Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients
<p>Median meropenem concentrations 8 h after dose predicted by pharmacokinetic model and MeroRisk-Calculator. Median predictions (PK model: stochastic simulations (<span class="html-italic">n</span> = 2000), MeroRisk-Calculator: classic theory of linear models [<a href="#B11-antibiotics-10-00468" class="html-bibr">11</a>]) for patients (<span class="html-italic">n</span> = 124) with creatinine clearance calculated using Cockcroft–Gault Equation (CLCRCG) > 50 mL/min (green triangles) and patients (<span class="html-italic">n</span> = 31) with CLCRCG ≤ 50 mL/min (red points) 8 h after standard dose (1 g meropenem, 0.5 h infusion). Line: Line of identity.</p> "> Figure 2
<p>Risk of target non-attainment predicted by MeroRisk-Calculator and by pharmacokinetic (PK) model. The risk of target non-attainment (unbound drug concentration below the minimum inhibitory concentration (MIC) 8 h after standard dose (1 g meropenem, 0.5 h infusion)) was assessed for 155 critically ill patients and selected minimum inhibitory concentrations. Solid line: Line of identity, dashed line: 95% risk predicted by the PK model.</p> "> Figure 3
<p>Graphical user interface of the extended MeroRisk-Calculator after risk calculation. Example for illustration: Patient-related and microbiological data: patients with creatinine clearance of 100 mL/min infected with Pseudomonas aeruginosa and no MIC value available. Red box: extended input possibilities for the microbiological data compared to the first version of the MeroRisk-Calculator. Abbreviations: CLCR<sub>CG</sub>, Creatinine clearance estimated according to Cockcroft and Gault equation [<a href="#B23-antibiotics-10-00468" class="html-bibr">23</a>]; CRRT, Continuous renal replacement therapy; C8h, Meropenem serum concentration 8 h after infusion start; MIC, Minimum inhibitory concentration.</p> "> Figure 4
<p>MeroRisk-Calculator predicted risk of target non-attainment for 6 clinically relevant pathogens. The risk of target non-attainment (unbound drug concentration 8 h after standard meropenem dosing below the minimum inhibitory concentration (MIC)) was assessed for critically ill patents (<span class="html-italic">n</span> = 155) using EUCAST MIC distributions of the investigated pathogens and cumulative fraction of response analysis. Risk predictions ≤10% (green), >10% to ≤50% (orange) and >50% (red).</p> "> Figure 5
<p>Stepwise evaluation strategy of the MeroRisk-Calculator using a clinical routine dataset. A direct, data-based evaluation of the MeroRisk-Calculator was not feasible due to the time variable sampling time points under routine conditions. A population pharmacokinetic (PK) model was evaluated for its potential to predict the concentrations observed at variable time points (Step 1) and the risk predictions by the PK model were used as a benchmark for the risk predictions of the MeroRisk-Calculator (Step 2).</p> ">
Abstract
:1. Introduction
2. Results
2.1. Data and Patients
2.2. Evaluation Step 1: Evaluation of the Potential of the Selected Meropenem Population Pharmacokinetic (PK) Model to Predict the Clinical Routine Dataset
2.3. Evaluation Step 2: Evaluation of the MeroRisk-Calculator Based on PK Model Predictions of Meropenem Concentrations 8 h after Dosing
2.4. Extending Risk Predictions to Include General Pathogen Sensitivity Data
3. Discussion
4. Materials and Methods
4.1. Evaluation Strategy for the MeroRisk-Calculator
- Step 1: Evaluation of the potential of the selected meropenem population pharmacokinetic (PK) model to predict the clinical routine dataset.
- Step 2: Evaluation of the MeroRisk-Calculator based on PK model predictions of meropenem concentrations 8 h after dosing.
