Autocatalytic-protection for an unknown locus CRISPR-Cas countermeasure for undesired mutagenic chain reactions.

The mutagenic chain reaction (MCR) is a genetic tool to use a CRISPR-Cas construct to introduce a homing endonuclease, allowing gene drive to influence whole populations in a minimal number of generations (Esvelt et al., 2014; Gantz and Bier, 2015; Gantz and Bier, 2016). The question arises: if an active genetic terror event is released into a population, could we prevent the total spread of the undesired allele (Gantz, et al., 2015; Webber et al., 2015)? Thus far, effective protection methods require knowledge of the terror locus (Grunwald et al., 2019). Here we introduce a novel approach, an autocatalytic-Protection for an Unknown Locus (a-PUL), whose aim is to spread through a population and arrest and decrease an active terror event's spread without any prior knowledge of the terror-modified locus, thus allowing later natural selection and ERACR drives to restore the normal locus (Hammond et al., 2017). a-PUL, using a mutagenic chain reaction, includes (i) a segment encoding a non-Cas9 endonuclease capable of homology-directed repair suggested as Type II endonuclease Cpf1 (Cas12a), (ii) a ubiquitously-expressed gene encoding a gRNA (gRNA1) with a U4AU4 3'-overhang specific to Cpf1 and with crRNA specific to some desired genomic sequence of non-coding DNA, (iii) a ubiquitously-expressed gene encoding two gRNAs (gRNA2/gRNA3) both with tracrRNA specific to Cas9 and crRNA specific to two distinct sites of the Cas9 locus, and (iv) homology arms flanking the Cpf1/gRNA1/gRNA2/gRNA3 cassette that are identical to the region surrounding the target cut directed by gRNA1 (Khan, 2016; Zetsche et al., 2015). We demonstrate the proof-of-concept and efficacy of our protection construct through a Graphical Markov model and computer simulation.

Type II odontoid fractures in the elderly presenting to the emergency department: an assessment of factors affecting in-hospital mortality and discharge to skilled nursing facilities.

BACKGROUND CONTEXT: Type II odontoid fractures (OF) are among the most common cervical spine injuries in the geriatric population. However, there is a paucity of literature regarding their epidemiology. Additionally, the optimal management of these injuries remains controversial, and no study has evaluated the short-term outcomes of geriatric patients presenting to emergency departments (ED). PURPOSE: This study aims to document the epidemiology of geriatric patients presenting to EDs with type II OFs and determine whether surgical management was associated with early adverse outcomes such as in-hospital mortality and discharge to skilled nursing facilities (SNF). STUDY DESIGN: This is a retrospective cohort study. PATIENT SAMPLE: Data was used from the 2016-2020 Nationwide Emergency Department Sample. Patient encounters corresponding to type II OFs were identified. Patients younger than 65 at the time of presentation to the ED and those with concomitant spinal pathology were excluded. OUTCOME MEASURES: The association between the surgical management of geriatric type II OFs and outcomes such as in-hospital mortality and discharge to SNFs. METHODS: Patient, fracture, and surgical management characteristics were recorded. A propensity score matched cohort was constructed to reduce differences in age, comorbidities, and injury severity between patients undergoing operative and nonoperative management. Additionally, to develop a positive control for the analysis of geriatric patients with type II OFs and no other concomitant spinal pathology, a cohort of patients that had been excluded due to the presence of a concomitant spinal cord injury (SCI) was also constructed. Multivariate regressions were then performed on both the matched and unmatched cohorts to ascertain the associations between surgical treatment and in-hospital mortality, inpatient length of stay, encounter charges, and discharge to SNFs. RESULTS: A total of 11,325 encounters were included. The mean total charge per encounter was $60,221. 634 (5.6%) patients passed away during their encounters. In total, 1,005 (8.9%) patients were managed surgically. Surgical management of type II OFs was associated with a 316% increase in visit charge (95% CI: 291%-341%, p<.001), increased inpatient length of stay (IRR: 2.87, 95% CI: 2.62-3.12, p<.001), and increased likelihood of discharge to SNFs (OR=2.62, 95% CI: 2.26-3.05, p<.001), but decreased in-hospital mortality (OR=0.32, CI: 0.21-0.45, p<.001). The propensity score matched cohort consisted of 2,010 patients, matching each of the 1,005 that underwent surgery to 1,005 that did not. These cohorts were well balanced across age (78.24 vs 77.91 years), Elixhauser Comorbidity Index (3.68 vs 3.71), and Injury Severity Score (30.15 vs 28.93). This matching did not meaningfully alter the associations determined between surgical management and in-hospital mortality (OR=0.34, CI=0.21-0.55, p<.001) or SNF discharge (OR=2.59, CI=2.13-3.16, p<.001). Lastly, the positive control cohort of patients with concurrent SCI had higher rates of SNF discharge (50.0% vs 42.6%, p<.001), surgical management (32.3% vs 9.7%, p<.001), and in-hospital mortality (28.9% vs 5.6%, p<.001). CONCLUSIONS: This study lends insight into the epidemiology of geriatric type II OFs and quantifies risk factors influencing adverse outcomes. Patient informed consent should include a discussion of the protective association between definitive surgical management and in-hospital mortality against potential operative morbidity, increased lengths of hospital stay, and increased likelihood of discharge to SNFs. This information may impact patient treatment selection and decision making.

