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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.

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.

Online patient reviews influence a patient's choice of a vascular surgeon. The aim of this study is to examine underlying factors that contribute to positive and negative patient reviews by leveraging sentiment analysis and machine learning methods.The Society of Vascular Surgeons publicly accessible member directory was queried and cross-referenced with a popular patient-maintained physician review website, healthgrades.com. Sentiment analysis and machine learning methods were used to analyze several parameters. Demographics (gender, age, and state of practice), star rating (of 5 stars), and written reviews were obtained for corresponding vascular surgeons. A sentiment analysis model was applied to patient-written reviews and validated against the star ratings. Student's t-test or one-way analysis of variance assessed demographic relationships with reviews. Word frequency assessments and multivariable logistic regression analyses were conducted to identify common and determinative components of written reviews.A total of 1,799 vascular surgeons had public profiles with reviews. Female gender of surgeon was associated with lower star ratings (male = 4.19, female = 3.95, P 0.01) and average sentiment score (male = 0.50, female = 0.40, P 0.01). Younger physician age was associated with higher star rating (P = 0.02) but not average sentiment score (P = 0.12). In the Best reviews, the most commonly used one-words were Care (N = 999), Caring (N = 767), and Kind (N = 479), while the most commonly used two-word pairs were Saved/Life (N = 189), Feel/Comfortable (N = 106), and Kind/Caring (N = 104). For the Worst reviews, the most commonly used one-words were Pain (N = 254) and Rude (N = 148), while the most commonly used two-word pairs were No/One (N = 27), Waste/Time (N = 25), and Severe/Pain (N = 18). In a multiple logistic regression, satisfactory reviews were associated with words such as Confident (odds ratio [OR] = 8.93), Pain-free (OR = 4.72), Listens (OR = 2.55), and Bedside Manner (OR = 1.70), while unsatisfactory reviews were associated with words such as Rude (OR = 0.01), Arrogant (OR = 0.09), Infection (OR = 0.20), and Wait (OR = 0.48).Female surgeons received significantly worse reviews and younger surgeons tended to receive better reviews. The positivity and negativity of reviews were largely related to words associated with the patient-doctor experience and pain. Vascular surgeons should focus on these 2 areas to improve patient experiences and their own reviews.

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.

Background: In 2020, the World Health Organization (WHO) published the 5th version of the soft tissue and bone tumor classification. Based on this novel classification system, we reviewed the current knowledge on all tumor entities with spinal manifestations, their biologic behavior, and most importantly the appropriate treatment options as well as surgical approaches. Methods: All tumor entities were extracted from the WHO Soft-Tissue and Bone Tumor Classification (5th Edition). PubMed and Google Scholar were searched for the published cases of spinal tumor manifestations for each entity, and the following characteristics were extracted: Growth pattern, ability to metastasize, peak age, incidence, treatment, type of surgical resection indicated, recurrence rate, risk factors, 5-year survival rate, key molecular or genetic alterations, and possible associated tumor syndromes. Surgical treatment strategies as well as nonsurgical treatment recommendations are presented based on the biologic behavior of each lesion. Results: Out of 163 primary tumor entities of bone and soft tissue, 92 lesions have been reported along the spinal axis. Of these 92 entities, 54 have the potential to metastasize. The peak age ranges from conatal lesions to 72 years. For each tumor entity, we present recommended surgical treatment strategies based on the ability to locally destruct tissue, to grow, recur after resection, undergo malignant transformation as well as survival rates. In addition, potential systemic treatment recommendations for each tumor entity are outlined. Conclusion: Based on the 5th Edition of the WHO bone and soft tumor classification, we identified 92 out of 163 tumor entities, which potentially can have spinal manifestations. Exact preoperative tissue diagnosis and interdisciplinary case discussions are crucial. Surgical resection is indicated in a significant subset of patients and has to be tailored to the specific biologic behavior of the targeted tumor entity based on the considerations outlined in detail in this article.

