Predictors of Augmented Renal Clearance in a Heterogeneous ICU Population as Defined by Creatinine and Cystatin C

The estimation of glomerular filtration in acute and critical illness: Challenges and opportunities

Recent events have made it apparent that the creatinine based estimating equations for glomerular filtration have their flaws. Some flaws have been known for some time; others have prompted radical modification of the equations themselves. These issues persist in part owing to the behaviour of the creatinine molecule itself, particularly in acute and critical illness. There are significant implications for patient treatment decisions, including drug and fluid therapies and choice of imaging modality (contrast vs. non-contrast CT scan for example). An alternative biomarker, Cystatin C, has been used with some success both alone and in combination with creatinine to help improve the accuracy of particular estimating equations. Problems remain in certain circumstances and costs may limit the more widespread use of the alternative assay. This review will explore both the historical and more recent evidence for glomerular filtration estimation, including options to directly measure glomerular filtration (rather than estimate), perhaps the holy grail for both Biochemistry and Nephrology.

Development and validation of the creatinine clearance predictor machine learning models in critically ill adults

BACKGROUND

In critically ill patients, measured creatinine clearance (CrCl) is the most reliable method to evaluate glomerular filtration rate in routine clinical practice and may vary subsequently on a day-to-day basis. We developed and externally validated models to predict CrCl one day ahead and compared them with a reference reflecting current clinical practice.

METHODS

A gradient boosting method (GBM) machine-learning algorithm was used to develop the models on data from 2825 patients from the EPaNIC multicenter randomized controlled trial database. We externally validated the models on 9576 patients from the University Hospitals Leuven, included in the M@tric database. Three models were developed: a "Core" model based on demographic, admission diagnosis, and daily laboratory results; a "Core + BGA" model adding blood gas analysis results; and a "Core + BGA + Monitoring" model also including high-resolution monitoring data. Model performance was evaluated against the actual CrCl by mean absolute error (MAE) and root-mean-square error (RMSE).

RESULTS

All three developed models showed smaller prediction errors than the reference. Assuming the same CrCl of the day of prediction showed 20.6 (95% CI 20.3-20.9) ml/min MAE and 40.1 (95% CI 37.9-42.3) ml/min RMSE in the external validation cohort, while the developed model having the smallest RMSE (the Core + BGA + Monitoring model) had 18.1 (95% CI 17.9-18.3) ml/min MAE and 28.9 (95% CI 28-29.7) ml/min RMSE.

CONCLUSIONS

Prediction models based on routinely collected clinical data in the ICU were able to accurately predict next-day CrCl. These models could be useful for hydrophilic drug dosage adjustment or stratification of patients at risk.

TRIAL REGISTRATION

Not applicable.

Effect of renal clearance on vancomycin area under the concentration-time curve deviations in critically ill patients

INTRODUCTION

Augmented renal clearance (ARC) increases vancomycin (VCM) clearance. Therefore, higher VCM doses are recommended in patients with ARC; however, impacts of ARC on the area under the concentration-time curve (AUC) discrepancies between initial dosing design and therapeutic drug monitoring (TDM) period remains unclear.

METHODS

We retrospectively collected data from critically ill patients treated with VCM. The primary endpoint was the association between ARC and AUC_24-48h deviations. ARC and AUC deviation were defined as a serum creatinine clearance (CCr) ≥130 mL/min/1.73 m² and an AUC at TDM 30% or more higher than the AUC at the initial dosing design, respectively. The pharmacokinetic profiles of VCM were analyzed with the trough levels or peak/trough levels using the Bayesian estimation software Practical AUC-guided TDM (PAT).

RESULTS

Among 141 patients (median [IQR]; 66 [58-74] years old; 30% women), 35 (25%) had ARC. AUC deviations were significantly more frequent in the ARC group than in the non-ARC group (20/35 [57.1%] and 17/106 [16.0%] patients, respectively, p < 0.001). Age- and sex-adjusted multivariate analyses revealed that the number of VCM doses before TDM ≥5 (odds ratio, 2.56; 95% confidence interval [CI]: 1.01-6.44, p = 0.047) and CCr ≥130 mL/min/1.73 m² were significantly associated with AUC deviations (odds ratio, 7.86; 95%CI: 2.91-21.19, p < 0.001).