Deep Learning Prediction of Cervical Spine Surgery Revision Outcomes Using Standard Laboratory and Operative Variables.

BACKGROUND: Cervical spine procedures represent a major proportion of all spine surgery. Mitigating the revision rate following cervical procedures requires careful patient selection. While complication risk has successfully been predicted, revision risk has proven more challenging. This is likely due to the absence of granular variables in claims databases. The objective of this study was to develop a state-of-the-art model of revision prediction of cervical spine surgery using laboratory and operative variables. METHODS: Using the Stanford Research Repository, patients undergoing a cervical spine procedure between 2016 and 2022 were identified (N = 3151), and recent laboratory values were collected. Patients were classified into separate cohorts by revision outcome and time frame. Machine and deep learning models were trained to predict each revision outcome from laboratory and operative variables. RESULTS: Red blood cell count, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, red blood cell distribution width, platelet count, carbon dioxide, anion gap, and calcium all were significantly associated with ≥1 revision cohorts. For the prediction of 3-month revision, the deep neural network achieved an area under the receiver operating characteristic curve of 0.833. The model demonstrated increased performance for anterior versus posterior and arthrodesis versus decompression procedures. CONCLUSIONS: Our deep learning approach successfully predicted 3-month revision outcomes from demographic variables, standard laboratory values, and operative variables in a cervical spine surgery cohort. This work used standard laboratory values and operative codes as meaningful predictive variables for revision outcome prediction. The increased performance on certain procedures evidences the need for careful development and validation of one-size-fits-all risk scores for spine procedures.

Rates and Predictors of Pain Reduction With Intracranial Stimulation for Intractable Pain Disorders.

BACKGROUND AND OBJECTIVES: Intracranial modulation paradigms, namely deep brain stimulation (DBS) and motor cortex stimulation (MCS), have been used to treat intractable pain disorders. However, treatment efficacy remains heterogeneous, and factors associated with pain reduction are not completely understood. METHODS: We performed an individual patient review of pain outcomes (visual analog scale, quality-of-life measures, complications, pulse generator implant rate, cessation of stimulation) after implantation of DBS or MCS devices. We evaluated 663 patients from 36 study groups and stratified outcomes by pain etiology and implantation targets. RESULTS: Included studies comprised primarily retrospective cohort studies. MCS patients had a similar externalized trial success rate compared with DBS patients (86% vs 81%; P = .16), whereas patients with peripheral pain had a higher trial success rate compared with patients with central pain (88% vs 79%; P = .004). Complication rates were similar for MCS and DBS patients (12% vs 15%; P = .79). Patients with peripheral pain had lower likelihood of device cessation compared with those with central pain (5.7% vs 10%; P = .03). Of all implanted patients, mean pain reduction at last follow-up was 45.8% (95% CI: 40.3-51.2) with a 31.2% (95% CI: 12.4-50.1) improvement in quality of life. No difference was seen between MCS patients (43.8%; 95% CI: 36.7-58.2) and DBS patients (48.6%; 95% CI: 39.2-58) or central (41.5%; 95% CI: 34.8-48.2) and peripheral (46.7%; 95% CI: 38.9-54.5) etiologies. Multivariate analysis identified the anterior cingulate cortex target to be associated with worse pain reduction, while postherpetic neuralgia was a positive prognostic factor. CONCLUSION: Both DBS and MCS have similar efficacy and complication rates in the treatment of intractable pain. Patients with central pain disorders tended to have lower trial success and higher rates of device cessation. Additional prognostic factors include anterior cingulate cortex targeting and postherpetic neuralgia diagnosis. These findings underscore intracranial neurostimulation as an important modality for treatment of intractable pain disorders.

