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  1. Saffian SM, Duffull SB, Wright D
    Clin. Pharmacol. Ther., 2017 Aug;102(2):297-304.
    PMID: 28160278 DOI: 10.1002/cpt.649
    There is preliminary evidence to suggest that some published warfarin dosing algorithms produce biased maintenance dose predictions in patients who require higher than average doses. We conducted a meta-analysis of warfarin dosing algorithms to determine if there exists a systematic under- or overprediction of dose requirements for patients requiring ≥7 mg/day across published algorithms. Medline and Embase databases were searched up to September 2015. We quantified the proportion of over- and underpredicted doses in patients whose observed maintenance dose was ≥7 mg/day. The meta-analysis included 47 evaluations of 22 different warfarin dosing algorithms from 16 studies. The meta-analysis included data from 1,492 patients who required warfarin doses of ≥7 mg/day. All 22 algorithms were found to underpredict warfarin dosing requirements in patients who required ≥7 mg/day by an average of 2.3 mg/day with a pooled estimate of underpredicted doses of 92.3% (95% confidence interval 90.3-94.1, I(2) = 24%).
  2. Saffian SM, Wright DF, Roberts RL, Duffull SB
    Ther Drug Monit, 2015 Aug;37(4):531-8.
    PMID: 25549208 DOI: 10.1097/FTD.0000000000000177
    The aim of this study was to compare the predictive performance of different warfarin dosing methods.
  3. Azemi NFN, Islahudin F, Khan RA, Saffian SM, Loon LC
    J Pharm Policy Pract, 2024;17(1):2337125.
    PMID: 38638422 DOI: 10.1080/20523211.2024.2337125
    INTRODUCTION: Trials have demonstrated the benefits of methylprednisolone in the treatment of coronavirus disease 2019 (COVID-19). However, data on optimal dose, duration and timing of administration are limited. This study investigates the outcome of various methylprednisolone treatment regimens among hospitalised COVID-19 patients.

    METHODS: A retrospective cohort study was conducted on hospitalised adult COVID-19 patients admitted between June and August 2021 in general COVID-19 wards, treated with methylprednisolone. Clinical outcomes evaluated include in-hospital mortality, thirty-day mortality, clinical efficacy (C-reactive protein (CRP), total white blood cells (TWBC) and oxygen requirement) as well as the safety of methylprednisolone.

    RESULTS: Of 278 patients, 1(0.4%) received weight-based dosing of 1 mg/kg/day, 101(36.3%) received weight-based dosing of 2 mg/kg/day, 130(46.8%) received fixed dosing methylprednisolone 250 mg/day and 46(16.5%) received fixed dosing methylprednisolone 500 mg/day. There was a significant difference in in-hospital mortality rates following different methylprednisolone doses whereby in-hospital mortality occurred in 22.5% (n = 23) of patients with 1 or 2 mg/kg/day methylprednisolone, 32.3% (n = 42) with 250 mg/day and 39.1% (n = 18) with 500 mg/day (p = 0.023). On the other hand, no significant difference in thirty-day mortality, clinical efficacy and safety was observed between different dosing regimens (p > 0.05).

    CONCLUSION: The use of methylprednisolone weight-based dosing in hospitalised COVID-19 patients should be considered due to the positive outcome associated with lower in-hospital mortality.

  4. Saffian SM, Duffull SB, Roberts RL, Tait RC, Black L, Lund KA, et al.
    Ther Drug Monit, 2016 12;38(6):677-683.
    PMID: 27855133
    BACKGROUND: A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions.

    METHODS: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values.

    RESULTS: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations.

    CONCLUSIONS: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.

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