Hospital adoption of interoperability functions

BACKGROUND

The seamless transmission of patient health information across health care settings, commonly referred to as interoperability, is a focal point of federal electronic health record (EHR) incentive programs. The objective of this study was to examine the extent to which interoperability functions outlined in Promoting Interoperability Stage 3 (PI3) requirements have been adopted by US hospitals, and barriers to interoperability.

METHODS

We conducted a cross-sectional analysis of 2781 non-federal, acute-care hospitals responding to the 2015 American Hospital Association Information Technology (IT) Supplement survey. We described the percentage of hospitals that adopted PI3 functionalities, identified hospital characteristics associated with adoption, and compared barriers to interoperability between hospitals that have and have not adopted PI3 functionalities.

RESULTS

Only 16.7% of hospitals had adopted all six core functionalities required to meet PI3 objectives. Over 70% of hospitals had implemented at least four of six functionalities, while 1.8% implemented none. Major teaching (adjusted odds ratio [aOR]=1.66), system affiliated (aOR=1.63), and regional health information exchange participating hospitals (aOR=1.86) were more likely to adopt PI3 functionalities, while for-profit hospitals (OR=0.11) were less likely. Hospitals that adopted PI3 functionalities more frequently reported experiencing barriers to interoperability, including the receiving provider's ability and interest to send/receive data.

CONCLUSIONS

While only a small proportion of hospitals had implemented all six PI3 functionalities at the time the requirements were finalized, the vast majority had already implemented most of the required functionalities. Still, several barriers stand in the way of achieving seamless interoperability, many of which lie outside hospitals' control.

Medicare Payment Penalties and Safety Net Hospital Profitability: Minimal Impact on These Vulnerable Hospitals

OBJECTIVE

To examine relationships between penalties assessed by Medicare's Hospital Readmission Reduction Program and Value-Based Purchasing Program and hospital financial condition.

DATA SOURCES/STUDY SETTING

Centers for Medicare and Medicaid Services, American Hospital Association, and Area Health Resource File data for 4,824 hospital-year observations.

STUDY DESIGN

Bivariate and multivariate analysis of pooled cross-sectional data.

PRINCIPAL FINDINGS

Safety net hospitals have significantly higher HRRP/VBP penalties, but, unlike nonsafety net hospitals, increases in their penalty rate did not significantly affect their total margins.

CONCLUSIONS

Safety net hospitals appear to rely on nonpatient care revenues to offset higher penalties for the years studied. While reassuring, these funding streams are volatile and may not be able to compensate for cumulative losses over time.

Transcatheter Versus Surgical Aortic Valve Replacement Episode Payments and Relationship to Case Volume

BACKGROUND

Transcatheter aortic valve replacement (TAVR) has increased in volume as an alternative to surgical aortic valve replacement (SAVR). Comparisons of total episode expenditures, although largely ignored thus far, will be key to the value proposition for payers.

METHODS

We evaluated 6,359 Blue Cross Blue Shield of Michigan and Medicare fee-for-service beneficiaries undergoing TAVR (17 hospitals, n = 1,655) or SAVR (33 hospitals, n = 4,704) in Michigan between 2012 and 2016. Payments through 90 post-discharge days between TAVR and SAVR were price-standardized and risk-adjusted. Centers were divided into terciles of procedural volume separately for TAVR and SAVR, and payments were compared between lowest and highest terciles.

RESULTS

Payments (± SD) were higher for TAVR than SAVR ($69,388 ± $22,259 versus $66,683 ± $27,377, p < 0.001), while mean hospital length of stay was shorter for TAVR (6.2 ± 5.6 versus 10.2 + 7.5 days, p < 0.001). Index hospitalization payments were $4,374 higher for TAVR (p < 0.001), whereas readmission and post-acute care payments were $1,150 (p = 0.001) and $739 (p = 0.004) lower, respectively, and professional payments were similar. For SAVR, high-volume centers had lower episode payments (difference: 5.0%, $3,255; p = 0.01) and shorter length of stay (10.0 ± 7.5 versus 11.1 ± 7.9 days, p = 0.002) than low volume centers. In contrast, we found no volume-payment relationship among TAVR centers.

CONCLUSIONS

Episode payments were higher for TAVR, despite shorter length of stay. Although not a driver for TAVR, center SAVR volume was inversely associated with payments. These data will be increasingly important to address value-based reimbursement in valve replacement surgery.

