Faculty Bibliography

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:

STUDY DESIGN/METHODS:A retrospective analysis of prospectively collected data. OBJECTIVE:The aim of this study was to determine the ability of Revised Cardiac Risk Index (RCRI) to predict adverse cardiac events following posterior lumbar decompression (PLD). SUMMARY OF BACKGROUND DATA/BACKGROUND:PLD is an increasingly common procedure used to treat a variety of degenerative spinal conditions. The RCRI is used to predict risk for cardiac events following noncardiac surgery. There is a paucity of literature that directly addresses the relationship between RCRI and outcomes following PLD, specifically, the discriminative ability of the RCRI to predict adverse postoperative cardiac events. METHODS:ACS-NSQIP was utilized to identify patients undergoing PLD from 2006 to 2014. Fifty-two thousand sixty-six patients met inclusion criteria. Multivariate and ROC analysis was utilized to identify associations between RCRI and postoperative complications. RESULTS:Membership in the RCRI=1 cohort was a predictor for myocardial infarction (MI) [odds ratio (OR) = 3.3, P = 0.002] and cardiac arrest requiring cardiopulmonary resuscitation (CPR) (OR = 3.4, P = 0.013). Membership in the RCRI = 2 cohort was a predictor for MI (OR = 5.9, P = 0.001) and cardiac arrest requiring CPR (OR = 12.5), Membership in the RCRI = 3 cohort was a predictor for MI (OR = 24.9) and cardiac arrest requiring CPR (OR = 26.9, P = 0.006). RCRI had a good discriminative ability to predict both MI [area under the curve (AUC) = 0.876] and cardiac arrest requiring CPR (AUC = 0.855). The RCRI had a better discriminative ability to predict these outcomes that did ASA status, which had discriminative abilities of "fair" (AUC = 0.799) and "poor" (AUC = 0.674), respectively. P < 0.001 unless otherwise specified. CONCLUSION/CONCLUSIONS:RCRI was predictive of cardiac events following PLD, and RCRI had a better discriminative ability to predict MI and cardiac arrest requiring CPR than did ASA status. Consideration of the RCRI as a component of preoperative surgical risk stratification can minimize patient morbidity and mortality. Studies such as this can allow for implementation of guidelines that better estimate the preoperative risk profile of surgical patients. LEVEL OF EVIDENCE/METHODS:3.

MOTIVATION:Radiologists have used algorithms for Computer-Aided Diagnosis (CAD) for decades. These algorithms use machine learning with engineered features, and there have been mixed findings on whether they improve radiologists' interpretations. Deep learning offers superior performance but requires more training data and has not been evaluated in joint algorithm-radiologist decision systems. RESULTS:We developed the Computer-Aided Note and Diagnosis Interface (CANDI) for collaboratively annotating radiographs and evaluating how algorithms alter human interpretation. The annotation app collects classification, segmentation, and image captioning training data, and the evaluation app randomizes the availability of CAD tools to facilitate clinical trials on radiologist enhancement. AVAILABILITY AND IMPLEMENTATION:Demonstrations and source code are hosted at (https://candi.nextgenhealthcare.org), and (https://github.com/mbadge/candi), respectively, under GPL-3 license. SUPPLEMENTARY INFORMATION:Supplementary material is available at Bioinformatics online.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocardial infarction (MI), and vancomycin antibiotic administration over two week patient ICU courses in the MIMIC-III dataset. These models achieved next-day predictive AUC of 0.876 for sepsis, 0.823 for MI, and 0.833 for vancomycin administration. Attention maps built from these models highlighted those times when input variables most influenced predictions and could provide a degree of interpretability to clinicians. These models appeared to attend to variables that were proxies for clinician decision-making, demonstrating a challenge of using flexible deep learning approaches trained with EHR data to build clinical decision support. While continued development and refinement is needed, we believe that such models could one day prove useful in reducing information overload for ICU physicians by providing needed clinical decision support for a variety of clinically important tasks.