282 CHAPTER 4.1 age, number of co-morbidities, number of medications, history of falls, history of hospital admissions, renal function, systolic blood pressure, and being housebound or not. Setting-related factors included ward type (medical vs surgical), admission type (elective vs non-elective), length of hospital stay and country of inclusion. Potential determinants with continuous values were dichotomised or categorised into tertiles based on patient distribution or based on clinically accepted cut-off values for measurements (renal function <30 ml/min, 30-50 ml/min, >50 ml/min, systolic blood pressure <120 mmHg, 120-140 mmHg, >140 mmHg). Data on potential determinants were captured during the index hospitalisation in an electronic case report form (eCRF) for all OPERAM patients. The included potential determinants were selected after expert consensus and based on a potential relation with STOPP/ START (e.g. falls – section STOPP K; renal function – section STOPP E, STOPP B7, START F1; systolic blood pressure – START A4, STOPP K3) and database availability. Data analysis Data analysis was performed with IBM SPSS Statistics v.25.0.0.2. An unpaired, two-sided student’s t-test (α = 0.05, β = 0.2) was used to test the difference in percentages of mean acceptance for STOPP vs START signals. The effect of patient- and setting-related determinants on mean acceptance was investigated separately for STOPP and START signals in a univariate linear regression analysis and entered in a multivariate linear regression model after examination of model assumptions. Results Study population A total of 2,008 patients were included in the OPERAM study, 963 of whom were assigned to the intervention group. Data on the CDSS-assisted structured medication review during hospital admission were incomplete for 137 (14.2%) intervention patients. The study population therefore consisted of 826 patients who underwent a structured in-hospital medication review as part of the OPERAM intervention (Figure 2).
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