Philippe Phan

A Modified Risser Grading System Predicts the Curve Acceleration Phase of Female Adolescent Idiopathic Scoliosis

BACKGROUND The Risser sign can be assessed with the United States method or the European method. The Tanner-Whitehouse method estimates skeletal age on the basis of hand radiography and digital skeletal age. Digital skeletal age scores between 400 and 425 are associated with the beginning of the curve acceleration phase or peak growth velocity in adolescent idiopathic scoliosis. The first objective of the present study was to evaluate the agreement between the two Risser grading systems. The second objective was to identify which grading system best predicts a digital skeletal age of between 400 and 425. The third objective was to explore a new system that could be used to replace the Risser method. METHODS One hundred female patients with adolescent idiopathic scoliosis were recruited in this cross-sectional descriptive study. Each patient was evaluated with posteroanterior spine and hand radiographs. The Risser sign was measured according to both the United States and European grading systems. Digital skeletal age was calculated, and triradiate cartilage ossification was assessed. RESULTS With use of kappa statistics, moderate agreement between the United States and European grading systems was seen. Risser stages alone were not good predictors of the curve acceleration phase. A new system with three groups was tested, and the second group (Risser 0 with closed triradiate cartilage and Risser 1) was the best predictor of a digital skeletal age score of between 400 and 425. CONCLUSIONS Two Risser grading systems coexist, and the agreement between them is moderate. No Risser stage was found to be a good clinical landmark for the beginning of the curve acceleration phase of adolescent idiopathic scoliosis. A new group, Risser 0 with closed triradiate cartilage and Risser 1, was the best predictor of the beginning of the curve acceleration phase. This new system is easy to implement and is based on findings that are available on spine radiographs. It should be used at the first visit and for scoliosis follow-up to assess skeletal maturity and correlation with the curve acceleration phase.

Computer algorithms and applications used to assist the evaluation and treatment of adolescent idiopathic scoliosis: a review of published articles 2000-2009.

Adolescent idiopathic scoliosis (AIS) is a complex spinal deformity whose assessment and treatment present many challenges. Computer applications have been developed to assist clinicians. A literature review on computer applications used in AIS evaluation and treatment has been undertaken. The algorithms used, their accuracy and clinical usability were analyzed. Computer applications have been used to create new classifications for AIS based on 2D and 3D features, assess scoliosis severity or risk of progression and assist bracing and surgical treatment. It was found that classification accuracy could be improved using computer algorithms that AIS patient follow-up and screening could be done using surface topography thereby limiting radiation and that bracing and surgical treatment could be optimized using simulations. Yet few computer applications are routinely used in clinics. With the development of 3D imaging and databases, huge amounts of clinical and geometrical data need to be taken into consideration when researching and managing AIS. Computer applications based on advanced algorithms will be able to handle tasks that could otherwise not be done which can possibly improve AIS patients’ management. Clinically oriented applications and evidence that they can improve current care will be required for their integration in the clinical setting.

A decision tree can increase accuracy when assessing curve types according to Lenke classification of adolescent idiopathic scoliosis

Study Design. The assignment of adolescent idiopathic scoliosis (AIS) curves into curve types (1–6), as described by Lenke et al, was evaluated by 12 independent observers using the original description versus a decisional tree algorithm. Objective. To determine whether a decision tree algorithm can improve classification accuracy using the Lenke classification for AIS. Summary of Background Data. Curve type classification in AIS relies on several parameters to consider, and its relative complexity has lead to conflicting studies that reported fair-to-excellent interobserver reliability. Kings classification reliability was shown to be improved using a rule-based automated algorithm. No similar algorithm for Lenkes classification currently exists. Methods. A clinical diagram derived from a decision tree was developed to help clinicians classify AIS curves. Twelve clinicians and research assistants were asked to classify AIS curves using 2 methods: the original Lenke chart alone and the decision tree diagram in addition to the Lenke Chart. Wilcoxon ranking tests were used to evaluate any difference in classification accuracy and speed for both methods. Mann-Whitney tests were used to compare experts and nonexperts results. Pearson correlation was calculated to evaluate the relationship between accuracy and time taken to classify. Results. Use of the decision tree for curve type determination improved classification accuracy from 77.2% to 92.9% (P = 0.005) without requiring more time to classify. This improvement was statistically significant (P < 0.05). A statistically significant correlation between accuracy and time spent classifying when the decision tree is used was also observed (R = 0.62, P = 0.032). Conclusion. Transfer of a computer algorithm, a decision tree, to a clinical diagram improved both accuracy ofAIS classification. Algorithmic diagrams could prove beneficial to increase classification reliability due to their systematic approach.

