The impact of anxiety and depression on lumbar spine surgical outcomes: a Michigan Spine Surgery Improvement Collaborative study

OBJECTIVE

The presence of depression and anxiety has been associated with negative outcomes in spine surgery patients. While it seems evident that a history of depression or anxiety can negatively influence outcome, the exact additive effect of both has not been extensively studied in a multicenter trial. The purpose of this study was to investigate the relationship between a patient's history of anxiety and depression and their patient-reported outcomes (PROs) after lumbar surgery.

METHODS

Patients in the Michigan Spine Surgery Improvement Collaborative registry undergoing lumbar spine surgery between July 2016 and December 2021 were grouped into four cohorts: those with a history of anxiety only, those with a history of depression only, those with both, and those with neither. Primary outcomes were achieving the minimal clinically important difference (MCID) for the Patient-Reported Outcomes Measurement Information System Physical Function 4-item Short Form (PROMIS PF), EQ-5D, and numeric rating scale (NRS) back pain and leg pain, and North American Spine Society patient satisfaction. Secondary outcomes included surgical site infection, hospital readmission, and return to the operating room. Multivariate Poisson generalized estimating equation models were used to report incidence rate ratios (IRRs) from patient baseline variables.

RESULTS

Of the 45,565 patients identified, 3941 reported a history of anxiety, 5017 reported a history of depression, 9570 reported both, and 27,037 reported neither. Compared with those who reported having neither, patients with both anxiety and depression had lower patient satisfaction at 90 days (p = 0.002) and 1 year (p = 0.021); PROMIS PF MCID at 90 days (p < 0.001), 1 year (p < 0.001), and 2 years (p = 0.006); EQ-5D MCID at 90 days (p < 0.001), 1 year (p < 0.001), and 2 years (p < 0.001); NRS back pain MCID at 90 days (p < 0.001) and 1 year (p < 0.001); and NRS leg pain MCID at 90 days (p < 0.001), 1 year (p = 0.024), and 2 years (p = 0.027). Patients with anxiety only (p < 0.001), depression only (p < 0.001), or both (p < 0.001) were more likely to be readmitted within 90 days. Additionally, patients with anxiety only (p = 0.015) and both anxiety and depression (p = 0.015) had higher rates of surgical site infection. Patients with anxiety only (p = 0.006) and depression only (p = 0.021) also had higher rates of return to the operating room.

CONCLUSIONS

The authors observed an association between a history of anxiety and depression and negative outcome after lumbar spine surgery. In addition, they found an additive effect of a history of both anxiety and depression with an increased risk of negative outcome when compared with either anxiety or depression alone.

Risk factors for not reaching minimal clinically important difference at 90 days and 1 year after elective lumbar spine surgery: a cohort study

OBJECTIVE

Patient-perceived functional improvement is a core metric in lumbar surgery for degenerative disease. It is important to identify both modifiable and nonmodifiable risk factors that can be evaluated and possibly optimized prior to elective surgery. This case-control study was designed to study risk factors for not achieving the minimal clinically important difference (MCID) in Patient-Reported Outcomes Measurement Information System Function 4-item Short Form (PROMIS PF) score.

METHODS

The authors queried the Michigan Spine Surgery Improvement Collaborative database to identify patients who underwent elective lumbar surgical procedures with PROMIS PF scores. Cases were divided into two cohorts based on whether patients achieved MCID at 90 days and 1 year after surgery. Patient characteristics and operative details were analyzed as potential risk factors.

RESULTS

The authors captured 10,922 patients for 90-day follow-up and 4453 patients (40.8%) did not reach MCID. At the 1-year follow-up period, 7780 patients were identified and 2941 patients (37.8%) did not achieve MCID. The significant demographic characteristic-adjusted relative risks (RRs) for both groups (RR 90 day, RR 1 year) included the following: symptom duration > 1 year (1.34, 1.41); previous spine surgery (1.25, 1.30); African American descent (1.25, 1.20); chronic opiate use (1.23, 1.25); and less than high school education (1.20, 1.34). Independent ambulatory status (0.83, 0.88) and private insurance (0.91, 0.85) were associated with higher likelihood of reaching MCID at 90 days and 1 year, respectively.

CONCLUSIONS

Several key unique demographic risk factors were identified in this cohort study that precluded optimal postoperative functional outcomes after elective lumbar spine surgery. With this information, appropriate preoperative counseling can be administered to assist in shaping patient expectations.

Risk factors of emergency department visits following elective cervical and lumbar surgical procedures: a multi-institution analysis from the Michigan Spine Surgery Improvement Collaborative

OBJECTIVE

Emergency department visits 90 days after elective spinal surgery are relatively common, with rates ranging from 9% to 29%. Emergency visits are very costly, so their reduction is of importance. This study's objective was to evaluate the reasons for emergency department visits and determine potentially modifiable risk factors.

METHODS

This study retrospectively reviewed data queried from the Michigan Spine Surgery Improvement Collaborative (MSSIC) registry from July 2020 to November 2021. MSSIC is a multicenter (28-hospital) registry of patients undergoing cervical and lumbar degenerative spinal surgery. Adult patients treated for elective cervical and/or lumbar spine surgery for degenerative pathology (spondylosis, intervertebral disc disease, low-grade spondylolisthesis) were included. Emergency department visits within 90 days of surgery (outcome measure) were analyzed utilizing univariate and multivariate regression analyses.

RESULTS

Of 16,224 patients, 2024 (12.5%) presented to the emergency department during the study period, most commonly for pain related to spinal surgery (31.5%), abdominal problems (15.8%), and pain unrelated to the spinal surgery (12.8%). On multivariate analysis, age (per 5-year increase) (relative risk [RR] 0.94, 95% CI 0.92-0.95), college education (RR 0.82, 95% CI 0.69-0.96), private insurance (RR 0.79, 95% CI 0.70-0.89), and preoperative ambulation status (RR 0.88, 95% CI 0.79-0.97) were associated with decreased emergency visits. Conversely, Black race (RR 1.30, 95% CI 1.13-1.51), current diabetes (RR 1.13, 95% CI 1.01-1.26), history of deep venous thromboembolism (RR 1.28, 95% CI 1.16-1.43), history of depression (RR 1.13, 95% CI 1.03-1.25), history of anxiety (RR 1.32, 95% CI 1.19-1.46), history of osteoporosis (RR 1.21, 95% CI 1.09-1.34), history of chronic obstructive pulmonary disease (RR 1.19, 95% CI 1.06-1.34), American Society of Anesthesiologists class > II (RR 1.18, 95% CI 1.08-1.29), and length of stay > 3 days (RR 1.29, 95% CI 1.16-1.44) were associated with increased emergency visits.

