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Movement system impairment model, Knee, Cluster analysis, Classification, Syndrome, What&,rsquo s Known The movement system impairment model has certain limitations in the classification of knee impairments. Partitioning around medoids clustering is a method to organize homogeneous groups with similar characteristics. What&,rsquo s New A new clinical knee examination model is introduced. The model uses the partitioning around medoids clustering method based on clinical signs and symptoms. The model successfully reduced the number of clinical examination variables and prioritized discriminative variables. IntroductionExtra-articular soft tissue injuries are the most common injuries of the knee joint, which may eventually lead to knee osteoarthritis with an estimated prevalence of 24% in adults. 1,, 2, Knee pain is a common musculoskeletal problem in patients referred to outpatient physical therapy centers. The prevalence of knee pain in the United States has increased by 65% over 20 years. 3, Conservative treatment of non-traumatic knee pain is usually the first line of management versus surgical treatments. 4, Over the years, various non-surgical treatments with an emphasis on correcting the impaired movement have been developed for knee pain. 5, Despite the availability of various treatments, it has been suggested that classifying patients into homogeneous groups may simplify the complexities of diagnosis and facilitate treatment options. 5,- 7, Clinical methods classify patients based on their distinct signs, symptoms, treatments, or psychosocial characteristics. 8,, 9, Among the most important clinical classification methods are the pathoanatomic and kinesiopathologic models. 10,- 12, The pathoanatomic model focuses on the effect of stresses on tissues and utilizes diagnostic labeling of the causative factors of the patient&,rsquo s pain (e.g., meniscal tear). However, sometimes patients are referred to a physical therapist with no definite diagnosis. Physical therapists typically treat patients based on their movement impairments rather than on the pathoanatomical source of the pain. As a result, they use the kinesiopathologic model, a movement system diagnostic classification method, as an alternative to the pathoanatomic model. 11, Among kinesiopathologic models, the movement system impairment (MSI) model is considered the most suitable classification approach and has been reviewed clinically and kinematically during the current decade. 5,, 13,, 14, The MSI model of the knee has been shown to have acceptable intratester reliability. 14, This model classifies patients with knee pain into seven subgroups based on examining the knee movement, correction of faulty posture, and re-evaluation of the symptoms. The subgroups are tibiofemoral rotation syndrome, tibiofemoral hypomobility syndrome, tibiofemoral accessory hypermobility syndrome, knee extension syndrome knee hyperextension syndrome, patellar lateral glide syndrome, and knee impairment. Previous studies have reported certain limitations in the MSI model, namely overlapping signs and symptoms, 13, time-consuming examination process, 5, challenges in determining the tibiofemoral rotation angle, 4, and the absence of differences in perceived pain levels between the usual and corrected movement conditions. 13, Attempts have been made to validate the classification of syndromes using statistical models such as Ward&,rsquo s clustering method for hip disorders 15, and hierarchical clustering method for non-specific patellofemoral pain for the lower extremity examination. 16, Statistical analysis can be used to describe the characteristics of a population in an idealized model with algorithms based on similarity and differences in signs and symptoms. 17,, 18, Statistical clustering is an unsupervised learning process in which the data are grouped in different clusters free from preconceptions. Unlike other clustering methods, the partitioning around medoids (PAM) model is a unique and flexible clustering method that allows the entry of various forms of variables (nominal, ordinal, or scalar) besides numeric variables. 17,, 18, In the PAM model, one case is selected as a medoid, and the data with the least dissimilarities to the medoid are clustered around it. The medoids are then replaced to gain the smallest dissimilarity. This process continues until no change takes place in medoids and the determined number of clusters itself. This unique process leads to separated data groups with the least within-group dissimilarity and maximum between-group dissimilarity. 19, This highly accurate method classifies patients into clusters based on their true dissimilarities and distinguishes the important variables for clustering. Thus, classifications based on this model would be simple, most time-efficient, and require less clinical examination variables.Studies evaluating the reliability of the MSI model have reported difficulties in classifying patients based on the signs and symptoms. 7,, 14, We believe that this could be due to difficulties in the judging part of the MSI model. Another study also reported that knee examination based on the MSI model is too time-consuming (about 45 minutes). 