Weekend Admission Increases Risk of Readmissions Following Elective Thoracolumbar Spinal Fusion

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.

Antecubital Burn Resulting From Antenna Coupling: A Case Report

CASE

We present the case of a 54-year-old man who underwent elective hip disarticulation complicated by third-degree burn of the left antecubital fossa requiring skin graft. After careful review, it was determined that "antenna coupling" as a result of electrosurgery was the likely cause. We present an experiment demonstrating this phenomenon.

CONCLUSION

Antenna coupling is a real but rare cause of intraoperative burns not previously described in the orthopaedic literature. Care should be taken to avoid coiling or running bovie or other electrosurgical device cords with other metallic cords or corded devices.

How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments

STUDY DESIGN

A Sentiment Analysis of online reviews of spine surgeons.

OBJECTIVES

Physician review websites have significant impact on a patient's provider selection. Written reviews are subjective, but sentiment analysis through machine learning can quantitatively analyze these reviews. This study analyzes online written reviews of spine surgeons and reports biases associated with demographic factors and trends in words utilized.

METHODS

Online written and star-reviews of spine surgeons were obtained from healthgrades.com. A sentiment analysis package was used to analyze the written reviews. The relationship of demographic variables to these scores was analyzed with t-tests and word and bigram frequency analyses were performed. Additionally, a multiple regression analysis was performed on key terms.

RESULTS

8357 reviews of 480 surgeons were analyzed. There was a significant difference between the means of sentiment analysis scores and star scores for both gender and age. Younger, male surgeons were rated more highly on average (P < .01). Word frequency analysis indicated that behavioral factors and pain were the main contributing factors to both the best and worst reviewed surgeons. Additionally, several clinically relevant words, when included in a review, affected the odds of a positive review.

CONCLUSIONS

The best reviews laud surgeons for their ability to manage pain and for exhibiting positive bedside manner. However, the worst reviews primarily focus on pain and its management, as exhibited by the frequency and multivariate analysis. Pain is a clear contributing factor to reviews, thus emphasizing the importance of establishing proper pain expectations prior to any intervention.

Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?

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.

Giant Cell Reparative Granuloma in the Thumb Distal Phalanx: A Case Report

Giant cell reparative granulomas (GCRG) often affect the bones of the hands and the feet. Treatment of this lesion depends on the exact location and amount of localized bony destruction. Ours is the first case report to discuss the nuances of treating this lesion in the thumb distal phalanx. A 19-year-old male presented with lytic, destructive expansion of his left thumb distal phalanx; imaging was suggestive of an aneurysmal bone cyst. Open biopsy was interpreted as giant cell reparative granuloma. Curettage and bone grafting resulted in complete healing of the distal phalanx with an excellent range of motion and interphalangeal joint stability. GCRG is a rare, benign entity typically presenting as a lytic bone lesion. Despite the initial massive bony destruction, this lesion nevertheless healed with curettage and bone grafting with maintained flexor pollicis longus and extensor pollicis longus function, permitting excellent active motion postoperatively.

Readmission and Associated Factors in Surgical Versus Non-Surgical Management of Spinal Epidural Abscess: A Nationwide Readmissions Database Analysis

STUDY DESIGN

Retrospective cohort study.

OBJECTIVES

Spinal epidural abscess (SEA) is a rare but potentially life-threatening infection treated with antimicrobials and, in most cases, immediate surgical decompression. Previous studies comparing medical and surgical management of SEA are low powered and limited to a single institution. As such, the present study compares readmission in surgical and non-surgical management using a large national dataset.

METHODS

We identified all hospital admissions for SEA using the Nationwide Readmissions Database (NRD), which is the largest collection of hospital admissions data. Patients were grouped into surgically and non-surgically managed cohorts using ICD-10 coding and compared using information retrieved from the NRD such as demographics, comorbidities, length of stay and cost of admission.

