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Alcohol use disorder has been associated with broad health consequences that may interfere with healing after total shoulder arthroplasty. The aim of this study was to explore the impact of alcohol use disorder on readmissions and complications following total shoulder arthroplasty.We used data from the Healthcare Cost and Utilization Project National Readmissions Database (NRD) from 2016 to 2018. Patients were included based on International Classification of Diseases, 10th Revision (ICD-10) procedure codes for anatomic total shoulder arthroplasty (aTSA) and reverse total shoulder arthroplasty (rTSA). Patients with an alcohol use disorder (AUD) were identified using the ICD-10 diagnosis code F10.20. Demographics, complications, and 30-day and 90-day readmission were collected for all patients. A univariate logistic regression was performed to investigate AUD as a factor affecting readmission and complication rates. A multivariate logistic regression model was created to assess the impact of alcohol use disorder on complications and readmission while controlling for demographic factors.In total, 164,527 patients were included, and 503 (0.3%) patients had a prior diagnosis of AUD. Revision surgery was more common in patients with an alcohol use disorder (8.8% vs. 6.2%; p = 0.022). Postoperative infection (p = 0.026), dislocation (p = 0.025), liver complications (p 0.01), and 90-day readmission (p 0.01) were more common in patients with a diagnosed AUD. On multivariate analysis, patients with an AUD were found to be at increased odds for liver complications (OR: 46.8; 95% CI: [32.8, 66.8]; p 0.01). Comparatively, mean age, length of stay, and over healthcare costs were also higher for patients with an AUD.Patients with a diagnosis of AUD were more likely to suffer from shoulder dislocation, liver complications, and 90-day readmission, while also being younger and having longer hospital stays. Therefore, surgeons should take caution to anticipate and prevent complications and readmissions following total shoulder arthroplasty in patients with an AUD.

Introduction: This report describes a novel approach for spinal cord decompression using C1–C2 atlantoaxial fusion with bilateral C1 hinge laminoplasty. Hypothetically, this approach ensures that the stenotic spine is decompressed while simultaneously retaining C1 density for subsequent fixation. Similar techniques have been used in previous case reports for revision procedures or for fusions beyond the atlantoaxial joint. Case Report: Here, an 83-year-old man with psoriatic arthritis, instability, paresthesia, and motor dysfunction regained strength without symptom progression at 7 months follow-up from cervical spine surgery. This patient had no prior surgical interventions at C1–C2. The pre-operative modified Japanese Orthopedic Association scale score was a 9 preoperatively and a 10 at final post-operative follow-up. Conclusion: This case provides an alternative to anterior cervical discectomy with fusion, laminectomy, or laminoplasty for progressive myelopathy relief in the geriatric population. Neurosurgeons and orthopedic spine surgeons alike may benefit from utilizing this technique, potentially reducing the occurrence of reoperations or revisions due to instability and or cervical stenosis.

To elucidate risk factors for 90-day readmission in anterior cervical discectomy and fusion (ACDF) for small, medium, and large hospitals. To assess differences in length of stay, charges, and complication rates across hospitals of different size.A retrospective analysis was performed using elective, single-level ACDF data from 2016 to 2018 in the Healthcare Cost and Utilization Project Nationwide Readmissions Database. Elective single-level ACDF cases were stratified into 3 groups by hospital bed size (small, medium, and large). All-cause complication rates, mean charges, length of stay, and 90-day readmission rates were compared across hospital size. Frequencies of specific comorbidities were compared between readmitted and nonreadmitted patients for each hospital size. Comorbidities significant on univariate analysis were evaluated as independent risk factors for 90-day readmission for each hospital size using multivariate regression.The overall 90-day readmission rate was 6.43% in 36,794 patients, and the rates for small, medium, and large hospitals were 6.25%, 6.28%, and 6.56%, respectively (P = 0.537). Length of stay increased significantly with hospital size (P0.001), and small hospitals had the lowest charges (P0.001). Although different independent predictors of 90-day readmission were identified for each hospital size, cardiac arrhythmia, chronic pulmonary disease, neurologic disorders, and rheumatic disease were identified as risk factors for hospitals of all sizes.Hospital size is a determining factor for charges and length of stay associated with elective single-level ACDF. Variation in risk factors for readmission exists across hospital size in context of similar 90-day readmission rates.

