Development and validation of the Adverse Inpatient Medication Event
Background Medication harm has negative clinical and economic
consequences, contributing to hospitalisation, morbidity and mortality.
The incidence ranges from four to 14%, of which up to 50% of events
may be preventable. A predictive model for identifying high-risk
inpatients can guide a timely and systematic approach to prioritisation.
Aim To develop and internally validate a risk prediction model, for
prioritisation of hospitalised patients, at risk of medication harm.
Methods A retrospective cohort study was conducted in general medical
and geriatric specialties at an Australian hospital, over six months.
Medication harm was identified using International Classification of
Disease (ICD-10) codes and the hospital’s incident database. Sixty-eight
variables, including medications and laboratory results, were extracted
from the hospital’s databases. Multivariable logistic regression was
used to develop the final risk model. Performance was evaluated using
area under the receiver operative characteristic curve (AuROC) and
clinical utility was determined using decision curve analysis. Results
The study cohort included 1982 patients median age 74 years, of which
136 (7%) experienced ≥1 adverse medication event(s). The model
included: length of stay, hospital re-admission within 12 months, venous
or arterial thrombosis &/or embolism, ≥ 8 medications, serum sodium
< 126 mmol/L, INR > 3, anti-psychotic,
antiarrhythmic and immunosuppressant medications, and history of
medication allergy. Validation gave an AuROC of 0.70 (95% CI:
0.65-0.74). Decision curve analysis identified that the AIME may be
clinically useful to help guide decision making in practice. Conclusion
We have developed a risk prediction model with reasonable performance.
Future steps include external validation.