RESULTS

We identified 350 surgically managed and 350 non-surgically managed patients. The 90-day readmission rates for surgical and non-surgical management were 26.0% and 35.1%, respectively (P < .05). Expectedly, surgical management was associated with a significantly higher charge and length of stay at index hospital admission. Surgically managed patients had a significantly lower risk of readmission for osteomyelitis (P < .05). Finally, in patients with a low comorbidity burden, we observed a significantly lower 90-day readmission rate for surgically managed patients (surgical: 23.0%, non-surgical: 33.8%, P < .05).

CONCLUSION

In patients with a low comorbidity burden, we observed a significantly lower readmission rate for surgically managed patients than non-surgically managed patients. The results of this study suggest a lower readmission rate as an advantage to surgical management of SEA and emphasize the importance of SEA as a not-to-miss diagnosis.

Previous Emergency Department Admission Is Associated With Increased 90-Day Readmission Following Cervical Spine Surgery: Evidenced Using Propensity Score Matching

STUDY DESIGN

This was a retrospective case-control study.

OBJECTIVE

The objective of this study was to evaluate whether prior emergency department admission was associated with an increased risk for 90-day readmission following elective cervical spinal fusion.

SUMMARY OF BACKGROUND DATA

The incidence of cervical spine fusion reoperations has increased, necessitating the improvement of patient outcomes following surgery. Currently, there are no studies assessing the impact of emergency department visits before surgery on the risk of 90-day readmission following elective cervical spine surgery. This study aimed to fill this gap and identify a novel risk factor for readmission following elective cervical fusion.

METHODS

The 2016-2018 Nationwide Readmissions Database was queried for patients aged 18 years and older who underwent an elective cervical fusion. Prior emergency admissions were defined using the variable HCUP_ED in the Nationwide Readmissions Database database. Univariate analysis of patient demographic details, comorbidities, discharge disposition, and perioperative complication was evaluated using a χ 2 test followed by multivariate logistic regression.

RESULTS

In all, 2766 patients fit the inclusion criteria, and 18.62% of patients were readmitted within 90 days. Intraoperative complications, gastrointestinal complications, valvular, uncomplicated hypertension, peripheral vascular disorders, chronic obstructive pulmonary disease, cancer, and experiencing less than 3 Charlson comorbidities were identified as independent predictors of 90-day readmission. Patients with greater than 3 Charlson comorbidities (OR=0.04, 95% CI 0.01-0.12, P <0.001) and neurological complications (OR=0.29, 95% CI 0.10-0.86, P =0.026) had decreased odds for 90-day readmission. Importantly, previous emergency department visits within the calendar year before surgery were a new independent predictor of 90-day readmission (OR=9.74, 95% CI 6.86-13.83, P <0.001).

CONCLUSIONS

A positive association exists between emergency department admission history and 90-day readmission following elective cervical fusion. Screening cervical fusion patients for this history and optimizing outcomes in those patients may reduce 90-day readmission rates.

Weekend Admission Increases Risk of Readmissions Following Elective Cervical Spinal Fusion

Objective: The “weekend effect” occurs when patients cared for during weekends versus weekdays experience worse outcomes. But reasons for this effect are unclear, especially amongst patients undergoing elective cervical spinal fusion (ECSF). Our aim was to analyze whether index weekend admission affects 30- and 90-day readmission rates post-ECSF.Methods: All ECSF patients > 18 years were retrospectively identified from the 2016–2018 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD), using unique patient linkage codes and International Classification of Diseases, Tenth Revision codes. Patient demographics, comorbidities, and outcomes were analyzed. Univariate logistic regression analyzed primary outcomes of 30- and 90-day readmission rates in weekday or weekend groups. Multivariate regression determined the impact of complications on readmission rates.Results: Compared to the weekday group (n = 125,590), the weekend group (n = 1,026) held a higher percentage of Medicare/Medicaid insurance, incurred higher costs, had longer length of stay, and fewer routine home discharge (all p < 0.001). There was no difference in comorbidity burden between weekend versus weekday admissions, as measured by the Elixhauser Comorbidity Index (p = 0.527). Weekend admissions had higher 30-day (4.30% vs. 7.60%, p < 0.001) and 90-day (7.80% vs. 16.10%, p < 0.001) readmission rates, even after adjusting for sex, age, insurance status, and comorbidities. All-cause complication rates were higher for weekend admissions (8.62% vs. 12.7%, p < 0.001), specifically deep vein thrombosis, infection, neurological conditions, and pulmonary embolism.Conclusion: Index weekend admission increases 30- and 90-day readmission rates after ECSF. In patients undergoing ECSF on weekends, postoperative care for patients at risk for specific complications will allow for improved outcomes and health care utilization.

