Scoping Review of Machine Learning and Patient-Reported Outcomes in Spine Surgery

Overview

Journal Bioengineering (Basel)

Date 2025 Feb 26

PMID 40001645

Authors

Christian Quinones

Deepak Kumbhare

Bharat Guthikonda

Stanley Hoang

Affiliations

Soon will be listed here.

Abstract

Machine learning is an evolving branch of artificial intelligence that is being applied in neurosurgical research. In spine surgery, machine learning has been used for radiographic characterization of cranial and spinal pathology and in predicting postoperative outcomes such as complications, functional recovery, and pain relief. A relevant application is the investigation of patient-reported outcome measures (PROMs) after spine surgery. Although a multitude of PROMs have been described and validated, there is currently no consensus regarding which questionnaires should be utilized. Additionally, studies have reported varying degrees of accuracy in predicting patient outcomes based on questionnaire responses. PROMs currently lack standardization, which renders them difficult to compare across studies. The purpose of this manuscript is to identify applications of machine learning to predict PROMs after spine surgery.

References

Rigoard P, Ounajim A, Goudman L, Louis P, Slaoui Y, Roulaud M . A Novel Multi-Dimensional Clinical Response Index Dedicated to Improving Global Assessment of Pain in Patients with Persistent Spinal Pain Syndrome after Spinal Surgery, Based on a Real-Life Prospective Multicentric Study (PREDIBACK) and Machine.... J Clin Med. 2021; 10(21). PMC: 8585086. DOI: 10.3390/jcm10214910. View

Young R . Emerging Role of Artificial Intelligence and Big Data in Spine Care. Int J Spine Surg. 2023; 17(S1):S3-S10. PMC: 10318907. DOI: 10.14444/8504. View

Berjano P, Langella F, Ventriglia L, Compagnone D, Barletta P, Huber D . The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach. J Pers Med. 2021; 11(12). PMC: 8705358. DOI: 10.3390/jpm11121377. View

Collins G, Moons K, Dhiman P, Riley R, Beam A, Van Calster B . TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024; 385:e078378. PMC: 11019967. DOI: 10.1136/bmj-2023-078378. View

Siccoli A, de Wispelaere M, Schroder M, Staartjes V . Machine learning-based preoperative predictive analytics for lumbar spinal stenosis. Neurosurg Focus. 2019; 46(5):E5. DOI: 10.3171/2019.2.FOCUS18723. View

Liew B, Peolsson A, Rugamer D, Wibault J, Lofgren H, Dedering A . Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach. Sci Rep. 2020; 10(1):16782. PMC: 7545179. DOI: 10.1038/s41598-020-73740-7. View

Beighley A, Zhang A, Huang B, Carr C, Mathkour M, Werner C . Patient-reported outcome measures in spine surgery: A systematic review. J Craniovertebr Junction Spine. 2023; 13(4):378-389. PMC: 9910127. DOI: 10.4103/jcvjs.jcvjs_101_22. View

Pedersen C, Andersen M, Carreon L, Eiskjaer S . Applied Machine Learning for Spine Surgeons: Predicting Outcome for Patients Undergoing Treatment for Lumbar Disc Herniation Using PRO Data. Global Spine J. 2020; 12(5):866-876. PMC: 9344505. DOI: 10.1177/2192568220967643. View

Karhade A, Fogel H, Cha T, Hershman S, Doorly T, Kang J . Development of prediction models for clinically meaningful improvement in PROMIS scores after lumbar decompression. Spine J. 2020; 21(3):397-404. DOI: 10.1016/j.spinee.2020.10.026. View

10.

Khan A, Mattei T, Mercier P, Cloney M, Dahdaleh N, Koski T . Outcome Reporting in Spine Surgery: A Review of Historical and Emerging Trends. World Neurosurg. 2023; 179:88-98. DOI: 10.1016/j.wneu.2023.07.067. View

11.

Staartjes V, de Wispelaere M, Vandertop W, Schroder M . Deep learning-based preoperative predictive analytics for patient-reported outcomes following lumbar discectomy: feasibility of center-specific modeling. Spine J. 2018; 19(5):853-861. DOI: 10.1016/j.spinee.2018.11.009. View

12.

McCormick J, Werner B, Shimer A . Patient-reported outcome measures in spine surgery. J Am Acad Orthop Surg. 2013; 21(2):99-107. DOI: 10.5435/JAAOS-21-02-99. View

13.

Moustafa I, Ozsahin D, Mustapha M, Ahbouch A, Oakley P, Harrison D . Utilizing machine learning to predict post-treatment outcomes in chronic non-specific neck pain patients undergoing cervical extension traction. Sci Rep. 2024; 14(1):11781. PMC: 11116459. DOI: 10.1038/s41598-024-62812-7. View

14.

Finkelstein J, Stark R, Lee J, Schwartz C . Patient factors that matter in predicting spine surgery outcomes: a machine learning approach. J Neurosurg Spine. 2021; 35(1):127-136. DOI: 10.3171/2020.10.SPINE201354. View

15.

Park C, Mummaneni P, Gottfried O, Shaffrey C, Tang A, Bisson E . Which supervised machine learning algorithm can best predict achievement of minimum clinically important difference in neck pain after surgery in patients with cervical myelopathy? A QOD study. Neurosurg Focus. 2023; 54(6):E5. DOI: 10.3171/2023.3.FOCUS2372. View

16.

Staartjes V, Stumpo V, Ricciardi L, Maldaner N, Eversdijk H, Vieli M . FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease. Eur Spine J. 2022; 31(10):2629-2638. DOI: 10.1007/s00586-022-07135-9. View

17.

Christodoulou E, Ma J, Collins G, Steyerberg E, Verbakel J, Van Calster B . A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019; 110:12-22. DOI: 10.1016/j.jclinepi.2019.02.004. View

18.

Jaeschke R, Singer J, Guyatt G . Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials. 1989; 10(4):407-15. DOI: 10.1016/0197-2456(89)90005-6. View

19.

Grob A, Rohr J, Stumpo V, Vieli M, Ciobanu-Caraus O, Ricciardi L . Multicenter external validation of prediction models for clinical outcomes after spinal fusion for lumbar degenerative disease. Eur Spine J. 2024; 33(9):3534-3544. DOI: 10.1007/s00586-024-08395-3. View

20.

Janssen E, Osong B, Soest J, Dekker A, van Meeteren N, Willems P . Exploring Associations of Preoperative Physical Performance With Postoperative Outcomes After Lumbar Spinal Fusion: A Machine Learning Approach. Arch Phys Med Rehabil. 2021; 102(7):1324-1330.e3. DOI: 10.1016/j.apmr.2021.02.013. View