In the UK, the RRec monitors AP dosing in patients (pts) with complex AP regimens – each AP dose is converted to % of relevant max dose by indication and titration stage in SPC. Total dose% >100% identifies pts at risk. A nested case control (NCC) study was undertaken as part of a PASS to explore possible dose-event response for somnolence/ sedation (SS) after starting a new AP formulation. The primary objective evaluated total daily dose (TDDmg)> 600.
An exploratory objective evaluated use of the RRec as an alternative indication-adjusted dose metric to model AP treatment effects.
An incidence density matched NCC study using a primary care cohort (13276) identified Sep2008-Feb2013. Of 756 cases, 212(28%) were randomly selected and 170 risk sets created. For all subjects, the reported TDDmg (start, maintenance and event) were converted to dose% (Non-licensed indications used SPC dose range for major depression). Fractional polynomial (FP) logistic models explored functional form; empirical and fitted within-person OLS dose trajectories described patterns over time.
SS cases tended have higher reported TDDmg vs controls at start [median 300 (IQR 200,600) vs 200mg (IQR 100,300), p<0.01] but the inverse was seen when the dose % metric was applied (median 50% (IQR 16,100) vs 75% (33,100), p=0.02). At maintenance and event, similar relationships were observed for reported TDDmg and dose % variables. SS risk was a negative function of TDDmg and dose%. No deviation from linear assumption was apparent for start and maintenance doses (FP2 vs linear, P>0.05), but was non-linear for dose% at event (p=0.04). In exploring pattern over time, the general trend was decreasing for both cases and controls, although slower for controls.
This exploratory analysis demonstrates the feasibility of the RRec as an alternative indication-adjusted variable for analysing dose, particularly where multiple indications may exist that use various dose ranges. Analytical advantages include avoiding creation of strata of small sample sizes. Limitations include possible metric under-estimation from missing data for other concomitant APs.