164 Analyses First, missing patient data were imputed (% missings = 0.6 to 10.6%) by performing 20 imputations using fully conditional specification, and predictive mean matching for continuous variables. The model included baseline variables associated with the outcomes to improve the prediction. For professional outcomes, we did not perform imputation because missing seemed not at random, as four of the seven non-returned questionnaires came from physicians. Second, we determined whether a two-level structure was present, with patients nested under professionals, by assessing the intra-class correlations at the professional level. These were close to zero, meaning that one- and two-level analyses yielded similar results. We reported the one-level analyses. We calculated baseline descriptive statistics and evaluated differences between the intervention group and care-as-usual. Then, we assessed the intervention effect by performing linear or multinomial logistic regressions, with significance set at p<0.05. We adjusted the patient analyses for baseline eGFR given a difference between the intervention group and care-as-usual. As a sensitivity analysis, we reran patient analyses with the non-imputed dataset and without correction for eGFR. We performed subgroup analyses for 1) patients at risk (i.e. with inadequate health behaviours, hypertension and perceiving the consultation quality low), 2) patients with LHL, 3) patients from general practices and nephrology clinics, and 4) patients who used ≥2 intervention components. In the process evaluation, we calculated descriptive statistics for all outcomes and analysed differences between patients with limited and adequate health literacy, and from general practices and clinics. RESULTS PARTICIPANT FLOW Flowcharts are in figure 5.1. Among patients, 169 (39%) consented and 155 were included, 86 in the intervention and 69 in care-as-usual. Fifty-three professionals (35%) were included, 48 filled in the baseline measure and 45 were in the final analyses.
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