Can a Novel Natural Language Processing Model and Artificial Intelligence Automatically Generate Billing Codes From Spine Surgical Operative Notes?

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.

How are Patients Describing You Online? A Natural Language Processing Driven Sentiment Analysis of Online Reviews on CSRS Surgeons

STUDY DESIGN

A quantitative analysis of written, online reviews of Cervical Spine Research Society (CSRS) surgeons.

OBJECTIVE

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.

SUMMARY OF BACKGROUND DATA

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.

METHODS

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.

RESULTS

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, P <0.01). There was no difference in review sentiment by provider sex. However, the age of surgeons showed a significant difference as those <55 had more positive reviews (mean=+0.50) than surgeons >=55 (mean=+0.37) ( P <0.01). 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.

CONCLUSION

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 OF EVIDENCE

Level 4-retrospective case-control study.

Development of a machine learning algorithm to identify total and reverse shoulder arthroplasty implants from X-ray images

Introduction

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.

Methods

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%.

Results

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.

Conclusion

This proof of concept study demonstrates that machine learning can assist with preoperative planning and improve cost-efficiency in shoulder surgery.

Sentiment Analysis of Online Patient-Written Reviews of Vascular Surgeons

BACKGROUND

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.

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.

RESULTS

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).

CONCLUSIONS

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.

Building better pediatric surgeons: A sentiment analysis of online physician review websites

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 < 0.01) and "comfortable" (p < 0.01) improved the odds of positive reviews, while "rude" (p < 0.01) and "unprofessional" (p < 0.01) decreased these odds. Comments regarding "pain" lowered the odds of positive reviews (p < 0.01), whereas "pain-free" increased these odds (p < 0.01).

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.

Clinically relevant biomechanical properties of three different fixation techniques of the upper instrumented vertebra in deformity surgery

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

The Impact of Computer-Assisted Navigation on Charges and Readmission in Lumbar Spinal Fusion

STUDY DESIGN

Retrospective National Database Study.

OBJECTIVES

The purpose of this study is to evaluate the cost and patient outcomes associated with the utilization of computer-assisted navigation (CAN) utilization on patients undergoing lumbar spinal fusion.

BACKGROUND

CAN systems have demonstrated comparable outcomes with instrumentation and procedural speed when compared with traditional techniques. In recent years, CAN systems have seen increased adoption in spinal surgery as they allow for better contextualization of anatomical structures with the goal of improving surgical accuracy and reproducibility.

METHODS

The 2016 National Readmission Database was queried for patients with lumbar spinal fusion ICD-10 codes, with 2 subgroups created based on computer-aided navigation ICD-10 codes. Nonelective cases and patients below 18 years of age were excluded. Univariate analysis on demographics, surgical data, and total charges was performed. Postoperative complication rates were calculated based on diagnosis. Lastly, multivariate analysis was performed to assess navigation's impact on cost and postoperative outcomes.