The purpose of this study was to analyze posts shared on social media sites, Twitter and Instagram, referencing scoliosis surgery for tone, content, and perspective of the posts.Public Twitter and Instagram posts from November 2020 to April 2021 were isolated using the hashtag #ScoliosisSurgery or the words "scoliosis surgery." A total of 5,022 Instagram and 1,414 Twitter posts were collected, of which 500 of each were randomly selected to be analyzed by the authors for the variables previously listed.Of the Instagram posts, 91.8% were associated with an image, and 47.8% were postoperative. 96.9% of the posts had either a positive or neutral tone. 38% delivered a progress update, and 29.9% disseminated education or sought to provide awareness. 48.6% of the posts were from the perspective of the patient. Of the Twitter posts, 60.1% contained only words, and 37.8% were postoperative. 75% of the posts had either a negative or neutral tone. 38.4% described a personal story, and 19.3% provided a progress update. 42.3% of the posts were from the perspective of the patient.Patients reported a positive tone on Instagram, displaying their progress updates and demonstrating contentment with scoliosis surgery, and a negative tone on Twitter, showing discontentment toward inadequate access to surgery. Although both platforms were used to distribute information and provide awareness, only a small percentage of posts were from physicians and hospitals, indicating opportunities for surgeons to use social media to connect with patients.

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.

Adjacent segment disease, junctional kyphosis/failure and pseudarthrosis can negatively impact the mid to long-term outcome in spinal deformity surgery. These complications might be influenced by upper instrumented vertebra (UIV) fixation techniques. In this study we analyze key biomechanical properties of three different UIV fixation techniques and define their ideal clinical use based on patient-specific risk profiles using a finite element analysis (FEA) model.A T9-pelvis posterior instrumented spinal fusion was assumed. Three different FEA models were created based on the UIV fixation technique: T9 pedicle screws (PS); T9 cortical bone screws (CBS); T9 transverse process hooks (TPH). The three FEA models consisted of T8-T10 bone and ligamentous anatomy derived from a CT scan of a healthy patient as well as spinal implants consisting of either pedicle screws, cortical bone screws or transverse process hooks as well as cobalt chromium rods. The FEA models were constrained at T10, axial load as assumed for a healthy 80 kg male during flexion, extension and lateral bending were applied. As surrogate markers for risk of proximal junctional kyphosis, proximal junctional failure, adjacent segment disease and pseudarthrosis the following biomechanical parameters were calculated: UIV range of motion (ROM); intradiscal stress at UIV/UIV + 1; UIV intravertebral stress and screw pull out forces. One-way ANOVA analyses have been performed to compare biomechanical outcome parameters between the three construct variants under investigation.UIV-ROM was restricted during flexion/extension/lateral bending by: PS: 73%/80%/86%, CBS: 71%/81%/85% and TPH: 62%/76%/85%. Average intradiscal stress at UIV/UIV + 1 during flexion/extension/lateral bending was (Mega Pascal, MPa): PS 0.42/0.44/0.38, CBS 0.49/0.4/0.44, TPH 0.66/0.51/0.58; average intravertebral stress of the UIV superior endplate during flexion/extension/lateral bending was (MPa): PS 2.23/2.12/2.21, CBS 1.87/1.98/1.8, TPH 1.67/0.98/1.53. Screw pull-out forces (N) at UIV during flexion/extension/lateral bending were: PS 476/320/375, CBS 444/245/308. Statistically significant differences were found for intradiscal stress as well as vertebral body average stress (p = 0.02 and p = 0.02).Different UIV fixation techniques carry different biomechanical properties. Pedicle screw fixation is the most rigid, leading to the highest UIV stress and UIV screw pull out forces. Cortical bones screw fixation is similarly rigid; however, UIV stress and UIV screw pull out is significantly lower. Transverse process hook fixation is the least rigid, with the lowest UIV stress, however highest intradiscal stress at UIV/UIV + 1. Thus, these biomechanical differences may help select optimal UIV fixation techniques according to patient specific risk factors.

