Introduction

In applied research studies, the focus is on population averages and traditional multivariate analysis to find the significant difference between variables [ 1 ]. Such a focus actively obscured the heterogeneity between subjects and considered it as a noise [ 2 ]. Misleading this heterogeneity can increase errors in estimating the effectiveness of interventions and also in the results of decision analytic models [ 3 ]. In many applied research studies, the most useful level of analysis involves the consideration of the entire case more than comparisons between variables within each case [ 1 ]. Cluster analysis (CA) is the process of grouping of seemingly homogenous objects into different subgroups [ 4 ]. Recently partitioning around mediods (PAM) analysis was suggested to discover and identify unknown patterns without any limitation.

Some evidence disclosed the heterogeneity in patients with low back pain (LBP) [ 5 , 6 ]. Several systems, such as “Mechanical Diagnosis and Therapy” [ 7 ], “Treatment-based Classification” [ 8 ], “Movement System Impairment” [ 9 ], and the “O’sullivan Classification System” [ 10 ] have been developed for classification of patients with LBP that all of these systems have focused on the traditional multivariate analyses for subgrouping in their methods [ 8 , 11 - 13 ]. A few studies also tried to directly uncover the heterogeneity through CA [ 14 - 17 ]. However, they only involved psychosocial and/or work related aspects of LBP and neglected physical examinations and biomechanical dimensions.

Among the different classification approaches, the treatment-based classification (TBC) system has received the highest priority in research [ 18 ]. This system firstly developed in 1995, and subsequent clinical trials have shown that clinical decision-making based on this system provides better outcome than no classification in patients with LBP [ 8 , 19 , 20 ], and has revised and updated many times based on clinical experience and scientific evidence [ 21 , 22 ]. The developers of TBC algorithm emphasized that it should be revised to optimize its comprehensiveness, refine current criteria and explore additional treatments [ 23 ]. This system, like other classification approaches, does not cover all patients with LBP and 25% of patients remained unclassified [ 23 ]. Some previous studies have reported moderate inter-observer reliability for this system [ 23 - 26 ].

Since all criteria in the TBC system were examined individually and separately, the behavior of one criterion in the presence or absence of the others may affect the final outcome of classification. The mentioned shortcomings were not addressed previously. Therefore, the aim of present study was to evaluate the reliability of TBC system using equivalent C3 cluster typology by PAM analysis.

Material and Methods

In this cross-sectional study, a convenience sample of 118 patients with low back problems was screened. Based on 8 primary criteria of the TBC system and considering 5 patients for each criterion included in the clustering, the initial sample size was calculated as 40 patients. Afterward, the initial clustering was performed to identify the number of criteria that was important in clustering. After initial clustering, the maximum number of possible influential criteria was determined 18 patients and the final sample size according to rule of thumb was determined as 90 patients.

Patients in this study were 50 males and 40 females aged 20 to 65 years, who met the eligibility criteria. Informed consent was obtained from all subjects before participation. The patients in the LBP group were selected if they reported pain or discomfort localized between T12 to the gluteal fold with or without referral pain to the legs [ 27 ]. In order to identify the patient, who candidate for rehabilitation treatment, TBC system (2015 version) was used to meet the first level of triage [ 22 ]. Patients with red flags of serious pathology (e.g. pathologic fractures, sacral stress fracture, acute spondylolisthesis, cancer, etc.) or serious comorbidities that do not respond to the standard rehabilitation management (e.g. rheumatoid arthritis, central sensitization) were excluded from the study [ 22 ]. The patients were also excluded if they were currently pregnant or if they had a positive history of back surgery in the preceding 6 months, history of using spinal fusion, scoliosis rod or screws, history of taking steroid medications in the last month or if they received physiotherapy at least 3 months prior to their entrance. We also excluded the patients, classified as self-care management based on 2015 TBC system. Therefore, if the patients had the Oxford depression questionnaire (ODQ) score of less than 6% [ 28 , 29 ] or pain score of less than 4 mm, were excluded from study [ 22 ].

Physical examinations and 2007 TBC criteria

All patients completed the Persian version of Oswestry disability questionnaire (ODI) for low back disability [ 30 ] and their pain was assessed by visual analogue pain scale [ 31 ]. They also completed the Persian version of Fear-Avoidance Beliefs Questionnaire (FABQ) to assess psychological aspects of their work and physical activity [ 32 ]. Since the TBC system (2015 version) [ 22 ] has not yet been completely developed, the TBC system (2007 version) [ 21 ] was used for classification in this study. Evaluation was done by a physical therapist familiar with the TBC system.

