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CLINICAL RESEARCH

Construct Validity and Precision of Different Patient-reported Outcome Measures During Recovery After Upper Extremity Fractures

Jayakumar, Prakash MBBS, BSc(Hons), MRCS(Eng), DPhil; Teunis, Teun MD, PhD; Vranceanu, Ana-Maria PhD; Lamb, Sarah DPhil; Williams, Mark DPhil; Ring, David MD, PhD; Gwilym, Stephen FRCS(Orth), PhD

Author Information
Clinical Orthopaedics and Related Research: November 2019 - Volume 477 - Issue 11 - p 2521-2530
doi: 10.1097/CORR.0000000000000928

Abstract

Introduction

Musculoskeletal healthcare professionals have access to a range of patient-reported outcome measures (PROMs) that are designed to quantify patient perceptions of illness and injury [23]. PROMs include general health measures (to assess physical limitations, symptom intensity, and emotional health), disease-specific measures (to assess physical limitations and symptom intensity that are specific to a condition), and region-specific or joint-specific measures (to assess physical limitations and symptom intensity in relation to an anatomic region) [23, 30]. They may be administered through fixed sets of questions developed using classical test theory–the idea that measurement of a construct depends on the reliability and completeness of a test–or computer adaptive tests based on item response theory–the idea that one learns from responses to questions that inform the delivery and number of subsequent questions, enabling faster arrival at sufficiently precise scores [6, 7, 11, 18, 19, 27].

Surgeons should understand the differences between commonly used PROMs alongside their validity (whether instruments measure what they claim to measure) and precision (how well items measure limitations, including susceptibility to censoring) if they are to make informed choices on outcome measures after orthopaedic trauma [19, 30]. Widely used fixed-scale and computer-adaptive PROMs in patients with upper extremity conditions include the Patient-reported Outcome Measurement Information System Physical Function (PROMIS PF) computer-adaptive test (a general measure of physical function) [15] and the upper-extremity version ([PROMIS UE], a computer-adaptive, region-specific measure) [14, 17], the QuickDASH (a fixed-scale, region-specific measure) [4, 22, 32], the Oxford Shoulder Score (OSS) [9], the Oxford Elbow Score (OES) [8], the Patient-rated Wrist Evaluation (PRWE) [21] (all of which are fixed-scale, joint-specific measures), and the European Quality of Life Index-3L ([EQ-5D-3L], a fixed-scale, general health measure) [31].

Although multiple studies have assessed the validity and precision of PROMs in the evidence, few have specifically applied a range of instruments that differ by type (like general health, region-specific, joint-specific) and mode of administration (such as fixed and computer adaptive) in the longitudinal assessment of focused populations recovering from a selection of the most common upper extremity fractures [18, 19]. Fracture patients have more frequently been included within heterogenous cohorts with chronic conditions [18, 19]. Instruments should be psychometrically evaluated for the population being assessed and not just the instruments themselves [29]. Further, almost all studies using computer adaptive PROMs have been conducted in the United States. This study provides an opportunity to assess performance of these PROMs in a relatively large trauma population in the United Kingdom. Evaluating the relative advantages of different PROMs in this context may better inform musculoskeletal professionals, healthcare systems, and policy makers in deciding which instruments to use in practice and research.

We therefore asked: (1) What is the strength of the correlation between different PROMs within 1 week, 2 to 4 weeks, and 6 to 9 months after shoulder, elbow, and wrist fractures? (2) Using a factor analysis, what underlying constructs are being measured by these PROMs? (3) Are there strong floor and ceiling effects with these instruments?