CONCLUSIONS

The most common reasons for emergency department visits were surgical pain, abdominal dysfunction, and pain unrelated to index spinal surgery. Increased focus on postoperative pain management and bowel regimen can potentially reduce emergency visits. The risks of diabetes, history of osteoporosis, depression, and anxiety are areas for additional preoperative screening.

Physical activity in recurrent colon cancer: Cancer and Leukemia Group B/SWOG 80702 (Alliance)

BACKGROUND

One in three patients with stage III colon cancer will experience tumor recurrence. It is uncertain whether physical activity during and after postoperative chemotherapy for stage III colon cancer improves overall survival after tumor recurrence.

METHODS

A prospective cohort study nested within a randomized multicenter trial of patients initially diagnosed with stage III colon cancer who experienced tumor recurrence (N = 399) was conducted. Postoperative physical activity before tumor recurrence was measured. Physical activity energy expenditure was quantified via metabolic equivalent task hours per week (MET-h/week). The primary end point was overall survival after tumor recurrence. Multivariable flexible parametric survival models estimated relative and absolute effects with two-sided hypothesis tests.

RESULTS

Compared with patients expending <3.0 MET-h/week of physical activity (comparable to <1.0 h/week of brisk walking), patients with ≥18.0 MET-h/week of physical activity (comparable to 6 h/week of brisk walking) had a 33% relative improvement in overall survival time after tumor recurrence (hazard ratio, 0.67; 95% CI, 0.42-0.96). The overall survival rate at 3 years after tumor recurrence was 61.3% (95% CI, 51.8%-69.2%) with <3.0 MET-h/week of physical activity and 72.2% (95% CI, 63.1%-79.6%) with ≥18 MET-h/week of physical activity (risk difference, 10.9 percentage points; 95% CI, 1.2-20.8 percentage points).

CONCLUSIONS

Higher postoperative physical activity is associated with improved overall survival after tumor recurrence in patients initially diagnosed with stage III colon cancer. These data may be relevant to patients who, despite optimal postoperative medical therapy, have a high risk of tumor recurrence.

Association of prolonged symptom duration with poor outcomes in lumbar spine surgery: a Michigan Spine Surgery Improvement Collaborative study

OBJECTIVE

There is a scarcity of large multicenter data on how preoperative lumbar symptom duration relates to postoperative patient-reported outcomes (PROs). The objective of this study was to determine the effect of preoperative and baseline symptom duration on PROs at 90 days, 1 year, and 2 years after lumbar spine surgery.

METHODS

The Michigan Spine Surgery Improvement Collaborative registry was queried for all lumbar spine operations between January 1, 2017, to December 31, 2021, with a follow-up of 2 years. Patients were stratified into three subgroups based on symptom duration: < 3 months, 3 months to < 1 year, and ≥ 1 year. The primary outcomes were reaching the minimal clinically important difference (MCID) for the PROs (i.e., leg pain, Patient-Reported Outcomes Measurement Information System Physical Function (PROMIS PF), EQ-5D, North American Spine Society satisfaction, and return to work). The EQ-5D score was also analyzed as a continuous variable to calculate quality-adjusted life years. Multivariable Poisson generalized estimating equation models were used to report adjusted risk ratios, with the < 3-month cohort used as the reference.

RESULTS

There were 37,223 patients (4670 with < 3-month duration, 9356 with 3-month to < 1-year duration, and 23,197 with ≥ 1-year duration) available for analysis. Compared with patients with a symptom duration of < 1 year, patients with a symptom duration of ≥ 1 year were significantly less likely to achieve an MCID in PROMIS PF, EQ-5D, back pain relief, and leg pain relief at 90 days, 1 year, and 2 years postoperatively. Similar trends were observed for patient satisfaction and return to work. With the EQ-5D score as a continuous variable, a symptom duration of ≥ 1 year was associated with 0.04, 0.05, and 0.03 (p < 0.001) decreases in EQ-5D score at 90 days, 1 year, and 2 years after surgery, respectively.

CONCLUSIONS

A symptom duration of ≥ 1 year was associated with poorer outcomes on several outcome metrics. This suggests that timely referral and surgery for degenerative lumbar pathology may optimize patient outcome.

An Exploration into Human-Computer Interaction: Hand Gesture Recognition Management in a Challenging Environment

Scientists are developing hand gesture recognition systems to improve authentic, efficient, and effortless human-computer interactions without additional gadgets, particularly for the speech-impaired community, which relies on hand gestures as their only mode of communication. Unfortunately, the speech-impaired community has been underrepresented in the majority of human-computer interaction research, such as natural language processing and other automation fields, which makes it more difficult for them to interact with systems and people through these advanced systems. This system's algorithm is in two phases. The first step is the Region of Interest Segmentation, based on the color space segmentation technique, with a pre-set color range that will remove pixels (hand) of the region of interest from the background (pixels not in the desired area of interest). The system's second phase is inputting the segmented images into a Convolutional Neural Network (CNN) model for image categorization. For image training, we utilized the Python Keras package. The system proved the need for image segmentation in hand gesture recognition. The performance of the optimal model is 58 percent which is about 10 percent higher than the accuracy obtained without image segmentation.

Correlation of mJOA, PROMIS physical function, and patient satisfaction in patients with cervical myelopathy: an analysis of the Michigan Spine Surgery Improvement Collaborative (MSSIC) database

BACKGROUND CONTEXT

Patient-reported outcomes (PROs) are increasingly utilized to evaluate the efficacy and value of spinal procedures. Among patients with cervical myelopathy, the modified Japanese Orthopaedic Association (mJOA) remains the standard instrument, with Patient-Reported Outcomes Measurement Information System (PROMIS) physical function (PF) and patient satisfaction also frequently assessed. These outcomes have not all been directly compared using a large spine registry at 2 years follow-up for cervical myelopathic patients undergoing surgery.

