Association between Sodium–Chloride Difference at ICU Admission and 30-Day Mortality: A Retrospective Cohort Study of Critically Ill Adults

Association between Sodium–Chloride Difference at ICU Admission and 30-Day Mortality: A Retrospective Cohort Study of Critically Ill Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between Sodium–Chloride Difference at ICU Admission and 30-Day Mortality: A Retrospective Cohort Study of Critically Ill Adults Rafał Świstek, Jakub Konieczyński, Jakub Droś, Patryk Kasongo, and 24 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7151264/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Nov, 2025 Read the published version in BMC Anesthesiology → Version 1 posted 16 You are reading this latest preprint version Abstract Background The difference between serum sodium and chloride concentrations (SCD), may reflect the strong ion difference (SID) and has emerged as a potential marker of acid–base status. We investigated whether SCD assessed upon ICU admission holds predictive value for short-term outcomes in critically ill adults. Methods We retrospectively analyzed 1,726 consecutive patients admitted to a mixed (medical-surgical) ICU (median age 67 years; 70.7% male). SCD values on admission were studied in relation to 30-day mortality using Cox proportional hazards models and restricted cubic spline regression to explore non-linear trends. Internal validation was performed using bootstrap resampling with 1,000 iterations. Results SCD levels were significantly lower among non-survivors compared to survivors (median [IQR]: 31.5 [28.0–34.0] vs. 33.0 [31.0–36.0] mmol/L, p = 0.002). The mortality risk curve revealed a non-linear relationship with SCD. Despite no difference in SOFA scores on admission between groups stratified by SCD (<30 vs. ≥30 mmol/L), lower SCD was associated with increased risk of death (unadjusted HR 1.50, 95% CI 1.26–1.78; p < 0.001; and adjusted for age and SOFA score HR 1.43, 95% CI 1.18–1.73; p < 0.001). Bootstrap resampling confirmed the robustness of this finding (HR 1.45, 95% CI 1.19–1.77; p < 0.001). Conclusions Low SCD on admission may indicate elevated short-term mortality risk in ICU patients, independent of conventional severity scores. Our findings may provide a reasonable pathophysiological explanation for the prognostic significance of sodium-chloride interplay in critically ill patients. sodium chloride strong ion difference Stewart’s theory sodium-chloride difference Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Disturbances in serum electrolyte levels and imbalances in acid–base status are frequently observed among patients in critical condition [ 1 ]. Chloride, being the predominant extracellular anion, contributes to the majority of all strong anionic charges and all negative charges in plasma (Fig. 1 ). Although it is involved in several physiological processes—such as acid–base regulation, osmotic balance, and neuromuscular function—its clinical significance is often underrecognized, especially compared to other electrolytes. resulting in the unintentionally underestimation of its clinical importance [ 2 ]. In routine clinical practice, serum chloride is seldom prioritized, typically remaining a background parameter rather than a central focus in diagnostic or therapeutic strategies [ 3 , 4 ]. Conversely, sodium is the main extracellular cation and plays an essential role in numerous physiological pathways, including fluid balance and pH homeostasis [ 5 ]. Its concentration in serum has long been investigated as a marker of clinical severity, and both hypo- and hypernatremia are known to correlate with unfavourable outcomes in critically ill populations [ 6 – 9 ]. Although electrolyte disturbances involving both sodium and chloride are common, the data remain inconclusive. Various studies report differing conclusions, and a comprehensive pathophysiological model explaining these associations is still lacking [ 10 ]. Some findings support a correlation between sodium and chloride levels, implying potential interdependence [ 11 ]. Others suggest that alterations in chloride alone may independently influence prognosis. However, most studies fail to explain the underlying mechanisms responsible for these observations, leaving the clinical relevance of these interactions uncertain [ 12 , 13 ]. One approach that may help clarify these relationships is the evaluation of the sodium–chloride difference (SCD), which in many contexts is proposed as an approximate surrogate for the strong ion difference (SID). This concept is rooted in the physicochemical model of acid–base balance introduced by Peter Stewart. According to Stewart's theory, the balance between fully dissociated plasma cations and anions determines the acid–base status of the organism. In this context, the gap between serum sodium and chloride concentrations can be interpreted as a simplified yet informative marker of this balance [ 14 , 15 ]. Our previous work demonstrated the prognostic value of SCD in patients with acute myocardial infarction [ 16 ]. In the present study, we sought to build upon those findings by evaluating SCD in a broader and more heterogeneous population of critically ill patients admitted to the intensive care unit (ICU). Although the Stewart approach to acid–base balance has gained increasing attention in anesthesia and intensive care, its prognostic applications—particularly in relation to SCD—remain underexplored in this setting. Given this gap in knowledge, we aimed to investigate the prognostic value of SCD assessed in critically ill patients upon admission to the ICU. Methods Ethical declaration The study protocol was approved by the Jagiellonian University Bioethics Committee (approval no. 118.0043.1.160.2024) and adhered to the principles outlined in the Declaration of Helsinki. The need for obtaining informed consent was waived because of the retrospective nature of the study. Human Ethics and Consent to Participate declarations: not applicable. Clinical trial number: not applicable. Study design and population We performed a retrospective analysis of clinical records from adult patients (aged ≥ 18 years) admitted consecutively to the intensive care unit (ICU) of the University Hospital in Kraków, Poland, between January 2020 and December 2022. We assessed serum sodium and chloride concentrations based on three successive blood samples: one taken immediately upon ICU admission, and two additional samples collected within the following 24 hours. All measurements were performed using the ABL90 FLEX point-of-care testing (POCT) device (Radiometer, Copenhagen, Denmark). Based on these values, we calculated the sodium–chloride difference (SCD). In cases where patients died within the first 24 hours of ICU admission, SCD was calculated using the data available: either a single value (if death occurred on day 0) or the mean of two measurements (if death occurred on day 1). For the remaining patients, the SCD was calculated as the average of all three available results. Additional clinical data—covering patient demographics, comorbidities, course of hospitalization, laboratory findings, treatment modalities, and in-hospital outcomes—were obtained from the hospital’s electronic medical documentation system. The Sequential Organ Failure Assessment (SOFA) score was determined for each patient based on data collected within the first 24 hours following ICU admission. Mortality within 30 days after ICU admission was determined based on information retrieved from the National Electronic Population Registration System in Poland. This manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies [ 17 ]. A completed STROBE checklist, indicating where each item is addressed in the manuscript, is provided as Supplementary Table S1. A visual overview of the patient inclusion process is presented in Fig. 2 . Statistical analysis Descriptive statistics were used to summarize the study data. Categorical variables were expressed as counts and percentages, while continuous variables were presented as means with standard deviations (SD) or as medians with interquartile ranges (IQR), depending on the distribution. The Shapiro–Wilk test was applied to assess normality, and Levene’s test was used to evaluate the homogeneity of variances. Comparisons between groups were conducted using Student’s t-test or Welch’s t-test for normally distributed variables, depending on the equality of variances. For non-normally distributed variables, the Mann–Whitney U test was employed. Ordinal data were analyzed using the Cochran–Armitage trend test. Categorical variables were compared using Pearson’s chi-squared test; when over 20% of expected cell counts were below 5, Monte Carlo simulation was applied as an alternative to the Fisher exact test. To explore the relationship between sodium–chloride difference (SCD) and 30-day mortality, we performed restricted cubic spline regression modeling, placing knots at the 25th, 50th, and 75th percentiles of the SCD distribution. The threshold value of 30 mmol/L was used as a reference point for further survival analyses. Multivariable Cox proportional hazards models were constructed to identify independent predictors of mortality. Two models were specified: Model 1 (unadjusted) and Model 2 (adjusted for age and SOFA score). Results were reported as hazard ratios (HR) with corresponding 95% confidence intervals (CI). The proportional hazards assumption was verified using the Schoenfeld residuals test and graphical methods. To assess the internal validity of the Cox proportional hazards model, we performed bootstrap resampling with 1000 iterations. In each iteration, a new dataset was generated by random sampling with replacement from the original cohort. A multivariable Cox regression model was then fitted using time-to-event data (30-day follow-up) with SCD group (< 30 vs. ≥30 mmol/L), age, and SOFA score as covariates. The hazard ratio (HR) for the SCD group was extracted from each model. The distribution of the HR estimates across all iterations was used to calculate the median HR and the 95% percentile-based confidence interval (bootstrap CI), providing