Identifying Acute Lumbar Spondylolysis in Young Athletes with Low Back Pain: Retrospective Classification and Regression Tree Analysis.
Journal
Spine
ISSN: 1528-1159
Titre abrégé: Spine (Phila Pa 1976)
Pays: United States
ID NLM: 7610646
Informations de publication
Date de publication:
01 Aug 2021
01 Aug 2021
Historique:
pubmed:
5
1
2021
medline:
17
7
2021
entrez:
4
1
2021
Statut:
ppublish
Résumé
Case-control study. The aim of this study was to establish an algorithm to distinguish acute lumbar spondylolysis (LS) from nonspecific low back pain (NSLBP) among patients in junior high school by classification and regression tree (CART) analysis. Rapid identification of acute LS is important because delayed diagnosis may result in pseudarthrosis in the pars interarticularis. To diagnose acute LS, magnetic resonance imaging (MRI) or computed tomography is necessary. However, not all adolescent patients with low back pain (LBP) can access these technologies. Therefore, a clinical algorithm that can detect acute LS is needed. The medical records of 223 junior high school-aged patients with diagnosed acute NSLBP or LS verified by MRI were reviewed. A total of 200 patients were examined for establishing the algorithm and 23 were employed for testing the performance of the algorithm. CART analysis was applied to establish the algorithm using the following data; age, sex, school grades, days after symptom onset, history of LBP, days of past LBP, height, passive straight leg raising test results, hours per week spent in sports activities, existence of spina bifida, lumbar lordosis angle, and lumbosacral joint angle. Sensitivity and specificity of the algorithm and the area under the ROC curve were calculated to assess algorithm performance. The algorithm revealed that sex, days after symptom onset, days of past LBP, hours per week spent in sports activities, and existence of spina bifida were key predictors for identifying acute LS versus NSLBP. Algorithm sensitivity was 0.64, specificity was 0.92, and the area under the ROC curve was 0.79. The algorithm can be used in clinical practice to distinguish acute LS from NSLBP in junior high school athletes, although referral to MRI may be necessary for definitive diagnosis considering the algorithm's sensitivity.Level of Evidence: 4.
Sections du résumé
STUDY DESIGN
METHODS
Case-control study.
OBJECTIVE
OBJECTIVE
The aim of this study was to establish an algorithm to distinguish acute lumbar spondylolysis (LS) from nonspecific low back pain (NSLBP) among patients in junior high school by classification and regression tree (CART) analysis.
SUMMARY OF BACKGROUND DATA
BACKGROUND
Rapid identification of acute LS is important because delayed diagnosis may result in pseudarthrosis in the pars interarticularis. To diagnose acute LS, magnetic resonance imaging (MRI) or computed tomography is necessary. However, not all adolescent patients with low back pain (LBP) can access these technologies. Therefore, a clinical algorithm that can detect acute LS is needed.
METHODS
METHODS
The medical records of 223 junior high school-aged patients with diagnosed acute NSLBP or LS verified by MRI were reviewed. A total of 200 patients were examined for establishing the algorithm and 23 were employed for testing the performance of the algorithm. CART analysis was applied to establish the algorithm using the following data; age, sex, school grades, days after symptom onset, history of LBP, days of past LBP, height, passive straight leg raising test results, hours per week spent in sports activities, existence of spina bifida, lumbar lordosis angle, and lumbosacral joint angle. Sensitivity and specificity of the algorithm and the area under the ROC curve were calculated to assess algorithm performance.
RESULTS
RESULTS
The algorithm revealed that sex, days after symptom onset, days of past LBP, hours per week spent in sports activities, and existence of spina bifida were key predictors for identifying acute LS versus NSLBP. Algorithm sensitivity was 0.64, specificity was 0.92, and the area under the ROC curve was 0.79.
CONCLUSION
CONCLUSIONS
The algorithm can be used in clinical practice to distinguish acute LS from NSLBP in junior high school athletes, although referral to MRI may be necessary for definitive diagnosis considering the algorithm's sensitivity.Level of Evidence: 4.