PURPOSE

To determine the correlation and association of PROMIS PF, mJOA, and patient satisfaction outcomes in patients undergoing surgery for cervical myelopathy.

STUDY DESIGN/SETTING

Retrospective review of a multicenter spine registry database.

PATIENT SAMPLE

Adult patients with cervical myelopathy who underwent cervical spine surgery between 2/26/2018 and 4/17/2021.

OUTCOME MEASURES

PROMIS PF, mJOA, and North American Spine Society (NASS) patient satisfaction index.

METHODS

The MSSIC database was accessed to gather pre- and postoperative outcome data on patients with cervical myelopathy. Spearman's correlation coefficients relating mJOA and PROMIS PF were quantified up to 2 years postoperatively. The correlations between patient satisfaction with mJOA and PROMIS were determined. Kappa statistics were used to evaluate for agreement between those reaching the minimum clinically important difference (MCID) for mJOA and PROMIS PF. Odds ratios were calculated to determine the association between patient satisfaction and those reaching MCID for mJOA and PROMIS PF. Support for MSSIC is provided by BCBSM and Blue Care Network as part of the BCBSM Value Partnerships program.

RESULTS

Data from 2,023 patients were included. Moderate to strong correlations were found between mJOA and PROMIS PF at all time points (p<.001). These outcomes had fair agreement at all postoperative time points when comparing those who reached MCID. Satisfaction was strongly related to changes from baseline for both mJOA and PROMIS PF at all time points (p<.001). Odds ratios associating satisfaction with PROMIS PF MCID were higher at all time points compared with mJOA, although the differences were not significant.

CONCLUSIONS

PROMIS PF has a strong positive correlation with mJOA up to 2 years postoperatively in patients undergoing surgery for cervical myelopathy, with similar odds of achieving MCID with both instruments. Patient satisfaction is predicted similarly by these outcome measures by 2 years postoperatively. These results affirm the validity of PROMIS PF in the cervical myelopathic population. Given its generalizability and ease of use, PROMIS PF may be a more practical outcome measure for clinical use compared with mJOA.

Top Ten Tips Palliative Care Clinicians Should Know About Caring for Chinese American Patients

The Chinese American population is one of the fastest-growing communities in the United States, composed of ∼5.4 million people, and represents ∼5.5% of overseas Chinese populations. With an expected exponential population rise, Chinese American patients who experience serious illness or approach end-of-life (EOL) may find their cultural values influencing the medical care they receive. Palliative care clinicians must recognize diverse cultural beliefs and preferences of Chinese American patients and their families. In this study, we provide 10 cultural pearls to guide the provision of palliative and EOL care for Chinese American patients, including discussions of Chinese traditions, communication strategies for Chinese patients and families, advance care planning, and EOL care beliefs.

The Potential Negative Effects of Smoking on Cervical and Lumbar Surgery Beyond Pseudarthrosis: A Michigan Spine Surgery Improvement Collaborative Study

OBJECTIVE

To review the Michigan Spine Surgery Improvement Collaborative registry to investigate the long-term associations between current smoking status and outcomes after elective cervical and lumbar spine surgery.

METHODS

Using the Michigan Spine Surgery Improvement Collaborative, we captured all cases from January 1, 2017, to November 21, 2020, with outcomes data available; 19,251 lumbar cases and 7936 cervical cases were included. Multivariate regression analyses were performed to assess the relationship of smoking with the clinical outcomes.

RESULTS

Current smoking status was associated with lower urinary retention and satisfaction for patients after lumbar surgery and was associated with less likelihood of achieving minimal clinically important difference in primary outcome measures including Patient-Reported Outcomes Measurement Information System, back pain, leg pain, and EuroQol-5D at 90 days and 1 year after surgery. Current smokers were also less likely to return to work at 90 days and 1 year after surgery. Among patients who underwent cervical surgery, current smokers were less likely to have urinary retention and dysphagia postoperatively. They were less likely to be satisfied with the surgery outcome at 1 year. Current smoking was associated with lower likelihood of achieving minimal clinically important difference in Patient-Reported Outcomes Measurement Information System, neck pain, arm pain, and EuroQol-5D at various time points. There was no difference in return-to-work status.

CONCLUSIONS

Our analysis suggests that smoking is negatively associated with functional improvement, patient satisfaction, and return-to-work after elective spine surgery.

Postoperative opioid prescription and patient-reported outcomes after elective spine surgery: a Michigan Spine Surgery Improvement Collaborative study

OBJECTIVE

This study was designed to assess how postoperative opioid prescription dosage could affect patient-reported outcomes after elective spine surgery.

METHODS

Patients enrolled in the Michigan Spine Surgery Improvement Collaborative (MSSIC) from January 2020 to September 2021 were included in this study. Opioid prescriptions at discharge were converted to total morphine milligram equivalents (MME). A reference value of 225 MME per week was used as a cutoff. Patients were divided into two cohorts based on prescribed total MME: ≤ 225 MME and > 225 MME. Primary outcomes included patient satisfaction, return to work status after surgery, and whether improvement of the minimal clinically important difference (MCID) of the Patient-Reported Outcomes Measurement Information System 4-question short form for physical function (PROMIS PF) and EQ-5D was met. Generalized estimated equations were used for multivariate analysis.

RESULTS

Regression analysis revealed that patients who had postoperative opioids prescribed with > 225 MME were less likely to be satisfied with surgery (adjusted OR [aOR] 0.81) and achieve PROMIS PF MCID (aOR 0.88). They were also more likely to be opioid dependent at 90 days after elective spine surgery (aOR 1.56).

CONCLUSIONS

The opioid epidemic is a serious threat to national public health, and spine surgeons must practice conscientious postoperative opioid prescribing to achieve adequate pain control. The authors' analysis illustrates that a postoperative opioid prescription of 225 MME or less is associated with improved patient satisfaction, greater improvement in physical function, and decreased opioid dependence compared with those who had > 225 MME prescribed.

