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 used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare.

Purpose/UNASSIGNED:To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). Materials and Methods/UNASSIGNED:= 110) populations. Bootstrapping was used to compute CIs. Results/UNASSIGNED:The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. Conclusion/UNASSIGNED:© RSNA, 2020.

Real-time seizure detection is a resource intensive process as it requires continuous monitoring of patients on stereoelectroencephalography. This study improves real-time seizure detection in drug resistant epilepsy (DRE) patients by developing patient-specific deep learning models that utilize a novel self-supervised dynamic thresholding approach. Deep neural networks were constructed on over 2000 h of high-resolution, multichannel SEEG and video recordings from 14 DRE patients. Consensus labels from a panel of epileptologists were used to evaluate model efficacy. Self-supervised dynamic thresholding exhibited improvements in positive predictive value (PPV; difference: 39.0%; 95% CI 4.5-73.5%; Wilcoxon-Mann-Whitney test; N = 14; p = 0.03) with similar sensitivity (difference: 14.3%; 95% CI - 21.7 to 50.3%; Wilcoxon-Mann-Whitney test; N = 14; p = 0.42) compared to static thresholds. In some models, training on as little as 10 min of SEEG data yielded robust detection. Cross-testing experiments reduced PPV (difference: 56.5%; 95% CI 25.8-87.3%; Wilcoxon-Mann-Whitney test; N = 14; p = 0.002), while multimodal detection significantly improved sensitivity (difference: 25.0%; 95% CI 0.2-49.9%; Wilcoxon-Mann-Whitney test; N = 14; p < 0.05). Self-supervised dynamic thresholding improved the efficacy of real-time seizure predictions. Multimodal models demonstrated potential to improve detection. These findings are promising for future deployment in epilepsy monitoring units to enable real-time seizure detection without annotated data and only minimal training time in individual patients.

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.

STUDY DESIGN/METHODS:Cross sectional database study. OBJECTIVE:To develop a fully automated artificial intelligence and computer vision pipeline for assisted evaluation of lumbar lordosis. METHODS:Lateral lumbar radiographs were used to develop a segmentation neural network (n = 629). After synthetic augmentation, 70% of these radiographs were used for network training, while the remaining 30% were used for hyperparameter optimization. A computer vision algorithm was deployed on the segmented radiographs to calculate lumbar lordosis angles. A test set of radiographs was used to evaluate the validity of the entire pipeline (n = 151). RESULTS:> .05). CONCLUSION/CONCLUSIONS:This study is the first to use artificial intelligence and computer vision in a combined pipeline to rapidly measure a sagittal spinopelvic parameter without prior manual surgeon input. The pipeline measures angles with no statistically significant differences from manual measurements by surgeons. This pipeline offers clinical utility in an assistive capacity, and future work should focus on improving segmentation network performance.

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:/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.

Brain-computer interface (BCI) technology is rapidly developing and changing the paradigm of neurorestoration by linking cortical activity with control of an external effector to provide patients with tangible improvements in their ability to interact with the environment. The sensor component of a BCI circuit dictates the resolution of brain pattern recognition and therefore plays an integral role in the technology. Several sensor modalities are currently in use for BCI applications and are broadly either electrode-based or functional neuroimaging-based. Sensors vary in their inherent spatial and temporal resolutions, as well as in practical aspects such as invasiveness, portability, and maintenance. Hybrid BCI systems with multimodal sensory inputs represent a promising development in the field allowing for complimentary function. Artificial intelligence and deep learning algorithms have been applied to BCI systems to achieve faster and more accurate classifications of sensory input and improve user performance in various tasks. Neurofeedback is an important advancement in the field that has been implemented in several types of BCI systems by showing users a real-time display of their recorded brain activity during a task to facilitate their control over their own cortical activity. In this way, neurofeedback has improved BCI classification and enhanced user control over BCI output. Taken together, BCI systems have progressed significantly in recent years in terms of accuracy, speed, and communication. Understanding the sensory components of a BCI is essential for neurosurgeons and clinicians as they help advance this technology in the clinical setting.