Faculty Bibliography

BACKGROUND:Modern science and healthcare generate vast amounts of data, and, coupled with the increasingly inexpensive and accessible computing, a tremendous opportunity exists to use these data to improve care. A better understanding of data science and its relationship to neurosurgical practice will be increasingly important as we transition into this modern "big data" era. METHODS:A review of the literature was performed for key articles referencing big data for neurosurgical care or related topics. RESULTS:In the present report, we first defined the nature and scope of data science from a technical perspective. We then discussed its relationship to the modern neurosurgical practice, highlighting key references, which might form a useful introductory reading list. CONCLUSIONS:Numerous challenges exist going forward; however, organized neurosurgery has an important role in fostering and facilitating these efforts to merge data science with neurosurgical practice.

BACKGROUND:Unplanned hospital readmissions constitute a significant cost burden in healthcare. Identifying factors contributing to readmission risk presents opportunities for actionable change to reduce readmission rates. OBJECTIVE:To combine machine learning classification and feature importance analysis to identify drivers of readmission in a large cohort of spine patients. METHODS:Cases involving surgical procedures for degenerative spine conditions between 2008 and 2016 were retrospectively reviewed. Of 11 150 cases, 396 patients (3.6%) experienced an unplanned hospital readmission within 30 d of discharge. Over 75 pre-discharge variables were collected and categorized into demographic, perioperative, and resource utilization feature domains. Random forest classification was used to construct predictive models for readmission from feature domains. An ensemble tree-specific method was used to quantify and rank features by relative importance. RESULTS:In the demographics domain, age and comorbidity burden were the most important features for readmission prediction. Surgical duration and intraoperative oral morphine equivalents were the most important perioperative features, whereas total direct cost and length of stay were most important in the resource utilization domain. In supervised learning experiments for predicting readmission, the demographic domain model performed the best alone, suggesting that demographic features may contribute more to readmission risk than perioperative variables following spine surgery. A predictive model, created using only enriched features showing substantial importance, demonstrated improved predictive capacity compared to previous models, and approached the performance of state-of-the-art, deep-learning models for readmission. CONCLUSION/CONCLUSIONS:This strategy provides insight into global patterns of feature importance and better understanding of drivers of readmissions following spine surgery.

BACKGROUND:/Objective: Subdural hematomas (SDH) are a common and dangerous condition, with potential for a rapid rise in incidence given the aging United States population, but the magnitude of this increase is unknown. Our objective was to characterize the number of SDH and practicing neurosurgeons from 2003-2016 and project these numbers to 2040. METHODS:Utilizing the National Inpatient Sample years 2003-2016 (nearly 500 million hospitalizations), all hospitalizations with a diagnosis of SDH were identified and grouped by age. Numerical estimates of SDH were projected to 2040 in ten-year increments for each age group using Poisson modeling with population estimates from the United States Census Bureau. The number of neurosurgeons who billed CMS from 2012 to 2017 was noted and linearly projected to 2040. RESULTS:From 2020-2040, SDH volume is expected to increase by 78.3%, from 135,859 to 208,212. Most of this increase will be seen in the elderly, as patients 75-84 years old will experience an increase from 37,941 to 69,914 and patients greater than 85 years old will experience an increase from 31,200 to 67,181. The number of neurosurgeons is projected to increase from 4,675 in 2020 to 6,252 in 2040. CONCLUSION/CONCLUSIONS:SDH is expected to increase significantly from 2020-2040, with the majority of this increase being concentrated in elderly patients. While the number of neurosurgeons will also increase, the ability of current neurosurgical resources to properly handle this expected increase in SDH will need to be addressed on a national scale.

OBJECTIVE:To predict surgical volumes for two common cervical spine procedures from 2020 to 2040. METHODS:Utilizing the National Inpatient Sample from 2003-2016, nationwide estimates of anterior cervical discectomy and fusion (ACDF) and posterior cervical decompression and fusion (PCDF) volumes were calculated using International Classification of Diseases, Ninth and Tenth Revision (ICD-9, ICD-10) procedure codes. With data from the United States Census Bureau, estimates of the United States population were used to create Poisson models controlling for age and sex. Age was categorized into ranges (<25 years old, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and >85), and estimates of surgical volume for each age group were created. RESULTS:From 2020-2040, increases in surgical volume from 13.3% (153,288 to 173,699) and 19.3% (29,620 to 35,335) are expected for ACDF and PCDF, respectively. For ACDF, the largest increases are expected in the 45-54 (42,077 to 49,827) and 75-84 (8,065 to 14,862) age groups, whereas for PCDF, the largest increases will be seen in the 75-84 (3,710 to 6,836) group. In accordance with an aging population, modest increases will be seen for ACDF (858 to 1,847) and PCDF (730 to 1,573) in the >85 years old cohort. CONCLUSION/CONCLUSIONS:As expected, large growth in cervical spine surgical volumes is likely to be seen, which could indicate a need for increased numbers of spinal neurosurgeons and orthopedic surgeons. Further studies are needed to investigate the needs of the field in light of these expected increases in volume.

PURPOSE/OBJECTIVE:To determine if weakly supervised learning with surrogate metrics and active transfer learning can hasten clinical deployment of deep learning models. MATERIALS AND METHODS/METHODS:test, Bland-Altman analysis, and intraclass correlation; survival analysis was performed with the Kaplan-Meier method and a Mantel-Cox test. RESULTS:= .0005). In overall survival analysis, predicted liver volumes using the best active learning-trained model (LiTS-OU) were at least comparable with liver volumes extracted from radiology reports and MELD-Na scores in predicting survival. CONCLUSION/CONCLUSIONS: