Philippe Phan1, Brandon Budhram2, Qiong Zhang3, Carly S Rivers4, Vanessa K Noonan3, Tova Plashkes4, Eugene K Wai5, Jérôme Paquet6, Darren M Roffey7, Eve Tsai8, Nader Fallah3. 1. Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Clinical Epidemiology Program, The Ottawa Hospital,, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada. Electronic address: pphan@toh.ca. 2. Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada. 3. Rick Hansen Institute, Blusson Spinal Cord Centre, 6400-818 W. 10th Ave, Vancouver, BC V5Z 1M9, Canada; The University of British Columbia, 2329 West Mall, Vancouver, BC V6T 1Z4, Canada. 4. Rick Hansen Institute, Blusson Spinal Cord Centre, 6400-818 W. 10th Ave, Vancouver, BC V5Z 1M9, Canada. 5. Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Division of Orthopaedic Surgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Clinical Epidemiology Program, The Ottawa Hospital,, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada. 6. Département Sciences Neurologiques, Pavillon Enfant-Jésus, CHU de Québec, 1401 18e rue, Québec, QC G1J 1Z4, Canada. 7. Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Clinical Epidemiology Program, The Ottawa Hospital,, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada. 8. Ottawa Combined Adult Spinal Surgery Program, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Clinical Epidemiology Program, The Ottawa Hospital,, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada; Division of Neurosurgery, Department of Surgery, Faculty of Medicine, University of Ottawa, The Ottawa Hospital, 1053 Carling Ave, Ottawa, ON K1Y 4E9, Canada.
Abstract
BACKGROUND CONTEXT: Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. PURPOSE: To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models. STUDY DESIGN: A prospective cohort study. PARTICIPANT SAMPLE: Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data. OUTCOME MEASURES: The FIM locomotor score was used to assess independent walking ability at 1-year follow-up. METHODS: Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). RESULTS: In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses. CONCLUSIONS: Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.
BACKGROUND CONTEXT: Models for predicting recovery in traumatic spinal cord injury (tSCI) patients have been developed to optimize care. Several models predicting tSCI recovery have been previously validated, yet recent findings question their accuracy, particularly in patients whose prognoses are the least predictable. PURPOSE: To compare independent ambulatory outcomes in AIS (ASIA [American Spinal Injury Association] Impairment Scale) A, B, C, and D patients, as well as in AIS B+C and AIS A+D patients by applying two existing logistic regression prediction models. STUDY DESIGN: A prospective cohort study. PARTICIPANT SAMPLE: Individuals with tSCI enrolled in the pan-Canadian Rick Hansen SCI Registry (RHSCIR) between 2004 and 2016 with complete neurologic examination and Functional Independence Measure (FIM) outcome data. OUTCOME MEASURES: The FIM locomotor score was used to assess independent walking ability at 1-year follow-up. METHODS: Two validated prediction models were evaluated for their ability to predict walking 1-year postinjury. Relative prognostic performance was compared with the area under the receiver operating curve (AUC). RESULTS: In total, 675 tSCI patients were identified for analysis. In model 1, predictive accuracies for 675 AIS A, B, C, and D patients as measured by AUC were 0.730 (95% confidence interval [CI] 0.622-0.838), 0.691 (0.533-0.849), 0.850 (0.771-0.928), and 0.516 (0.320-0.711), respectively. In 160 AIS B+C patients, model 1 generated an AUC of 0.833 (95% CI 0.771-0.895), whereas model 2 generated an AUC of 0.821 (95% CI 0.754-0.887). The AUC for 515 AIS A+D patients was 0.954 (95% CI 0.933-0.975) with model 1 and 0.950 (0.928-0.971) with model 2. The difference in prediction accuracy between the AIS B+C cohort and the AIS A+D cohort was statistically significant using both models (p=.00034; p=.00038). The models were not statistically different in individual or subgroup analyses. CONCLUSIONS: Previously tested prediction models demonstrated a lower predictive accuracy for AIS B+C than AIS A+D patients. These models were unable to effectively prognosticate AIS A+D patients separately; a failure that was masked when amalgamating the two patient populations. This suggests that former prediction models achieved strong prognostic accuracy by combining AIS classifications coupled with a disproportionately high proportion of AIS A+D patients.
Authors: Stephanie K Rigot; Michael L Boninger; Dan Ding; Gina McKernan; Edelle C Field-Fote; Jeanne Hoffman; Rachel Hibbs; Lynn A Worobey Journal: Arch Phys Med Rehabil Date: 2021-04-08 Impact factor: 3.966