the first activity was significantly greater in Summer (58.5 ± 41.7 min), compared to Winter (51.9 ± 40.0 min). However, intensity (4.5 ± 2.0 METS) and frequency (3.0 ± 1.9 sessions per week) of the first activity did not differ among seasons. A second activity was performed by 1319 of active individuals. Percentage of respondents performing a second leisure-time activity was greater in the Spring (47.7%) and Summer (53.9%) compared to Fall (42.7%) and Winter (40.0%) (X2: P < 0.01). CONCLUSION: Weekly leisure-time energy expenditure averaged ± 15-20% higher during the Spring and Summer. Much of this difference was due to active respondents participating in only one rather than two activities during the Fall and Winter.
Background: Inadequate transitional care contributes to poor patient outcomes including hospital readmissions, delayed recovery, and reduced quality of life. We report perspectives from healthcare providers on challenges and stroke educational needs which were collected to inform an intervention study designed to improve stroke transitions following discharge to home. Methods: During the development phase of the Michigan Stroke Transitions Trial (MISTT), a randomized trial testing a Social Work Case Management (SWCM) intervention, we convened 4 stakeholder meetings with healthcare providers. We aimed to understand provider perspectives regarding patient and system-level challenges and to identify patient educational needs. Michigan providers, including social workers, nurses, neurologists, physiatrists, rehabilitation therapists, and managers were invited to attend the focus groups. Transcriptions were analyzed for common patient and system-level challenges (themes) using qualitative methods and educational needs were determined. Results: Thirty-four providers, representing hospitals, rehabilitation facilities, nursing homes, and home healthcare agencies, attended the 4 meetings. They identified 43 patient-level, 36 healthcare-level, and 13 community-level challenges. The most common challenges were related to medical follow-up (n=10), consistency of care planning across settings (n=10), ability of patients to retain information (n=10), and unrealistic patient expectations (n=10). The most common stroke educational needs were stroke signs and symptoms recognition (n=10), post-stroke expectations (n=10), stroke risk factors (n=9), and post-stroke depression (n=9). Common medication topics included strategies for medication management (n=13), understanding the importance of medications (n=12), managing side effects (n=7), and communicating with physicians (n=5). Conclusion: These data were essential in directing the scope and organization of the MISTT case management intervention. Addressing common challenges and targeting stroke educational needs offers the best opportunity for in-home transitional support to maximize stroke recovery during the early transitional period.
After acute ischemic stroke (AIS), older adults tend to have worse outcomes compared to young adults.One potential explanation is differences in cerebral collateral circulation (CCC).Multiple studies have shown that a robust CCC is associated with smaller infarcts, improved clinical outcomes, and lower rates of hemorrhagic transformation.We undertook this study to examine CCC status and the role it plays in determining infarct size and outcome in young and older adults with AIS.
Background: Poor transitions adversely affect stroke survivors and contribute to readmissions and poor quality of life. More randomized trials are needed to identify effective interventions to improve patient wellbeing during the transitional period. However, the post-discharge period is complex and presents many practical challenges for transition trials when patients move between different care settings. We report on recruitment and retention challenges in the Michigan Stroke Transitions Trial (MISTT) designed to improve the transition experience of stroke survivors. Methods: MISTT, a 3 group pragmatic randomized trial, tested the efficacy of an in-home social work case management (SWCM) program and SWCM with an informational website against usual care. Eligible subjects were acute stroke patients discharged directly home or who had an expected rehab stay <4 weeks. Randomization occurred the day subjects were discharged home. Patient-centered outcomes were collected by phone 90-days later. Results: Figure 1 shows recruitment and retention data during the first 15 months. Of 242 subjects enrolled, 40 (17%) were dropped pre-randomization largely due to extended rehab stays. Of 136 subjects randomized to SWCM or SWCM+website, the intervention was not started in 14 (10%) due to early withdrawals or unwillingness to participate. A total of 188 subjects were eligible for 90 day follow-up. Lost to follow-up (n=43) occurred in 20%, 24% and 21% of the usual care, SWCM, and SWCM+Website groups, respectively. Half of the LTFU was due to patient withdrawals (n=23); reasons included study demands (n=9), protocol deviations (n=6), and deteriorating health conditions (n=5). Conclusion: The complexity of stroke transitions creates challenges for recruiting and retaining study participants in transition trials. These issues are important to consider in future study design and are relevant to understanding the validity of study results.