PURPOSE Physician review websites have significant influence on a patient's selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback. The objective of this study is to quantitatively analyze the written reviews of members of the Scoliosis Research Society (SRS) through sentiment analysis. METHODS Online written reviews and star-rating reviews of SRS surgeons were obtained from healthgrades.com, and a sentiment analysis package was used to obtain compound scores of each physician's reviews. A t test and ANOVA was performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was performed to provide context to words used to describe surgeons. RESULTS Seven hundred and twenty-one SRS surgeon's reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (r2 = 0.5, p

PURPOSE The purpose of this study is to analyze posts shared on Instagram, Twitter, and Reddit referencing scoliosis surgery to evaluate content, tone, and perspective. METHODS Public posts from Instagram, Twitter, and Reddit were parsed in 2020-2021 and selected based on inclusion of the words 'scoliosis surgery' or '#scoliosissurgery. 100 Reddit posts, 5022 Instagram posts, and 1414 tweets were included in analysis. The Natural Language Toolkit (NLTK) python library was utilized to perform computational text analysis to determine content and sentiment analysis to estimate the tone of posts across each platform. RESULTS 46.4% of Tweets were positive in tone, 39.4% were negative, and 13.8% were neutral. Positive content focused on patients, friends, or hospitals sharing good outcomes after a patient's surgery. Negative content focused on long wait times to receive scoliosis surgery. 64.7% of Instagram posts were positive in tone, 16.3% were negative, and 19.0% were neutral. Positive content centered around post-operative progress reports and educational resources, while negative content focused on long-term back pain. 37% of Reddit posts were positive in tone, 38% were negative, and 25% were neutral. Positive posts were about personal post-operative progress reports, while negative posts were about fears prior to scoliosis surgery and questions about risks of the procedure. CONCLUSION This study highlights scoliosis surgery content in social media formats and stratifies how this content is portrayed based on the platform it is on. Surgeons can use this knowledge to better educate and connect with their own patients, thus harnessing the power and reach of social media. LEVEL OF EVIDENCE IV.

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patientspecific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.

Objective: Subsidence following anterior cervical discectomy and fusion (ACDF) may lead to disruptions of cervical alignment and lordosis. The purpose of this study was to evaluate the effect of subsidence on segmental, regional, and global lordosis.Methods: This was a retrospective cohort study performed between 2016–2021 at a single institution. All measurements were performed using lateral cervical radiographs at the immediate postoperative period and at final follow-up greater than 6 months after surgery. Associations between subsidence and segmental lordosis, total fused lordosis, C2–7 lordosis, and cervical sagittal vertical alignment change were determined using Pearson correlation and multivariate logistic regression analyses.Results: One hundred thirty-one patients and 244 levels were included in the study. There were 41 one-level fusions, 67 two-level fusions, and 23 three-level fusions. The median follow-up time was 366 days (interquartile range, 239–566 days). Segmental subsidence was significantly negatively associated with segmental lordosis change in the Pearson (r = -0.154, p = 0.016) and multivariate analyses (beta = -3.78; 95% confidence interval, -7.15 to -0.42; p = 0.028) but no associations between segmental or total fused subsidence and any other measures of cervical alignment were observed.Conclusion: We found that subsidence is associated with segmental lordosis loss 6 months following ACDF. Surgeons should minimize subsidence to prevent long-term clinical symptoms associated with poor cervical alignment.

Study design Cross-sectional Study. Objective The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of ACDF plates from smartphone images of AP cervical spine radiographs. Summary of background data Identification of existing instrumentation is a critical step in planning revision surgery for anterior cervical discectomy and fusion (ACDF). Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. Methods 402 smartphone images containing 15 different types of ACDF plates were gathered. 275 images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. 127 (∼30%) images were held out to test algorithm performance. Results The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855 respectively. Conclusion This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Purpose: Physician review websites are a heavily utilized patient tool for finding, rating, and reviewing surgeons. Natural language processing such as sentiment analysis provides a comprehensive approach to better understand the nuances of patient perception. This study utilizes sentiment analysis to examine how specific patient sentiments correspond to positive and negative experiences in online reviews of pediatric orthopedic surgeons. Methods: The online written reviews and star ratings of pediatric surgeons belonging to the Pediatric Orthopaedic Society of North America were obtained from healthgrades.com . A sentiment analysis package obtained compound scores of each surgeon’s reviews. Inferential statistics analyzed relationships between demographic variables and star/sentiment scores. Word frequency analyses and multiple logistic regression analyses were performed on key terms. Results: A total of 749 pediatric surgeons (3830 total online reviews) were included. 80.8% were males and 33.8% were below 50 years of age. Male surgeons and younger surgeons had higher mean star ratings. Surgeon attributes including “confident” (p Conclusion: Pediatric surgeons who were younger, communicated effectively, eased pain, and curated a welcoming office setting were more likely to receive positively written online reviews. This suggests that a spectrum of interpersonal and ancillary factors impact patient experience and perceptions beyond surgical skill. These outcomes can advise pediatric surgeons on behavioral and office qualities that patients and families prioritize when rating/recommending surgeons online. Level of evidence: IV

