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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 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.

Tendon injuries are common and debilitating, with non-regenerative healing often resulting in chronic disease. While there has been considerable progress in identifying the cellular and molecular regulators of tendon healing, the role of inflammation in tendon healing is less well understood. While inflammation underlies chronic tendinopathy, it also aids debris clearance and signals tissue repair. Here, we highlight recent findings in this area, focusing on the cells and cytokines involved in reparative inflammation. We also discuss findings from other model systems when research in tendon is minimal, and explore recent studies in the treatment of human tendinopathy to glean further insights into the immunobiology of tendon healing.

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.

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.

BACKGROUND Occipitocervical fusion (OCF) procedures are increasing due to an aging population and the prevalence of trauma, rheumatoid arthritis, and tumors. Reoperation rates and readmission risk factors for cervical fusions have been established, but in relation to OCF they have not been explored. This study investigates the patterns of readmissions and complications following OCF using a national database. METHODS The 2016 U.S. Nationwide Readmissions Database was used for sample collection. Adults (>18 years) who underwent OCF were identified using the 2016 ICD-10 coding system, and we examined the readmission rates (30-day and 90-day) and reoperation rates. RESULTS Between January and September 2016, a total of 477 patients underwent OCF; the 30-day and 90-day readmission rates were 10.4% and 22.4%, respectively. The 90-day reoperation rate related to the index surgery was 5.7%. Mean age (68.58 years) was significantly greater in the readmitted group versus nonreadmitted group (61.76 years) (P < 0.001). The readmitted group had a significantly higher Charlson Comorbidity Index and Elixhauser Comorbidity Index (5.00 and 2.41, respectively) than the nonreadmitted group (3.25 and 1.15, respectively; P < 0.001). Nonelective OCF showed a higher readmission rate (29.18%) versus elective OCF (12.23%) (P < 0.001). Medicare and Medicaid patients showed the highest rates of readmission (27.27% and 20.41%, respectively). Readmitted patients had higher total health care costs. CONCLUSIONS Nonelective OCF was found to have a readmission rate of almost 2½× that of elective OCF. Understanding risk factors associated with OCF will help with operative planning and patient optimization.

Study design Systematic review and meta-analysis. Objective To compare complication rates and clinical and radiologic outcome between the mini-open prepsoas and mini-open transpsoas approaches for lateral lumbar interbody fusion. Summary of background data Both approaches are believed to be safe with similar complication rates. Previous studies suggest that the rate of neurological injury might be higher in the transpsoas group, whereas visceral or vascular injury might be more frequent in the prepsoas group. Methods A systematic review of the literature was performed. Data were extracted from original publications up until December 26, 2018. Evidence was extracted from well-designed case-control or cohort studies and sorted in 2 groups, the prepsoas and transpsoas approaches. A meta-analysis was performed using a random-effects model (I statistic >50% for all analyses). Results A total of 115 studies included data of 13,260 patients, 2450 in the prepsoas group and 10,810 in the transpsoas group. Demographics for prepsoas versus transpsoas group were (N-weighted means): age 61.9 versus 60.9 years; %female sex 53% versus 63%, levels fused 1.4 versus 2.6, blood loss 52.4 versus 122.3 mL, and operating time 125.1 versus 200.7 min. The following statistically significant differences in complication rates between prepsoas and transpsoas approaches were found: transient psoas weakness or thigh/groin numbness 4% versus 26% [95% confidence interval (CI): 11%-17%], motor neural injury 0.4% versus 1.3% (95% CI: 16%-62.3%); no statistically significant differences were found for: major vascular injury 2% versus 1% (95% CI: 1.04%-2.31%), kidney or ureter injury 0.04% versus 0.08% (95% CI: 0.057%-5.2%), injury pleural/peritoneal structures 0.6% versus 0.2% (95% CI: 0.89%-6.58%), cage subsidence 5% versus 4% (95% CI: 0.9%-1.97%), surgical site infection 1% versus 1% (95% CI: 0.57%-1.66%), abdominal wall pseudohernia 1% versus 1% (95% CI: 0.07%-21.22%), sympathetic chain injury 5% versus 0% (95% CI: 0.34%-97.86%), and directly procedure-related death 0.04% versus 0% (95% CI: 0.127%-76.8%). Pooled mean perioperative changes between prepsoas and transpsoas approaches were: segmental sagittal Cobb angle 3.07 versus 1.99 degrees; foraminal height 2 versus 6.96 mm. Conclusions The prepsoas had fewer complications than the transpsoas approach. Furthermore, the prepsoas approach showed superior restoration of segmental lordosis, whereas foraminal height restoration was superior with the transpsoas approach. This could be explained by the differences in location of the interbody device placement in relation to the center of rotation of the spine between the 2 surgical techniques.