Patients and Methods

We approached 775 new adult patients with isolated proximal humerus, elbow, and distal radius fractures at a Level I trauma center. This was a secondary analysis of a prospective institutional review committee-approved study, adhering to Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines, assessing factors that influence physical limitations after upper extremity fractures in patients recruited at our center between January 1, 2016 and August 31, 2016. We included patients in this study if they were fluent in English, were aged 18 years or older, and were able to provide informed consent. Those with injuries concurrent with a shoulder, elbow, or wrist fracture, fractures of the same arm or any other region (for example, as part of complex polytrauma), refracture during recovery from a previous injury, fracture dislocations, and periprosthetic fracture near the site of previous fixation or joint replacement were ineligible for inclusion. Of the 775 patients, 31 declined to participate because of time constraints, four patients died of unrelated illness before completing PROMs at 6 to 9 months after injury, and six patients could not be contacted. The final sample consisted of 734 patients (498 women) with a mean age of 59 years ± 20 (Table 1).

Table 1.
Table 1.:
Patient demographics

Participants provided demographic details including education level; marital, social, and work status; and arm dominance. We gathered clinical variables such as prior arm injuries, including side, neurovascular compromise, and open or closed fracture; procedures; and adverse events from health records. PROMs were completed on a secure, encrypted, internet-based data collection platform (Assessment CenterSM, Northwestern University, Chicago, IL, USA) [12]. Data were captured at baseline (initial orthopaedic visit, usually within 1 week after injury), early follow-up (2 to 4 weeks), and assessment 6 to 9 months later. Time points were selected with the aim of capturing patient outcomes as close as possible to the injury event, an early stage where most patients have gained pain control, early stabilization and definitive treatment, and a longer-term follow-up where patients often reach a plateau with acceptable return to activities of daily life. These also aligned with our clinical care pathway and intention to limit the amount of responder burden.

In all, 70% of patients (514 of 734) completed assessments in person (on tablet or laptop), 25% (184 of 734) did so by telephone (contacted by a researcher not connected with the clinical care of the patient), and 5% (37 of 734) responded via email using an electronic link. We made three attempts to contact participants by phone for the 6- to 9-month evaluation. Injuries were classified by energy level (high-level injuries such as a high-speed traffic collision and low-level injuries such as falling from standing height) and categorized by region; that is, any proximal humerus, elbow (including radial head, distal humerus, and proximal ulna fractures), and distal radius fractures. Use of opioid analgesia was defined as regular use of any opiates beyond 2 weeks after injury, and those using opiates before injury were only included if there was an increase in opioid use because of their fracture. We categorized antidepressant use as treatment for a preexisting diagnosis of depression as well as newly diagnosed major depression within the first month after injury.

We used the PROMIS PF [15], the PROMIS UE [14, 17], the QuickDASH [4, 22, 32], the OSS [9], the OES [16], the PRWE [21], and the EQ-5D-3L [31]) in this study (see Appendix, Supplemental Digital Content 1, http://links.lww.com/CORR/A218) and obtained scores from each at the specified time points (Table 2). Although most patients answered baseline survey questions by reflecting on their current post-injury condition, some questioned whether they should base these on a recollection of their pre-injury state. In these rare instances, participants were advised to consider their current experience after fracture. This approach was standardized as a single researcher conducted collection of all PROMs.

Table 2.
Table 2.:
PROM scores at 1 week, 2 to 4 weeks and 6 to 9 weeks after injury

Statistical Analyses

We performed descriptive statistics; discrete variables are presented as frequencies and percentages, and continuous variables are presented as the mean, SD, and range.

We performed bivariate correlation analyses among physical-function PROMs at three timepoints: 1 week, 2 to 4 weeks, and 6 to 9 months after injury. The strength of correlations between PROMs were classified as high (> 0.7), high-moderate (0.61 to 0.69), moderate (0.4 to 0.6), moderate-weak (0.31 to 0.39), or weak (< 0.3) [28].