Demand for total shoulder arthroplasty (TSA) has risen significantly and is projected to continue growing. From 2012 to 2017, the incidence of reverse total shoulder arthroplasty (rTSA) rose from 7.3 cases per 100,000 to 19.3 per 100,000. Anatomical TSA saw a growth from 9.5 cases per 100,000 to 12.5 per 100,000. Failure to identify implants in a timely manner can increase operative time, cost and risk of complications. Several machine learning models have been developed to perform medical image analysis. However, they have not been widely applied in shoulder surgery. The authors developed a machine learning model to identify shoulder implant manufacturers and type from anterior-posterior X-ray images.The model deployed was a convolutional neural network (CNN), which has been widely used in computer vision tasks. 696 radiographs were obtained from a single institution. 70% were used to train the model, while evaluation was done on 30%.On the evaluation set, the model performed with an overall accuracy of 93.9% with positive predictive value, sensitivity and F-1 scores of 94% across 10 different implant types (4 reverse, 6 anatomical). Average identification time was 0.110 s per implant.This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.

Purpose The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19. Materials and methods This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation. Results 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p, Highlights • Machine learning on routinely collected health data can predict future intubation status among COVID-19 patients. • Learned decision criteria by the model closely matched normal-abnormal boundary thresholds for vitals and laboratory values. • Patients alerted by the model were more likely to require intubation during their admission.

Study Design Retrospective cohort. Objective Billing and coding-related administrative tasks are a major source of healthcare expenditure in the United States. We aim to show that a second-iteration Natural Language Processing (NLP) machine learning algorithm, XLNet, can automate the generation of CPT codes from operative notes in ACDF, PCDF, and CDA procedures. Methods We collected 922 operative notes from patients who underwent ACDF, PCDF, or CDA from 2015 to 2020 and included CPT codes generated by the billing code department. We trained XLNet, a generalized autoregressive pretraining method, on this dataset and tested its performance by calculating AUROC and AUPRC. Results The performance of the model approached human accuracy. Trial 1 (ACDF) achieved an AUROC of .82 (range: .48-.93), an AUPRC of .81 (range: .45-.97), and class-by-class accuracy of 77% (range: 34%-91%); trial 2 (PCDF) achieved an AUROC of .83 (.44-.94), an AUPRC of .70 (.45-.96), and class-by-class accuracy of 71% (42%-93%); trial 3 (ACDF and CDA) achieved an AUROC of .95 (.68-.99), an AUPRC of .91 (.56-.98), and class-by-class accuracy of 87% (63%-99%); trial 4 (ACDF, PCDF, CDA) achieved an AUROC of .95 (.76-.99), an AUPRC of .84 (.49-.99), and class-by-class accuracy of 88% (70%-99%). Conclusions We show that the XLNet model can be successfully applied to orthopedic surgeon’s operative notes to generate CPT billing codes. As NLP models as a whole continue to improve, billing can be greatly augmented with artificial intelligence assisted generation of CPT billing codes which will help minimize error and promote standardization in the process.

Delays in postoperative referrals to rehabilitation or skilled nursing facilities contribute toward extended hospital stays. Facilitating more efficient referrals through accurate preoperative prediction algorithms has the potential to reduce unnecessary economic burden and minimize risk of hospital-acquired complications. We develop a robust machine learning algorithm to predict non-home discharge after thoracolumbar spine surgery that generalizes to unseen populations and identifies markers for prediction.Retrospective electronic health records were obtained from our single-center data warehouse (SCDW) to identify patients undergoing thoracolumbar spine surgeries between 2008 and 2019 for algorithm development and internal validation. The National Inpatient Sample (NIS) database was queried to identify thoracolumbar surgeries between 2009 and 2017 for out-of-sample validation. Ensemble decision trees were constructed for prediction and area under the receiver operating characteristic curve (AUROC) was used to assess performance. Shapley additive explanations values were derived to identify drivers of non-home discharge for interpretation of algorithm predictions.A total of 5224 cases of thoracolumbar spine surgeries were isolated from the SCDW and 492,312 cases were identified from NIS. The model achieved an AUROC of 0.81 (standard deviation [SD] = 0.01) on the SCDW test set and 0.77 (SD = 0.01) on the nationwide NIS data set, thereby demonstrating robust prediction of non-home discharge across all diverse patient cohorts. Age, total Elixhauser comorbidities, Medicare insurance, weighted Elixhauser score, and female sex were among the most important predictors of non-home discharge.Machine learning algorithms reliably predict non-home discharge after thoracolumbar spine surgery across single-center and national cohorts and identify preoperative features of importance that elucidate algorithm decision-making.

Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.Machine learning will soon become critical in the day-to-day operations of orthopaedic practice; therefore, it is imperative that providers become accustomed to and familiar with not only the terminology but also the fundamental techniques behind the technology.A foundation of knowledge regarding machine learning is critical for physicians so they can begin to understand the details in the algorithms that are being developed, which provide improved accuracy compared with clinicians, decreased time required, and a heightened ability to triage patients.

Study Design Retrospective Cohort Study. Objectives Using natural language processing (NLP) in combination with machine learning on standard operative notes may allow for efficient billing, maximization of collections, and minimization of coder error. This study was conducted as a pilot study to determine if a machine learning algorithm can accurately identify billing Current Procedural Terminology (CPT) codes on patient operative notes. Methods This was a retrospective analysis of operative notes from patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. Algorithm performance was measured by performing receiver operating characteristic (ROC) analysis, calculating the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC). A deep learning NLP algorithm and a Random Forest algorithm were both trained and tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model. Results The random forest machine learning model had an AUC of .94 and an AUPRC of .85. The deep learning model had a final AUC of .72 and an AUPRC of .44. The random forest model had a weighted average, class-by-class accuracy of 87%. The LSTM deep learning model had a weighted average, class-by-class accuracy 0f 59%. Conclusions Combining natural language processing with machine learning is a valid approach for automatic generation of CPT billing codes. The random forest machine learning model outperformed the LSTM deep learning model in this case. These models can be used by orthopedic or neurosurgery departments to allow for efficient billing.

Orthopaedic surgical trays contain unused instruments, but we do not know which specific instruments go unused nor do we know the savings from eliminating them from a given tray. This was a single-site, observational study conducted at an academic medical center. The primary outcome was type of unused instruments and percentage of instruments used in two commonly used surgical trays. The secondary outcome was cost savings in United States dollars (USD) that could be attained by eliminating these instruments. In the first tray, five instruments (10.6%) were unused in any of 37 observed cases. In the second tray, nineteen instruments (19.6%) were unused in 37 observed cases. The total annual savings from replacement cost analysis and reprocessing cost analysis was $6,597.00 USD. Unused instruments are common in surgical trays. Eliminating unused instruments can result in immediate cost savings. (Journal of Surgical Orthopaedic Advances 30(3):131-135, 2021).

Cross-sectional study.The purpose of this study is to develop and validate a machine learning algorithm for the automated identification of anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs.Identification of existing instrumentation is a critical step in planning revision surgery for ACDF. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification.A total of 402 smartphone images containing 15 different types of ACDF plates were gathered. Two hundred seventy-five images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of images from radiographs. One hundred twenty-seven (∼30%) images were held out to test algorithm performance.The algorithm performed with an overall accuracy of 94.4% and 85.8% for top-3 and top-1 accuracy, respectively. Overall positive predictive value, sensitivity, and f1-scores were 0.873, 0.858, and 0.855, respectively.This algorithm demonstrates strong performance in the classification of ACDF plates from smartphone images and will be deployed as an accessible smartphone application for further evaluation, improvement, and eventual widespread use.Level of Evidence: 3.

Physician review websites have significant influence on a patient's selection of a provider, but written reviews are subjective. Sentiment analysis of writing through artificial intelligence can quantify surgeon reviews to provide actionable feedback. The objective of this study is to quantitatively analyze the written reviews of members of the Scoliosis Research Society (SRS) through sentiment analysis.Online written reviews and star-rating reviews of SRS surgeons were obtained from healthgrades.com, and a sentiment analysis package was used to obtain compound scores of each physician's reviews. A t test and ANOVA was performed to determine the relationship between demographic variables and average sentiment score of written reviews. Positive and negative word and word-pair frequency analysis was performed to provide context to words used to describe surgeons.Seven hundred and twenty-one SRS surgeon's reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (rThe most frequently used word pairs used to describe top-rated surgeons describe compassionate providers and efficiency in pain management. Conversely, those with the worst reviews are characterized as unable to relieve pain. Through quantitative analysis of physician reviews, pain is a clear factor contributing to both positive and negative reviews of surgeons, reinforcing the need to properly manage pain expectations.IV.