CONCLUSION

Our study clarifies that the AUC of VCM in patients with ARC is higher at the time of TDM than at the time of dosage design.

External Validation of the Augmented Renal Clearance Predictor in Critically Ill COVID-19 Patients

The ARC predictor is a prediction model for augmented renal clearance (ARC) on the next intensive care unit (ICU) day that showed good performance in a general ICU setting. In this study, we performed a retrospective external validation of the ARC predictor in critically ill coronavirus disease 19 (COVID-19) patients admitted to the ICU of the University Hospitals Leuven from February 2020 to January 2021. All patient-days that had serum creatinine levels available and measured creatinine clearance on the next ICU day were enrolled. The performance of the ARC predictor was evaluated using discrimination, calibration, and decision curves. A total of 120 patients (1064 patient-days) were included, and ARC was found in 57 (47.5%) patients, corresponding to 246 (23.1%) patient-days. The ARC predictor demonstrated good discrimination and calibration (AUROC of 0.86, calibration slope of 1.18, and calibration-in-the-large of 0.14) and a wide clinical-usefulness range. At the default classification threshold of 20% in the original study, the sensitivity and specificity were 72% and 81%, respectively. The ARC predictor is able to accurately predict ARC in critically ill COVID-19 patients. These results support the potential of the ARC predictor to optimize renally cleared drug dosages in this specific ICU population. Investigation of dosing regimen improvement was not included in this study and remains a challenge for future studies.

Glomerular filtration rate correlation and agreement between common predictive equations and standard 24-hour urinary creatinine clearance in medical critically ill patients

Background

Determining kidney function in critically ill patients is paramount for the dose adjustment of several medications. When assessing kidney function, the glomerular filtration rate (GFR) is generally estimated either by calculating urine creatinine clearance (UCrCl) or using a predictive equation. Unfortunately, all predictive equations have been derived for medical outpatients. Therefore, the validity of predictive equations is of concern when compared with that of the UCrCl method, particularly in medical critically ill patients. Therefore, we conducted this study to assess the agreement of the estimated GFR (eGFR) using common predictive equations and UCrCl in medical critical care setting.

Methods

This was the secondary analysis of a nutrition therapy study. Urine was collected from participating patients over 24 h for urine creatinine, urine nitrogen, urine volume, and serum creatinine measurements on days 1, 3, 5, and 14 of the study. Subsequently, we calculated UCrCl and eGFR using four predictive equations, the Cockcroft-Gault (CG) formula, the four and six-variable Modification of Diet in Renal Disease Study (MDRD-4 and MDRD-6) equations, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The correlation and agreement between eGFR and UCrCl were determined using the Spearman rank correlation coefficient and Bland-Altman plot with multiple measurements per subject, respectively. The performance of each predictive equation for estimating GFR was reported as bias, precision, and absolute percentage error (APE).

Results

A total of 49 patients with 170 urine samples were included in the final analysis. Of 49 patients, the median age was 74 (21-92) years-old and 49% was male. All patients were hemodynamically stable with mean arterial blood pressure of 82 (65-108) mmHg. Baseline serum creatinine was 0.93 (0.3-4.84) mg/dL and baseline UCrCl was 46.69 (3.40-165.53) mL/min. The eGFR from all the predictive equations showed modest correlation with UCrCl (r: 0.692 to 0.759). However, the performance of all the predictive equations in estimating GFR compared to that of UCrCl was poor, demonstrating bias ranged from -8.36 to -31.95 mL/min, precision ranged from 92.02 to 166.43 mL/min, and an unacceptable APE (23.01% to 47.18%). Nevertheless, the CG formula showed the best performance in estimating GFR, with a small bias (-2.30 (-9.46 to 4.86) mL/min) and an acceptable APE (14.72% (10.87% to 23.80%)), especially in patients with normal UCrCl.

