European Journal of Cardiovascular Medicine

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection, representing a major global health challenge that affects approximately 48.9 million people worldwide annually and accounts for 19.7% of all global deaths [1, 2]. This pathological syndrome involves complex interactions between systemic inflammation, immune dysfunction, and multi-organ failure, with mortality rates ranging from 10% to over 60% depending on disease severity and the presence of septic shock [1,3 ,4]. The heterogeneous nature of sepsis makes early identification and prognostic assessment crucial for optimizing patient outcomes and resource allocation in intensive care settings.

The pathophysiology of sepsis involves recognition of pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) by toll-like receptors, initiating a cascade of inflammatory responses that can lead to widespread tissue damage and organ failure [4]. Acute kidney injury (AKI) develops in approximately two-thirds of patients with septic shock and represents one of the earliest manifestations of sepsis-induced organ dysfunction [5]. Sepsis-associated AKI (SA-AKI) is characterized by inflammation, microcirculatory dysfunction, and metabolic reprogramming, resulting in mortality rates that are six to eight-fold higher than sepsis alone [5-7].

Traditional scoring systems such as the Sequential Organ Failure Assessment (SOFA) and quick SOFA (qSOFA) have been widely implemented for sepsis severity assessment and mortality prediction [8-10]. The qSOFA score, incorporating blood pressure, respiratory rate, and mental status, provides a simple bedside tool for risk stratification with specificity of 83% but limited sensitivity of 51% [10,11]. While these clinical scores remain valuable, their moderate discriminatory performance (AUC 0.60-0.72) highlights the need for more precise prognostic biomarkers [8-10].

Lactate serves as a well-established biomarker of tissue hypoperfusion and anaerobic metabolism, with elevated levels (>2 mmol/L) incorporated into the Sepsis-3 definition of septic shock [12-14]. However, lactate elevation can result from both hypoxic and non-hypoxic mechanisms, including enhanced glycolysis and decreased hepatic clearance [13,15]. Albumin, a negative acute-phase protein, decreases during inflammation due to reduced hepatic synthesis, increased vascular permeability, and enhanced catabolism [16-19] Hypoalbuminemia (<3.5 g/dL) is independently associated with increased mortality in critically ill patients [18].

Recent studies have demonstrated that the lactate-to-albumin ratio (LAR) provides superior prognostic performance compared to individual biomarkers [20-23]. LAR simultaneously captures metabolic dysfunction (elevated lactate) and inflammatory response with nutritional depletion (decreased albumin), offering a more comprehensive assessment of disease severity [21,24]. Multiple investigations have reported LAR AUC values ranging from 0.65 to 0.97 for mortality prediction in sepsis, with optimal cut-off values between 0.5 and 1.5 demonstrating sensitivities of 59-100% and specificities of 62-88% [20-23].

In tertiary care hospitals, sepsis represents a significant burden with ICU mortality rates of 32.5% and prolonged length of stay averaging 10 days [25,26] The Medical Intensive Care Unit setting provides optimal conditions for comprehensive sepsis management, including advanced monitoring, multidisciplinary care, and adherence to evidence-based protocols [25,27]. However, variability in patient outcomes necessitates improved risk stratification tools to guide clinical decision-making and resource allocation [26,27].

The integration of LAR with established scoring systems like qSOFA may enhance prognostic accuracy and facilitate early identification of high-risk patients requiring intensive interventions. Given the superior performance of LAR demonstrated in recent literature and the critical need for precise prognostic tools in sepsis-associated AKI, this study aims to evaluate the relationship between LAR and qSOFA scores in predicting outcomes among sepsis patients admitted to the Medical Intensive Care Unit at a tertiary care hospital.

This observational, prospective cohort study was conducted at the Department of Medicine, Sri Aurobindo Institute of Medical Sciences and Post Graduate Institute, Indore, Madhya Pradesh, over a duration of 18 months. The study aimed to evaluate the prognostic value of the serum lactate/albumin ratio (LAR) in critically ill patients with Acute Kidney Injury (AKI). The research adhered to ethical standards and was approved by the institutional ethics committee.

Study Population The study included patients who were critically ill with sepsis or septic shock and had been admitted to the ICU. Inclusion criteria were:

Inclusion Criteria

• Patients presenting with fever, abdominal pain, vomiting
• Patients who has raised TLC
• Patients who provided informed consent to participate in the study.

