OBJECTIVE

The development of risk models for 16 preventable adverse drug events (pADEs) and their aggregation into the final complexity score (C-score) are described.

METHODS

Using data from 2 tertiary care facilities, logistic regression models were constructed for the first 5 hospital days that admissions were at risk for each of 16 pADEs. The best model for each pADE was validated in 100 bootstrap samples. The C-score was then aggregated and predicted individual pADE risk as the probability to develop at least 1 pADE. Using the 100 bootstrap samples for each pADE, 100 C-scores for validation were generated.

RESULTS

We utilized electronic health records (EHR) data from 65,518 admissions to UF Health Shands and 18,269 admissions to UF Health Jacksonville to develop risk models for 16 pADEs. Most models had very strong discriminant validity (C-statistic>> 0.8), with the highest predicted decile representing about half of manifest pADEs. Among admissions in the highest C-score decile, about two thirds experienced at least 1 pADE (C-statistic, 0.838; 95% confidence interval, 0.838-0.839). C-score precision, defined as the percentage of patients consistently (i.e., at least 95 of 100 samples) ranked in the 90th percentile, was 80-84%.

CONCLUSIONS

The C-score was developed and validated for the identification of hospitalized patients at highest risk for pADEs. Aggregation of individual prediction models into a single score reduced its predictive power for most pADEs, compared with the individual risk models, but concentrated in the highest C-score decile a patient group more than two thirds of whom experienced at least 1 pADE.