Evaluating Computer Vision, Large Language, and Genome-Wide Association Models in a Limited Sized Patient Cohort for Pre-Operative Risk Stratification in Adult Spinal Deformity Surgery.

Background: Adult spinal deformities (ASD) are varied spinal abnormalities, often necessitating surgical intervention when associated with pain, worsening deformity, or worsening function. Predicting post-operative complications and revision surgery is critical for surgical planning and patient counseling. Due to the relatively small number of cases of ASD surgery, machine learning applications have been limited to traditional models (e.g., logistic regression or standard neural networks) and coarse clinical variables. We present the novel application of advanced models (CNN, LLM, GWAS) using complex data types (radiographs, clinical notes, genomics) for ASD outcome prediction. Methods: We developed a CNN trained on 209 ASD patients (1549 radiographs) from the Stanford Research Repository, a CNN pre-trained on VinDr-SpineXR (10,468 spine radiographs), and an LLM using free-text clinical notes from the same 209 patients, trained via Gatortron. Additionally, we conducted a GWAS using the UK Biobank, contrasting 540 surgical ASD patients with 7355 non-surgical ASD patients. Results: The LLM notably outperformed the CNN in predicting pulmonary complications (F1: 0.545 vs. 0.2881), neurological complications (F1: 0.250 vs. 0.224), and sepsis (F1: 0.382 vs. 0.132). The pre-trained CNN showed improved sepsis prediction (AUC: 0.638 vs. 0.534) but reduced performance for neurological complication prediction (AUC: 0.545 vs. 0.619). The LLM demonstrated high specificity (0.946) and positive predictive value (0.467) for neurological complications. The GWAS identified 21 significant (p < 10-5) SNPs associated with ASD surgery risk (OR: mean: 3.17, SD: 1.92, median: 2.78), with the highest odds ratio (8.06) for the LDB2 gene, which is implicated in ectoderm differentiation. Conclusions: This study exemplifies the innovative application of cutting-edge models to forecast outcomes in ASD, underscoring the utility of complex data in outcome prediction for neurosurgical conditions. It demonstrates the promise of genetic models when identifying surgical risks and supports the integration of complex machine learning tools for informed surgical decision-making in ASD.

Machine Learning in Neurosurgery: Toward Complex Inputs, Actionable Predictions, and Generalizable Translations.

Machine learning can predict neurosurgical diagnosis and outcomes, power imaging analysis, and perform robotic navigation and tumor labeling. State-of-the-art models can reconstruct and generate images, predict surgical events from video, and assist in intraoperative decision-making. In this review, we will detail the neurosurgical applications of machine learning, ranging from simple to advanced models, and their potential to transform patient care. As machine learning techniques, outputs, and methods become increasingly complex, their performance is often more impactful yet increasingly difficult to evaluate. We aim to introduce these advancements to the neurosurgical audience while suggesting major potential roadblocks to their safe and effective translation. Unlike the previous generation of machine learning in neurosurgery, the safe translation of recent advancements will be contingent on neurosurgeons' involvement in model development and validation.

Impact of Supine versus Prone Positioning on Segmental Lumbar Lordosis in Patients Undergoing ALIF Followed by PSF: A Comparative Study.