Artificial neural networks assessing adolescent idiopathic scoliosis: comparison with Lenke classification

BACKGROUND CONTEXT Variability in classifying and selecting levels of fusion in adolescent idiopathic scoliosis (AIS) has been repeatedly documented. Several computer algorithms have been used to classify AIS based on the geometrical features, but none have attempted to analyze its treatment patterns. PURPOSE To use self-organizing maps (SOM), a kind of artificial neural networks, to reliably classify AIS cases from a large database. To analyze surgeons treatment pattern in selecting curve regions to fuse in AIS using Lenke classification and SOM. STUDY DESIGN This is a technical concept article on the possibility and benefits of using neural networks to classify AIS and a retrospective analysis of AIS curve regions selected for fusion. PATIENT SAMPLE A total of 1,776 patients surgically treated for AIS were prospectively enrolled in a multicentric database. Cobb angles were measured on AIS patient spine radiographies, and patients were classified according to Lenke classification. OUTCOME MEASURES For each patient in the database, surgical approach and levels of fusion selected by the treating surgeon were recorded. METHODS A Kohonen SOM was generated using 1,776 surgically treated AIS cases. The quality of the SOM was tested using topological error. Percentages of prediction of fusion based on Lenke classification for each patient in the database and for each node in the SOM were calculated. Lenke curve types, treatment pattern, and kappa statistics for agreement between fusion realized and fusion recommended by Lenke classification were plotted on each node of the map. RESULTS The topographic error for the SOM generated was 0.02, which demonstrates high accuracy. The SOM differentiates clear clusters of curve type nodes on the map. The SOM also shows epicenters for main thoracic, double thoracic, and thoracolumbar/lumbar curve types and transition zones between clusters. When cases are taken individually, Lenke classification predicted curve regions fused by the surgeon in 46% of cases. When those cases are reorganized by the SOM into nodes, Lenke classification predicted the curve regions to fuse in 82% of the nodes. Agreement with Lenke classification principles was high in epicenters for curve types 1, 2, and 5, moderate in cluster for curve types 3, 4, and 6, and low in transition zones between curve types. CONCLUSIONS An AIS SOM with high accuracy was successfully generated. Lenke classification principles are followed in 46% of the cases but in 82% of the nodes on the SOM. The SOM highlights the tendency of surgeons to follow Lenke classification principles for similar curves on the SOM. Self-organizing map classification of AIS could be valuable to surgeons because it bypasses the limitations imposed by rigid classification such as cutoff values on Cobb angle to define curve types. It can extract similar cases from large databases to analyze and guide treatment.

Prior abdominal surgery is associated with an increased risk of postoperative complications after anterior lumbar interbody fusion.

