4CPS-189 Fall incidents in nursing home patients: development of a predictive clinical rule (finder)

V Milosevic, B Winkens, B Oijen, K Hurkens, A Linkens, C Mestres-Gonzalvo, H Kuy
2020 Section 4: Clinical Pharmacy Services   unpublished
drugs prescribed per patient were 7 days and 10±3.5 drugs, respectively. Fifty-nine patients (72%) had at least one DACE prescribed (an average of two DACE per patient). Most common DACE grouped by ATC were: anxiolytics (N05B, n=30), antidepressants (N06A, n=28), antipsychotics (N05A, n=22), opioids (N02A, n=16) and antiepileptic (N03A, n=14). Thirty-two (39%) patients had a moderate anticholinergic risk (median DBI 0.6) and 27 (33%) patients had a HAR (median DBI 1.5). Four out of 27 (15%)
more » ... out of 27 (15%) interventions were accepted and consisted of two dose reductions and two DACE deprescriptions. The interventions were not accepted mainly because the drugs were part of the patient's chronic psychiatric or neurological treatment, the presence of refractory pain or insomnia disorders. Conclusion and relevance Our pharmacological intervention was poorly accepted by physicians. During the hospitalisation process it is difficult to re-evaluate the need for adjusting chronic medication, especially related to psychiatric or neurological pathologies. For future studies we believe that this type of study would have more impact at the primary care level. Background and importance Fall incidents are common among nursing home patients. Different tools have been developed in the prevention of fall incidents but with unsatisfactory results. Aim and objectives To develop (part I) and validate (part II) a clinical rule (CR) that can predict a fall risk in nursing home patients. Material and methods The study was conducted in two parts. In part I, the variables which could lead to an increased risk of falls were determined and implemented in the predictive clinical rule. Subsequently, data from a retrospective cohort study were used to validate the developed clinical rule. Multiple linear regression analysis was conducted to identify the fall risk variables in part I. With these, a predictive fall risk algorithm was developed where the overall prediction quality was assessed using the area under the receiver operating characteristic curve (AUROC), and a cut-off value was determined for the predicted risk ensuring a sensitivity !0.85. This prediction model and cut-off value were externally validated in part II. Results A total of 1668 (824 in part I, 844 in part II) nursing home patients were included in the study. Eleven fall risk variables were identified in part I. The externally validated AUROC of the prediction model, obtained in part II, was 0.603 (95% CI 0.565-0.641) with a sensitivity of 83.41% (95% CI 79.44-86.76%) and a specificity of 27.25% (95% CI 23.11-31.81%).
doi:10.1136/ejhpharm-2020-eahpconf.290 fatcat:xrwjl7e6cvf4vlp2xpwxkv2bna