A quantitative analysis of written, online reviews of Cervical Spine Research Society (CSRS) surgeons.This study quantitatively analyzes the written reviews of members of the CSRS to report biases associated with demographic factors and frequently used words in reviews to help aid physician practices.Physician review websites have 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.Online written and star-rating reviews of CSRS surgeons were obtained from healthgrades.com. A sentiment analysis package was used to obtain compound scores of each physician's reviews. The relationship between demographic variables and average sentiment score of written reviews were evaluated through t-tests. Positive and negative word and bigram frequency analysis was performed to indicate trends in the reviews' language.In all, 2239 CSRS surgeon's reviews were analyzed. Analysis showed a positive correlation between the sentiment scores and overall average star-rated reviews (r2=0.60, P0.01). There was no difference in review sentiment by provider sex. However, the age of surgeons showed a significant difference as those55 had more positive reviews (mean=+0.50) than surgeons=55 (mean=+0.37) (P0.01). The most positive reviews focused both on pain and behavioral factors, whereas the most negative focused mainly on pain. Behavioral attributes increased the odds of receiving positive reviews while pain decreased them.The top-rated surgeons were described as considerate providers and effective at managing pain in their most frequently used words and bigrams. However, the worst-rated ones were mainly described 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 for proper pain expectation management.Level 4-retrospective case-control study.

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 review. Objectives: (1) Identify the 90-day rate of readmission following revision lumbar fusion, (2) identify independent risk factors associated with increased rates of readmission within 90 days, (3) and identify the hospital costs associated with revision lumbar fusion and subsequent readmission within 90 days. Methods: Utilizing 2014 data from the Nationwide Readmissions Database, patients undergoing elective revision lumbar fusion were identified. With this sample, multivariate logistic regression was utilized to identify independent predictors of readmission within 90 days. An analysis of total hospital costs was also conducted. Results: In 2014, an estimated 14 378 patients underwent elective revision lumbar fusion. The readmission rate at 90 days was 3.1% (n = 446). Diabetes with chronic complications was the only comorbidity found to carry significantly increased odds of readmission. Surgical complications such as deep venous thrombosis, surgical wound disruption, hematoma, and pneumonia (experienced during the index admission) were also independent predictors of readmission. Anterior approaches were associated with increased odds of readmission. The most common related diagnoses on readmission were hardware issues, postoperative infection, and disc herniation. Readmissions were associated with an average of $96 152 in increased hospital costs per patient compared with those not readmitted. Conclusion: Relevant patient comorbidities and surgical complications were associated with increased readmission within 90 days. Readmission within 90 days was associated with significant increases in hospital costs.

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.

Study design Secondary analysis of a large administrative database. Objective The objectives of this study are to: 1) identify the incidence and cause of 90-day readmissions following primary elective lumbar spine surgery, 2) offer insight into potential risk factors that contribute to these readmissions, and 3) quantify the cost associated with these readmissions. Summary of background data As bundled-payment models for the reimbursement of surgical services become more popular in spine, the focus is shifting toward long-term patient outcomes in the context of 90-day episodes of care. With limited data available on national 90-day readmission statistics available, we hope to provide evidence that will aid in the development of more cost-effective perioperative care models. Methods Using ICD-9 coding, we identified all patients 18 years of age and older in the 2014 Nationwide Readmissions Database (NRD) who underwent an elective, inpatient, primary lumbar spine surgery. Using multivariate logistic regression, we identified independent predictors of 90-day readmission while controlling for a multitude of confounding variables and completed a comparative cost analysis. Results We identified 169,788 patients who underwent a primary lumbar spine procedure. In total 4268 (2.5%) were readmitted within 90 days. There was no difference in comorbidity burden between cohorts (readmitted vs. not readmitted) as quantified by the Elixhauser Comorbidity index. Independent predictors of increased odds of 90-day readmission were: anemia, uncomplicated diabetes and diabetes with chronic complications, surgical wound disruption and acute myocardial infarction at the time of the index admission, self-pay status, and an anterior surgical approach. Implant complications were identified as the primary related cause of readmission. These readmissions were associated with a significant cost increase. Conclusion There are clearly identifiable risk factors that increase the odds of hospital readmission within 90 days of primary lumbar spine surgery. An overall 90-day readmission rate of 2.5%, while relatively low, carries significantly increased cost to both the patient and hospital. Level of evidence 3.