Study Design. Retrospective medical record review. Objective. The purpose of this study was to determine whether a history of abdominal/pelvic surgery confers an increased risk of retroperitoneal anterior approach–related complications when undergoing anterior lumbar interbody fusion. Summary of Background Data. As anterior lumbar interbody fusion gains popularity, both anterior retroperitoneal approach have become increasingly used. Methods. The records of 263 patients, who underwent infraumbilical retroperitoneal approach to the anterior aspect of the lower lumbar spine for a degenerative spine condition between 2007 and 2011 were retrospectively reviewed. Patients demographics, risk factors, preoperative diagnosis, surgical history, level of the anterior fusion, and perioperative complications were collected. Anterior retroperitoneal approach to the spine was carried out by a single general surgeon. Results. Ninety-seven patients (37%) developed at least 1 complication. Forty-nine percent of patients with a history of abdominal surgery developed a postoperative complication compared with 28% of patients without such history (RR = 1.747, P⩽ 0.001). After controlling for other factors such as age, sex, body mass index, diagnostic groups, and preoperative comorbidities (hypertension, diabetes, and smoking status), these differences remained statistically significant. When each type of complication was considered separately, there was a statistically significant difference in the incidence of general complications (RR = 2.384, P = 0.007), instrumentation-related complications (RR = 2.954, P = 0.010), and complications related to the anterior approach (RR = 1.797, P = 0.021). Conclusion. Anterior lumbar interbody fusion via a midline incision and a retroperitoneal approach was associated with 37% overall rate of complication. Patients with a history of abdominal or pelvic surgery are at a higher risk of developing general, instrumentation, and anterior approach–related complications. Level of Evidence: 4

Can Surgeons Adequately Capture Adverse Events Using the Spinal Adverse Events Severity System (SAVES) and OrthoSAVES

BackgroundPhysicians have consistently shown poor adverse-event reporting practices in the literature and yet they have the clinical acumen to properly stratify and appraise these events. The Spine Adverse Events Severity System (SAVES) and Orthopaedic Surgical Adverse Events Severity System (OrthoSAVES) are standardized assessment tools designed to record adverse events in orthopaedic patients. These tools provide a list of prespecified adverse events for users to choose from—an aid that may improve adverse-event reporting by physicians.Questions/PurposesThe primary objective was to compare surgeons’ adverse-event reporting with reporting by independent clinical reviewers using SAVES Version 2 (SAVES V2) and OrthoSAVES in elective orthopaedic procedures.MethodThis was a 10-week prospective study where SAVES V2 and OrthoSAVES were used by six orthopaedic surgeons and two independent, non-MD clinical reviewers to record adverse events after all elective procedures to the point of patient discharge. Neither surgeons nor reviewers received specific training on adverse-event reporting. Surgeons were aware of the ongoing study, and reported adverse events based on their clinical interactions with the patients. Reviewers recorded adverse events by reviewing clinical notes by surgeons and other healthcare professionals (such as nurses and physiotherapists). Adverse events were graded using the severity-grading system included in SAVES V2 and OrthoSAVES. At discharge, adverse events recorded by surgeons and reviewers were recorded in our database.ResultsAdverse-event data for 164 patients were collected (48 patients who had spine surgery, 51 who had hip surgery, 34 who had knee surgery, and 31 who had shoulder surgery). Overall, 99 adverse events were captured by the reviewers, compared with 14 captured by the surgeons (p < 0.001). Surgeons adequately captured major adverse events, but failed to record minor events that were captured by the reviewers. A total of 93 of 99 (94%) adverse events reported by reviewers required only simple or minor treatment and had no long-term adverse effect. Three patients experienced adverse events that resulted in use of invasive or complex treatment that had a temporary adverse effect on outcome.ConclusionUsing SAVES V2 and OrthoSAVES, independent reviewers reported more minor adverse events compared with surgeons. The value of third-party reviewers requires further investigation in a detailed cost-benefit analysis.Level of EvidenceLevel II, therapeutic study.

A Computer-Aided Method for Scoliosis Fusion Level Selection by a Topologicaly Ordered Self Organizing Kohonen Network

Surgical instrumentation for the Adolescent idiopathic scoliosis (AIS) is a complex procedure involving many difficult decisions. Selection of the appropriate fusion level remains one of the most challenging decisions in scoliosis surgery. Currently, the Lenke classification model is generally followed in surgical planning. The purpose of our study is to investigate a computer aided method for Lenke classification and scoliosis fusion level selection. The method uses a self organizing neural network trained on a large database of surgically treated AIS cases. The neural network produces two maps, one of Lenke classes and the other of fusion levels. These two maps show that the Lenke classes are associated with the the proper fusion level categories everywhere in the map except at the Lenke class transitions. The topological ordering of the Cobb angles in the neural network justifies determining a patient scoliotic treatment instrumentation using directly the fusion level map rather than via the Lenke classification.