4.2. Clinical Data and Patients
4.3. Evaluation Step 1: Evaluation of the Potential of the Selected Meropenem Population Pharmacokinetic (PK) Model to Predict the Clinical Routine Dataset
4.4. Evaluation Step 2: Evaluation of the MeroRisk-Calculator Based on PK Model Predictions of Meropenem Concentrations 8 h after Dosing
4.5. Integration of Risk Assessment Based on Pathogen-Specific MIC Distribution
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Engel, C.; Brunkhorst, F.M.; Bone, H.-G.; Brunkhorst, R.; Gerlach, H.; Grond, S.; Gruendling, M.; Huhle, G.; Jaschinski, U.; John, S.; et al. Epidemiology of Sepsis in Germany: Results from a National Prospective Multicenter Study. Intensiv. Care Med. 2007, 33, 606–618. [Google Scholar] [CrossRef]
- Vincent, J.-L.; Rello, J.; Marshall, J.; Silva, E.; Anzueto, A.; Martin, C.D.; Moreno, R.; Lipman, J.; Gomersall, C.; Sakr, Y.; et al. International Study of the Prevalence and Outcomes of Infection in Intensive Care Units. JAMA 2009, 302, 2323–2329. [Google Scholar] [CrossRef] [Green Version]
- Cecconi, M.; Evans, L.; Levy, M.; Rhodes, A. Sepsis and Septic Shock. Lancet 2018, 392, 75–87. [Google Scholar] [CrossRef]
- Kumar, A.; Roberts, D.; Wood, K.E.; Light, B.; Parrillo, J.E.; Sharma, S.; Suppes, R.; Feinstein, D.; Zanotti, S.; Taiberg, L.; et al. Duration of Hypotension before Initiation of Effective Antimicrobial Therapy Is the Critical Determinant of Survival in Human Septic Shock. Crit. Care Med. 2006, 34, 1589–1596. [Google Scholar] [CrossRef]
- Ferrer, R.; Martin-Loeches, I.; Phillips, G.; Osborn, T.M.; Townsend, S.; Dellinger, R.P.; Artigas, A.; Schorr, C.; Levy, M.M. Empiric Antibiotic Treatment Reduces Mortality in Severe Sepsis and Septic Shock from the First Hour: Results from a Guideline-Based Performance Improvement Program. Crit. Care Med. 2014, 42, 1749–1755. [Google Scholar] [CrossRef]
- Kim, R.Y.; Ng, A.M.; Persaud, A.K.; Furmanek, S.P.; Kothari, Y.N.; Price, J.D.; Wiemken, T.L.; Saad, M.A.; Guardiola, J.J.; Cavallazzi, R.S. Antibiotic Timing and Outcomes in Sepsis. Am. J. Med. Sci. 2018, 355, 524–529. [Google Scholar] [CrossRef] [PubMed]
- Gonçalves-Pereira, J.; Póvoa, P. Antibiotics in Critically Ill Patients: A Systematic Review of the Pharmacokinetics of Beta-Lactams. Crit. Care 2011, 1–7. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Udy, A.A.; Roberts, J.A.; Lipman, J. Clinical Implications of Antibiotic Pharmacokinetic Principles in the Critically Ill. Intensiv. Care Med. 2013, 39, 2070–2082. [Google Scholar] [CrossRef] [PubMed]
- Roberts, J.A.; Paul, S.K.; Akova, M.; Bassetti, M.; Waele, J.J.D.; Dimopoulos, G.; Kaukonen, K.-M.; Koulenti, D.; Martin, C.; Montravers, P.; et al. DALI: Defining Antibiotic Levels in Intensive Care Unit Patients: Are Current β-Lactam Antibiotic Doses Sufficient for Critically Ill Patients? Clin. Infect. Dis. 2014, 58, 1072–1083. [Google Scholar] [CrossRef]