We performed factor analysis, a method which generates multiple factor sets (or unobserved, latent variables) from large amounts of data, that can identify common underlying themes (also known as constructs). These themes can be labelled based on the composition of groups of measured variables, in this case PROMs, which are variably organized among the factor sets. For example, QuickDASH and PROMIS UE both claim to measure upper extremity limitations and may therefore group together under the same factor. Alternatively, QuickDASH also measures interference with social activities and so may also group with other variables that seem to capture aspects of social interaction. Rotation describes the way a factor analysis program handles the variables to determine the best fit or correlation between variables and latent factors. Promax rotation is described as an oblique rotation method that is useful for large datasets, while Varimax rotation is described as an orthogonal rotation method that aims to simplify the interpretation of factors. Promax rotation was suitable for this large dataset, except for proximal humerus fractures after 2 to 4 weeks, where Varimax rotation was preferred as it simplified an overly complex model. All relevant factors (factors with variable loading ≥ 0.30) were selected. Of the initial 744 patients, 4% (30) had missing data and were omitted from the factor analysis.

We assessed the precision of PROMs by assessing floor and ceiling effects, which can bias the results of analyses. These were measured by calculating the percentage of respondents with the absolute lowest (worst) and highest (best) possible scores for a given PROM [28].

An a priori power analysis indicated that a minimum sample size of 165 patients would provide 90% power to detect a correlation of 0.25 for each diagnosis-specific disability measure, with alpha set at 0.05. All statistical analyses were performed using STATA 14.0 (StataCorp LP, College Station, TX, USA). No sources of funding were related to this work.

Results

Strength of Correlation Between Different PROMs After Upper Extremity Fractures

We found that all PROMs were interrelated, with the correlation strengthening increasing over time (PROMIS UE and QuickDASH: r = -0.4665 at less than 1 week (Table 3), r = -0.7763 at 2 to 4 weeks (Table 4), and r = -0.8326 at 6 to 9 months; p < 0.001) (Table 5). Moderate-to-high correlations were observed at 6 to 9 months (the PROMIS PF was moderately correlated with the EQ-5D-3L [r = 0.6077; p < 0.001] and the PROMIS PF was highly correlated with the OSS [r = 0.8595; p < 0.001]). Although some correlations were weak at less than 1 week after injury (for example, PROMIS UE and PRWE: r = -0.1556; p = 0.0022), most interactions were moderate at this early stage (PROMIS UE and QuickDASH: r = -0.4665; p < 0.001).

Table 3.
Table 3.:
Correlation matrix of correlation coefficients for PROMs at less than 1 week after injury
Table 4.
Table 4.:
Correlation matrix of correlation coefficients for PROMs at 2 to 4 weeks after injury
Table 5.
Table 5.:
Correlation matrix of correlation coefficients for PROMs at 6 to 9 months after injury

What Underlying Constructs are Being Measured by These PROMs?

The factor analysis demonstrated the presence of an underlying construct reflecting capability (the perceived ability to perform or engage in activities) that was separate and distinct from quality of life (an overall sense of health and wellbeing). When all fractures were combined, this difference was observed within 1 week and at 6 to 9 months after injury, while at 2 to 4 weeks, all PROMs appeared to measure a single, common underlying construct (Table 6). The factor analysis of anatomic region demonstrated that there was a distinction between capability and quality of life at all three timepoints for patients with elbow fractures but only within 1 week after injury for patients with shoulder and wrist fractures. At 2 to 4 weeks and 6 to 9 months, all PROMs measured a single common underlying construct after fractures of the shoulder and wrist.

Table 6.
Table 6.:
Results of the factor analysis showing groupings of PROMs into two factors at less than 1 week, 2 to 4 weeks, and 6 to 9 months after injury

Are There Strong Floor and Ceiling Effects with These Instruments?

The PROMIS PF, PROMIS UE, and QuickDASH had no floor or ceiling effects throughout recovery (Table 7). The OSS, OES, and PRWE showed no floor effect at any timepoint, but there was a severe ceiling effect at 6 to 9 months after injury. The EQ-5D-3L also showed no floor effect but exhibited more severe ceiling effects as the follow-up duration increased.

