• Biomedical engineering 
  • Biostatistics 
  • Infectious disease 
  • Surgery and subspecialties 

Location: Ochsner Clinical School

Type fo student:

  • PGY1
  • PGY2

Type of work:

  • Qualitative methods 
  • Secondary data analysis 
  • Statistical analysis 
  • Systematic review 

Brief synopsis:

The complexity of spine pathology, specifically the three dimensional aspects of anatomic abnormality like
curvature, vertebral distortion, and pelvic positioning, is an important contributing factor to surgical outcomes.

Complex spine disease is a leading cause of back pain and is a frequent indication for spine imaging,
opiate use, and ultimately surgery. Spinal surgery has increased >6-fold in the last two decades, from
61,000 procedures in 1993 to 450,000 procedures in 2011 with an estimated $100 billion in annual direct costs. The U.S. population ≥65 years of age is estimated to grow from 46 million in 2014 to 88 million in 2050 which will result in an increasing prevalence, morbidity, and associated cost of spine disease. Despite this growth, overall efficacy of spine surgery in functional optimization remains highly variable. A great number of questions about spinal disease and its treatment, particularly surgical intervention, remain unanswered due to insufficient evidence. Patient selection, surgical decision making, and even outcome measurement remains inconsistent in complex spine disease requiring surgical intervention. Multiple randomized trials comparing lumbar vertebral fusion, a complex surgical intervention, to medical treatment have not shown clinically meaningful differences in disability outcomes in chronic low back pain. Without improvements in surgical intervention, patients with spine disease will continue to have functional impairment and exhaust medical treatment options which often leads to opioid analgesics as a last resort. In fact, back pain is one of the most common reasons for opiate prescriptions and over 60% of back pain sufferers use opiates.

The rapid emergence of 3D Printing (3DP) and Virtual Reality (VR) has created exciting opportunities in
medical education, patient engagement, and clinical care delivery. Spine disease is an ideal application of
3DP and VR due to the inherently complex three dimensional nature of the pathology in conjunction with the
limitations of the current standard flat or two dimensional imaging. The ability of 3DP and VR to produce highly customized solid and virtual models that can not only be visualized but also manipulated in three dimensions by touch has shown promise as a valuable tool in a diverse range of surgical fields. Preliminary studies have shown that these technologies are particularly powerful tools in higher complexity disease presentations. When compared to standard 2D imaging, utilizing 3DP and VR models may improve pre-operative planning and clinical outcomes while decreasing operative time and cost. Data examining the impact of 3DP and VR anatomical models to improve clinical outcomes, optimize resource utilization, and enhance patient experience; however, is quite limited. There is a great need to systematically investigate if and how using 3DP and VR models can improve clinical care delivery in complex spine pathologies being considered for surgical intervention. This knowledge gap prevents the wide-spread adoption of 3DP and VR in planning and execution of complex spine surgical interventions and relegates this promising technology to the realm of curiosity. The proposed work will set the stage for understanding how these technologies can serve as effective tools in healthcare rather than interesting but superfluous toys.

The long-term goal of my research is to assess the application of 3DP and VR modeling to create more
personalized and effective interventions for neurologic disorders like complex spine disease. The overall
objective of the proposed study, which is the next logical step in achieving my long-term goal, is to facilitate
preliminary validation of patient-specific 3DP and VR modeling and lay the groundwork for future studies
looking to apply these models for clinical triage, procedural selection and planning for spine disease requiring surgical intervention. Creating a specific spine complexity metric that predicts utility of 3DP and VR modeling is necessary to facilitate appropriate selection and stratification of eligible participants for a future randomized controlled trial. To accomplish this objective, we will use patient-specific 3DP and VR anatomical models and leverage a comprehensive institutional spine registry to assess the impact of these models on utilization and clinical outcome metrics. We will address this knowledge gap through the following specific aims:

Specific Aim#1. Develop and validate a spine complexity metric based on clinical findings, functional status,
and imaging findings available in our comprehensive spine registry. We hypothesize that this metric will
effectively characterize clinical outcomes after spine surgery. We also hypothesize that we can externally
validate the metric for prediction of complex surgical outcomes in collaboration with Louisiana State University (LSU) Shreveport Medical Center by examining its performance characteristics, including sensitivity, specificity, and area under the curve.

Specific Aim#2. Assess feasibility and efficacy of integrating this spine disease complexity metric into our
selection process for 3DP and VR modeling. We hypothesize that outcomes among patients for whom 3DP
and VR modeling was used will be superior when compared to controls matched based on demographics and our complexity metric. We also hypothesize that clinicians and patients are willing to adopt patient-specific 3DP and VR spine models by using a validated technology acceptance model (TAM). The impact of the proposed study will be to develop the necessary tools that are required to support a randomized controlled trial evaluating the efficacy of patient-specific 3DP and VR anatomical models on clinical outcomes and resource utilization in complex spine surgery.