- Abdul-Aziz, M.H.; Lipman, J.; Roberts, J.A. Identifying “at-Risk” Patients for Sub-Optimal Beta-Lactam Exposure in Critically Ill Patients with Severe Infections. Crit. Care 2017, 21, 283. [Google Scholar] [CrossRef] [Green Version]
- Ehmann, L.; Zoller, M.; Minichmayr, I.K.; Scharf, C.; Maier, B.; Schmitt, M.V.; Hartung, N.; Huisinga, W.; Vogeser, M.; Frey, L.; et al. Role of Renal Function in Risk Assessment of Target Non-Attainment after Standard Dosing of Meropenem in Critically Ill Patients: A Prospective Observational Study. Crit. Care 2017, 21, 263. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Scharf, C.; Paal, M.; Schroeder, I.; Vogeser, M.; Draenert, R.; Irlbeck, M.; Zoller, M.; Liebchen, U. Therapeutic Drug Monitoring of Meropenem and Piperacillin in Critical Illness—Experience and Recommendations from One Year in Routine Clinical Practice. Antibiotics 2020, 9, 131. [Google Scholar] [CrossRef] [Green Version]
- Wu, C.-C.; Tai, C.H.; Liao, W.-Y.; Wang, C.-C.; Kuo, C.-H.; Lin, S.-W.; Ku, S.-C. Augmented Renal Clearance Is Associated with Inadequate Antibiotic Pharmacokinetic/Pharmacodynamic Target in Asian ICU Population: A Prospective Observational Study. Infect. Drug Resist. 2019, 12, 2531–2541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Keough, L.A.; Krauss, A.; Hudson, J.Q. Inadequate Antibiotic Dosing in Patients Receiving Sustained Low Efficiency Dialysis. Int. J. Clin. Pharm. 2018, 40, 1250–1256. [Google Scholar] [CrossRef] [PubMed]
- Udy, A.A.; Roberts, J.A.; Boots, R.J.; Paterson, D.L.; Lipman, J. Augmented Renal Clearance: Implications for Antibacterial Dosing in the Critically Ill. Clin. Pharmacokinet. 2010, 49, 1–16. [Google Scholar] [CrossRef] [PubMed]
- Ehmann, L.; Zoller, M.; Minichmayr, I.K.; Scharf, C.; Huisinga, W.; Zander, J.; Kloft, C. Development of a Dosing Algorithm for Meropenem in Critically Ill Patients Based on a Population Pharmacokinetic/Pharmacodynamic Analysis. Int. J. Antimicrob. Agents 2019, 54, 309–317. [Google Scholar] [CrossRef]
- Baldwin, C.M.; Lyseng-Williamson, K.A.; Keam, S.J. Meropenem: A Review of Its Use in the Treatment of Serious Bacterial Infections. Drugs 2008, 68, 803–838. [Google Scholar] [CrossRef]
- Craig, W.A. Interrelationship between Pharmacokinetics and Pharmacodynamics in Determining Dosage Regimens for Broad-Spectrum Cephalosporins. Diagn. Microbiol. Infect. Dis. 1995, 22, 89–96. [Google Scholar] [CrossRef]
- Liebchen, U.; Paal, M.; Scharf, C.; Schroeder, I.; Grabein, B.; Zander, J.; Siebers, C.; Zoller, M. The ONTAI Study—A Survey on Antimicrobial Dosing and the Practice of Therapeutic Drug Monitoring in German Intensive Care Units. J. Crit. Care 2020, 60, 260–266. [Google Scholar] [CrossRef]
- German S2k Guideline Parenteral Antibiotics. Available online: https://www.awmf.org/uploads/tx_szleitlinien/S82-006l_S2k_Parenterale_Antibiotika_2018-1.pdf (accessed on 18 November 2020).