Table 7.
Table 7.:
Floor and ceiling effects of each PROM at less than 1 week, 2 to 4 weeks, and 6 to 9 months after injury

Discussion

As musculoskeletal healthcare providers, healthcare systems, and policymakers consider the use of a consistent set of PROMs to assess patient perceptions of their health and the value of care received, it becomes necessary to analyze an instrument’s performance characteristics in specific populations and geographical contexts. This may supersede PROM selection based on personal, institutional, and cultural preferences alone. Few studies have evaluated the validity and precision of a range of fixed and computer-adaptive PROMs that variably assess general, region-specific, and joint-specific health, longitudinally in a population of patients acutely recovering from common upper extremity fractures. Further, studies have rarely assessed computer-adaptive PROMs outside the United States; establishing the utility and relative advantages of instruments in different settings is needed, especially if we are to standardize and compare patient outcomes. Commonly used PROMs are interrelated and their correlation strength increases over time with the highest associations occurring several months after injury. These PROMs appear to variably reflect the constructs of capability and quality of life depending on the stage of recovery and injury type. Computer adaptive tests of physical function and the QuickDASH offer the most suitable options for assessing outcomes up to 9 months after upper extremity fracture based on limiting censorship over time. These findings should be considered when incorporating PROMs in research, clinical practice, health policy and payment models, and in seeking a single measure or set of measures for assessing limitations and impact on quality of life.

The results of this study should be viewed in light of several limitations. First, despite wide-ranging patient demographics, the generalizability of this study may be challenged because it was performed at a single institution. This was an academic level I major trauma center serving a varied urban population with special communities, including socially deprived and ethnic minorities, rural inhabitants, students, military personnel, and residents from overseas. Readers should consider the profile of the populations served by their own institutions when considering our findings. Further studies should translate our assessment method in regional trauma centers and units in more cosmopolitan areas. Second, for logistical reasons, patients completed PROMs in person, via telephone, or online. Although most evaluations were conducted in person, other modes of administration may have introduced varying levels of procedural, measurement, and responder bias. Although evidence suggests these effects are small and relatively inconsequential, readers should take this into consideration if their predominant mode of capture is substantially different [5]. Third, some responders may have found the process burdensome, especially when similar-sounding items of these PROMs were administered sequentially. This may have introduced some responder bias and made the correlations look stronger than they might otherwise be. Item randomization on the digital platform may have minimized this aspect. Testing the impact of item randomization and different combinations of PROMs provides an opportunity for future studies. Fourth, few studies have used computerized adaptive tests in fracture populations outside the United States where these instruments were mostly developed. While translations in different languages may account for terms and contexts specific to US populations, such as walking blocks, performing yard work, and passing turkey legs or ham at the dinner table, interpretation for patients in other English-speaking countries where these terms are not familiar should be assessed. Different English versions of these instruments may be necessary if they are to be adopted universally. Finally, floor-to-ceiling effects were assessed in the entire group of patients. A subgroup analysis, especially of higher-performing individuals, may have yielded different results. This was not directly within the scope of this study; however, future evaluations are planned to assess the impact of patient characteristics and performance on instrument properties for each main fracture type.

We found that PROMs quantifying limitations after common upper limb fractures correlated substantially with one another, irrespective of the area of focus (joint-specific, region-specific, or general quality of life) or mode of administration (fixed-scale or computer-adaptive test). There was a stepwise increase in the correlation strength from less than 1 week to 2 to 4 weeks to 6 to 9 months. This correlation could be explained by the growing body of evidence showing that psychological and social determinants are common underlying factors that influence physical limitations at the level of tasks, broader acts, and participatory roles [24, 25, 34]. Furthermore, the different increases in correlation strength over time among the various PROMs could be because region-specific measures may have greater discriminatory potential than general health measures early after injury, while both types of measurement become less differentiated over time. That is, over time, the impact of the injury on arm-specific physical limitations decreases with healing, and all instruments provide similar information. Longer-term outcomes are potentially less related to the injury and more related to effective coping strategies and adaptation (resiliency). For example, opening a jam jar and walking long distances may be impossible within weeks of a shoulder fracture, but both activities may be easier to perform several months after injury through restored actual ability or adaptation (for example, opening the jar by placing it between the legs to grip it). Moderate-to-high correlations between the PROMIS PF and QuickDASH [26], between the PROMIS UE and QuickDASH [10] and between the PROMIS UE and PROMIS PF and EQ-5D have been shown in nontraumatic and subacute conditions [1, 2]. Notably, we used the EQ-5D-3L as the preferred PROM for measuring general health as is done in many European institutions. Further studies could assess for any differences using PROMIS Global Health from the PROMIS suite of questionnaires (which also contain physical and mental health items) to evaluate the quality of life construct of PROMIS PF and PROMIS UE after fractures.