Exclusion Criteria

• Patients were excluded from the study if they had:
• Elevated renal parameters.
• Patients who do not have sepsis
• Patients who did not consent to participate in the study.

Sample Size

A total of 348 patients who were treated for sepsis or septic shock at the ICU of Sri Aurobindo Institute of Medical Sciences , between January 2023 and December 2024,

Study Design

The study was designed as an observational prospective cohort study. Patients were assessed for baseline characteristics and then followed for clinical outcomes. Data on mortality were collected for both short-term (in-hospital) and long-term outcomes (up to 2 years).

Data Collection

• Hemoglobin levels
• Total and differential leukocyte count
• Erythrocyte sedimentation rate (ESR)
• Liver function tests (SGOT, SGPT, serum bilirubin)
• Serum electrolytes (including magnesium levels)
• Serum cholesterol
• Serum proteins and urine albumin.
• ABG

Serum Lactate/Albumin Ratio (LAR)

The primary measure for this study was the serum lactate/albumin ratio (LAR). The LAR was calculated by dividing serum lactate levels by serum albumin levels, obtained from the initial blood sample collected within the first 24 hours of ICU admission.

Study Endpoints and Follow-up

The primary outcome of the study was all-cause mortality. In-hospital mortality and long-term mortality (follow-up up to 2 years) were both tracked. Mortality data was collected from patient records or through contact with patients for those who were discharged. The prognostic value of LAR in predicting mortality was assessed using Receiver Operating Characteristic (ROC) curve analysis to determine the optimal cut-off value.

Statistical Analysis

Data were analyzed using Microsoft excel Normally distributed data were expressed as mean ± standard error of the mean (SEM), and differences between independent groups were assessed using ANOVA. Categorical data were presented as numbers and percentages, with the chi-square test used to assess differences between groups. Survival rates were calculated using a chi-square test for in-hospital mortality, and univariate and multivariate Cox regression analyses were conducted to adjust for confounding factors in long-term mortality. Variables with a p-value <0.10 in univariate analysis were included in the multivariate model.

Procedure Planned

The study included the calculation of the SAPS2 and APACHE II scores within 24 hours of ICU admission. Baseline clinical and laboratory characteristics, such as liver and kidney parameters, were compared between patients with high and low LAR values. Survival data were also analyzed for both short-term and long-term mortality, and the association of LAR with mortality was tested using ROC curves and hazard ratios (HR). The Youden’s index was used to identify the optimal cut-off value of LAR for predicting mortality, and both univariate and multivariate regression analyses were performed to explore the prognostic significance of LAR.

Table 1: Demographic and Clinical Characteristics

This table presents the demographic and clinical features of survivors and non-survivors. It highlights significant differences in age, with survivors being younger (54.6 ± 13.9 years) compared to non-survivors (65.5 ± 12.0 years), with a p-value of <0.001. Several clinical parameters, such as LAR, qSOFA score, SOFA score, lactate levels, and albumin levels, also show significant disparities between the two groups. The survivors tend to have better clinical outcomes, reflected by lower scores and values across these metrics, with all comparisons showing statistically significant results.

Table 2: LAR Cut-off Values and Diagnostic Performance

The optimal LAR cut-off of 1.2 demonstrated the highest accuracy (85.9%) with balanced sensitivity (81.9%) and specificity (88.7%).

This table outlines the diagnostic performance of the LAR at various cut-off points. As the LAR cut-off increases, specificity improves while sensitivity decreases. The optimal cut-off of 1.2 offers the highest accuracy (85.9%) with balanced sensitivity (81.9%) and specificity (88.7%). This indicates that the 1.2 threshold provides the best balance between detecting true positives and minimizing false positives, making it a suitable cut-off for prognostic evaluation.

Table 3: qSOFA Score Distribution and Mortality Rates

qSOFA Performance:

• qSOFA ≥2: 100/159 deaths (62.9% mortality)
• qSOFA <2: 44/189 deaths (23.3% mortality)
• Chi-square test p-value: <0.001

The table provides a breakdown of the qSOFA scores and their corresponding mortality rates. As the qSOFA score increases, mortality rates also rise significantly. For instance, a qSOFA score of 3 is associated with a mortality rate of 76.7%, while a score of 0 has a mortality rate of only 22.7%. The chi-square test confirms the statistical significance of these findings, with a p-value of <0.001. This highlights the importance of qSOFA as a prognostic tool in predicting patient outcomes.

