Turn-over model characterizing effect of colistin on serum-creatinine in critically ill patients
Viktor Rognås (1), Johan Spånberg (1), Emanuele Durante-Mangoni (2), Leonard Leibovici (3), Yehuda Carmeli (4), George L Daikos (5), Mical Paul (6,7), Mats O Karlsson (1), Lena E Friberg (1)
(1) Department of Pharmacy, Uppsala University, Uppsala, Sweden, (2) Internal Medicine, University of Campania ‘L Vanvitelli’, and AORN dei Colli-Monaldi Hospital, Napoli, Italy, (3) Department of Medicine, Rabin Medical Center, Beilinson Hospital, Petah Tikva, Israel, (4) Sackler Faculty of Medicine, Tel Aviv University, Ramat-Aviv, Israel, (5) First Department of Medicine, Laikon General Hospital, Athens, Greece, (6) Institute of Infectious Diseases, Rambam Health Care Campus, Haifa, Israel, (7) The Ruth and Bruce Rappaport Faculty of Medicine, Techion – Israel Institute of Technology, Haifa, Israel
Objectives: In the randomized clinical trial AIDA [1], surviving patients experienced Acute Kidney Injury (AKI, as measured by the RIFLE score) at day 14 (39%; 153 /249) and day 28 (27%; 120/165). Additionally, there was a tendency for protective effects of meropenem against colistin-associated kidney toxicity. Furthermore, a previous survival analysis [2] indicated a positive correlation between higher colistin exposure and a a higher hazard of death. However, since colistin is nephrotoxic and its prodrug (CMS) is renally cleared, the survival analysis might be confounded by kidney function. Therefore, in this study, we aimed to explore the relation between colistin exposure and creatinine (SCr) changes using a model-based approach.
Methods: Three hundred and fifty-six critically ill patients (17-95 years, median 69 years), not on any renal replacement therapy, contributed colistin and creatinine measurements to the analysis. Patients had Gram-negative bacterial infections that were carbapenem-resistant (MIC ≥ 2 mg/L) and colistin susceptible (MIC ≤ 2 mg/L). Both arms received CMS (9 MU load, 4.5 MU q12h maintenance, 30 min infusion); the second arm received meropenem in addition. Collected PK measurements were used to predict daily (24 hours) colistin average concentrations (Cavg, daily) for each subject, using a previously published model [2]. The number of SCr measurements per patient ranged from 1–8 from index date to 28 days after randomization (end of trial). Measured SCr ranged from 0.10–7.23 mg/dL, median 0.91 mg/dL. Estimation was done with the FOCEI method in NONMEM software using the ADVAN13 (LSODA) subroutine. The SCr (mg/dL) baseline was estimated using the first SCr measurement as a covariate (not used as an observation in the model), allowing it to vary with the individual residual error magnitude. Creatinine clearance (CrCL) baseline (at first SCr measurement) was calculated using the Cockcroft and Gault equation [3]. Volume of distribution for creatinine was assumed to be equal to total body water [4]. Covariates were explored using a stepwise approach, with forward-addition (p<0.05) and backward-deletion (p<0.001).
Results: A turn-over model was used, with a direct-effect Emax model of colistin Cavg, daily. Disease progression (percent change from baseline in CrCL) was modeled with an exponential function that could monotonically increase or decrease with time, accounting for changes not related to colistin exposure [5]. Estimated median baseline SCr was 0.88 mg/dL. The predicted median baseline half-life of SCr was 5.5 hours, and the estimated baseline median CrCL was 77 mL/min (range 9.3–592 mL/min). The median endogenous creatinine production rate was 2.67 mg/dL/day. The effect of colistin on CrCL was described using a direct-effect maximum inhibition model where Imax was estimated to 68%. The typical colistin Cavg, daily needed for half Imax (IC50) was 6.82 mg/L. Thus, a typical patient on ten days of colistin treatment would experience an almost linear decrease of CrCL to about 85% of baseline at 14 days after randomization. No significant covariates were found. Estimation equations for eGFR (MDRD, CKD-EPI) were not found superior to Cockcroft and Gault. Model predictions of AKI incidence (95%CI) in this group of patients around day 14 and 28 after randomization were 24–35%, and 22–34%, respectively.
Conclusions: Visual predictive checks and model predictions demonstrated that a turn-over model for SCr (adjusted for exponential progression) with colistin exposure affecting CrCL described the longitudinal data well. In addition, the model could characterize the change in CrCL over time for patients getting worse or better throughout the study period. AKI was adequately predicted for day 28 after randomization and slightly underpredicted for day 14 after randomization.
References:
[1] Paul M, et al. Colistin alone versus colistin plus meropenem for treatment of severe infections caused by carbapenem-resistant Gram-negative bacteria: an open-label, randomised controlled trial. Lancet Infect Dis 2018;18:391–400. https://doi.org/10.1016/S1473-3099(18)30099-9
[2] Kristoffersson A, et al. Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria. Clin Microb Infec 2020. https://doi.org/10.1016/j.cmi.2020.03.016
[3] Cockcroft DW, Gault MH. Prediction of Creatinine Clearance from Serum Creatinine. Nephron 1976;16:31–41
[4] Watson PE et al. Total body water volumes for adult males and females estimated from simple anthropometric measurements. Am Journal of Clin Nutr 1980;1:27–39
[5] Sunder S et al. Estimation of renal function in the intensive care unit: the covert concepts brought to light. Journal of Intensive Care 2014;2:31 http://www.jintensivecare.com/content/2/1/31