- Roberts, J.A.; Kumar, A.; Lipman, J. Right Dose, Right Now: Customized Drug Dosing in the Critically Ill. Crit. Care Med. 2017, 45, 331–336. [Google Scholar] [CrossRef]
- Charmillon, A.; Novy, E.; Agrinier, N.; Leone, M.; Kimmoun, A.; Levy, B.; Demoré, B.; Dellamonica, J.; Pulcini, C. The Antibioperf Study: A Nationwide Cross-Sectional Survey about Practices for β-Lactam Administration and Therapeutic Drug Monitoring among Critically Ill Patients in France. Clin. Microbiol. Infect. 2016, 22, 625–631. [Google Scholar] [CrossRef] [Green Version]
- Cockcroft, D.W.; Gault, M.H. Prediction of Creatinine Clearance from Serum Creatinine. Nephron 1976, 16, 31–41. [Google Scholar] [CrossRef]
- Sherwin, C.M.T.; Kiang, T.K.L.; Spigarelli, M.G.; Ensom, M.H.H. Fundamentals of Population Pharmacokinetic Modelling. Clin. Pharm. 2012, 18. [Google Scholar] [CrossRef]
- McBride, G. A Proposal of Strength-of-Agreement Criteria for Lins Concordance Correlation Coefficient; National Institute of Water & Atmospheric Research Ltd.: Hamilton, New Zealand, 2020. [Google Scholar]
- Mouton, J.W.; Dudley, M.N.; Cars, O.; Derendorf, H.; Drusano, G.L. Standardization of Pharmacokinetic/Pharmacodynamic (PK/PD) Terminology for Anti-Infective Drugs: An Update. J. Antimicrob. Chemother. 2005, 55, 601–607. [Google Scholar] [CrossRef] [Green Version]
- Neely, M.; Philippe, M.; Rushing, T.; Fu, X.; van Guilder, M.; Bayard, D.; Schumitzky, A.; Bleyzac, N.; Goutelle, S. Accurately Achieving Target Busulfan Exposure in Children and Adolescents with Very Limited Sampling and the Best Dose Software. Therapeutic Drug Monit. 2016, 38, 332–342. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Heil, E.L.; Nicolau, D.P.; Farkas, A.; Roberts, J.A.; Thom, K.A. Pharmacodynamic Target Attainment for Cefepime, Meropenem, and Piperacillin-Tazobactam Using a Pharmacokinetic/Pharmacodynamic-Based Dosing Calculator in Critically Ill Patients. Antimicrob. Agents Chemother. 2018, 62. [Google Scholar] [CrossRef] [Green Version]
- Mould, D.; D’Haens, G.; Upton, R. Clinical Decision Support Tools: The Evolution of a Revolution. Clin. Pharmacol. Ther. 2016, 99, 405–418. [Google Scholar] [CrossRef] [PubMed]
- Roberts, J.A.; Abdul-Aziz, M.H.; Lipman, J.; Mouton, J.W.; Vinks, A.A.; Felton, T.W.; Hope, W.W.; Farkas, A.; Neely, M.N.; Schentag, J.J.; et al. Individualised Antibiotic Dosing for Patients Who Are Critically Ill: Challenges and Potential Solutions. Lancet Infect. Dis. 2014, 14, 498–509. [Google Scholar] [CrossRef] [Green Version]
- Dhaese, S.A.M.; Farkas, A.; Colin, P.; Lipman, J.; Stove, V.; Verstraete, A.G.; Roberts, J.A.; De Waele, J.J. Population Pharmacokinetics and Evaluation of the Predictive Performance of Pharmacokinetic Models in Critically Ill Patients Receiving Continuous Infusion Meropenem: A Comparison of Eight Pharmacokinetic Models. J. Antimicrob. Chemother. 2019, 74, 432–441. [Google Scholar] [CrossRef]
- Heine, R.; Keizer, R.J.; Steeg, K.; Smolders, E.J.; Luin, M.; Derijks, H.J.; Jager, C.P.C.; Frenzel, T.; Brüggemann, R. Prospective Validation of a Model-informed Precision Dosing Tool for Vancomycin in Intensive Care Patients. Br. J. Clin. Pharmacol. 2020. [Google Scholar] [CrossRef]
- Mattioli, F.; Fucile, C.; Del Bono, V.; Marini, V.; Parisini, A.; Molin, A.; Zuccoli, M.L.; Milano, G.; Danesi, R.; Marchese, A.; et al. Population Pharmacokinetics and Probability of Target Attainment of Meropenem in Critically Ill Patients. Eur. J. Clin. Pharmacol. 2016, 72, 839–848. [Google Scholar] [CrossRef]
