薬剤疫学
Online ISSN : 1882-790X
Print ISSN : 1342-0445
ISSN-L : 1342-0445
早期公開論文
早期公開論文の1件中1~1を表示しています
  • Tomomi KIMURA, Toshifumi SUGITANI, Takuya NISHIMURA, Masanori ITO
    原稿種別: research-article
    論文ID: 25.e1
    発行日: 2020年
    [早期公開] 公開日: 2020/01/31
    ジャーナル フリー 早期公開
    電子付録

    Objective: To validate and recalibrate Charlson and Elixhauser comorbidity indices (CCI and ECI, respectively) in a Japanese hospital-based administrative database.

    Methods: In this retrospective, cohort study, derivation and validation cohorts were developed to include all hospitalizations for patients aged ≥ 18 years at admission and discharged in 2015 or 2016, respectively, from an administrative database based on 287 hospitals. Seventeen CCI and 30 ECI conditions were identified using the International Classification of Diseases (ICD) -10 codes at admission or during the stay. Predictability for hospital death was evaluated using C statistics from multivariable logistic regression models including age, sex, and individual CCI/ECI conditions or the CCI/ECI score in the derivation cohort. After stepwise selection, weighted risk scores were re-assigned to each condition based on the odds ratios (CCI) or beta-coefficient (ECI), and these modified models were evaluated in the validation cohort.

    Results: The original CCI/ECI had good predictive abilities for hospital death: C statistics (95% confidence interval) for individual comorbidities and score models were 0.764 (0.762-0.765) and 0.731 (0.729-0.733) for CCI, and 0.783 (0.781-0.784) and 0.750 (0.748-0.752) for ECI, respectively. Modified CCI and ECI had 13 and 27 conditions, respectively, but maintained comparable predictive abilities: C statistics for modified individual comorbidities and score models were 0.761 (0.759-0.763) and 0.759 (0.757-0.760) for CCI, and 0.784 (0.782-0.785) and 0.783 (0.781-0.785) for ECI, respectively.

    Conclusions: The original and modified CCI/ECI models, with reduced numbers of conditions, had sufficient and comparable predictive abilities for hospital death and can be used in future studies using this administrative database.

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