an internal validation of the model’s stability and robustness. A post-hoc power analysis was performed to evaluate the ability of our study to detect the observed difference in 30-day mortality between patients with SCD < 30 mmol/L and those with SCD ≥ 30 mmol/L. Based on the total sample size (n = 1,726), the number of events (n = 803), and the observed hazard ratio of 1.43, the calculated statistical power exceeded 80% at a two-sided significance level of 0.05. All statistical procedures were carried out using R software (Version R 4.4.1). Sample size adequacy was confirmed via power analysis conducted in G*Power. Results Clinical characteristics of study subjects The cohort analyzed in this study encompassed 1726 adult individuals admitted to the intensive care unit over the specified study period. The median age of the population was 67 years (interquartile range: 59–76), with a predominance of male patients (70.7%). Of all admissions, 59.5% were classified as medical, while 40.5% followed surgical procedures. The mean SOFA score was 10,18 ± 3,84. Thirty-day all-cause mortality was observed in 803 cases, corresponding to a rate of 46.5%. Initial laboratory data, obtained via point-of-care testing, revealed a median serum sodium concentration of 140.0 mmol/L (IQR: 137.0–143.0), while chloride levels had a median of 106.67 mmol/L (IQR: 103.67–109.46). The sodium–chloride difference (SCD), calculated as the difference between these two ions, was 33.00 mmol/L (IQR: 31.00–35.50). Relationship between Sodium–Chloride Difference and 30-day mortality When stratifying the cohort by 30-day survival status, differences in SCD values became apparent (Table 1 and Table 2 ). Patients who did not survive exhibited lower SCD levels at admission compared to those who survived the follow-up period. Specifically, the median SCD among non-survivors was 33.00 mmol/L (IQR: 31.00–35.50), while survivors had a significantly higher value of 33.33 mmol/L (IQR: 31.33–35.66, p = 0.007). To explore this relationship further, we applied a restricted cubic spline regression model, which demonstrated a non-linear trend linking lower SCD values to an increased likelihood of mortality. (Fig. 3 ). Based on visual analysis and data distribution, a cut-off point of 30 mmol/L was identified as the threshold of prognostic relevance. Patients were subsequently categorized according to this threshold. Even tough, there was no difference in SOFA score on admission between patients with SCD < 30 mmol/L (in comparison to SCD ≥ 30 mmol/L); (10.0 (8.0–13.0) vs 10.0 (8.0–13.0); p = 0.582), however higher mortality rate was observed in patients with SCD < 30 mmol/L in comparison to patients with SCD ≥ 30 mmol/L (n = 161, 57.3% vs n = 642; 44.4%, p < 0.001). (Fig. 4 ) Among individuals with SCD values below 30 mmol/L, the risk of death within 30 days was markedly elevated. This observation was consistent across both unadjusted and adjusted analyses. In univariable Cox regression, the hazard ratio for mortality in this group was 1.498 (95% CI: 1.260–1.780; p < 0.001). After controlling for age and SOFA score, the association remained significant with an adjusted hazard ratio of 1.432 (95% CI: 1.182–1.734; p < 0.001). (Table 3 .) To validate the internal consistency of our Cox regression model, we performed bootstrap resampling (1000 iterations). The model included sodium–chloride difference group (< 30 vs. ≥30 mmol/L), age, and SOFA score as covariates. The bootstrap-derived hazard ratio for SCD < 30 mmol/L was 1.445 (95% CI: 1.188–1.770), supporting the robustness of the association. Table 1 Baseline clinical characteristics according to survival status in 30-day follow up. Parameter Survivors (n = 923; 53.5%) Non-survivors (n = 803; 46.5%) p value Age, years, median (IQR) 62.00 (45.00–70.00) 64.00 (50.00–73.00) < 0.001 Male, n (%) 574 (62.2) 503 (62.6) 0.443 BMI a , mean (SD) 27.78 (6.10) 28.19 (6.09) 0.176 Comorbidities, n (%) Diabetes mellitus, n (%) 218 (23.6) 234 (29.1) 0.005 Arterial hypertension, n (%) 436 (47.2) 451 (56.2) < 0.001 COPD, n (%) 61 (6.6) 79 (9.8) 0.009 Ischemic heart disease, n (%) 122 (13.2) 176 (21.9) < 0.001 Chronic kidney disease, n (%) 105 (11.4) 115 (14.3) 0.123 Chronic hepatic failure, n (%) 23 (2.5) 49 (6.1) < 0.001 Heart failure, n (%) 139 (15.1) 170 (21.2) 0.001 Active malignancy, n (%) 110 (11.9) 111 (13.8) 0.134 History of stroke, n (%) 56 (6.1) 47 (5.9) 0.554 Admission category Medical, n (%) 516 (55.9) 511 (63.6) 0.001 Surgical, n (%) 407 (44.1) 292 (36.4) 0.001 Admission diagnosis category Respiratory, n (%) 274 (29,68) 261 (32,50) 0.206 Cardiovascular b , n (%) 177 (19,18) 217 (27,02) 0.001 Other medical, n (%) 13 (1,41) 5 (0,62) 0.109 Non-operative trauma, n (%) 52 (5,63) 28 (3,49) 0.034 Postoperative, n (%) 407 (44,10) 292 (36,36) 0.001 Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; IQR, interquartile range;. a Data available in 1637 patients for BMI. b The cardiovascular admission category includes admissions due to cardiovascular failure or insufficiency from hypertensive crisis, rhythm disturbances, acute decompensation of heart failure, hemorrhagic/hypovolemic shock, sepsis and dissecting aortic aneurysm. Table 2 Clinical, laboratory and prognostic parameters on admission to ICU according to survival status in 30-days follow up. Parameter Survivors (n = 923; 53.5%) Non-survivors (n = 803; 46.5%) p value Lactates on admission[mmol/L], median (IQR) 1.70 (1.10–3.10) 2.10 (1.20–4.70) < 0.001 pH on admission, median (IQR) 7.320 (7.249–7.390) 7.302 (7.207–7.380) < 0.001 Na + a [mmol/L], median (IQR) 139.67 (137.33-142.67) 140.00 (137.00-143.00) 0.505 Cl − a [mmol/L], median (IQR) 106.67 (103.67–109.00) 106.67 (103.67–109.50) 0.009 SCD a (mmol/L), median (IQR) 33.33 (31.33–35.66) 33.00 (31.00-35.50) 0.007 Mean arterial pressure on admission[mmHg], median (IQR) 80.00 (65.00-93.33) 76.67 (60.00–90.00) < 0.001 Heart rate [bpm], median (IQR) 90.00 (75.00-105.00) 90.00 (75.00-110.00) < 0.001 Noradrenaline dose on admission, [ucg/kg/min], median (IQR) 0.06 (0.00-0.20) 0.10 (0.00-0.30) < 0.001 SOFA score, mean SD 9.06 (3.49) 11.55 (3.81) < 0.001 ICU length of stay, median [IQR], (days) 13.00 (5.00–30.00) 9.00 (2.00–20.00) < 0.001 Hospital length of stay [days], median (IQR) 28.00 (17.00–41.00) 17.00 (8.00–30.00) < 0.001 Abbreviations: SOFA- Sequential Organ Failure Assessment; BE-base excess; bpm – beats per minute; ICU – intensive care unit; SCD – sodium chloride difference; IQR - interquartile range. a an average value of three consecutive blood sample measurements obtained after ICU admission (first measurement was obtained immediately and 2nd and 3rd in 24-hour interval) (survivors: 3 measurements, n = 923, (100%), non-survivors: 3 measurements, n = 551 (68.6%), 2 measurements, n = 127 (15.8%) and 1 measurement, n = 125 (15.6%)) Table 3 Multiple Cox regression models demonstrating the hazard ratio for 30-days mortality. Model 1 a Model 2 b Model 3 c SCD HR (95% CI) p value HR (95% CI) p value Bootstrap HR (95% CI) p value SCD ≥ 30 mmol/L 1.0 (reference) 1.0 (reference) 1.0 (reference) SCD < 30 mmol/L 1.498 (1.260–1.780) < 0.001 1.432 (1.182–1.734) < 0.001 1.445 (1.188–1.770) < 0.001 a non-adjusted model b adjusted for age (HR 1.015; 95%CI: 1.010–1.020, p < 0.001) and SOFA score (HR 1.158; 95%CI: 1.134–1.183, p < 0.001) c bootstrap-validated; adjusted for age and SOFA score Abbreviations: CI, confidence interval; SCD, sodium chloride concentration difference; SOFA score, sequential organ failure assessment score; HR, hazard ratio. Discussion In this study, we investigated the prognostic value of the serum sodium-chloride difference (SCD) in a population of critically ill patients admitted to the intensive care unit (ICU). Our findings demonstrated a non-linear relationship between SCD values and 30-day mortality, with an elevated risk of death observed in patients whose SCD was below 30.0 mmol/L, even after adjusting for potential confounders. These results support the potential utility of SCD as an early, accessible prognostic indicator in ICU settings and provide a pathophysiological rationale for considering sodium–chloride dynamics in critically ill individuals. Although ICU patients represent a highly diverse population in terms of diagnoses and disease severity, they share a common need for rapid risk assessment upon admission to guide early management decisions. While established prognostic scoring systems such as APACHE II or SOFA are valuable, they often require the integration of numerous variables, making them less practical for immediate use in emergency contexts. Consequently, there is a pressing need for simple and quickly obtainable biomarkers to support triage and therapeutic planning in this high-risk group [18.19]. To date, prognostic research in critical care has predominantly focused on dysnatremia, with less attention paid to chloride abnormalities or the functional interplay between these electrolytes. Despite chloride being the primary anion in extracellular fluid, its clinical implications remain underappreciated. However, many of these investigations fail to provide a mechanistic explanation for such associations, and few have examined the combined significance of sodium and chloride concentrations [ 10 – 13 ]. Chloride represents the most abundant anion in the extracellular compartment and has been implicated as a prognostic indicator in various clinical contexts, including sepsis and postoperative states following non-cardiac surgery [20,21]. Moreover, in selected cohorts of critically ill patients, aberrant serum chloride levels have been independently associated with prolonged hospitalization, deterioration of renal function, and increased short-term mortality [10,12,21–24]. Nevertheless, the majority of these investigations have not sufficiently elucidated the underlying pathophysiological mechanisms that might account for these associations. This gap in understanding may, in part, reflect the historically marginal role attributed to chloride in clinical assessment. Its contribution to acid–base disturbances and its interaction with other major electrolytes within the extracellular fluid