Neurological examinations, including muscle strength, sensation, reflexes and the result of straight-leg-raise (SLR) test were recorded [ 21 ]. The range of motion (ROM) for SLR test was measured using an inclinometer in both sides [ 33 ]. Spinal movements were evaluated for presence of any aberrant motion. To detect path of directional preference (DP), active single and repeated spinal movements in standing, and sustained positions in standing and prone position were assessed [ 23 ]. Passive hip internal rotation range of motion (ROM) was measured using a goniometer in prone position [ 34 ]. Spinal mobility and pain arising from applying spring test from L1 to L5 spinous processes was recorded. If any pain was produced by spring test, the prone instability test (PIT) was done afterward [ 35 ].

Traditional classification based on 2007 TBC algorithm is consisted of 2 stages. Initially, 8 criteria sets hierarchically to guide the therapist assigning the patients to specific exercise, manipulation and stabilization subgroups, respectively. If a patient did not meet criteria of any subgroups in stage one, in the second stage 13 additional criteria is conveyed. In this stage, the therapist determined the patient’s best fitted subgroup based on analyzing factors favoring and against each subgroup’s criteria. All TBC criteria are listed in Table 1.

Statistical analysis

To assess the strength of 2007 TBC system, an equivalent clinical C3 CA typology was conducted using PAM analysis. Because the kappa coefficient is a chance-corrected measure of agreement, it is the appropriate reliability coefficient for evaluating the reliability of the 2007 TBC system. Therefore, Cohen’s kappa was run to examine the level of agreement between the TBC system and the C3 cluster typology [ 36 ]. The Silhouette width was used to assess the strength of clustering. The Silhouette width measures a combination of intra cluster homogeneity and inter-cluster heterogeneity. Discriminative power and prevalence of selected criteria was determined for each variable in each cluster. By means of a one-sided chai-square test it was determined whether the frequency of positive variables differed significantly per cluster. All statistical analyses were performed in R 3.5.0 and SPSS 19.0 software. Also for PAM analysis VarSelLCM packages of R software (R Core Team, Ver. 3.5.3, New Zealand) were used.

Results

Distribution of quantitative variables within TBC subgroups presented in Table 2 for a total sample of 90 participants (50 males and 40 females). The ratios of male/female in TBC system were 24/15 for DP flexion, 16/10 for manipulation and 10/15 for stabilization subgroups.

PAM analysis revealed the evidence of clustering for a C3 cluster typology (Average Silhouette width 0.12). Discriminative power and prevalence of selected criteria for C3 typology was shown in Table 3. The prevalence of the TBC subgroups within C3 clusters was also reported. The majority of patients in the cluster 1 (63%) were those with DP flexion based on the TBC system. Also, the majority of patients in cluster 2 (71%) were the manipulation subgroup and about 51% of cluster 3 were the stabilization exercise subgroup. The highest heterogeneity was observed in cluster 3 (Table 3).

The estimation of correlation between each variable and individual subgroups were further evaluated by considering overlap between the ranges of variable’s result (from 0-100%) for each subgroup in adjoining clusters (Table 3). Accordingly, the lower the overlap, the higher the correlation, i.e. the amount of 0% or 100% for the results of variable of “age<40”, indicates the minimum amount of overlap and therefore the maximum amount of correlation. The maximum amount of overlap with minimum correlation is where the results of the variable were achieved to 50%. When the patients were divided into 3 clusters, these overlaps were considerable and the average of Silhouette width is low. In the C3 typology, 10 out of 21 TBC criteria were selected by PAM that could differentiate 100% of patients. None of the criteria associated with fear-avoidance beliefs were included in the new C3 typology.

Cohen’s Kappa revealed fair agreement between the TBC system and C3 cluster typology (Percent of agreement 61%, Kappa=0.36, P<0.001). The results of chai-square test showed that despite the fact that there was no significant difference in the prevalence of “SLR ROM” and “aberrant motion” between C3 clusters, these criteria were selected by PAM as effective criteria for discrimination (Table 3). Therefore, in the C3 typology, 5 symptom criteria, 3 mobility and 2 control criteria were included to differentiate between clusters.

Discussion

The original 2007 TBC criteria by PAM analysis was employed to evaluate the agreement of original TBC and new CA system. Our results revealed that the TBC system has fair agreement with the new 3 cluster typology with 61% of agreement. The lower values of kappa are attributable to increase the probability of chance agreement between two classification approaches [ 36 ].