Customer Behavioral Trends in Online Grocery Shopping During COVID-19

The evolution of online shopping started when big players like Amazon began selling all types of merchandise. Customers understood the ease of shopping online, so the trend grew even stronger. It is therefore essential to conduct a study of online shopping usage and the perception of customers during COVID-19, especially in the grocery sector. In this study, approximately 28 respondents from 50 specifically targeted groups were surveyed, and data collection was undertaken through a structured questionnaire. The regression method was conducted to analyze the collected data. Additionally, 5 interviews were conducted to validate and support the findings. Customers definitely preferred online grocery shopping (OGS) services during COVID-19 due to safety, convenience, and government restrictions. The influential factors were very important in this case, like delivery times, good discounts, and the quality of products. Secondly, OGS services were more stable and alert during the pandemic situation, following the government's rules and restrictions. Customers were extremely satisfied with the safety precautions during COVID-19, the assistance provided through helplines for support, and the increased customer reach to make groceries as accessible as other reputable online departments.

Rates and reasons for reoperation within 30 and 90 days following cervical spine surgery: a retrospective cohort analysis of the Michigan Spine Surgery Improvement Collaborative (MSSIC) registry

BACKGROUND CONTEXT

Reoperation following cervical spinal surgery negatively impacts patient outcomes and increases health care system burden. To date, most studies have evaluated reoperations within 30 days after spine surgery and have been limited in scope and focus. Evaluation within the 90-day period, however, allows a more comprehensive assessment of factors associated with reoperation.

PURPOSE

The purpose of this study is to assess the rates and reasons for reoperations after cervical spine surgery within 30 and 90 days.

DESIGN

We performed a retrospective analysis of a state-wide prospective, multi-center, spine-specific database of patients surgically treated for degenerative disease.

PATIENT SAMPLE

Patients 18 years of age or older who underwent cervical spine surgery for degenerative pathologies from February 2014 to May 2019. Operative criteria included all degenerative cervical spine procedures, including those with cervical fusions with contiguous extension down to T3.

OUTCOME MEASURES

We determined causes for reoperation and independent surgical and demographic risk factors impacting reoperation.

METHODS

Patient-specific and surgery-specific data was extracted from the registry using ICD-10-DM codes. Reoperations data was obtained through abstraction of medical records through 90 days. Univariate analysis was done using chi-square tests for categorical variables, t-tests for normally distributed variables, and Wilcoxon rank-sum tests for variables with skewed distributions. Odds ratios for return to the operating room (OR) were evaluated in multivariate analysis.

RESULTS

A total of 13,435 and 13,440 patients underwent cervical spine surgery and were included in the 30 and 90-day analysis, respectively. The overall reoperation rate was 1.24% and 3.30% within 30 and 90 days, respectively. Multivariate analysis showed within 30 days, procedures involving four or more levels, posterior only approach, and longer length of stay had increased odds of returning to the OR (p<.05), whereas private insurance had a decreased odds of return to OR (p<.05). Within 90 days, male sex, coronary artery disease (CAD), previous spine surgery, procedures with 4 or more levels, and longer length of stay had significantly increased odds of returning to the OR (p<.05). Non-white race, independent ambulatory status pre-operatively, and having private insurance had decreased odds of return to the OR (p<.05). The most common specified reasons for return to the OR within 30 days was hematoma (19%), infection (17%), and wound dehiscence (11%). Within 90 days, reoperation reasons were pain (10%), infection (9%), and hematoma (8%).

CONCLUSION

Reoperation rates after elective cervical spine surgery are 1.24% and 3.30% within 30 and 90 days, respectively. Within 30 days, four or more levels, posterior approach, and longer length of stay were risk factors for reoperation. Within 90 days, male sex, CAD, four or more levels, and longer length of hospital stay were risk factors for reoperation. Non-white demographic and independent preoperative ambulatory status were associated with decreased reoperation rates.

A comparison of clinical development pathways to advance tuberculosis regimen development

BACKGROUND

Current tuberculosis (TB) regimen development pathways are slow and in urgent need of innovation. We investigated novel phase IIc and seamless phase II/III trials utilizing multi-arm multi-stage and Bayesian response adaptive randomization trial designs to select promising combination regimens in a platform adaptive trial.

METHODS

Clinical trial simulation tools were built using predictive and validated parametric survival models of time to culture conversion (intermediate endpoint) and time to TB-related unfavorable outcome (final endpoint). This integrative clinical trial simulation tool was used to explore and optimize design parameters for aforementioned trial designs.

RESULTS

Both multi-arm multi-stage and Bayesian response adaptive randomization designs were able to reliably graduate desirable regimens in ≥ 95% of trial simulations and reliably stop suboptimal regimens in ≥ 90% of trial simulations. Overall, adaptive phase IIc designs reduced patient enrollment by 17% and 25% with multi-arm multi-stage and Bayesian response adaptive randomization designs respectively compared to the conventional sequential approach, while seamless designs reduced study duration by 2.6 and 3.5 years respectively (typically ≥ 8.5 years for standard sequential approach).

CONCLUSIONS

In this study, we demonstrate that adaptive trial designs are suitable for TB regimen development, and we provide plausible design parameters for a platform adaptive trial. Ultimately trial design and specification of design parameters will depend on clinical trial objectives. To support decision-making for clinical trial designs in contemporary TB regimen development, we provide a flexible clinical trial simulation tool that can be used to explore and optimize design features and parameters.

A review on Natural Language Processing Models for COVID-19 research

This survey paper reviews Natural Language Processing Models and their use in COVID-19 research in two main areas. Firstly, a range of transformer-based biomedical pretrained language models are evaluated using the BLURB benchmark. Secondly, models used in sentiment analysis surrounding COVID-19 vaccination are evaluated. We filtered literature curated from various repositories such as PubMed and Scopus and reviewed 27 papers. When evaluated using the BLURB benchmark, the novel T-BPLM BioLinkBERT gives groundbreaking results by incorporating document link knowledge and hyperlinking into its pretraining. Sentiment analysis of COVID-19 vaccination through various Twitter API tools has shown the public’s sentiment towards vaccination to be mostly positive. Finally, we outline some limitations and potential solutions to drive the research community to improve the models used for NLP tasks.