Study design Nationwide Readmissions Database Study. Objective To investigate the patterns of readmissions and complications following hospitalization for elective single level anterior lumbobsacral interbody fusion. Summary of background data Lumbar interbody spine fusions for degenerative disease have increased annually in the United States, including associated hospital costs. Anterior lumbar interbody fusions (ALIFs) have become popularized secondary to higher rates of fusion compared with posterior procedures, and preservation of posterior elements. Prior national databases have sought to study readmission rates with some limitations due to older diagnosis and procedure codes. The newer 2016 International Classification of Diseases Tenth Revision, Clinical Modification (ICD-10 CM) includes more specification of the surgical site. Methods We utilized the 2016 United States Nationwide Readmissions Database (NRD), this nationally representative, all-payer database that includes weighted probability sample of inpatient hospitalizations for all ages. We identified all adults (≥ 18 yrs) using the 2016 ICD-10 coding system who underwent elective primary L5-S1 ALIF and examined rates of readmissions within 90 days of discharge. Results Between January and September 2016, a total of 7029 patients underwent elective stand-alone L5-S1 ALIF who were identified from NRD of whom 497 (7.07%) were readmitted within 90 days of their procedure. No differences in sex were appreciated. Medicare patients had statistically significant higher readmission rates (47.69%) among all payer types. With respect to intraoperative complications, vascular complications had statistically significant increased odds of readmission (OR, 3.225, 95% CI, 0.59 -1.75; P = 0.0001). Readmitted patients had higher total healthcare costs. Conclusion The 90-day readmission rate following stand-alone single level lumbosacral (L5-S1) ALIF was 7.07%. ALIF procedures have increased in frequency, and an understanding of the comorbidities, age-related demographics, and costs associated with 90-day readmissions are critical. Surgeons should consider these risk factors in preoperative planning and optimization. Level of evidence 3.

Background Machine learning (ML) techniques have been shown to successfully predict postoperative complications for high-volume orthopedic procedures such as hip and knee arthroplasty and to stratify patients for risk-adjusted bundled payments. The latter has not been done for more heterogeneous, lower-volume procedures such as total shoulder arthroplasty (TSA) with equally limited discussion around strategies to optimize the predictive ability of ML algorithms. The purpose of this study was to (1) assess which of 5 ML algorithms best predicts 30-day readmission, (2) test select ML strategies to optimize the algorithms, and (3) report on which patient variables contribute most to risk prediction in TSA across algorithms. Methods We identified 9043 patients in the American College of Surgeons National Surgical Quality Improvement Database who underwent primary TSA between 2011 and 2015. Predictors included demographics, comorbidities, laboratory data, and intraoperative variables. The outcome of interest was 30-day unplanned readmission. Five ML algorithms—support-vector machine (SVM), logistic regression, random forest (RF), an adaptive boosting algorithm, and neural network—were trained on the derivation cohort (2011-2014 TSA patients) to predict 30-day unplanned readmission rates. After training, weights for each respective model were fixed and the classifiers were evaluated on the 2015 TSA cohort to simulate a prospective evaluation. C-statistic and f1 scores were used to assess the performance of each classifier. After evaluation, features were removed independently to assess which features most affected classifier performance. Results The derivation and validation cohorts comprised 5857 and 3186 primary TSA patients, respectively, with similar demographics, comorbidities, and 30-day unplanned readmission rates (2.9% vs. 2.7%). Of the ML algorithms, SVM performed the worst with a c-statistic of 0.54 and an f1-score of 0.07, whereas the random-forest classifier performed the best with the highest c-statistic of 0.74 and an f1-score of 0.18. In addition, SVM was most sensitive to loss of single features, whereas the performance of RF did not dramatically decrease after loss of single features. Within the trained RF classifier, 5 variables achieved weights >0.5 in descending order: high bilirubin (>1.9 mg/dL), age >65, race, chronic obstructive pulmonary disease, and American Society of Anesthesiologists' scores ≥3. In our validation cohort, we observed a 2.7% readmission rate. From this cohort, using the RF classifier we were then able to identify 436 high-risk patients with a predicted risk score >0.6, of whom 36 were readmitted (readmission rate of 8.2%). Conclusion Predictive analytics algorithms can achieve acceptable prediction of unplanned readmission for TSA with the RF classifier outperforming other common algorithms.

Markov model analysis.The aim of this study was to determine the 7-year cost-effectiveness of single-level anterior cervical discectomy and fusion (ACDF) versus cervical disc replacement (CDR) for the treatment of cervical disc degeneration.Both ACDF and CDR are acceptable surgical options for the treatment of symptomatic cervical disc degeneration. Past studies have demonstrated at least equal effectiveness of CDR when compared with ACDF in large randomized Investigational Device Exemption (IDE) studies. Short-term cost-effectiveness analyses at 5 years have suggested that CDR may be the preferred treatment option. However, adjacent segment disease and other postoperative complications may occur after 5 years following surgery.A Markov model analysis was used to evaluate data from the LDR Mobi-C IDE study, incorporating five Markov transition states and seven cycles with each cycle set to a length of 1 year. Transition state probabilities were determined from complication rates, as well as index and adjacent segment reoperation rates from the IDE study. Raw SF-12 data were converted to health state utility values using the SF-6D algorithm for 174 CDR patients and 79 ACDF patients.Assuming an ideal operative candidate who is 40-years-old and failed appropriate conservative care, the 7-year cost was $103,924 for ACDF and $105,637 for CDR. CDR resulted in the generation of 5.33 quality-adjusted life-years (QALYs), while ACDF generated 5.16 QALYs. Both ACDF and CDR were cost-effective, but the incremental cost-effectiveness ratio (ICER) was $10,076/QALY in favor of CDR, which was less than the willingness-to-pay (WTP) threshold of $50,000/QALY.ACDF and CDR are both cost-effective strategies for the treatment of cervical disc degeneration. However, CDR is the more cost-effective procedure at 7 years following surgery. Further long-term studies are needed to validate the findings of this model.1.

