Dear Hans, If you are estimating GFR with C-G then you already have age (along with weight, sex and SeCr). Standardising SeCr is easy, for example:
TVCL = THETA(1)*(STDCR/SECR)**THETA(2) where STDCR is the typical value of SeCr for that age (and/or sex in adults). You can find values for expected SeCr ranges for age usually reported alongside the measured level, from which you can take the mean or median as STDCR, or you could just use a published value. In adults STDCR differs between men and women, not so in children (the grey area of adolescence requires an extrapolation - see Johansson et al). If you are feeling particularly flashy you might want to use a published equation for predicting STDCR with age in children, like the Ceriotti 2008 model, that even goes down then up to account for maternal creatinine: STDCR = -2.37330-12.91367*LOG(AGE)+23.93581*AGE**0.5 ; Mean SeCr, age adjusted (F. Ceriotti et al, Clinical Chemistry 54:3 559-566 (2008)) Another excellent paper where this method was used: Hennig S et al, Clin Pharmacokinet. 2013;52(4):289-301. How would you suggest smoothing is performed between Schwartz and C-G methods? Best wishes, Joe ________________________________________ From: [email protected] [[email protected]] On Behalf Of J.H.Proost [[email protected]] Sent: 04 September 2013 11:42 To: Standing Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUST); Matt Hutmacher; 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] Time-varing covariate and renal function as a covariate Dear Joe, You bring a new covariate, age, in the discussion. Welcome! This also introduces a new complexity: the correlation between age and weight. But again, the approach advocated by me, using non-normalised CLCR for the renal part of clearance, remains still valid. Indeed, separate equations for children and adults may introduce some problem, but this can be solved by using some smooth change between the scales. In addition, I don't see how to get a 'measured creatinine normalised to the age-adjusted value'. This sound very tricky and I would be reluctant to use it as a covariate. best regards, Hans Johannes H. Proost Dept. of Pharmacokinetics, Toxicology and Targeting University Centre for Pharmacy Antonius Deusinglaan 1 9713 AV Groningen, The Netherlands tel. 31-50 363 3292 fax 31-50 363 3247 Email: [email protected] <[email protected]<javascript:main.compose('new',%20'[email protected]')>> On 04-09-13, "Standing Joseph (GREAT ORMOND STREET HOSPITAL FOR CHILDREN NHS FOUNDATION TRUST)" <[email protected]> wrote: Dear Matt, Your first hypothetical scenario is an argument not to use CRCL because as you point out, weight is entering the model twice: once to predict renal function and once to scale for size. Other problems of using models that predict CLCr (when really you are interested in your drug CL, not the CL of endogenous creatinine) is that they are only valid for certain populations so if you have say adults and children in your dataset, at what point do you switch between C-G and the Schwartz method? Perhaps a better way is to scale clearance with measured creatinine normalised to the age-adjusted value (which if your drug is renally cleared to any extent should be correlated in some way), and then have a separate weight scaling - that way age, weight and SeCr all only enter into the model once, and can be updated as often as they are measured for time-varying techniques. You can then try different metrics for weight if you have some obese subjects (FFM, LBW...). This approach has been used a couple of times: Johansson ÅM et al. TDM. 2011;33(6):711-8. Germovsek E et al. Age-Corrected Creatinine is a Significant Covariate for Gentamicin Clearance in Neonates. PAGE 2013. BW, Joe Joseph F Standing MRC Fellow, UCL Institute of Child Health Antimicrobial Pharmacist, Great Ormond Street Hospital Tel: +44(0)207 905 2370 Mobile: +44(0)7970 572435 ________________________________________ From: [email protected] [[email protected]] On Behalf Of Matt Hutmacher [[email protected]] Sent: 03 September 2013 18:00 To: 'Nick Holford'; 'nmusers' Subject: RE: [NMusers] Time-varing covariate and renal function as a covariate Hello Nick, Hans Thanks for the replies and sorry for being so vague. I wanted to get your opinions about such a scenario without providing information that might "steer" the dialogue. Perhaps a hypothetical will help clarify my scientific curiosity. Let's say at baseline we take measurements of weight (WT), etc. Assume Cockcroft-Gault is used to predict CLCR. We formulate a model either by Nick's or Hans' method below to relate WT and CLCR (a function of WT) to CL of the drug. However, over time, the drug changes WT... in some way the ratio of fat to lean mass is altered by the drug. Should we expect the same structural relationship to hold as we would have assumed at baseline (before we knew the drug changes WT)? And if so, then should we assume the same coefficients (exponents) for CLCR and WT would hold over time in such a model, such that just adjusting CLCR and WT as time varying covariates is all that is needed to be predictive? As another example, let's assume we do a pooled population PK using healthy volunteers and obese patients. Then, say a drug is administered that reduces WT. Should we use the same exponent (coefficient) for healthy volunteers and obese patients? And if the drug works, should at what point should we treat the obese patients as healthy volunteers - or would just using WT and CLCR take care of it?. Best regards, Matt -----Original Message----- From: [email protected] [mailto:[email protected]] On Behalf Of Nick Holford Sent: Monday, September 02, 2013 12:16 To: 'nmusers' Subject: Re: [NMusers] Time-varing covariate and renal function as a covariate Matt, Thanks for your interest in this question. Hans and I have differing approaches for including 'renal function' but I think we agree on 'size'. Our differences of approach to 'renal function' are not very important for those who understand the biology and pharmacology. But its different when we have to talk to statisticians. While I recognize that you are not typical of statisticians (you know something about biology and pharmacology) it would help me (and probably Hans) if you stated more precisely what you mean by 'renal function' and 'size' and why you think there is a challenge if weight changes over time? Best wishes, Nick On 2/09/2013 9:04 a.m., J.H. Proost wrote: > Dear Matt, > I'm not quite sure that I fully understand your question. I would say > that a changing renal function and a changing weight over time can be > handled as described earlier by Nick Holford, or by the modified > approach I suggested. An important point is how to express renal > function. > Nick's method implies that 'size' should be excluded from 'renal > function', so CLCR needs to be normalized / standardized, e.g. using > CLCR in ml/min/1.73m2. Now, CLCR is a 'pure' measure of the kidney > function (of course, we know that its precision is rather poor, but > that is a different topic, interesting as well!). The factor > WEIGHT^0.75 deals with the factor 'size'. This approach treats CLCR as > a covariate similar to other covariates, making it more suitable for a > standardized approach for covariate analysis. > In the approach proposed by me, CLCR should be the 'individual's renal > clearance of creatinine', so it should expressed in ml/min (or > converted to e.g. l/h), and it should not be normalized / > standardized. Here, CLCR includes both kidney function and size (in > Nick's view a disadvantage, in my view an advantage), and the renal > part of the equation does not need further modification to take 'size' > into account. This approach treats CLCR as a 'special' covariate, > directly related to the renal clearance of the drug. This may be > advantageous for clinical purposes, e.g. dose calculation and > therapeutic drug monitoring. > In my view, both approaches have advantages and disadvantages. > best regards, > Hans Proost > Johannes H. Proost > Dept. of Pharmacokinetics, Toxicology and Targeting University Centre > for Pharmacy Antonius Deusinglaan 1 > 9713 AV Groningen, The Netherlands > tel. 31-50 363 3292 > fax 31-50 363 3247 > Email: [email protected] <mailto:[email protected]> > ******************************************************************************************************************** This message may contain confidential information. If you are not the intended recipient please inform the sender that you have received the message in error before deleting it. Please do not disclose, copy or distribute information in this e-mail or take any action in reliance on its contents: to do so is strictly prohibited and may be unlawful. Thank you for your co-operation. 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