Williams, Linda S.

Abstract Background Mortality prediction data may aid in identifying seriously ill transferred patients at high risk of dying and facilitate early goals-of-care discussions (GOCD); however, this is rarely evaluated. We recently developed a model for predicting 30-day inpatient mortality, which may be useful for promoting early GOCD. Objectives Our objectives were to examine the effects of sharing model-generated mortality risk with hospitalists by assessing (1) if hospitalists agreed with the mortality risk prediction, (2) if they planned to conduct GOCD or consult palliative care within 72 hours of transfer, and (3) if the communication alert affected GOCD timing and other clinical outcomes. We also aimed to measure the association between both the model-generated and hospitalists' stratified risk assessments with patient mortality. Methods This was a nonrandomized quasi-experimental pilot study with a historical control group. On the second day of hospitalization, the model-generated risk was communicated to the hospitalists. Hospitalists were asked to answer questions via a HIPAA (Health Insurance Portability and Accountability Act)-compliant mobile communication system, and clinical outcomes were extracted via chart review. Results Eighty-four patients (42 in the control and 42 in the intervention group) were included in this study. Hospitalists agreed that all patients in the intervention group were at risk for inpatient mortality. Hospitalists were more likely to indicate a plan to conduct GOCD in the intervention group (n = 9) compared with the control group (n = 4, p < 0.001). In this subset of patients, GOCD was completed within 72 hours in 78% of intervention patients (n = 7) as compared with 50% in the control group (n = 2). The greater absolute value of the model-generated mortality risk was significantly associated with deaths (p = 0.01), similar to the hospitalists' prediction of the mortality risk (p = 0.02). Conclusion Communicating model-generated mortality risk to hospitalists is a promising approach to promote timely GOCD.

OBJECTIVE Stroke symptoms among individuals reporting no physician diagnosis of stroke are associated with an increased risk of future stroke. Few studies have assessed whether individuals with diabetes or prediabetes, but no physician diagnosis of stroke, have an increased prevalence of stroke symptoms. RESEARCH DESIGN AND METHODS This study included 25,696 individuals aged ≥45 years from the REasons for Geographic And Racial Differences in Stroke (REGARDS) study who reported no history of stroke or transient ischemic attack at baseline (2003–2007). Glucose measurements, medication use, and self-reported physician diagnosis were used to categorize participants into diabetes, prediabetes, or normal glycemia groups. The presence of six stroke symptoms was assessed using a validated questionnaire. RESULTS The prevalence of any stroke symptom was higher among participants with diabetes (22.7%) compared with those with prediabetes (15.6%) or normal glycemia (14.9%). In multivariable models, diabetes was associated with any stroke symptom (prevalence odds ratio [POR] 1.28 [95% CI 1.18–1.39]) and two or more stroke symptoms (1.26 [1.12–1.43]) compared with normal glycemia. In analyses of individual stroke symptoms, diabetes was associated with numbness (1.15 [1.03–1.29]), vision loss (1.52 [1.31–1.76]), half-vision loss (1.54 [1.30–1.84]), and lost ability to understand people (1.34 [1.12–1.61]) after multivariable adjustment. No association was present between prediabetes and stroke symptoms. CONCLUSIONS In this population-based study, almost one in four individuals with diabetes reported stroke symptoms, which suggests that screening for stroke symptoms in diabetes may be warranted.

Background and Purpose — The Questionnaire for Verifying Stroke-Free Status (QVSFS) is an 8-item structured interview designed to identify stroke-free individuals. Previously, the QVSFS was validated with medical record review in a cohort with a low prevalence (7.1%) of stroke or transient ischemic attack (TIA). The objective of this study was to evaluate the validity of the QVSFS by comparing it with stroke status as determined by neurological history and examination in a population with a higher prevalence of stroke. Methods — A research assistant administered the QVSFS to outpatients from Veterans Administration stroke and general medicine clinics. Subjects were defined as QVSFS negative if responses to all 8 questions were negative. Questions requiring rephrasing or clarification were noted. Neurologists, blinded to QVSFS scores, interviewed and examined all subjects to determine stroke-free status, defined as no history or examination findings of previous stroke and/or TIA. Results — One hundred fifty-five subjects were examined; mean age was 70 years; 98.1% were male. Seventy-eight subjects were determined to be stroke free by the neurologist. The negative predictive value of the QVSFS was 0.96, with positive predictive value of 0.71. No question required rephrasing or clarification >5 times. Twenty-two subjects (14.2%) required rephrasing or clarification of at least 1 question. Conclusions — The QVSFS can effectively identify stroke-free individuals with a high degree of accuracy, even in a population with a large proportion of patients with prior stroke or TIA. Accuracy for identifying subjects with stroke and/or TIA is lower, but the QVSFS may still be useful as a screening tool in that regard.