Study Design: Retrospective national database study.Purpose: This study is conducted to assess the trends in the charges and usage of computer-assisted navigation in cervical and thoracolumbar spinal surgery.Overview of Literature: This study is the first of its kind to use a nationwide dataset to analyze trends of computer-assisted navigation in spinal surgery over a recent time period in terms of use in the field as well as the cost of the technology.Methods: Relevant data from the National Readmission Database in 2015–2018 were analyzed, and the computer-assisted procedures of cervical and thoracolumbar spinal surgery were identified using International Classification of Diseases 9th and 10th revision codes. Patient demographics, surgical data, readmissions, and total charges were examined. Comorbidity burden was calculated using the Charlson and Elixhauser comorbidity index. Complication rates were determined on the basis of diagnosis codes.Results: A total of 48,116 cervical cases and 27,093 thoracolumbar cases were identified using computer-assisted navigation. No major differences in sex, age, or comorbidities over time were found. The utilization of computer-assisted navigation for cervical and thoracolumbar spinal fusion cases increased from 2015 to 2018 and normalized to their respective years’ total cases (Pearson correlation coefficient=0.756, p =0.049; Pearson correlation coefficient=0.9895, p =0.010). Total charges for cervical and thoracolumbar cases increased over time (Pearson correlation coefficient=0.758, p =0.242; Pearson correlation coefficient=0.766, p =0.234).Conclusions: The use of computer-assisted navigation in spinal surgery increased significantly from 2015 to 2018. The average cost grossly increased from 2015 to 2018, and it was higher than the average cost of nonnavigated spinal surgery. With the increased utilization and standardization of computer-assisted navigation in spinal surgeries, the cost of care of more patients might potentially increase. As a result, further studies should be conducted to determine whether the use of computer-assisted navigation is efficient in terms of cost and improvement of care.

Retrospective cohort study of 2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD).The aim was to evaluate cost and outcomes associated with navigation use on posterior cervical fusion (PCF) surgery patients.Computer-assisted navigation systems demonstrate comparable outcomes with hardware placement and procedural speed compared with traditional techniques. Innovations in technology continue to improve surgeons' performance in complicated procedures, causing need to analyze the impact on patient care.The 2016 NRD was queried for patients with PCF surgery ICD-10 codes. Cost and readmission rates were compared with and without navigation. Nonelective cases and patients below 18 years of age were excluded. Univariate analysis on demographics, surgical data, and total charges was performed. Lastly, multivariate analysis was performed to assess navigation's impact on cost and postoperative outcomes.A total of 11,834 patients were identified, with 137 (1.2%) patients undergoing surgery with navigation and 11,697 (98.8%) patients without. Average total charge was $131,939.47 and $141,270.1 for the non-navigation and navigation cohorts, respectively ( P =0.349). Thirty-day and 90-day readmission rates were not significantly lower in patients who received navigation versus those that did not ( P =0.087). This remained insignificant after adjusting for several variables, age above 65, sex, medicare status, mental health history, and comorbidities. The model adjusting for demographic and comorbidities maintained insignificant results of navigation being associated with decreased 30-day and 90-day readmissions ( P =0.079).Navigation use in PCF surgery was not associated with increased cost, and patients operated on with navigation did not significantly have increased routine discharge or decreased 90-day readmission. As a result, future studies must continue to evaluate the cost-benefit of navigation use for cervical fusion surgery.Level III.

STUDY DESIGN Retrospective questionnaire analysis. OBJECTIVES The goal of this study was to analyze patients' understanding and preferences for Minimally Invasive Spine (MIS) versus open spine surgery. SUMMARY OF BACKGROUND DATA MIS surgery is increasing in prevalence. However, there is insufficient literature to evaluate how the availability of MIS surgery influences the patients' decision-making process and perceptions of spine procedures. METHODS A survey was administered to patients who received a microdiscectomy or transforaminal lumbar interbody fusion between 2016 and 2020. All eligible patients were stratified into two cohorts based on the use of minimally invasive techniques. Each cohort was administered a survey that evaluated patient preferences, perceptions, and understanding of their surgery. RESULTS 152 patients completed surveys (MIS: 88, Open: 64). There was no difference in time from surgery to survey (MIS: 2.1 ± 1.4 years, Open: 1.9 ± 1.4 years; p = 0.36) or sex (MIS: 56.8% male, Open: 53.1% male; p = 0.65). The MIS group was younger (MIS: 53.0 ± 16.9 years, Open: 58.2 ± 14.6 years; p = 0.05). More MIS patients reported that their technique influenced their surgeon choice (MIS: 64.0%, Open: 37.5%; p

