New tricks for old NODs

The mammalian NOD-like receptor NLRX1 appears to have a role as a regulator of antiviral response pathways

Impact of Hospital Admission for Patients with Transient Ischemic Attack

OBJECTIVES: To determine the impact of admission among transient ischemic attack (TIA) patients in the emergency department (ED). STUDY DESIGN: Retrospective cohort study using national Veterans Health Administration data (2008). METHODS: We first analyzed whether admitted patients were discharged from the hospital with a diagnosis of TIA. We then analyzed whether admission was associated with a composite outcome (new stroke, new myocardial infarction, or death in the year after TIA) using multivariate logistic regression modeling with propensity score matching. RESULTS: Among 3623 patients assigned a diagnosis of TIA in the ED, 2118 (58%) were admitted to the hospital or placed in observation compared with 1505 (42%) who were discharged from the ED. Among the 2118 patients who were admitted, 903 (43% of admitted group) were discharged from the hospital with a diagnosis of TIA, and 548 (26% of admitted group) were discharged with a diagnosis of stroke. Admitted patients were more likely than nonadmitted patients to receive processes of care (i.e., brain imaging, carotid imaging, echocardiography). In matched analyses using propensity scores, the 1-year composite outcome in the admitted group (15.3%) was not lower than the discharged group (13.3%, OR 1.17 [.94-1.46], P = .17). CONCLUSIONS: Less than half of patients admitted with a diagnosis of TIA retained that diagnosis at hospital discharge. Although admitted patients were more likely to receive diagnostic procedures, we did not identify improvements in outcomes among admitted patients; however, evaluating care for patients with TIA is limited by the reliability of secondary data analysis

Uncertainty as a Key Influence in the Decision To Admit Patients with Transient Ischemic Attack

Background Patients with transient ischemic attacks (TIA) are at high risk of subsequent vascular events. Hospitalization improves quality of care, yet admission rates for TIA patients vary considerably. Objectives We sought to identify factors associated with the decision to admit patents with TIA. Design We conducted a secondary analysis of a prior study’s data including semi-structured interviews, administrative data, and chart review. Participants We interviewed multidisciplinary clinical staff involved with TIA care. Administrative data included information for TIA patients in emergency departments or inpatient settings at VA medical centers (VAMCs) for fiscal years (FY) 2011 and 2014. Chart reviews were conducted on a subset of patients from 12 VAMCs in FY 2011. Approach For the qualitative data, we focused on interviewees’ responses to the prompt: “Tell me what influences you in the decision to or not to admit TIA patients.” We used administrative data to identify admission rates and chart review data to identify ABCD2 scores (a tool to classify stroke risk after TIA). Key Results Providers’ decisions to admit TIA patients were related to uncertainty in several domains: lack of a facility TIA-specific policy, inconsistent use of ABCD2 score, and concerns about facilities’ ability to complete a timely workup. There was a disconnect between staff perceptions about TIA admission and facility admission rates. According to chart review data, staff at facilities with higher admission rates in FY 2011 reported consistent reliance on ABCD2 scores and related guidelines in admission decision-making. Conclusions Many factors contributed to decisions regarding admitting a patient with TIA; however, clinicians’ uncertainty appeared to be a key driver. Further quality improvement interventions for TIA care should focus on facility adoption of TIA protocols to address uncertainty in TIA admission decision-making and to standardize timely evaluation of TIA patients and delivery of secondary prevention strategies

Predicting clinical outcomes of cancer patients with a p53 deficiency gene signature

The tumor suppressor p53, encoded by the TP53 gene, is mutated or nullified in nearly 50% of human cancers. It has long been debated whether TP53 mutations can be utilized as a biomarker to predict clinical outcomes of cancer patients. In this study, we applied computational methods to calculate p53 deficiency scores (PDSs) that reflect the inactivation of the p53 pathway, instead of TP53 mutation status. Compared to TP53 mutation status, the p53 deficiency gene signature is a powerful predictor of overall survival and drug sensitivity in a variety of cancer types and treatments. Interestingly, the PDSs predicted clinical outcomes more accurately than drug sensitivity in cell lines, suggesting that tumor heterogeneity and/or tumor microenvironment may play an important role in predicting clinical outcomes using p53 deficiency gene signatures

Using Administrative Databases to Calculate Framingham Scores within a Large Healthcare Organization

Background and Purpose—Framingham calculators are typically implemented in 1-on-1 settings to determine if a patient is at high risk for development of cardiovascular disease in the next 10 years. Because health care administrative datasets are including more clinical information, we explored how well administrative data-derived Framingham scores could identify persons who would have stroke develop in the next year. Methods—Using a nested case-control design, we compared all 313 persons who had a first-time stroke at 5 Veterans Administration Medical Centers with a random sample of 25 361 persons who did not have a first-time stroke in 2008. We compared Framingham scores and risk using administrative data available at the end of 2007. Results—Stroke patients had higher risk profile than controls: older age, higher systolic blood pressure and total cholesterol, more likely to have diabetes, cardiovascular disease, left ventricular hypertrophy, and more likely to use treatment for blood pressure (P<0.05). The mean Framingham generalized cardiovascular disease score (18.0 versus 14.5) as well as the mean Framingham stroke-specific score (13.2 versus 10.2) was higher for stroke cases than controls (both P<0.0001). The c-statistic for the generalized cardiovascular disease score was 0.68 (95% CI, 0.65–0.70) and for the stroke score was 0.64 (95% CI, 0.62–0.67). Conclusions—Persons who had a stroke develop in the next year had a worse Framingham risk profile, as determined by administrative data. Future studies should examine how to improve the stroke predictive tools and to identify the appropriate populations and uses for applying stroke risk predictive tools

Lower use of carotid artery imaging at minority-serving hospitals

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