Background: Anterior lumbar interbody fusion (ALIF) and posterior spinal fusion (PSF) play pivotal roles in restoring lumbar lordosis in spinal surgery. There is an ongoing debate between combined single-position surgery and traditional prone-position PSF for optimizing segmental lumbar lordosis. Methods: This retrospective study analyzed 59 patients who underwent ALIF in the supine position followed by PSF in the prone position at a single institution. Cobb angles were measured preoperatively, post-ALIF, and post-PSF using X-ray imaging. One-way repeated measures ANOVA and post-hoc analyses with Bonferroni adjustment were employed to compare mean Cobb angles at different time points. Cohen's d effect sizes were calculated to assess the magnitude of changes. Sample size calculations were performed to ensure statistical power. Results: The mean segmental Cobb angle significantly increased from preoperative (32.2 ± 13.8 degrees) to post-ALIF (42.2 ± 14.3 degrees, Cohen's d: -0.71, p < 0.0001) and post-PSF (43.6 ± 14.6 degrees, Cohen's d: -0.80, p < 0.0001). There was no significant difference between Cobb angles after ALIF and after PSF (Cohen's d: -0.10, p = 0.14). The findings remained consistent when Cobb angles were analyzed separately for single-screw and double-screw ALIF constructs. Conclusions: Both supine ALIF and prone PSF significantly increased segmental lumbar lordosis compared to preoperative measurements. The negligible difference between post-ALIF and post-PSF lordosis suggests that supine ALIF followed by prone PSF can be an effective approach, providing flexibility in surgical positioning without compromising lordosis improvement.

Demonstrating the successful application of synthetic learning in spine surgery for training multi-center models with increased patient privacy.

From real-time tumor classification to operative outcome prediction, applications of machine learning to neurosurgery are powerful. However, the translation of many of these applications are restricted by the lack of "big data" in neurosurgery. Important restrictions in patient privacy and sharing of imaging data reduce the diversity of the datasets used to train resulting models and therefore limit generalizability. Synthetic learning is a recent development in machine learning that generates synthetic data from real data and uses the synthetic data to train downstream models while preserving patient privacy. Such an approach has yet to be successfully demonstrated in the spine surgery domain. Spine radiographs were collected from the VinDR-SpineXR dataset, with 1470 labeled as abnormal and 2303 labeled as normal. A conditional generative adversarial network (GAN) was trained on the radiographs to generate a spine radiograph and normal/abnormal label. A modified conditional GAN (SpineGAN) was trained on the same task. A convolutional neural network (CNN) was trained using the real data to label abnormal radiographs. A CNN was trained to label abnormal radiographs using synthetic images from the GAN and in a separate experiment from SpineGAN. Using the real radiographs, an AUC of 0.856 was achieved in abnormality classification. Training on synthetic data generated by the standard GAN (AUC of 0.814) and synthetic data generated by our SpineGAN (AUC of 0.830) resulted in similar classifier performance. SpineGAN generated images with higher FID and lower precision scores, but with higher recall and increased performance when used for synthetic learning. The successful application of synthetic learning was demonstrated in the spine surgery domain for the classification of spine radiographs as abnormal or normal. A modified domain-relevant GAN is introduced for the generation of spine images, evidencing the importance of domain-relevant generation techniques in synthetic learning. Synthetic learning can allow neurosurgery to use larger and more diverse patient imaging sets to train more generalizable algorithms with greater patient privacy.

Understanding transfer learning for chest radiograph clinical report generation with modified transformer architectures.