Study Design:Retrospective cohort study.Objective:Malnutrition has been shown to be a risk factor for poor perioperative outcomes in multiple surgical subspecialties, but few studies have specifically investigated the effect of hypoalbuminemia in patients undergoing operative treatment of metastatic spinal tumors. The aim of this study was to assess the role of hypoalbuminemia as an independent risk factor for 30-day perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors using the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database from 2011 to 2014.Methods:We identified 1498 adult patients in the ACS-NSQIP database who underwent laminectomy and excision of metastatic extradural spinal tumors. Patients were categorized into normoalbuminemic and hypoalbuminemic (ie, albumin level Results:Hypoalbuminemia was associated with increased risk of perioperative mortality, any complication, sepsis, intra- or postoperative transfusion, prolonged hospitalization, and non-home discharge. However, albumin depletion was also associated with decreased risk of readmission. There was an albumin level–dependent effect of increasing mortality and complication rates with worsening albumin depletion.Conclusions:Hypoalbuminemia is an independent risk factor for perioperative mortality and morbidity following surgical decompression of metastatic spinal tumors with a dose-dependent effect on mortality and complication rates. Therefore, it is important to address malnutrition and optimize nutritional status prior to surgery.

Systematic review and meta-analysis.Examine the functional outcomes and complications following laminectomy for thoracic myelopathy due to ossification of the ligamentum flavum (OLF).OLF is a rare condition that can cause thoracic myelopathy. Laminectomy is a procedure that can be performed to decompress the spinal cord in patients with thoracic myelopathy due to OLF. Few studies have examined postoperative outcomes and complications following laminectomy for thoracic myelopathy secondary to OLF.A systematic review and meta-analysis was performed. Literature search yielded six studies that met our selection criteria. Study characteristics and baseline patient demographics were extracted from each study. Primary outcomes included pre- and postoperative Japanese Orthopedic Association (JOA) scores and perioperative complications including dural tears, cerebrospinal fluid (CSF) leaks, neurological deficits, surgical site infections, and other complications. We calculated pooled proportion estimates for JOA scores and complications using a random effects model.A total of 137 patients were included. The pooled pre- and postoperative JOA scores were 5.08 (95% confidence interval [CI], 2.70-7.47; I = 98%) and 8.29 (95% CI, 7.73-8.85; I = 18%), respectively, with a mean improvement of +3.03 points (95% CI, 1.08-4.98; I = 88%). Pooled proportion estimates for dural tears, CSF leaks, infections, and early neurological deficits were 18.4% (95% CI, 12.6-26.1; I = 0%), 12.1% (95% CI, 6.6-21.2; I = 0%), 5.8% (95% CI, 2.1-15.4; I = 0%), and 5.7% (95% CI, 2.2-14.3; I = 0%), respectively.Thoracic myelopathy secondary to OLF can be treated with laminectomy. However, despite some improvement in JOA score, functional status remains poor postoperatively. Perioperative complications are common, with dural tears and CSF leaks occurring most frequently. OLF is an uncommon condition and more research is needed to better understand how we can improve the outcomes of laminectomy alone for the treatment of thoracic myelopathy due to OLF.3.

Study Design: Meta-analysis. Objective: Proximal junctional kyphosis (PJK) is a complication of surgical management for adult spinal deformity with a multifactorial etiology. Many risk factors are controversial and their relative importance are not fully understood. We aimed to identify the surgical, radiographic, and patient-related risk factors associated with PJK and proximal junctional failure (PJF). Methods: A systematic literature search was performed using PubMed, Cochrane Database of Systematic Reviews, and EMBASE. The inclusion criteria included prospective randomized control trials and prospective/retrospective cohort studies of adult patients with radiographic evidence of PJK, which was defined as a proximal junctional sagittal Cobb angle ≥10° and at least 10° greater than the preoperative measurement. Studies required a minimum of 10 patients and 12 months of follow-up. Results: A total of 14 unique studies, including 1908 patients were included. The pooled analysis showed significant differences between the PJK and non-PJK groups in age (weighted mean difference [WMD] −3.80; P = .03), prevalence of osteopenia/osteoporosis (odds ratio [OR] 1.99; P = .0004), preoperative sagittal vertical axis (SVA) (WMD −17.52; P = .02), preoperative lumbar lordosis (LL) (WMD −1.22; P = .002), pedicle screw instrumentation at the upper instrumented vertebra (UIV) (OR 1.67; P = .02), change in SVA (WMD −11.87; P = .01), fusion to sacrum/pelvis/ilium (OR 2.14; P < .00 001), change in LL (WMD −5.61; P = .01), and postoperative SVA (WMD −7.79; P = .008). Conclusions: Our meta-analysis suggests that age, osteopenia/osteoporosis, high preoperative SVA, high postoperative SVA, low preoperative LL, use of pedicle screws at the UIV, SVA change/correction, LL change/correction, and fusion to sacrum/pelvis/iliac region are risk factors for PJK.

Study Design: Retrospective propensity score matched analysis. Objective: To compare the incidence of any 30-day perioperative complication following primary and revision discectomy for lumbar disc herniation. Methods: The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) was used to identify patients undergoing primary or revision lumbar discectomy from 2005 to 2012. Propensity score matching was performed to create matched pairs of primary and revision discectomy cases for analysis. Univariate analysis was then performed to compare 30-day morbidity and mortality between propensity score–matched pairs. Results: We identified 4730 cases of primary discectomy performed through a minimally invasive or open approach and 649 revision discectomy cases. Baseline patient characteristics and comorbidities were compared and then propensity score–matched adjustments were made to create 649 matched pairs of primary and revision cases. On univariate analysis, there were no significant differences in 30-day perioperative outcomes between the 2 groups. Conclusion: While there were no significant differences in 30-day perioperative complications between patients undergoing primary lumbar discectomy and those undergoing revision lumbar discectomy, this finding should be interpreted with caution since the ACS-NSQIP database lacks functional and pain outcomes, and also does not include dural tear or durotomy as a complication. Future large-scale and long-term prospective studies including these variables are needed to better understand the outcomes and complications following primary versus revision discectomy for lumbar disc herniation.

