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INTRODUCTION
Sepsis is a severe life-threatening condition that can lead to various complications including acute kidney injury (AKI). The occurrence of AKI is associated with a 6- to 8-fold increase in the risk of hospital mortality and a three-fold increase in the risk of chronic kidney disease [1]. The current Surviving Sepsis Campaign guidelines recommend administering crystalloid fluids within the first 3 hours after sepsis-induced hypoperfusion or septic shock for resuscitation purposes to maintain fluid balance in the circulatory system and restore homeostasis. Balanced crystalloids are now recommended as the primary fluid choice instead of normal saline for treating sepsis patients [2] based on findings from the SMART (Isotonic Solutions and Major Adverse Renal Events Trial) trial that the saline group experienced a significantly higher rate of major adverse kidney events than the balanced crystalloid group [3]. Meta-analysis studies comparing balanced crystalloids and normal saline in sepsis patients also found that balanced crystalloids were associated significantly with lower risk of mortality and AKI compared to the group receiving saline fluids [4].
Normal saline remains an alternative fluid choice for resuscitation in sepsis patients due to its effectiveness, cost-efficiency, and widespread availability in healthcare facilities. Despite the development of numerous AKI predictive models in critically ill or sepsis patients, many of these models do not specifically address the risk of AKI in sepsis patients receiving normal saline [5-10]. Therefore, this study identified the risk factors and developed and validated a clinical risk score to predict the occurrence of AKI in sepsis patients receiving normal saline. Our findings will help healthcare professionals make appropriate fluid choices and reduce the risk of AKI to mitigate the occurrence of potentially life-threatening situations.
MATERIALS AND METHODS
This study followed the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines (Supplementary Table 1) [11].
Study Design, Setting, and Participants
This retrospective observational cohort study was conducted from January 2018 to May 2020 at Phrae Hospital, a secondary hospital with 500 beds in Northern Thailand. The study included patients who were at least 18 years old, were diagnosed with sepsis or septic shock according to the Sepsis-3 diagnostic criteria [2], and received normal saline for resuscitation and maintenance in sepsis treatment. Patients who were pregnant, undergoing renal replacement therapy (such as hemodialysis, peritoneal dialysis, or kidney transplant), those who expected to receive renal replacement therapy within 6 hours, those referred to other hospitals, those already diagnosed with AKI before receiving normal saline, or those who had incomplete data were excluded from the study.
Ethics
This study was performed in line with the principles of the Declaration of Helsinki, and the protocol was approved by the Phrae Hospital Ethical Committee for Clinical Research (No. 5/2563). Due to the minimal risk involved, the requirement for written informed consent was waived.
Data Collection and Candidate Variables
All data were extracted from the medical record electronic database and used to develop the AKI clinical predictive risk score for individuals with sepsis or septic shock who were treated with normal saline. The data included demographics (gender, age, and body mass index), pre-existing medical conditions (hypertension, diabetes mellitus, dyslipidemia, chronic kidney disease, liver disease, and/or malignancy), historical use of angiotensin-converting enzyme inhibitors (ACEIs) or angiotensin receptor blockers (ARBs), concurrent exposure to nephrotoxic agents (antimicrobial drugs, vasopressors, inotropic drugs, and/or contrast media), type of infection, respiratory failure with invasive mechanical ventilation requirement, assessment of illness severity via the Sequential Organ Failure Assessment (SOFA) score, the Acute Physiology and Chronic Health Evaluation (APACHE) II score, and laboratory results at sepsis diagnosis.
Study Outcome
According to the Kidney Disease Improving Global Outcomes (KDIGO) 2012 serum creatinine (sCr) criteria, a person had AKI if their sCr level rose by at least 0.3 mg/dl within 48 hours or 1.5 times from baseline within 7 days [12,13] of being diagnosed based on the Sepsis-3 criteria [14]. The baseline sCr was defined as the sCr level at the time of initial diagnosis of sepsis. We compared the baseline sCr level to the sCr levels over the past year, requiring an increase by less than 0.3 mg/dl, or 1.5 times, to exclude AKI at the time of sepsis diagnosis. Additionally, this study excluded urine volumes from the AKI diagnostic criteria due to missing or inaccurate data.