Study Design Retrospective cohort analysis. Objectives To identify the effects of preoperative steroid therapy on 30-day perioperative complications after adult spinal deformity (ASD) surgery. Summary of Background Data Chronic steroid therapy has demonstrated therapeutic effects in the treatment of various medical conditions but is also known to be associated with surgical complications. There remains a gap in the literature regarding the impact of chronic steroid therapy in predisposing patients to perioperative complications after elective surgery for ASD. Methods We performed a retrospective analysis of data from the 2008-2015 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database. Patients were divided into two groups based on preoperative steroid therapy. Differences in baseline patient characteristics, comorbidities, and operative variables were assessed. Univariate analysis was performed to compare the incidence of perioperative complications. Multivariate stepwise logistic regression models were then used to adjust for baseline patient and operative variables in order to identify perioperative complications that were associated with preoperative steroid therapy. Results We identified 7,936 patients who underwent surgery for ASD, of which 418 (5.3%) were on preoperative steroid therapy. Preoperative steroid therapy was an independent risk factor for four perioperative complications, including mortality (odds ratio [OR] 2.42, 95% confidence interval [CI] 1.30, 4.51; p = .005), wound dehiscence (OR 3.12, 95% CI 1.45, 6.70; p = .004), deep vein thrombosis (DVT) (OR 2.10, 95% CI 1.24, 3.55; p = .006), and blood transfusion (OR 1.34, 95% CI 1.08, 1.66; p Conclusions Patients on preoperative steroid therapy are at increased risk of 30-day mortality, wound dehiscence, DVT, and blood transfusion after surgery for ASD. An interdisciplinary approach to the perioperative management of steroid regimens is critical. Level of Evidence Level III.

Study Design: 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: The U-Net segmentation achieved a test dataset dice score of 0.821, an area under the receiver operating curve of 0.914, and an accuracy of 0.862. The computer vision algorithm identified the L1 and S1 vertebrae on 84.1% of the test set with an average speed of 0.14 seconds/radiograph. From the 151 test set radiographs, 50 were randomly chosen for surgeon measurement. When compared with those measurements, our algorithm achieved a mean absolute error of 8.055° and a median absolute error of 6.965° (not statistically significant, P > .05). Conclusion: 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.

Cervical spinal deformity (CSD) in adult patients is a relatively uncommon yet debilitating condition with diverse etiologies and clinical manifestations. Similar to thoracolumbar deformity, CSD can be broadly divided into scoliosis and kyphosis. Severe forms of CSD can lead to pain; neurologic deterioration, including myelopathy; and cervical spine-specific symptoms such as difficulty with horizontal gaze, dysphagia, and dyspnea. Recently, an increased interest is shown in systematically studying CSD with introduction of classification schemes and treatment algorithms. Both major and minor complications after surgical intervention have been analyzed and juxtaposed to patient-reported outcomes. An ongoing effort exists to better understand the relationship between cervical and thoracolumbar spinal alignment, most importantly in the sagittal plane.

To identify independent risk factors for non-home discharge in patients undergoing laminectomy for intradural extramedullary spinal tumors.We performed a retrospective cohort analysis of data from the American College of Surgeons National Surgical Quality Improvement Program from 2011 to 2014. Adult patients who underwent laminectomy for the excision of intradural extramedullary spinal tumors were included and divided into 2 groups based on home or non-home discharge disposition. We compared baseline patient characteristics, comorbidities, and operative factors between the 2 groups, and then performed multivariate regression analyses to identify independent risk factors for non-home discharge.A total of 1232 patients were included, of whom 248 (20.1%) were discharged to a non-home facility. Univariate analysis demonstrated that patients discharged to a non-home facility were more frequently aged ≥65 years and American Society of Anesthesiologists classification ≥3 with obesity, diabetes, dyspnea, functional dependence, cardiac comorbidity, renal comorbidity, and anemia. Operative factors correlated with non-home discharge were operative time of ≥4 hours and tumor location in the cervical or thoracic spine. Multivariate regression analysis identified age ≥65 years (odds ratio [OR] 2.73; confidence interval [CI] 1.80-4.13; P0.001), American Society of Anesthesiologists classification ≥3 (OR 2.36; CI 1.53-3.65; P0.001), dependent functional status (OR 4.30; CI 1.95-9.48; P0.001), hospital-acquired conditions (OR 2.32; CI 1.15-4.68; P = 0.019), and prolonged length of stay (OR 4.05; CI 2.72-6.03; P0.001) as predictors of non-home discharge.Early identification of patients at risk for non-home discharge is important in order to implement comprehensive discharge planning protocols that reduce inpatient length of stay, as well as associated complications and costs.