The image captioning task is increasingly prevalent in artificial intelligence applications for medicine. One important application is clinical report generation from chest radiographs. The clinical writing of unstructured reports is time consuming and error-prone. An automated system would improve standardization, error reduction, time consumption, and medical accessibility. In this paper we demonstrate the importance of domain specific pre-training and propose a modified transformer architecture for the medical image captioning task. To accomplish this, we train a series of modified transformers to generate clinical reports from chest radiograph image input. These modified transformers include: a meshed-memory augmented transformer architecture with visual extractor using ImageNet pre-trained weights, a meshed-memory augmented transformer architecture with visual extractor using CheXpert pre-trained weights, and a meshed-memory augmented transformer whose encoder is passed the concatenated embeddings using both ImageNet pre-trained weights and CheXpert pre-trained weights. We use BLEU(1-4), ROUGE-L, CIDEr, and the clinical CheXbert F1 scores to validate our models and demonstrate competitive scores with state of the art models. We provide evidence that ImageNet pre-training is ill-suited for the medical image captioning task, especially for less frequent conditions (e.g.: enlarged cardiomediastinum, lung lesion, pneumothorax). Furthermore, we demonstrate that the double feature model improves performance for specific medical conditions (edema, consolidation, pneumothorax, support devices) and overall CheXbert F1 score, and should be further developed in future work. Such a double feature model, including both ImageNet pre-training as well as domain specific pre-training, could be used in a wide range of image captioning models in medicine.

The Impact of Preoperative Myelopathy on Postoperative Outcomes among Anterior Cervical Discectomy and Fusion Procedures in the Nonelderly Adult Population: A Propensity-Score Matched Study.

STUDY DESIGN: Retrospective cohort study. PURPOSE: Anterior cervical discectomy and fusion (ACDF) is a common surgical intervention for patients diagnosed with cervical degenerative diseases with or without myelopathy. A thorough understanding of outcomes in patients with and without myelopathy undergoing ACDF is required because of the widespread utilization of ACDF for these indications. OVERVIEW OF LITERATURE: Non-ACDF approaches achieved inferior outcomes in certain myelopathic cases. Studies have compared patient outcomes across procedures, but few have compared outcomes concerning myelopathic versus nonmyelopathic cohorts. METHODS: The MarketScan database was queried from 2007 to 2016 to identify adult patients who were ≤65 years old, and underwent ACDF using the international classification of diseases 9th version and current procedural terminology codes. Nearest neighbor propensity-score matching was employed to balance patient demographics and operative characteristics between myelopathic and nonmyelopathic cohorts. RESULTS: Of 107,480 patients who met the inclusion criteria, 29,152 (27.1%) were diagnosed with myelopathy. At baseline, the median age of patients with myelopathy was higher (52 years vs. 50 years, p <0.001), and they had a higher comorbidity burden (mean Charlson comorbidity index, 1.92 vs. 1.58; p <0.001) than patients without myelopathy. Patients with myelopathy were more likely to undergo surgical revision at 2 years (odds ratio [OR], 1.63; 95% confidence interval [CI], 1.54-1.73) or are readmitted within 90 days (OR, 1.27; 95% CI, 1.20-1.34). After patient cohorts were matched, patients with myelopathy remained at elevated risk for reoperation at 2 years (OR, 1.55; 95% CI, 1.44-1.67) and postoperative dysphagia (2.78% vs. 1.68%, p <0.001) compared to patients without myelopathy. CONCLUSIONS: We found inferior postoperative outcomes at baseline for patients with myelopathy undergoing ACDF compared to patients without myelopathy. Patients with myelopathy remained at significantly greater risk for reoperation and readmission after balancing potential confounding variables across cohorts, and these differences in outcomes were largely driven by patients with myelopathy undergoing 1-2 level fusions.

Sigma-1 receptor expression in a subpopulation of lumbar spinal cord microglia in response to peripheral nerve injury.

