Abstract
Background
Standard base excess (SBE) is a pivotal indicator of metabolic acid–base status and has been associated with adverse outcomes in critically ill patients. Nevertheless, the evidence regarding its association with mortality risk in sepsis‐associated acute kidney injury (SA‐AKI) remains limited.
Methods
The present study utilized the MIMIC‐IV database and comprised 16,842 adult patients with SA‐AKI. Patients were stratified into quartiles based on SBE levels. The association between SBE and 28‐day and 180‐day all‐cause mortality was evaluated using Cox proportional hazards models. Restricted cubic spline analysis was performed to assess potential nonlinear relationships. Multivariable adjustments and subgroup analyses were conducted to ensure the robustness of the findings.
Results
Multivariate Cox regression analysis demonstrated that patients in the higher SBE quartiles had significantly lower risks of 28‐day and 180‐day mortality compared to those in the lowest quartile. Kaplan–Meier survival curves and restricted cubic spline analysis further confirmed a significant association between SBE levels and mortality risk, with evidence of a nonlinear relationship. Subgroup analyses showed different interaction effects between the primary and secondary outcomes.
Conclusions
SBE was associated with mortality risk in patients with SA‐AKI, with lower levels of SBE being associated with worse outcomes. As a routine blood gas parameter, SBE may serve as a useful indicator for risk stratification and prognostic assessment in this population.
Keywords: MIMIC-IV database, mortality, sepsis-associated acute kidney injury, standard base excess
1. Introduction
Sepsis‐associated acute kidney injury (SA‐AKI) is among the most prevalent and challenging complications in critical care [1]. As delineated by the 28th Acute Disease Quality Initiative (ADQI), SA‐AKI is defined as acute kidney injury (AKI) occurring in the context of sepsis, driven by a complex interplay of systemic inflammation, renal microcirculatory dysfunction, oxidative stress, and immune dysregulation [2]. Mortality rates in patients with SA‐AKI are significantly higher than in those without SA‐AKI, with in‐hospital mortality ranging from 11% to 77% [2, 3]. The long‐term prognosis is also poor, with a significant proportion of patients at risk of progressing to chronic kidney disease (CKD) or end‐stage renal disease (ESKD). This has a considerable impact on the quality of life and places a substantial burden on healthcare systems and society [4, 5].
In this context, the effective assessment of the prognostic risk in patients with SA‐AKI has become a critical focus in clinical practice. Base excess (BE), a frequently employed marker of metabolic acid–base disturbances, is widely utilized to reflect the severity of nonrespiratory acidosis or alkalosis [6]. However, the reliability and representativeness of BE measurements are potentially limited by multiple physiological variables, including concentrations of plasma proteins and hemoglobin. Conversely, the standard base excess (SBE), as determined by the Van Slyke formula, is advocated as the optimal clinical metric, exhibiting superior in vivo precision and reliability [7]. A number of earlier studies have demonstrated that BE, as an integrated indicator of acid–base balance, is closely associated with mortality, organ dysfunction, and clinical outcomes across a range of critical illnesses. These findings underscore its potential as a prognostic marker [8–10].
However, there is a notable absence of systematic research on the clinical relevance of SBE in the SA‐AKI population. It is evident that SA‐AKI is frequently accompanied by profound acid–base disturbances and multiorgan dysfunction. In such cases, SBE can serve as a straightforward metabolic marker, aiding in the evaluation of disease severity and mortality risk. The objective of this study is to examine the relationship between SBE levels and both short‐term and long‐term all‐cause mortality in patients with SA‐AKI. A large‐scale clinical database will be utilized to establish novel reference criteria for prognostic assessment in this high‐risk population.
2. Methods
2.1. Study Design and Data Source
The present retrospective cohort study, based on the Medical Information Mart for Intensive Care IV (MIMIC‐IV, version 3.1) database, aims to examine the association between SBE and both short‐term (28‐day) and long‐term (180‐day) all‐cause mortality in patients with SA‐AKI. The MIMIC‐IV database, developed collaboratively by the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center, contains comprehensive clinical data on over 90,000 adult intensive care unit (ICU) admissions between 2008 and 2022, including demographics, vital signs, laboratory results, medication records, diagnostic codes, and mortality outcomes. MIMIC‐IV is a widely used open‐access clinical big data resource, which offers a robust foundation for observational research [11, 12]. The present study is conducted in strict accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines [13] and adheres to the ethical standards set forth in the Declaration of Helsinki. As all patient data in the MIMIC‐IV database are deidentified and appropriate data usage permissions have been obtained, additional ethical approval was not required.
