Faculty Bibliography

Early admission to the neurosciences intensive care unit (NSICU) is associated with improved patient outcomes. Natural language processing offers new possibilities for mining free text in electronic health record data. We sought to develop a machine learning model using both tabular and free text data to identify patients requiring NSICU admission shortly after arrival to the emergency department (ED). We conducted a single-center, retrospective cohort study of adult patients at the Mount Sinai Hospital, an academic medical center in New York City. All patients presenting to our institutional ED between January 2014 and December 2018 were included. Structured (tabular) demographic, clinical, bed movement record data, and free text data from triage notes were extracted from our institutional data warehouse. A machine learning model was trained to predict likelihood of NSICU admission at 30 min from arrival to the ED. We identified 412,858 patients presenting to the ED over the study period, of whom 1900 (0.5%) were admitted to the NSICU. The daily median number of ED presentations was 231 (IQR 200-256) and the median time from ED presentation to the decision for NSICU admission was 169 min (IQR 80-324). A model trained only with text data had an area under the receiver-operating curve (AUC) of 0.90 (95% confidence interval (CI) 0.87-0.91). A structured data-only model had an AUC of 0.92 (95% CI 0.91-0.94). A combined model trained on structured and text data had an AUC of 0.93 (95% CI 0.92-0.95). At a false positive rate of 1:100 (99% specificity), the combined model was 58% sensitive for identifying NSICU admission. A machine learning model using structured and free text data can predict NSICU admission soon after ED arrival. This may potentially improve ED and NSICU resource allocation. Further studies should validate our findings.

'Federated Learning' (FL) is a method to train Artificial Intelligence (AI) models with data from multiple sources while maintaining anonymity of the data thus removing many barriers to data sharing. During the SARS-COV-2 pandemic, 20 institutes collaborated on a healthcare FL study to predict future oxygen requirements of infected patients using inputs of vital signs, laboratory data, and chest x-rays, constituting the "EXAM" (EMR CXR AI Model) model. EXAM achieved an average Area Under the Curve (AUC) of over 0.92, an average improvement of 16%, and a 38% increase in generalisability over local models. The FL paradigm was successfully applied to facilitate a rapid data science collaboration without data exchange, resulting in a model that generalised across heterogeneous, unharmonized datasets. This provided the broader healthcare community with a validated model to respond to COVID-19 challenges, as well as set the stage for broader use of FL in healthcare.

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:The severe acute respiratory syndrome coronavirus 2 pandemic has created challenges to neurosurgical patient care. Despite editorials evaluating neurosurgery responses to 2019 novel coronavirus disease (COVID-19), data reporting effects of COVID-19 on neurosurgical case volume, census, and resident illness are lacking. The aim of this study was to present a real-world analysis of neurosurgical volumes, resident deployment, and unique challenges encountered during the severe acute respiratory syndrome coronavirus 2 outbreak peak in New York City. METHODS:Daily census and case volume data were prospectively collected throughout the severe acute respiratory syndrome coronavirus 2 outbreak in spring 2020. Neurosurgical census was compared against COVID-19 system-wide data. Neurosurgical cases during the crisis were analyzed and compared with 7-week periods from 2019 and early 2020. Resident deployment and illness were reviewed. RESULTS:From March 16, 2020, to May 5, 2020, residents participated in 72 operations and 69 endovascular procedures compared with 448 operations and 253 endovascular procedures from January 2020 to February 2020 and 530 operations and 340 endovascular procedures from March 2019 to May 2019. There was a 59% reduction in neurosurgical census during the outbreak (median 24 patients, 2.75 average total cases daily). COVID-19 neurosurgical admissions peaked in concert with the system-wide pandemic. Three residents demonstrated COVID-19 symptoms (no hospitalizations occurred) for a total 24 workdays lost (median 7 workdays). CONCLUSIONS:These data provide real-world guidance on neurosurgical infrastructure needs during a COVID-19 outbreak. While redeployment to support the COVID-19 response was required, a significant need remained to continue to provide critical neurosurgical service.

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.