Sigma-1 Receptor has been shown to localize to sites of peripheral nerve injury and back pain. Radioligand probes have been developed to localize Sigma-1 Receptor and thus image pain source. However, in non-pain conditions, Sigma-1 Receptor expression has also been demonstrated in the central nervous system and dorsal root ganglion. This work aimed to study Sigma-1 Receptor expression in a microglial cell population in the lumbar spine following peripheral nerve injury. A publicly available transcriptomic dataset of 102,691 L4/5 mouse microglial cells from a sciatic-sural nerve spared nerve injury model and 93,027 age and sex matched cells from a sham model was used. At each of three time points-postoperative day 3, postoperative day 14, and postoperative month 5-gene expression data was recorded for both spared nerve injury and Sham cell groups. For all cells, 27,998 genes were sequenced. All cells were clustered into 12 distinct subclusters and gene set enrichment pathway analysis was performed. For both the spared nerve injury and Sham groups, Sigma-1 Receptor expression significantly decreased at each time point following surgery. At the 5-month postoperative time point, only one of twelve subclusters showed significantly increased Sigma-1 Receptor expression in spared nerve injury cells as compared to Sham cells (p = 0.0064). Pathway analysis of this cluster showed a significantly increased expression of the inflammatory response pathway in the spared nerve injury cells relative to Sham cells at the 5-month time point (p = 6.74e-05). A distinct subcluster of L4/5 microglia was identified which overexpress Sigma-1 Receptor following peripheral nerve injury consistent with neuropathic pain inflammatory response functioning. This indicates that upregulated Sigma-1 Receptor in the central nervous system characterizes post-acute peripheral nerve injury and may be further developed for clinical use in the differentiation between low back pain secondary to peripheral nerve injury and low back pain not associated with peripheral nerve injury in cases where the pain cannot be localized.

Clinical Outcomes and Cost Profiles for Cage and Allograft Anterior Cervical Discectomy and Fusion Procedures in the Adult Population: A Propensity Score-Matched Study.

STUDY DESIGN: Retrospective cohort study. PURPOSE: To characterize the postoperative outcomes and economic costs of anterior cervical discectomy and fusion (ACDF) procedures using synthetic biomechanical intervertebral cage (BC) and structural allograft (SA) implants. OVERVIEW OF LITERATURE: ACDF is a common spine procedure that typically uses an SA or BC for the cervical fusion. Previous studies that compared the outcomes between the two implants were limited by small sample sizes, short-term postoperative outcomes, and procedures with single-level fusion. METHODS: Adult patients who underwent an ACDF procedure in 2007-2016 were included. Patient records were extracted from MarketScan, a national registry that captures person-specific clinical utilization, expenditures, and enrollments across millions of inpatient, outpatient, and prescription drug services. Propensity-score matching (PSM) was employed to match the patient cohorts across demographic characteristics, comorbidities, and treatments. RESULTS: Of 110,911 patients, 65,151 (58.7%) received BC implants while 45,760 (41.3%) received SA implants. Patients who underwent BC surgeries had slightly higher reoperation rates within 1 year after the index ACDF procedure (3.3% vs. 3.0%, p=0.004), higher postoperative complication rates (4.9% vs. 4.6%, p=0.022), and higher 90-day readmission rates (4.9% vs. 4.4%, p =0.001). After PSM, the postoperative complication rates did not vary between the two cohorts (4.8% vs. 4.6%, p=0.369), although dysphagia (2.2% vs. 1.8%, p<0.001) and infection (0.3% vs. 0.2%, p=0.007) rates remained higher for the BC group. Other outcome differences, including readmission and reoperation, decreased. Physician's fees remained high for BC implantation procedures. CONCLUSIONS: We found marginal differences in clinical outcomes between BC and SA ACDF interventions in the largest published database cohort of adult ACDF surgeries. After adjusting for group-level differences in comorbidity burden and demographic characteristics, BC and SA ACDF surgeries showed similar clinical outcomes. Physician's fees, however, were higher for BC implantation procedures.

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

Vertebrae segmentation in reduced radiation CT imaging for augmented reality applications.