RESULTS

A total of 88,445 lumbar fusion surgery patients were identified. Of the total, 2478 (2.8%) patients underwent lumbar fusion with navigation utilization, while 85,967 (97.2%) patients underwent surgery without navigation. The average total charges were $150,947 ($150,058, $151,836) and $161,018 ($155,747, $166,289) for the non-CAN and CAN groups, respectively ( P <0.001). The 30-day readmission rates were 5.3% for the non-CAN cohort and 3.1% for the CAN cohort ( P <0.05). The 90-day readmission rates were 8.8% for the non-CAN cohort and 5.2% for the CAN cohort ( P <0.001).

CONCLUSIONS

CAN use was found to be significantly associated with increased cost and decreased 30-day and 90-day readmissions. Although patients operated on with CAN had increased routine discharge and decreased readmission risk, future studies must continue to evaluate the cost-benefit of CAN. Limitations include ICD-10 codes for CAN utilization being specific to region of surgery, not to exact type.

LEVEL OF EVIDENCE

Level III.

The Impact of Computer-Assisted Navigation on Charges and Readmission in Patients Undergoing Posterior Cervical Fusion Surgery

STUDY DESIGN

Retrospective cohort study of 2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database (NRD).

OBJECTIVE

The aim was to evaluate cost and outcomes associated with navigation use on posterior cervical fusion (PCF) surgery patients.

SUMMARY OF BACKGROUND DATA

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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 OF EVIDENCE

Level III.

Trends in the Charges and Utilization of Computer-Assisted Navigation in Cervical and Thoracolumbar Spinal Surgery

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.

Identification of Anterior Cervical Spinal Instrumentation Using a Smartphone Application Powered by Machine Learning

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 anterior cervical discectomy and fusion (ACDF) plates from smartphone images of anterior-posterior (AP) cervical spine radiographs.

SUMMARY OF BACKGROUND DATA

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.

METHODS

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.

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.

What Are Patients Saying About Minimally Invasive Spine Surgeons Online: A Sentiment Analysis of 2,235 Physician Review Website Reviews

Objective

Physician review websites are becoming increasingly popular for patients to find and review healthcare providers. The goal of this study was to utilize quantitative analyses to understand trends in ratings and written comments on physician review websites for Society of Minimally Invasive Spine Surgery (SMISS) members.

Methods

This is a cross-sectional study. The reviews of SMISS surgeons were obtained from healthgrades.com, and sentiment analysis was used to obtain compound scores of each physicians' reviews. SMISS surgeons who were international or had fewer than three written reviews, often consisting of residents and fellows, were excluded. Inferential statistics were utilized, and word frequency analysis reported the phrases used to characterize reviews.

Results

One hundred sixty-nine surgeons met the inclusion criteria. 98.6% were males and the mean age was 51.7 years old. A total of 2,235 written reviews were analyzed. Younger surgeons were significantly more likely to receive higher star ratings (p<0.01). Positive behavioral characteristics, such as “kind” and “bedside manner,” conferred significantly improved odds of receiving positive reviews (p<0.01). Ancillary “staff” conferred a 2x greater odds of receiving a positive review whereas a comment on “wait” times halved a surgeon’s odds (p<0.01). Sentences describing pain drove down the odds of positive reviews whereas those describing pain relief produced greater odds of positive reviews (p<0.01).

Conclusion

Physicians who were younger, personable, provided sufficient pain relief, and who worked in favorable offices received the most positive reviews. This study informs SMISS members on the traits deemed important by patients who ultimately review surgeons online.

Understanding Artificial Intelligence and Predictive Analytics: A Clinically Focused Review of Machine Learning Techniques

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Machine learning and artificial intelligence have seen tremendous growth in recent years and have been applied in numerous studies in the field of orthopaedics.

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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.

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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.

What are patients saying about you online? A sentiment analysis of online written reviews on Scoliosis Research Society surgeons

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 < 0.01). There was no difference in review sentiment by provider gender. However, the age of surgeons showed a significant difference as younger surgeons, on average, had more positive reviews (p < 0.01).

CONCLUSION

The 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.

LEVEL OF EVIDENCE

IV.

