Research Themes

We investigate outcomes and quality of life in patients undergoing spine surgery, aiming to refine surgical techniques and postoperative care for spinal disorders. Projects in this area analyze both institutional and national data to understand how different treatment approaches affect patient recovery. By studying spine surgery outcomes at scale, the lab provides evidence to guide surgical decision-making and optimize long-term patient results.

Neurotrauma research in the WONDER Lab focuses on traumatic brain injury (TBI) and spinal trauma, with the goal of improving acute management and long-term outcomes for these patients. We leverage large datasets to uncover predictors of recovery after head and spine injuries and to inform best practices in critical care. By analyzing traumatic injury cases across hospital systems, we aim to guide interventions that maximize neurologic recovery, reduce complications, and ensure that patients with brain or spine trauma receive the most effective, evidence-based care.

Recognizing the importance of value-based care, the WONDER Lab integrates health economic analysis into our outcomes research. We study the cost-effectiveness of neurosurgical interventions to understand how resource utilization relates to patient outcomes. These projects merge clinical outcome data with cost data (such as hospital length of stay, readmissions, and procedure costs) to identify strategies that improve care efficiency. By quantifying the value (outcomes achieved per dollar spent) of neurosurgical treatments, our findings help inform policy and decision-making – ensuring that innovations in neurosurgery are both clinically effective and economically sustainable.

A core strength of the lab is applying advanced analytics to predict neurosurgical outcomes and risks. We develop and utilize predictive models – including machine learning algorithms and statistical risk scores – to forecast which patients may be at higher risk for complications or suboptimal outcomes after surgery. By training models on large patient datasets, the lab can uncover complex patterns and predictors of surgical morbidity that might be overlooked by traditional analyses. Ongoing projects involve creating prediction tools for outcomes such as stroke recovery, spinal fusion complications, or tumor surgery results, incorporating variables ranging from clinical factors (e.g. patient comorbidities, imaging findings) to social determinants of health. Through predictive modeling, the WONDER Lab aims to enhance pre-surgical planning and patient counseling – ultimately personalizing neurosurgical care by stratifying risk and tailoring interventions to each patient’s profile.

Many of our projects utilize large national databases to research neurosurgical outcomes on a broad scale. By tapping into datasets such as the National (Nationwide) Inpatient Sample (NIS) and other multi-center registries, we can identify trends and insights across thousands of patients and hospitals. These big-data studies enable us to address questions about disparities, temporal trends, and rare outcomes with robust statistical power. By leveraging big data, the WONDER Lab can influence practice beyond our institution, providing evidence that helps shape neurosurgical guidelines and health policy at the national scale.