Activities performed and treatments conducted before consultation with a spine surgeon: are patients and clinicians following evidence-based clinical practice guidelines?

BACKGROUND CONTEXT Clinical practice guidelines (CPGs) are designed to ensure that evidence-based treatment is easily put into action. Whether patients and clinicians follow these guidelines is equivocal. PURPOSE The objectives of this study were to examine how many patients complaining of low back pain (LBP) underwent evidence-based medical interventional treatment in line with CPG recommendations before consultation with a spine surgeon, and to evaluate any associations between adherence to CPG recommendations and baseline factors. STUDY DESIGN/SETTING This is a cross-sectional cohort analysis at a tertiary care center. PATIENT SAMPLE A total of 229 patients were referred for surgical consultation for an elective lumbar spinal condition. OUTCOME MEASURES The outcome measures include the number of CPG-recommended treatments undertaken by patients at or before the time of referral, the validated pain score, the EuroQol-5D (EQ-5D) health status, and the Oswestry Disability Index (ODI) score. METHODS Questionnaires assessing demographic and functional characteristics as well as overall health care use were sent to patients immediately after their referral was received by the surgeons office. RESULTS Medications were the most common modality before consultation (74.2% of patients), of which 46.3% received opioids. The number of medications taken was significantly related to a higher ODI score (R=0.23, p=.0004), a higher pain score (R=0.15, p=.026), and a lower EQ-5D health status (R=-0.15, p=.024). In contrast, a lower pain score (7.2 vs. 7.7, p=.037) and a lower ODI score (26.6 vs. 29.9, p=.0023) were associated with performing adequate amounts of exercise. There was a significant association between lower numbers of treatments received and higher numerical pain rating scores (R=-0.14, p=.035). The majority (61.1%) of patients received two or less forms of treatment. CONCLUSIONS Evidence-based medical interventional treatments for patients with LBP are not being taken advantage of before spine surgery consultation. If more patients were to undertake CPG-endorsed conservative modalities, it may result in fewer unnecessary referrals from primary care physicians, and patients might not deteriorate as much while lingering on long wait lists. Further studies incorporating knowledge translation or health system pathway changes are necessary.

A Kohonen neural network description of scoliosis fused regions and their corresponding Lenke classification

PurposeSurgical instrumentation for adolescent idiopathic scoliosis (AIS) is a complex procedure where selection of the appropriate curve segment to fuse, i.e., fusion region, is a challenging decision in scoliosis surgery. Currently, the Lenke classification model is used for fusion region evaluation and surgical planning. Retrospective evaluation of Lenke classification and fusion region results was performed.MethodsUsing a database of 1,776 surgically treated AIS cases, we investigated a topologically ordered self organizing Kohonen network, trained using Cobb angle measurements, to determine the relationship between the Lenke class and the fusion region selection. Specifically, the purpose was twofold (1) produce two spatially matched maps, one of Lenke classes and the other of fusion regions, and (2) associate these two maps to determine where the Lenke classes correlate with the fused spine regions.ResultsTopologically ordered maps obtained using a multi-center database of surgically treated AIS cases, show that the recommended fusion region agrees with the Lenke class except near boundaries between Lenke map classes. Overall agreement was 88%.ConclusionThe Lenke classification and fusion region agree in the majority of adolescent idiopathic scoliosis when reviewed retrospectively. The results indicate the need for spinal fixation instrumentation variation associated with the Lenke classification.

Highlighting Discrepancies in Walking Prediction Accuracy for Patients with Traumatic Spinal Cord Injury: an Evaluation of Validated Prediction Models using a Canadian Multi-centre Spinal Cord Injury Registry

BACKGROUND CONTEXT Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. PURPOSE To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models. STUDY DESIGN A prospective cohort study. PARTICIPANT SAMPLE Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data. OUTCOME MEASURES The FIM locomotor score was used to assess independent walking ability at 1-year follow-up. METHODS Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). RESULTS In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses. CONCLUSIONS Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.