Using Sentiment Analysis to Understand What Patients Are Saying About Hand Surgeons Online

BACKGROUND

Physician review websites have influence on a patient's selection of a provider. Written reviews are subjective and difficult to quantitatively analyze. Sentiment analysis of writing can quantitatively assess surgeon reviews to provide actionable feedback for surgeons to improve practice. The objective of this study is to quantitatively analyze large subset of written reviews of hand surgeons using sentiment analysis and report unbiased trends in words used to describe the reviewed surgeons and biases associated with surgeon demographic factors.

METHODS

Online written and star-rating reviews of hand surgeons were obtained from healthgrades.com and webmd.com. A sentiment analysis package was used to calculate compound scores of all reviews. Mann-Whitney U tests were 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 also performed.

RESULTS

A total of 786 hand surgeons' reviews were analyzed. Analysis showed a significant relationship between the sentiment scores and overall average star-rated reviews (r² = 0.604, P ≤ .01). There was no significant difference in review sentiment by provider sex; however, surgeons aged 50 years and younger had more positive reviews than older (P < .01). The most frequently used bigrams used to describe top-rated surgeons were associated with good bedside manner and efficient pain management, whereas those with the worst reviews are often characterized as rude and unable to relieve pain.

CONCLUSIONS

This study provides insight into both demographic and behavioral factors contributing to positive reviews and reinforces the importance of pain expectation management.

Emerging Technologies in the Treatment of Adult Spinal Deformity

Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patient-specific 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.

Primary extradural tumors of the spinal column: A comprehensive treatment guide for the spine surgeon based on the 5th Edition of the World Health Organization bone and soft-tissue tumor classification

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.

Significance of Hospital Size in Outcomes of Single-Level Elective Anterior Cervical Discectomy and Fusion: A Nationwide Readmissions Database Analysis

OBJECTIVE

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.

METHODS

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.

RESULTS

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 (P < 0.001), and small hospitals had the lowest charges (P < 0.001). 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.

CONCLUSIONS

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.

Reparative and Maladaptive Inflammation in Tendon Healing

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.

Deep Learning Automates Measurement of Spinopelvic Parameters on Lateral Lumbar Radiographs

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.

Spondylectomy in the treatment of neoplastic spinal lesions - A retrospective outcome analysis of 582 patients using a patient-level meta-analysis

This study aims at identifying predictors of postoperative complications, lesion recurrence, and overall survival in patients undergoing en bloc spondylectomy (EBS) for spinal tumors. For this purpose a systematic review of the literature was conducted and patient-level data extracted. Linear-regression models were calculated to predict postoperative complications, lesion recurrence and overall survival based on age, tumor etiology, surgical approach, mode of resection (extra- vs. intralesional), tumor extension, and number of levels treated. A total of 582 patients were identified from the literature: 45% of females, median age 46 years (5-78); most common etiologies were: sarcoma (46%), metastases (31%), chordoma (11%); surgical approach was anterior (2.5%), combined (45%), and posterior (52.4%); 68.5% underwent EBS; average levels resected were 1.6 (1-6); average survival was 2.6 years; Complication rate was 17.7%. The following significant correlations were found: postoperative complications and resection mode (Odds ratio [OR] 1.35) as well as number of levels treated (OR 1.35); tumor recurrence and resection mode (OR 0.78); 5-year survival and age (OR 0.79), tumor grade (OR 0.65), tumor stage at diagnosis (OR 0.79), and resection mode (OR 1.68). EBS was shown to improve survival, decreases recurrence rates but also has a higher complication rate. Interestingly, the complication rate was not influenced by tumor extension or tumor etiology.

Spondylectomy in the Treatment of Neoplastic Spinal Lesions – A Retrospective Outcome Analysis of 582 Patients Using a Patient-Level Meta-Analysis

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.

Risk Factors Associated with 90-Day Readmissions Following Occipitocervical Fusion-A Nationwide Readmissions Database Study

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.