Three years in - changing plan features in the U.S. health insurance marketplace

Background

A central objective of recent U.S. healthcare policy reform, most notably the Affordable Care Act’s (ACA) Health Insurance Marketplace, has been to increase access to stable, affordable health insurance. However, changing market dynamics (rising premiums, changes in issuer participation and plan availability) raise significant concerns about the marketplaces’ ability to provide a stable source of healthcare for Americans that rely on them. By looking at the effect of instability on changes in the consumer choice set, we can analyze potential incentives to switch plans among price-sensitive enrollees, which can then be used to inform policy going forward.

Methods

Data on health plan features for non-tobacco users in 2512 counties in 34 states participating in federally-facilitated exchanges from 2014 to 2016 was obtained from the Centers for Medicaid & Medicare Services. We examined how changes in individual plan features, including premiums, deductibles, issuers, and plan types, impact consumers who had purchased the lowest-cost silver or bronze plan in their county the previous year. We calculated the cost of staying in the same plan versus switching to another plan the following year, and analyzed how costs vary across geographic regions.

Results

In most counties in 2015 and 2016 (53.7 and 68.2%, respectively), the lowest-cost silver plan from the previous year was still available, but was no longer the cheapest plan. In these counties, consumers who switched to the new lowest-cost plan would pay less in monthly premiums on average, by $51.48 and $55.01, respectively, compared to staying in the same plan. Despite potential premium savings from switching, however, the majority would still pay higher average premiums compared to the previous year, and most would face higher deductibles and an increased probability of having to change provider networks.

Conclusion

While the ACA has shown promise in expanding healthcare access, continued changes in the availability and affordability of health plans are likely to result in churning and switching among enrollees, which may have negative ramifications for their health going forward. Future healthcare policy reform should aim to stabilize marketplace dynamics in order to encourage greater care continuity and limit churning.

Regional Variation in 30-Day Ischemic Stroke Outcomes for Medicare Beneficiaries Treated in Get With The Guidelines-Stroke Hospitals

BACKGROUND

We explored regional variation in 30-day ischemic stroke mortality and readmission rates and the extent to which regional differences in patients, hospitals, healthcare resources, and a quality of care composite care measure explain the observed variation.

METHODS AND RESULTS

This ecological analysis aggregated patient and hospital characteristics from the Get With The Guidelines-Stroke registry (2007-2011), healthcare resource data from the Dartmouth Atlas of Health Care (2006), and Medicare fee-for-service data on 30-day mortality and readmissions (2007-2011) to the hospital referral region (HRR) level. We used linear regression to estimate adjusted HRR-level 30-day outcomes, to identify HRR-level characteristics associated with 30-day outcomes, and to describe which characteristics explained variation in 30-day outcomes. The mean adjusted HRR-level 30-day mortality and readmission rates were 10.3% (SD=1.1%) and 13.1% (SD=1.1%), respectively; a modest, negative correlation (r=-0.17; P=0.003) was found between one another. Demographics explained more variation in readmissions than mortality (25% versus 6%), but after accounting for demographics, comorbidities accounted for more variation in mortality compared with readmission rates (17% versus 7%). The combination of hospital characteristics and healthcare resources explained 11% and 16% of the variance in mortality and readmission rates, beyond patient characteristics. Most of the regional variation in mortality (65%) and readmission (50%) rates remained unexplained.

CONCLUSIONS

Thirty-day mortality and readmission rates vary substantially across HRRs and exhibit an inverse relationship. While regional variation in 30-day outcomes were explained by patient and hospital factors differently, much of the regional variation in both outcomes remains unexplained.