In this study, selected criteria by PAM analysis were different with original TBC system. Main subgrouping criteria for original TBC system were the primary 8 criteria, setting hierarchically to assign patients to subgroups. Main PAM selected criteria for C3 typology also were those of 7 primary criteria of TBC system with higher discriminative power. This indicates the logical agreement between the TBC system and the C3 clustering. However, the percent of agreement in C3 typology is not a desirable limit for a clinical clustering model [ 37 ]. If we consider the PAM analysis as a valid classification method, this relatively low agreement revealed the weakness of the 2007 TBC system.

A moderate [ 23 - 26 ] and moderate to good [ 21 , 29 , 38 , 39 ] inter-observer reliability have been reported for the TBC system. To the authors’ knowledge, this is the first study that examine the TBC classification using clustering by PAM analysis. All previous studies evaluated the inter-observer or the intra-observer reliability of the TBC approach. Therefore, their results cannot be compared with our results.

Stanton et al. showed that the TBC criteria do not guarantee that patients comply with a single intervention, but that 25% of patients can meet criteria for more than one subgroup [ 23 ]. In addition, the prevalence of patients with “unclear classification” in early versions of TBC system has been estimated between 26 and 50 percent [ 23 , 24 , 38 , 40 ]. The developers of the 2007 TBC system also stated that this system had its limitations and needed to refine the algorithm [ 22 ]. In line with previous studies [ 23 , 40 , 41 ], our results disclosed the evidence of within group inhomogeneity and between group overlapping in the 2007 TBC system. Decision making based on C3 system is inconclusive and it is not comprehensive enough to consider the various clinical presentations of patients with LBP. This may be due to lower average silhouette width of 0.12 and omission of more informative clinical criteria such as aberrant motion, positive PIT test and SLR ROM. The PAM analysis also omitted the results of the spring test in the clustering. These criteria provided beneficial information about the mobility of the lumbar spine. The imperfect combination of criteria in the C3 typology uncover the multifaceted interaction of the variables. It seems that the patients with LBP could be classified into more than 3 clusters to address all presentations of LBP.

The results of Chai-square test showed that the relying only on the traditional statistical methods and their P-values cannot determine whether a criterion is effective or not in a classification. These results are in agreement with recent statistical paradigms related to concept of “statistical significance” [ 42 , 43 ]. According to recent paradigms, we cannot interpret the results based on P-values only. Thus, the phrase of “no significant” does not mean that there is “no relationship” or “no effectiveness” for an intervention approach. We need other statistical indices for better analysis and interpretation of the results [ 42 , 43 ].

Since, all variables in the TBC system were examined individually and separately, the behavior of one variable in the presence or absence of the others, may affect the final outcome of the classification. The CA approach in our study may be contrasted against traditional multivariate methods [ 21 , 23 - 26 , 38 - 41 ] that typically analyze for significant differences between variables. In many applied research studies, the most useful level of analysis involves the consideration of the entire case more than comparisons between variables within each case [ 1 ]. Clustering tries to discover and identify unknown patterns without any limitation. PAM analysis is one advantages of this study that could handle the mixed data sets without challenging [ 1 , 4 ].

One limitation of the present study was the inclusion of 21 criteria in the clustering for the sample size of 90 patients that may affect the strength of C3 typology. Another limitation was related to inherent weakness of the 2007 TBC system. Due to the limited examination criteria and the lack of reliable tests in the diagnosis of movement control disorders in the 2007 TBC system, the C3 typology failed to identify properly the criteria of the stabilization exercise subgroup.

Conclusion

Higher probability of chance agreement was observed between two classification methods. The classification of patients with LBP into C3 typology decreased the strength of clustering markedly. The C3 cluster typology provides the evidence that LBP is a heterogeneous condition with vast combination of signs and symptoms. To that end, traditional classification of patients without considering simultaneous interaction of selected criteria may lead to erroneous results.

Acknowledgment

This article has been derived from the research done by Esmaeil Shokri as a part of the PhD thesis and financially supported by Shiraz University of Medical Sciences, Shiraz, Iran (Grant No 12195-06-01-95).

Authors’ Contribution

E. Shokri contributed to idea formation, proposal writing, data collection and analysis, and drafting the manuscript. A. Motealleh was consulted during idea development and data collection. H. Raeisi Shahraki was consulted during data collection and interpretation processes and evaluating statistical models. R. Jalli took part in data collection and interpretation. M. Razeghi supervised the research project as the principal person and reviewing the manuscript. All authors reviewed and approved the final manuscript.

Ethical Approval

The research was approved by the ethical committee of the Shiraz University of Medical Sciences (IR.SUMS.REC.1395.183).

Informed Consent

A consent form was signed by each participant before data collection.

Funding

The project was supported by Shiraz University of Medical Sciences, Shiraz, Iran (Grant No 12195-06-01-95).

Conflict of Interest

None

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