INTRODUCTION:

An elevated creatinine clearance of more than 130 ml/min/m², as determined by urine collection over an 8-24 hour period, is currently considered as Augmented Renal Clearance (ARC). This condition has been described over the first 5 days of ICU Admission¹. The definitive causes of ARC continue to remain unclear and generally difficult to elucidate².

According to the SIRS theory, when patients are in conditions such as severe trauma, burns, sepsis, and major surgery that is related or unrelated to infection, cytokines and pro-inflammatory mediators are released, which may decrease vascular resistance and increase cardiac output and capillary permeability^3,4,5,6. In previous studies, factors that indicate a high risk for ARC include younger age, male sex, lower severity of illness, vasopressor therapy, sepsis, trauma, general surgery, neurosurgery, febrile neutropenia, burn injury, and cystic fibrosis^7,8,9,10. ARC plays an important role in preventing underdosing of drugs, individualizing dosage regimens, and improving patient outcome. In the case of antimicrobial therapy, there is a chance of antibiotic under-dosing and the development of drug resistance^11-16. One-fifth of the patients in the ICU do not reach the maximum peak drug concentration^17. Several studies have been conducted focusing on antimicrobial dose adjustments in patients with ARC, notably antibiotics such as vancomycin, amikacin, and piperacillin-tazobactam^18-22. It is also noted that ARC is associated with longer ICU stays and therapeutic failure in critically ill patients. The study aims to estimate the prevalence and risk factors of ARC in critically ill ICU patients and to compare the estimated values of Glomerular Filtration Rate (GFR) between the Cockcroft Gault equation (CG equation), Modification of Diet for Renal Disease equation (MDRD equation), and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI equation).

METHODOLOGY:

Study Design:

It is a cross-sectional study on the assessment of ARC and estimation of GFR in ICU patients. Participants in the study were recruited using a purposive sampling technique. The research protocol adhered to the Declaration of Helsinki and received approval from the Institutional Ethics Committee for Biomedical Health Research, (BMHR/2023/0090). We acquired informed consent from each patient or their family members.

Study Setting:

Tertiary Care Hospital, Chennai

Inclusion And Exclusion Criteria:

Patients who are 18 years and above, critically ill, admitted to the ICU for at least 24 hours with normal renal function irrespective of their social habits and comorbidities. Patients who are less than 18 years of age, patients with renal impairment and renal transplantation, patients with muscular dystrophies and obesity, and pregnant women were excluded from the study.

Sample Size Calculation:

The sample size for this study was calculated using OpenEpi online software available at https://www.openepi.com/SampleSize/SSPropor.htm. Based on the number of patients admitted to the intensive care unit throughout the study period, the sample size was determined. The estimated sample size of the total study population was calculated as N=100 who were admitted to the ICU were enrolled in the study at the margin error ±5% and CI=95%. The sample size was thus determined to be 80.

Study Procedure:

A preliminary literature survey was conducted to outline our protocol and we have used MD+CALC to estimate creatinine clearance with CG equation, MDRD equation, and CKD-EPI equation. Data collection was performed over 3 months. Various parameters like age, weight, past medical history, diagnosis, plan, length of ICU stay, antibiotics prescribed, and serum creatinine level were collected and documented. Data were collected through data collection forms from various medical and surgical ICUs of a tertiary care hospital in Chennai.

Statistical Analysis:

The Statistical Package for Social Sciences, version 23, was used to analyze the data. The presentation of continuous data was done as median ± interquartile range. The frequency and percentage were used for presenting categorical data. Univariate logistic regression analysis was performed to compare the characteristics of patients who developed ARC and patients who did not develop ARC. The chi-square test or Fisher exact test was used to compare nominal data, whereas the Mann-Whitney U test was used to analyze continuous data. For factors that were significant with a p-value <0.2, in univariate analysis, multivariate logistic regression was performed to identify the significant risk factors for ARC. The goodness of fit of the regression model was performed using the Hosmer-Lemeshow test. A 2-sided p-value of <0.05 was considered statistically significant, and all analyses were performed using SPSS version 23. The agreement between the individual eGFRs by the CG, MDRD, and CKD-EPI formula was analyzed using a Bland-Altman plot with the bias representing the mean difference between each variable and precision being ±2SD from the mean.

