LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,089
تعداد مقالات9,995
تعداد مشاهده مقاله26,491,403
تعداد دریافت فایل اصل مقاله7,294,375
Farazdaghi, Mohammad Reza, Razeghi, Mohsen, Sobhani, Sobhan, Raeisi Shahraki, Hadi, Motealleh, Alireza. (1399). A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model. سامانه مدیریت نشریات علمی, 45(6), 451-462. doi: 10.30476/ijms.2019.82033.
Mohammad Reza Farazdaghi; Mohsen Razeghi; Sobhan Sobhani; Hadi Raeisi Shahraki; Alireza Motealleh. "A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model". سامانه مدیریت نشریات علمی, 45, 6, 1399, 451-462. doi: 10.30476/ijms.2019.82033.
Farazdaghi, Mohammad Reza, Razeghi, Mohsen, Sobhani, Sobhan, Raeisi Shahraki, Hadi, Motealleh, Alireza. (1399). 'A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model', سامانه مدیریت نشریات علمی, 45(6), pp. 451-462. doi: 10.30476/ijms.2019.82033.
Farazdaghi, Mohammad Reza, Razeghi, Mohsen, Sobhani, Sobhan, Raeisi Shahraki, Hadi, Motealleh, Alireza. A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model. سامانه مدیریت نشریات علمی, 1399; 45(6): 451-462. doi: 10.30476/ijms.2019.82033.