PURPOSE: There is growing evidence for the use of augmented reality (AR) navigation in spinal surgery to increase surgical accuracy and improve clinical outcomes. Recent research has employed AR techniques to create accurate auto-segmentations, the basis of patient registration, using reduced radiation dose intraoperative computed tomography images. In this study, we aimed to determine if spinal surgery AR applications can employ reduced radiation dose preoperative computed tomography (pCT) images. METHODS: We methodically decreased the imaging dose, with the addition of Gaussian noise, that was introduced into pCT images to determine the image quality threshold that was required for auto-segmentation. The Gaussian distribution's standard deviation determined noise level, such that a scalar multiplier (L: [0.00, 0.45], with steps of 0.03) simulated lower doses as L increased. We then enhanced the images with denoising algorithms to evaluate the effect on the segmentation. RESULTS: The pCT radiation dose was decreased to below the current lowest clinical threshold and the resulting images produced segmentations that were appropriate for input into AR applications. This held true at simulated dose L = 0.06 (estimated 144 mAs) but not at L = 0.09 (estimated 136 mAs). The application of denoising algorithms to the images resulted in increased artifacts and decreased bone density. CONCLUSIONS: The pCT image quality that is required for AR auto-segmentation is lower than that which is currently employed in spinal surgery. We recommend a reduced radiation dose protocol of approximately 140 mAs. This has the potential to reduce the radiation experienced by patients in comparison to procedures without AR support. Future research is required to identify the specific, clinically relevant radiation dose thresholds required for surgical navigation.

Chronic Opioid Use Prior to ACDF Surgery Is Associated with Inferior Postoperative Outcomes: A Propensity-Matched Study of 17,443 Chronic Opioid Users.

OBJECTIVE: Candidates for anterior cervical discectomy and fusion (ACDF) have a higher rate of opioid use than does the public, but studies on preoperative opioid use have not been conducted. We aimed to understand how preoperative opioid use affects post-ACDF outcomes. METHODS: The MarketScan Database was queried from 2007 to 2015 to identify adult patients who underwent an ACDF. Patients were classified into separate cohorts based on the number of separate opioid prescriptions in the year before their ACDF. Ninety-day postoperative complications, postoperative readmission, reoperation, and total inpatient costs were compared between opioid strata. Propensity score-matched patient cohorts were calculated to balance comorbidities across groups. RESULTS: Of 81,671 ACDF patients, 31,312 (38.3%) were nonusers, 30,302 (37.1%) were mild users, and 20,057 (24.6%) were chronic users. Chronic opioid users had a higher comorbidity burden, on average, than patients with less frequent opioid use (P < 0.001). Chronic opioid users had higher rates of postoperative complications (9.1%) than mild opioid users (6.0%) and nonusers (5.3%) (P < 0.001) and higher rates of readmission and reoperation. After balancing opioid nonusers versus chronic opioid users along with demographic characteristics, preoperative comorbidity, and operative characteristics, postoperative complications remained elevated for chronic opioid users relative to opioid nonusers (8.6% vs. 5.7%; P < 0.001), as did rates of readmission and reoperation. CONCLUSIONS: Chronic opioid users had more comorbidities than opioid nonusers and mild opioid users, longer hospitalizations, and higher rates of postoperative complication, readmission, and reoperation. After balancing patients across covariates, the outcome differences persisted, suggesting a durable association between preoperative opioid use and negative postoperative outcomes.

Comparison of Deep Learning and Classical Machine Learning Algorithms to Predict Postoperative Outcomes for Anterior Cervical Discectomy and Fusion Procedures With State-of-the-art Performance.

STUDY DESIGN: Retrospective cohort. OBJECTIVE: Due to anterior cervical discectomy and fusion (ACDF) popularity, it is important to predict postoperative complications, unfavorable 90-day readmissions, and two-year reoperations to improve surgical decision-making, prognostication, and planning. SUMMARY OF BACKGROUND DATA: Machine learning has been applied to predict postoperative complications for ACDF; however, studies were limited by sample size and model type. These studies achieved ≤0.70 area under the curve (AUC). Further approaches, not limited to ACDF, focused on specific complication types and resulted in AUC between 0.70 and 0.76. MATERIALS AND METHODS: The IBM MarketScan Commercial Claims and Encounters Database and Medicare Supplement were queried from 2007 to 2016 to identify adult patients who underwent an ACDF procedure (N=176,816). Traditional machine learning algorithms, logistic regression, and support vector machines, were compared with deep neural networks to predict: 90-day postoperative complications, 90-day readmission, and two-year reoperation. We further generated random deep learning model architectures and trained them on the 90-day complication task to approximate an upper bound. Last, using deep learning, we investigated the importance of each input variable for the prediction of 90-day postoperative complications in ACDF. RESULTS: For the prediction of 90-day complication, 90-day readmission, and two-year reoperation, the deep neural network-based models achieved AUC of 0.832, 0.713, and 0.671. Logistic regression achieved AUCs of 0.820, 0.712, and 0.671. Support vector machine approaches were significantly lower. The upper bound of deep learning performance was approximated as 0.832. Myelopathy, age, human immunodeficiency virus, previous myocardial infarctions, obesity, and documentary weakness were found to be the strongest variable to predict 90-day postoperative complications. CONCLUSIONS: The deep neural network may be used to predict complications for clinical applications after multicenter validation. The results suggest limited added knowledge exists in interactions between the input variables used for this task. Future work should identify novel variables to increase predictive power.