compartment remain underappreciated in daily critical care practice. The Stewart model offers a theoretical framework for understanding the acid–base consequences of shifts in strong ions, positing that the difference between fully dissociated cations and anions—i.e., the strong ion difference (SID)—is a fundamental determinant of blood pH [ 14 ]. In clinical practice, SCD serves as a simplified approximation of SID, based on the assumption that sodium and chloride are the predominant ions in plasma [ 25 ]. Our findings suggest that SCD may function not only as a reflection of acid–base status but also as a meaningful prognostic signal in patients admitted to the ICU. While several studies have highlighted the predictive relevance of acid–base parameters, most have relied on traditional measures such as the anion gap or base excess. In contrast, investigations into SCD as a surrogate marker of SID remain relatively limited and have yielded mixed conclusions [ 10 ]. Some reports indicate that reduced SCD is associated with adverse outcomes such as acute kidney injury or prolonged hospitalization, especially in patients with sepsis or COVID-19, though evidence linking SCD to mortality remains inconsistent [ 26 ]. Some reports indicate that reduced SCD is associated with adverse outcomes such as acute kidney injury or prolonged hospitalization, especially in patients with sepsis or COVID-19, though evidence linking SCD to mortality remains inconsistent [ 27 ]. For example, while Cortés-Román et al. identified an SCD < 31 mmol/L as a predictor of 30-day mortality in septic shock, other studies failed to replicate this association in broader ICU cohorts [28]. Furthermore, recent analyses suggest that isolated evaluation of strong ion gap (SIG) or individual electrolyte disturbances may have limited prognostic value compared to integrated assessments like SCD [29]. Notably, hyperchloremia at ICU admission continues to emerge as a robust predictor of mortality, highlighting the need to reconsider how electrolyte data is interpreted in critical care [ 30 , 31 ]. Drawing unambiguous conclusions about the prognostic utility of SCD, SID, or chloride levels alone in mortality prediction remains challenging based on current literature. Therefore, in daily clinical practice, our understanding of the prognostic significance of SCD and SID still remains insufficient and our results, at least partially, fill this gap. From a clinical standpoint, our results advocate for a more nuanced approach to electrolyte interpretation—one that considers the combined influence of sodium and chloride on acid–base homeostasis. A low SCD may reflect either a relative excess of chloride or a depletion of sodium and may be associated with hidden acidosis or impaired tissue perfusion. Recognizing such imbalances early can inform therapeutic strategies, including fluid selection, diuretic management, and circulatory support. In this context, interventions aimed at restoring physiological SID—such as avoiding high-chloride fluids or correcting hypo/hypernatremia—may improve outcomes. Although some clinicians recognize that dysnatremia and dyschloremia do not occur in isolation, clinical practice often assumes that changes in sodium concentration will be accompanied by corresponding changes in serum chloride levels (or vice versa). However, according to Stewart’s theory and our findings, a low SCD—resulting from an uncompensated excessive increase in chloride anion concentration and/or an excessive decrease in sodium cation concentration—holds greater prognostic significance. Therefore, instead of focusing solely on correcting individual electrolyte imbalances (primarily dysnatremia), the assessment of critically ill patients should take into account both dysnatremia and dyschloremia, as their interaction may reflect the SCD level. When a low SCD is detected, prompt action should be taken to restore the balance. Having in mind that SCD corresponds to SID, we can consider that the above parameters also reflect acid-base imbalance and in this group of patients, confirmation of low SCD requires not only correction of electrolyte imbalances but also implementing interventions to optimize tissue perfusion, such as the use of catecholamines, diuretics, fluid supplementation, oxygen therapy, and hemoglobin level correction. Finally, our study underscores the potential of SCD to enhance awareness among ICU clinicians regarding fluid therapy. Particular caution should be exercised when administering solutions with a high chloride content (e.g., normal saline) to patients already exhibiting low SCD, as this may exacerbate acid–base disturbances and worsen prognosis. Limitations This study has several important limitations. It was conducted retrospectively at a single tertiary care center, which introduces potential risks of selection and misclassification biases. These inherent limitations of retrospective design may affect the generalizability of our findings. Another limitation is the unequal number of sodium and chloride measurements among patients, particularly those who died within the first 24 hours of ICU admission. While this may affect the precision of SCD estimation in this subgroup, we deliberately included these patients to avoid survivorship bias and to ensure the representativeness of high-risk individuals in our analysis. An additional potential limitation of our study is the lack of external validation, which restricts the generalizability of our findings to other ICU populations or clinical settings. However, this limitation is partially mitigated by the internal validation we performed using bootstrap resampling, which demonstrated consistent effect estimates and supports the robustness of our primary results. Furthermore, unmeasured confounding variables may have influenced the relationship between serum sodium–chloride difference (SCD) and 30-day mortality. Although sodium and chloride are the predominant strong ions in both intra- and extracellular compartments—as outlined in Stewart’s physicochemical model—other less abundant anions, which were not evaluated in our dataset, could potentially contribute to acid–base disturbances. Another limitation is the lack of longitudinal assessment of SCD. Changes in this parameter over time might offer valuable insights into patient trajectory and prognosis, but such dynamics were not captured in this analysis. Additionally, we did not assess the impact of medications, such as diuretics, which are known to alter sodium and chloride concentrations and might influence SCD-related outcomes. Finally, while Stewart’s model also accounts for factors like partial pressure of carbon dioxide and concentrations of nonvolatile weak acids (e.g., albumin), we were unable to analyze these parameters due to incomplete data availability. Therefore, we did not explore the potential associations between SCD and other indicators of acid–base balance, such as base excess or anion gap—areas that merit future investigation. Further research is needed to determine whether targeted correction of low SCD could meaningfully influence acid–base homeostasis and clinical outcomes in critically ill patients. Conclusions In critically ill patients admitted to the ICU, a reduced sodium–chloride difference at admission is independently associated with higher 30-day mortality. This simple, routinely available parameter may support early risk stratification in the intensive care setting and our findings may provide a reasonable pathophysiological explanation for the prognostic significance of sodium-chloride interplay in critically ill patients. Abbreviations AG Anion gap APACHE II Acute Physiology and Chronic Health Evaluation II BE Base excess CI Confidence interval HR Hazard ratio ICU Intensive care unit IQR Interquartile range POCT Point–of–care testing RCS Restricted cubic spline SCD Sodium–chloride difference SD Standard deviation SID Strong ion difference SIG Strong ion gap SOFA Sequential Organ Failure Assessment STROBE Strengthening the Reporting of Observational Studies in Epidemiology Declarations Authors’ contributions RŚ, JK, JD, PK, WS, TL, AKw, RD, TD, and MT contributed to conceptualization, methodology, investigation, and supervision . EG, JJ, MZ, PM, PG, JP, JC, ET, AKo, AH, EI, AW, KM, ASi, GP, AWł, BM, and AG were involved in investigation, data curation, literature review, and visualization . RŚ, TD, and MT performed formal analysis, validation, writing – original draft, and visualization . MT contributed to project administration, resources, writing – review & editing, and funding acquisition . All authors read and approved the final version of the manuscript. Funding No external funding was obtained for the conduct of this study, its authorship, or publication. Data availability The underlying data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to appropriate institutional permissions and ethical oversight. Ethics approval and consent to participate The study protocol was approved by the Jagiellonian University Bioethics Committee (approval no. 118.0043.1.160.2024) and was conducted in accordance with the principles outlined in the Declaration of Helsinki. Consent for publication Not applicable. Competing interests None of the authors have any financial or personal relationships that could have inappropriately influenced the work reported in this article. References Gunnerson KJ, Kellum JA. Acid-base and electrolyte analysis in critically ill patients: are we ready for the new millennium? 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The sodium–chloride difference: A marker of prognosis in patients with acute myocardial infarction. Eur J Clin Invest. 2024;54(5):e14157. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, STROBE Initiative. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. Kądziołka I, Świstek R, Borowska K, Tyszecki P, Serednicki W. Validation of APACHE II and SAPS II scales at the intensive care unit along with assessment of SOFA scale at the admission as an isolated risk of death predictor. Anaesthesiol Intensive Ther. 2019;51(2):107–11. Knaus WA, Draper EA, Wagner DP, Zimmerman JE, Karkouti K, Wijeysundera D, Minkovich L, Tait G, Beattie WS. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818 – 29. Neyra JA, CanepaEscaro F, Li X, Manllo J, AdamsHuet B, Yee J, Yessayan L. Association of hyperchloremia with hospital mortality in critically ill septic patients. Crit Care Med. 2015;43(9):1938-44. McCluskey SA, Karkouti K, Wijeysundera D, Minkovich L, Tait G, Beattie WS Hyperchloremia after noncardiac surgery is independently associated with increased morbidity and mortality: a propensity-matched cohort study. Anesth Analg. 2013;117(2):412 – 21. Goyal A, Kaur S, Singh B, Tandon R, Chhabra ST, Aslam N, Mohan B, Wander GS. Admission serum chloride levels as predictor of stay duration in acute decompensated heart failure. J Assoc Physicians India. 2020;68(10):34 – 8. Ma Q, Tian W, Wang K, Xu B, Lou T. Association of serum chloride levels with allcause mortality among patients in surgical intensive care units: a retrospective analysis of the MIMICIV database. BMC Anesthesiol. 2025;25:3. Song K, Yang T, Gao W. Association of hyperchloremia with allcause mortality in patients admitted to the surgical intensive care unit: a retrospective cohort study. BMC Anesthesiol. 2022;22:14. Story DA, Morimatsu H, Bellomo R. Strong ions, weak acids and base excess: a simplified Fencl-Stewart approach to clinical acid-base disorders. Br J Anaesth. 2004;92(1):54–60. Mallat J, Barrailler S, Lemyze M, Pepy F, Gasan G, Tronchon L, Thevenin D. Use of sodium-chloride difference and corrected anion gap as surrogates of Stewart variables in critically ill patients. PLoS ONE. 2013;8(2):e56635. Núñez-Martínez FJ, Luna-Montalbán R, Orozco-Juárez K, Chávez-Lárraga AJ, Velasco-Santos JI, Verazaluce-Rodríguez BE, Lan KM, Williams NS, Harahsheh TA, Chapman Y, Dobb AR, Magder GJ. Sodium-chloride difference as a prognostic predictor in adult patients diagnosed with COVID-19. Rev Med Inst Mex Seguro Soc. 2022;60(4):440-6. SánchezDíaz JS, PenicheMoguel KG, MartínezRodríguez EA, RiveraSolís G, Del CarpioOrantes L, PérezNieto OR, ZamarrónLópez EI, MonaresZepeda E. Acidosis metabolica: de principio a fin. Med Int Mex. 2022;38(5):1050-62. Ho KM, Lan, NS, Williams, TA, Harahsheh, Y, Chapman, AR, Dobb, GJ, Magder S. A comparison of prognostic significance of strong ion gap (SIG) with other acid-base markers in the critically ill: a cohort study. J Intensive Care. 2016;4:43. Van Regenmortel N, Verbrugghe W, Van den Wyngaert T, Jorens PG. Impact of chloride and strong ion difference on ICU and hospital mortality in a mixed intensive care population. Ann Intensive Care. 2016;6(1):91. Li Z, Xing C, Li T, Du L, Wang N. Hypochloremia is associated with increased risk of all-cause mortality in patients in the coronary care unit: a cohort study. J Int Med Res. 2020;48(4):300060520911500. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Nov, 2025 Read the published version in BMC Anesthesiology → Version 1 posted Editorial decision: Revision requested 18 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 30 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 28 Jul, 2025 Reviewers agreed at journal 26 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor invited by journal 24 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Submission checks completed at journal 24 Jul, 2025 First submitted to journal 17 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7151264","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491494100,"identity":"f8d19bad-2fb2-4c4d-bcd9-c217fcc82203","order_by":0,"name":"Rafał Świstek","email":"","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Rafał","middleName":"","lastName":"Świstek","suffix":""},{"id":491494101,"identity":"b8de4a39-a27d-408f-aafe-a15b6ee2dec8","order_by":1,"name":"Jakub Konieczyński","email":"","orcid":"","institution":"Jagiellonian University Medical 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Terlecki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie3Pv0oDMRzA8V8IxOVs1oSIPoFwh1AdCr5Kb+pkEQRxKicH6XI4K+g79BESDpyCrjfe1KnCjQd1MA0idkikW5F8h/yDDz8CEIvtYxgIwLU7FQCKHdsFILH3QZCkwACQI2c/hPjnbBHIi81biJzO8bLtU5hRoe/b3lxMFlqh9kPC1EeGNTnPKjuFP+RlVjXsaqEUzl4k3PhJQlhiSWqQFNA5QsShhFwGCP+05NKg+dqSSarUwfovItyUBEkMDRtbQnCYkKE4Shl/NKjklWHZk9Ilf35j/r+810u+uhtRWmHd9a+zk0FT6251O5pSD/mO/T7rYrOOw2IrWrhtFxKLxWL/uy8YClTfGcLENwAAAABJRU5ErkJggg==","orcid":"","institution":"Jagiellonian University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Michał","middleName":"","lastName":"Terlecki","suffix":""}],"badges":[],"createdAt":"2025-07-17 17:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7151264/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7151264/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12871-025-03438-8","type":"published","date":"2025-11-11T15:57:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87804732,"identity":"a7ff97fc-420a-44f4-b801-8bcf571a4af3","added_by":"auto","created_at":"2025-07-29 08:21:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61616,"visible":true,"origin":"","legend":"\u003cp\u003eGamblegram - normal plasma electrolyte distribution with electrical neutrality (equal sum concentration of cations and anions).\u003c/p\u003e\n\u003cp\u003eAbbreviations: A\u003csup\u003e-\u003c/sup\u003e, Albumin; Lac\u003csup\u003e-\u003c/sup\u003e, lactate; SID, strong ion difference; SCD, sodium chloride concentration difference\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7151264/v1/c5687d321367dd3d744f9c71.png"},{"id":87804734,"identity":"0c3b9174-77fc-46cd-9e24-7c95570049ee","added_by":"auto","created_at":"2025-07-29 08:21:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":88676,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study group\u003c/p\u003e\n\u003cp\u003eAbbreviations: ICU – intensive care unit\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7151264/v1/baf01bec5b580af946fb64c8.png"},{"id":87804735,"identity":"047deb60-6926-4826-8edb-eeac0fe68fba","added_by":"auto","created_at":"2025-07-29 08:21:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65023,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline regression analysis of SCD with all-cause mortality in 30-day follow-up. Heavy central lines represent the estimated adjusted hazard ratios, with shaded ribbons denoting 95% confidence intervals. A value of 30 mmol/L for SCD was chosen as the cutoff point.\u003c/p\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; SCD, sodium chloride concentration difference\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7151264/v1/73721c79c43c8bbfb562b5d6.png"},{"id":87806092,"identity":"75f34aa0-12a3-45af-8c68-857b52dc42cf","added_by":"auto","created_at":"2025-07-29 08:37:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":22578,"visible":true,"origin":"","legend":"\u003cp\u003eBox and whisker plot showing the distributions of SCD in survivors and non-survivors.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SCD, sodium chloride concentration difference.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7151264/v1/d2fe0b59a571a578d5d78ac2.png"},{"id":96105371,"identity":"886c5c58-067f-4949-af14-cfb95e7912cb","added_by":"auto","created_at":"2025-11-17 16:11:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1182558,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7151264/v1/5e7a6b21-22fc-420b-8e30-39342377ec26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Sodium–Chloride Difference at ICU Admission and 30-Day Mortality: A Retrospective Cohort Study of Critically Ill Adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDisturbances in serum electrolyte levels and imbalances in acid–base status are frequently observed among patients in critical condition [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Chloride, being the predominant extracellular anion, contributes to the majority of all strong anionic charges and all negative charges in plasma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although it is involved in several physiological processes—such as acid–base regulation, osmotic balance, and neuromuscular function—its clinical significance is often underrecognized, especially compared to other electrolytes. resulting in the unintentionally underestimation of its clinical importance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In routine clinical practice, serum chloride is seldom prioritized, typically remaining a background parameter rather than a central focus in diagnostic or therapeutic strategies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConversely, sodium is the main extracellular cation and plays an essential role in numerous physiological pathways, including fluid balance and pH homeostasis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Its concentration in serum has long been investigated as a marker of clinical severity, and both hypo- and hypernatremia are known to correlate with unfavourable outcomes in critically ill populations [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough electrolyte disturbances involving both sodium and chloride are common, the data remain inconclusive. Various studies report differing conclusions, and a comprehensive pathophysiological model explaining these associations is still lacking [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Some findings support a correlation between sodium and chloride levels, implying potential interdependence [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Others suggest that alterations in chloride alone may independently influence prognosis. However, most studies fail to explain the underlying mechanisms responsible for these observations, leaving the clinical relevance of these interactions uncertain [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOne approach that may help clarify these relationships is the evaluation of the sodium–chloride difference (SCD), which in many contexts is proposed as an approximate surrogate for the strong ion difference (SID). This concept is rooted in the physicochemical model of acid–base balance introduced by Peter Stewart. According to Stewart's theory, the balance between fully dissociated plasma cations and anions determines the acid–base status of the organism. In this context, the gap between serum sodium and chloride concentrations can be interpreted as a simplified yet informative marker of this balance [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOur previous work demonstrated the prognostic value of SCD in patients with acute myocardial infarction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In the present study, we sought to build upon those findings by evaluating SCD in a broader and more heterogeneous population of critically ill patients admitted to the intensive care unit (ICU). Although the Stewart approach to acid–base balance has gained increasing attention in anesthesia and intensive care, its prognostic applications—particularly in relation to SCD—remain underexplored in this setting. Given this gap in knowledge, we aimed to investigate the prognostic value of SCD assessed in critically ill patients upon admission to the ICU.