Adult spinal deformity surgical decision-making score

2019 ◽  
Vol 28 (7) ◽  
pp. 1652-1660 ◽  
Author(s):  
Takashi Fujishiro ◽  
◽  
Louis Boissière ◽  
Derek Thomas Cawley ◽  
Daniel Larrieu ◽  
...  
Keyword(s):  
Decision Making ◽  
Spinal Deformity ◽  
Adult Spinal Deformity ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals Upper-thoracic versus lower-thoracic upper instrumented vertebra in adult spinal deformity patients undergoing fusion to the pelvis: surgical decision-making and patient outcomes

2020 ◽  
Vol 32 (4) ◽  
pp. 600-606 ◽  
Author(s):  
Alan H. Daniels ◽  
Daniel B. C. Reid ◽  
Wesley M. Durand ◽  
D. Kojo Hamilton ◽  
Peter G. Passias ◽  
...  
Keyword(s):  
Decision Making ◽  
Blood Loss ◽  
Spinal Deformity ◽  
Adult Spinal Deformity ◽  
Complication Rates ◽  
Sagittal Vertical Axis ◽  
Surgical Decision ◽  
Estimated Blood Loss ◽  
Upper Thoracic ◽  
Surgical Decision Making

OBJECTIVEOptimal patient selection for upper-thoracic (UT) versus lower-thoracic (LT) fusion during adult spinal deformity (ASD) correction is challenging. Radiographic and clinical outcomes following UT versus LT fusion remain incompletely understood. The purposes of this study were: 1) to evaluate demographic, radiographic, and surgical characteristics associated with choice of UT versus LT fusion endpoint; and 2) to evaluate differences in radiographic, clinical, and health-related quality of life (HRQOL) outcomes following UT versus LT fusion for ASD.METHODSRetrospective review of a prospectively collected multicenter ASD database was performed. Patients with ASD who underwent fusion from the sacrum/ilium to the LT (T9–L1) or UT (T1–6) spine were compared for demographic, radiographic, and surgical characteristics. Outcomes including proximal junctional kyphosis (PJK), reoperation, rod fracture, pseudarthrosis, overall complications, 2-year change in alignment parameters, and 2-year HRQOL metrics (Lumbar Stiffness Disability Index, Scoliosis Research Society-22r questionnaire, Oswestry Disability Index) were compared after controlling for confounding factors via multivariate analysis.RESULTSThree hundred three patients (169 LT, 134 UT) were evaluated. Independent predictors of UT fusion included greater thoracic kyphosis (odds ratio [OR] 0.97 per degree, p = 0.0098), greater coronal Cobb angle (OR 1.06 per degree, p < 0.0001), and performance of a 3-column osteotomy (3-CO; OR 2.39, p = 0.0351). While associated with longer operative times (ratio 1.13, p < 0.0001) and greater estimated blood loss (ratio 1.31, p = 0.0018), UT fusions resulted in greater sagittal vertical axis improvement (−59.5 vs −41.0 mm, p = 0.0035) and lower PJK rates (OR 0.49, p = 0.0457). No significant differences in postoperative HRQOL measures, reoperation, or overall complication rates were detected between groups (all p > 0.1).CONCLUSIONSGreater deformity and need for 3-CO increased the likelihood of UT fusion. Despite longer operative times and greater blood loss, UT fusions resulted in better sagittal correction and lower 2-year PJK rates following surgery for ASD. While continued surveillance is necessary, this information may inform patient counseling and surgical decision-making.

Clinical Performance and Concurrent Validity of the Adult Spinal Deformity Surgical Decision-making Score

Spine ◽  
2020 ◽  
Vol 45 (14) ◽  
pp. E847-E855
Author(s):  
Takashi Fujishiro ◽  
Louis Boissière ◽  
Derek Thomas Cawley ◽  
Daniel Larrieu ◽  
Olivier Gille ◽  
...  
Keyword(s):  
Decision Making ◽  
Spinal Deformity ◽  
Concurrent Validity ◽  
Clinical Performance ◽  
Adult Spinal Deformity ◽  
Surgical Decision ◽  
Surgical Decision Making

The impact of preoperative supine radiographs on surgical strategy in adult spinal deformity