Intervertebral kinematics during neck motion 6.5 years after fusion and artificial disc replacement

BACKGROUND

Arthroplasty with artificial disc replacement for surgical treatment of cervical spine degeneration was introduced with the notion that motion-preserving approaches would prevent development of adjacent segment disease. Though clinical outcomes favor arthroplasty over the commonly used anterior cervical discectomy with fusion approach, clinical studies confirming the biomechanical basis of these results are lacking. The aim of this study was to compare intervertebral kinematics between arthroplasty and fusion patients 6.5 years post-surgery during physiological motion of the neck.

METHODS

Using a biplane dynamic X-ray system, computed tomography imaging and model based tracking algorithms, three dimensional intervertebral kinematics were measured during neck axial rotation and extension in 14 patients treated for cervical radiculopathy with fusion (n = 8) or arthroplasty (n = 6). The measurements were performed at 2-year (baseline) and 6.5 year post-surgical time points, with the main interest being in the interaction between surgery types and time points. 3 translations and 3 rotations were investigated for the index (C5C6), and upper- (C4C5) and lower adjacent levels (C6C7).

FINDINGS

Surgery-time interaction was significant for axial rotation (P < 0.04) and flexion-extension rotation (P < 0.005) in C4C5 during neck axial rotation, left-right translation (P < 0.04) in C5C6 and anterior-posterior translation in C6C7 (P < 0.04) during neck extension. In contrast with the expectations, axial rotation and flexion-extension decreased in C4C5 during neck rotation and anterior-posterior translation decreased in C6C7 during neck extension for fusion.

INTERPRETATION

The findings do not support the notion that adjacent segment motion increases after fusion.

ADOPT: automatic deep learning and optimization-based approach for detection of novel coronavirus COVID-19 disease using X-ray images

In the hospital, because of the rise in cases daily, there are a small number of COVID-19 test kits available. For this purpose, a rapid alternative diagnostic choice to prevent COVID-19 spread among individuals must be implemented as an automatic detection method. In this article, the multi-objective optimization and deep learning-based technique for identifying infected patients with coronavirus using X-rays is proposed. J48 decision tree approach classifies the deep feature of corona affected X-ray images for the efficient detection of infected patients. In this study, 11 different convolutional neural network-based (CNN) models (AlexNet, VGG16, VGG19, GoogleNet, ResNet18, ResNet50, ResNet101, InceptionV3, InceptionResNetV2, DenseNet201 and XceptionNet) are developed for detection of infected patients with coronavirus pneumonia using X-ray images. The efficiency of the proposed model is tested using k-fold cross-validation method. Moreover, the parameters of CNN deep learning model are tuned using multi-objective spotted hyena optimizer (MOSHO). Extensive analysis shows that the proposed model can classify the X-ray images at a good accuracy, precision, recall, specificity and F1-score rates. Extensive experimental results reveal that the proposed model outperforms competitive models in terms of well-known performance metrics. Hence, the proposed model is useful for real-time COVID-19 disease classification from X-ray chest images.Communicated by Ramaswamy H. Sarma.

Towards Reviewing an Immediate Impact of COVID-19 on the Integrative World Economy

The coronavirus (CoV) belongs to Severe Acute Respiratory Syndrome (SARS) species that lead to infection, causing illness, starting from common cold to some serious sickness. Finally, on 11 March 2020, the WHO Director-General Dr. Tedros Adhanom Ghebreyesus announced the outbreak as a pandemic. As the fear and ambiguity rose among companies and firms, the profit rate seemed to be lower due to the Covid-19 global impact, say nearly US$6 trillion in wealth from 24th to 28 February 2020 of the stock market has been wiped out. There was a great decrease in value over the S&P index, which abolished over $5 trillion in the same week. However, the largest ten companies of S&P faced a loss of $1.4 trillion. The investors make an analytical prediction that firms' profits may drop in response to the impact of coronavirus. Our prime focus is on the importance of digital business practices and how different sectors have been affected in terms of economic loss during this pandemic outbreak in this paper.

Knowledge Graph and Deep Learning-based Text-to-GQL Model for Intelligent Medical Consultation Chatbot

Text-to-GQL (Text2GQL) is a task that converts the user's questions into GQL (Graph Query Language) when a graph database is given. That is a task of semantic parsing that transforms natural language problems into logical expressions, which will bring more efficient direct communication between humans and machines. The existing related work mainly focuses on Text-to-SQL tasks, and there is no available semantic parsing method and data set for the graph database. In order to fill the gaps in this field to serve the medical Human-Robot Interactions (HRI) better, we propose this task and a pipeline solution for the Text2GQL task. This solution uses the Adapter pre-trained by "the linking of GQL schemas and the corresponding utterances" as an external knowledge introduction plug-in. By inserting the Adapter into the language model, the mapping between logical language and natural language can be introduced faster and more directly to better realize the end-to-end human-machine language translation task. In the study, the proposed Text2GQL task model is mainly constructed based on an improved pipeline composed of a Language Model, Pre-trained Adapter plug-in, and Pointer Network. This enables the model to copy objects' tokens from utterances, generate corresponding GQL statements for graph database retrieval, and builds an adjustment mechanism to improve the final output. And the experiments have proved that our proposed method has certain competitiveness on the counterpart datasets (Spider, ATIS, GeoQuery, and 39.net) converted from the Text2SQL task, and the proposed method is also practical in medical scenarios.

Loss of function STK11 alterations and poor outcomes in non-small-cell lung cancer: Literature and case series of US Veterans

Emerging evidence suggests that STK11 alterations, frequently found in non-small-cell lung cancers, may be prognostic and/or predictive of response to therapy, particularly immunotherapy. STK11 affects multiple important cellular pathways, and mutations lead to tumor growth by creating an immunosuppressive and altered metabolic environment through changes in AMPK, STING, and vascular endothelial growth factor pathways. We illustrate the questions surrounding STK11 genomic alteration in NSCLC with a case series comprising six United States Veterans from a single institution. We discuss the history of STK11, review studies on its clinical impact, and describe putative mechanisms of how loss of STK11 might engender resistance to immunotherapy or other therapies. While the exact impact of STK11 alteration in non-small-cell lung cancer remain to be fully elucidated, future research and ongoing clinical trials will help us better understand its role in cancer development and devise more effective treatment strategies.

