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Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach to develop a classification model to predict individual patient outcome after surgery for DCM. Patients undergoing surgery for DCM as a part of the AOSpine CSM-NA or CSM-I prospective, multi-centre studies were included in the analysis. Out of 757 patients 605, 583, and 539 patients had complete follow-up information at 6, 12, and 24 months respectively and were included in the analysis. The primary outcome was improvement in the SF-6D quality of life indicator score by the minimum clinically important difference (MCID). The secondary outcome was improvement in the modified Japanese Orthopedic Association (mJOA) score by the MCID. Predictor variables reflected information about pre-operative disease severity, disease presentation, patient demographics, and comorbidities. A machine learning approach of feature engineering, data pre-processing, and model optimization was used to create the most accurate predictive model of outcome after surgery for DCM. Following data pre-processing 48, 108, and 101 features were chosen for model training at 6, 12, and 24 months respectively. The best performing predictive model used a random forest structure and had an average area under the curve (AUC) of 0.70, classification accuracy of 77%, and sensitivity of 78% when evaluated on a testing cohort that was not used for model training. Worse pre-operative disease severity, longer duration of DCM symptoms, older age, higher body weight, and current smoking status were associated with worse surgical outcomes. We developed a model that predicted positive surgical outcome for DCM with good accuracy at the individual patient level on an independent testing cohort. Our analysis demonstrates the applicability of machine-learning to predictive modeling in spine surgery.

Although there is a strong biological rationale for early decompression of the injured spinal cord, the influence of the timing of surgical decompression for acute spinal cord injury (SCI) remains debated, with substantial variability in clinical practice. We aimed to objectively evaluate the effect of timing of decompressive surgery for acute SCI on long-term neurological outcomes. We did a pooled analysis of individual patient data derived from four independent, prospective, multicentre data sources, including data from December, 1991, to March, 2017. Three of these studies had been published; of these, only one study previously specifically analysed the effect of the timing of surgical decompression. These four datasets were selected because they were among the highest quality acute SCI datasets available and contained highly granular data. Individual patient data were obtained by request from study authors. All patients who underwent decompressive surgery for acute SCI within these datasets were included. Patients were stratified into early (<24 h after spinal injury) and late (≥24 h after spinal injury) decompression groups. Neurological outcomes were assessed by American Spinal Injury Association (ASIA), or International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI), examination. The primary endpoint was change in total motor score from baseline to 1 year after spinal injury. Secondary endpoints were ASIA Impairment Scale (AIS) grade and change in upper-extremity motor, lower-extremity motor, light touch, and pin prick scores after 1 year. One-stage meta-analyses were done by hierarchical mixed-effects regression adjusting for baseline score, age, mechanism of injury, AIS grade, level of injury, and administration of methylprednisolone. Effect sizes were summarised by mean difference (MD) for sensorimotor scores and common odds ratio (cOR) for AIS grade, with corresponding 95% CIs. As a secondary analysis, change in total motor score was regressed against time to surgical decompression (h) as a continuous variable, using a restricted cubic spline with adjustment for the same covariates as in the primary analysis. We identified 1548 eligible patients from the four datasets. Outcome data at 1 year after spinal injury were available for 1031 patients (66·6%). Patients who underwent early surgical decompression (n=528) experienced greater recovery than patients who had late decompression surgery (n=1020) at 1 year after spinal injury; total motor scores improved by 23·7 points (95% CI 19·2–28·2) in the early surgery group versus 19·7 points (15·3–24·0) in the late surgery group (MD 4·0 points [1·7–6·3]; p=0·0006), light touch scores improved by 19·0 points (15·1–23·0) vs 14·8 points (11·2–18·4; MD 4·3 [1·6–7·0]; p=0·0021), and pin prick scores improved by 18·3 points (13·7–22·9) versus 14·2 points (9·8–18·6; MD 4·0 [1·5–6·6]; p=0·0020). Patients who had early decompression also had better AIS grades at 1 year after surgery, indicating less severe impairment, compared with patients who had late surgery (cOR 1·48 [95% CI 1·16–1·89]; p=0·0019). When time to surgical decompression was modelled as a continuous variable, there was a steep decline in change in total motor score with increasing time during the first 24–36 h after injury (p<0·0001); and after 36 h, change in total motor score plateaued. Surgical decompression within 24 h of acute SCI is associated with improved sensorimotor recovery. The first 24–36 h after injury appears to represent a crucial time window to achieve optimal neurological recovery with decompressive surgery following acute SCI. None.

Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volume of medical imaging data and provide initial interpretation of images gathered in a primary-care setting. We aimed to develop and validate a deep learning model for detection of cervical spinal cord compression in MRI scans. Patients undergoing surgery for DCM as a part of the AO Spine CSM-NA or CSM-I prospective cohort studies were included in our study. Patients were divided into a training/validation or holdout dataset. Images were labelled by two specialist physicians. We trained a deep convolutional neural network using images from the training/validation dataset and assessed model performance on the holdout dataset. The training/validation cohort included 201 patients with 6588 images and the holdout dataset included 88 patients with 2991 images. On the holdout dataset the deep learning model achieved an overall AUC of 0.94, sensitivity of 0.88, specificity of 0.89, and f1-score of 0.82. This model could improve the efficiency and objectivity of the interpretation of cervical spine MRI scans.

Abstract BACKGROUND There is controversy over the optimal treatment strategy for patients with mild degenerative cervical myelopathy (DCM). OBJECTIVE To evaluate the degree of impairment in baseline quality of life as compared to population norms, as well as functional, disability, and quality of life outcomes following surgery in a prospective cohort of mild DCM patients undergoing surgical decompression. METHODS We identified patients with mild DCM (modified Japanese Orthopaedic Association [mJOA] 15 to 17) enrolled in the prospective, multicenter AOSpine CSM-NA or CSM-I trials. Baseline quality of life Short Form-36 version 2 (SF-36v2) was compared to population norms by the standardized mean difference (SMD). Outcomes, including functional status (mJOA, Nurick grade), disability (NDI [Neck Disability Index]), and quality of life (SF-36v2), were evaluated at baseline and 6 mo, 1 yr, and 2 yr after surgery. Postoperative complications within 30 d of surgery were monitored. RESULTS One hundred ninety-three patients met eligibility criteria. Mean age was 52.4 yr. There were 67 females (34.7%). Patients had significant impairment in all domains of the SF-36v2 compared to population norms, greatest for Social Functioning (SMD –2.33), Physical Functioning (SMD –2.31), and Mental Health (SMD –2.30). A significant improvement in mean score from baseline to 2-yr follow-up was observed for all major outcome measures, including mJOA (0.87, P < .01), Nurick grade (–1.13, P < .01), NDI (–12.97, P < .01), and SF-36v2 Physical Component Summary (PCS) (5.75, P < .01) and Mental Component Summary (MCS) (6.93, P < .01). The rate of complication was low. CONCLUSION Mild DCM is associated with significant impairment in quality of life. Surgery results in significant gains in functional status, level of disability, and quality of life.

The association of the patient's electric shock-like sensations prompted us to do a dynamic cervical spine MRI study with the neck in positions of flexion and extension: MRI showed that the spinal cord was being compressed—most noticeably at the level of the fifth cervical vertebral body—and that the dorsal epidural space was abnormally expanding while in a flexed position (figure). Hirayama disease—first described by Keizo Hirayama in 1959 as a juvenile, unilateral, muscular atrophy of the upper limb—predominantly affects adolescent men and is thought to be caused by dynamic mechanical or ischaemic injury to the ventral cervical motor neurons. The imaging findings are key to differentiating the disease from more common disorders—such as amyotrophic lateral sclerosis, multiple sclerosis, cervical spondylotic myelopathy, tabes dorsalis, spinal muscular atrophy, or syringomyelia—and allowing for appropriate surgical action, as in our patient.

Frailty, as measured by the modified frailty index-5 (mFI-5), and older age are associated with increased mortality in the setting of spinal cord injury (SCI). However, there is limited evidence demonstrating an incremental prognostic value derived from patient mFI-5. We conducted a retrospective cohort study to evaluate in-hospital mortality among adult complete cervical SCI patients at participating centers of the Trauma Quality Improvement Program from 2010 to 2018. Logistic regression was used to model in-hospital mortality, and the area under the receiver operating characteristic curve (AUROC) of regression models with age, mFI-5, or age with mFI-5 was used to compare the prognostic value of each model. 4733 patients were eligible. We found that both age (80 y versus 60 y: OR 3.59 95% CI [2.82 4.56], P  < 0.001) and mFI-5 (score ≥ 2 versus < 2: OR 1.53 95% CI [1.19 1.97], P  < 0.001) had statistically significant associations with in-hospital mortality. There was no significant difference in the AUROC of a model including age and mFI-5 when compared to a model including age without mFI-5 (95% CI Δ AUROC [− 8.72 × 10 –4 0.82], P  = 0.199). Both models were superior to a model including mFI-5 without age (95% CI Δ AUROC [0.06 0.09], P  < 0.001). Our findings suggest that mFI-5 provides minimal incremental prognostic value over age with respect to in-hospital mortality for patients complete cervical SCI.

