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