ABSTRACT 49th ESCP virtual symposium on clinical pharmacy 19.10.2021–21.10.2021 Clinical pharmacy, working collaboratively in mental health care Published online: 29 November 2021  Springer Nature Switzerland AG 2021 Oral communications and Posters ORAL COMMUNICATION I OR01.1 Developing and validating a prediction model for in-hospital mortality associated with anticholinergic drug burden among older hospitalised patients with dementia Angela E. Lisibach1, 2, 3, 4, Nora Haltinner* 4, 5, Patrick E. Beeler6, Andrea Burden5, Chantal Csajka1, 2, 3, Monika Lutters4, 5 1Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, University of Lausanne, 2School of Pharmaceutical Sciences, University of Geneva, Geneva, 3Center for Research and Innovation in Clinical Pharmaceutical Sciences, University Hospital and University of Lausanne, Lausanne, 4Departement of Medical Services, Clinical Pharmacy, Cantonal Hospital of Baden, Baden, 5Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, ETH Zurich, Zurich, 6Division of Occupational and Environmental Medicine, Epidemiology, Biostatistics and Prevention Institute, University of Zurich and University Hospital Zurich, Zu¨rich, Switzerland Background and Objective: After cardiovascular diseases and can- cer, dementia is the third most frequent cause of death in Switzerland among patients aged 65 years and older. A recent cohort study con- ducted in a regional hospital in Switzerland showed that a high anticholinergic drug burden in older patients is signiﬁcantly associ- ated with increased in-hospital mortality. We aimed to develop and validate a risk prediction model for in-hospital mortality in older patients with dementia and consider their exposure to anticholinergic drugs as a predeﬁned predictor. Design: Electronic health record data within the ﬁrst 24h of admis- sion was collected from 2015-2018. Included were inpatients C65 years old, hospitalised C48h, diagnosed with dementia according to ICD-10 codes. Outpatients and patients with stays [24h in the intensive care unit were excluded. The outcome was in-hospital mortality. The developed model was based on Naı¨ve Bayes using data from 2015–2016 as training set, the years 2017–2018 served as val- idation set. The candidate variables were obtained with the Minimum Redundancy Maximum Relevance (MRMR) variable selection algo- rithm and backward selection. Synthetic Minority Oversampling Technique (SMOTE) was applied within the training-set. Perfor- mance measures of interest were AUC, sensitivity and speciﬁcity. Results: In total, 2,595 patients were included. The training set contained 1266 patients (mean age 83.2±6.54 years, 60% female) of which 63 (5.0%) died during hospitalisation. Variables selected for prediction were: self-care index, delirium observation screening score, sodium, CRP, creatinine, GFR, chronic heart failure, hemi-/paraplegia, COPD, cancer, diabetes and the anticholinergic burden scales Cancelli, ACL, ADS and SCDL. Further, blood pres- sure, polypharmacy, age, BMI, and temperature were included. A ﬁrst validation for the ﬁnal model using the training set showed an AUC of 0.872, a sensitivity of 90.44% and a speciﬁcity of 68.50%. Using the validation set, we obtained an AUC of 0.771, a sensitivity of 63.01% and a speciﬁcity of 73.65%. Conclusion: The developed prediction model for in-hospital mor- tality showed an acceptable performance. It might be a useful tool to identify patients with dementia with a high risk for in-hospital mor- tality in order to implement interventions and management strategies during hospitalisation to improve patient outcome, however, further validation is required. OR01.2 Undergraduate students and educators’ views on interprofessional behaviour change support education in chronic disease Isa B. Fe´lix1, Carla Nascimento1, Patrı´cia Pereira1, Katja Braam2, Cathal Cadogan3, Judith Strawbridge4, Leona Cilar5, Lucija Gosak5, Nuno Pime