Ninety-Day Readmission in Elective Revision Lumbar Fusion Surgery in the Inpatient Setting

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.

Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19

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 < 0.0001).

CONCLUSION

In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care.

Anticoagulation, Bleeding, Mortality, and Pathology in Hospitalized Patients With COVID-19

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 postmortem findings.

Objectives

The purpose of this study was to examine association of AC with in-hospital outcomes and describe thromboembolic findings on autopsies.

Methods

This retrospective analysis examined the association of AC with mortality, intubation, and major bleeding. Subanalyses were also conducted on the association of therapeutic versus prophylactic AC initiated ≤48 h from admission. Thromboembolic disease was contextualized by premortem AC among consecutive autopsies.

Results

Among 4,389 patients, median age was 65 years with 44% women. Compared with no AC (n = 1,530; 34.9%), therapeutic AC (n = 900; 20.5%) and prophylactic AC (n = 1,959; 44.6%) were associated with lower in-hospital mortality (adjusted hazard ratio [aHR]: 0.53; 95% confidence interval [CI]: 0.45 to 0.62 and aHR: 0.50; 95% CI: 0.45 to 0.57, respectively), and intubation (aHR: 0.69; 95% CI: 0.51 to 0.94 and aHR: 0.72; 95% CI: 0.58 to 0.89, respectively). When initiated ≤48 h from admission, there was no statistically significant difference between therapeutic (n = 766) versus prophylactic AC (n = 1,860) (aHR: 0.86; 95% CI: 0.73 to 1.02; p = 0.08). Overall, 89 patients (2%) had major bleeding adjudicated by clinician review, with 27 of 900 (3.0%) on therapeutic, 33 of 1,959 (1.7%) on prophylactic, and 29 of 1,530 (1.9%) on no AC. Of 26 autopsies, 11 (42%) had thromboembolic disease not clinically suspected and 3 of 11 (27%) were on therapeutic AC.

Conclusions

AC was associated with lower mortality and intubation among hospitalized COVID-19 patients. Compared with prophylactic AC, therapeutic AC was associated with lower mortality, although not statistically significant. Autopsies revealed frequent thromboembolic disease. These data may inform trials to determine optimal AC regimens.

Automated Measurement of Lumbar Lordosis on Radiographs Using Machine Learning and Computer Vision

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.

Analysis of 90-Day Readmissions After Total Shoulder Arthroplasty

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.

Hypoalbuminemia as an Independent Risk Factor for Perioperative Complications Following Surgical Decompression of Spinal Metastases

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 <3.5 g/dL) groups. Univariate and multivariate regression analyses were performed to examine the association between preoperative hypoalbuminemia and 30-day perioperative mortality and morbidity. Subgroup analysis was performed in the hypoalbuminemic group to assess the dose-dependent effect of albumin depletion.

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.

Ninety-day readmissions following reverse total shoulder arthroplasty

Background

An adequate characterization of 90-day readmissions after primary reverse total shoulder arthroplasty (RTSA) on a national level remains to be undertaken. As bundled payment models become more prevalent, an improved understanding of readmission data will help to predict resource utilization and expenses.

Methods

All adult patients who underwent elective primary RTSA in 2014 in the National Readmission Database were included in the analysis. Two cohorts were created based on 90-day readmission status. Multivariate analysis was then performed to determine predictors of 90-day readmissions. Reasons for 30-, 60-, and 90-day readmissions were identified. Total hospital resource utilization was calculated.

Results

An estimated 25,196 patients were identified. The 30-, 60-, and 90-day rates of readmissions were 0.6%, 1.2%, and 1.7%, respectively. Diabetes (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.14-1.78), hypertension (OR, 1.63; 95% CI, 1.28-2.08), paralysis (OR, 3.61; 95% CI, 1.63-7.97), and solid tumor without metastasis (OR, 2.72; 95% CI, 1.21-6.12) were identified as independent predictors of 90-day readmission. Ninety-day readmissions were associated with a significant increase in cost (P = .02). The most common related reason for 90-day readmission was hardware-related complications at all time points.

