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Developing PHarmacie-R: A bedside risk prediction tool with a medicines management focus to identify risk of hospital
Deirdre T Criddle1, Benjamin Devine2, Kevin Murray3
1Pharmacy Department, Sir Charles Gairdner Hospital, Perth, 6009, Australia; Complex Needs Coordination Team, South Metropolitan Health Service, Murdoch Drive, Perth, 6150, Australia; Medicines Solutions, Dianella, 6059, Australia.
Background:
The imperative to identify patients at risk of medication-related harm has never been greater. Hospital clinicians cannot easily predict risk of readmission or harm. Candidate variables associated with medication-related harm derived from the literature or significantly represented in a complex patient cohort have been previously described by PHarmacie-4. With a focus on polypharmacy and high-risk medicines in vulnerable patient cohorts, PHarmacie-4 was easy to use and highlighted risks. However it over-estimated risk, reducing its usefulness in stratifying risk of readmission.
Objective:
Develop a risk prediction tool built into a smart phone app, enabling clinicians to identify and refer high-risk patients for an early post-discharge medicines review. Demonstrate usability, real world application and validity in an independent dataset.
Methods:
A retrospective, observational study was conducted with 1201 randomly selected patients admitted to Sir Charles Gairdner Hospital between June 1, 2016 to December 31, 2016. Patient characteristics and outcomes of interest were reported, including unplanned hospital utilisation at 30, 60 and 90 days post-discharge. Using multivariable logistic regression modelling, an algorithm was developed, built into a smart phone app and used and validated in an independent dataset.
Results:
738 patients (61%) were included in the derivation sample. The best predictive performance was achieved by PHarmacie-R (C-statistic 0.72, 95% CI 0.68-0.75) which included PHarmacie-4 risk variables, a non-linear effect of age, unplanned hospital utilisation in the preceding six months and gender. The independent validation dataset had a C-statistic of 0.64 (95% CI 0.56-0.72).
Conclusion:
PHarmacie-R is the first readmission risk prediction tool, built into a smart phone app, focussing on polypharmacy and high-risk medicines in vulnerable patients. It can assist clinical pharmacists to identify medical inpatients who may benefit from early post-discharge medication management services. External validation is needed to enable application in other clinical settings.
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