2021 ◽  
pp. 1-7
Author(s):  
Francis Lovecchio ◽  
Jonathan Charles Elysee ◽  
Renaud Lafage ◽  
Jeff Varghese ◽  
Mathieu Bannwarth ◽  
...  
Keyword(s):  
Spinal Deformity ◽  
Lumbar Lordosis ◽  
Adult Spinal Deformity ◽  
Surgical Decision ◽  
Spinal Flexibility ◽  
Grade 3 ◽  
Upper Instrumented Vertebra ◽  
Invasive Techniques ◽  
The Impact ◽  
Surgical Decision Making

OBJECTIVE Preoperative planning for adult spinal deformity (ASD) surgery is essential to prepare the surgical team and consistently obtain postoperative alignment goals. Positional imaging may allow the surgeon to evaluate spinal flexibility and anticipate the need for more invasive techniques. The purpose of this study was to determine whether spine flexibility, defined by the change in alignment between supine and standing imaging, is associated with the need for an osteotomy in ASD surgery. METHODS A single-center, dual-surgeon retrospective analysis was performed of adult patients with ASD who underwent correction of a thoracolumbar deformity between 2014 and 2018 (pelvis to upper instrumented vertebra between L1 and T9). Patients were stratified into osteotomy (Ost) and no-osteotomy (NOst) cohorts according to whether an osteotomy was performed (Schwab grade 2 or higher). Demographic, surgical, and radiographic parameters were compared. The sagittal correction from intraoperative prone positioning alone (sagittal flexibility percentage [Sflex%]) was assessed by comparing the change in lumbar lordosis (LL) between preoperative supine to standing radiographs and preoperative to postoperative alignment. RESULTS Demographics and preoperative and postoperative sagittal alignment were similar between the Ost (n = 60, 65.9%) and NOst (n = 31, 34.1%) cohorts (p > 0.05). Of all Ost patients, 71.7% had a grade 2 osteotomy (mean 3 per patient), 21.7% had a grade 3 osteotomy, and 12.5% underwent both grade 3 and grade 2 osteotomies. Postoperatively, the NOst and Ost cohorts had similar pelvic incidence minus lumbar lordosis (PI-LL) mismatch (mean PI-LL 5.2° vs 1.2°; p = 0.205). Correction obtained through positioning (Sflex%) was significantly lower for in the osteotomy cohort (38.0% vs 76.3%, p = 0.004). A threshold of Sflex% < 70% predicted the need for osteotomy at a sensitivity of 78%, specificity of 56%, and positive predictive value of 77%. CONCLUSIONS The flexibility of the spine is quantitatively related to the use of an osteotomy. Prospective studies are needed to determine thresholds that may be used to standardize surgical decision-making in ASD surgery.

1228: Assessing the Surgical Decision-Making Ability of Novice and Proficient Urologists

2007 ◽  
Vol 177 (4S) ◽  
pp. 405-405
Author(s):  
Suman Chatterjee ◽  
Jonathon Ng ◽  
Edward D. Matsumoto
Keyword(s):  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

Myocardial infarction – An important factor for surgical decision-making?

2008 ◽  
Vol 56 (S 1) ◽  
Author(s):  
B Osswald ◽  
U Tochtermann ◽  
S Keller ◽  
D Badowski-Zyla ◽  
V Gegouskov ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Decision Making ◽  
Surgical Decision ◽  
Surgical Decision Making

scholarly journals 3132 Machine Learning for Prediction of Pathologic Pneumatosis Intestinalis Using CT Scans

2019 ◽  
Vol 3 (s1) ◽  
pp. 60-61
Author(s):  
Kadie Clancy ◽  
Esmaeel Dadashzadeh ◽  
Christof Kaltenmeier ◽  
JB Moses ◽  
Shandong Wu
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Pneumatosis Intestinalis ◽  
Learning Models ◽  
Surgical Decision ◽  
Prediction Task ◽  
Svm Model ◽  
Surgical Decision Making ◽  
Intraoperative Visualization ◽  
Machine Learning Models

