A simplified clinical prediction rule for prognosticating independent walking after spinal cord injury: a prospective study from a Canadian multicenter spinal cord injury registry.

BACKGROUND CONTEXT: Traumatic spinal cord injury (SCI) is a debilitating condition with limited treatment options for neurologic or functional recovery. The ability to predict the prognosis of walking post injury with emerging prediction models could aid in rehabilitation strategies and reintegration into the community. PURPOSE: To revalidate an existing clinical prediction model for independent ambulation (van Middendorp et al., 2011) using acute and long-term post-injury follow-up data, and to investigatethe accuracy of a simplified model using prospectively collected data from a Canadian multicenter SCI database, the Rick Hansen Spinal Cord Injury Registry (RHSCIR). STUDY DESIGN: Prospective cohort study. PARTICIPANT SAMPLE: The analysis cohort consisted of 278 adult individuals with traumatic SCI enrolled in the RHSCIR for whom complete neurologic examination data and Functional Independence Measure (FIM) outcome data were available. OUTCOME MEASURES: The FIM locomotor score was used to assess independent walking ability (defined as modified or complete independence in walk or combined walk and wheelchair modality) at 1-year follow-up for each participant. METHODS: A logistic regression (LR) model based on age and four neurologic variables was applied to our cohort of 278 RHSCIR participants. Additionally, a simplified LR model was created. The Hosmer-Lemeshow goodness of fit test was used to check if the predictive model is applicable to our data set. The performance of the model was verified by calculating the area under the receiver operating characteristic curve (AUC). The accuracy of the model was tested using a cross-validation technique. This study was supported by a grant from The Ottawa Hospital Academic Medical Organization ($50,000 over 2 years). The RHSCIR is sponsored by the Rick Hansen Institute and is supported by funding from Health Canada, Western Economic Diversification Canada, and the provincial governments of Alberta, British Columbia, Manitoba, and Ontario. ET and JP report receiving grants from the Rick Hansen Institute (approximately $60,000 and $30,000 per year, respectively). DMR reports receiving remuneration for consulting services provided to Palladian Health, LLC and Pacira Pharmaceuticals, Inc ($20,000-$30,000 annually), although neither relationship presents a potential conflict of interest with the submitted work. KEH received a grant for involvement in the present study from the Government of Canada as part of the Canada Summer Jobs Program ($3,000). JP reports receiving an educational grant from Medtronic Canada outside of the submitted work ($75,000 annually). TM reports receiving educational fellowship support from AO Spine, AO Trauma, and Medtronic; however, none of these relationships are financial in nature. All remaining authors have no conflicts of interest to disclose. RESULTS: The fitted prediction model generated 85% overall classification accuracy, 79% sensitivity, and 90% specificity. The prediction model was able to accurately classify independent walking ability (AUC 0.889, 95% confidence interval [CI] 0.846-0.933, p<.001) compared with the existing prediction model, despite the use of a different outcome measure (FIM vs. Spinal Cord Independence Measure) to qualify walking ability. A simplified, three-variable LR model based on age and two neurologic variables had an overall classification accuracy of 84%, with 76% sensitivity and 90% specificity, demonstrating comparable accuracy with its five-variable prediction model counterpart. The AUC was 0.866 (95% CI 0.816-0.916, p<.01), only marginally less than that of the existing prediction model. CONCLUSIONS: A simplified predictive model with similar accuracy to a more complex model for predicting independent walking was created, which improves utility in a clinical setting. Such models will allow clinicians to better predict the prognosis of ambulation in individuals who have sustained a traumatic SCI.

Minimal important differences in the EORTC QLQ-C15-PAL to determine meaningful change in palliative advanced cancer patients.

AIMS: Quality of life (QOL) is important for advanced cancer patients. Brief questionnaires are advantageous to reduce patient burden. In large clinical trials, statistically significant small changes can be achieved; however, whether such change is clinically relevant is unknown. The purpose of this study was to determine the minimal important differences (MID) of the European Organisation for Research and Treatment of Cancer quality of life core 15 palliative questionnaire (EORTC QLQ-C15-PAL). METHODS: Patients undergoing palliative radiotherapy completed the EORTC QLQ-C15-PAL at baseline and 1 month later. Anchor and distribution-based assessments were employed to determine the MID associated with this instrument. The anchor of overall QOL was used to determine meaningful change. RESULTS: In all, 276 patients were included in MID calculation. Mean age was 65 years and primary lung, breast or prostate cancers were most common. Statistically significant MID for improvement was seen in emotional functioning and pain (20.9 and 15.6, respectively). MID for deterioration required a 20.4, 24.5, 17.1 and 23.0 change in physical functioning, fatigue, pain and appetite loss, respectively, to constitute meaningful change. Distribution-based estimates of MID were closest to the standard error of measurement. MID for brain and bone metastases patients yielded MID larger than previously determined in the incorporation of all patients. CONCLUSION: Meaningful change in the EORTC QLQ-C15-PAL is important for clinicians to determine the impact of treatment on the QOL of patients and can aid in determining the sample size required for clinical trials. Future studies should investigate MID in subgroups using symptom-specific modules.