Patients and Sample Size
According to the recommended minimum of 10 events per variable for a binary outcome, the effective sample size was estimated to increase statistical power [15,16]. The model was comprised of 28 potential predictors; thus, there should be approximately 280 (28×10) cases of AKI. A previous study found that 38.9% of patients had AKI [3], indicating that a sample size of at least 720 cases (100/38.9×280) was required to establish the scoring system. Our dataset included 735 cases, which is more than sufficient for building the model.
Statistical Analysis
Categorical data are represented by frequencies (n) and percentages (%) and were compared using the Fisher’s exact test. Continuous data are presented as mean and standard deviation or median and interquartile range and were compared using Student t-test or Wilcoxon’s rank-sum test depending on the suitability of the statistical method.
Model Development
Multicollinearity among the variables was evaluated using the variance inflation factor (VIF), with multicollinearity considered present if the VIF was ≥5 [17]. Both univariate and multivariate logistic regression models adhered to the guidelines outlined in the TRIPOD statement [11]. Only candidate predictive variables with a P-value <0.20 from the univariate analysis were included in the logistic regression model. The multivariable logistic regression analysis employed the stepwise forward technique, utilizing the likelihood ratio test. Variables with P-values <0.05 in the multivariate logistic regression model were used to identify predictors of AKI for inclusion in the final model. Weights were assigned to the coefficients of significant factors identified in the multivariate analyses and transformed into item scores by dividing each regression coefficient by the smallest coefficient in the model and then rounding to the nearest number. The cutoff scores were created based on the probability of AKI. Finally, we measured the applicability of the clinical risk score to clinical practice through decision curve analysis (DCA). The discriminatory capability of each model to assess performance was evaluated using the area under the receiver operating characteristic (AUROC) curve ranging from 0.5 to 1.0 (with 1.0 indicating perfect discrimination). A calibration assessment was also conducted by examining the expected-to-observed (E/O) ratio.
Model Validation
We evaluated the internal validity of the final model using a bootstrapping procedure involving 500 bootstrap replicates. Subsequently, we used an estimated shrinkage factor from these bootstrap samples to alter the model coefficients, reducing the extremes of future forecasts. We re-estimated the model intercepts following shrinkage to mitigate the potential for systematic underestimation or overestimation of risks. All statistical analyses were conducted using Stata software version 18 (StataCorp LP.), with statistical significance defined as a P-value less than 0.05.
RESULTS
Patient Characteristics
The participant flow diagram is presented in Figure 1. Of the 735 participants, 211 (28.7%) developed AKI, and the time to AKI onset after sepsis diagnosis was 31.5±22.2 hours. The clinical and demographic characteristics of the study participants are summarized in Table 1. Average age was 65.8 years, 353 (48.0%) of the participants were female, 269 (36.6%) had hypertension, 144 (19.6%) had diabetes mellitus, and 99 (13.5%) had chronic kidney disease, while 123 (16.7%) reported using ACEIs/ARBs.
Bacteremia, urinary tract infection, and pneumonia were the primary causes of infection, with 330 (44.90%), 177 (24.08%), and 165 (22.45%) cases, respectively. There was also a significant statistical association between the AKI and non-AKI groups when considering AKI stage, 28-day mortality, and the need for renal replacement therapy (P<0.001, P<0.001, and P<0.001, respectively) (Table 1).
Model Development and Internal Validation to Predict the Risk of AKI
No evidence of multicollinearity was detected among the variables, with a mean VIF of 1.47 (range, 1.11–3.05) (Supplementary Table 2). Univariate and multivariate analyses revealed that factors significantly associated with AKI in sepsis patients receiving normal saline included patients with chronic liver disease, those receiving ACEIs/ARBs before sepsis diagnosis, patients with respiratory failure requiring invasive ventilator support, APACHE II score ≥18 points, hyperchloremia (serum chloride level, ≥110 mEq/L), sCr level ≥1.5 g/dl, exposure to norepinephrine and exposure to nephrotoxic antimicrobial drugs such as amphotericin B, carbapenems, colistin, piperacillin/tazobactam, or vancomycin at the time of sepsis (Supplementary Table 3). The logistic coefficient of each predictor was weighted for score transformation (Table 2).
