BACKGROUND: Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. OBJECTIVE: To explore the relationship between predictive models and the MCID cutpoint on the ODI. SETTING: Data were collected from 16 outpatient physical therapy clinics in 10 states. DESIGN: Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). PARTICIPANTS AND INTERVENTIONS: One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. RESULTS: The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 001). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. LIMITATIONS: The primary limitations include sub-optimal sample size and study design. CONCLUSIONS: There is extreme variability among predictive models created using different MCIDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice.
RCT Entities:
BACKGROUND: Minimal clinically important difference (MCID) scores for outcome measures are frequently used evidence-based guides to gage meaningful changes. There are numerous outcome instruments used for analyzing pain, disability, and dysfunction of the low back; perhaps the most common of these is the Oswestry disability index (ODI). A single agreed-upon MCID score for the ODI has yet to be established. What is also unknown is whether selected baseline variables will be universal predictors regardless of the MCID used for a particular outcome measure. OBJECTIVE: To explore the relationship between predictive models and the MCID cutpoint on the ODI. SETTING: Data were collected from 16 outpatient physical therapy clinics in 10 states. DESIGN: Secondary database analysis using backward stepwise deletion logistic regression of data from a randomized controlled trial (RCT) to create prognostic clinical prediction rules (CPR). PARTICIPANTS AND INTERVENTIONS: One hundred and forty-nine patients with low back pain (LBP) were enrolled in the RCT. All were treated with manual therapy, with a majority also receiving spine-strengthening exercises. RESULTS: The resultant predictive models were dependent upon the MCID used and baseline sample characteristics. All CPR were statistically significant (P < 001). All six MCID cutpoints used resulted in completely different significant predictor variables with no predictor significant across all models. LIMITATIONS: The primary limitations include sub-optimal sample size and study design. CONCLUSIONS: There is extreme variability among predictive models created using different MCIDs on the ODI within the same patient population. Our findings highlight the instability of predictive modeling, as these models are significantly affected by population baseline characteristics along with the MCID used. Clinicians must be aware of the fragility of CPR prior to applying each in clinical practice.
Entities:
Keywords:
Clinical prediction rule; Low back pain; Minimal clinically important difference; Prognosis
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