Conclusion

Although uncommon, 90-day readmissions after primary RTSA are associated with significant patient morbidity and consequently substantial hospital costs.

Surgical, Radiographic, and Patient-Related Risk Factors for Proximal Junctional Kyphosis: A Meta-Analysis

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.

Predicting Surgical Complications in Adult Patients Undergoing Anterior Cervical Discectomy and Fusion Using Machine Learning

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.

Primary Versus Revision Discectomy for Adults With Herniated Nucleus Pulposus: A Propensity Score-Matched Multicenter Study

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.

Role of Posterior Ligamentous Reinforcement in Proximal Junctional Kyphosis: A Cadaveric Biomechanical Study

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.

Substrate micropatterns produced by polymer demixing regulate focal adhesions, actin anisotropy, and lineage differentiation of stem cells

Stem cells are adherent cells whose multipotency and differentiation can be regulated by numerous microenvironmental signals including soluble growth factors and surface topography. This study describes a simple method for creating distinct micropatterns via microphase separation resulting from polymer demixing of poly(desaminotyrosyl-tyrosine carbonate) (PDTEC) and polystyrene (PS). Substrates with co-continuous (ribbons) or discontinuous (islands and pits) PDTEC regions were obtained by varying the ratio of PDTEC and sacrificial PS. Human mesenchymal stem cells (MSCs) cultured on co-continuous PDTEC substrates for 3 days in bipotential adipogenic/osteogenic (AD/OS) induction medium showed no change in cell morphology but exhibited increased anisotropic cytoskeletal organization and larger focal adhesions when compared to MSCs cultured on discontinuous micropatterns. After 14 days in bipotential AD/OS induction medium, MSCs cultured on co-continuous micropatterns exhibited increased expression of osteogenic markers, whereas MSCs on discontinuous PDTEC substrates showed a low expression of adipogenic and osteogenic differentiation markers. Substrates with graded micropatterns were able to reproduce the influence of local underlying topography on MSC differentiation, thus demonstrating their potential for high throughput analysis. This work presents polymer demixing as a simple, non-lithographic technique to produce a wide range of micropatterns on surfaces with complex geometries to influence cellular and tissue regenerative responses.

STATEMENT OF SIGNIFICANCE

A better understanding of how engineered microenvironments influence stem cell differentiation is integral to increasing the use of stem cells and materials in a wide range of tissue engineering applications. In this study, we show the range of topography obtained by polymer demixing is sufficient for investigating how surface topography affects stem cell morphology and differentiation. Our findings show that co-continuous topographies favor early (3-day) cytoskeletal anisotropy and focal adhesion maturation as well as long-term (14-day) expression of osteogenic differentiation markers. Taken together, this study presents a simple approach to pattern topographies that induce divergent responses in stem cell morphology and differentiation.

Mechanobiology of limb musculoskeletal development

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.

High-content image informatics of the structural nuclear protein NuMA parses trajectories for stem/progenitor cell lineages and oncogenic transformation

Stem and progenitor cells that exhibit significant regenerative potential and critical roles in cancer initiation and progression remain difficult to characterize. Cell fates are determined by reciprocal signaling between the cell microenvironment and the nucleus; hence parameters derived from nuclear remodeling are ideal candidates for stem/progenitor cell characterization. Here we applied high-content, single cell analysis of nuclear shape and organization to examine stem and progenitor cells destined to distinct differentiation endpoints, yet undistinguishable by conventional methods. Nuclear descriptors defined through image informatics classified mesenchymal stem cells poised to either adipogenic or osteogenic differentiation, and oligodendrocyte precursors isolated from different regions of the brain and destined to distinct astrocyte subtypes. Nuclear descriptors also revealed early changes in stem cells after chemical oncogenesis, allowing the identification of a class of cancer-mitigating biomaterials. To capture the metrology of nuclear changes, we developed a simple and quantitative "imaging-derived" parsing index, which reflects the dynamic evolution of the high-dimensional space of nuclear organizational features. A comparative analysis of parsing outcomes via either nuclear shape or textural metrics of the nuclear structural protein NuMA indicates the nuclear shape alone is a weak phenotypic predictor. In contrast, variations in the NuMA organization parsed emergent cell phenotypes and discerned emergent stages of stem cell transformation, supporting a prognosticating role for this protein in the outcomes of nuclear functions.