The newly derived predictive risk, named the NACl RENAl-Cr score, was used to predict the occurrence of AKI in sepsis patients receiving normal saline. Each predictor in the optimism-corrected NACl RENAL-Cr score was assigned specific points with 1 point for nephrotoxic antimicrobial drugs and norepinephrine exposure at the time of sepsis diagnosis and 1.5 points for sCr levels ≥1.5 g/dl, serum chloride level ≥110 mEq/L, APACHE II score ≥18 points, and patients with respiratory failure requiring invasive mechanical ventilator support. Two points were allocated for a history of ACEIs/ARBs before sepsis diagnosis, with an additional 2.5 points for patients with chronic liver disease. Finally, cutoff scores were implemented to create three risk groups based on the probability of AKI as low risk (0–2.5 points), moderate risk (2.5–7 points), and high risk (7.5–12.5 points) (Figure 2).
DCA is a technique employed to evaluate the overall clinical advantage of predictive models. In our research, we assessed the practicality of the well-established NACl RENAL-Cr score using DCA (Figure 3). The findings demonstrated that the score provided satisfactory clinical utility, effectively aiding clinicians in accurately predicting AKI. The NACl RENAL-Cr score demonstrated robust discriminative ability with an AUROC of 0.79; 95% CI, 0.72–0.83, and excellent calibration ability with E/O =1 (Figure 4). The sensitivity, specificity, positive predictive value, negative predictive value, and correct classification rate of the score were 71.6%, 72.5%, 51.2%, 86.4%, and 72.2%, respectively. The model maintained strong discriminative ability (AUROC, 0.79; 95% CI, 0.77–0.79) after internal validation using the bootstrapping method (500 replications) (Table 3).
DISCUSSION
AKI is a common and life-threatening complication in sepsis patients resulting from various causes including the use of normal saline. A meta-analysis revealed an association between the administration of normal saline and the occurrence of AKI in sepsis patients. Current studies indicate that AKI is related to sepsis in 21.6 to 57.3% of cases [18,19]. These results aligned with the findings of the present study, which determined a 28.71% incidence of AKI in sepsis patients receiving normal saline.
The occurrence of AKI in sepsis patients who received normal saline was evaluated by gathering various parameters from a literature review, and predictive models were used to anticipate the onset of AKI in patients with sepsis or other critical conditions. Twenty-eight variables were considered using multiple regression analysis, and eight significant variables were identified as integral components of the NACl RENAL-Cr score. This scoring model encompassed patients with chronic liver disease, individuals using ACEIs or ARBs before the diagnosis of sepsis, patients with respiratory failure necessitating invasive mechanical ventilator support, those with an APACHE II score ≥18 points, individuals with serum chloride ≥110 mEq/L or sCr level ≥1.5 g/dl, and those exposed to norepinephrine or nephrotoxic antibiotics at the time of sepsis.
All these predictors, with the exception of hyperchloremia (serum chloride ≥110 mEq/L), have been previously utilized as significant variables to predict AKI in sepsis or critical care patients [5-10]. The relationship between hyperchloremia and AKI occurrence in sepsis patients remains unclear, but several observational studies have indicated that an increase in serum chloride from intensive care unit admission or delta chloride was associated with AKI. However, the objective of this study was to develop a predictive risk model to anticipate AKI occurrence in sepsis patients and assist in decision-making to select appropriate fluid therapy. Therefore, predictors were selected that could be assessed at the time of sepsis diagnosis. Results show that hyperchloremia is significantly associated with AKI. Hyperchloremia contributes to vascular smooth muscle contraction, leading to increased vasoconstriction induced by norepinephrine and angiotensin II, resulting in reduced renal blood flow via tubulo-glomerular feedback, and ultimately leading to the development of AKI [20,21].
Model performance demonstrated good calibration of the NACl RENAL-Cr score (calibration in the large, 0.000; C-slope, 1.00; and O/E, 1) and discrimination (AUROC, 0.78). Consequently, high-risk sepsis patients with a NACl RENAL-Cr score of 7.5 points or higher should not be given normal saline for resuscitation. For the group of moderate-risk sepsis patients with a NACl RENAL-Cr score between 2.5 and 7.5 points, close monitoring for development of AKI is recommended.