Study Design Retrospective database study. Objectives The goal of this study was to assess the influence of weekend admission on patients undergoing elective thoracolumbar spinal fusion by investigating hospital readmission outcomes and analyzing differences in demographics, comorbidities, and postoperative factors. Methods The 2016-2018 Nationwide Readmission Database was used to identify adult patients who underwent elective thoracolumbar spinal fusion. The sample was divided into weekday and weekend admission patients. Demographics, comorbidities, complications, and discharge status data were compiled. The primary outcomes were 30-day and 90-day readmission. Univariate logistic regression analyzed the relationship between weekday or weekend admission and 30- or 90-day readmission, and multivariate regression determined the impact of covariates. Results 177,847 patients were identified in total, with 176,842 in the weekday cohort and 1005 in the weekend cohort. Multivariate regression analysis found that 30-day readmissions were significantly greater for the weekend cohort after adjusting for sex, age, Medicare or Medicaid status, and comorbidity status (OR 2.00, 95% CI: 1.60-2.48; P < .001), and 90-day readmissions were also greater for the weekend cohort after adjustment (OR 2.01, 95% CI: 1.68-2.40, P < .001). Conclusions Patients undergoing elective thoracolumbar spinal fusion surgery who are initially admitted on weekends are more likely to experience hospital readmission. These patients have increased incidence of deep vein thrombosis, postoperative infection, and non-routine discharge status. These factors are potential areas of focus for reducing the impact of the “weekend effect” and improving outcomes for elective thoracolumbar spinal fusion.

Objective Machine learning algorithms excel at leveraging big data to identify complex patterns that can be used to aid in clinical decision-making. The objective of this study is to demonstrate the performance of machine learning models in predicting postoperative complications following anterior cervical discectomy and fusion (ACDF). Methods Artificial neural network (ANN), logistic regression (LR), support vector machine (SVM), and random forest decision tree (RF) models were trained on a multicenter data set of patients undergoing ACDF to predict surgical complications based on readily available patient data. Following training, these models were compared to the predictive capability of American Society of Anesthesiologists (ASA) physical status classification. Results A total of 20,879 patients were identified as having undergone ACDF. Following exclusion criteria, patients were divided into 14,615 patients for training and 6,264 for testing data sets. ANN and LR consistently outperformed ASA physical status classification in predicting every complication (p < 0.05). The ANN outperformed LR in predicting venous thromboembolism, wound complication, and mortality (p < 0.05). The SVM and RF models were no better than random chance at predicting any of the postoperative complications (p < 0.05). Conclusion ANN and LR algorithms outperform ASA physical status classification for predicting individual postoperative complications. Additionally, neural networks have greater sensitivity than LR when predicting mortality and wound complications. With the growing size of medical data, the training of machine learning on these large datasets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios.

Study Design: Review article. Objectives: A review of the literature on postoperative spinal infections, their diagnosis, and management. Methods: A systematic computerized Medline literature search was performed using PubMed, Cochrane Database of Systematic Reviews, and EMBASE. The electronic databases were searched for publication dates from the last 10 years. The searches were performed from Medical Subject Headings (MeSH) used by the National Library of Medicine. Specifically, MeSH terms “spine,” “infections,” “management,” and “diagnosis” were used. Results: Currently, the gold standard for diagnosis of postoperative spine infection is positive deep wound culture. Many of the current radiologic and laboratory tests can assist with the initial diagnosis and monitoring treatment response. Currently erythrocyte sedimentation rate, C-reactive protein, computed tomography scan, and magnetic resonance imaging with and without contrast are used in combination to establish diagnosis. Management of postoperative spine infection involves thorough surgical debridement and targeted antibiotic therapy. Conclusions: Postoperative spine infection is a not uncommon complication following surgery that may have devastating consequences for a patient’s short- and long-term health. A high index of suspicion is needed to make an early diagnosis.