Identifiants
pubmed: 33395023
doi: 10.1097/BRS.0000000000003922
pii: 00007632-202108010-00013
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1026-1032Informations de copyright
Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.
Références
Leone A, Cianfoni A, Cerase A, et al. Lumbar spondylolysis: a review. Skeletal Radiol 2011; 40:683–700.
Stanitski CL. Spondylolysis and spondylolisthesis in athletes. Oper Tech Sports Med 2006; 14:141–146.
Sakai T, Sairyo K, Takao S, et al. Incidence of lumbar spondylolysis in the general population in Japan based on multi-detector computed tomography scans from two thousand subjects. Spine (Phila Pa 1976) 2009; 34:2346–2350.
Yin J, Peng BG, Li YC, et al. Differences of sagittal lumbosacral parameters between patients with lumbar spondylolysis and normal adults. Chin Med J 2016; 129:1166–1170.
Wren TAL, Ponrartana S, Aggabao PC, et al. Increased lumbar lordosis and smaller vertebral cross-sectional area are associated with spondylolysis. Spine (Phila Pa 1976) 2018; 43:833–838.
Lawrence KJ, Elser T, Stromberg R. Lumbar spondylolysis in the adolescent athlete. Phys Ther Sport 2016; 20:56–60.
Thein-Nissenbaum J, Boissonnault WG. Differential diagnosis of spondylolysis in a patient with chronic low back pain. J Orthop Sports Phys Ther 2005; 35:319–326.
Yanagisawa R, Tsukagoshi Y, Nakashima K, et al. Analysis of the physique of adolescents with lumbar spondylolysis. J Clin Sports Med 2018; 26:242–246. (Japanese).
Kobayashi A, Kobayashi T, Kato K, et al. Diagnosis of radiographically occult lumbar spondylolysis in young athletes by magnetic resonance imaging. Am J Sports Med 2013; 41:169–176.
Alqarni AM, Schneiders AG, Cook CE, et al. Clinical tests to diagnose lumbar spondylolysis and spondylolisthesis: a systematic review. Phys Ther Sport 2015; 16:268–275.
Sundell CG, Jonsson H, Adin L, et al. Clinical examination, spondylolysis and adolescent athletes. Int J Sports Med 2013; 34:263–267.
The SK, Zheng W, Ho KY, et al. Diagnosis of gastric cancer using near-infrared raman spectroscopy and classification and regression tree techniques. J Biomed Opt 2008; 13:1–8.
Bittencourt NF, Ocarino JM, Mendonça LD, et al. Foot and hip contributions to high frontal plane knee projection angle in athletes: a classification and regression tree approach. J Orthop Sports Phys Ther 2012; 42:996–1004.
Mendonça LD, Ocarino JM, Bittencourt NFN, et al. Association of hip and foot factors with patellar tendinopathy (jumper's knee) in athletes. J Orthop Sports Phys Ther 2018; 48:676–684.
Majlesi J, Togay H, Unalan H, et al. The sensitivity and specificity of the slump and the straight leg raising tests in patients with lumbar disc herniation. J Clin Rheumatol 2008; 14:87–91.
Caglayan M, Tacar O, Demirant A, et al. Effects of lumbosacral angles on development of low back pain. J Musculoskelet Pain 2014; 22:251–255.
Schaffer C. Overfitting avoidance as bias. Machine Learning 1993; 10:153–178.
Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 2010; 5:1315–1316.
Sairyo K, Katoh S, Komatsubara S, et al. Spondylolysis fracture angle in children and adolescents on CT indicates the fracture producing force vector: a biomechanical rationale. Internet J Spine Surg 2005; 1:1–6.
Pedersen AB, Mikkelsen EM, Cronin-Fenton D, et al. Missing data and multiple imputation in clinical epidemiological research. Clin Epidemiol 2017; 9:157–166.
DataCamp.com. R Documentation and manuals. Available at: https://www.rdocumentation.org/ . Accessed May 26, 2020.