On the relation of gene essentiality to intron structure: a computational and deep learning approach.

Essential genes have been studied by copy number variants and deletions, both associated with introns. The premise of our work is that introns of essential genes have distinct characteristic properties. We provide support for this by training a deep learning model and demonstrating that introns alone can be used to classify essentiality. The model, limited to first introns, performs at an increased level, implicating first introns in essentiality. We identify unique properties of introns of essential genes, finding that their structure protects against deletion and intron-loss events, especially centered on the first intron. We show that GC density is increased in the first introns of essential genes, allowing for increased enhancer activity, protection against deletions, and improved splice site recognition. We find that first introns of essential genes are of remarkably smaller size than their nonessential counterparts, and to protect against common 3' end deletion events, essential genes carry an increased number of (smaller) introns. To demonstrate the importance of the seven features we identified, we train a feature-based model using only these features and achieve high performance.

Lateralized Deficits in Motor, Sensory, and Olfactory Domains in Dementia.

BACKGROUND: There exist functional deficits in motor, sensory, and olfactory abilities in dementias. Measures of these deficits have been discussed as potential clinical markers. OBJECTIVE: We measured the deficit of motor, sensory, and olfactory functions on both the left and right body side, to study potential body lateralizations. METHODS: This IRB-approved study (N = 84) performed left/right clinical tests of gross motor (dynamometer test), sensory (Von Frey test), and olfactory (peppermint oil test) ability. The Mini-Mental Status Exam was administered to determine level of dementia; medical and laboratory data were collected. RESULTS: Sensory and olfactory deficits lateralized to the left side of the body, while motor deficits lateralized to the right side. We found clinical correlates of motor lateralization: female, depression, MMSE <15, and diabetes. While clinical correlates of sensory lateralization: use of psychotherapeutic agent, age ≥85, MMSE <15, and male. Lastly, clinical correlates of olfactory lateralization: age <85, number of medications >10, and male. CONCLUSION: These lateralized deficits in body function can act as early clinical markers for improved diagnosis and treatment. Future research should identify correlates and corresponding therapies to strengthen at-risk areas.

OnabotulinumtoxinA injections: treatment of reversible cerebral vasoconstriction syndrome chronic daily headaches.

Reversible cerebral vasoconstriction syndrome (RCVS) is a rare condition characterised by repetitive, multifocal, vasofluctuations of cerebral arteries. A key symptom is chronic, disabling 'thunderclap' headaches, which are extremely difficult to treat as established medications may exacerbate the pathophysiology of RCVS. OnabotulinumtoxinA (OBT-A) injections are used for the prophylaxis of chronic daily headaches (CDH). The mechanism of action of OBT-A significantly differs from oral headache treatments. Thus, OBT-A may be an effective, safe treatment of RCVS-CDH. A 51-year-old woman with RCVS-CDH presented to outpatient clinic. This case report describes the first, believed, documented treatment of RCVS-CDH by OBT-A injections. In 2018, the consented patient received a total of 200 units of OBT-A, 155 units to the 31 approved U.S. Food and Drug Administration (FDA) sites and 45 units injected into the bilateral occipital belly of occipitofrontalis muscles. The patient reported 3 months of excellent pain relief (60% reduction). Three rounds of OBT-A injection, each 3 months apart, resulted in 80% reduction. OBT-A injections may prove a successful, novel treatment for RCVS-CDH.