Most Hospitals Received Annual Penalties For Excess Readmissions, But Some Fared Better Than Others

The Hospital Readmissions Reduction Program (HRRP) initiated by the Affordable Care Act levies financial penalties against hospitals with excess thirty-day Medicare readmissions. We sought to understand the penalty burden over the program's first five years, focusing on characteristics of hospitals that received penalties during all five years, how penalties changed over time, and the relationship between baseline and subsequent performance. More than half of participating hospitals were penalized by the Centers for Medicare and Medicaid Services in all five years of the program. From fiscal years 2013 to 2017, the growth in average penalties was modest, doubling from 0.29 percent to 0.60 percent, despite increasing opportunities for penalization. The penalty burden was greater in hospitals that were urban, major teaching, large, or for-profit and that treated larger shares of Medicare or socioeconomically disadvantaged patients. Surprisingly, hospitals treating greater proportions of medically complex Medicare patients had a lower cumulative penalty burden compared to those treating fewer proportions of these patients. Lastly, we found that hospitals with high baseline penalties in the first year continued to receive significantly higher penalties in subsequent years. For many hospitals, the HRRP leads to persistent penalization and limited capacity to reduce penalty burden. Alternative structures might avoid persistent penalization, while still motivating reductions in hospital readmissions.

Abstract 231: Does the Implementation of Pneumonia Prevention Practices Reduce Risk of Pneumonia Following Cardiac Surgery?

Purpose: Pneumonia is the most prevalent healthcare-associated infection following isolated coronary artery bypass surgery (CABG) and is associated with increased length of stay and mortality. Bundled infection prevention practices have been proposed as a tool for quality improvement, however the extent to which their adoption is associated with reducing risk of pneumonia is unknown.

Method: We undertook a cohort study of 2,482 patients undergoing CABG from 2016 to 2017 across 18 centers participating in a statewide collaborative. Three pneumonia prevention practices were identified via structured literature review and benchmarking site visits: 1) preoperative nasal and oral antibiotic prophylaxis, 2) lung protective ventilation, and 3) goal-directed postoperative ambulation. These practices were implemented as a bundle, with a composite (bundle) score calculated as the total number of practices received by each patient. We estimated the association between composite score and development of pneumonia using logistic regression, adjusting for baseline risk (using a published risk model). Sensitivity analyses were conducted by age, sex, chronic lung disease, and operative status.

Results: Pneumonia occurred in 2.4% (n/N = 60/2482) of patients. Bundle scores ranged from 0 to 3, with 75% of patients receiving a score of 1 or 2, and 22% having a score of 3. Crude and adjusted rates of pneumonia were lower in patients with higher scores (p-trend < 0.01; Figure); this finding was consistent across clinically important subgroups. Lung protective ventilation (OR adj : 0.42) and goal-directed postoperative ambulation (OR adj : 0.11) were significantly associated with lower odds of pneumonia (both p < 0.01); preoperative nasal and oral antibiotic prophylaxis was non-significantly protective of pneumonia (OR adj : 0.63, p = 0.11). Each 1-unit increase in bundle score was associated with a 57% decrease in operative mortality, and a more than 1 day reduction in (i.e., -27 hours) ICU length of stay (both p < 0.01).

Conclusion: In this statewide study, we identified components of a bundle associated with reduced odds of pneumonia. Broader adoption of this bundle may serve as an effective strategy for improving value for cardiac surgical patients.

Abstract 18: Center Variation in 90-Day Episode Expenditures for Cardiac Surgery - The Role of Healthcare-Associated Pnuemonia

Objective: Pneumonia is the most common healthcare-associated infection following cardiac surgery, and associated with poorer clinical outcomes and substantially higher hospital costs. Less understood is the role that the care and treatment of post-operative pneumonia may have on a hospital’s 90-day episode payments. We hypothesize that expenditures associated with pneumonia may significantly impact a hospital’s 90-day episode payments for coronary artery bypass graft (CABG) surgery.

Methods and Results: Using Medicare Part A and B claims data, we identified 49,573 patients undergoing isolated CABG in 1,001 hospitals with greater than 10 cases (2014-15). We applied an established claims-based algorithm to identify 3,135 (6.3%) patients as having a new onset of pneumonia during their index admission and after their surgical procedure. Using hierarchical logistic regression models, we estimated risk-adjusted hospital-level pneumonia rates, adjusted for age, sex, race, Medicaid eligibility, Elixhauser comorbidities, and hospital-random effect. There was weak correlation (r=0.20, p<0.001) between observed and predicted (adjusting for only patient factors) hospital-level pneumonia rates, indicating patient factors explained little of the variation between hospitals. We placed patients into quartiles based on rank-order of hospital risk-adjusted pneumonia rates; the pneumonia rate in the lowest and highest quartile was 3.4% and 13.9% (p<0.001), respectively (Table). Average risk-adjusted 90-day episode expenditures were 10% higher for patients in the highest quartile hospitals compared to the lowest quartile ($41,936 vs. $46,095 vs., p<0.001). Payments for outlier hospitalizations were 100% greater in the highest quartile hospitals compared to the lowest quartile, and accounted for 28.5% of the total difference between high and low spending hospitals.

