Marjon Borgert

114 Chapter 6 Types of measurements for care bundle compliance Four di erent types of measurements were described in the literature to calculate the levels of bundle compliance: 1) ‘AON-measurement’, which calculates the percentage of all indicated elements the patients actually have received, unless medically contraindicated 4,24,25 ; 2) composite measurement, which can be calculated as a ratio between care that was actually givendividedby the care that shouldhavebeengiven 24,25 ; 3) item-by-item measurement, which presents the nominator and denominator of each bundle element separately 25 ; 4) lowest level of compliance, which means that the lowest level of compliance to one of the elements is considered as the total bundle compliance. 5,7 Data analysis/synthesis We used the compliance levels, which were last recorded as the measure of e ect of implementation. Compliance was summarised as a percentage and, if applicable, as a numerator and a denominator. When studies were described as quality improvement initiatives, we further classi ed the nature of the study design by two reviewers independently. In case of discrepancies, consensus was achieved through discussion. We determined if selective reporting of compliance levels occurred within the included studies. Data analysis was performed in two phases. Firstly, overviews were given of all included studies to give insight in the study characteristics, compliance levels, the implementation strategies used, the number and types of bundles and their elements and the methods used to calculate compliance. In this phase, studies were not excluded based on their methodological quality. Secondly, a subgroup analysis was performed. For the subgroup analysis, the methodological quality of studies was assessed. In case a study scored less than 14 points, i.e. poor quality, it was excluded. Furthermore, subgroup analysis was not performed if less than three data points were available per subgroup. Studies were strati ed and analysed by study design, quality assessment outcome, type of compliance measurement and by type of bundle. Subsequently, data were grouped and analysed by factors that could in uence compliance, i.e. number of implementation strategies, bundle elements, methods for calculating compliance. From this, we attempted to identify patterns in compliance levels. Pearson’s product-moment correlation coe cient or Spearman’s rank-order were used to assess the relationship of compliance to the number of implementation strategies and the relationship between compliance and the number of elements. Kendall’s rank-correlation assessed the relationship of compliance to the time frame in which compliance was calculated. R (version: 3.1.3; R Foundation for Statistical Computing, Vienna, Austria) was used to perform subgroup analysis. Although a meta-analysis was planned, this could not be conducted due to the heterogeneity of the data in study designs, interventions and