RESULTS:

Patient Characteristics:

We enrolled 80 patients in this study with a median age of 55 years (40.50-68.75 years), of which 52.5% were men. Of these 30(37.5%) were identified as manifesting ARC. The median weight and median serum creatinine values of our study population were 63kg (56-76.67kg) and 0.7mg/dl (0.6-0.9mg/dl) respectively. A median length of stay of 4.6 days (2.2-11.1 days) was observed. The majority of patients involved had hypertension (46.3%) and diabetes mellitus (40%). The main characteristics of our study population are shown in table 1

Table 1: Overall Descriptives of demographics, comorbidities, treatment plan, and estimated creatinine clearance

Sl. No	Parameters	Total (n=80)	Patients with ARC (n=30)	Patients Without ARC (n=50)	p Value
Demographics
1	Age	55(40.50-68.75)	45(33-61.25)	61(50.75-71.25)	0.003
2	Male Gender	42(52.5)	16(53.3)	26(52)	0.655
3	Weight	63(56-76.67)	61(55-76)	63.25(56-78.15)	0.390
4	Serum Creatinine	0.7(0.6-0.9)	0.6(0.4-0.83)	0.7(0.6-0.9)	0.035
Comorbidities
5	Hypertension	37(46.3)	21(70)	16(32)	0.001
6	Diabetes Mellitus	7(8.75)	1(3.3)	6(12.0)	0.713
7	Thyroid Disorder	2(2.5)	0(0)	2(4)	0.270
8	Asthma and COPD	1(1.3)	0(0)	1(2)	0.439
9	CAD and Cardiac Disorders	2(2.5)	0(0)	2(4)	0.270
10	Cancer	4(5)	1(3.3)	3(6)	0.599
11	Seizures	1(1.3)	0(0)	1(2)	0.439
12	Cerebrovascular Accident	32(40)	18(60)	14(28.0)	0.005
13	Other Comorbidities	9(11.3)	2(6.7)	7(14)	0.318
Plan
Post Surgical Management
14	Neurosurgery	25(31.3)	14(46.7)	11(22.0)	0.02
15	Abdominal Surgery	5(6.3)	2(6.7)	3(6.0)	0.95
16	Orthopedic Surgery	7(8.8)	2(6.7)	5(10.0)	0.612
17	Obstetric and Gynecological Surgery	3(3.8)	2(6.7)	1(2.0)	0.291
18	Conservative Oncologic Surgery	6(7.5)	1(3.3)	5(10)	0.276
19	Other Surgery	3(3.8)	0(0)	3(6)	0.174
Medical Management
20	Infectious Disease	7(8.8)	0(0)	7(14)	0.133
21	Neuromedicine	11(13.8)	6(20)	5(10)	0.211
22	Cardiopulmonary	3(3.8)	0(0)	3(6)	0.174
23	Other Medical Management	11(13.8)	3(10)	8(16.0)	0.453
24	LOS in ICU	4.6(2.2-11.1)	5.1(3.0-14.8)	3.9(1.9-7.8)	0.030
Estimated Creatinine Clearance
25	CG Equation	125.5(77-172)	180(160-243)	94(68 -123)	<0.0001
26	MDRD Equation	127(95-180)	183(156-233)	93.30(80-122)	<0.0001
27	CKD-EPI Equation	114.5(96-132)	133(131-137)	100(90-110)	<0.0001

Prevalence of ARC: Among the 80 patients included, ARC was present in 30 (37.5%) patients based on the CKD-EPI equation. Of the patients who manifested ARC, 14(46.7%) were female and 16(53.3%) were male. The occurrence of ARC was higher in males when compared to females. Patients who developed ARC tended to be significantly (p=0.003) younger (median age 45 years) compared with those who did not develop ARC (median age 61 years). The serum creatinine of patients who had ARC (p=0.035, median serum creatinine 0.6mg/dl) was significantly lower than those who did not develop ARC (median serum creatinine 0.7mg/dl). Patients with ARC had a length of ICU stay (median LOS 5.1 days, p=0.03) was significant. The median eGFR for patients with ARC was 180ml/min/1.73m², 183ml/min/1.73m², and 133ml/min/1.73m²for the CG equation, MDRD equation and, CKD-EPI equation respectively. Comparison between patients with and without ARC are shown in table 1

Risk Factors Assessment:

Multivariate logistics regression analysis was performed for the 5 variables that were found to be significant in the univariate logistic regression which showed that age (p = 0.013), cerebrovascular accident (p = 0.010), and hypertension (p = 0.014) were independent risk factors for ARC and is shown in table 2.

Table 2: Multivariable Logistic Regression of risk factors of Augmented Renal Clearance

Sl No.	Risk Factors	OR	95% CI	p Value
1	Age	1.044	1.009 – 1.081	0.013
2	Low Serum creatinine	3.035	0.396 – 23.276	0.286
3	Hypertension	4.222	1.346 – 13.245	0.014
4	Cerebrovascular accident	4.599	1.441 – 14.673	0.010
5	Neurosurgery	1.854	0.551 – 6.237	0.318

Comparison of Method for Renal Function Assessment:

In order to assess normal renal function, the median eGFR for the total population was calculated which showed 125.5ml/min/1.73m², 127ml/min/1.73m², and 114.55ml/min/1.73m²for the CG, MDRD, and CKD-EPI equations respectively. The correlation coefficient revealed only a moderate positive correlation (R_{s =}0.763), although a statistically significant correlation for the CG equation compared with the CKD-EPI equation as shown in figure 1. The same pattern was noted between the MDRD equation (R_{s =}0.743) and the CKD-EPI equation figure 2. The Hosmer-Lemeshow statistic showed a significant value of p=0.459, indicating satisfactory goodness of fit.

