D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
Predicting Disc Re-Herniation After Lumbar Decompression:
A Machine Learning Approach
Artificial Intelligence
Dino Samartzis
Garrett K. Harada, MD; Zakariah K. Siyaji, BS; G. Michael Mallow, BS; Alexander L. Hornung, BS; Fayyazul Hassan, MS;
Bryce A. Basques, MD; Haseeb A. Mohammed, BS; Arash J. Sayari, MD; Dino Samartzis, DSc; Howard S. An, MD
Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA
D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
Introduction
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1. Peul et al. NEJM 2007. 3. Parker et al. CORR 2015.
2. Lurie et al. Spine 2014.
• Lumbar microdiscectomy is an effective therapy for HNPs
– Faster relief of pain symptoms1
– Superior outcomes with careful patient selection2
• Roughly 6% may experience recurrent lumbar disc herniation3
Purpose: To develop a machine learning model to identify patients at risk for lumbar re-herniation
D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
Methods• Retrospective cohort study
– N=2,630 HNP patients (61% maes) (2004-2018)
– Lumbar microdiscectomy
– 2 year mean f/up
• Patient charts reviewed– Demographic, clinical, operative, patient-reported
outcomes, imaging parameters
• Extreme Gradient Boost Model– Cutoff score with Youden’s Index
– Development of web application calculator
– Recursive Feature Elimination
– Cross Validation
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D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
Results
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Variables Re-HNP Control Univariate
Mean±SD Mean±SD p-value
Follow Up (months) 38.3 ± 40.5 27.6 ± 37.9 p < 0.001
Patient Age 52.4±15.2 47.4±14.8 p < 0.001
MIS Technique 34.2% 35.4% 0.59
30-Day Readmission 2.6% 3.4% 0.34
Workers' Compensation Insurance 0.0±0.3 0.1±0.3 0.31
ASC (As opposed to Hospital OR) 14.0% 16.7% 0.46
Female 43.0% 38.9% 0.38
Current Smoker 23.9% 24.6% 0.86
Diagnosis of Diabetes Mellitus 8.9% 8.6% 0.89
Body Mass Index 35.4±6.94 29.4±5.49 p < 0.001
ASA 1.9±0.5 1.9±0.6 0.99
CCI 0.8±1.2 0.8±1.4 0.32
Number of Herniated Levels 1.2±0.4 1.3±0.6 0.09
Central Herniation 16.3% 18.8% 0.06
Lateral Recess Herniation 67.4% 60.2% p < 0.001
Far Lateral Herniation 15.9% 18.4% 0.16
Duration of Symptoms (months) 14.2±15.3 14.0±26.0 0.92
Leg Pain 88.5% 73.1% p < 0.001
Motor Weakness 30.8% 23.1% 0.06
Cauda Equina Syndrome 6.7% 5.2% 0.40
Oswestry Disability Index 48.9±8.0 44.0±11.7 0.91
Visual Analog Scale - Back 5.6±1.5 5.6±1.7 0.62
Visual Analog Scale - Leg 6.2±1.2 6.3±1.6 0.61
SF12 - Mental Component 48.7±6.9 49.4±7.41 0.37
SF12 - Physical Component 30.7±5.00 31.1±5.59 0.45
VR12 - Mental Component 50.0±6.4 50.6±7.2 0.38
VR12 - Physical Component 32.2±6.0 32.6±6.2 0.33
Coronal Angulation 4.4±3.0 5.0±3.9 0.16
Sacral Slope 34.0±9.1 34.8±8.5 0.34
Pelvic Tilt 22.6±6.3 22.1±7.8 0.53
Pelvic Incidence 56.8±11.3 57.0±11.0 0.85
Lumbar Lordosis 42.2±12.2 46.2±12.9 p < 0.001
PI-LL Mismatch 16.4±14.5 9.8±12.2 p < 0.001
Table 1. Univariate Analysis Between Re-HNP and Control for Preoperative Patient Characteristics
Bolding indicates statistical significance; SD=standard deviation; MIS = Minimally-Invasive; ASC = Ambulatory Surgery Center; ASA =
American Society of Anesthesiologists; CCI = Charlson Comorbidity Index; PI = Pelvic Incidence; LL = Lumbar Lordosis
4.3% re-herniation
D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
ResultsModel Performance and Variable Selection
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Feature Selection through Recursive Feature Elimination
D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
AUC: 0.72F-Score: 0.72Recall: 0.80
Precision: 0.64
ResultsWeb-Based Risk Calculator
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D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
Conclusion
• Re-herniation after lumbar decompression is a relatively common post-operative outcome
• The RAD profile is a novel web tool that can be used to accurately risk-stratify patients for re-herniation
• This may be used to pre-operatively counsel patients and may help reduce long periods of ineffective non-operative therapy
• Future studies should aim to validate this tool in other patient populations
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D E P A R T M E N T O F O R T H O P A E D I C S U R G E R Y
International Spine Research& Innovation Initiative (ISRII)
Twitter@DinoSamartzis