Posttraumatic epilepsy

Vagus nerve stimulation for refractory posttraumatic epilepsy: Efficacy and predictors of seizure outcome

Background

Traumatic brain injury (TBI) has been recognized as an important and common cause of epilepsy since antiquity. Posttraumatic epilepsy (PTE) is usually associated with drug resistance and poor surgical outcomes, thereby increasing the burden of the illness on patients and their families. Vagus nerve stimulation (VNS) is an adjunctive treatment for medically refractory epilepsy. This study aimed to determine the efficacy of VNS for refractory PTE and to initially evaluate the potential predictors of efficacy.

Methods

We retrospectively collected the outcomes of VNS with at least a 1-year follow-up in all patients with refractory PTE. Subgroups were classified as responders and non-responders according to the efficacy of VNS (≥50% or <50% reduction in seizure frequency). Preoperative data were analyzed to screen for potential predictors of VNS efficacy.

Results

In total, forty-five patients with refractory PTE who underwent VNS therapy were enrolled. Responders were found in 64.4% of patients, and 15.6% of patients achieved seizure freedom at the last follow-up. In addition, the responder rate increased over time, with 37.8, 44.4, 60, and 67.6% at the 3-, 6-, 12-, and 24-month follow-ups, respectively. After multivariate analysis, generalized interictal epileptic discharges (IEDs) were found to be a negative predictor (OR: 4.861, 95% CI: 1.145–20.632) of VNS efficacy.

Conclusion

The results indicated that VNS therapy was effective in refractory PTE patients and was well tolerated over a 1-year follow-up period. Patients with focal or multifocal IEDs were recognized to have better efficacy after VNS therapy.

Appropriateness of ICD-coded Diagnostic Inpatient Hospital Discharge Data for Medical Practice Assessment

Objectives: We performed a systematic review to investigate the quality of diagnostic hospital discharge data (DHDD) in order to gain insight in the usefulness of these data for medical practice assessment. We investigated the methods used to evaluate data quality, factors that determine data quality and its consequences for medical practice assessment.

Methods: We selected studies in which both completeness (or sensitivity: SENS) and correctness (or positive predictive value: PPV) were measured. We used the random-effects model to calculate mean SENS and PPV and to explore the effect of a number of covariates.

Results: The 101 included studies were very heterogeneous. We distinguished six typical study designs. We found a mean SENS of 0.67 (95%CI: 0.62– 0.73) and PPV of 0.76 (95%CI: 0.73– 0.79); SENS was significantly lower for comorbidity and complication studies than for some single disease studies. PPV was significantly higher for Scandinavian countries than for other countries. Recoding compared to re-abstracting of the medical record as a gold standard gave a significantly lower PPV. Diagnostic data were considered appropriate by the authors of the studies for quality of care purposes when both SENS and PPV were at least 0.85. Only 13% of the studies fulfilled this criterion.

Conclusions: Variability in quality of care between settings can easily be overshadowed by variability in data quality. However, the use of DHDD by physicians to evaluate their own medical practice may be useful. But only if physicians are willing to critically interpret the meaning of the information for their medical practice assessment.

MyRisk_Stroke Calculator: A Personalized Stroke Risk Assessment Tool for the General Population

BACKGROUND AND PURPOSE

There is a variety of stroke risk factors, and engaging individuals in reducing their own personal risk is hugely relevant and could be an optimal dissemination strategy. The aim of the present study was to estimate the stroke risk for specific combinations of health- and lifestyle-related factors, and to develop a personalized stroke-risk assessment tool for health professionals and the general population (called the MyRisk_Stroke Calculator).

METHODS

This population-based, longitudinal study followed a historical cohort formed from the 1992 or 1998 Santé Québec Health Surveys with information for linkage to health administrative databases. Stroke risk factors were ascertained at the time of survey, and stroke was determined from hospitalizations and death records. Cox proportional hazards models were used, modeling time to stroke in relationship to all variables.

RESULTS

A total of 358 strokes occurred among a cohort of 17805 persons (men=8181) who were followed for approximately 11 years (i.e., -200000 person-years). The following regression parameters were used to produce 10-year stroke-risk estimates and assign risk points: for age (1 point/year after age 20 years), male sex (3 points), low education (4 points), renal disease (8 points), diabetes (7 points), congestive heart failure (5 points), peripheral arterial disease (2 points), high blood pressure (2 points), ischemic heart disease (1 point), smoking (8 points), >7 alcoholic drinks per week (3 points), low physical activity (2 points), and indicators of anger (4 points), depression (4 points), and anxiety (3 points). According to MyRisk_Stroke Calculator, a person with <50, 75, and 90 risk points has a 10-year stroke risk of <3%, 28%, and >75%, respectively.

CONCLUSIONS

The MyRisk_Stroke Calculator is a simple method of disseminating information to the general population about their stroke risk.