Table 4: Correlation Analysis

The LAR showed moderate positive correlation with qSOFA (r=0.286) and strong correlation with SOFA scores (r=0.476).

This table shows the correlation between the LAR and various clinical parameters. There are moderate to strong correlations between LAR and the qSOFA score (r=0.286), SOFA score (r=0.476), lactate levels (r=0.305), and albumin (r=-0.355), all with p-values less than 0.001. Age also shows a moderate correlation (r=0.243) with LAR. These findings indicate that LAR is significantly associated with other established prognostic markers, underscoring its potential clinical relevance.

Table 5: Prognostic Performance Comparison

This table compares the prognostic performance of LAR, qSOFA, and SOFA using the area under the curve (AUC). LAR demonstrates the highest AUC of 0.932, indicating excellent discriminatory power for predicting mortality. The optimal cut-off of 1.34 yields a high specificity (92.6%) and a reasonable sensitivity (79.2%). In comparison, qSOFA and SOFA have lower AUC values and less favorable sensitivity-specificity trade-offs, confirming that LAR is a superior predictor of patient outcomes in this context.

This study provides compelling evidence that several clinical parameters differ significantly between survivors and non-survivors among sepsis patients with acute kidney injury. The observed differences in age, qSOFA scores, SOFA scores, lactate levels, and albumin between outcome groups are consistent with established sepsis literature and underscore the complex pathophysiology underlying poor outcomes. The lactate-to-albumin ratio (LAR) emerged as a particularly promising prognostic marker, demonstrating strong correlations with established severity scores and superior predictive performance compared to qSOFA and SOFA scores individually.

The findings that non-survivors were older and exhibited higher severity scores align with numerous previous investigations. Zhang et al. reported similar patterns, demonstrating that LAR was significantly elevated in non-survivors and correlated strongly with APACHE II and SOFA scores [28]. Likewise, recent prospective studies by Shadvor et al. found that LAR at 6 hours from sepsis onset was associated with mortality, ICU length of stay, and mechanical ventilation duration [29]. The negative correlation between albumin and LAR observed in our study reflects the underlying pathophysiological mechanisms, where systemic inflammation leads to decreased albumin synthesis and increased vascular permeability, while tissue hypoperfusion drives lactate accumulation [30,31].

The optimal LAR cut-off of 1.2 demonstrated exceptional diagnostic performance with balanced sensitivity (81.9%) and specificity (88.7%), achieving an overall accuracy of 85.9%. This performance significantly surpasses that of qSOFA alone, which showed an AUC of 0.726 in our analysis. These findings are consistent with recent meta-analytical evidence by Yoon et al., who reported pooled sensitivity and specificity of 0.71 and 0.68 respectively for LAR in predicting sepsis mortality, with an AUC of 0.74 [32]. However, our study's superior performance may reflect the specific population of sepsis patients with AKI, where LAR may have enhanced discriminatory power.

The high specificity of LAR (88.7%) is particularly valuable in clinical decision-making, as it substantially reduces false-positive rates compared to other scoring systems. Wang et al. demonstrated similar findings, reporting AUCs of 0.90 for LAR versus 0.69 for qSOFA in predicting sepsis mortality [28]. The practical significance of this improved specificity cannot be overstated, as it enables more precise identification of truly high-risk patients while avoiding unnecessary intensive interventions in lower-risk individuals.

The relationship between qSOFA scores and mortality rates confirms the clinical utility of this bedside tool, with mortality increasing progressively from 22.7% in patients with qSOFA=0 to 76.7% in those with qSOFA=3. However, the sensitivity of qSOFA for predicting mortality in patients with scores <2 remains relatively low (46.7%), limiting its effectiveness for early risk stratification. This finding is consistent with multiple large-scale studies and meta-analyses. Seymour et al. reported qSOFA sensitivity of 61% and specificity of 72% for sepsis diagnosis [33], while a comprehensive meta-analysis by Song et al. found pooled sensitivity of 0.46 and specificity of 0.82 for qSOFA in predicting mortality [34].

The moderate discriminatory ability of qSOFA (AUC=0.726) reflects inherent limitations in clinical scoring systems that rely solely on physiological parameters without incorporating biochemical markers. This limitation becomes more pronounced in sepsis-associated AKI, where traditional clinical signs may be confounded by underlying renal dysfunction and fluid management complexities [35].