BACKGROUND CONTEXT Anterior cervical discectomy and fusion (ACDF) is a common procedure used in the treatment of the cervical spine. Estimates of revision rate following this procedure range between 10-20%. Identification of existing instrumentation is a critical step in planning revision surgery following ACDF. In many cases, information about the existing instrumentation is unavailable, and identification must be performed visually. This process may be time-consuming and subject to biases. As such, a need exists for fast, objective, automated methods for identifying instrumentation from radiographs. Machine learning algorithms that are known to be adept at image classification may be applied to the problem of ACDF plate identification. Furthermore, due to the ubiquity of smartphone use in clinical practice, an algorithm that could be deployed on a smartphone would be particularly useful. PURPOSE 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. STUDY DESIGN/SETTING Cross-sectional study. PATIENT SAMPLE A total of 402 smartphone images of deidentified AP cervical spine radiographs containing 15 different types of ACDF plates were gathered from publicly available data sources via internet search. Image selection required implant identities to be confirmable by the authors, so the images could not have severe artifacts. ACDF plates for 1-level, 2-level, and 3-level fusions were included. OUTCOME MEASURES Algorithm performance was evaluated using accuracy, positive predictive value, sensitivity, and f-1 score. Qualitative analysis was performed using saliency heatmaps to evaluate if algorithm decisions were based on reasonable implant features. METHODS The smartphone images of AP cervical spine radiographs were stratified by implant type and divided into a training and a testing dataset. 275 images (∼70%) were used to train and validate a convolution neural network (CNN) for classification of implants from the 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. Saliency heatmaps revealed that the algorithm examined unique features of the implants that a human observer might use for implant identification. CONCLUSIONS 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. FDA DEVICE/DRUG STATUS Aesculap ABC (Approved for this indication), Alphatec Deltaloc (Approved for this indication), Alphatec Deltaloc Reveal (Approved for this indication), Depuy Synthes CSLP (Approved for this indication), Depuy Synthes Skyline (Approved for this indication), Medtronic Atlantis (Approved for this indication), Medtronic Atlantis Vision (Approved for this indication), Medtronic Orion (Approved for this indication), Medtronic Premier (Approved for this indication), Medtronic Zephir (Approved for this indication), Stryker Aline (Approved for this indication), Stryker Aviator (Approved for this indication), Stryker Reflex Hybrid (Approved for this indication), Xtant Spider (Approved for this indication), Zimmer Maxan (Approved for this indication)

BACKGROUND CONTEXT Computer-assisted navigation (CAN) was developed to improve visualization inside the human body during surgery. This technology allows surgeons to more readily contextualize anatomical structures during complex procedures. The goal of CAN is to improve the accuracy and reproducibility of surgeries. PURPOSE Given the increased adoption of CAN for spine surgery, the objective of this study is to assess the charges, trends and usage of CAN in thoracolumbar spine surgery over time in order to better inform surgeons to changes in practice. METHODS The 2012-2016 National Readmission Databases (NRD) were queried for patients with ICD-9 and ICD-10 codes for computer-assisted procedures of the thoracolumbar region. Nonelective cases and patients RESULTS Following inclusion and exclusion criteria, 6,598 total patients were included, with 246 in 2012, 134 in 2013, 30 in 2014, 2,169 in 2015, and 4,019 in 2016 (Pearson correlation coefficient = 0.866, p = 0.057). Average total charge to a patient in 2016 was $142,013, which is greater than the 2012-2014 mean total charges of $126,698, $111,738, $115,120, and similar to the 2015 mean total charge of $148,134 (Pearson correlation coefficient = 0.660, p = 0.225). The ratio of patients covered under Medicare to those covered by private insurance increased from 0.822 in 2012 to 1.27 in 2016 (Pearson correlation coefficient 0.687, p = 0.313). CONCLUSIONS The use of and charges related to CAN in thoracolumbar surgeries has increased overall from 2012-2016, most notably indicated by the steep upward trend in use of this technology in 2016. This indicates an increasing adoption of a more recent innovation in assistive surgical technology, and an associated cost burden that comes with it. CAN has the potential to improve surgical outcomes, but as utilization grows and CAN becomes the standard in thoracolumbar spine surgeries, the charges for care also increase for more patients. As a result, further studies need to be conducted to determine whether the use of CAN is efficient in terms of cost and improvement of patient care. Limitations of this include the fact that there is no ICD-9 code for the use of CAN in thoracolumbar surgery specifically, so patient population was estimated using ICD-9 codes for CAN general use coupled with ICD-9 codes for thoracolumbar surgery. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

