Skip to main content
Top

Open Access 29-12-2023 | Artificial Intelligence | Original Article

Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery

Authors: Babak Saravi, Alisia Zink, Sara Ülkümen, Sebastien Couillard-Despres, Gernot Lang, Frank Hassel

Published in: European Spine Journal

Login to get access

Abstract

Purpose

A common spine surgery procedure involves decompression of the lumbar spine. The impact of the surgeon’s learning curve on relevant clinical outcomes is currently not well examined in the literature. A variety of machine learning algorithms have been investigated in this study to determine how a surgeon's learning curve and other clinical parameters will influence prolonged lengths of stay (LOS), extended operating times (OT), and complications, as well as whether these clinical parameters can be reliably predicted.

Methods

A retrospective monocentric cohort study of patients with lumbar spinal stenosis treated with microsurgical (MSD) and full-endoscopic (FED) decompression was conducted. The study included 206 patients with lumbar spinal stenosis who underwent FED (63; 30.6%) and MSD (118; 57.3%). Prolonged LOS and OT were defined as those exceeding the 75th percentile of the cohort. Furthermore, complications were assessed as a dependent variable. Using unsupervised learning, clusters were identified in the data, which helped distinguish between the early learning curve (ELC) and the late learning curve (LLC). From 15 algorithms, the top five algorithms that best fit the data were selected for each prediction task. We calculated the accuracy of prediction (Acc) and the area under the curve (AUC). The most significant predictors were determined using a feature importance analysis.

Results

For the FED group, the median number of surgeries with case surgery type at the time of surgery was 72 in the ELC group and 274 in the LLC group. FED patients did not significantly differ in outcome variables (LOS, OT, complication rate) between the ELC and LLC group. The random forest model demonstrated the highest mean accuracy and AUC across all folds for each classification task. For OT, it achieved an accuracy of 76.08% and an AUC of 0.89. For LOS, the model reached an accuracy of 83.83% and an AUC of 0.91. Lastly, in predicting complications, the random forest model attained the highest accuracy of 89.90% and an AUC of 0.94. Feature importance analysis indicated that LOS, OT, and complications were more significantly affected by patient characteristics than the surgical technique (FED versus MSD) or the surgeon's learning curve.