Conclusion: New onset pneumonia after cardiac surgery varies widely across hospitals, and counter to conventional wisdom, is not driven by patient risk. Cardiac surgical programs should consider the prevention and management post-operative pneumonia as a component of their overall strategy for reducing 90-day episode payments.

Community navigators reduce hospital utilization in super-utilizers

OBJECTIVES

Super-utilizers place a significant burden on the healthcare system. Blending the roles of patient navigators and community health workers may address the clinical and social needs of these patients. This study evaluated the effectiveness of community navigators in reducing hospital utilization and costs among super-utilizers from a low-income area in Memphis, Tennessee.

STUDY DESIGN

Controlled pre-post (difference-in-differences [DID]) design using Methodist Le Bonheur Healthcare electronic health records from 2013 to 2016.

METHODS

Data were abstracted for 1 year pre- and post intervention for super-utilizers working with a community navigator (n = 159) and a control group of similar super-utilizers (n = 280). We compared utilization (hospital encounters, total hospital days, days between encounters, 30-day readmissions) and costs before and after working with a navigator for the intervention group with utilization and costs in a control group not working with a navigator and compared relative changes using a DID approach.

RESULTS

Utilization and cost outcomes for intervention and control groups declined significantly from the pre- to postintervention periods. Relative to the control group, super-utilizers working with community navigators had an additional 13% reduction in hospital encounters (95% CI, -19% to -6%), 8% reduction in total hospital days (95% CI, -14% to -2%), and 9% increase in days between encounters (95% CI, 4%-15%). The intervention group also had additional reductions in 30-day readmissions (-18%; 95% CI, -44% to 22%) and costs (-$4903; 95% CI, -$13,579 to $3774), but these were not statistically significant.

CONCLUSIONS

Community navigators can reduce subsequent hospital utilization in super-utilizers. Expansions of this model should examine the model's effectiveness in other populations and outcomes.

Abstract 028: Electronic Health Record Meaningful Use Did Not Improve Pay-for-Performance Outcomes

Objective: With the goal of improving healthcare quality, Medicare has implemented a series of pay-for-performance initiatives and allocated substantial financial resources to promote meaningful use of electronic health records (EHRs). The purpose of this study was to examine whether hospitals achieving EHR meaningful use improved hospital 30-day risk-standardized mortality (RSMR) and readmission (RSRR) rates used in Medicare pay-for-performance (P4P) initiatives.

Methods: We used publically available data on Medicare EHR Incentive Program achievement (2014 to 2015) to categorize hospitals as achieving two years, one year, or no years of stage 2 meaningful use (i.e. comprehensive EHR) from 2014-2015. Using generalized linear models, we compared the change in publically reported 30-day RSMRs and RSRRs for acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia (PN) from 2012 to 2016 by years of stage 2 meaningful use. Models were adjusted for hospital teaching status, system affiliation, ownership status, urban/rural location, bed size, and safety-net status obtained from the American Hospital Association Annual Survey and Medicare Impact File (both 2009-2011).

Results: From the 4,755 hospitals participating in the Medicare EHR program, 19.8% and 46.0% had two years and one year of stage 2 meaningful use, while 34.2% never achieved stage 2 meaningful use. The Figure shows that from 2012 to 2016 thirty-day mortality for AMI decreased (-1.2% to -1.4%), increased modestly for CHF (+0.5), and increased for PN (+4.1 to +4.6%). All thirty-day readmission rates decreased during this time, with decreases greater in AMI and CHF (both -2.7% to -2.8%) than in PN (-1.3% to -1.4%). We found that there were no significant differences in risk-standardized mortality or readmission rate changes by years of stage 2 meaningful use, even after adjusting for hospital characteristics (all comparisons p>0.05).

Conclusions: While RSMRs and RSRRs have changed substantially from 2012 to 2016 for most conditions, changes were similar for hospitals with two years, one year, or no years of stage 2 EHR meaningful use. Our findings suggest that adoption of more comprehensive EHRs did not improve hospital P4P outcomes. Figure. Change in RSMRs and RSRRs rates by years of stage 2 meaningful use.