Fig.1 Correlation between CKD-EPI and CG equation

Fig.2 Correlation between CKD-EPI and MDRD equation

The agreement between the three approaches was further evaluated using the Bland-Altman Plot. Bias, as illustrated by the mean difference was clinically negligible for the CG and CKD-EPI equation was -4.84ml/min/1.73m²whereas for MDRD and CKD-EPI equation has a bias of -12.09ml/ min/1.73m² as shown in table 3.

Table 3: Bias and Measures of Agreement Between CKD EPI and Other Estimates.

Estimated GFR Formulae	Bias	Upper Limit of Agreement	Lower Limit of Agreement
CG- Equation	-4.84	42.14	-51.84
MDRD Equation	-12.09	42.34	-66.53

Fig. 3: Bland Altman plot for CKD-EPI and CG equation

Fig. 4: Bland Altman plot for CKD-EPI and MDRD equation

DISCUSSION:

This study demonstrates a high prevalence of ARC (37.5%) in mixed medical and surgical ICUs. The gross prevalence shows that a significant number of patients in the ICU develop ARC. Age, hypertension, and cerebrovascular accident were identified as the independent risk factors for the development of ARC according to the multivariate logistic regression in our study. These factors were also reported as an independent risk factor in previous studies.

In our study the median age of ARC was 45, ARC is rarely found in patients over 50 years, and it is reported in <58 years as a screening cut-off for ARC²³. In conjunction with age-related structural alterations including tubulointerstitial fibrosis and decreased renal mass, a decline in GFR most likely explains the pattern of decreased ARC with advancing age in our study²⁴. Age should be used only as a screening tool for identifying patients with ARC and it is necessary to evaluate GFR for diagnosing ARC in ICU patients.

Many studies have found that ARC was more prevalent in male patients^9,25 while our study did not find any association between ARC and the male gender.

Multiple studies have shown trauma as a risk factor for ARC, although the exact mechanism by which trauma causes ARC is unknown²³.This applies to our study, where cerebrovascular accidents have a p-value <0.05.

The pattern of antibiotic usage in the ICU was also noted, which highlighted that most of them were renally secreted. According to studies, people with infections and ARC have increased renal antibiotic clearance, potentially lowering systemic exposure. ARC is a major concern as inadequate antimicrobial therapy can lead to antimicrobial resistance, therapeutic failure, and mortality^14,26,27. In addition to antibiotics, there are other kinds of drugs with the risk of underexposure in patients with ARC such as some antiepileptic drugs and low molecular weight heparins^28,29,30. These drug dosages in ARC should be revised, dosing intervals may need to be shortened with higher doses³¹.

The study compared the estimated GFR using predictive equations, the national kidney foundations recommend using the CKD EPI equation to estimate the GFR which can also be calculated using CG and MDRD equations. However, these equations yield large variations in GFR rates. The CG, MDRD, and CKD EPI equations showed median GFRs of 125ml/min/1.73m², 127 ml/min/1.73m², and 114.5 ml/min/1.73m²respectively. The study suggests that selecting the formula with the lowest bias may help in reducing undesired complications and improves kidney function assessment.

ABBREVIATIONS:

ARC –Augmented Renal Clearance

ICU- Intensive Care Unit

CG – Cockcroft-Gault

MDRD- Modification of Diet in Renal Disease

CKD EPI- Chronic Kidney Disease-Epidemiology Collaboration

CrCl- Creatinine clearance

GFR- Glomerular Filtration Rate

SIRS- Systematic Inflammatory Response Syndrome

CI- Confidence Interval

COPD- Chronic Obstructive Pulmonary Disease

CAD- Coronary Artery Disease

LOS- Length of stay

eGFR- Estimated Glomerular Filtration Rate

ACKNOWLEDGEMENT:

We would like to express our sincere gratitude to our beloved Principal Dr. C. N. Nalini M.Pharm., Ph.D., and the college for providing all the necessary facilities for the successful completion.

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Received on 29.12.2024 Revised on 14.04.2025

Accepted on 15.06.2025 Published on 13.01.2026

Available online from January 17, 2026

Research J. Pharmacy and Technology. 2026;19(1):346-351.

DOI: 10.52711/0974-360X.2026.00050

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