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Introduction

Musculoskeletal pain is a major cause of personal suffering and economic loss following work-related injury (Schulte 2005). In the state of New South Wales, over 36 000 workplace injuries occur annually (WorkCover NSW 2004/5). Musculoskeletal sprains and strains account for 62% of these injuries at a cost of AU $434 million. The percentage of injured workers who develop persistent pain and activity limitation has risen by 16% over the past decade (WorkCover NSW 2004/5). These findings are consistent with other westernised countries. Globally it is estimated that 100 million workplace injures occur annually (Leigh et al 1999) and the subsequent development of persistent pain and activity limitation has been recognised as a major public health problem (Feuerstein 2005).

The emerging paradigm in occupational injury management is early identification of injured workers at risk of chronic pain and activity limitation (Gatchel 2005) particularly for those with back pain (Fritz and George 2002, Hogg-Johnson and Cole 2003, Linton et al 2005, Pransky et al 2006, Schulz et al 2005, Turner et al 2006). While there is a clear imperative to recognise workers with poor prognosis in the early stage after injury, the current reality is that the population of workers with long-term musculoskeletal pain continues to rise (Waddell 1996). To date, there has been

limited research into prognosis for this population (Dionne et al 2005, Proctor et al 2005, Sullivan et al 2005, van der Giezen et al 2000) and available research has focused predominantly on identifying prognostic factors. It is not immediately clear how prognostic factors alone can be used to facilitate clinical management. However, predictors can be combined to develop clinical prediction rules. This approach can enable calculation of the likelihood of an outcome for individual patients (Childs and Cleland 2006, Randolph et al 1998) and can minimise bias in judgment, in accordance with current principles of evidence-based health care. This process is becoming increasingly recognised as a useful adjunct to the evaluative process in clinical decision-making, particularly for patients with musculoskeletal injuries (Beattie and Nelson 2006, Childs and Cleland 2006). To date, there are few clinical prediction rules for patients with work-related injuries, and there are no published rules for those injured in an Australian workplace setting. In a landmark study in Canada, Dionne and colleagues (2005) developed a clinical prediction rule to identify workers with persistent back pain at risk of poor occupational outcome. The predictive accuracy of return to work was not much better than chance. Further research is clearly needed to provide clinical prediction rules with higher predictive accuracy for this population.

Clinical prediction rules can be derived and validated for injured Australian workers with persistent musculoskeletal

pain: an observational study

Jennifer A Hewitt1, Julia M Hush2, Melody H Martin1, Robert D Herbert2 and Jane Latimer2

1Rehab One Physiotherapy 2University of Sydney Australia

Questions: Can clinical prediction rules be derived for injured Australian workers with persistent musculoskeletal pain? Are they valid? Design: Longitudinal observational study. Participants: 847 injured workers with persistent musculoskeletal pain undergoing rehabilitation. Outcome measures: At baseline, 12 putative predictors were measured. At 9 weeks, short-term outcomes such as pain (visual analogue scale), activity limitation (Functional Rating Index) and work upgrade (increase in work hours or duties) were measured. At 6 months, long-term work status (working or not working) was measured. Results: Data were obtained from 85% of the participants who were followed up at both 9 weeks (720 of 847) and 6 months (247 of 290). Predictors of outcome included high baseline pain and activity limitation, long duration of previous intervention, not working, non-English speaking background, and the area of pain. Accuracy was highest for clinical prediction rules predicting pain and level of activity limitation at 9 weeks (R2 = 0.67 and 0.69 respectively) and work status at 6 months (LR– = 0.24). Conclusion: Accurate clinical prediction rules have been derived and validated for injured workers with persistent musculoskeletal pain, predicting activity limitation, pain, and work outcomes following exercise-based rehabilitation. Further research to validate these prediction rules in other populations and to assess the effectiveness of tailoring intervention based on the estimated prognosis would be valuable. [Hewitt JA, Hush JM, Martin MH, Herbert RD, Latimer J (2007) Clinical prediction rules can be derived and validated for injured Australian workers with persistent musculoskeletal pain: an observational study. Australian Journal of Physiotherapy 53: 269–276]

Key words: Prognosis, Physical Therapy (Specialty), Work

Australian Journal of Physiotherapy 2007 Vol. 53 – © Australian Physiotherapy Association 2007 269