Validation of the Benefits of Ambulation Within 8 Hours of Elective Cervical and Lumbar Surgery: A Michigan Spine Surgery Improvement Collaborative Study

BACKGROUND

Early ambulation is considered a key element to Enhanced Recovery After Surgery protocol after spine surgery.

OBJECTIVE

To investigate whether ambulation less than 8 hours after elective spine surgery is associated with improved outcome.

METHODS

The Michigan Spine Surgery Improvement Collaborative database was queried to track all elective cervical and lumbar spine surgery between July 2018 and April 2021. In total, 7647 cervical and 17 616 lumbar cases were divided into 3 cohorts based on time to ambulate after surgery: (1) <8 hours, (2) 8 to 24 hours, and (3) >24 hours.

RESULTS

For cervical cases, patients who ambulated 8 to 24 hours (adjusted odds ratio [aOR] 1.38; 95% CI 1.11-1.70; P = .003) and >24 hours (aOR 2.20; 95% CI 1.20-4.03; P = .011) after surgery had higher complication rate than those who ambulated within 8 hours of surgery. Similar findings were noted for lumbar cases with patients who ambulated 8 to 24 hours (aOR 1.31; 95% CI 1.12-1.54; P < .001) and >24 hours (aOR 1.96; 95% CI 1.50-2.56; P < .001) after surgery having significantly higher complication rate than those ambulated <8 hours after surgery. Analysis of secondary outcomes for cervical cases demonstrated that <8-hour ambulation was associated with home discharge, shorter hospital stay, lower 90-day readmission, and lower urinary retention rate. For lumbar cases, <8-hour ambulation was associated with shorter hospital stay, satisfaction with surgery, lower 30-day readmission, home discharge, and lower urinary retention rate.

CONCLUSION

Ambulation within 8 hours after surgery is associated with significant improved outcome after elective cervical and lumbar spine surgery.

Prevalence and outcomes of WPW pattern in the young: a report from a nationwide cardiac screening programme

Funding Acknowledgements

Type of funding sources: None.

Background

Individuals with accessory pathways, also referred as the Wolff-Parkinson-White pattern (WPWp), are usually asymptomatic and fatal arrhythmias may be the first presentation. Risk stratification is recommended, with electrophysiological study (EPS) being the gold standard. Catheter ablation offers a widely available, curative option, and provides incentive for screening, particularly in high-risk populations such as athletes.

Purpose

We sought to assess the prevalence, real-world clinical practice and outcomes of WPWp in young individuals identified through a nationwide cardiac screening programme.

Method

Between 2008 and 2018, 113,209 consecutive individuals (14 to 35 years old, 38% female) underwent cardiac screening with a health questionnaire, 12-lead electrocardiogram (ECG) and cardiology consultation. Individuals with suspected cardiac disease were referred for further investigations as necessary. Clinical outcomes were obtained via an online questionnaire and matched de-identified records from the Office of National Statistics database. Individuals were classified as elite athletes, non-elite athletes and non-athletes.

Results

A total of 214 (0.2%) individuals were referred for secondary evaluation with a suspected WPWp (Figure 1). Clinical outcomes were unavailable for 30 (14%) individuals. Follow-up confirmed an additional 13 individuals with a WPWp who were deemed to have normal investigations at screening, of which 8 (61%) had reported cardiovascular symptoms on their HQ and, in retrospect, 4 (30%) demonstrated subtle WPWp on their ECG. A WPWp was confirmed in 187 individuals (0.2%), of which 149 (80%) individuals were considered non-athletes, 114 (61%) reported cardiovascular symptoms, 116 (62%) underwent risk stratification with an EPS and 95 (43%) underwent catheter ablation. Report of palpitation was significantly greater in non-athletes than athletes (p=0.034). In asymptomatic individuals, those engaging in sports at elite level were more likely to be referred for an EPS (p=0.04). Four adverse cardiac events were reported over a follow up period of 7.4 years (IQR 4.9 to 9.2), including 3 individuals (16 years, 20 years, 40 years) with life-threatening arrhythmic events (LAE) requiring cardioversion, and 1 individual (20 years) with sudden cardiac death. Of these 4 individuals, only 1 with a LAE had undergone risk stratification with EPS.

Conclusions

The incidence of SCD in this young screened population was 0.72 per 1000 person-years. Clinical practice was variable and EPS was not offered to a significant proportion of patients, despite inadequate non-invasive risk stratification. The indication to EPS was driven by the athletic status in the asymptomatic individuals. The WPW syndrome may manifest with subtle ECG changes, posing a diagnostic challenge to interpreting physicians.

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

Age as a Risk Factor for Complications Following Anterior Cervical Discectomy and Fusion: Analysis From the Michigan Spine Surgery Improvement Collaborative (MSSIC)

STUDY DESIGN

Retrospective analysis of prospectively collected registry data using multivariable analyses of imputed data.

OBJECTIVE

We sought to demonstrate that age would not be associated with complications in patients undergoing anterior cervical discectomy and fusion (ACDF).

SUMMARY OF BACKGROUND DATA

Elderly patients (≥70 yrs) undergoing ACDF are considered a higher risk for complications. However, conclusive evidence is lacking. The Michigan Spine Surgery Improvement Collaborative (MSSIC) is a quality improvement collaborative with 30 hospitals across Michigan.

METHODS

The study included all patients who had 1 to 4 level ACDF (September 2015-August 2019) for 90-day complications. Major and minor complications were defined using a validated classification. Multiple imputations were used to generate complete covariate datasets. Generalized estimating equation model was used to identify associations with complications using the whole cohort and elderly subgroup analyses. Bonferroni correction was used.

RESULTS

Nine thousand one hundred thirty five patients (11.1% ≥ 70 yrs and 88.9% <70 yrs) with 2266 complications were analyzed. Comparing elderly versus non-elderly, the elderly had a significantly higher rate of any complications (31.5% vs. 24.0%, P < 0.001) and major complications (14.1% vs. 7.0%, P < 0.001). On multivariable analysis, age was not independently associated with any complication. POD#0 ambulation and preop independent ambulation were independently associated with significantly decreased odds of any complication. In the elderly, independent preoperative ambulation was protective for any complication (odds ratio [OR] 0.53, 0.39-0.73 95% confidence interval [CI]), especially major complications (OR 0.41, 0.27-0.61 95% CI).