- Burger, R.; Guidi, M.; Calpini, V.; Lamoth, F.; Decosterd, L.; Robatel, C.; Buclin, T.; Csajka, C.; Marchetti, O. Effect of Renal Clearance and Continuous Renal Replacement Therapy on Appropriateness of Recommended Meropenem Dosing Regimens in Critically Ill Patients with Susceptible Life-Threatening Infections. J. Antimicrob. Chemother. 2018, 73, 3413–3422. [Google Scholar] [CrossRef] [PubMed]
- Singbartl, K.; Kellum, J.A. AKI in the ICU: Definition, Epidemiology, Risk Stratification, and Outcomes. Kidney Int. 2012, 81, 819–825. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Moon, Y.S.K.; Chung, K.C.; Gill, M.A. Pharmacokinetics of Meropenem in Animals, Healthy Volunteers, and Patients. Clin. Infect. Dis. 1997, 24, S249–S255. [Google Scholar] [CrossRef] [PubMed]
- Paal, M.; Zoller, M.; Schuster, C.; Vogeser, M.; Schütze, G. Simultaneous Quantification of Cefepime, Meropenem, Ciprofloxacin, Moxifloxacin, Linezolid and Piperacillin in Human Serum Using an Isotope-Dilution HPLC–MS/MS Method. J. Pharm. Biomed. Anal. 2018, 152, 102–110. [Google Scholar] [CrossRef] [PubMed]
- Lin, L.I. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 1989, 45, 255–268. [Google Scholar] [CrossRef]
Patient Characteristic | |
---|---|
Categorical | n (%) |
No. of patients No. of male patients No. of meropenem samples | 155 101 (65.2) 891 |
No. of meropenem samples collected during extracorporeal membrane oxygenation | 64 (7.18) |
Continuous (unit) | Median (5th–95th percentile) |
Meropenem concentration (mg/L) | 9.05 (1.09–36.5) |
Age (years) | 57.0 (33.7–79.0) |
Weight (kg) | 73.0 (50.0–97.3) |
Creatinine clearance # (mL/min) | 86.4 (35.4–161) |
Serum albumin concentration (g/dL) | 2.5 (2.3–3.2) |
MIC (mg/L) | Lin’s Concordance Correlation Coefficient (95% CI) | |
---|---|---|
All Patients | CLCRCG > 50 mL/min | |
0.125 | 0.791 (0.746–0.830) * | 0.999 (0.998–0.999) **** |
0.25 | 0.845 (0.811–0.872) * | 0.997 (0.996–0.998) **** |
0.5 | 0.894 (0.869–0.914) * | 0.992 (0.991–0.994) **** |
1 | 0.921 (0.899–0.938) * | 0.930 (0.910–0.946) ** |
2 | 0.957 (0.942–0.967) ** | 0.919 (0.893–0.938) * |
4 | 0.979 (0.972–0.984) *** | 0.954 (0.938–0.967) ** |
8 | 0.857 (0.834–0.877) * | 0.978 (0.970–0.984) *** |
16 | 0.087 (0.077–0.097) * | 0.945 (0.925–0.960) ** |
0.125–16 | 0.983 (0.981–0.984) *** | 0.990 (0.988–0.991) *** |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Liebchen, U.; Klose, M.; Paal, M.; Vogeser, M.; Zoller, M.; Schroeder, I.; Schmitt, L.; Huisinga, W.; Michelet, R.; Zander, J.; et al. Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients. Antibiotics 2021, 10, 468. https://doi.org/10.3390/antibiotics10040468
Liebchen U, Klose M, Paal M, Vogeser M, Zoller M, Schroeder I, Schmitt L, Huisinga W, Michelet R, Zander J, et al. Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients. Antibiotics. 2021; 10(4):468. https://doi.org/10.3390/antibiotics10040468
Chicago/Turabian StyleLiebchen, Uwe, Marian Klose, Michael Paal, Michael Vogeser, Michael Zoller, Ines Schroeder, Lisa Schmitt, Wilhelm Huisinga, Robin Michelet, Johannes Zander, and et al. 2021. "Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients" Antibiotics 10, no. 4: 468. https://doi.org/10.3390/antibiotics10040468
APA StyleLiebchen, U., Klose, M., Paal, M., Vogeser, M., Zoller, M., Schroeder, I., Schmitt, L., Huisinga, W., Michelet, R., Zander, J., Scharf, C., Weinelt, F. A., & Kloft, C. (2021). Evaluation of the MeroRisk Calculator, A User-Friendly Tool to Predict the Risk of Meropenem Target Non-Attainment in Critically Ill Patients. Antibiotics, 10(4), 468. https://doi.org/10.3390/antibiotics10040468