Objective Anterior lumbar fusion (ALF) is performed by both neurosurgeons and orthopaedic surgeons. The aim of this study was to determine differences between the 2 surgical subspecialties in terms of patient selection and postoperative outcomes after ALF. Methods A retrospective cohort study of adult patients undergoing ALF in the American College of Surgeons National Surgical Quality Improvement Program database from 2010 to 2014 was performed. Univariate analyses were performed to identify differences in baseline patient demographics, comorbidities, operative characteristics, and 30-day postoperative outcomes between neurosurgery and orthopaedic surgery patients. Multivariate logistic regression analysis was used to determine whether surgical subspecialty was an independent risk factor for postoperative complications. Results The study included 3182 patients, with 1629 (51.2%) neurosurgery patients and 1553 (48.8%) orthopaedic surgery patients. A greater proportion of neurosurgery patients were >65 years old, were being treated with preoperative steroids, had cardiac or pulmonary comorbidities, and had an American Society of Anesthesiologists classification III or higher. ALF procedures performed by neurosurgeons more frequently involved use of intervertebral devices and bone graft. On multivariate logistic regression analysis, ALF procedures performed by neurosurgeons were independently associated with a higher risk of reoperation (odds ratio = 1.61; 95% confidence interval, 1.02–2.56; P = 0.042) and urinary tract infection (odds ratio = 1.94; 95% confidence interval, 1.02–3.68; P = 0.043). Conclusions In addition to differences in baseline patient demographics and comorbidities and operative characteristics, ALF performed by neurosurgeons had a higher risk of 30-day reoperation and urinary tract infection compared with ALF performed by orthopaedic surgeons.

BACKGROUND This study aimed to investigate the impact of segmental artery reimplantation and its patency on spinal cord ischemia (SCI) in thoracoabdominal aorta replacement. METHODS For 193 patients who underwent early postoperative computed tomographic (CT) angiography after thoracoabdominal aorta replacement, the technique of segmental artery reimplantation, their patency, and postoperative SCI were retrospectively investigated. RESULTS The early patency rate of reimplanted segmental artery was 83.3% (210 of 252), as 13 were taken down intraoperatively and 42 were not visualized in the postoperative CT angiography. The patency rate differed according to the reimplantation technique: 93.6% (131/140) for en bloc patch, 95.6% (43/45) for small individual patch, and 53.7% (36/67) for graft interposition. SCI occurred in 13 (6.3%) patients, 4 of whom (2.0%) remained paraplegic permanently. SCI was significantly more frequent (P=0.044) in the patients in whom segmental artery reimplantation was not successful (take-down or occlusion, 6/37=16.2%) than in those who had all segmental arteries sacrificed intentionally (2/64=3.1%) and those who showed patency of all reimplanted segmental arteries (5/92=5.4%). Especially, there was no permanent paraplegia in the last group. Failure of intended segmental artery reimplantation was a significant risk factor of postoperative SCI in logistic regression analysis (P=0.012; odds ratio 4.65, 95% confidence interval 1.41-15.36). CONCLUSIONS During thoracoabdominal aorta replacement, attention should be paid to the segmental artery reimplantation technique, which affects the risk of occlusion or intraoperative takedown and thereby may have impact on postoperative SCI.

BACKGROUND: Generally, most spine surgeons agree that increased segmental motion viewed on flexion-extension radiographs is a reliable predictor of instability; however, these views can be limited in several ways and may underestimate the instability at a given lumbar segment. METHODS: Consecutively collected adult (≥18 years old) patients with symptomatic single-level lumbar spondylolisthesis were reviewed from a two-surgeon database from 2015 to 2019. Routine standing lumbar X-rays (neutral, flexion, extension) and supine lumbar MRI (sagittal T2-weighted imaging sequence) were performed. Patients were excluded if they had prior lumbar surgery, missing radiographic data, or if the time between X-rays and MRI was >6 months. RESULTS: All 39 patients with symptomatic, single-level lumbar spondylolisthesis were identified. The mean age was 57.3±16.7 years and 66% were female. There was good intra- and inter-rater reliability agreement between measured values on the presence of instability. The slip percentage (SP) difference was significantly highest in the flexion-supine (FS) (5.7 mm, 12.3%) and neutral standing-supine (NS) (4.3 mm, 8.7%) groups, both of which were significantly higher compared with the flexion-extension (FE) group (1.8 mm, 4.5%, P8% was observed more frequently in FS (79.5%) and NS (52.6%) groups compared with FE group (16.7%, P