Conclusion

From our finding, CG formula was the best eGFR formula in the medical critically ill patients, which demonstrated the least bias and acceptable APE, especially in normal UCrCl patients. However, the predictive equation commonly used to estimate GFR in critically ill patients must be cautiously applied due to its large bias, wide precision, and unacceptable error, particularly in renal function impairment.

Augmented Renal Clearance in Severe Infections-An Important Consideration in Vancomycin Dosing: A Narrative Review

Vancomycin is a hydrophilic antibiotic widely used in severe infections, including bacteremia and central nervous system (CNS) infections caused by Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus (MRSA), coagulase-negative staphylococci and enterococci. Appropriate antimicrobial dosage regimens can help achieve the target exposure and improve clinical outcomes. However, vancomycin exposure in serum and cerebrospinal fluid (CSF) is challenging to predict due to rapidly changing pathophysiological processes and patient-specific factors. Vancomycin concentrations may be decreased for peripheral infections due to augmented renal clearance (ARC) and increased distribution caused by systemic inflammatory response syndrome (SIRS), increased capillary permeability, and aggressive fluid resuscitation. Additionally, few studies on vancomycin’s pharmacokinetics (PK) in CSF for CNS infections. The relationship between exposure and clinical response is unclear, challenging for adequate antimicrobial therapy. Accurate prediction of vancomycin pharmacokinetics/pharmacodynamics (PK/PD) in patients with high interindividual variation is critical to increase the likelihood of achieving therapeutic targets. In this review, we describe the interaction between ARC and vancomycin PK/PD, patient-specific factors that influence the achievement of target exposure, and recent advances in optimizing vancomycin dosing schedules for severe infective patients with ARC.

Biomarkers Predicting Tissue Pharmacokinetics of Antimicrobials in Sepsis: A Review

The pathophysiology of sepsis alters drug pharmacokinetics, resulting in inadequate drug exposure and target-site concentration. Suboptimal exposure leads to treatment failure and the development of antimicrobial resistance. Therefore, we seek to optimize antimicrobial therapy in sepsis by selecting the right drug and the correct dosage. A prerequisite for achieving this goal is characterization and understanding of the mechanisms of pharmacokinetic alterations. However, most infections take place not in blood but in different body compartments. Since tissue pharmacokinetic assessment is not feasible in daily practice, we need to tailor antibiotic treatment according to the specific patient’s pathophysiological processes. The complex pathophysiology of sepsis and the ineffectiveness of current targeted therapies suggest that treatments guided by biomarkers predicting target-site concentration could provide a new therapeutic strategy. Inflammation, endothelial and coagulation activation markers, and blood flow parameters might be indicators of impaired tissue distribution. Moreover, hepatic and renal dysfunction biomarkers can predict not only drug metabolism and clearance but also drug distribution. Identification of the right biomarkers can direct drug dosing and provide timely feedback on its effectiveness. Therefore, this might decrease antibiotic resistance and the mortality of critically ill patients. This article fills the literature gap by characterizing patient biomarkers that might be used to predict unbound plasma-to-tissue drug distribution in critically ill patients. Although all biomarkers must be clinically evaluated with the ultimate goal of combining them in a clinically feasible scoring system, we support the concept that the appropriate biomarkers could be used to direct targeted antibiotic dosing.