CONCLUSION

Age was not an independent risk factor for complications in patients that underwent ACDF. In the elderly, independent preoperative ambulation was especially protective for major complications.Level of Evidence: 3.

A Matched Cohort Analysis of Drain Usage in Elective Anterior Cervical Discectomy and Fusion: A Michigan Spine Surgery Improvement Collaborative (MSSIC) Study

STUDY DESIGN

This is a retrospective, cohort analysis of multi-institutional database.

OBJECTIVE

This study was designed to analyze the impact of drain use following elective anterior cervical discectomy and fusion (ACDF) surgeries.

SUMMARY OF BACKGROUND DATA

After ACDF, a drain is often placed to prevent postoperative hematoma. However, there has been no high quality evidence to support its use with ACDF despite the theoretical benefits and risks of drain placement.

METHODS

The Michigan Spine Surgery Improvement Collaborative database was queried to identify all patients undergoing elective ACDF between February 2014 and October 2019. Cases were divided into two cohorts based on drain use. Propensity-score matching was utilized to adjust for inherent differences between the two cohorts. Measured outcomes included surgical site hematoma, length of stay, surgical site infection, dysphagia, home discharge, readmission within 30 days, and unplanned reoperation.

RESULTS

We identified 7943 patients during the study period. Propensity-score matching yielded 3206 pairs. On univariate analysis of matched cohorts, there were no differences in rate of postoperative hematoma requiring either return to OR or readmission. We noted patients with drains had a higher rate of dysphagia (4.6% vs. 6.3%; P = 0.003) and had longer hospital stay (P < 0.001). On multivariate analysis, drain use was associated with significantly increased length of stay (relative risk 1.23, 95% confidence interval [CI] 1.13-1.34; P < 0.001). There were no significant differences in other outcomes measured.

CONCLUSION

Our analysis demonstrated that drain use is associated with significant longer hospital stay.Level of Evidence: 3.

The role of postoperative antibiotic duration on surgical site infection after lumbar surgery

OBJECTIVE

Despite a general consensus regarding the administration of preoperative antibiotics, poorly defined comparison groups and underpowered studies prevent clear guidelines for postoperative antibiotics. Utilizing a data set tailored specifically to spine surgery outcomes, in this clinical study the authors aimed to determine whether there is a role for postoperative antibiotics in the prevention of surgical site infection (SSI).

METHODS

The Michigan Spine Surgery Improvement Collaborative registry was queried for all lumbar operations performed for degenerative spinal pathologies over a 5-year period from 2014 to 2019. Preoperative prophylactic antibiotics were administered for all surgical procedures. The study population was divided into three cohorts: no postoperative antibiotics, postoperative antibiotics ≤ 24 hours, and postoperative antibiotics > 24 hours. This categorization was intended to determine 1) whether postoperative antibiotics are helpful and 2) the appropriate duration of postoperative antibiotics. First, multivariable analysis with generalized estimating equations (GEEs) was used to determine the association between antibiotic duration and all-type SSI with adjusted odds ratios; second, a three-tiered outcome-no SSI, superficial SSI, and deep SSI-was calculated with multivariable multinomial logistical GEE analysis.

RESULTS

Among 37,161 patients, the postoperative antibiotics > 24 hours cohort had more men with older average age, greater body mass index, and greater comorbidity burden. The postoperative antibiotics > 24 hours cohort had a 3% rate of SSI, which was significantly higher than the 2% rate of SSI of the other two cohorts (p = 0.004). On multivariable GEE analysis, neither postoperative antibiotics > 24 hours nor postoperative antibiotics ≤ 24 hours, as compared with no postoperative antibiotics, was associated with a lower rate of all-type postoperative SSIs. On multivariable multinomial logistical GEE analysis, neither postoperative antibiotics ≤ 24 hours nor postoperative antibiotics > 24 hours was associated with rate of superficial SSI, as compared with no antibiotic use at all. The odds of deep SSI decreased by 45% with postoperative antibiotics ≤ 24 hours (p = 0.002) and by 40% with postoperative antibiotics > 24 hours (p = 0.008).

CONCLUSIONS

Although the incidence of all-type SSI was highest in the antibiotics > 24 hours cohort, which also had the highest proportions of risk factors, duration of antibiotics failed to predict all-type SSI. On multinomial subanalysis, administration of postoperative antibiotics for both ≤ 24 hours and > 24 hours was associated with decreased risk of only deep SSI but not superficial SSI. Spine surgeons can safely consider antibiotics for 24 hours, which is equally as effective as long-term administration for prophylaxis against deep SSI.

Development of a Diabetes Diagnosis System Using Machine Learning Algorithms

This paper describes how to develop diabetes diagnosis through the combined use of the support vector machine, the Decision Tree, Naive Bayes, K-nearest and finally, Random Forest (RF) algorithms. These methods are useful to predict diabetes jointly. The appropriateness of ML-depended techniques to tackle this issue has been revealed. This diabetes diagnosis system using machine-learning algorithms is used to review papers. This project was based on developing python-based code for machine learning algorithms to perform large scales of diabetes analysis. The hardware requirement of machine learning is RAM that is 128 GB DDR4 2133 MHz and 2 TB Hard Disk and needs 512 GB SSD. One standard library is NumPy that uses to support multi-dimensional arrays objects, various components, and matrices. The Random Forest Prediction representing the pictorial visualization of the model and the accuracy for the data analysis using the Random Forest is 76%.

Plant Disease Detection Using Sequential Convolutional Neural Network

The main warning in the area of food preservation and care is on topmost are crop diseases. It has been recognized speedily, but it is not as easy as in any area of the world because no required framework exists. Both the healthy and diseased plant leaves were gathered and collected under the condition and circumstances. For this purpose, a public set of information was used. It was 20,639 images of plants that were infected and healthy. In order to recognize three different crops and 12 diseases, a sequential convolutional neural network from Keras was trained and applied. The perfection and exactness was 98.18 % onset of information of the above trained mentioned model using CNN . It has also indicated the probability and possibility of this strategy and procedure. The over-fitting occurs and neutralizes by putting the dropout value to 0.25.