Hewitt et al: Clinical prediction rules for injured Australian workers

for the first 6 to 9 weeks. Sessions were carried out in a gymnasium for one hour, three times per week, and included prescription of graded exercises (strength, endurance, fitness, stretch) and activities (simulated work) based on injury and work goals. Participants also performed a daily home program of activities and exercises and completed a diary that was checked at each physiotherapy session to monitor compliance. All physiotherapists had received training in cognitive behavioural techniques (education, goal setting, and pacing). Education included reassurance about their condition, explanation of scan findings, and discussion of the benefits of activity and the consequences of extended down-time. After the supervised component of the program, participants were instructed to exercise independently, and long-term self-management was encouraged. Professional interpreters were used as required. Short term outcomes (pain, activity limitation, upgrade in hours or duties at work)

Australian Journal of Physiotherapy 2007 Vol. 53 – © Australian Physiotherapy Association 2007270

Research

The research questions investigated in this study were:

1. Can clinical prediction rules be derived for injured Australian workers with persistent musculoskeletal pain?

2. Are they valid?

Method

Design

People with sub-acute or chronic musculoskeletal pain in NSW, Australia, referred to a private physiotherapy practice for exercise following a work injury were observed longitudinally. Typically, their previous intervention consisted of passive techniques that focused on symptom relief. First, putative predictors were measured. Participants then took part in an exercise-based physiotherapy program

Week

Exercise-based physiotherapy program

0

9

26

Injured workers referred for exercise-based physiotherapy program

Measured 12 putative predictors(n = 847)

Measured pain, activity limitation, work upgrade(n = 720)

Measured work status(n = 247)

Figure 1. Design and flow of participants through the study.

Participants lost to follow-up (n = 127) Doctor’s decision (n = 53) Participant’s decision (n= 74)

Randomly split

Randomly split

Predictors identified(n = 360)

Clinical Prediction Rule validated(n = 360)

Predictors identified(n = 124)

Clinical Prediction Rule validated(n = 123)



were measured 9 weeks after commencing the program. Long-term measurement of work status (working or not working) was carried out at 6 months. Measurements were collected by physiotherapists not involved in the intervention program. The data from 9 weeks and 6 months were each randomly divided into two halves. The first half was used to identify predictors from which a clinical prediction rule for each outcome was developed. The second half was used to test the predictive accuracy of each model. The design of the study is illustrated in Figure 1. The study was approved by the University of Sydney Human Research Ethics Committee.

Participants

Patients were considered eligible for inclusion in the study if they presented with work-related musculoskeletal pain of ≥ 6 weeks’ duration, had been given a medical clearance by their doctor to participate in the exercise-based physiotherapy program, and had been given approval by the third party payer (insurance company) to attend the program. They were excluded from the program if they demonstrated signs or symptoms suggestive of a red flag condition (eg, tumour, systemic illness, inflammatory disease or infection, screened using the Modified Core Network Screening Questionnaire, Maher et al 2001), were in the acute stage of their injury (0-6 weeks), or demonstrated signs of entrenched psychological or psychosocial factors warranting referral to a multidisciplinary pain clinic (Guzman et al 2003, Haldorsen et al 2002). Physiotherapists performed comprehensive assessments incorporating subjective, physical, functional, and psychosocial measures to assist in determining each participant’s eligibility for inclusion in the program.

Outcome measures

At baseline, 12 putative predictors were measured. In order to increase the clinical utility of the findings for physiotherapists, putative predictors were selected that were clinical features, easily and routinely recorded during a standard physiotherapy assessment (Box 1).

At 9 weeks, outcomes were selected that addressed issues relevant to the individual (such as pain and activity limitation) as well as economic issues (upgrade in work hours and duties) which are of particular interest to third

party payers, and workers’ compensation authorities. Pain was measured using a 10-cm visual analogue scale where 0 = no pain, and 10 = the worst pain imaginable, and activity limitation was measured using the Functional Rating Index scored as a percentage. The Functional Rating Index is a hybrid instrument of the Oswestry Disability Questionnaire and the Neck Disability Index and has been shown to be psychometrically sound with regard to reliability, validity, and responsiveness in patients with spinal pain (Fiese et al 2001, Hush 2006). In the present study, a small modification of the questionnaire was made to enable use with all participants. The modification was made to the statement

Box 1: The twelve putative predictors measured at baseline.

• Duration of injury (mth)• Duration of previous intervention (mth)

• Surgery to compensable area (y/n)• Area injured (non-spinal/spine)

• Pain intensity (10-cm VAS)

• Activity limitation (FRI) (%)

• Gender (M/F)

• Age (yr)

• Time off work (mth)

• Work status (working/not working)

• Non-English speaking background (y/n)

• Interpreter required (y/n)

Table 1. Characteristics of participants at baseline.

Characteristic All participants (n = 847)

Participants measured at 9 weeks (n = 290)

Duration of injury (mth), mean (SD)

11 (14.6) 9.91 (9.6)

Duration of previous intervention (mth), mean (SD)

5.23 (5.6) 4.91 (5.4)

Surgery n (%)SpineNot spineCompensable

126 (15)720 (85)153 (18)

59 (20)232 (80)58 (20)

Area injured (primary), n (%)Lumbar spineShoulderCervical spineLower limbUpper limbThoracic spineOther

474 (56)110 (13)101 (12)76 (9)51 (6)17 (2)18 (2)

154 (53)32 (11)

6 (2)44 (15)23 (8)

29 (10)6 (2)

Pain intensity (10-cm VAS), mean (SD)

5.8 (2.9) 5.6 (2.1)

Activity limitation FRI (0 to 100), mean (SD)

52 (17) 51 (17)

Gender, n male (%) 559 (66) 197 (68)Age (yr), mean (SD) 39.7 (10.7) 39.7 (10.8) Time off work (mth), mean (SD)

2.4 (4.5) 2.2 (3.4)

Work status, n (%)Working Normal dutiesSuitable dutiesFull timePart timeReduced hours

609 (72)119 (14)727 (86)441 (52)101 (12)305 (36)

214 (74)32 (11)

258 (89)160 (55)

26 (9)104 (36)

Non-English speaking background, n (%)

295 (35) 78 (27)

Interpreter required, n (%) 102 (12) 32 (11)Fear avoidance beliefs (FABQ)

Physical activity (0 to 24), mean (SD)Work (0 to 42), mean (SD)

16.4 (6.0)

28.7 (9.8)

16.9 (5.7)

27.6 (9.9)

at the top of the questionnaire: ‘We wish to understand how much your pain [instead of your back/neck pain] has affected your ability to manage your everyday activities’. None of the items was altered and no items refer to the area of pain. High reliability and responsiveness of this modified version of the Functional Rating Index has been reported (Chansirinukor et al 2004). This process has also been performed on other back pain-specific scales for application to the wider chronic pain population (eg, Roland Morris was adapted in Stroud et al 2004, and the Oswestry Disability Index was adapted in Whittink et al 2004, and Fish and Chang 2007). Work upgrade (whether the participant returned to work or upgraded work hours or duties) was measured categorically as Yes/No.

At 6 months, long-term measurement of work status (measured as Yes/No) was achieved by postal questionnaire

and phone call to the participants, or by contacting the insurance company covering the workers’ compensation claim. Long-term follow up was limited to the first consecutive 290 participants to complete the program due to limited resources.

Data analysis

Separate models were developed to predict each outcome of interest, using multiple regression analyses. A split-half approach was used whereby the data set was randomly divided into two halves. The first half was used to identify predictors by stepwise regression. A stepwise approach (p to enter = 0.05, p to remove = 0.10) was used to identify significant predictors from the 12 putative predictors. The second half was used to test the predictive accuracy of each model. For each outcome, a clinical prediction rule was developed from the regression coefficients of each predictor.


Research

Table 2. Mean (95% CI) regression coefficients of predictors, clinical prediction rule and accuracy of prediction for activity limitation at 9 weeks.

Regression coefficients of predictors Baseline activity limitation = 0.72 (0.63 to 0.82) Interpreter required = –8.93 (–13.79 to –4.08) Duration of previous intervention = 2.31 (0.89 to 3.72) Baseline work status = –4.15 (–7.74 to –0.56) Constant = 13.66 (5.59 to 21.74)Clinical Prediction Rule Activity limitation = 0.72 (baseline activity limitation) – 8.93 (interpreter required) + 2.31 (duration of previous intervention) – 4.15 (baseline work status) + 13.66Accuracy of prediction R2 = 0.69

Activity limitation 0–100 FRI scale; interpreter requirement (0 = yes, 1 = no); duration previous symptom focused treatment = natural log (months of previous treatment + 0.125); work status (0 = not working, 1 = working).

Table 3. Mean (95% CI) regression coefficients of predictors, clinical prediction rule and accuracy of prediction for pain intensity at 9 weeks.

Regression coefficients of predictors Baseline pain = 0.41 (0.28 to 0.53) Baseline activity limitation = 0.04 (0.02 to 0.05) Non-English speaking background = –0.94 (–1.38 to –0.51) Duration of previous intervention = 0.27 (0.06 to 0.49) Constant = 0.41 (–0.42 to 1.23)Clinical Prediction Rule Pain = 0.41 (baseline pain) + 0.04 (baseline activity limitation) – 0.94 (Non-English speaking background) + 0.27 (duration of previous intervention) + 0.41Accuracy of prediction R2 = 0.67

Pain intensity scale 0–10; activity limitation 0–100; FRI scale (0 = minimum activity limitation, 100 = maximum activity limitation); Non-English speaking background (0 = other, 1 = English); duration of previous intervention = natural log (months of previous intervention + 0.125).

The predictive accuracy of models with continuous outcomes was expressed as R2 values. For dichotomous outcomes, positive and negative likelihood ratios (LR+, LR–) were calculated because clinicians can use these ratios to determine the probability of outcomes and they have been reported previously for occupational prognosis (Fritz and George 2002). Additionally, odds ratios were calculated for dichotomous predictors to indicate the strength of individual prognostic factors.

Results

Participants

Eight hundred and forty-seven injured workers commenced the exercise-based physiotherapy program. Baseline characteristics of these participants are summarised in Table 1. All participants were compensable, being referred following a work injury under the Workers’ Compensation system in New South Wales, Australia. Participants had sustained injuries, on average, 11 months previously from manual handling (55%), falls, slips and trips (15%), motor vehicle accident (12%), repetitive factory work (7%), being hit by an object (4%), repetitive office work (1%), and other

mechanisms (6%). Of the 847 participants, 720 (85%) completed the program and were measured at 9 weeks. Participants were lost to follow-up because they chose to withdraw from the program (n = 74), or were advised by their treating doctor to cease exercise because of medical concerns unrelated to their injury (n = 41) or related to their work injury (n = 12). Long-term follow-up of work status was conducted at 6 months on the first 290 participants for whom data had been obtained at 9 weeks. The baseline characteristics of the 290 participants followed up at 6 months were similar to those of the whole cohort (Table 1). Of the 290 participants, 247 (85%) were measured at 6 months.

Clinical prediction rules

The four variables predictive of a higher level of activity limitation at 9 weeks were high baseline activity limitation, requirement for an interpreter, long duration of previous intervention, and being off work at baseline (Table 2). The R2 value of the clinical prediction rule for this outcome is 0.69 (Table 2). The variables predictive of higher pain intensity at 9 weeks were high baseline pain intensity, high baseline activity limitation, a non-English speaking background and



Table 4: Mean (95% CI) regression coefficients of predictors, clinical prediction rule and accuracy of prediction for work upgrade at 9 weeks.

Regression coefficients of predictors Baseline work status = 1.63 (1.03 to 2.23) Duration of previous intervention = –0.41 (–0.69 to –0.14) Area injured = –0.72 (–1.27 to –0.16) Interpreter required = 0.97 (0.16 to 1.77) Constant = –1.00 (–1.97 to –0.03)Clinical Prediction Rule Odds (work upgrade) = e–1.00 x e1.63 (baseline work status) x e–0.41 (duration previous intervention) x e– 0.72 (area injured) x e0.97 (interpreter)

Probability of a work upgrade = Odds (work upgrade) / Odds (work upgrade) + 1Accuracy of prediction LR+ = 1.53 LR– = 0.52

Work status (0 = not working, 1 = working); duration previous intervention = natural log (months of previous intervention + 0.125); area injured (0 = not spine, 1 = spine); interpreter requirement (0 = yes, 1 = no).

Table 5. Mean (95% CI) regression coefficients of predictors, clinical prediction rule and accuracy of prediction for work status at 6 months.

Regression coefficients of predictors Baseline work status = 1.33 (0.66 to 1.99) Area injured = 0.77 (0.04 to 1.51) Constant = –0.12 (–0.80 to 0.55)Clinical Prediction Rule Odds (work status) = e–0.12 x e1.33 (baseline work status) x e0.77 (area injured)

Probability of being at work = Odds (work status) / Odds (work status) + 1Accuracy of prediction LR+ = 1.21 LR– = 0.24

Work status (0 = not working, 1 = working); area injured (0 = not spine, 1 = spine).

long duration of previous intervention (Table 3). The R2 value for this clinical prediction rule is 0.67 (Table 3).

Variables predictive of not upgrading at work by 9 weeks included being off work at baseline (OR 5.10, 95% CI 2.81 to 9.26), longer duration of previous intervention, an injury to the spine (OR 0.49, 95% CI 0.28 to 0.85), and requirement for an interpreter (OR 2.63, 95% CI 1.18 to 5.88) (Table 4). The likelihood ratios (LR+ 1.53, LR– 0.52) suggest the predictive accuracy of this clinical prediction rule is limited (Table 4).

Work status at 6 months was strongly predicted by two factors: baseline work status (OR 3.77, 95% CI 1.93 to 7.34) and area of injury (OR 2.17, 95% CI 1.12 to 4.20), such that participants had lower odds of working at 6 months if they had been off work at baseline and had a non-spinal injury (Table 5). The likelihood ratios for the clinical prediction rule for work status at 6 months (LR+ 1.21 and LR– 0.24), indicate that this prediction rule should be most useful for identifying injured workers who are at higher risk of being off work 6 months after commencing the rehabilitation program (Table 5). Table 6 illustrates how two of the clinical prediction rules could be used to estimate patient outcomes, at 9 weeks (Cases 1 and 2) and 6 months (Cases 3 and 4).

Discussion

This longitudinal study has investigated prognosis in a large cohort of people with persisting musculoskeletal pain following a work injury. Baseline predictors of poor outcome included being off work, longer duration of previous intervention, high activity limitation and pain, and being of a non-English speaking background or requiring an interpreter. Clinical prediction rules to estimate prognosis were developed and validated. The strengths of this study are the prospective design, large sample size of consecutive

participants, adequate follow-up, and validation of the algorithms.

The clinical prediction rules with the highest accuracy are those that predict activity limitation and pain at 9 weeks and poor work status at 6 months. In a previous study that attempted to develop a clinical prediction rule to predict outcome for workers with persistent back pain (Dionne et al 2005), the predictive accuracy was not much more than chance. Other published algorithms also report limited accuracy. For instance, the models developed by Pransky and colleagues (2006) explained only 12% of the variance when predicting activity limitation. Therefore, the clinical prediction rules developed in this study have predictive accuracy that exceeds that of previous algorithms.

The high predictive accuracy of the models in the present study is particularly interesting given that psychosocial factors were not considered. There is some evidence that such factors are predictive of occupational outcome in people with persistent musculoskeletal pain (van der Giezen et al 2000, Linton et al 2005) as well as for those with acute occupational injuries (Burton et al 1995, Gatchel 2005, Turner et al 2006). However, the intention of this study was to evaluate the predictive capacity of measures taken during a routine physiotherapy assessment, without the need for specialised screening instruments or expertise. For that reason, psychosocial factors were not evaluated in this study. A possible direction for future research is to evaluate whether inclusion of psychosocial factors in these prediction rules further improves the predictive accuracy. In the clinical setting, combining findings from the clinical prediction rules from this study with findings from psychosocial screening tools (eg, the Orebro Musculoskeletal Pain Questionnaire, Linton and Boersma 2003) may assist in further estimating prognosis using a biopsychosocial model.


Research

Table 6. Predicted outcome using clinical prediction rules for four cases.

Case Baseline score Predicted outcomeCase 1 Activity limitation [0–100%] 50 Interpreter required [0 = yes, 1 = no] 1 Duration of previous intervention [months] 1.5 Work status [0 = no, 1 = yes] 1

Predicted activity limitation at 9 weeks = 38%Case 2 Activity limitation score [0–100%] 73 Interpreter required [0 = yes, 1 = no] 0 Duration of previous intervention [months] 6.5 Work status [0 = no, 1 = yes] 0

Predicted activity limitation at 9 weeks = 71%Case 3 Work status [0 = no, 1 = yes] 1 Area injured [0 = not spine, 1 = spine] 1

Predicted probability of working at 6 months = 88%Case 4 Work status [0 = no, 1 = yes] 0 Area injured [0 = not spine, 1 = spine] 0

Predicted probability of working at 6 months = 47%

Baseline work status was found to be a strong predictor of work upgrade at 9 weeks and work status at 6 months. Proctor and colleagues (2005) investigated prognosis in a large study of injured workers with occupational musculoskeletal disorders with an average duration of 19 months since injury, representing a population similar to the present study. These authors found that return to work reliably predicted completion of a functional restoration program. Previous research has reported a relationship between length of time off work and the likelihood of returning to work (Fordyce 1995). However in our analyses, time off work did not appear in the final multivariate models.