Differences in physician income by gender have been described in numerous jurisdictions, but few studies have looked at a Canadian cohort with adjustment for confounders. In this study, we aimed to understand differences in fee-for-service payments to men and women physicians in Ontario. We conducted a cross-sectional analysis of all Ontario physicians who submitted claims to the Ontario Health Insurance Plan (OHIP) in 2017. For each physician, we gathered demographic information from the College of Physicians and Surgeons of Ontario registry. We compared differences in physician claims between men and women in the entire cohort and within each specialty using multivariable linear regressions, controlling for length of practice, specialty and practice location. We identified a cohort of 30 167 physicians who submitted claims to OHIP in 2017, including 17 992 men and 12 175 women. When controlling for confounding variables in a linear mixed-effects regression model, annual physician claims were $93 930 (95% confidence interval $88 434 to $99 431) higher for men than for women. Women claimed 74% as much as men when adjusting for covariates. This discrepancy was present in nearly all specialty categories. Men claimed more than women throughout their careers, with the greatest gap 10–15 years into practice. We found a gender gap in fee-for-service claims in Ontario, with women claiming less than men overall and in nearly every specialty. Further work is required to understand the root causes of the gender pay gap.

Study Design: Narrative review. Objective: There is a strong biological rationale to perform early decompression after traumatic spinal cord injury (SCI). With an enlarging clinical evidence base, most spine surgeons internationally now favor early decompression for the majority of SCI patients; however, a number of pertinent questions remain surrounding this therapy. Methods: A narrative review evaluating the status of early surgery for SCI. In particular, we addressed the following questions: (1) Which patients stand to benefit most from early surgery? 2) What is the most appropriate time threshold defining early surgery? Results: Although heterogeneity exists, the evidence generally seems to support early surgery. While the best evidence exists for cervical SCI, there is insufficient data to support a differential effect for early surgery depending on neurological level or injury severity. When comparing thresholds to define early versus late surgery—including a later threshold (48-72 hours), an earlier threshold (24 hours), and an ultra-early threshold (8-12 hours)—the 2 earlier time points seem to be associated with the greatest potential for improved outcomes. However, existing prehospital and hospital logistics pose barriers to early surgery in a significant proportion of patients. An overview of recommendations from the recent AOSpine guidelines is provided. Conclusion: In spite of increasing acceptance of early surgery post SCI, further research is needed to (1) identify subgroups of patients who stand to derive particular benefit—in particular to develop more evidence-based approaches for central cord syndrome and (2) investigate the efficacy and feasibility of ultra-early surgery targeting more aggressive timelines.

Study Design: Broad narrative review. Objective: To review and summarize the current literature on guidelines, outcomes, techniques and indications surrounding multiple modalities of minimizing blood loss in spine surgery. Methods: A thorough review of peer-reviewed literature was performed on the guidelines, outcomes, techniques, and indications for multiple modalities of minimizing blood loss in spine surgery. Results: There is a large body of literature that provides a consensus on guidelines regarding the appropriate timing of discontinuation of anticoagulation, aspirin, nonsteroidal anti-inflammatory drugs (NSAIDs), and herbal supplements prior to surgery. Additionally, there is a more heterogenous discussion the utility of preoperative autologous blood donation facilitated by erythropoietin and iron supplementation for healthy patients slated for procedures with high anticipated blood loss and for whom allogeneic transfusion is likely. Intraoperative maneuvers available to minimize blood loss include positioning and maintaining normothermia. Tranexamic acid (TXA), bipolar sealer electrocautery, and topical hemostatic agents, and hypotensive anesthesia (mean arterial pressure (MAP) <65 mm Hg) should be strongly considered in cases with larger exposures and higher anticipated blood loss. There is strong level 1 evidence for the use of TXA in spine surgery as it reduces the overall blood loss and transfusion requirements. Conclusion: As the volume and complexity of spinal procedures rise, intraoperative blood loss management has become a pivotal topic of research within the field. There are many tools for minimizing blood loss in patients undergoing spine surgery. The current literature supports combining techniques to use a cost- effective multimodal approach to minimize blood loss in the perioperative period.

There are limited data pertaining to current practices in timing of surgical decompression for acute thoracolumbar spinal cord injury (SCI). We conducted a retrospective cohort study to evaluate variability in timing between- and within-trauma centers in North America; and to identify patient- and hospital-level factors associated with treatment delay. Adults with acute thoracolumbar SCI who underwent decompressive surgery within five days of injury at participating trauma centers in the American College of Surgeons Trauma Quality Improvement Program were included. Mixed-effects regression with a random intercept for trauma center was used to model the outcome of time to surgical decompression and assess risk-adjusted variability in surgery timeliness across centers. 3,948 patients admitted to 214 TQIP centers were eligible. 28 centers were outliers, with a significantly shorter or longer time to surgery than average. Case-mix and hospital characteristics explained < 1% of between-hospital variability in surgical timing. Moreover, only 7% of surgical timing variability within-centers was explained by case-mix characteristics. The adjusted intraclass correlation coefficient of 12% suggested poor correlation of surgical timing for patients with similar characteristics treated at the same center. These findings support the need for further research into the optimal timing of surgical intervention for thoracolumbar SCI.