The factor analysis showed that PROMs either measured a common underlying construct of capability or quality of life, and this varied by recovery stage and fracture region. Region-specific and generic quality of life measures were distinct from those reflecting patient perceptions of physical capabilities throughout recovery after elbow fractures. This distinction was only present at less than 1 week after shoulder and wrist fractures and 6 to 9 months after distal radius fracture. All PROMs otherwise assessed the same underlying construct (that is, quality of life) at 2 to 4 weeks after shoulder and wrist fractures and 6 to 9 months after shoulder fracture. This variation could partly be explained by differences in demographic and injury characteristics between patients with elbow fractures (who are mostly younger and active working adults with injuries that can be managed nonoperatively using early mobilization and relatively straightforward pain control) compared with those with fractures of the shoulder and wrist (who are mostly older, have lower demands, often have an initial period of restricted mobility, are exposed to a range of management options, and have relatively slower recovery and more complex analgesic requirements). A distinction between the patient’s perception of his or her capabilities and quality of life may have been clearer for many patients with elbow fractures from the time of injury throughout recovery. In contrast, the perceptions of patients with shoulder and wrist fractures may have been more difficult to differentiate after the initial week because of ongoing interventions and lengthy recovery. The duration of recovery and rehabilitation may be longer after shoulder fractures compared with wrist fractures where patients may more easily differentiate between capability and quality of life impact by 6 to 9 months. Although both injuries may still be disabling at 6 to 9 months, it could be perceived that shoulder fractures confer a greater limitation upon the whole arm compared with wrist fractures, making the delineation between physical limitations and quality of life impact less distinct even at this stage. Researchers and policymakers should define what construct they want to measure, when they wish to do this, and make their selection of PROMs; our findings provide some guidance with this in mind. Based on ceiling effects, it appears preferable to use the PROMIS PF, PROMIS UE, and QuickDASH over joint-specific and generic health measures, especially when assessing limitations in the later stages of recovery after upper limb fractures [16]. Therefore, it may be prudent to retroactively assess such effects in high-impact, large-scale, controlled trials that have used some of these joint-specific and general-health PROMs without necessarily understanding this potential shortcoming [13]. Studies have highlighted the ceiling effects of both the PROMIS PF and PROMIS UE in higher-functioning patients [2, 3, 17, 20], while others demonstrated minimal or no ceiling effects with a range of upper limb conditions [1, 33]. However, none of these studies involve the administration of these tools in a relatively large population focused on upper extremity fractures.

Conclusions

Musculoskeletal researchers should be mindful of which health constructs a given PROM is measuring (for example, is the instrument predominantly measuring capabilities alone or quality of life?) and how the scoring characteristics of PROMs change during recovery because this may influence the selection of the measurement tool. The choice of PROM can be guided by practical considerations based on the area of focus and mode of administration, the timepoint of administration, avoidance of censoring, and preference to focus on capability or quality of life. This study suggests that the PROMIS UE, PROMIS PF, and QuickDASH are the most useful PROMs for people recovering from an upper extremity fracture. Further studies should be conducted to assess other psychometric properties of these measures, such as reliability and responsiveness, alongside their performance in higher functioning patients. Combined knowledge may ultimately lead to a choice instrument for assessing patient outcomes throughout the recovery trajectory after upper extremity fractures.

Acknowledgments

None.

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© 2019 by the Association of Bone and Joint Surgeons