Browsing by Subject "Spine surgery"
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Item Open Access Predicting Patient-Centered Outcomes from Spine Surgery Using Risk Assessment Tools: a Systematic Review.(Current reviews in musculoskeletal medicine, 2020-06) White, Hannah J; Bradley, Jensyn; Hadgis, Nicholas; Wittke, Emily; Piland, Brett; Tuttle, Brandi; Erickson, Melissa; Horn, Maggie EPurpose of review
The purpose of this systematic review is to evaluate the current literature in patients undergoing spine surgery in the cervical, thoracic, and lumbar spine to determine the available risk assessment tools to predict the patient-centered outcomes of pain, disability, physical function, quality of life, psychological disposition, and return to work after surgery.Recent findings
Risk assessment tools can assist surgeons and other healthcare providers in identifying the benefit-risk ratio of surgical candidates. These tools gather demographic, medical history, and other pertinent patient-reported measures to calculate a probability utilizing regression or machine learning statistical foundations. Currently, much is still unknown about the use of these tools to predict quality of life, disability, and other factors following spine surgery. A systematic review was conducted using PRISMA guidelines that identified risk assessment tools that utilized patient-reported outcome measures as part of the calculation. From 8128 identified studies, 13 articles met inclusion criteria and were accepted into this review. The range of c-index values reported in the studies was between 0.63 and 0.84, indicating fair to excellent model performance. Post-surgical patient-reported outcomes were identified in the following categories (n = total number of predictive models): return to work (n = 3), pain (n = 9), physical functioning and disability (n = 5), quality of life (QOL) (n = 6), and psychosocial disposition (n = 2). Our review has synthesized the available evidence on risk assessment tools for predicting patient-centered outcomes in patients undergoing spine surgery and described their findings and clinical utility.Item Open Access Preoperative optimization for patients undergoing elective spine surgery.(Clinical neurology and neurosurgery, 2021-01-14) Wang, Timothy Y; Price, Meghan; Mehta, Vikram A; Bergin, Stephen M; Sankey, Eric W; Foster, Norah; Erickson, Melissa; Gupta, Dhanesh K; Gottfried, Oren N; Karikari, Isaac O; Than, Khoi D; Goodwin, C Rory; Shaffrey, Christopher I; Abd-El-Barr, Muhammad MItem Open Access Social Determinants of Health and Disparities in Spine Surgery: A Ten-Year Analysis of 8,565 Cases using Ensemble Machine Learning and Multilayer Perceptron.(The spine journal : official journal of the North American Spine Society, 2024-07) Shin, David; Razzouk, Jacob; Thomas, Jonathan; Nguyen, Kai; Cabrera, Andrew; Bohen, Daniel; Lipa, Shaina A; Bono, Christopher M; Shaffrey, Christopher I; Cheng, Wayne; Danisa, OlumideBackground context
The influence of SDOH on spine surgery is poorly understood. Historically, researchers commonly focused on the isolated influences of race, insurance status, or income on healthcare outcomes. However, analysis of SDOH is becoming increasingly more nuanced as viewing social factors in aggregate rather than individually may offer more precise estimates of the impact of SDOH on healthcare delivery.Purpose
The aim of this study was to evaluate the effects of patient social history on length of stay (LOS) and readmission within 90 days following spine surgery using ensemble machine learning and multilayer perceptron.Study design
Retrospective chart review PATIENT SAMPLE: 8,565 elective and emergency spine surgery cases performed from 2013-2023 using our institution's database of longitudinally collected electronic medical record information.Outcomes measures
Patient LOS, discharge disposition, and rate of 90-day readmission.Methods
Ensemble machine learning and multilayer perceptron were employed to predict LOS and readmission within 90 days following spine surgery. All other subsequent statistical analysis was performed using SPSS version 28. To further assess correlations among variables, Pearson's correlation tests and multivariate linear regression models were constructed. Independent sample t-tests, paired sample t-tests, one-way analysis of variance (ANOVA) with post-hoc Bonferroni and Tukey corrections, and Pearson's chi-squared test were applied where appropriate for analysis of continuous and categorical variables.Results
Black patients demonstrated a greater LOS compared to white patients, but race and ethnicity were not significantly associated with 90-day readmission rates. Insured patients had a shorter LOS and lower readmission rates compared to non-insured patients, as did privately insured patients compared to publicly insured patients. Patients discharged home had lower LOS and lower readmission rates, compared to patients discharged to other facilities. Marriage decreased both LOS and readmission rates, underweight patients showcased increased LOS and readmission rates, and religion was shown to impact LOS and readmission rates. When utilizing patient social history, lab values, and medical history, machine learning determined the top 5 most-important variables for prediction of LOS -along with their respective feature importances-to be insurance status (0.166), religion (0.100), ICU status (0.093), antibiotic use (0.061), and case status: elective or urgent (0.055). The top 5 most-important variables for prediction of 90-day readmission-along with their respective feature importances-were insurance status (0.177), religion (0.123), discharge location (0.096), emergency case status (0.064), and history of diabetes (0.041).Conclusions
This study highlights that SDOH is influential in determining patient length of stay, discharge disposition, and likelihood of readmission following spine surgery. Machine learning was utilized to accurately predict LOS and 90-day readmission with patient medical history, lab values, and social history, as well as social history alone.