title: Renal elimination of amikacin and the aging process.
abstract: OBJECTIVE
  Although amikacin is primarily eliminated via glomerular filtration, drug concentrations are not consistently predicted in all patients. To better describe the relationship between amikacin clearance and both age and renal function, we used a new heuristic approach involving statistical analysis of dependence.
  
  
  METHODS
  Retrospective pharmacokinetic study using data from seven centres in France.
  
  
  METHODS
  634 patients with sepsis aged between 18 and 98 years of age who received intravenous amikacin.
  
  
  METHODS
  Clearance of amikacin was modelled using the NonParametric EM algorithm for a two-compartment model (NPEM2) with intravenous infusion.
  
  
  RESULTS
  A total of 2499 serum amikacin determinations was available for analysis. The relationship between the clearance of amikacin and age was weak. Interestingly, the Z method, which filters data based on dependence criteria, selected data that were best fitted by a polynomial function (r = 0.90; p < 0.001). This representation of the polynomial function was similar to a previously proposed theoretical model describing covariations between the clearance of amikacin and age. However, the polynomial function applied to only 33% of the patients that were selected by the Z method. The correlation between the clearance of amikacin and renal function was also relatively low (r = 0.39). The Z method exhibited a continuous and strong dependence pattern between the clearance of amikacin and age for 49% of the patients.
  
  
  CONCLUSIONS
  The Z methodology, which filters data using dependence criteria, confirms that age, renal function and amikacin clearance are strongly related, but only in less than half of a large sample of patients with sepsis without renal pathology. These results suggest that other variables should be taken into account in order to improve the description of the behaviour of amikacin. The Z methodology improved the classical description of relationships between variables, and should be applied to better select pertinent variables in pharmacokinetic studies.
authors.affiliation: Hôpital A. Charial, Francheville, France. ducher@infobiontat.net
authors.forename: M
authors.initials: M
authors.lastname: Ducher
authors.forename: P
authors.initials: P
authors.lastname: Maire
authors.forename: C
authors.initials: C
authors.lastname: Cerutti
authors.forename: Y
authors.initials: Y
authors.lastname: Bourhis
authors.forename: F
authors.initials: F
authors.lastname: Foltz
authors.forename: P
authors.initials: P
authors.lastname: Sorensen
authors.forename: R
authors.initials: R
authors.lastname: Jelliffe
authors.forename: J P
authors.initials: JP
authors.lastname: Fauvel
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000378:metabolism*; D000583:Amikacin / Q000493:pharmacokinetics* / Q000627:therapeutic use; D000900:Anti-Bacterial Agents / Q000493:pharmacokinetics* / Q000627:therapeutic use; D005602:France; D006801:Humans; D007668:Kidney / Q000378:metabolism*; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D015337:Multicenter Studies as Topic; D011336:Probability; D012189:Retrospective Studies; D018805:Sepsis / Q000188:drug therapy / Q000378:metabolism
chemical_list: D000900:Anti-Bacterial Agents; D000583:Amikacin