Date Published: May 1, 2017
Publisher: Public Library of Science
Author(s): Lina Schelin, Eva Tengman, Patrik Ryden, Charlotte Häger, David S. Logerstedt.
Clinical test batteries for evaluation of knee function after injury to the Anterior Cruciate Ligament (ACL) should be valid and feasible, while reliably capturing the outcome of rehabilitation. There is currently a lack of consensus as to which of the many available assessment tools for knee function that should be included. The present aim was to use a statistical approach to investigate the contribution of frequently used tests to avoid redundancy, and filter them down to a proposed comprehensive and yet feasible test battery for long-term evaluation after ACL injury.
In total 48 outcome variables related to knee function, all potentially relevant for a long-term follow-up, were included from a cross-sectional study where 70 ACL-injured (17–28 years post injury) individuals were compared to 33 controls. Cluster analysis and logistic regression were used to group variables and identify an optimal test battery, from which a summarized estimator of knee function representing various functional aspects was derived.
As expected, several variables were strongly correlated, and the variables also fell into logical clusters with higher within-correlation (max ρ = 0.61) than between clusters (max ρ = 0.19). An extracted test battery with just four variables assessing one-leg balance, isokinetic knee extension strength and hop performance (one-leg hop, side hop) were mathematically combined to an estimator of knee function, which acceptably classified ACL-injured individuals and controls. This estimator, derived from objective measures, correlated significantly with self-reported function, e.g. Lysholm score (ρ = 0.66; p<0.001). The proposed test battery, based on a solid statistical approach, includes assessments which are all clinically feasible, while also covering complementary aspects of knee function. Similar test batteries could be determined for earlier phases of ACL rehabilitation or to enable longitudinal monitoring. Such developments, established on a well-grounded consensus of measurements, would facilitate comparisons of studies and enable evidence-based rehabilitation.
Rupture of the anterior cruciate ligament (ACL) is a common injury especially in individuals who participate in sports [1, 2]. Treatment involves either physiotherapy in combination with reconstructive surgery, or physiotherapy alone. Regardless of treatment, individuals still often suffer from varying extents of impaired knee function, both in the short [3, 4] and long-term perspective despite completing rehabilitation [5–8]. Such reduced knee function may be manifested by, for instance, instability, pain, swelling, decreased range of motion, joint stiffness, reduced physical capacity or decreased activity level in everyday tasks, but particularly with regard to sports and recreational activities. Consequently, attempts to determine knee function often combine several assessment tools covering different aspects of knee function based mainly on clinical examination, knee-specific scores and functional tests. The latter are aimed at capturing indicators of physical capacity, e.g. muscular strength, balance, motor coordination etc. There is, however, still no consensus on which outcome measures to use, which makes comparisons across studies difficult and leads to a lack of evidence for specific interventions. In the clinic, self-reported questionnaires and examiner-administrated knee scores such as the International Knee Documentation Committee 2000 subjective form (IKDC) , Knee injury and Osteoarthritis Outcome Score (KOOS)  or Lysholm questionnaire  are commonly used, and often in combination with a strength measurement and a hop task. Regarding functional assessments, different test batteries have been suggested [12–14]. A test battery in this context refers to a set of functional tests. A test battery consisting of three commonly used hop tests (vertical hop, one-leg hop for distance, and side hop), has shown a high ability to discriminate between the injured and non-injured leg of individuals with ACL injury . Another test battery consisting of four hop tests (one-leg hop for distance, 6-m timed hop, triple hop for distance and crossover hop for distance) has also been demonstrated to be reliable and valid [14, 15]. Yet another test battery, consisting of knee-extension, knee-flexion and leg-press tests, discriminates between strength of the injured and the non-injured leg . The full potential of such test batteries is not always achieved, since the specific test results are most often evaluated separately. The statistical methodology for a research question related to a single outcome variable is often straightforward. Typically two (or more) groups are compared with respect to a single variable using a statistical test [e.g. [5, 6, 8]]. Such tests are sometimes suitable to answer research questions, but single outcome variable analysis might not reveal all of the information contained in the data. It is, for example, possible to find significant differences between two groups when studying two variables simultaneously, while a separate analysis for each of them would not reveal any significant group differences. Hence, it would be desirable to analyze several variables simultaneously.
The data from the KACL20-study used in the present paper included data from both healthy-knee controls and ACL-injured individuals. When applying correlation analysis combined with hierarchical cluster analysis the variables fell into five major clusters that in fact represented clinically meaningful dimensions of knee functions. Generally, the pairwise correlation within the clusters was significantly higher than between the clusters (p-value = 0.005), see Fig 2. Each cluster broadly represents different dimensions of knee function; Cluster I: the Hop performance and knee strength included all absolute variables from the functional tests with the exception of the variables from RC and B. Cluster II: the Perceived knee function included most of the self-reported questionnaires and examiner-administrated scores related to perceived knee function, including the five sub scores of KOOS, Lysholm, SF36-bp and SF36-pf. Cluster III: Knee function reflected in activity and health was the most diverse group and included variables related to activity (Tegner, PAS) and general health (SF36), RC (RC-c, RC-i, RC-LSI), and B (B-c, B-i). Cluster IV: the Knee strength ratio and Cluster V: the Limb asymmetry were rather closely related and contained all the relative functional tests variables with the exception of RC-LSI. The average absolute correlations between variables within cluster I-V were 0.55 (SD = 0.11), 0.61 (SD = 0.15), 0.14 (SD = 0.13), 0.39 (SD = 0.08), and 0.29, (SD = 0.13) respectively. The average absolute correlation between variables from different clusters was 0.12 (SD = 0.05).
The aim of this paper was to suggest a solid statistical selection process to derive a comprehensive and yet feasible clinical test battery with different functional aspects to be used in rehabilitation after ACL injury. The test battery may be used to characterize knee function following an ACL injury. In this specific case, for the purpose of suggesting a test battery for long-term follow-up after ACL injury, we used data from the KACL20-study to investigate which combination of variables that optimally distinguished ACL-injured and healthy-knee controls, while still being feasible and clinically relevant. We extracted a test battery with four variables related to functional tests that may be used as a complement to questionnaires and scores in the long-term perspective after injury. It is a true challenge to define “good knee function” and many dimensions need to be considered. Previously reported test batteries have used data without healthy-knee controls, i.e. they compared knee function between the injured and the non-injured leg [12, 14, 15]. However, several studies show that there might be decreased bilateral function after a unilateral ACL injury [16–18]. Therefore, it is essential that a test battery can also reliably discriminate between persons with an ACL injury and healthy-knee controls, since knee function may vary substantially across individuals, whether injured or not. This is accomplished by the suggested test battery, which distinguishes those with good knee function from those with less good knee function.
The present study shows that with a solid statistical approach, we were able to construct a comprehensive and yet feasible test battery for evaluation of knee function after ACL injury which is appropriate in the long-term perspective. Our estimator of knee function combined several aspects, and could be said to more coherently represent true knee function than a single variable is able to. Consensus regarding clinical functional test batteries for various stages of rehabilitation, along with a general health score and a knee-specific health score, would ensure evidence-based assessment of knee function in patients following an ACL injury and enable reliable monitoring of knee function throughout the different phases of rehabilitation. Further, it would make it possible to carry out powerful retrospective and prospective studies over longer timespans post-injury while facilitating comparisons across studies.