Although several predictive models have been developed to predict the occurrence of AKI in sepsis or critical patients. The NACl RENAL-Cr score is specifically designed to predict AKI in sepsis patients who receive normal saline. This study identified patients with sepsis by calculating SOFA scores according to three sepsis diagnostic criteria. Our method resulted in greater accuracy in classifying patients compared to using the International Classification of Diseases codes in the database. Twenty-eight variables were collected and analyzed including hyperchloremia, which was expected to be associated with the development of AKI following normal saline administration. Hyperchloremia is a crucial component of this model. Each predictor in the model constituted basic variables that can be assessed in hospitals at all levels, without the need for reliance on experts. Medical professionals can promptly assess the situation and make decisions before opting for fluid resuscitation.
Some study limitations warrant further attention. First, hypoalbuminemia and acidosis, variables previously associated with the occurrence of AKI in sepsis patients, were not analyzed in this study because laboratory test results for blood albumin levels and arterial blood gas are not part of the routine assessment in our setting. Therefore, this study could not examine the correlation between hypoalbuminemia and acidosis with the occurrence of AKI, and these variables were not included as predictors in our model. Consequently, caution should be exercised when using this scoring system in sepsis patients with hypoalbuminemia or acidosis. Additionally, if future studies find a significant correlation between hypoalbuminemia and acidosis and the development of AKI, it may lead to modifications in these scoring systems to enhance predictive accuracy. Second, even though the NACl RENAL-Cr score has been established based on patients diagnosed with sepsis and AKI according to the current standard treatment guidelines, it was developed and validated using sepsis patients admitted to a single-center cohort, with all participants being Thai. This represents a potential limitation when applying the tool to generalized populations and various healthcare settings. Third, the variable of nephrotoxic antimicrobial drugs in this scoring system did not include aminoglycosides because this study did not observe the usage of aminoglycosides in our patient population. However, this drug group is widely recognized as a potential nephrotoxic drug. Therefore, we recommend exercising caution and closely monitoring for AKI in sepsis patients receiving aminoglycosides, especially when used concomitantly with normal saline. Finally, this is a retrospective data collection study, which inherently limits the precise documentation of the quantity of normal saline used. Moreover, it aimed to develop a tool for guiding the initiation of normal saline in sepsis patients. As a result, we did not investigate the relationship between elevated serum chloride level and the occurrence of AKI. Nevertheless, current research indicates that elevated serum chloride levels following normal saline administration are associated with AKI. Therefore, we strongly advise close monitoring for AKI in sepsis patients receiving normal saline or experiencing significant increases in serum chloride levels. The retrospective study design has other limitations, such as the accuracy and completeness of medical records, which may lead to potential biases. Therefore, external validation in a diverse and independent cohort is crucial to confirm the tool's applicability across different settings.
Implications
Normal saline serves as an alternative fluid for resuscitation in sepsis patients, but it has been linked to the development of AKI. However, given its effectiveness in treating sepsis, cost-effectiveness, and widespread availability in all hospitals, we developed the NACl RENAL-Cr score to assist healthcare professionals in deciding on the choice of fluid for resuscitation in sepsis patients. In light of our findings, we suggest avoiding the use of normal saline for resuscitation in high-risk sepsis patients with scores of 7.5 points or higher and closely monitoring moderate-risk sepsis patients (2.5–7 points) for the occurrence of AKI when considering normal saline for their treatment. However, our prediction model did not include several factors like hypoalbuminemia, metabolic acidosis, or the use of aminoglycosides, which are known to increase the risk of AKI. Therefore, if patients present with these factors, we recommend extra caution and close monitoring when using normal saline for resuscitation in sepsis patients.
Conclusions
The NACl RENAL-Cr score consists of eight clinical factors, forming a simplified scoring model to predict the risk of AKI in sepsis patients receiving normal saline. This tool demonstrated good performance in terms of discrimination, calibration, and clinical application. Healthcare professionals can use this tool to assess risk and make decisions regarding fluid selection for the treatment of sepsis patients, as well as monitor the occurrence of AKI in sepsis patients receiving normal saline. However, further studies should include external validation to evaluate the performance of this tool in broader sepsis populations.
KEY MESSAGES
▪ Normal saline is an effective fluid for resuscitation in patients with sepsis and is cost-effective and readily available in hospitals at all levels.
▪ Normal saline is utilized as an alternative fluid for resuscitation in septic patients because of the more frequent major adverse kidney events compared to those in the balanced crystalloid group.
▪ The NACl RENAL-Cr score is a predictive model developed to forecast the occurrence of acute kidney injury in septic patients; this scoring system aids healthcare professionals in making decisions about appropriate fluid resuscitation for treating sepsis.
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