METHODS: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.
RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.
CONCLUSIONS: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.
MATERIAL AND METHODS: Thirtyfour patients with rheumatoid arthritis and 34 non-rheumatoid arthritis individuals were included in this cross-sectional study. Anti-RA33 antibody and rheumatoid factor were performed on all samples.
RESULTS: The sensitivity, specificity, positive and negative predictive value for anti-RA33 antibody and rheumatoid factor were 41.1%, 97.1%, 93.3%, 62.3% and 64.7%, 79.4%, 75.9%, 69.2% respectively. The overall sensitivity and specificity if either anti-RA33 antibody or rheumatoid factor are positive were 79.4% and 76.47% respectively.
CONCLUSION: Anti-RA33 antibody showed good specificity and positive predictive value and could be considered as a potential serological marker for rheumatoid arthritis diagnosis.
MATERIALS AND METHODS: The addressed focused question was "Is there a difference in the resistin levels between individuals with CP and those without CP?" four electronic databases: Medline, PubMed (National Institutes of Health, Bethesda), EMBASE, and Science direct databases from 1977 up to March 2016 for appropriate articles addressing the focused question. EMBASE and Medline were accessed using OVID interface which facilitated simultaneous search of text words, MeSH or Emtree. Unpublished studies (gray literature) were identified by searching the Open-GRAY database and references of the included studies (cross referencing) were performed to obtain new studies. In-vitro studies, animal studies, studies that reported levels of other cytokines but not resistin, letters to the editor and review papers were excluded.
RESULTS: Ten studies were included. Nine studies compared resistin levels between CP and periodontally healthy (H) individuals and reported higher mean serum and GCF levels of resistin in CP patients than the H controls. Two studies showed comparable resistin levels from GCF and serum between diabetes mellitus with CP (DMCP) and CP groups. Three studies included obese subjects and showed comparable serum and GCF resistin levels between obese subjects with CP (OBCP) and CP subjects.
CONCLUSIONS: CP patients were presented with elevated levels of GCF or serum resistin as compared with H individuals. Resistin modulates inflammation in chronic periodontal disease and may be used as surrogate measure to identify subjects at risk for periodontitis. Resistin levels in patients with CP and systemic inflammatory disorders such as diabetes, obesity, or rheumatoid arthritis was not significantly higher than the levels in patients with only CP.