Analysis of Factors Associated with Return-to-Work After Lumbar Surgery up to 2-Years Follow-up: A Michigan Spine Surgery Improvement Collaborative (MSSIC) Study

STUDY DESIGN

Michigan Spine Surgery Improvement Collaborative (MSSIC) prospectively collects data on all patients undergoing operations for degenerative and/or deformity indications.

OBJECTIVE

We aimed to identify which factors are significantly associated with return-to-work after lumbar surgery at long-term follow-up.

SUMMARY OF BACKGROUND DATA

Prior publications have created a clinically relevant predictive model for return-to-work, wherein education, gender, race, comorbidities, and preoperative symptoms increased likelihood of return-to-work at 3 months after lumbar surgery. We sought to determine if these trends 1) persisted at 1 year and 2 years postoperatively; or 2) differed among preoperatively employed versus unemployed patients.

METHODS

MSSIC was queried for all patients undergoing lumbar operations (2014-2019). All patients intended to return-to-work postoperatively. Patients were followed for up to 2 years postoperatively. Measures of association were calculated with multivariable generalized estimating equations.

RESULTS

Return-to-work increased from 63% (3542/5591) at 90 days postoperatively to 75% (3143/4147) at 1 year and 74% (2133/2866) at 2 years postoperatively. Following generalized estimating equations, neither clinical nor surgical variables predicted return-to-work at all three time intervals: 90 days, 1 year, and 2 years postoperatively. Only socioeconomic factors reached statistical significance at all follow-up points. Preoperative employment followed by insurance status had the greatest associations with return-to-work. In a subanalysis of patients who were preoperatively employed, insurance was the only factor with significant associations with return-to-work at all three follow-up intervals. The return-to-work rates among unemployed patients at baseline increased from 29% (455/1100) at 90 days, 44% (495/608) at 1 year, and 46% (366/426) at 2 years postoperatively. The only two significant factors associated with return-to-work at all three follow-up intervals were Medicaid, as compared with private insurance, and male gender.

CONCLUSION

In patients inquiring about long-term return-to-work after lumbar surgery, insurance status represents the important determinant of employment status.Level of Evidence: 2.

Analyzing the Impact of Machine Learning on Cancer Treatments

The survival rate of breast cancer prediction has been a significant issue for researchers. Nowadays, the health care industry has completely transformed by using modern technologies and their applications for medical services. Among those technologies, machine learning is one of them, which has gained attention by people that its new advanced technology would give accurate results by using modeling methods for prediction. As this is a branch of artificial intelligence, it employs various statics, probabilistic and optimistic tools. This is applied to medical services, especially which are based on proteomic and genomic measurements. The aim is to use the dataset of cancer treatment and predict the results of patients by using machine learning with its modeling methods for accurate results. Recently experts have even used this machine learning in cancer for prognosis and forecasting.

Towards Perceived Playfulness and Adoption of Hearables in Smart Cities of China

'Hearables' have become important in the aging population. This study investigates whether smart technologies help middle-aged and elderly people accept hearing aid devices in smart cities of China. The authors adopt the PLS-SEM framework to analyze the factors that affect behavioral intention towards adopting hearing aids in smart cities. In order to avoid common method bias, Harman's single factor method is also carried out to make sure the instrument does not introduce a bias. The findings suggest that perceived playfulness and perceived usefulness are principal determinants of hearing aids adoption. In contrast, perceived ease of use, a factor always stressed in literature, does not matter significantly. The results reveal that smart technologies enable patients to access professional services and instructions playfully, which reduces obstacles to adopt hearing aids. This study provides novel insights for policymakers and manufacturers to expand hearing aid adoption by facilitating smart infrastructure and technologies.

The association of patient education level with outcomes after elective lumbar surgery: a Michigan Spine Surgery Improvement Collaborative study

OBJECTIVE

Socioeconomic factors have been shown to impact a host of healthcare-related outcomes. Level of education is a marker of socioeconomic status. This study aimed to investigate the relationship between patient education level and outcomes after elective lumbar surgery and to characterize any education-related disparities.

METHODS

The Michigan Spine Surgery Improvement Collaborative registry was queried for all lumbar spine operations. Primary outcomes included patient satisfaction determined by the North American Spine Society patient satisfaction index, and reaching the minimum clinically important difference of Patient-Reported Outcomes Measurement Information System Physical Function score and return to work up to 2 years after surgery. Multivariate Poisson generalized estimating equation models reported adjusted risk ratios.

RESULTS

A total of 26,229 lumbar spine patients had data available for inclusion in this study. On multivariate generalized estimating equation analysis all comparisons were done versus the high school (HS)/general equivalency development (GED)-level cohort. For North American Spine Society satisfaction scores after surgery the authors observed the following: at 90 days the likelihood of satisfaction significantly decreased by 11% (p < 0.001) among < HS, but increased by 1% (p = 0.52) among college-educated and 3% (p = 0.011) among postcollege-educated cohorts compared to the HS/GED cohort; at 1 year there was a decrease of 9% (p = 0.02) among < HS and increases of 3% (p = 0.02) among college-educated and 9% (p < 0.001) among postcollege-educated patients; and at 2 years, there was an increase of 5% (p = 0.001) among postcollege-educated patients compared to the < HS group. The likelihood of reaching a minimum clinically important difference of Patient-Reported Outcomes Measurement Information System Physical Function score at 90 days increased by 5% (p = 0.005) among college-educated and 9% (p < 0.001) among postcollege-educated cohorts; at 1 year, all comparison cohorts demonstrated significance, with a decrease of 12% (p = 0.007) among < HS, but an increase by 6% (p < 0.001) among college-educated patients and 14% (p < 0.001) among postcollege-educated compared to the HS/GED cohort; at 2 years, there was a significant decrease by 19% (p = 0.003) among the < HS cohort, an increase by 8% (p = 0.001) among the college-educated group, and an increase by 16% (p < 0.001) among the postcollege-educated group. For return to work, a significant increase was demonstrated at 90 days and 1 year when comparing the HS or less group with college or postcollege cohorts.

CONCLUSIONS

This study demonstrated negative associations on all primary outcomes with lower levels of education. This finding suggests a potential disparity linked to education in elective spine surgery.