Reliability of 30-Day Readmission Measures Used in the Hospital Readmission Reduction Program

OBJECTIVE

To assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).

DATA SOURCES

State Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.

STUDY DESIGN

Hierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.

PRINCIPAL FINDINGS

Only RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.

CONCLUSIONS

Many of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.

Quantifying Selection Bias in National Institute of Health Stroke Scale Data Documented in an Acute Stroke Registry

BACKGROUND

As a measure of stroke severity, the National Institutes of Health Stroke Scale (NIHSS) is an important predictor of patient- and hospital-level outcomes, yet is often undocumented. The purpose of this study is to quantify and correct for potential selection bias in observed NIHSS data.

METHODS AND RESULTS

Data were obtained from the Michigan Stroke Registry and included 10 262 patients with ischemic stroke aged ≥65 years discharged from 23 hospitals from 2009 to 2012, of which 74.6% of patients had documented NIHSS. We estimated models predicting NIHSS documentation and NIHSS score and used the Heckman selection model to estimate a correlation coefficient (ρ) between the 2 model error terms, which quantifies the degree of selection bias in the documentation of NIHSS. The Heckman model found modest, but significant, selection bias (ρ=0.19; 95% confidence interval: 0.09, 0.29; P<0.001), indicating that because NIHSS score increased (ie, strokes were more severe), the probability of documentation also increased. We also estimated a selection bias-corrected population mean NIHSS score of 4.8, which was substantially lower than the observed mean NIHSS score of 7.4. Evidence of selection bias was also identified using hospital-level analysis, where increased NIHSS documentation was correlated with lower mean NIHSS scores (r=-0.39; P<0.001).

CONCLUSIONS

We demonstrate modest, but important, selection bias in documented NIHSS data, which are missing more often in patients with less severe stroke. The population mean NIHSS score was overestimated by >2 points, which could significantly alter the risk profile of hospitals treating patients with ischemic stroke and subsequent hospital risk-adjusted outcomes.

Abstract 19: Using the Heckman Selection Model to Assess Selection Bias in Ischemic Stroke Patients with Documented NIHSS

Objective: Complete documentation in large scale datasets such as administrative data or disease registries is often difficult. Given that the subset of patients with complete data documentation are most likely not a random sample of patients, selection bias threatens the validity of results if a complete case analysis is used. To demonstrate, we will assess the presence and magnitude of selection bias in ischemic stroke patients with documented National Institute of Health Stroke Scale (NIHSS) [[Unable to Display Character: &#8211;]] which is often incomplete [[Unable to Display Character: &#8211;]] using the Heckman Selection Model.

Methods: Patient level variables including demographics, comorbidities, clinical EMS and admission variables, and medical history/comorbidities were obtained from 10,717 ischemic stroke patients aged 65 and older in the Michigan Stroke Registry in 2009-2012. The Heckman Selection Model assesses the presence and magnitude of selection bias by estimating a correlation coefficient between error components of a linear regression model predicting patient NIHSS score [[Unable to Display Character: &#8211;]] the outcome model [[Unable to Display Character: &#8211;]] and a binary probit model predicting NIHSS documentation [[Unable to Display Character: &#8211;]] the selection model [[Unable to Display Character: &#8211;]] conditional on patient and hospital predictors. The outcome model predicting NIHSS score was specified using a backward selection process with stepwise deletion of non-significant predictors. The selection model included all variables in the outcome model, plus additional significant predictors of NIHHS documentation. Quasi-maximum likelihood estimation was used to produce robust standard errors. All analyses were done using PROC QLIM procedure in SAS.

Results: 7,956 cases (74.2%) of cases had NIHSS documented. Significant predictors in the outcome and selection models are shown in the Table. The Heckman Selection Model found a statistically significant but modest correlation coefficient of ρ =0.1089 (SE=0.0119, p<0.0001). The positive correlation indicates that NIHSS was more likely to be documented in patients with higher NIHSS scores, i.e., more severe strokes.

Conclusions: We found statistically significant albeit weak selection bias in the documentation of NIHSS in stroke patients. The Heckman Selection Model is a novel method that can be used to assess the presence and magnitude of selection bias when missing data is common.