Study design Cadaveric biomechanical study. Purpose The purpose of this study was to biomechanically evaluate the effect of preserving or augmenting the interspinous ligament (ISL) and supraspinous ligament (SSL; ISL/SSL) complex between the upper instrumented vertebra (UIV) and UIV+1 using a cadaveric model. Overview of literature Adult spinal deformity is becoming an increasingly prevalent disorder, and proximal junctional kyphosis (PJK) is a well-known postoperative complication following long spinal fusion. Methods Pure moments of 4 and 8 Nm were applied to the native and instrumented spine, respectively (n=8). The test conditions included the following: native spine (T7-L2), fused spine (T10-L2), fused spine with a hand-tied suture loop through the spinous processes at T9-T10, and fused spine with severed T9-T10 ISL/SSL complex. Results The flexion range of motion (ROM) at T9-T10 of the fused spine loaded at 8 Nm increased by 62% compared to that of the native spine loaded at 4 Nm. The average flexion ROM at T9-T10 for the suture loop and severed ISL/SSL spines were 141% (p=0.13) and 177% (p=0.66) of the native spine at 4 Nm, respectively (p-values vs. fused). Conclusions Transection of the ISL/SSL complex did not significantly change flexion ROM at the proximal junctional segment following instrumented spinal fusion. Furthermore, augmentation of the posterior ligamentous tension band with a polyester fiber suture loop did not mitigate excessive flexion loads on the proximal junctional segment. We postulate that the role of the posterior ligamentous tension band in mitigating PJK is secondary to the anterior column support provided by the vertebral body and intervertebral disc.

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.

BACKGROUND CONTEXT Rising health care expenditures in the United States place an imperative on providers, payers, and policymakers to more closely evaluate hospital charges. While previous spine surgery studies have compared charges across procedures and in the presence of comorbidities, the association between charges and hospital size in Anterior Cervical Discectomy and Fusion (ACDF) has yet to be understood. PURPOSE To assess the differences in charges for elective single-level ACDF procedures across hospitals of different size. STUDY DESIGN/SETTING This was a retrospective analysis utilizing elective, single-level ACDF data from January 2016 to September 2016 in the HCUP Nationwide Readmissions Database (NRD). PATIENT SAMPLE A total of 13,776 cases of elective, single-level ACDF were identified. Patients who underwent single-level ACDF were identified using the ICD-10 procedural codes for single-level anterior cervical fusion. Patients who underwent cervical disc arthroplasty were excluded from this study. Additional exclusion criteria were death during hospitalization, nonelective surgery, use of emergency services, and age OUTCOME MEASURES Total charges were compared across hospital sizes (small, medium, and large). METHODS Elective single-level ACDF cases were stratified into three groups by hospital bedsize (small, medium, and large) as classified by the NRD. As per the NRD, about one-third of the hospitals within a specific teaching status, location, and region grouping are assigned to each bedsize category. Mean total charges were compared across hospital size utilizing one-way analysis of variance (ANOVA) and subsequently the post-hoc Tukey's range test. RESULTS There was significant variation in mean total charges across small ($59,010.97 ± $55,211.71), medium ($63,775.99 ± $50,823.21) and large ($63,546.59 ± $54,831.45) hospitals (p=0.002). Post-hoc testing revealed significant differences in charge between small and medium hospitals (p=0.004) and between small and large hospitals (p=0.002). CONCLUSIONS Hospital size is a determining factor for cost of elective single-level ACDF. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

STUDY DESIGN Nationwide Readmissions Database Study. OBJECTIVE The aim of this study was to investigate readmission rates and factors related to readmission after surgical and nonsurgical management of odontoid fractures. SUMMARY OF BACKGROUND DATA Management of odontoid fractures, which are the most common isolated spine fracture in the elderly, continues to be debated. The choice between surgical or nonsurgical treatment has been reported to impact mortality and might influence readmission rates. Hospital readmissions represent a large financial burden upon our healthcare system. Factors surrounding hospital readmissions would benefit from a better understanding of their associated causes to lower health care costs. METHODS A retrospective study was performed using the 2016 Healthcare Utilization Project (HCUP) Nationwide Readmission Database (NRD). Demographic information and factors associated with readmission were collected. Readmission rates, complications, length of hospital stay were collected. Patients treated operatively, nonoperatively, and patients who were readmitted or not readmitted were compared. Statistical analysis was performed using open source software SciPy (Python v1.3.0) for all analyses. RESULTS We identified 2921 patients who presented with Type II dens fractures from January 1, 2016 to September 30, 2016, 555 of which underwent surgical intervention. The readmission rate in patients who underwent surgery was 16.4% (91/555) and 29.4% (696/2366) in the nonoperative group. Hospital costs for readmitted and nonreadmitted patients were $353,704 and $174,922, and $197,099 and $80,715 for nonoperatively managed patients, respectively. Medicaid and Medicare patients had the highest readmission rate in both groups. Charlson and Elixhauser comorbidity indices were significantly higher in patients who were readmitted (P