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cem\u003eEthical declaration\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe study protocol was approved by the Jagiellonian University Bioethics Committee (approval no. 118.0043.1.160.2024) and adhered to the principles outlined in the Declaration of Helsinki. The need for obtaining informed consent was waived because of the retrospective nature of the study. Human Ethics and Consent to Participate declarations: not applicable. Clinical trial number: not applicable.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy design and population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe performed a retrospective analysis of clinical records from adult patients (aged ≥ 18 years) admitted consecutively to the intensive care unit (ICU) of the University Hospital in Kraków, Poland, between January 2020 and December 2022. We assessed serum sodium and chloride concentrations based on three successive blood samples: one taken immediately upon ICU admission, and two additional samples collected within the following 24 hours. All measurements were performed using the ABL90 FLEX point-of-care testing (POCT) device (Radiometer, Copenhagen, Denmark).\u003c/p\u003e\u003cp\u003eBased on these values, we calculated the sodium–chloride difference (SCD). In cases where patients died within the first 24 hours of ICU admission, SCD was calculated using the data available: either a single value (if death occurred on day 0) or the mean of two measurements (if death occurred on day 1). For the remaining patients, the SCD was calculated as the average of all three available results. Additional clinical data—covering patient demographics, comorbidities, course of hospitalization, laboratory findings, treatment modalities, and in-hospital outcomes—were obtained from the hospital’s electronic medical documentation system. The Sequential Organ Failure Assessment (SOFA) score was determined for each patient based on data collected within the first 24 hours following ICU admission. Mortality within 30 days after ICU admission was determined based on information retrieved from the National Electronic Population Registration System in Poland. This manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guidelines for reporting observational studies [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A completed STROBE checklist, indicating where each item is addressed in the manuscript, is provided as Supplementary Table S1. A visual overview of the patient inclusion process is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were used to summarize the study data. Categorical variables were expressed as counts and percentages, while continuous variables were presented as means with standard deviations (SD) or as medians with interquartile ranges (IQR), depending on the distribution. The Shapiro–Wilk test was applied to assess normality, and Levene’s test was used to evaluate the homogeneity of variances. Comparisons between groups were conducted using Student’s t-test or Welch’s t-test for normally distributed variables, depending on the equality of variances. For non-normally distributed variables, the Mann–Whitney U test was employed. Ordinal data were analyzed using the Cochran–Armitage trend test. Categorical variables were compared using Pearson’s chi-squared test; when over 20% of expected cell counts were below 5, Monte Carlo simulation was applied as an alternative to the Fisher exact test. To explore the relationship between sodium–chloride difference (SCD) and 30-day mortality, we performed restricted cubic spline regression modeling, placing knots at the 25th, 50th, and 75th percentiles of the SCD distribution. The threshold value of 30 mmol/L was used as a reference point for further survival analyses. Multivariable Cox proportional hazards models were constructed to identify independent predictors of mortality. Two models were specified: Model 1 (unadjusted) and Model 2 (adjusted for age and SOFA score). Results were reported as hazard ratios (HR) with corresponding 95% confidence intervals (CI). The proportional hazards assumption was verified using the Schoenfeld residuals test and graphical methods. To assess the internal validity of the Cox proportional hazards model, we performed bootstrap resampling with 1000 iterations. In each iteration, a new dataset was generated by random sampling with replacement from the original cohort. A multivariable Cox regression model was then fitted using time-to-event data (30-day follow-up) with SCD group (\u0026lt; 30 vs. ≥30 mmol/L), age, and SOFA score as covariates. The hazard ratio (HR) for the SCD group was extracted from each model. The distribution of the HR estimates across all iterations was used to calculate the median HR and the 95% percentile-based confidence interval (bootstrap CI), providing an internal validation of the model’s stability and robustness. A post-hoc power analysis was performed to evaluate the ability of our study to detect the observed difference in 30-day mortality between patients with SCD \u0026lt; 30 mmol/L and those with SCD ≥ 30 mmol/L. Based on the total sample size (n = 1,726), the number of events (n = 803), and the observed hazard ratio of 1.43, the calculated statistical power exceeded 80% at a two-sided significance level of 0.05. All statistical procedures were carried out using R software (Version R 4.4.1). Sample size adequacy was confirmed via power analysis conducted in G*Power.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eClinical characteristics of study subjects\u003c/em\u003e\u003c/p\u003e\u003cp\u003eThe cohort analyzed in this study encompassed 1726 adult individuals admitted to the intensive care unit over the specified study period. The median age of the population was 67 years (interquartile range: 59\u0026ndash;76), with a predominance of male patients (70.7%). Of all admissions, 59.5% were classified as medical, while 40.5% followed surgical procedures. The mean SOFA score was 10,18\u0026thinsp;\u0026plusmn;\u0026thinsp;3,84. Thirty-day all-cause mortality was observed in 803 cases, corresponding to a rate of 46.5%. Initial laboratory data, obtained via point-of-care testing, revealed a median serum sodium concentration of 140.0 mmol/L (IQR: 137.0\u0026ndash;143.0), while chloride levels had a median of 106.67 mmol/L (IQR: 103.67\u0026ndash;109.46). The sodium\u0026ndash;chloride difference (SCD), calculated as the difference between these two ions, was 33.00 mmol/L (IQR: 31.00\u0026ndash;35.50).\u003c/p\u003e\u003cp\u003e\u003cem\u003eRelationship between Sodium\u0026ndash;Chloride Difference and 30-day mortality\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhen stratifying the cohort by 30-day survival status, differences in SCD values became apparent (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients who did not survive exhibited lower SCD levels at admission compared to those who survived the follow-up period. Specifically, the median SCD among non-survivors was 33.00 mmol/L (IQR: 31.00\u0026ndash;35.50), while survivors had a significantly higher value of 33.33 mmol/L (IQR: 31.33\u0026ndash;35.66, p\u0026thinsp;=\u0026thinsp;0.007). To explore this relationship further, we applied a restricted cubic spline regression model, which demonstrated a non-linear trend linking lower SCD values to an increased likelihood of mortality. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Based on visual analysis and data distribution, a cut-off point of 30 mmol/L was identified as the threshold of prognostic relevance. Patients were subsequently categorized according to this threshold. Even tough, there was no difference in SOFA score on admission between patients with SCD\u0026thinsp;\u0026lt;\u0026thinsp;30 mmol/L (in comparison to SCD\u0026thinsp;\u0026ge;\u0026thinsp;30 mmol/L); (10.0 (8.0\u0026ndash;13.0) vs 10.0 (8.0\u0026ndash;13.0); p\u0026thinsp;=\u0026thinsp;0.582), however higher mortality rate was observed in patients with SCD\u0026thinsp;\u0026lt;\u0026thinsp;30 mmol/L in comparison to patients with SCD\u0026thinsp;\u0026ge;\u0026thinsp;30 mmol/L (n\u0026thinsp;=\u0026thinsp;161, 57.3% vs n\u0026thinsp;=\u0026thinsp;642; 44.4%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) Among individuals with SCD values below 30 mmol/L, the risk of death within 30 days was markedly elevated. This observation was consistent across both unadjusted and adjusted analyses. In univariable Cox regression, the hazard ratio for mortality in this group was 1.498 (95% CI: 1.260\u0026ndash;1.780; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After controlling for age and SOFA score, the association remained significant with an adjusted hazard ratio of 1.432 (95% CI: 1.182\u0026ndash;1.734; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.) To validate the internal consistency of our Cox regression model, we performed bootstrap resampling (1000 iterations). The model included sodium\u0026ndash;chloride difference group (\u0026lt;\u0026thinsp;30 vs. \u0026ge;30 mmol/L), age, and SOFA score as covariates. The bootstrap-derived hazard ratio for SCD\u0026thinsp;\u0026lt;\u0026thinsp;30 mmol/L was 1.445 (95% CI: 1.188\u0026ndash;1.770), supporting the robustness of the association.