2.2. Study Population
The present study comprised patients admitted to the ICU between 2008 and 2022 from the MIMIC‐IV database. The inclusion criteria comprised the following: (1) was over 18 years of age; (2) had been diagnosed with SA‐AKI according to the consensus criteria of the 28th ADQI [2]; and (3) only the first ICU admission was considered to prevent duplicate counting. SA‐AKI was defined as AKI occurring in the context of confirmed sepsis, with sepsis identified per Sepsis‐3 criteria as infection accompanied by organ dysfunction (Sequential Organ Failure Assessment [SOFA] score increase ≥ 2) [14] and AKI defined according to Kidney Disease: Improving Global Outcomes (KDIGO) guidelines [15].
In order to enhance the quality of the data and the analytical reliability, patients were excluded for the following reasons: Firstly, patients who had an ICU stay shorter than 24 h were excluded, thus minimizing the impact of brief admissions on outcomes. Secondly, patients who lacked key laboratory data, including arterial blood bicarbonate and pH values, necessary for calculating SBE, were also excluded (Supporting Figure 1).
2.3. Data Extraction
The present study extracted the necessary clinical data from the MIMIC‐IV (version 3.1) database using Structured Query Language (SQL). The extracted variables encompassed demographic characteristics, illness severity scores, vital signs, laboratory results, comorbidities, and clinical outcomes. For variables that exhibited multiple measurements (e.g., vital signs and laboratory tests), the initial value recorded after ICU admission was utilized to minimize bias and standardize the assessment timeframe [16].
2.4. SBE
SBE is a widely utilized arterial blood gas parameter that is employed for the assessment of metabolic acid–base status. This is achieved by quantifying the excess or deficit of buffering bases in the blood, thereby indicating the presence and severity of metabolic acidosis or alkalosis [17]. The SBE was calculated using the following formula: SBE (mmol/L) = HCO3 - (mmol/L) – 24.8 + 16.2 × (pH‐7.4) [7]. The pH and HCO3 - values were extracted from arterial blood gas measurements in the MIMIC‐IV database, and SBE was subsequently calculated. The earliest available measurements within 24 h after ICU admission were used to represent the initial SBE level. Patients were stratified into quartiles (Q1–Q4), and the associations between SBE levels and short‐term and long‐term all‐cause mortality were assessed.
2.5. Outcomes and Follow‐Up
The primary outcome of this study was a 28‐day all‐cause mortality, with a 180‐day all‐cause mortality serving as the secondary outcome. Mortality data were obtained from the date of death (DOD) recorded in the MIMIC‐IV database. The temporal parameters of follow‐up time and survival status were calculated from the initial ICU admission time (icu_intime). The definition of survival duration was as the interval between DOD and icu_intime in days. For patients without a recorded death, the follow‐up process was concluded at the last documented discharge date or the final postdischarge follow‐up available in the database.
2.6. Statistical Analysis
Statistical analyses were conducted using R (version 4.2.2), with significance defined as p < 0.05.
The baseline characteristics were summarized in a descriptive manner. Continuous variables were presented as the mean ± standard deviation (SD) or as the median with interquartile range (M [Q1, Q3]) based on the distribution, while categorical variables were reported as frequencies and percentages. Between‐group comparisons employed one‐way ANOVA for normally distributed continuous variables, the Kruskal–Wallis H test for non‐normal continuous variables, and the chi‐squared test (χ 2) for categorical variables. Survival analyses were conducted utilizing multivariate Cox proportional hazards models to evaluate the association between SBE levels and 28‐day and 180‐day all‐cause mortality. Models were adjusted for prognostically relevant confounders, including demographics, comorbidities, illness severity scores, and laboratory parameters (Supporting Table 1). SBE was analyzed both as a categorical variable by quartiles and as a continuous variable using restricted cubic spline (RCS) regression to assess potential nonlinear relationships with mortality risk.
Furthermore, Kaplan–Meier (KM) survival curves were generated for each SBE quartile group, with log‐rank tests applied to compare survival differences across groups, thereby visually elucidating the relationship between SBE levels and patient outcomes. All findings were expressed as hazard ratios (HRs) with corresponding 95% confidence intervals (95% CIs).
2.7. Subgroup Analyses
In order to further evaluate the stability and consistency of the association between SBE and all‐cause mortality across diverse populations, exploratory subgroup analyses were performed. The predefined subgroups encompassed four clinical variables: age (≤ 67 vs. > 67 years), gender (male vs. female), race (white vs. nonwhite), and SOFA score (≤ 10 vs. > 10). The age cutoffs were determined based on the median age of the cohort, while the SOFA threshold of 10 was identified as the optimal discriminator using the “survminer” package in R.
Subsequently, separate multivariate Cox proportional hazards models were constructed for each subgroup in order to assess the relationship between SBE quartiles and 28‐day and 180‐day mortality. HRs with 95% CIs were reported for each subgroup. Interaction terms were incorporated into the study to ascertain statistically significant effect modifications, thereby identifying populations in which the impact of SBE on mortality may vary.