The correlation analysis revealed that LAR demonstrates significant associations with multiple clinical parameters, including qSOFA (r=0.286), SOFA (r=0.476), lactate (r=0.305), and albumin (r=-0.355). These correlations provide important insights into the pathophysiological mechanisms underlying sepsis-associated AKI. The moderate correlation with qSOFA suggests that LAR captures both the clinical manifestations measured by qSOFA and additional pathophysiological processes not reflected in bedside assessments.

The negative correlation with albumin (r=-0.355) is particularly significant, as hypoalbuminemia in sepsis results from multiple mechanisms including decreased hepatic synthesis, increased vascular permeability, and enhanced catabolism [30,31]. Vincent et al. demonstrated in a meta-analysis that hypoalbuminemia is independently associated with increased mortality in acute illness, with each 10 g/L decrease in albumin associated with approximately 130-140% increase in odds of death [30]. Similarly, recent studies by Arnau-Barrés et al. found that albumin levels <2.6 g/dL were independently associated with 30-day mortality in elderly sepsis patients [36].

The superior prognostic performance of LAR (AUC=0.932) compared to qSOFA (AUC=0.726) and SOFA (AUC=0.857) represents a clinically meaningful improvement in mortality prediction. This finding is supported by multiple recent studies and meta-analyses. A systematic review by Yoon et al. encompassing 4,723 sepsis patients reported that LAR demonstrated moderate predictive ability with pooled AUC of 0.74, superior to individual lactate or albumin measurements [32].

More recent investigations have shown even more impressive results. A study by Singh et al. reported LAR AUC of 0.976 for predicting mortality in sepsis patients, with optimal cut-off of 0.96 achieving 100% sensitivity and 88% specificity [37]. Similarly, Huang et al. demonstrated LAR AUC of 0.868 for 28-day mortality prediction, significantly outperforming lactate alone (AUC=0.814) [38].

The enhanced performance of LAR likely stems from its ability to simultaneously capture two critical aspects of sepsis pathophysiology: tissue hypoperfusion (reflected by lactate) and inflammatory response with capillary leak (reflected by albumin) [28,39]. This dual representation provides a more comprehensive assessment of disease severity than either parameter alone.

The clinical implications of these findings are substantial. LAR can be calculated from readily available laboratory parameters without additional cost or complexity, making it immediately implementable in clinical practice. The superior discriminatory ability of LAR suggests it could enhance existing risk stratification protocols and improve clinical decision-making in sepsis-associated AKI.

However, several important considerations must be acknowledged. The optimal LAR cut-off values may vary across different populations and healthcare settings. Multi-center validation studies are essential to establish standardized thresholds and confirm the generalizability of these findings. Additionally, the dynamic nature of sepsis suggests that serial LAR measurements may provide additional prognostic value beyond single-point assessments [29].

Study Limitations

This analysis has several limitations that warrant consideration. The retrospective, single-center design limits the generalizability of findings and precludes establishment of causal relationships. The reliance on admission values for LAR calculation may not capture the dynamic changes in lactate and albumin that occur during sepsis progression. Furthermore, the study population was limited to sepsis patients with AKI, which may limit applicability to broader sepsis populations.

Missing variables and potential unmeasured confounders may have influenced the results despite statistical adjustment. The absence of long-term follow-up also limits assessment of LAR's prognostic value beyond in-hospital outcomes. Future prospective, multi-center studies with larger sample sizes are needed to validate these findings and establish evidence-based guidelines for LAR implementation.

This study demonstrates that LAR is a superior prognostic marker compared to qSOFA and SOFA scores for predicting mortality in sepsis patients with acute kidney injury. The optimal LAR cut-off of 1.2 provides excellent discriminatory power (AUC=0.932) with balanced sensitivity and specificity, significantly outperforming traditional scoring systems. The strong correlations between LAR and established clinical parameters, combined with its superior predictive performance, support its integration into clinical practice for enhanced risk stratification and early identification of high-risk patients.

The clinical utility of LAR extends beyond its statistical superiority, offering a practical, cost-effective tool that can be immediately implemented using routine laboratory parameters. As sepsis management continues to evolve toward precision medicine approaches, LAR represents a valuable addition to the clinician's prognostic toolkit. However, successful implementation will require prospective validation studies, standardization of cut-off values, and integration into existing clinical workflows. The potential for LAR to improve patient outcomes through enhanced risk stratification and timely intervention warrants continued investigation and clinical adoption.

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