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline clinical characteristics according to survival status in 30-day follow up.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;923; 53.5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;803; 46.5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.00 (45.00\u0026ndash;70.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.00 (50.00\u0026ndash;73.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e574 (62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e503 (62.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.443\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI \u003csup\u003ea\u003c/sup\u003e, mean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.78 (6.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.19 (6.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eComorbidities, n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e218 (23.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e234 (29.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArterial hypertension, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e436 (47.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e451 (56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCOPD, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIschemic heart disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e122 (13.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e176 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic kidney disease, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e105 (11.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (14.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic hepatic failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23 (2.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart failure, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eActive malignancy, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e110 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e111 (13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHistory of stroke, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e56 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.554\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAdmission category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedical, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e516 (55.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e511 (63.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e407 (44.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292 (36.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eAdmission diagnosis category\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRespiratory, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274 (29,68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e261 (32,50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.206\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular\u003csup\u003eb\u003c/sup\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e177 (19,18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e217 (27,02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther medical, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13 (1,41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (0,62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-operative trauma, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (5,63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28 (3,49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostoperative, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e407 (44,10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292 (36,36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; IQR, interquartile range;. \u003csup\u003ea\u003c/sup\u003eData available in 1637 patients for BMI. \u003csup\u003eb\u003c/sup\u003eThe cardiovascular admission category includes admissions due to cardiovascular failure or insufficiency from hypertensive crisis, rhythm disturbances, acute decompensation of heart failure, hemorrhagic/hypovolemic shock, sepsis and dissecting aortic aneurysm.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinical, laboratory and prognostic parameters on admission to ICU according to survival status in 30-days follow up.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSurvivors\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;923; 53.5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-survivors (n\u0026thinsp;=\u0026thinsp;803; 46.5%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLactates on admission[mmol/L], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.70 (1.10\u0026ndash;3.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.10 (1.20\u0026ndash;4.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003epH on admission, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.320 (7.249\u0026ndash;7.390)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.302 (7.207\u0026ndash;7.380)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNa\u003csup\u003e+\u0026thinsp;a\u003c/sup\u003e [mmol/L], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e139.67 (137.33-142.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140.00 (137.00-143.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCl\u003csup\u003e\u0026minus; a\u003c/sup\u003e [mmol/L], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106.67 (103.67\u0026ndash;109.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e106.67 (103.67\u0026ndash;109.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCD\u003csup\u003ea\u003c/sup\u003e (mmol/L), median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.33 (31.33\u0026ndash;35.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.00 (31.00-35.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean arterial pressure on admission[mmHg], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.00 (65.00-93.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e76.67 (60.00\u0026ndash;90.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeart rate [bpm], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e90.00 (75.00-105.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e90.00 (75.00-110.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNoradrenaline dose on admission, [ucg/kg/min], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.00-0.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10 (0.00-0.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSOFA score, mean SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.06 (3.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.55 (3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eICU length of stay, median [IQR], (days)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.00 (5.00\u0026ndash;30.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e9.00 (2.00\u0026ndash;20.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospital length of stay [days], median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28.00 (17.00\u0026ndash;41.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.00 (8.00\u0026ndash;30.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: SOFA- Sequential Organ Failure Assessment; BE-base excess; bpm \u0026ndash; beats per minute; ICU \u0026ndash; intensive care unit; SCD \u0026ndash; sodium chloride difference; IQR - interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003e an average value of three consecutive blood sample measurements obtained after ICU admission (first measurement was obtained immediately and 2nd and 3rd in 24-hour interval) (survivors: 3 measurements, n\u0026thinsp;=\u0026thinsp;923, (100%), non-survivors: 3 measurements, n\u0026thinsp;=\u0026thinsp;551 (68.6%), 2 measurements, n\u0026thinsp;=\u0026thinsp;127 (15.8%) and 1 measurement, n\u0026thinsp;=\u0026thinsp;125 (15.6%))\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultiple Cox regression models demonstrating the hazard ratio for 30-days mortality.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBootstrap HR\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCD\u0026thinsp;\u0026ge;\u0026thinsp;30 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.0 (reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSCD\u0026thinsp;\u0026lt;\u0026thinsp;30 mmol/L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.498 (1.260\u0026ndash;1.780)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.432 (1.182\u0026ndash;1.734)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.445 (1.188\u0026ndash;1.770)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ea\u003c/sup\u003e non-adjusted model\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eb\u003c/sup\u003eadjusted for age (HR 1.015; 95%CI: 1.010\u0026ndash;1.020, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and SOFA score (HR 1.158; 95%CI: 1.134\u0026ndash;1.183, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003ec\u003c/sup\u003e bootstrap-validated; adjusted for age and SOFA score\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eAbbreviations: CI, confidence interval; SCD, sodium chloride concentration difference; SOFA score, sequential organ failure assessment score; HR, hazard ratio.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the prognostic value of the serum sodium-chloride difference (SCD) in a population of critically ill patients admitted to the intensive care unit (ICU). Our findings demonstrated a non-linear relationship between SCD values and 30-day mortality, with an elevated risk of death observed in patients whose SCD was below 30.0 mmol/L, even after adjusting for potential confounders. These results support the potential utility of SCD as an early, accessible prognostic indicator in ICU settings and provide a pathophysiological rationale for considering sodium–chloride dynamics in critically ill individuals.