BACKGROUND CONTEXT Preoperative imaging is essential in surgical planning for spinal deformity. While MRIs have reduced the need for invasive imaging in most cases, some patients, especially patients with prior hardware, still require CT myelograms, which involve injection of contrast into the thecal sac. Creating an algorithm that can generate a “predicted CT myelogram” may help reduce the risks associated with invasive imaging while providing valuable clinical information. PURPOSE Our study aims to develop an algorithm that utilizes the recent advances in computer vision, GANs in particular, to generate predicted CT myelograms from noncontrast CT images of the spine to reduce the need for unnecessary invasive imaging. STUDY DESIGN/SETTING Cross-sectional database review study. PATIENT SAMPLE Routine imaging data from 32 patients aged 18 or older from 01/01/2005 to 11/01/2020 that underwent both noncontrast CT and CT myelogram at a major academic center. OUTCOME MEASURES Qualitative assessment of predicted CT myelograms compared to actual CT myelograms on the 10-patient hold-out dataset. METHODS Using routine imaging data from 22 patients, we trained a GAN to generate predicted CT myelograms from noncontrast CTs. The GAN's effectiveness was then evaluated on a hold-out dataset of 10 additional patients. RESULTS We successfully trained a GAN using 1875 slices of noncontrast CT and CT myelogram data from 22 patients. When evaluated on data from a 10 patient hold-out dataset, the GAN was able to enhance the thecal sac on some of the patient images. CONCLUSIONS GANs can enhance presence of the thecal sac using information present in noncontrast CT images, but may require additional optimizations to generate complete contrast. Further optimizations and training with more data may allow GANs to reduce resource utilization and patient harms by decreasing the need for invasive imaging in the future. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

While there has been considerable progress in identifying molecular regulators of musculoskeletal development, the role of physical forces in regulating induction, differentiation, and patterning events is less well understood. Here, we highlight recent findings in this area, focusing primarily on model systems that test the mechanical regulation of skeletal and tendon development in the limb. We also discuss a few of the key signaling pathways and mechanisms that have been implicated in mechanotransduction and highlight current gaps in knowledge and opportunities for further research in the field.

BACKGROUND CONTEXT Medical notes contain a rich supply of medical data, yet the format of unstructured text precludes this data from being readily used by computers for data mining. 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. PURPOSE We hypothesize that a machine learning algorithm can accurately identify billing CPT codes on unstructured patient operative notes. STUDY DESIGN/SETTING This was a retrospective analysis of medical notes from a large, single-center academic institution's database comprised of cases from a single surgeon. PATIENT SAMPLE Inclusion criteria included patients who underwent elective spine surgery by a single senior surgeon from 9/2015 to 1/2020. OUTCOME MEASURES Algorithm performance was measured by performing receiver-operating characteristic (ROC) analysis and calculating the area under the ROC curve (AUC). METHODS Inclusion criteria included 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 and calculating the area under the ROC curve (AUC). The data was randomized with 70% used for training and 30% used for testing. Labels (CPT codes) were generated by the billing and coding department. NLP (natural language processing) techniques were used to analyze standard operative notes and train an algorithm to automatically generate CPT codes. A deep learning NLP algorithm (bidirectional long short-term memory network with attention) was tested on operative notes to predict CPT codes. CPT codes generated by the billing department were compared to those generated by our model. RESULTS A total of 391 operative dictations fit our inclusion criteria. The NLP algorithm identified the correct CPT codes on the validation set with a final accuracy of 98%. The overall AUC was 80% for the top 36 CPT Codes. The average class by class accuracy for the top 36 CPT Codes was 80%. CONCLUSIONS Combining NLP with ML is a valid approach for automatic generation of CPT billing codes, and it can be used by departments to allow for efficient billing, maximization of collections and minimization of coder error. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

The coronavirus disease-2019 (COVID-19) pandemic has created unprecedented challenges in clinical research activities; its effects on ongoing clinical trials has not been yet quantified ([1][1],[2][2]). In this report, we provide a description of the effect of the COVID-19 pandemic on ongoing

Open reduction with internal fixation (ORIF) and intramedullary nailing (IMN) have similar union rates for treating humerus shaft fractures, but IMN leads to increased incidence of shoulder impingement and reoperation. The difference in 30-day readmission rate and length of stay (LOS) between these procedures is unknown. The objective of the study was to compare 30-day unplanned readmissions and length of stay between humeral shaft fractures fixed with either ORIF or IMN.The nationwide readmissions database (NRD) was queried for patients with humeral shaft fractures treated with ORIF or IMN between 2015-2016. IMN cases were propensity matched to ORIF cases based on demographic and co-morbid variables. Multivariable logistic regression determined if treatment modality was an independent risk factor for 30-day readmission or length of stay3 days.There were 406 patients treated with IMN matched to 406 patients treated with ORIF. The 30-day readmission rate was 6.4% for IMN and 4.9% for ORIF (p = 0.45), and the median LOS was 3 days for each group (p = 0.45). Congestive heart failure (CHF)(OR=2.7, p = 0.04), depression (OR=3.3, p = 0.0008), and electrolyte abnormality (OR=3.6, p = 0.0003) were independent risk factors for readmission. Older age (OR=1.02, p = 0.03), CHF (OR=2.4, p = 0.03), electrolyte abnormality (OR=2.6, p = 0.0001), obesity (OR=2.8, p0.0001), Medicaid (OR=2.1, p = 0.04), discharge to a facility (OR = 5.2, p0.0001), discharge with home health services (OR=2.4, p = 0.0003), and open fracture (OR=2.3, p = 0.01) were independent risk factors for LOS3 days. Procedure (ORIF vs. IMN) was not a predictor of 30-day readmission or LOS3 days.Comorbid conditions are risk factors for 30-day readmission and increased LOS. Comorbidity, discharge disposition, and open fractures are risk factors for increased LOS. Treating humeral shaft fractures with either ORIF or IMN did not affect readmission or length of stay.