Study design Cross-sectional database study. Objective The objective of this study was to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. Summary of background data Sagittal alignment measurements are important for the evaluation of spinal disorders. Manual measurement methods are time-consuming and subject to rater-dependent error. Thus, a need exists to develop automated methods for obtaining sagittal measurements. Previous studies of automated measurement have been limited in accuracy, inapplicable to common plain films, or unable to measure pelvic parameters. Methods Images from 816 patients receiving lateral lumbar radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. A total of 653 (80%) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a pipeline for the fully automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 163 (20%) of radiographs were used to test this pipeline. Forty radiographs were selected from the test set and manually measured by three surgeons for comparison. Results The CNN achieved an area under the receiver-operating curve of 0.956. Algorithm measurements of L1-S1 cobb angle, pelvic incidence, pelvic tilt, and sacral slope were not significantly different from surgeon measurement. In comparison to criterion standard measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30° ± 4.14° vs. 4.99° ± 5.34°), pelvic tilt (2.14° ± 6.29° vs. 1.58° ± 5.97°), pelvic incidence (4.56° ± 5.40° vs. 3.74° ± 2.89°), and sacral slope (4.76° ± 6.93° vs. 4.75° ± 5.71°). Conclusion This algorithm measures spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. The algorithm could be used to streamline clinical workflow or perform large scale studies of spinopelvic parameters.Level of Evidence: 3.

Supplemental Digital Content is available in the text., Objectives: To examine whether increasing time between admission and intubation was associated with mortality in patients with coronavirus disease 2019 who underwent mechanical ventilation. Design: Retrospective cohort study of patients with severe acute respiratory syndrome coronavirus 2 infection who were admitted between January 30, 2020, and April 30, 2020, and underwent intubation and mechanical ventilation prior to May 1, 2020. Patients were followed up through August 15, 2020. Setting: Five hospitals within the Mount Sinai Health System in New York City, NY. Patients: Adult patients with severe acute respiratory syndrome coronavirus 2 infection who underwent intubation and mechanical ventilation. Interventions: Tracheal intubation and mechanical ventilation. Measurements and Main Results: The primary outcome was in-hospital mortality. A hospital-stratified time-varying Cox model was used to evaluate the effect of time from admission to intubation on in-hospital death. A total of 755 adult patients out of 5,843 admitted with confirmed severe acute respiratory syndrome coronavirus 2 infection underwent tracheal intubation and mechanical ventilation during the study period. The median age of patients was 65 years (interquartile range, 56–72 yr) and 64% were male. As of the time of follow-up, 121 patients (16%) who were intubated and mechanically ventilated had been discharged home, 512 (68%) had died, 113 (15%) had been discharged to a skilled nursing facility, and 9 (1%) remained in the hospital. The median time from admission to intubation was 2.3 days (interquartile range, 0.6–6.3 d). Each additional day between hospital admission and intubation was significantly associated with higher in-hospital death (adjusted hazard ratio, 1.03; 95% CI, 1.01–1.05). Conclusions: Among patients with coronavirus disease 2019 who were intubated and mechanically ventilated, intubation earlier in the course of hospital admission may be associated with improved survival.

Background Thromboembolic disease is common in coronavirus disease-2019 (COVID-19). There is limited evidence on the association of in-hospital anticoagulation (AC) with outcomes and postm...

Pediatric bone age assessment is clinically valuable for the evaluation of a variety of pediatric endocrine and orthopedic conditions. Recent studies have explored automated methods for bone age assessment using machine learning techniques, yielding impressive results. However, many state-of-the-art methods rely on manual, fine-grained segmentation of phalanges and have not been validated on an external hospital site. The purpose of this study was to examine the efficacy of a deep learning algorithm for pediatric bone age assessment without the need for time-intensive segmentation. We utilize a novel training regime to achieve results on par with existing approaches, present a systematic analysis of experimental findings via an ablation study, and evaluate generalizability on an external dataset as a function of training data size. The final optimized model achieves mean absolute error of 7.59 months upon internal validation and 11.02 upon validation with data from an external hospital site.