Feasibility of coding-based Charlson comorbidity index for hospitalized patients in China, a representative developing country.

2020 
BACKGROUND: The Charlson Comorbidity Index (CCI) can be automatically calculated from the International Classification of Disease (ICD) code. However, the feasibility of this transformation has not been acknowledged, particularly in hospitals without a qualified ICD coding system. Here, we investigated the utility of coding-based CCI in China. METHODS: A multi-center, population-based, retrospective observational study was conducted, using a dataset incorporating 2,464,395 adult subjects from 15 hospitals. CCI was calculated using both ICD-10-based and diagnosis-based method, according to the transformation rule reported previously and to the literal description from discharge diagnosis, respectively. A κ coefficient of variation was used as a measure of agreement between the above two methods for each hospital. The discriminative abilities of the two methods were compared using the receiver-of-operating characteristic curve (ROC) for prediction of in-hospital mortality. RESULTS: Total agreement between the ICD-based and diagnosis-based CCI for each index ranged from 86.1 to 100%, with κ coefficients from 0.210 [95% confidence interval (CI) 0.208-0.212] to 0.932 (95% CI 0.924-0.940). None of the 19 indices of CCI had a κ coefficient > 0.75 in all the hospitals included for study. The area under the curve of ROC for in-hospital mortality of all 15 hospitals was significantly lower for ICD-based than diagnosis-based CCI [0.735 (0.732, 0.739) vs 0.760 (0.757, 0.764)], indicative of more limited discriminative ability of the ICD-based calculation. CONCLUSIONS: CCI calculated using ICD-10 coding did not agree with diagnosis-based CCI. ICD-based CCI displayed diminished discrimination performance in terms of in-hospital mortality, indicating that this method is not promising for CCI scoring in China under the present circumstances.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    17
    References
    3
    Citations
    NaN
    KQI
    []