Back pain is one of the most common causes of work loss due to disability, posing an economic burden on society in the United States. Understanding what are the patient features that are highly predictive for patient-reported outcomes, is essential to help to provide the best cares for patients and more cost-effective interventions.
The goal of this project is to provide best practices for treatment care and management of patients who underwent surgical interventions.
To identify clinical features and patient-reported outcomes among patients undergoing spine surgeries, I have designed a retrospective cohort study and analyzed it by using a multivariable logistic regression model to evaluate the effect of age, fusion levels and their interactions on patient-reported outcomes. I found that age affects the discharge destination after a one- or multi-level fusion and intraoperative/postoperative blood transfusion after a one-level fusion.
- Pennicooke, B., Santacatterina, M., et.al., The effect of age, fusion levels and their interactions on unfavorable outcomes and complications: A Retrospective Study of 60,000 patients. (Submitted to Clinical Spine Surgery), 2020
- Pennicooke, B., Santacatterina, M., et.al., Machine Learning Algorithm for Prediction of Patient-Reported Outcomes with Age- Adjusted Modeling in Patients with Grade 1 Spondylolisthesis (Work in progress), 2020
Because spine surgery is especially susceptible to malpractice claims, which drives up medical costs, I designed a study and analyzed the relationship of malpractice claims on outcomes following spine surgical interventions, using a inverse-probability-weighted regression-adjustment estimator. I also applied gradient boosting classifiers and regressor to predict the outcomes as function of the patients’ characteristics. I found that malpractice claims were associated with an increased odds of non-home discharge, longer LOS, and higher total charges.
- Chan, A., Santacatterina, M., et. al., Does state malpractice environment affect outcomes following spinal fusions? A robust statistical and machine learning analysis of 549,775 discharges following spinal fusion surgery in the United States, Submitted to Journal of Neurosurgery: Neurosurgical Focus, 2020