OBJECTIVES/SPECIFIC AIMS: This retrospective study aims to create and train machine learning models using a radiomic-based feature extraction method for two classification tasks: benign vs. pathologic PI and operation of benefit vs. operation not needed. The long-term goal of our study is to build a computerized model that incorporates both radiomic features and critical non-imaging clinical factors to improve current surgical decision-making when managing PI patients. METHODS/STUDY POPULATION: Searched radiology reports from 2010-2012 via the UPMC MARS Database for reports containing the term “pneumatosis” (subsequently accounting for negations and age restrictions). Our inclusion criteria included: patient age 18 or older, clinical data available at time of CT diagnosis, and PI visualized on manual review of imaging. Cases with intra-abdominal free air were excluded. Collected CT imaging data and an additional 149 clinical data elements per patient for a total of 75 PI cases. Data collection of an additional 225 patients is ongoing. We trained models for two clinically-relevant prediction tasks. The first (referred to as prediction task 1) classifies between benign and pathologic PI. Benign PI is defined as either lack of intraoperative visualization of transmural intestinal necrosis or successful non-operative management until discharge. Pathologic PI is defined as either intraoperative visualization of transmural PI or withdrawal of care and subsequent death during hospitalization. The distribution of data samples for prediction task 1 is 47 benign cases and 38 pathologic cases. The second (referred to as prediction task 2) classifies between whether the patient benefitted from an operation or not. “Operation of benefit” is defined as patients with PI, be it transmural or simply mucosal, who benefited from an operation. “Operation not needed” is defined as patients who were safely discharged without an operation or patients who had an operation, but nothing was found. The distribution of data samples for prediction task 2 is 37 operation not needed cases and 38 operation of benefit cases. An experienced surgical resident from UPMC manually segmented 3D PI ROIs from the CT scans (5 mm Axial cut) for each case. The most concerning ~10-15 cm segment of bowel for necrosis with a 1 cm margin was selected. A total of 7 slices per patient were segmented for consistency. For both prediction task 1 and prediction task 2, we independently completed the following procedure for testing and training: 1.) Extracted radiomic features from the 3D PI ROIs that resulted in 99 total features. 2.) Used LASSO feature selection to determine the subset of the original 99 features that are most significant for performance of the prediction task. 3.) Used leave-one-out cross-validation for testing and training to account for the small dataset size in our preliminary analysis. Implemented and trained several machine learning models (AdaBoost, SVM, and Naive Bayes). 4.) Evaluated the trained models in terms of AUC and Accuracy and determined the ideal model structure based on these performance metrics. RESULTS/ANTICIPATED RESULTS: Prediction Task 1: The top-performing model for this task was an SVM model trained using 19 features. This model had an AUC of 0.79 and an accuracy of 75%. Prediction Task 2: The top-performing model for this task was an SVM model trained using 28 features. This model had an AUC of 0.74 and an accuracy of 64%. DISCUSSION/SIGNIFICANCE OF IMPACT: To the best of our knowledge, this is the first study to use radiomic-based machine learning models for the prediction of tissue ischemia, specifically intestinal ischemia in the setting of PI. In this preliminary study, which serves as a proof of concept, the performance of our models has demonstrated the potential of machine learning based only on radiomic imaging features to have discriminative power for surgical decision-making problems. While many non-imaging-related clinical factors play a role in the gestalt of clinical decision making when PI presents, we have presented radiomic-based models that may augment this decision-making process, especially for more difficult cases when clinical features indicating acute abdomen are absent. It should be noted that prediction task 2, whether or not a patient presenting with PI would benefit from an operation, has lower performance than prediction task 1 and is also a more challenging task for physicians in real clinical environments. While our results are promising and demonstrate potential, we are currently working to increase our dataset to 300 patients to further train and assess our models. References DuBose, Joseph J., et al. “Pneumatosis Intestinalis Predictive Evaluation Study (PIPES): a multicenter epidemiologic study of the Eastern Association for the Surgery of Trauma.” Journal of Trauma and Acute Care Surgery 75.1 (2013): 15-23. Knechtle, Stuart J., Andrew M. Davidoff, and Reed P. Rice. “Pneumatosis intestinalis. Surgical management and clinical outcome.” Annals of Surgery 212.2 (1990): 160.

Sign in / Sign up

Export Citation Format

Share Document