To compare surgical outcomes between seven different approaches for thoracolumbar corpectomy/spondylectomy in the setting of spinal metastasis. A systematic review of literature was performed including articles on corpectomy for thoracolumbar spinal metastasis. Data were extracted and sorted by surgical approach: en bloc spondylectomy (group 1), transpedicular (group 2), costotransversectomy (group 3), mini-open retropleural/retroperitoneal (group 4a), lateral extracavitary approach (group 4b), open transthoracic/transretroperitoneal (group 5), and thoracoscopic (group 6). Comparison of demographics, blood loss, directly procedure related complications, operating time, and postoperative improvement of pain. A total of 63 articles were included comprising data of 774 patients with various primary tumor entities. Mean age was 51.8 years, 54% of patients were female, on average 1.46 levels were treated per patient, and mean follow-up was 1.59 years. The following statistically significant findings were observed: Blood loss was lowest for the mini-open retropleural/retroperitoneal (917 ml), thoracoscopic (1107 ml) and transthoracic approach (1172 ml) versus the posterior approach groups (1633–2261 ml); directly procedure related complications were lowest for mini-open retropleural/retroperitoneal and thoracoscopic approach (0% each) versus 7–15% in the other groups; operating time was lowest in mini-open retropleural/retroperitoneal approach (184 min) versus 300–588 min in the other groups. Less invasive approaches (mini-open retropleural/retroperitoneal and thoracoscopic) not only had superior outcome in terms of blood loss and operating time, but also were shown to be safe techniques in cancer patients with low rates of procedure-related complications. These slides can be retrieved under Electronic Supplementary Material.

Background: En-bloc spondylectomy in the treatment of spinal tumors is a complex procedure with potential complications. This study aims at identifying predictors of postoperative complications, lesion recurrence and overall survival. Methods: A systematic review of the literature was conducted, and patient-level data extracted from the included studies. Multiple linear-regression models were calculated to predict the occurrence of postoperative complications, lesion recurrence and overall survival based on age, tumor etiology, surgical approach, mode of resection (en-bloc versus intralesional), tumor extension based on Weinstein-BorianiBiagini classification system and number of levels treated. Results: Data of 582 individual adult and pediatric patients were extracted from the literature; Patient characteristics are: 45% female, median age of 46 years (range 5-78); most common etiologies were: sarcoma (46%), metastases (31%) and chordoma (11%). The surgical technique was: anterior (2.5%), combined (45%) and posterior approach (52.4%); 68.5% underwent en-bloc spondylectomy; average levels resected were 1.6 (1-6); 65% of patients had neurologic deficits at presentation, average survival was 2.6 years; Direct procedure-related complications were observed in 17.7%, with the most prevalent being CSF leaks, wound infections and neural injury. For postoperative complications, recurrence and 5-year survival significant regression equations were found (F(7,90)=2.57, p=0.018) with an R2 of 0.1; (F(5,147)=2.35, p=0.044) with an R2 of 0.07 and (F(4,101)=7.2, p=0.01) with an R2 of 0.38. Odds ratio for predicted complications was 1.35 for en-bloc resection and 1.25 for more than one level treated. The odds ratio for tumor recurrence was 0.78 for en-bloc resection; odds ratio for 5-year survival were 0.79 for increased patient age, 0.65 for increasing tumor grade, 0.79 for tumor dissemination at diagnosis and 1.68 for en-bloc resection. Conclusion: En-bloc spondylectomy provides improved survival and lower recurrence rates but also higher operative complication rates when compared to intralesional resections. Interestingly the complication rate was not influenced by tumor stage (WBB scale) and tumor etiology.

BACKGROUND CONTEXT Sagittal alignment measurements are increasingly recognized as an important quantitative metric for the evaluation of spinal disorders. Manual methods used to derive these measurements are time consuming and subject to rater-dependent error. Thus, a need exists to develop fast and precise automated methods for obtaining sagittal measurements. This study aims to develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with comparable accuracy to surgeons. PURPOSE We hypothesize that we could develop an algorithm for the automated measurement of spinopelvic parameters on lateral lumbar radiographs with accuracy comparable to surgeons. STUDY DESIGN/SETTING This was a retrospective analysis of lumbar radiographs from Mount Sinai Medical Center. PATIENT SAMPLE Images from 816 patients receiving sagittal lumbar spine radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. OUTCOME MEASURES The area under the receiver operating curve (AUROC) for the algorithm was calculated. Radiographs were randomly selected from the test set and manually measured by three surgeons for comparison to those measurements made by the algorithm. METHODS Images from 816 patients receiving sagittal lumbar spine radiographs were collected sequentially and used to develop a convolutional neural network (CNN) segmentation algorithm. Eighty percent (n =653) of these radiographs were used to train and validate the CNN. This CNN was combined with a computer vision algorithm to create a complete pipeline for the fully-automated measurement of spinopelvic parameters from lateral lumbar radiographs. The remaining 20% (n =163) of radiographs were used to test this pipeline. After exclusion of radiographs which did not contain two femoral heads, 40 radiographs were randomly selected from the test set and manually measured by three surgeons for comparison. RESULTS Following training of the segmentation algorithm, a final validation accuracy of 0.978 was achieved. The area under the receiver operating curve (AUROC) was 0.942, 0.960, and 0.962 for segmentation of the sacrum, vertebrae, and femoral head classes, respectively. Wilcoxon rank-sum tests revealed algorithm measurements of L1-S1 cobb angle, pelvic incidence, and pelvic tilt were not significantly different from surgeon measurement (p > 0.05). In comparison to a gold standard surgeon measurement, the algorithm performed with a similar mean absolute difference to spine surgeons for L1-S1 Cobb angle (4.30 ± 4.14˚ vs 6.00 ± 4.96˚), pelvic tilt (2.14 ± 6.29˚ vs 1.82 ± 5.88˚), pelvic incidence (4.56 ± 5.40˚ vs 4.39 ± 3.48˚), and sacral slope (4.76 ± 6.93˚ vs 5.43 ± 5.54˚). CONCLUSIONS The results of this study indicate that this algorithm is comparable to surgeons in the evaluation of spinopelvic parameters on lateral lumbar radiographs. The algorithm may find use in clinical workflow through integration into existing spine measurement tools to provide default measurement suggestions. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.

