Evaluation of two anti-seizure medication strategies in refractory epilepsy patients from a tertiary center with complementary insights from data visualization.

OBJECTIVE: To evaluate the healthcare resources in a tertiary center related to exclusive use of non-enzyme inducing anti-seizure medications relative to concomitant use of enzyme-inducing anti-seizure medications in patients with refractory epilepsy. METHODS: In this retrospective case-time-control study, we compared the effects of two anti-seizure medication strategies: exclusively non-inducing anti-seizure medications (NIND) or a combination of NIND and inducing anti-seizure medications (IND+). The primary outcome parameter was the number of consultations with relevant healthcare professionals in our tertiary center, assessed with a negative binomial regression model, adjusting for several covariates like blood drug level and time interval (TI). Results from statistical models were visualized to explore the contribution of all covariates on the outcome in the total population and in subgroups. RESULTS: From the 21538 patients with refractory epilepsy referred to our center 1648 patients met the inclusion criteria. The regression model showed that the IND + strategy was significantly associated with fewer consultations compared to the NIND strategy (p < 0.001), reflected in an incidence risk ratio (IRR) of 0.844 (0.799-0.890). Visualization of subgroups, defined by anti-seizure medications strategy, revealed patterns in contribution of blood drug level measurements on the outcome. Although sex was not included as a covariate in the regression model, as it was eliminated by the backward-elimination approach, visualization of this subgroup showed differences in effects of blood drug level and TI. CONCLUSION: For patients with refractory epilepsy in our tertiary center, treatment following the IND + strategy is associated with fewer consultations with healthcare professionals compared to the NIND strategy. Comprehensive visualization of the results facilitated the exploration of effects of covariates across subgroups.

RegressionExplorer: Interactive Exploration of Logistic Regression Models with Subgroup Analysis.

We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.