\u003c/p\u003e\u003cp\u003eAlthough ICU patients represent a highly diverse population in terms of diagnoses and disease severity, they share a common need for rapid risk assessment upon admission to guide early management decisions. While established prognostic scoring systems such as APACHE II or SOFA are valuable, they often require the integration of numerous variables, making them less practical for immediate use in emergency contexts. Consequently, there is a pressing need for simple and quickly obtainable biomarkers to support triage and therapeutic planning in this high-risk group [18.19].\u003c/p\u003e\u003cp\u003eTo date, prognostic research in critical care has predominantly focused on dysnatremia, with less attention paid to chloride abnormalities or the functional interplay between these electrolytes. Despite chloride being the primary anion in extracellular fluid, its clinical implications remain underappreciated. However, many of these investigations fail to provide a mechanistic explanation for such associations, and few have examined the combined significance of sodium and chloride concentrations [\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eChloride represents the most abundant anion in the extracellular compartment and has been implicated as a prognostic indicator in various clinical contexts, including sepsis and postoperative states following non-cardiac surgery [20,21]. Moreover, in selected cohorts of critically ill patients, aberrant serum chloride levels have been independently associated with prolonged hospitalization, deterioration of renal function, and increased short-term mortality [10,12,21–24]. Nevertheless, the majority of these investigations have not sufficiently elucidated the underlying pathophysiological mechanisms that might account for these associations. This gap in understanding may, in part, reflect the historically marginal role attributed to chloride in clinical assessment. Its contribution to acid–base disturbances and its interaction with other major electrolytes within the extracellular fluid compartment remain underappreciated in daily critical care practice.\u003c/p\u003e\u003cp\u003eThe Stewart model offers a theoretical framework for understanding the acid–base consequences of shifts in strong ions, positing that the difference between fully dissociated cations and anions—i.e., the strong ion difference (SID)—is a fundamental determinant of blood pH [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In clinical practice, SCD serves as a simplified approximation of SID, based on the assumption that sodium and chloride are the predominant ions in plasma [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our findings suggest that SCD may function not only as a reflection of acid–base status but also as a meaningful prognostic signal in patients admitted to the ICU.\u003c/p\u003e\u003cp\u003eWhile several studies have highlighted the predictive relevance of acid–base parameters, most have relied on traditional measures such as the anion gap or base excess. In contrast, investigations into SCD as a surrogate marker of SID remain relatively limited and have yielded mixed conclusions [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSome reports indicate that reduced SCD is associated with adverse outcomes such as acute kidney injury or prolonged hospitalization, especially in patients with sepsis or COVID-19, though evidence linking SCD to mortality remains inconsistent [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Some reports indicate that reduced SCD is associated with adverse outcomes such as acute kidney injury or prolonged hospitalization, especially in patients with sepsis or COVID-19, though evidence linking SCD to mortality remains inconsistent [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. For example, while Cortés-Román et al. identified an SCD \u0026lt; 31 mmol/L as a predictor of 30-day mortality in septic shock, other studies failed to replicate this association in broader ICU cohorts [28].\u003c/p\u003e\u003cp\u003eFurthermore, recent analyses suggest that isolated evaluation of strong ion gap (SIG) or individual electrolyte disturbances may have limited prognostic value compared to integrated assessments like SCD [29]. Notably, hyperchloremia at ICU admission continues to emerge as a robust predictor of mortality, highlighting the need to reconsider how electrolyte data is interpreted in critical care [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Drawing unambiguous conclusions about the prognostic utility of SCD, SID, or chloride levels alone in mortality prediction remains challenging based on current literature. Therefore, in daily clinical practice, our understanding of the prognostic significance of SCD and SID still remains insufficient and our results, at least partially, fill this gap.\u003c/p\u003e\u003cp\u003eFrom a clinical standpoint, our results advocate for a more nuanced approach to electrolyte interpretation—one that considers the combined influence of sodium and chloride on acid–base homeostasis. A low SCD may reflect either a relative excess of chloride or a depletion of sodium and may be associated with hidden acidosis or impaired tissue perfusion. Recognizing such imbalances early can inform therapeutic strategies, including fluid selection, diuretic management, and circulatory support. In this context, interventions aimed at restoring physiological SID—such as avoiding high-chloride fluids or correcting hypo/hypernatremia—may improve outcomes.\u003c/p\u003e\u003cp\u003eAlthough some clinicians recognize that dysnatremia and dyschloremia do not occur in isolation, clinical practice often assumes that changes in sodium concentration will be accompanied by corresponding changes in serum chloride levels (or vice versa). However, according to Stewart’s theory and our findings, a low SCD—resulting from an uncompensated excessive increase in chloride anion concentration and/or an excessive decrease in sodium cation concentration—holds greater prognostic significance. Therefore, instead of focusing solely on correcting individual electrolyte imbalances (primarily dysnatremia), the assessment of critically ill patients should take into account both dysnatremia and dyschloremia, as their interaction may reflect the SCD level. When a low SCD is detected, prompt action should be taken to restore the balance. Having in mind that SCD corresponds to SID, we can consider that the above parameters also reflect acid-base imbalance and in this group of patients, confirmation of low SCD requires not only correction of electrolyte imbalances but also implementing interventions to optimize tissue perfusion, such as the use of catecholamines, diuretics, fluid supplementation, oxygen therapy, and hemoglobin level correction.\u003c/p\u003e\u003cp\u003eFinally, our study underscores the potential of SCD to enhance awareness among ICU clinicians regarding fluid therapy. Particular caution should be exercised when administering solutions with a high chloride content (e.g., normal saline) to patients already exhibiting low SCD, as this may exacerbate acid–base disturbances and worsen prognosis.\u003c/p\u003e"},{"header":"Limitations","content":"\u003cp\u003eThis study has several important limitations. It was conducted retrospectively at a single tertiary care center, which introduces potential risks of selection and misclassification biases. These inherent limitations of retrospective design may affect the generalizability of our findings. Another limitation is the unequal number of sodium and chloride measurements among patients, particularly those who died within the first 24 hours of ICU admission. While this may affect the precision of SCD estimation in this subgroup, we deliberately included these patients to avoid survivorship bias and to ensure the representativeness of high-risk individuals in our analysis. An additional potential limitation of our study is the lack of external validation, which restricts the generalizability of our findings to other ICU populations or clinical settings. However, this limitation is partially mitigated by the internal validation we performed using bootstrap resampling, which demonstrated consistent effect estimates and supports the robustness of our primary results. Furthermore, unmeasured confounding variables may have influenced the relationship between serum sodium–chloride difference (SCD) and 30-day mortality. Although sodium and chloride are the predominant strong ions in both intra- and extracellular compartments—as outlined in Stewart’s physicochemical model—other less abundant anions, which were not evaluated in our dataset, could potentially contribute to acid–base disturbances. Another limitation is the lack of longitudinal assessment of SCD. Changes in this parameter over time might offer valuable insights into patient trajectory and prognosis, but such dynamics were not captured in this analysis. Additionally, we did not assess the impact of medications, such as diuretics, which are known to alter sodium and chloride concentrations and might influence SCD-related outcomes. Finally, while Stewart’s model also accounts for factors like partial pressure of carbon dioxide and concentrations of nonvolatile weak acids (e.g., albumin), we were unable to analyze these parameters due to incomplete data availability. Therefore, we did not explore the potential associations between SCD and other indicators of acid–base balance, such as base excess or anion gap—areas that merit future investigation. Further research is needed to determine whether targeted correction of low SCD could meaningfully influence acid–base homeostasis and clinical outcomes in critically ill patients.