Study design A cross-sectional database study. Objective The aim of this study was to train and validate machine learning models to identify risk factors for complications following posterior lumbar spine fusion. Summary of background data Machine learning models such as artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex datasets. ANNs have yet to be used for risk factor analysis in orthopedic surgery. Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent posterior lumbar spine fusion. This query returned 22,629 patients, 70% of whom were used to train our models, and 30% were used to evaluate the models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society for Anesthesiology (ASA) class ≥3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating curves (AUC) was used to determine the accuracy of our machine learning models. Results On the basis of AUC values, ANN and LR both outperformed ASA class for predicting all four types of complications. ANN was the most accurate for predicting cardiac complications, and LR was most accurate for predicting wound complications, VTE, and mortality, though ANN and LR had comparable AUC values for predicting all types of complications. ANN had greater sensitivity than LR for detecting wound complications and mortality. Conclusion Machine learning in the form of logistic regression and ANNs were more accurate than benchmark ASA scores for identifying risk factors of developing complications following posterior lumbar spine fusion, suggesting they are potentially great tools for risk factor analysis in spine surgery. Level of evidence 3.

Study design A retrospective cohort study from 2011 to 2014 was performed using the American College of Surgeons National Surgical Quality Improvement Program database. Objective The purpose of this study was to assess the impact of tumor location in the cervical, thoracic, or lumbosacral spine on 30-day perioperative mortality and morbidity after surgical decompression of metastatic extradural spinal tumors. Summary of background data Operative treatment of metastatic spinal tumors involves extensive procedures that are associated with significant complication rates and healthcare costs. Past studies have examined various risk factors for poor clinical outcomes after surgical decompression procedures for spinal tumors, but few studies have specifically investigated the impact of tumor location on perioperative mortality and morbidity. Methods We identified 2238 patients in the American College of Surgeons National Surgical Quality Improvement Program database who underwent laminectomy for excision of metastatic extradural tumors in the cervical, thoracic, or lumbosacral spine. Baseline patient characteristics were collected from the database. Univariate and multivariate regression analyses were performed to examine the association between spinal tumor location and 30-day perioperative mortality and morbidity. Results On univariate analysis, cervical spinal tumors were associated with the highest rate of pulmonary complications. Multivariate regression analysis demonstrated that cervical spinal tumors had the highest odds of multiple perioperative complications. However, thoracic spinal tumors were associated with the highest risk of intra- or postoperative blood transfusion. In contrast, patients with metastatic tumors in the lumbosacral spine had lower odds of perioperative mortality, pulmonary complications, and sepsis. Conclusion Tumor location is an independent risk factor for perioperative mortality and morbidity after surgical decompression of metastatic spinal tumors. The addition of tumor location to existing prognostic scoring systems may help to improve their predictive accuracy. Level of evidence 3.

Study Design Cross-sectional database study. Objective To train and validate machine learning models to identify risk factors for complications following surgery for adult spinal deformity (ASD). Summary of Background Data Machine learning models such as logistic regression (LR) and artificial neural networks (ANNs) are valuable tools for analyzing and interpreting large and complex data sets. ANNs have yet to be used for risk factor analysis in orthopedic surgery. Methods The American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) database was queried for patients who underwent surgery for ASD. This query returned 4,073 patients, which data were used to train and evaluate our models. The predictive variables used included sex, age, ethnicity, diabetes, smoking, steroid use, coagulopathy, functional status, American Society of Anesthesiologists (ASA) class >3, body mass index (BMI), pulmonary comorbidities, and cardiac comorbidities. The models were used to predict cardiac complications, wound complications, venous thromboembolism (VTE), and mortality. Using ASA class as a benchmark for prediction, area under receiver operating characteristic curves (AUC) was used to determine the accuracy of our machine learning models. Results The mean age of patients was 59.5 years. Forty-one percent of patients were male whereas 59.0% of patients were female. ANN and LR outperformed ASA scoring in predicting every complication (p Conclusions Machine learning algorithms outperform ASA scoring for predicting individual risk prognosis. These algorithms also outperform LR in predicting individual risk for all complications except VTE. With the growing size of medical data, the training of machine learning on these large data sets promises to improve risk prognostication, with the ability of continuously learning making them excellent tools in complex clinical scenarios. Level of Evidence Level III.

