Evaluation of Clinical Usefulness
Figure 2 shows the decision curve, with the range of threshold
probabilities for which the AIME model could be clinically useful. The
grey lines indicate the net benefit when pharmacist interventions are
provided for all patients (“treat all”), or for the horizontal black
line, when no patient receives pharmacist intervention (“treat none”).
These serve as a reference to judge whether the AIME and the
Polypharmacy models are clinically useful (indicated by the positive
values above the “treat all” and “treat none” lines). The turquoise
line shows the clinical value of the binary Polypharmacy model (with a
single predictor) for medication harm. This model shows minimal clinical
usefulness when compared with a “treat all” approach. The red lines
show the clinical usefulness of the AIME model. Between the threshold
probabilities of approximately 0.05 and 0.7 the AIME model has greater
net benefit (i.e. clinically useful) than a ‘treat all’ and ‘treat none’
approach, reinforcing that prioritisation of patients with risk above
the 5% probability threshold would be a useful approach.
Insert Figure 2
Discussion
Key findings
We report the development and internal validation of the AIME model, for
predicting the risk of inpatient medication harm. The model was
developed in a quaternary Australian hospital, in the general medical
and geriatric setting. The final model consisted of 10 variables,
including high-risk medications, laboratory tests, history of medication
allergies and hospital utilisation data, extracted from digital
databases at the hospital. The AIME model demonstrated reasonable
discrimination (AuROC = 0.70), similar to that of previously published
models [11-12, 35-36], (AuROC 0.70-0.74), and superior to three
existing inpatient models (AuROC 0.59-0.63) [37-39].
The AIME model compared with existing
models
Outcome
The selection of a study outcome is crucial to developing a useful
model. Currently, there are a limited number of models that predict
actual medication harm, with some using medication errors or
medication-related problems, as their primary outcome [17]. In this
study, only events that were likely to have resulted in clinical or
physiological harm to the patient were included. This is important given
that around 90% of medication errors cause no patient harm [18].
Therefore, a key focus of prognostic studies should be on outcomes or
actual patient harm, versus medication errors where clinical
consequences are unknown . In our study, the rate of harm was
approximately 7%, which is consistent with local and international data
[6, 36, 40]. Causality assessment showed similarities between event
likelihood and severity, to the findings of the study by Tangiisuran et
al and the BADRI ADR risk score – a European model for inpatient harm
[11].
Cardiovascular medications were the most common medication groups
leading to inpatient harm (n=36). This was similar to the findings of
the GerontoNet risk score where the most common adverse events included
cardiovascular and antiarrhythmic complications [41]. Adverse events
associated with cardiovascular agents have been highlighted by multiple
studies [42-44]. A recent Australian study of 768 hospital
admissions identified the use of multiple antihypertensives as the most
significant predictor of medication-related hospitalisation [43].
Similarly, a study of inpatient medication harm, using the Institute for
Healthcare Improvement’s ADE Trigger Tool, found that cardiovascular
agents were the most common drug class association with harm [6].
Given the rise in the prescription of cardiovascular therapies, in
particular in the aging population, it is essential that there is a
greater focus on these medications [45].
Regular medication review and deprescribing, in particular in older
frail adults, where less aggressive blood pressure control is desirable,
should be a key focus for clinicians [35]. As half of the events in
this study were either possibly or definitely preventable, collaborative
medication management, in particular at transitions in care, can play a
major role in improving patient outcomes [5]. A model such as the
AIME will help identify and prioritise high-risk patients for timely
interventions at transition.