Purpose: Venous thromboembolism (VTE) is a leading cause of preventable morbidity and mortality, with total hip arthroplasty (THA) and total knee arthroplasty (TKA) at the highest risk. Safe and appropriate thromboprophylaxis is essential. However, investigations into prescribing practices have only had limited investigation. Aims: To describe current VTE prophylaxis regimens in Australian patients following an elective THA/TKA and compare these regimens to an international standard. Methods: A retrospective multisite cohort study of patients admitted for a THA/TKA in six tertiary hospitals in Queensland, Australia was conducted over 12 months. Patient and medication data were collected following surgery and for 60 days after discharge to determine changes to their thromboprophylaxis regimen. Results were summarised and compared to NICE guidelines. Results: 1,011 patients (43.1% THA, 56.9% TKA) were included and thromboprophylaxis was used in 98.1% of inpatients and in 94.3% of discharge patients for 5.2 (±5.23) and 29.2 (±15.9) days, respectively. Low-molecular-weight heparins were the primary drugs for inpatients (71.2%), and aspirin 150mg for discharge (42.0%), most commonly for 6 weeks (31.8%). Generally, a two-staged prophylaxis regimen was implemented; most commonly any anticoagulant as an inpatient, followed by rivaroxaban on discharge (32.7%) or an anticoagulant as an inpatient with aspirin on discharge (26.4%). Overall, adherence to NICE guidelines was low; THA: 8.7%, TKA: 5.9%. Conclusion: VTE prophylaxis regimens varied considerably and consequently, adherence to international guidelines was low. There is a need for local, peer-led guidelines to ensure consistent, safe, and effective prophylaxis.

BACKGROUND: Clinic pharmacists have been shown to identify and resolve medication related problems post-discharge, however, the impact on patient clinical outcomes is unclear. AIMS: To identify hospital-based post-discharge pharmacist clinics that provide medication review; report the patient clinical outcomes measured; and describe the activities of the clinical pharmacist. METHODS: Published studies evaluating a patient clinical outcome following a post-discharge hospital clinic pharmacy service were included. All studies needed a comparative design (intervention vs usual care). Pubmed, Embase, CINAHL, PsycnINFO, Web of Science, IPA and APAIS-Health databases were searched to identify studies. The type of clinic and the clinical pharmacist activities were linked to patient clinical outcomes. RESULTS: Fifty-seven studies were included in the final analysis, 14 randomised controlled trials and 43 non-randomised studies. Three key clinic types were identified: post-discharge pharmacist review alone, inpatient care plus post-discharge review and post-discharge collaborative clinics. The three main outcome metrics identified were hospital readmission and/or representation, adverse drug events, and improved disease state metrics. There was often a mix of these outcomes reported as primary and secondary outcomes. High heterogeneity of interventions and clinical pharmacist activities reported meant it was difficult to link clinical pharmacist activities with the outcomes reported. CONCLUSIONS: A post-discharge clinic pharmacist may improve patient clinical outcomes such as hospital readmission and representation rates. Future research needs to provide a clearer description of the clinical pharmacist activities provided in both arms of comparative studies.

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.