Faculty Bibliography

BACKGROUND:Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to develop explainable machine learning models to understand such interactions in a large cohort of patients treated with spine surgery. METHODS:Of 11,150 patients who underwent surgery for degenerative spine conditions at a single institution, 3,310 (29.7%) were characterized as having prolonged LOS. Machine learning models predicting LOS were built for each patient. Shapley additive explanation (SHAP) values were calculated for each patient model to quantify the importance of features and variable interaction effects. RESULTS:Models using features identified by SHAP values were highly predictive of prolonged LOS risk (mean C-statistic = 0.87). Feature importance analysis revealed that prolonged LOS risk is multifactorial. Non-elective admission produced elevated SHAP values, indicating a clear, strong risk of prolonged LOS. In contrast, intraoperative and sociodemographic factors displayed bidirectional influences on risk, suggesting potential protective effects with optimization of factors such as estimated blood loss, surgical duration, and comorbidity burden. CONCLUSIONS:Meticulous management of patients with high comorbidity burdens or Medicaid insurance who are admitted non-electively or spend clinically indicated time in the intensive care unit (ICU) during their hospitalization course may be warranted to reduce their risk of unanticipated prolonged LOS following spine surgery. LEVEL OF EVIDENCE/METHODS:Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

BACKGROUND:/Objective: With a growing aging population in the United States, the number of operative lumbar spine pathologies continues to grow. Therefore, our objective was to estimate the future demand for lumbar spine surgery volumes for the United States to the year 2040. METHODS:The National/Nationwide Inpatient Sample was queried for years 2003-2015 for anterior interbody and posterior lumbar fusions (ALIF, PLF) to create national estimates of procedural volumes for those years. The average age and comorbidity burden was characterized, and Poisson modeling controlling for age and sex allowed for surgical volume prediction to 2040 in ten year increments. Age was grouped into categories (<25 years old, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, and >85), and estimates of surgical volumes for each age subgroup were created RESULTS: ALIF volume is expected to increase from 46,903 to 55,528, while PLF volume is expected to increase from 248,416 to 297,994 from 2020 to 2040. For ALIF, the largest increases are expected in the 45-54 year old (10,316 to 12,216) and 75-84 year old (2,898 to 5,340) age groups. Similarly the largest increases in PLF will be seen in the 65-74 year old (71,087 to 77,786) and 75-84 year old (28,253 to 52,062) age groups. CONCLUSION/CONCLUSIONS:The large increases in expected volumes of ALIF and PLF could necessitate training of more spinal surgeons and an examination of projected costs. Further analyses are needed to characterize the needs of this increasingly large population of surgical patients.

OBJECTIVE:Few studies have examined the impact of teaching status and location on outcomes in subarachnoid hemorrhage (SAH). The objective of the present study was to compare mortality and functional outcomes between urban teaching, urban nonteaching, and rural centers for hospitalizations with SAH. METHODS:The National Inpatient Sample (NIS) years 2003-2016 was queried for hospitalizations with aneurysmal SAH from 2003 to 2017. Cohorts treated at urban teaching, urban nonteaching, and rural centers were compared with the urban teaching center cohort acting as the reference. The NIS-SOM, a validated measure of SAH functional outcome, was used as a co-primary outcome with mortality. Multivariable models adjusted for age, sex, NIH-SSS score, hypertension, and hospital bedsize. Trends in SAH mortality rates were calculated. RESULTS:There were 379,716 SAH hospitalizations at urban teaching centers, 105,638 at urban nonteaching centers, and 17,165 at rural centers. Adjusted mortality rates for urban teaching centers were lower than urban nonteaching (21.90% vs. 25.00%, p<0.0001) and rural (21.90% vs. 30.90%, p<0.0001) centers. While urban teaching (24.74% to 21.22%) and urban nonteaching (24.78% to 23.68%) had decreases in mortality rates over the study period, rural hospitals showed increased mortality rates (25.67% to 33.38%). CONCLUSIONS:Rural and urban nonteaching centers have higher rates of mortality from SAH than urban teaching centers. Further study is necessary to understand drivers of these differences.

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.