Abstract 305: The Accuracy of Profiling Methods to Identify Hospital Outliers for Ischemic Stroke Mortality in the Presence of Missing Data: Effect of Hospital Volume

Objective: We used simulation modeling to assess the impact of missing risk adjustment data, exemplified by incomplete National Institute of the Health Stroke Scale (NIHSS) documentation, on the ability of risk adjustment models to accurately identify hospital ischemic stroke mortality performance outliers using a complete case analysis.

Methods: Simulation parameters were generated from analysis of 10,716 ischemic stroke patients aged 65 and older from the Michigan Stroke Registry (MSR). 250 datasets were simulated in a way such that distributions of risk score (including NIHSS) and mortality within and between 100 hospitals were similar to the MSR. We randomly assigned missing NIHSS data to each simulated dataset using a plausible range of hospital-level NIHSS documentation (30%-100%). Hospitals were assigned a random intercept from a distribution of intercepts observed in the MSR (true performance), and estimated from a hierarchical mortality model using a complete case analysis of the simulated data (observed performance). If the assigned (true) or estimated (observed) random intercepts were outside their respective 95% CIs, they were considered outliers. Accuracy was assessed as the percent of true outliers and non-outliers correctly identified by the hierarchical model, i.e., sensitivity (Se) and specificity (Sp). To understand the impact of case volume, we modified hospital patient volume as 100, 300 and 500.

Results: As NIHSS documentation fell, Se decreased while Sp increased. (Figure) The relative change in both Se and Sp was strongly impacted by patient volume: in hospitals with low stroke volume (n=100), Se was <50% even with complete documentation, while Sp remained high (>90%). In moderate (n=300) and high (n=500) stroke volume hospitals when NIHSS documentation was very high, Se was near perfect, but declined rapidly with reduced documentation. Conversely, as NIHSS documentation fell, Sp increased in both moderate and high volume hospitals.

Conclusions: Reductions in sample size when using a complete case analysis in the presence of missing data have a dramatic effect on correctly identifying performance outliers, especially in lower case volumes. Limitations to identifying ischemic stroke performance mortality outliers - even with complete NIHSS documentation - should be appreciated.

Bartonella henselae-mediated disease in solid organ transplant recipients: two pediatric cases and a literature review

Bartonella henselae, the etiologic agent of cat-scratch disease, causes a well-defined, self-limited syndrome of fever and regional lymphadenopathy in immunocompetent hosts. In immunocompromised hosts, however, B. henselae can cause severe disseminated disease and pathologic vasoproliferation known as bacillary angiomatosis (BA) or bacillary peliosis. BA was first recognized in patients infected with human immunodeficiency virus. It has become more frequently recognized in solid organ transplant (SOT) recipients, but reports of pediatric cases remain rare. Our review of the literature revealed only one previously reported case of BA in a pediatric SOT recipient. We herein present 2 pediatric cases, one of which is the first reported case of BA in a pediatric cardiac transplant recipient, to our knowledge. In addition, we review and summarize the literature pertaining to all cases of B. henselae-mediated disease in SOT recipients.

Abstract 168: Assessing The Utility Of The Modified Rankin Scale (mRS) At Discharge To Predict Day 90 Outcomes In Acute Stroke Registries

Objective: The modified Rankin Scale (mRS) is a commonly used measure of functional stroke recovery, which is typically measured at 30 or 90 days post event. Given the difficulty of follow-up, most hospital-based stroke registries are usually limited to assessments at discharge. Studies have shown that mRS assessed 30 days post-stroke is a sufficient proxy for 90 day mRS, but little research has explored the utility of mRS assessment at discharge. The objective of this study was to assess the utility of mRS at discharge in predicting 90 day stroke outcomes.

Methods: Data were obtained from a follow-up study of 373 acute stroke discharges from a statewide hospital-based acute stroke registry. Discharge mRS was obtained by chart review and 90 day outcomes data measured by the Barthel Index (BI) were obtained by phone. BI data were converted to 3 mRS levels: 0-2 (low to no), 3 (moderate), and 4-5 (severe) using validated methods. Weighted kappa was used to assess agreement between discharge and 90 day mRS. Logistic regression was used to assess the independent association between discharge mRS and disability at 90 days (defined as mRS 3-5 vs. 0-2).

Results: Average age of the patients was 65 years, 56% were female, 28% were non-white, 74% had ischemic stroke, 14% had TIA, and 12% had hemorrhagic stroke. Distribution of mRS scores for 261 patients who completed the 90 day follow up is shown in Figure 1. The weighted kappa between discharge and 90 day mRS was 0.28 (95% CI 0.18, 0.38), indicating only fair agreement. After adjustment, those with moderate (3) or severe disability (4-5) at discharge were 2.49 (1.13, 5.54) and 2.55 (1.17-5.79) times more likely to have moderate to severe disability (3-5) at 90 days. (Table 1)

Conclusion: Although discharge mRS was a strong independent predictor of disability at 90 days, the modest agreement between discharge and 90 day mRS indicate that it is not a sufficient proxy for 90 day outcomes. Further research is needed to identify predictors of stroke recovery based on assessments at hospital discharge.

Cloning, mapping and association studies of the ovine ABCG2 gene with facial eczema disease in sheep

Facial eczema (FE) is a hepatogenous mycotoxicosis in sheep caused by the fungal toxin sporidesmin. Resistance to FE is a multigenic trait. To identify QTL associated with this trait, a scan of ovine chromosomes was implemented. In addition, ABCG2 was investigated as a possible positional candidate gene because of its sequence homology to the yeast PDR5 protein and its functional role as a xenobiotic transporter. The sequence of ovine ABCG2 cDNA was obtained from liver mRNA by RT-PCR and 5' and 3' RACE. The predicted protein sequence shares >80% identity with other mammalian ABCG2 proteins. SNPs were identified within exon 6, exon 9 and intron 4. The intron 4 SNP was used to map ABCG2 to ovine chromosome 6 (OAR6), about 2 cM distal to microsatellite marker OarAE101. Interestingly, this chromosomal region contains weak evidence for a FE QTL detected in a previous genome-scan experiment. To further investigate the association of ABCG2 with FE, allele frequencies for the three SNPs plus three neighbouring microsatellite markers were tested for differences in sheep selected for and against FE. Significant differences were detected in the allele frequencies of the intronic SNP marker among the resistant, susceptible and control lines. No difference in the levels of ABCG2 expression between the resistant and susceptible animals was detected by Northern hybridisation of liver RNA samples. However, significantly higher expression was observed in sporidesmin-dosed sheep compared with naïve animals. Our inference is that the ABCG2 gene may play a minor role in FE sensitivity in sheep, at least within these selection lines.

Autocrine lymphotoxin production in Epstein-Barr virus-immortalized B cells: induction via NF-kappaB activation mediated by EBV-derived latent membrane protein 1

Epstein-Barr virus (EBV)-immortalized lymphoblastoid cells express high levels of lymphotoxin and use this molecule as an autocrine growth factor. We hypothesized that the EBV-derived latent membrane protein 1 (LMP1) mediates lymphotoxin production by inducing NF-kappaB binding to the lymphotoxin promoter. We assessed lymphotoxin production, LMP1 expression, and NF-kappaB activation in Z-43 (EBV-positive lymphoblastoid cells), Daudi (EBV-positive Burkitt's cells), and 3A4 (EBV-negative Burkitt's cells containing a stably transfected tetracycline-inducible LMP1 construct). Z-43 cells expressed high levels of LMP1 (immunoblot) and lymphotoxin (ELISA); the EBV-positive Burkitt's lymphoma line Daudi expressed neither LMP1 nor lymphotoxin. Similarly, induction of LMP1 in the 3A4 cells (exposed to tetracycline) was accompanied by a 13-fold increase in lymphotoxin levels (ELISA) as compared to uninduced (LMP1-negative) cells. EMSAs demonstrated high levels of NF-kappaB activation in Z-43 and tetracycline-induced 3A4 cells, but much lower levels in the uninduced 3A4 cells. Exposure of these cells to Bay 11-7082 (an inhibitor of IkappaB phosphorylation and, therefore, NF-kappaB activation) abrogated NF-kappaB binding and lymphotoxin production in a dose-dependent manner in both Z-43 and 3A4 cells. Therefore, in our model system, autocrine lymphotoxin production is largely driven by NF-kappaB activation, which is in turn mediated by EBV-derived LMP1 signaling.