Background: The number of total shoulder arthroplasty (TSA) procedures performed annually is increasing as a result of an aging population and an increased access to subspecialty-trained upper extremity arthroplasty surgeons. An up-to-date analysis of the incidence of, risk factors for, and reasons for 90-day readmissions in primary anatomic TSA has yet to be performed. Purpose: To characterize 90-day readmissions on a national level. An understanding of these data will help to predict resource utilization and expenses in shoulder arthroplasty. Study Design: Case-control study; Level of evidence, 3. Methods: All adult patients undergoing elective primary TSA in 2014 who were included in the National Readmission Database were included in the analysis. Two cohorts were created according to 90-day readmission status. Multivariable analysis was then performed to determine predictors of 90-day readmissions. Reasons for 30-, 60-, and 90-day readmissions were identified, and total hospital resource utilization was calculated. Results: An estimated 26,023 patients were identified. The 30-, 60-, and 90-day rates of readmissions were 0.6%, 1.2%, and 1.7%, respectively. There was no difference in comorbidity burden between the cohorts. Medicare payer status (odds ratio [OR], 1.63; 95% CI, 1.00-2.65; P = .05), transfer to a skilled nurse facility (OR, 1.50; 95% CI, 1.05-2.14; P = .02), and chronic obstructive pulmonary disease (OR, 1.32; 95% CI, 1.04-1.66; P = .02) were identified as predictors of 90-day readmission. Female sex decreased odds of 90-day readmission (OR, 0.72; 95% CI, 0.59-0.87; P = .001). Ninety-day readmissions were associated with significant cost increases ( P < .001). The most common identifiable reason for related readmissions was a hardware-related complication at all time points. Conclusion: While uncommon, 90-day readmissions after primary TSA are associated with significant patient morbidity and ultimately substantial hospital costs. Truncating readmission analysis at a 30-day period will miss most arthroplasty-related hospital readmissions.

Background/Objectives: The advent of Wireless Sensor Networks (WSNs) have revolutionized the field of automation in many ways. This paper presents the implementation of one such WSN useful for industrial applications. Methods/Statistical Analysis: The establishment of the WSN cluster using low cost MSP430 processor and RF transceivers is explained. This network could be easily modified to suit the needs of a particular process in an industry. Findings: A basic hardware system comprising of a cluster having five nodes is tested with real time control signals. Such a network was found to be reliable, low cost and low power consuming. This cluster can be duplicated and such clusters can be integrated with the use of gateways to form a large network to monitor and control the various processes in an industry simultaneously. In the consumer end, these networks will be more user friendly and available at low cost. Applications/Improvements: The WSN clusters can be used for automation of various industries with the possibility of easy modification or expansion in the future.

With the rapid development in the field of solar energy, photovoltaic (PV) modules are being used in a wide range of moving applications like cars, trains, ships and a number of space applications that are subjected to dynamic partial shading. In such applications the power-voltage (P-V) characteristics of the PV module continuously changes with time. The conventional Maximum Power Point Tracking (MPPT) techniques may not have a good conversion efficiency under these scenarios. This paper proposes a novel MPPT algorithm, namely Recurrent Scan and Track (RST) method that was designed in a manner so as to keep up with the changes in the output characteristics due to dynamic shading and track the Global Maximum Power Point (GMPP) at all times. Simulations and outdoor experimental tests taking into account three different dynamic partial shading conditions were carried out to prove the effectiveness of the proposed RST method.

BACKGROUND CONTEXT Quality improvement studies have provided surgeons with systematic insight on how to make changes to their practice to improve patient outcomes and avoid complications. However, currently most automated methods to identify patient complications for quality improvement studies rely on prelabeled administrative codes sampling data from fixed electronic medical databases. PURPOSE Medical notes contain a rich supply of medical data, yet the format of unstructured text has precluded these data from use in large retrospective analyses. Therefore, the utilization of free-text from electronic medical records using natural-language processing in combination with machine learning can provide an additional tool for patient complication surveillance and identification. The purpose of this study was to develop a machine learning algorithm that can identify postsurgical sepsis based on unstructured patient notes. STUDY DESIGN/SETTING This was a retrospective analysis of medical notes from the Medical Information Mart for Intensive Care (MIMIC III), a large, single-center database comprised of intensive care unit admissions data from 2001 to 2012. PATIENT SAMPLE A total of 947 patients from the orthopedic service were identified. Patients who experienced sepsis were identified using ICD-9 code (99592). A total of 15,004 notes were analyzed in total from the 947 patients. Of the 15,004 notes, 3,547 notes were from patients positive for sepsis and 11,457 notes were negative for sepsis defined by ICD-9 code. OUTCOME MEASURES Area under the receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity of the model were evaluated to assess performance of the model. METHODS Natural language processing was used to analyze unstructured narrative discharge notes and train a support vector machine algorithm to train a classifier to automatically identify patients with sepsis based solely on unstructured discharge notes. The data were randomized with 70% used for training and 30% used for testing. RESULTS The SVM algorithm identified sepsis notes with a sensitivity and specificity of 79% and 85%, respectively. The overall accuracy was 83.3% with area-under-receiver operating characteristic curve of 0.82. CONCLUSIONS Combining natural language processing with machine learning is a valid approach for automatic identification of sepsis using unstructured text from discharge notes. This method provides a robust survey of surgical encounters and may be used for real-time surveillance of postoperative complication risk and for automated identification of patient complications such as sepsis for quality improvement studies. FDA DEVICE/DRUG STATUS This abstract does not discuss or include any applicable devices or drugs.