EL R And BMI Case Study

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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
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