Organizational metrics of interchromatin speckle factor domains: integrative classifier for stem cell adhesion & lineage signaling

Stem cell fates on biomaterials are influenced by the complex confluence of microenvironmental cues emanating from soluble growth factors, cell-to-cell contacts, and biomaterial properties. Cell-microenvironment interactions influence the cell fate by initiating a series of outside-in signaling events that traverse from the focal adhesions to the nucleus via the cytoskeleton and modulate the sub-nuclear protein organization and gene expression. Here, we report a novel imaging-based framework that highlights the spatial organization of sub-nuclear proteins, specifically the splicing factor SC-35 in the nucleoplasm, as an integrative marker to distinguish between minute differences of stem cell lineage pathways in response to stimulatory soluble factors, surface topologies, and microscale topographies. This framework involves the high resolution image acquisition of SC-35 domains and imaging-based feature extraction to obtain quantitative nuclear metrics in tandem with machine learning approaches to generate a predictive cell state classification model. The acquired SC-35 metrics led to >90% correct classification of emergent human mesenchymal stem cell (hMSC) phenotypes in populations of hMSCs exposed for merely 3 days to basal, adipogenic, or osteogenic soluble cues, as well as varying levels of dexamethasone-induced alkaline phosphatase (ALP) expression. Early osteogenic cellular responses across a series of surface patterns, fibrous scaffolds, and micropillars were also detected and classified using this imaging-based methodology. Complex cell states resulting from inhibition of RhoGTPase, β-catenin, and FAK could be classified with >90% sensitivity on the basis of differences in the SC-35 organizational metrics. This indicates that SC-35 organization is sensitively impacted by adhesion-related signaling molecules that regulate osteogenic differentiation. Our results show that diverse microenvironment cues affect different attributes of the SC-35 organizational metrics and lead to distinct emergent organizational patterns. Taken together, these studies demonstrate that the early organization of SC-35 domains could serve as a "fingerprint" of the intracellular mechanotransductive signaling that governs growth factor- and topography-responsive stem cell states.

Uniform derandomization from pathetic lower bounds

The notion of probabilistic computation dates back at least to Turing, who also wrestled with the practical problems of how to implement probabilistic algorithms on machines with, at best, very limited access to randomness. A more recent line of research, known as derandomization, studies the extent to which randomness is superfluous. A recurring theme in the literature on derandomization is that probabilistic algorithms can be simulated quickly by deterministic algorithms, if one can obtain impressive (i.e. superpolynomial, or even nearly exponential) circuit size lower bounds for certain problems. In contrast to what is needed for derandomization, existing lower bounds seem rather pathetic. Here, we present two instances where 'pathetic' lower bounds of the form n(1+ε) would suffice to derandomize interesting classes of probabilistic algorithms. We show the following: -If the word problem over S(5) requires constant-depth threshold circuits of size n(1+ε) for some ε > 0, then any language accepted by uniform polynomial size probabilistic threshold circuits can be solved in subexponential time (and, more strongly, can be accepted by a uniform family of deterministic constant-depth threshold circuits of subexponential size). -If there are no constant-depth arithmetic circuits of size n(1+ε) for the problem of multiplying a sequence of n 3×3 matrices, then, for every constant d, black-box identity testing for depth-d arithmetic circuits with bounded individual degree can be performed in subexponential time (and even by a uniform family of deterministic constant-depth AC(0) circuits of subexponential size).