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn critically ill patients admitted to the ICU, a reduced sodium\u0026ndash;chloride difference at admission is independently associated with higher 30-day mortality. This simple, routinely available parameter may support early risk stratification in the intensive care setting and our findings may provide a reasonable pathophysiological explanation for the prognostic significance of sodium-chloride interplay in critically ill patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAnion gap\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAPACHE II\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAcute Physiology and Chronic Health Evaluation II\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBase excess\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eICU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eIntensive care unit\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eInterquartile range\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePOCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePoint\u0026ndash;of\u0026ndash;care testing\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSCD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSodium\u0026ndash;chloride difference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandard deviation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSID\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrong ion difference\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSIG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrong ion gap\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSOFA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eSequential Organ Failure Assessment\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSTROBE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStrengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRŚ, JK, JD, PK, WS, TL, AKw, RD, TD, and MT\u003c/strong\u003econtributed to\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003econceptualization, methodology, investigation, and supervision\u003c/strong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEG, JJ, MZ, PM, PG, JP, JC, ET, AKo, AH, EI, AW, KM, ASi, GP, AWł, BM, and AG\u003c/strong\u003ewere involved\u003c/p\u003e\n\u003cp\u003ein \u003cstrong\u003einvestigation, data curation, literature review, and visualization\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRŚ, TD, and MT\u003c/strong\u003eperformed\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eformal analysis, validation, writing – original draft, and visualization\u003c/strong\u003e.\u003cbr\u003e \u003cstrong\u003eMT\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003econtributed to\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eproject administration, resources, writing – review \u0026amp; editing, and funding acquisition\u003c/strong\u003e.\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was obtained for the conduct of this study, its authorship, or publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe underlying data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to appropriate institutional permissions and ethical oversight.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Jagiellonian University Bioethics Committee (approval no. 118.0043.1.160.2024) and was conducted in accordance with the principles outlined in the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have any financial or personal relationships that could have inappropriately influenced the work reported in this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGunnerson KJ, Kellum JA. Acid-base and electrolyte analysis in critically ill patients: are we ready for the new millennium? Curr Opin Crit Care. 2003;9(6):468\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYunos NM, Bellomo R, Story D, Kellum J. Bench-to-bedside review: Chloride in critical illness. Crit Care. 2010;14(4):226.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarunaka Y. Physiological roles of chloride ions in bodily and cellular functions. J Physiol Sci. 2023;73(1):31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBerend K, van Hulsteijn LH, Gans RO. Chloride: the queen of electrolytes? Eur J Intern Med. 2012;23(3):203\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBie P. Mechanisms of sodium balance: total body sodium, surrogate variables, and renal sodium excretion. 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PLoS ONE. 2013;8(2):e56635.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eN\u0026uacute;\u0026ntilde;ez-Mart\u0026iacute;nez FJ, Luna-Montalb\u0026aacute;n R, Orozco-Ju\u0026aacute;rez K, Ch\u0026aacute;vez-L\u0026aacute;rraga AJ, Velasco-Santos JI, Verazaluce-Rodr\u0026iacute;guez BE, Lan KM, Williams NS, Harahsheh TA, Chapman Y, Dobb AR, Magder GJ. Sodium-chloride difference as a prognostic predictor in adult patients diagnosed with COVID-19. \u003cem\u003eRev Med Inst Mex Seguro Soc.\u003c/em\u003e 2022;60(4):440-6. S\u0026aacute;nchezD\u0026iacute;az JS, PenicheMoguel KG, Mart\u0026iacute;nezRodr\u0026iacute;guez EA, RiveraSol\u0026iacute;s G, Del CarpioOrantes L, P\u0026eacute;rezNieto OR, Zamarr\u0026oacute;nL\u0026oacute;pez EI, MonaresZepeda E. Acidosis metabolica: de principio a fin. \u003cem\u003eMed Int Mex.\u003c/em\u003e 2022;38(5):1050-62. Ho KM, Lan, NS, Williams, TA, Harahsheh, Y, Chapman, AR, Dobb, GJ, Magder S. A comparison of prognostic significance of strong ion gap (SIG) with other acid-base markers in the critically ill: a cohort study. \u003cem\u003eJ Intensive Care.\u003c/em\u003e 2016;4:43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVan Regenmortel N, Verbrugghe W, Van den Wyngaert T, Jorens PG. Impact of chloride and strong ion difference on ICU and hospital mortality in a mixed intensive care population. Ann Intensive Care. 2016;6(1):91.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Z, Xing C, Li T, Du L, Wang N. Hypochloremia is associated with increased risk of all-cause mortality in patients in the coronary care unit: a cohort study. J Int Med Res. 2020;48(4):300060520911500.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sodium, chloride, strong ion difference, Stewart’s theory, sodium-chloride difference","lastPublishedDoi":"10.21203/rs.3.rs-7151264/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7151264/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eThe difference between serum sodium and chloride concentrations (SCD), may reflect the strong ion difference (SID) and has emerged as a potential marker of acid–base status. We investigated whether SCD assessed upon ICU admission holds predictive value for short-term outcomes in critically ill adults.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe retrospectively analyzed 1,726 consecutive patients admitted to a mixed (medical-surgical) ICU (median age 67 years; 70.7% male). SCD values on admission were studied in relation to 30-day mortality using Cox proportional hazards models and restricted cubic spline regression to explore non-linear trends. Internal validation was performed using bootstrap resampling with 1,000 iterations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eSCD levels were significantly lower among non-survivors compared to survivors (median [IQR]: 31.5 [28.0–34.0] vs. 33.0 [31.0–36.0] mmol/L, p = 0.002). The mortality risk curve revealed a non-linear relationship with SCD. Despite no difference in SOFA scores on admission between groups stratified by SCD (\u0026lt;30 vs. ≥30 mmol/L), lower SCD was associated with increased risk of death (unadjusted HR 1.50, 95% CI 1.26–1.78; p \u0026lt; 0.001; and adjusted for age and SOFA score HR 1.43, 95% CI 1.18–1.73; p \u0026lt; 0.001). Bootstrap resampling confirmed the robustness of this finding (HR 1.45, 95% CI 1.19–1.77; p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eLow SCD on admission may indicate elevated short-term mortality risk in ICU patients, independent of conventional severity scores. Our findings may provide a reasonable pathophysiological explanation for the prognostic significance of sodium-chloride interplay in critically ill patients.\u003c/p\u003e","manuscriptTitle":"Association between Sodium–Chloride Difference at ICU Admission and 30-Day Mortality: A Retrospective Cohort Study of Critically Ill Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 08:21:23","doi":"10.21203/rs.3.rs-7151264/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-18T08:30:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T17:47:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T06:17:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-09T03:46:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-07T22:28:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T13:56:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107436083610935735376934533355138252378","date":"2025-07-30T13:09:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"181142194859248290294540399753322831591","date":"2025-07-28T13:51:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264769836321410513545957399259438025105","date":"2025-07-28T13:30:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36420681617115362775580997694314747755","date":"2025-07-28T04:42:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36011244607946581189666815366515354616","date":"2025-07-26T21:58:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-25T00:26:31+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-24T10:48:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-24T10:07:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-24T10:07:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Anesthesiology","date":"2025-07-17T17:05:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-anesthesiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bane","sideBox":"Learn more about [BMC Anesthesiology](http://bmcanesthesiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bane","title":"BMC Anesthesiology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8dc29159-50c5-459a-90be-ca77d2ab1482","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:07:34+00:00","versionOfRecord":{"articleIdentity":"rs-7151264","link":"https://doi.org/10.1186/s12871-025-03438-8","journal":{"identity":"bmc-anesthesiology","isVorOnly":false,"title":"BMC Anesthesiology"},"publishedOn":"2025-11-11 15:57:54","publishedOnDateReadable":"November 11th, 2025"},"versionCreatedAt":"2025-07-29 08:21:23","video":"","vorDoi":"10.1186/s12871-025-03438-8","vorDoiUrl":"https://doi.org/10.1186/s12871-025-03438-8","workflowStages":[]},"version":"v1","identity":"rs-7151264","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7151264","identity":"rs-7151264","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}