Conclusions

A median of 72 cases of FED surgeries led to comparable clinical outcomes in the early learning curve phase compared to experienced surgeons. These outcomes seem to be more significantly affected by patient characteristics than the learning curve or the surgical technique. Several study variables, including the learning curve, can be used to predict whether lumbar decompression surgery will result in an increased LOS, OT, or complications. To introduce the provided prediction tools into clinics, the algorithms need to be implemented into open-source software and externally validated through large-scale randomized controlled trials.
Literature
3.
go back to reference Deyo RA, Mirza SK, Martin BI et al (2010) Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA 303:1259–1265CrossRefPubMedPubMedCentral Deyo RA, Mirza SK, Martin BI et al (2010) Trends, major medical complications, and charges associated with surgery for lumbar spinal stenosis in older adults. JAMA 303:1259–1265CrossRefPubMedPubMedCentral
5.
12.
go back to reference Perez-Cruet MJ, Fessler RG, Perin NI (2002) Review: complications of minimally invasive spinal surgery. Neurosurgery 51:S26-36CrossRefPubMed Perez-Cruet MJ, Fessler RG, Perin NI (2002) Review: complications of minimally invasive spinal surgery. Neurosurgery 51:S26-36CrossRefPubMed
15.
go back to reference Dagenais S, Roffey DM, Wai EK et al (2009) Can cost utility evaluations inform decision making about interventions for low back pain? Spine J 9(11):944–957CrossRefPubMed Dagenais S, Roffey DM, Wai EK et al (2009) Can cost utility evaluations inform decision making about interventions for low back pain? Spine J 9(11):944–957CrossRefPubMed
16.
go back to reference Saravi B, Ülkümen S, Lang G, Couillard-Després S, Hassel F (2023) Case-matched radiological and clinical outcome evaluation of interlaminar versus microsurgical decompression of lumbar spinal stenosis. Eur Spine J 32:2863–2874CrossRefPubMed Saravi B, Ülkümen S, Lang G, Couillard-Després S, Hassel F (2023) Case-matched radiological and clinical outcome evaluation of interlaminar versus microsurgical decompression of lumbar spinal stenosis. Eur Spine J 32:2863–2874CrossRefPubMed
20.
go back to reference Hoda M, El Saddik A, Wai E, Phan P (2019) Predicting spine surgery complications using machine learning. In: 2019 IEEE international conference on multimedia & expo workshops (ICMEW). IEEE, Shanghai, China, pp 49–53 Hoda M, El Saddik A, Wai E, Phan P (2019) Predicting spine surgery complications using machine learning. In: 2019 IEEE international conference on multimedia & expo workshops (ICMEW). IEEE, Shanghai, China, pp 49–53
24.
go back to reference Rong L-M, Xie P-G, Shi D-H et al (2008) Spinal surgeons’ learning curve for lumbar microendoscopic discectomy: a prospective study of our first 50 and latest 10 cases. Chin Med J (Engl) 121:2148–2151CrossRefPubMed Rong L-M, Xie P-G, Shi D-H et al (2008) Spinal surgeons’ learning curve for lumbar microendoscopic discectomy: a prospective study of our first 50 and latest 10 cases. Chin Med J (Engl) 121:2148–2151CrossRefPubMed
27.
go back to reference Siccoli A, de Wispelaere MP, Schröder ML (2019) Machine learning–based preoperative predictive analytics for lumbar spinal stenosis. Neurosurg Focus 46:5CrossRef Siccoli A, de Wispelaere MP, Schröder ML (2019) Machine learning–based preoperative predictive analytics for lumbar spinal stenosis. Neurosurg Focus 46:5CrossRef
28.
go back to reference Dibra FF, Silverberg AJ, Vasilopoulos T et al (2019) Arthroplasty care redesign impacts the predictive accuracy of the risk assessment and prediction tool. J Arthroplast 34:2549–2554CrossRef Dibra FF, Silverberg AJ, Vasilopoulos T et al (2019) Arthroplasty care redesign impacts the predictive accuracy of the risk assessment and prediction tool. J Arthroplast 34:2549–2554CrossRef
30.
go back to reference Biron DR, Sinha I, Kleiner JE (2019) A novel machine learning model developed to assist in patient selection for outpatient total shoulder arthroplasty. J Am Acad Orthop Surg 28:580–585CrossRef Biron DR, Sinha I, Kleiner JE (2019) A novel machine learning model developed to assist in patient selection for outpatient total shoulder arthroplasty. J Am Acad Orthop Surg 28:580–585CrossRef
31.
go back to reference Navarro SM, Wang EY, Haeberle HS (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33:3617–3623CrossRefPubMed Navarro SM, Wang EY, Haeberle HS (2018) Machine learning and primary total knee arthroplasty: patient forecasting for a patient-specific payment model. J Arthroplasty 33:3617–3623CrossRefPubMed
33.
go back to reference Fontana MA, Lyman S, Sarker GK et al (2019) Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clinical orthopaedics and related research. Lippincott Williams Wilkins 477:1267–1279 Fontana MA, Lyman S, Sarker GK et al (2019) Can machine learning algorithms predict which patients will achieve minimally clinically important differences from total joint arthroplasty? Clinical orthopaedics and related research. Lippincott Williams Wilkins 477:1267–1279
34.
go back to reference Malik AT, Khan SN (2019) Predictive modeling in spine surgery. Ann Transl Med 7:173CrossRef Malik AT, Khan SN (2019) Predictive modeling in spine surgery. Ann Transl Med 7:173CrossRef
35.
go back to reference Kobayashi K, Ando K, Kato F et al (2019) Predictors of prolonged length of stay after lumbar interbody fusion: a multicenter study. Glob Spine J 9:466–472CrossRef Kobayashi K, Ando K, Kato F et al (2019) Predictors of prolonged length of stay after lumbar interbody fusion: a multicenter study. Glob Spine J 9:466–472CrossRef
36.
go back to reference Adogwa O, Lilly DT, Khalid S et al (2019) Extended length of stay after lumbar spine surgery: sick patients, postoperative complications, or practice style differences among hospitals and physicians? World Neurosurg 123:734–739CrossRef Adogwa O, Lilly DT, Khalid S et al (2019) Extended length of stay after lumbar spine surgery: sick patients, postoperative complications, or practice style differences among hospitals and physicians? World Neurosurg 123:734–739CrossRef
37.
go back to reference Kim BD, Hsu WK, De Oliveira GS et al (2014) Operative duration as an independent risk factor for postoperative complications in single-level lumbar fusion: an analysis of 4588 surgical cases. Spine 39:510–520CrossRefPubMed Kim BD, Hsu WK, De Oliveira GS et al (2014) Operative duration as an independent risk factor for postoperative complications in single-level lumbar fusion: an analysis of 4588 surgical cases. Spine 39:510–520CrossRefPubMed
42.
45.
go back to reference Brewster N, Guthrie C, McBirnie J (1994) CRP levels as a measure of surgical trauma: a comparison of different general surgical procedures. J R Coll Surg Edinb 39:86–88PubMed Brewster N, Guthrie C, McBirnie J (1994) CRP levels as a measure of surgical trauma: a comparison of different general surgical procedures. J R Coll Surg Edinb 39:86–88PubMed
Metadata
Title
Artificial intelligence-based analysis of associations between learning curve and clinical outcomes in endoscopic and microsurgical lumbar decompression surgery
Authors
Babak Saravi
Alisia Zink
Sara Ülkümen
Sebastien Couillard-Despres
Gernot Lang
Frank Hassel
Publication date
29-12-2023
Publisher
Springer Berlin Heidelberg
Published in
European Spine Journal
Print ISSN: 0940-6719
Electronic ISSN: 1432-0932
DOI
https://doi.org/10.1007/s00586-023-08084-7