preview

EL R And BMI Case Study

Decent Essays
Regardless of the reason why these biological factors impact treatment outcomes, the ability of both the EL.R and BMI to predict percentage negative opioid screens, is a finding that would set the stage for personalising treatment intensities and durations. A treatment algorithm, based on the predicted value of observed and or imputed percentage negative opioid screens with a cut-off percentage of 95% and 75% respectively, would be reasonable (figure 19). Under this algorithm, patients who fail the target percentage are directed to a different course of pharmacotherapy and would not continue on BNX. Patients with predictions above the target cut-off are assigned to BNX based treatment pathways, according to psychological parameters and degree of family involvement. Other cut-off thresholds can be set for each treatment facility and population.…show more content…
Despite that MET plasma concentration correlates well with MET dose; it is not clear why TDM has not been adopted in mainstream MET assisted treatment. For BUP, TDM was assumed to have a role in monitoring compliance but have no role in optimising clinical responses [Marque and Kintz, 2004]. Similarly, BUP received level 2 recommendation for TDM, which encourages TDM in dose titration or in special indications or problem solving [Hiemke, Baumann, Bergemann, Conca, 2011]. In our study, identifying non-adherence and potential diversion would translate to problem solving. Non-adherence to BNX and possible diversion were identified through contrasting observed BUP levels with the predicted levels for each patient. While no direct comparisons for BNX non-adherence were retrieved in literature; data on diversion and abuse was used as comparative with non-adherence observed in
Get Access