A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model

Iranian Journal of Medical Sciences
مقاله 6، دوره 45، شماره 6، بهمن 2020، صفحه 451-462    اصل مقاله (298.59 K)
نوع مقاله: Original Article(s)
شناسه دیجیتال (DOI): 10.30476/ijms.2019.82033.
نویسندگان
Mohammad Reza Farazdaghi1؛ Mohsen Razeghi1؛ Sobhan Sobhani1؛ Hadi Raeisi Shahraki2؛ Alireza Motealleh* 1
1Department of Physical Therapy, School of Rehabilitation Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
2Department of Epidemiology and Biostatistics, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
چکیده
Background: The movement system impairment (MSI) model is a clinical model that can be used for the classification, diagnosis, and treatment of knee impairments. By using the partitioning around medoids (PAM) clustering method, patients can be easily clustered in homogeneous groups through the determination of the most discriminative variables. The present study aimed to reduce the number of clinical examination variables, determine the important variables, and simplify the MSI model using the PAM clustering method.
Methods: The present cross-sectional study was performed in Shiraz, Iran, during February-December 2018. A total of 209 patients with knee pain were recruited. Patients’ knee, femoral and tibial movement impairments, and the perceived pain level were examined in quiet standing, sitting, walking, partial squatting, single-leg stance (both sides), sit-to-stand transfer, and stair ambulation. The tests were repeated after correction for impairments. Both the pain pattern and the types of impairment were subsequently used in the PAM clustering analysis.
Results: PAM clustering analysis categorized the patients in two main clusters (valgus and non-valgus) based on the presence or absence of valgus impairment. Secondary analysis of the valgus cluster identified two sub-clusters based on the presence of hypomobility. Analysis of the non-valgus cluster showed four sub-clusters with different characteristics. PAM clustering organized important variables in each analysis and showed that only 23 out of the 41 variables were essential in the sub-clustering of patients with knee pain.
Conclusion: A new direct knee examination method is introduced for the organization of important discriminative tests, which requires fewer clinical examination variables.
کلیدواژه‌ها
Movement system impairment model؛ Knee؛ Cluster analysis؛ Classification؛ Syndrome
سایر فایل های مرتبط با مقاله
مراجع
  1. Kraus T, Svehlik M, Singer G, Schalamon J, Zwick E, Linhart W. The epidemiology of knee injuries in children and adolescents. Arch Orthop Trauma Surg. 2012;132:773-9. doi: 10.1007/s00402-012-1480-0. PubMed PMID: 22358221.
  2. Pereira D, Peleteiro B, Araujo J, Branco J, Santos RA, Ramos E. The effect of osteoarthritis definition on prevalence and incidence estimates: a systematic review. Osteoarthritis Cartilage. 2011;19:1270-85. doi: 10.1016/j.joca.2011.08.009. PubMed PMID: 21907813.
  3. Nguyen US, Zhang Y, Zhu Y, Niu J, Zhang B, Felson DT. Increasing prevalence of knee pain and symptomatic knee osteoarthritis: survey and cohort data. Ann Intern Med. 2011;155:725-32. doi: 10.7326/0003-4819-155-11-201112060-00004. PubMed PMID: 22147711; PubMed Central PMCID: PMCPMC3408027.
  4. Harris-Hayes M, Sahrmann SA, Norton BJ, Salsich GB. Diagnosis and management of a patient with knee pain using the movement system impairment classification system. J Orthop Sports Phys Ther. 2008;38:203-13. doi: 10.2519/jospt.2008.2584. PubMed PMID: 18434664.
  5. Kajbafvala M, Ebrahimi-Takamjani I, Salavati M, Saeedi A, Ashnagar Z, Pourahmadi MR, et al. Validation of the movement system impairment-based classification in patients with knee pain. Man Ther. 2016;25:19-26. doi: 10.1016/j.math.2016.05.333. PubMed PMID: 27422593.
  6. Fritz JM, Delitto A, Erhard RE. Comparison of classification-based physical therapy with therapy based on clinical practice guidelines for patients with acute low back pain: a randomized clinical trial. Spine (Phila Pa 1976). 2003;28:1363-71. doi: 10.1097/01.BRS.0000067115.61673.FF. PubMed PMID: 12838091.
  7. Van Dillen LR, Sahrmann SA, Norton BJ, Caldwell CA, McDonnell MK, Bloom NJ. Movement system impairment-based categories for low back pain: stage 1 validation. J Orthop Sports Phys Ther. 2003;33:126-42. doi: 10.2519/jospt.2003.33.3.126. PubMed PMID: 12683688.
  8. Karayannis NV, Jull GA, Hodges PW. Physiotherapy movement based classification approaches to low back pain: comparison of subgroups through review and developer/expert survey. BMC Musculoskelet Disord. 2012;13:24. doi: 10.1186/1471-2474-13-24. PubMed PMID: 22348236; PubMed Central PMCID: PMCPMC3395852.
  9. Whatman C, Hume P, Hing W. The reliability and validity of physiotherapist visual rating of dynamic pelvis and knee alignment in young athletes. Phys Ther Sport. 2013;14:168-74. doi: 10.1016/j.ptsp.2012.07.001. PubMed PMID: 23107340.
  10. Sahrmann S. Movement system impairment syndromes of the extremities, cervical and thoracic spines-e-book. Amsterdam: Elsevier Health Sciences; 2010.
  11. Ludewig PM, Lawrence RL, Braman JP. What’s in a name? Using movement system diagnoses versus pathoanatomic diagnoses. J Orthop Sports Phys Ther. 2013;43:280-3. doi: 10.2519/jospt.2013.0104. PubMed PMID: 23636096.
  12. Willis S, Rosedale R, Rastogi R, Robbins SM. Inter-rater reliability of the McKenzie System of Mechanical Diagnosis and Therapy in the examination of the knee. J Man Manip Ther. 2017;25:83-90. doi: 10.1080/10669817.2016.1229396. PubMed PMID: 28559667; PubMed Central PMCID: PMCPMC5430453.
  13. Salsich GB, Graci V, Maxam DE. The effects of movement pattern modification on lower extremity kinematics and pain in women with patellofemoral pain. J Orthop Sports Phys Ther. 2012;42:1017-24. doi: 10.2519/jospt.2012.4231. PubMed PMID: 22960572; PubMed Central PMCID: PMCPMC4383765.
  14. Kajbafvala M, Ebrahimi-Takamjani I, Salavati M, Saeedi A, Pourahmadi MR, Ashnagar Z, et al. Intratester and intertester reliability of the movement system impairment-based classification for patients with knee pain. Man Ther. 2016;26:117-24. doi: 10.1016/j.math.2016.07.014. PubMed PMID: 27544452.
  15. Bierma-Zeinstra SM, Bohnen AM, Bernsen RM, Ridderikhoff J, Verhaar JA, Prins A. Hip problems in older adults: classification by cluster analysis. J Clin Epidemiol. 2001;54:1139-45. doi: 10.1016/s0895-4356(01)00398-5. PubMed PMID: 11675165.
  16. Selfe J, Janssen J, Callaghan M, Witvrouw E, Sutton C, Richards J, et al. Are there three main subgroups within the patellofemoral pain population? A detailed characterisation study of 127 patients to help develop targeted intervention (TIPPs). Br J Sports Med. 2016;50:873-80. doi: 10.1136/bjsports-2015-094792. PubMed PMID: 26834185; PubMed Central PMCID: PMCPMC4975826.
  17. Kassambara A. Practical guide to cluster analysis in R: Unsupervised machine learning. London: Sthda; 2017.
  18. Lesmeister C. Mastering machine learning with R. Birmingham: Packt Publishing Ltd; 2015.
  19. Verma R, Puntambekar D. Comparison of partitioning algorithms for categorical data in cluster. Int J Eng Sci. 2018;18701.
  20. Van Belle G. Statistical rules of thumb. New Jersey: John Wiley & Sons; 2011.
  21. Negahban H, Mostafaee N, Sohani SM, Mazaheri M, Goharpey S, Salavati M, et al. Reliability and validity of the Tegner and Marx activity rating scales in Iranian patients with anterior cruciate ligament injury. Disabil Rehabil. 2011;33:2305-10. doi: 10.3109/09638288.2011.570409. PubMed PMID: 21491968.
  22. Rousseeuw PJ. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics. 1987;20:53-65. doi: 10.1016/0377-0427(87)90125-7.
  23. Witvrouw E, Werner S, Mikkelsen C, Van Tiggelen D, Vanden Berghe L, Cerulli G. Clinical classification of patellofemoral pain syndrome: guidelines for non-operative treatment. Knee Surg Sports Traumatol Arthrosc. 2005;13:122-30. doi: 10.1007/s00167-004-0577-6. PubMed PMID: 15703965.
  24. Stefanik JJ, Neogi T, Niu J, Roemer FW, Segal NA, Lewis CE, et al. The diagnostic performance of anterior knee pain and activity-related pain in identifying knees with structural damage in the patellofemoral joint: the Multicenter Osteoarthritis Study. J Rheumatol. 2014;41:1695-702. doi: 10.3899/jrheum.131555. PubMed PMID: 24931959; PubMed Central PMCID: PMCPMC4182011.
  25. Keays SL, Mason M, Newcombe PA. Individualized physiotherapy in the treatment of patellofemoral pain. Physiother Res Int. 2015;20:22-36. doi: 10.1002/pri.1593. PubMed PMID: 24852505.
  26. Nakagawa TH, Maciel CD, Serrao FV. Trunk biomechanics and its association with hip and knee kinematics in patients with and without patellofemoral pain. Man Ther. 2015;20:189-93. doi: 10.1016/j.math.2014.08.013. PubMed PMID: 25261089.
آمار
تعداد مشاهده مقاله: 1,724
تعداد دریافت فایل اصل مقاله: 1,950


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب