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An algorithm comprising 7 baseline variables predicted the 2 year work disability status in non-specific back pain Arthur T Evans and Nortin M Hadler Evid. Based Med. 2005;10;187doi:10.1136/ebm.10.6.187
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CLINICAL PREDICTION GUIDE
187
An algorithm comprising 7 baseline variables predicted the 2 year work disability status in non-specific back pain Dionne CE, Bourbonnais R, Fremont P, et al. A clinical return-to-work rule for patients with back pain. CMAJ 2005;172:1559–67. Clinical impact ratings GP/FP/Primary care wwwwwqq
...............................................................................................................................
with non-specific back pain associated with >1 day’s absence from work, what variable or set of variables Q Inbestpatients predicts the 2 year work disability status? CONCLUSIONS
METHODS Design: a cohort study (Recherche sur les Affections MusculoSquelettiques [RAMS]- Prognosis Study) with a qualitative phase to identify additional predictors, and a quantitative phase for prediction analysis. More than 100 potential predictors were measured at baseline and at 6 and 12 weeks. Predictive models of 2 year outcome were developed with recursive partitioning on a 40% random sample of the cohort, and validated in the rest.
In patients with non-specific back pain associated with >1 day’s absence from work, the best, although limited, prediction of the 2 year work disability status was obtained with 7 baseline variables. Abstract and commentary also appears in ACP Journal Club.
Commentary
Setting: 7 primary care settings in Quebec City, Quebec, Canada. Patients: 860 adult workers (mean age 39 y, 58% men) who consulted for non-specific back pain associated with >1 day’s absence from work. Description of prediction guide: the final model had 7 questions pertaining to patients’ recovery expectations, radiating pain, previous back surgery, self reported pain severity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping. Outcomes: return to work in good health (RWGH) categorised as success, partial success, failure after attempt, and failure.
MAIN RESULTS The probability of success was highest (0.84, 95% CI 0.77 to 0.91) for patients without previous back surgery who expected to recover within 3 months and rated their pain as 4–10 (on a scale of none [0] to 10) but who did not change their positions frequently to get comfortable; and lowest (0.25, CI 0.18 to 0.32) in patients with radiating pain (into the arms or legs) who did not expect to recover within 3 months. Patients with the lowest probability of success also had the highest probability of failure (0.46, CI 0.38 to 0.54). The probability of partial success varied from 0.08, CI 0.02 to 0.14 (in patients with the highest probability of success) to 0.45, CI 0.30 to 0.60 (in patients without previous back surgery who anticipated to recover within 3 months, rated the pain as 4–10, changed positions often to get comfortable, were more irritable than usual but who slept as usual). Sensitivity and specificity and positive and negative likelihood ratios for the collapsed outcome (success plus partial success v failure after attempt plus failure) are in the table. ............................................................. For correspondence: Dr C E Dionne, Ho ˆ pital du Saint-Sacrement, Que´bec, Que´bec, Canada.
[email protected] Source of funding: Quebec Institute for Occupational Safety and Health.
T
he bottom line for clinicians: don’t use the prediction rule by Dionne et al for several reasons. Firstly, it should be validated in another setting, and its clinical use should be shown to cause more good than harm. Secondly, the rule’s potential use is suspect considering that 40% of predictions in the validation sample were erroneous. Finally, the predicted outcome—RWGH—is an unstable psychosocial construct that is highly influenced by interpersonal, economic, and political factors. Wasson et al warned against using such outcomes.1 The psychosocial nature of the outcome is underscored by the fact that the best single predictor was the patient’s own premonition of their future status: ‘‘Do you think you will be back to your normal work within 3 months?’’ Is this an example of dispassionate foresight or self fulfilling prophecy? Because the rule predicted failure to return to work 2–3 times more often than what was actually observed, it is possible that informing patients of an ominous prediction would adversely influence outcomes further. Another striking observation is that outcome status at 12 weeks had a predictive accuracy .90%. That prolonged disability seals one’s fate is a long standing reproach to all involved in workplace health and safety. How the persistence of disability renders back pain unremitting is a conundrum for clinical investigation. The answer may be hiding in studies such as this by Dionne et al. Living with back pain reflects the psychosocial assaults on our coping capacity that operate at home and at work.2 Furthermore, this psychosocial context has a temporal component, includes the vortex of disability determination itself, and is bedevilled by the idiosyncrasies of life. No wonder a ‘‘predictive rule’’ for job absenteeism in the setting of back pain is a will-o-the-wisp. Arthur T Evans, MD, MPH Cook County Hospital and Rush Medical College Chicago, Illinois, USA Nortin M Hadler, MD University of North Carolina at Chapel Hill Chapel Hill, North Carolina, USA 1 Wasson JH, Sox HC, Neff RK, et al. Clinical prediction rules. Applications and methodological standards. N Engl J Med 1985;313:793–9. 2 Hadler NM. Occupational musculoskeletal disorders. 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2005.
Measures of validity for a clinical prediction rule developed from 7 baseline variables for predicting the 2 year work disability status in non-specific back pain* Sample
Cut point
Sensitivity (95% CI)À
Specificity (CI)À
+LR
2LR
Validation set (n = 506)
Failure or FAA v success or partial success
74% (70 to 78)
62% (58 to 66)
1.95
0.42
*FAA = failure after attempt. Diagnostic terms defined in glossary; LRs calculated from data in article. 95% CIs provided by author.
EBM Volume 10 December 2005
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An algorithm comprising 7 baseline variables predicted the 2 year work d...fic back pain -- Evans and Hadler 10 (6): 187 -- Evidence-Based Medicine
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Evidence-Based Medicine 2005;10:187; doi:10.1136/ebm.10.6.187 © 2005 by BMJ Publishing Group Ltd This Article
Clinical prediction guide
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An algorithm comprising 7 baseline variables predicted the 2 year work disability status in non-specific back pain Dionne CE, Bourbonnais R, Fremont P, et al. A clinical return-to-work rule for patients with back pain. CMAJ 2005;172:1559–67.[Abstract/ Free Full Text]
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Q In patients with non-specific back pain associated with 1 day’s absence from work, what variable or set of variables best predicts the 2 year work disability status?
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Key Words: back pain • disability evaluation
METHODS Design: a cohort study (Recherche sur les Affections Musculo-Squelettiques [RAMS]- Prognosis Study) with a qualitative phase to identify additional predictors, and a quantitative phase for prediction analysis. More than 100 potential predictors were measured at baseline and at 6 and 12 weeks. Predictive models of 2 year outcome were developed with recursive partitioning on a 40% random sample of the cohort, and validated in the rest.
Setting: 7 primary care settings in Quebec City, Quebec, Canada.
Patients: 860 adult workers (mean age 39 y, 58% men) who consulted for non-specific back pain associated with 1 day’s absence from work.
http://ebm.bmj.com/cgi/content/full/10/6/187 (1 sur 4)2007-04-25 07:23:26
An algorithm comprising 7 baseline variables predicted the 2 year work d...fic back pain -- Evans and Hadler 10 (6): 187 -- Evidence-Based Medicine
Description of prediction guide: the final model had 7 questions pertaining to patients’ recovery expectations, radiating pain, previous back surgery, self reported pain severity, frequent change of position because of back pain, irritability and bad temper, and difficulty sleeping.
Outcomes: return to work in good health (RWGH) categorised as success, partial success, failure after attempt, and failure.
MAIN RESULTS The probability of success was highest (0.84, 95% CI 0.77 to 0.91) for patients without previous back surgery who expected to recover within 3 months and rated their pain as 4–10 (on a scale of none [0] to 10) but who did not change their positions frequently to get comfortable; and lowest (0.25, CI 0.18 to 0.32) in patients with radiating pain (into the arms or legs) who did not expect to recover within 3 months. Patients with the lowest probability of success also had the highest probability of failure (0.46, CI 0.38 to 0.54). The probability of partial success varied from 0.08, CI 0.02 to 0.14 (in patients with the highest probability of success) to 0.45, CI 0.30 to 0.60 (in patients without previous back surgery who anticipated to recover within 3 months, rated the pain as 4–10, changed positions often to get comfortable, were more irritable than usual but who slept as usual). Sensitivity and specificity and positive and negative likelihood ratios for the collapsed outcome (success plus partial success v failure after attempt plus failure) are in the table .
View this table: Measures of validity for a clinical prediction rule developed from 7 baseline variables for predicting [in this window] the 2 year work disability status in non-specific back pain* [in a new window]
CONCLUSIONS In patients with non-specific back pain associated with 1 day’s absence from work, the best, although limited, prediction of the 2 year work disability status was obtained with 7 baseline variables. Abstract and commentary also appears in ACP Journal Club.
FOOTNOTES For correspondence: Dr C E Dionne, Hôpital du Saint-Sacrement, Québec, Québec, Canada. clermont.dionne{at}uresp.ulaval. ca Source of funding: Quebec Institute for Occupational Safety and Health.
Commentary Arthur T Evans, MD, MPH1 and Nortin M Hadler, MD2 1
Cook County Hospital and Rush Medical College Chicago, Illinois, USA 2 University of North Carolina at Chapel Hill Chapel Hill, North Carolina, USA
The bottom line for clinicians: don’t use the prediction rule by Dionne et al for several reasons. Firstly, it should be validated in another setting, and its clinical use should be shown to cause more good than harm. Secondly, the rule’s potential use is suspect considering that 40% of predictions in the validation sample were erroneous. Finally, the predicted outcome—RWGH—is an
http://ebm.bmj.com/cgi/content/full/10/6/187 (2 sur 4)2007-04-25 07:23:26
An algorithm comprising 7 baseline variables predicted the 2 year work d...fic back pain -- Evans and Hadler 10 (6): 187 -- Evidence-Based Medicine
unstable psychosocial construct that is highly influenced by interpersonal, economic, and political factors. Wasson et al warned against using such outcomes.1 The psychosocial nature of the outcome is underscored by the fact that the best single predictor was the patient’s own premonition of their future status: "Do you think you will be back to your normal work within 3 months?" Is this an example of dispassionate foresight or self fulfilling prophecy? Because the rule predicted failure to return to work 2–3 times more often than what was actually observed, it is possible that informing patients of an ominous prediction would adversely influence outcomes further. Another striking observation is that outcome status at 12 weeks had a predictive accuracy >90%. That prolonged disability seals one’s fate is a long standing reproach to all involved in workplace health and safety. How the persistence of disability renders back pain unremitting is a conundrum for clinical investigation. The answer may be hiding in studies such as this by Dionne et al. Living with back pain reflects the psychosocial assaults on our coping capacity that operate at home and at work.2 Furthermore, this psychosocial context has a temporal component, includes the vortex of disability determination itself, and is bedevilled by the idiosyncrasies of life. No wonder a "predictive rule" for job absenteeism in the setting of back pain is a will-o-the-wisp.
REFERENCES 1. Wasson JH, Sox HC, Neff RK, et al.. Clinical prediction rules. Applications and methodological standards. N Engl J Med 1985;313:793–9.[Abstract] 2. Hadler NM. Occupational musculoskeletal disorders. 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2005.
eLetters: Read all eLetters Sometimes small things can make a big difference: Dionne et al. respond to Evans and Hadler Clermont E. Dionne, et al. EBM Online, 12 Jan 2006 [Full text]
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http://ebm.bmj.com/cgi/content/full/10/6/187 (3 sur 4)2007-04-25 07:23:26
An algorithm comprising 7 baseline variables predicted the 2 year work d...fic back pain -- Evans and Hadler 10 (6): 187 -- Evidence-Based Medicine
PubMed Articles by Evans, A. T Articles by Hadler, N. M
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An algorithm comprising 7 baseline variables predicted the 2 year work disability status in non-specific back pain Arthur T Evans and Nortin M Hadler Evid Based Med 2005; 10: 187 [Full text] [PDF]
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Electronic letters published: Sometimes small things can make a big difference: Dionne et al. respond to Evans and Hadler Clermont E. Dionne, Renée Bourbonnais, Pierre Frémont, Michel Rossignol, Susan R. Stock and Isabelle Larocque (12 January 2006)
Sometimes small things can make a big difference: Dionne et al. respond to Evans and Hadler Clermont E. Dionne, Investigator Population Health Research Unit, Research Center of the Laval University Affiliated Hospital, Renée Bourbonnais, Pierre Frémont, Michel Rossignol, Susan R. Stock and Isabelle Larocque Send letter to journal: Re: Sometimes small things can make a big difference: Dionne et al. respond to Evans and Hadler clermont.dionne{at} uresp.ulaval.ca Clermont E. Dionne, et al.
12 January 2006
Dear Editor, We agree with doctors Evans and Hadler that our clinical decision rule[1] should be validated in another setting and that it should be shown to cause more good than harm before it could be widely used. Our study was a first step in the process of developing a predictive tool for the occupational outcome of back pain. In a next phase of development, not only should the rule be validated in a new group of subjects, but it should also be compared with the judgment of clinicians; a decision tool that is no better than the clinician’s own judgment is of no interest. If the tool adds significantly to the clinician’s judgment, then the impacts of its systematic use could be further studied.[2] Evans and Hadler are also right to be cautious about the performance of the rule. However, it includes a subset of predictors selected among more than 100 variables, several of which measured at baseline, 6 weeks and 12 weeks. We are confident to have identified the best predictors. As we have recognized ourselves in the paper, the rule is however far from perfect. But in its appreciation, it is also very important to understand the limitations of the methods that are currently available to quantify the performance of our model: for example, because classical measures of concurrent validity ask for dichotomous outcomes, we had to regroup the outcome categories and this tended to dilute the performance of the rule. The alternative approach of
http://ebm.bmj.com/cgi/eletters/10/6/187 (1 sur 3)2007-04-25 07:24:09
EBM -- eLetters for Evans and Hadler, 10 (6) 187
comparing each group with the “Success” group would have resulted in much better indices of predictive validity, but they would however have been artificial, since the physician always works from the complete pool of patients. Again, the real test will come from comparisons with the clinicians’ judgment. We do not believe that “return to work in good health” is an “unstable psychosocial construct”. It was clearly defined and is certainly clinically important. In many instances, this is the kind of outcomes on which the primary care physician is asked to give a prognosis. It is, obviously, not easy to predict. Should we then abandon our efforts because the task is too difficult? The interpretation Evans and Hadler seem to give to our results, as to the association of the patient’s “own premonition” with their future status, is clearly flawed. In a predictive statistical approach as the one we have used, there is no intent to identify causal associations and there is no adjustment for confounders.[3] All that can be said about this association is that this variable is one of the most useful to predict the outcome. Why not use it then? Others have also found patients’ recovery expectations to be strongly linked with different health outcomes.[4-7] Finally, interpreting Figure 2 of the paper to say that the outcome at 12 weeks had a predictive accuracy of >90% is, unfortunately, too simple: at 12 weeks, only 52% of subjects were in the “Success” category. Forty-eight percent were in the other categories, and there was a difference of 7.3% in “Failures” between 12 weeks and 2 years. Given that the largest part (>80%) of the costs of back pain comes from a small minority (