Prevalence and Risk Factors of Augmented Renal Clearance: A Systematic Review and Meta-Analysis

Kidney function assessment in the critically ill overlooks the possibility for hyperfunctioning kidneys, known as augmented renal clearance (ARC), which could contribute to therapeutic failures in the intensive care unit (ICU). The aim of this research is to conduct a systematic review and meta-analysis of prevalence and risk factors of ARC in the critically ill. MEDLINE, Embase, Cochrane Library, CINAHL, Scopus, ProQuest Dissertations and Theses Global databases were searched on 27 October 2020. We included studies conducted in critically ill adults who reported the prevalence and/or risk factors of ARC. We evaluated study quality using the Joanna Briggs Institute appraisal tool. Case reports, reviews, editorials and commentaries were excluded. We generated a random-effects meta-analytic model using the inverse variance method and visualized the pooled estimates using forest plots. Seventy studies were included. The pooled prevalence (95% CI) was 39% (34.9–43.3). Prevalence for neuro, trauma, mixed and sepsis ICUs were 74 (55–87), 58 (48–67), 36 (31–41) and 33 (21–48), respectively. Age, male sex and trauma were associated with ARC with pooled OR (95% CI) of 0.95 (0.93–0.96), 2.36 (1.28–4.36), 2.60 (1.21–5.58), respectively. Limitations included variations in ARC definition, inclusion and exclusion criteria and studies design. In conclusion, ARC is prevalent in critically ill patients, especially those in the neurocritical care and trauma ICU population. Young age, male sex and trauma are risk factors for ARC in those with apparently normal renal function. Further research on optimal dosing of drugs in the setting of ARC is warranted. (Prospero registration: CRD42021246417).

Augmented Renal Clearance: What Have We Known and What Will We Do?

Augmented renal clearance (ARC) is a phenomenon of increased renal function in patients with risk factors. Sub-therapeutic drug concentrations and antibacterial exposure in ARC patients are the main reasons for clinical treatment failure. Decades of increased research have focused on these phenomena, but there are still some existing disputes and unresolved issues. This article reviews information on some important aspects of what we have known and provides suggestion on what we will do regarding ARC. In this article, we review the current research progress and its limitations, including clinical identification, special patients, risk factors, metabolism, animal models and clinical treatments, and provide some promising directions for further research in this area.

Issues in antibiotic therapy for hospital-acquired and ventilator-associated pneumonia: emerging concepts to improve outcomes

Introduction:Ventilator-associated pneumonia (VAP) and hospital-acquired pneumonia (HAP) result in significant morbidity and mortality. The emergence of multi-drug resistant organisms has complicated the matter, as many of these pathogens now represent key causes of VAP and HAP. While anumber of new medications have been approved, acomprehensive appreciation of pharmacokinetic and pharmacodynamic principles, which, are often neglected, is key to effective treatment.Areas covered: The authors discuss the central pharmacokinetic and pharmacodynamic principles underlying antibiotic utilization, especially as they pertain to the treatment of VAP and HAP. They further address the concept of and implications of augmented renal clearance for the patient with nosocomial pneumonia. Finally, the authors review the evolving data on colistin and inhaled antibiotics in the management of pneumonia.Expert opinion: An enhanced understanding of the pharmacokinetic and pharmacodynamic principles along with insight into the concept of augmented renal clearance can help guide drug development and improve the way we currently dose and deliver most antibiotics. There is now mounting data on the limited efficacy and substantial nephrotoxicity of colistin, which makes it difficult to justify its continued use. While the concept of inhaled antibiotics is enticing, we lack conclusive data proving the efficacy of this paradigm.

Augmented Renal Clearance in a Case of Sepsis Leading to Vancomycin Failure Despite Increasing Dose As per the Estimated Glomerular Filtration Rate

Augmented renal clearance (ARC) is a unique clinical scenario observed in critically ill patients. We present a case of a 30-year-old male with sepsis secondary to methicillin-resistant Staphylococcus aureus (MRSA) bacteremia treated with vancomycin. ARC was observed in the patient with a maximum estimated glomerular filtration rate (eGFR) of 161.9 ml/min/1.73 m², and therapeutic drug monitoring was used to adjust the vancomycin dosage. Despite the maximal recommended dose of vancomycin, the therapeutic vancomycin level was not achieved, leading to treatment failure and subsequent mortality. Our case report suggests the necessity of other strategies, such as early dose adjustment of vancomycin based on vancomycin clearance and continuous vancomycin infusion, not merely conventional adjustment based on eGFR and vancomycin levels.