scholarly journals Exploring associations of preoperative physical performance with postoperative outcomes after lumbar spinal fusion: a machine learning approach

2021 ◽  
Author(s):  
Esther R.C. Janssen ◽  
Biche Osong ◽  
Johan van Soest ◽  
Andre Dekker ◽  
Nico L.U. van Meeteren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Physical Performance ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Lumbar Spinal Fusion ◽  
Machine Learning Approach ◽  
Lumbar Spinal

Predicting Readmission After Anterior, Posterior, and Posterior Interbody Lumbar Spinal Fusion: A Neural Network Machine Learning Approach

2021 ◽  
Author(s):  
Eren O. Kuris ◽  
Ashwin Veeramani ◽  
Christopher L. McDonald ◽  
Kevin J. DiSilvestro ◽  
Andrew S. Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Spinal Fusion ◽  
Learning Approach ◽  
Lumbar Spinal Fusion ◽  
Machine Learning Approach ◽  
Lumbar Spinal ◽  
Anterior Posterior

scholarly journals The effect of timing of rehabilitation on physical performance after lumbar spinal fusion: a randomized clinical study

2013 ◽  
Vol 22 (8) ◽  
pp. 1884-1890 ◽  
Author(s):  
Lisa G. Oestergaard ◽  
Claus V. Nielsen ◽  
Cody E. Bünger ◽  
Karen Svidt ◽  
Finn B. Christensen
Keyword(s):  
Clinical Study ◽  
Spinal Fusion ◽  
Physical Performance ◽  
Lumbar Spinal Fusion ◽  
Randomized Clinical Study ◽  
Lumbar Spinal

Importance of anthropometric features to predict physical performance in elite youth soccer: a machine learning approach

2020 ◽  
pp. 1-12 ◽  
Author(s):  
Tindaro Bongiovanni ◽  
Athos Trecroci ◽  
Luca Cavaggioni ◽  
Alessio Rossi ◽  
Enrico Perri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Physical Performance ◽  
Learning Approach ◽  
Youth Soccer ◽  
Machine Learning Approach ◽  
Anthropometric Features

Prediction of major complications and readmission after lumbar spinal fusion: a machine learning-driven approach

2021 ◽  
Author(s):  
Akash A. Shah ◽  
Sai K. Devana ◽  
Changhee Lee ◽  
Amador Bugarin ◽  
Elizabeth L. Lord ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Lumbar Spinal Fusion ◽  
Major Complications ◽  
Lumbar Spinal

scholarly journals A machine learning approach to predict early outcomes after pituitary adenoma surgery

2018 ◽  
Vol 45 (5) ◽  
pp. E8 ◽  
Author(s):  
Todd C. Hollon ◽  
Adish Parikh ◽  
Balaji Pandian ◽  
Jamaal Tarpeh ◽  
Daniel A. Orringer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Pituitary Adenoma ◽  
Predictive Model ◽  
Odds Ratio ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Ratio Analysis ◽  
Machine Learning Approach ◽  
The Mean

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.

scholarly journals Spine Treatment Appraisal Report (STAR): Does Marijuana Use Affect Postoperative Outcomes in Patients Receiving Transforaminal Lumbar Interbody Fusion (TLIF)?

2021 ◽  
pp. 219256822199740
Author(s):  
Joseph R. Dettori
Keyword(s):  
Spinal Fusion ◽  
Interbody Fusion ◽  
Marijuana Use ◽  
Transforaminal Lumbar Interbody Fusion ◽  
Postoperative Outcomes ◽  
Lumbar Interbody Fusion ◽  
Lumbar Spinal Fusion ◽  
Lumbar Spinal

Jakoi AM, Kirchner GJ, Kerbel YE, Iorio JA, Khalsa AS. The effects of marijuana use on lumbar spinal fusion. Spine (Phila Pa 1976). 2020;45(9):629-634.

P30. Development of a machine learning algorithm for prediction of complications and readmission after lumbar spinal fusion

2021 ◽  
Vol 21 (9) ◽  
pp. S154-S155
Author(s):  
Akash A. Shah ◽  
Sai Devana ◽  
Changhee Lee ◽  
Amador Bugarin ◽  
Alexander Upfill-Brown ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Lumbar Spinal Fusion ◽  
Prediction Of Complications ◽  
Lumbar Spinal

Can a machine learning model accurately predict patient resource utilization following lumbar spinal fusion?

2020 ◽  
Vol 20 (3) ◽  
pp. 329-336 ◽  
Author(s):  
Jaret M. Karnuta ◽  
Joshua L. Golubovsky ◽  
Heather S. Haeberle ◽  
Prashant V. Rajan ◽  
Sergio M. Navarro ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spinal Fusion ◽  
Resource Utilization ◽  
Learning Model ◽  
Lumbar Spinal Fusion ◽  
Machine Learning Model ◽  
Lumbar Spinal

scholarly journals Predicting Early Postoperative Outcomes after Pituitary Adenoma Surgery Using a Machine Learning Approach

2018 ◽  
Vol 79 (S 01) ◽  
pp. S1-S188
Author(s):  
Todd Hollon ◽  
Adish Parikh ◽  
Jamaal Tarpeh ◽  
Nadine Ibrahim ◽  
Ariel Barkan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pituitary Adenoma ◽  
Postoperative Outcomes ◽  
Learning Approach ◽  
Machine Learning Approach
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