• Clinical epidemiology · Jan 2017

    Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients.

    • Knut Stavem, Henrik Hoel, Stein Arve Skjaker, and Rolf Haagensen.
    • Division of Medicine and Laboratory Sciences (AHUSKIL), Campus Ahus, Institute of Clinical Medicine, University of Oslo, Oslo.
    • Clin Epidemiol. 2017 Jan 1; 9: 311-320.

    PurposeThis study compared the Charlson comorbidity index (CCI) information derived from chart review and administrative systems to assess the completeness and agreement between scores, evaluate the capacity to predict 30-day and 1-year mortality in intensive care unit (ICU) patients, and compare the predictive capacity with that of the Simplified Acute Physiology Score (SAPS) II model.Patients And MethodsUsing data from 959 patients admitted to a general ICU in a Norwegian university hospital from 2007 to 2009, we compared the CCI score derived from chart review and administrative systems. Agreement was assessed using % agreement, kappa, and weighted kappa. The capacity to predict 30-day and 1-year mortality was assessed using logistic regression, model discrimination with the c-statistic, and calibration with a goodness-of-fit statistic.ResultsThe CCI was complete (n=959) when calculated from chart review, but less complete from administrative data (n=839). Agreement was good, with a weighted kappa of 0.667 (95% confidence interval: 0.596-0.714). The c-statistics for categorized CCI scores from charts and administrative data were similar in the model that included age, sex, and type of admission: 0.755 and 0.743 for 30-day mortality, respectively, and 0.783 and 0.775, respectively, for 1-year mortality. Goodness-of-fit statistics supported the model fit.ConclusionThe CCI scores from chart review and administrative data showed good agreement and predicted 30-day and 1-year mortality in ICU patients. CCI combined with age, sex, and type of admission predicted mortality almost as well as the physiology-based SAPS II.

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