14, To address these issues, the main objective of the present study was to use the PAM clustering method for the classification of patients based on their signs and symptoms derived from the MSI model. In addition, we aimed to identify those key tests directly related to knee examination for the sole purpose of reducing examination time.Materials and MethodsIn the present cross-sectional study, patients with knee pain were recruited from several orthopedic and rehabilitation clinics affiliated to Shiraz University of Medical Sciences (Shiraz, Iran) during February-December 2018. The participants were selected using the convenient sampling method, and the sample size was estimated in accordance with previous studies, 5,, 14, and by using the statistical rules of thumb. 20, A total of 209 patients with knee pain referred for physical therapy by an orthopedic surgeon were recruited in the study. The study was approved by the local Medical Ethics Committee (number, IR.SUMS.REC.1396.S993), and written informed consent was obtained from the participants.The inclusion criteria were aged 18-60 years and experiencing non-traumatic pain around the knee (scoring between 3 and 7 in a standing position on a numerical rating scale) for the last two months. 5,, 14, The exclusion criteria were any history of surgery (bone osteotomy, bone fracture repair or surgical correction of structural deformities in the trunk or lower extremity), major general metabolic or systemic diseases, neurological diseases (radiculopathy), obvious leg length discrepancy leading to limping, pregnancy and the use of walking aids, analgesic, and anti-inflammatory drugs up to or at the day of examination. 5,, 14, The examiner was a physical therapist with 12 years of experience in treating patients using the MSI model. All procedures were conducted according to the Declaration of Helsinki. The examination procedure began with a documentation of the patient&,rsquo s history (height, weight, age, sex, pain location, and intensity) followed by a physical examination. The activity level was measured using the Persian version of the Tegner questionnaire, which was previously validated with an acceptable level of reliability. 21, The questionnaire is a self-administered activity rating system (based on a scale of 0 to 10) for patients with various knee disorders. The physical postural examination included assessment of correct alignment (anterior, posterior, and side views) in standing and sitting positions as described in the MSI model. 10, The knee, femoral, and tibial movements were evaluated during different activities such as walking, half-squatting, single-leg stance (comparing both sides), and stair ambulation. A walkway and an adjustable chair were used to perform the walking and sit-to-stand transfer, respectively. Climbing was performed on stairs of 20 cm height. The initial perceived level of pain was recorded during each movement. In case of faulty movement patterns, the therapist trained the participants on the correct pattern, requesting repetition of the movement, and report of the perceived pain level. The difference in the pain level between faulty and corrected movements was coded for each activity and rated from 0 to 7 (0, no change, 1, valgus correction, 2, varus correction, 3, hyperextension correction, 4, patellar movement correction, 5, no immediate correction available, 6, no alignment deficit, and 7, increased pain). This process was repeated in all positions. The examiner palpated each segment during movements, and if an activity was performed incorrectly (with valgus or excessive tibial external rotation), the participant was informed about the correct movement pattern. The movement was then repeated in the corrected form (if possible) and re-evaluated by the examiner. If an obvious difference was found between the previous and corrected movements, the faulty movement was recorded as the impairment. Impairments that could not be corrected immediately (hypomobility) were recorded without repetition of the movement. The deployed coding system was based on the MSI model and previous studies. 5,, 14, A total of 41 scalars (pain) and nominal (alignment deficits) variables were counted during different activities. A muscle stiffness-flexibility test was performed in the supine position to determine the muscle-joint relative flexibility. It consisted of a two-joint hip flexors length test, and the result was reported positive if the tibia was abducted or rotated laterally while lowering the hip in extension. The joint integrity was measured by assessing the accessory motions of the patellofemoral and tibiofemoral joints. The McConnell patellar test 10, and patellar grind test were also performed. The foot arch was measured by determining the Feiss line and classified as high, low, or normal arch. The variables collected during examination based on the PAM clustering analysis are presented in table 1,. Manual muscle testing was also performed for the muscles of the lower extremity. However, the results were not used in the PAM analysis, since muscle power is not a discriminating factor for clustering.PositionSymptoms Signs WalkingSymptom alleviation with the correction of walkingKnee valgus and varus Femoral abduction and adduction |