4.4 Article

Developing an adapted Charlson comorbidity index for ischemic stroke outcome studies

Journal

BMC HEALTH SERVICES RESEARCH
Volume 19, Issue 1, Pages -

Publisher

BMC
DOI: 10.1186/s12913-019-4720-y

Keywords

Charlson comorbidity; Ischemic stroke; Administrative data; Risk adjustment; Mortality

Funding

  1. ICES
  2. Canadian Institutes of Health Research (CIHR) [FDN 143303]
  3. Ontario Stroke Network (OSN)
  4. Ontario Ministry of Health and Long-Term Care (MOHLTC)

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Background: The Charlson comorbidity index (CCI) is commonly used to adjust for patient casemix. We reevaluated the CCI in an ischemic stroke (IS) cohort to determine whether the original seventeen comorbidities and their weights are relevant. Methods: We identified an IS cohort (N = 6988) from the Ontario Stroke Registry (OSR) who were discharged from acute hospitals (N = 100) between April 1, 2012 and March 31, 2013. We used hospital discharge ICD-10-CA data to identify Charlson comorbidities. We developed a multivariable Cox model to predict one-year mortality retaining statistically significant (P < 0.05) comorbidities with hazard ratios >= 1.2. Hazard ratios were used to generate revised weights (1-6) for the comorbid conditions. The performance of the IS adapted Charlson comorbidity index (ISCCI) mortality model was compared to the original CCI using the c-statistic and continuous Net Reclassification Index (cNRI). Results: Ten of the 17 Charlson comorbid conditions were retained in the ISCCI model and 7 had reassigned weights when compared to the original CCI model. The ISCCI model showed a small but significant increase in the c-statistic compared to the CCI for 30-day mortality (c-statistic 0.746 vs. 0.732, p = 0.009), but no significant increase in c-statistic for in-hospital or one-year mortality. There was also no improvement in the cNRI when the ISCCI model was compared to the CCI. Conclusions: The ISCCI model had similar performance to the original CCI model. The key advantage of the ISCCI model is it includes seven fewer comorbidities and therefore easier to implement in situations where coded data is unavailable.

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