Using Charlson Comorbidity Index to Predict Short-Term Clinical Outcomes in Hospitalized Older Adults

Abstract

This study examined the prognostic value of the Charlson Comorbidity Index (CCI) in predicting short-term clinical outcomes in hospitalized older adults. We conducted a retrospective cohort study of patients, older than 75 years, admitted to the medicine service at a large tertiary hospital (New York). We used the Enhanced International Classification of Disease, 9th Revision, Clinical Modification adaptation to abstract the CCI from electronic medical records. The CCI scores were compared, using the standard Deyo version and the Schneeweiss version. Outcome measures included in-hospital mortality, length of stay (LOS), and 30-day readmissions. When comparing Charlson/Deyo and Charlson/Deyo/Schneeweiss with and without age, we found similar significant association with regard to in-hospital mortality, with a moderate predictive ability (area under the curve [AUC]: 0.5906-0.6433). However, for 30-day readmissions and LOS, the predictive ability was poor (AUC: 0.5598-0.6106 and ρ: 0.11-0.12, respectively). The CCI is, at most, a moderate predictor of in-hospital mortality and a poor predictor of other important healthcare outcomes relevant to administrative healthcare practices.

Risk factors for drug-related problems in a general hospital: A large prospective cohort

“…In our study, the analysis of variables on hospital admission indicated that elective surgery admission had lower risk of potential DRP, most likely due to a shorter length of stay, less comorbidities, lower complexity of the processes of care and, consequently, fewer medication orders. In contrast, recurrent hospitalizations within the same year, indicating a need to care of previously unresolved and complex health problems, as well as Charlson's comorbidity index � 5 are related to increased complexity of care [36]. Patient variables such as body mass index below the normal range, increased heart rate, anemia, and leukocytosis are clinical signs common to several serious conditions, especially infections and blood abnormalities [37].…”

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“…Demographic variables included age, sex, chart documented race (Asian, Black, White, "other or unknown"), and ethnicity (Hispanic or Other). Patient characteristics included dichotomized smoking history ("current or formal smoker" or "never smoker"); body mass index (BMI), which was calculated from weight and height, using the formula BMI = weight (kg)/height (m) 2 ; and comorbidity index, which was derived using the Charlson Comorbidity Index without the age component of the score (21,22). BMI < 14 (below one percentile of our data; N = 20) was regarded as missing, due to likely data input errors.…”

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