Health care utilisation (> 30 visits) was also found to be highly predictive of outcome in the Proctor (2005) study. This concurs with our results that a long duration of previous intervention is predictive of high levels of activity limitation, pain, and reduced likelihood of upgrading at work. Notably, this effect is independent of chronicity, which was also entered into the regression model. This finding also aligns with current evidence regarding efficacy of interventions for persisting occupational conditions. Evidence-based occupational health guidelines (Waddell and Burton 2001) recommend explicitly that back pain participants having difficulty returning to normal activities at 4–12 weeks should cease symptomatic intervention, including manual therapy and electrotherapy, and commence active exercise-based intervention.

Self-reported pain and activity limitation were identified as prognostic factors in this study. This finding is supported by research that has consistently found these variables to be positively associated with the duration of activity limitation, in occupational and back injuries (Hogg-Johnson and Cole 2003, Sullivan et al 2005, Turner et al 2000).

A notable feature of the present study is the evaluation of non-English speaking background (or requirement for an interpreter) as predictive of outcome, as these individuals have frequently been excluded from previous studies. These variables were found to predict pain and activity limitation, and likelihood of a work upgrade. Interpretation of these results is complex. One possible explanation is that cultural issues may impact on the perception or reporting of pain. Translators may also influence delivery of the program particularly when providing reassurance and education. Further research is required to better understand this finding.

A surprising feature of the results was that spinal pain was predictive of failing to upgrade at work in the short term, however having non-spinal pain was predictive of being off work in the long term. One explanation for this may be the high proportion of participants with upper or lower limb injuries requiring surgery, indicating that the injuries were severe. Conflicting findings regarding area of injury as a prognostic factor have been reported previously (Hogg-Johnson 1998, Turner et al 2000).

Valid prognostic evidence is recognised as useful in providing participants with information about what the future is likely to hold for them and their injury (Straus et al 2005). The models developed in this study may be clinically useful for this purpose, particularly following validation in other populations. A further application of these findings would be to use the clinical prediction rules to assist in planning intervention. The rationale is that participants identified at risk of poor prognosis may achieve better outcomes if they

receive more comprehensive or tailored interventions. The current study does not provide data to support this assertion, as clinical trials are required to test whether selection of intervention based on estimated patient prognosis at baseline is effective. However, one study by Haldorsen and colleagues (2002) does provide data that confirm this approach. The authors found that participants classified as having a high risk for poor outcome achieved better outcomes with an extensive multidisciplinary program, whereas those who were estimated to have a good outcome did equally well with the brief program. These results support the proposal that clinical prediction rules may be helpful in optimising choice of intervention. Nonetheless, clinical prediction rules should not be considered a replacement for clinical judgment: expert opinion is still the cornerstone of clinical decision making (Beattie and Nelson 2006). Where poor prognosis is indicated, maximising resources rather than withdrawal of intervention is more likely to result in better outcomes. Evaluation of the efficacy of this approach is required.

Further, validation of these clinical prediction rules in other clinical populations would enhance generalisability. It should be noted that the sample from which these clinical prediction rules were derived consisted of injured workers referred for secondary intervention programs within the New South Wales Workers Compensation system. The use of the split-half approach in this study to evaluate the predictive accuracy of each model enhances the ability to generalise these prediction rules to similar populations.

This study would have been strengthened by collecting 6 month outcomes from all 720 participants who completed the program rather than the first 290 participants. Limited resources prevented this. However, this sub-cohort appeared to be representative of the total cohort. Data were sought via postal questionnaire to participants. Attempts were made to collect data from non-respondents by telephone and by contacting the participant’s insurance company. In future, collaborative research between clinicians and insurance companies may provide improved systems for longer term follow-up.

A further limitation of this study was that information about the characteristics of the injured workers who dropped out of the program was not obtained. However, the percentage of participants who dropped out of the program (15%) is unlikely to seriously threaten the validity of the study (Straus et al 2005).

In conclusion, this study makes a unique contribution to the body of literature regarding prognosis of injured workers. This is the first report of clinical prediction rules with high predictive accuracy that have been developed for injured workers with persisting musculoskeletal pain. These algorithms may be a useful adjunct to assist in clinical decision-making. Further validation of these models on new samples would be useful as would evaluating the effectiveness of choice of intervention based on patient prognoses determined using these clinical prediction rules.

Acknowledgements: This study was supported by Rehab One Physiotherapy Pty Ltd, Sydney, who provided baseline and follow-up data for all participants in this study. The authors would like to acknowledge the staff of Rehab One Physiotherapy for their co-operation, support, and for contributing data to this study.



Correspondence: Dr Julia Hush, Faculty of Health Sciences, University of Sydney, PO Box 170, Lidcombe, NSW 1825, Australia. Email: [email protected]

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