2.8. Missing Value
The missing data rate for variables included in the final analyses was below 10% (Supporting Figure 2). Variables with substantial missingness, including neutrophils (16.38%), monocytes (16.65%), lymphocytes (16.95%), lactate (30.91%), and height (34.47%), were excluded. In order to mitigate the effects of missing data while maintaining the original sample size, multiple imputation was employed. Specifically, the random forest method within the R “mice” package was utilized to impute missing covariate values, with imputation restricted to predictor variables; outcome variables were not imputed. All statistical analyses were performed on the fully imputed dataset in order to ensure the validity and robustness of the results.
3. Results
3.1. Baseline Information of Patients With SA‐AKI
The present study comprised 16,842 patients who were admitted to the ICU for the first time and met the diagnostic criteria for SA‐AKI. Patients were stratified into quartiles based on SBE levels. Table 1 provides a synopsis of the demographic characteristics, disease severity scores, physiological parameters, and treatment‐related factors across the SBE groups. The mean age of the cohort was 67.55 ± 15.27 years, with females accounting for 41.15% of the population. A significant upward trend in age was observed with increasing SBE quartiles (H = 79.847, p < 0.001). Statistically significant differences were also noted in gender, race, and year of admission across quartiles. A notable variation was observed in the comorbidity burden and illness severity scores according to the severity of SBE levels. With regard to therapeutic interventions, patients in the lower SBE quartiles exhibited significantly higher rates of mechanical ventilation, renal replacement therapy (RRT), vasopressor use, septic shock, and CKD (all p < 0.001).
TABLE 1.
Descriptive statistics of the study population.
| Variables | Total (n = 16,842) | Quartile 1 (n = 4183) | Quartile 2 (n = 4163) | Quartile 3 (n = 4219) | Quartile 4 (n = 4277) | Statistics | p value |
|---|---|---|---|---|---|---|---|
| Age, mean ± SD | 67.55 ± 15.27 | 65.90 ± 16.60 | 67.46 ± 15.63 | 67.41 ± 14.50 | 69.41 ± 14.06 | H = 79.847 | < 0.001 |
| Sex, n(%) | χ2 = 96.506 | < 0.001 | |||||
| Male | 9912 (58.85) | 2248 (53.74) | 2495 (59.93) | 2702 (64.04) | 2467 (57.68) | ||
| Female | 6930 (41.15) | 1935 (46.26) | 1668 (40.07) | 1517 (35.96) | 1810 (42.32) | ||
| Race, n(%) | χ2 = 172.375 | < 0.001 | |||||
| Asian | 440 (2.61) | 127 (3.04) | 118 (2.83) | 105 (2.49) | 90 (2.1) | ||
| Black | 1699 (10.09) | 486 (11.62) | 400 (9.61) | 350 (8.3) | 463 (10.83) | ||
| Hispanic/Latino | 565 (3.35) | 163 (3.9) | 127 (3.05) | 139 (3.29) | 136 (3.18) | ||
| Other | 2901 (17.22) | 922 (22.04) | 706 (16.96) | 672 (15.93) | 601 (14.05) | ||
| White | 11,237 (66.72) | 2485 (59.41) | 2812 (67.55) | 2953 (69.99) | 2987 (69.84) | ||
| Admission year, n(%) | χ2 = 646.595 | < 0.001 | |||||
| 2008–2010 | 5624 (33.39) | 1089 (26.03) | 1300 (31.23) | 1400 (33.18) | 1835 (42.9) | ||
| 2011–2013 | 3639 (21.61) | 790 (18.89) | 860 (20.66) | 990 (23.47) | 999 (23.36) | ||
| 2014–2016 | 3523 (20.92) | 821 (19.63) | 959 (23.04) | 972 (23.04) | 771 (18.03) | ||
| 2017–2019 | 2502 (14.86) | 865 (20.68) | 656 (15.76) | 544 (12.89) | 437 (10.22) | ||
| 2020–2022 | 1554 (9.23) | 618 (14.77) | 388 (9.32) | 313 (7.42) | 235 (5.49) | ||
| ICU type, n(%) | χ2 = 950.090 | < 0.001 | |||||
| CCU | 1704 (10.12) | 502 (12) | 408 (9.8) | 363 (8.6) | 431 (10.08) | ||
| CVICU | 3941 (23.4) | 330 (7.89) | 1183 (28.42) | 1399 (33.16) | 1029 (24.06) | ||
| MICU | 4379 (26) | 1493 (35.69) | 917 (22.03) | 865 (20.5) | 1104 (25.81) | ||
| MICU/SICU | 2755 (16.36) | 781 (18.67) | 617 (14.82) | 592 (14.03) | 765 (17.89) | ||
| Other ICU | 4063 (24.12) | 1077 (25.75) | 1038 (24.93) | 1000 (23.7) | 948 (22.17) | ||
| Weight, kg, Mean ± SD | 83.43 ± 22.60 | 81.16 ± 21.99 | 82.77 ± 21.60 | 84.67 ± 22.18 | 85.06 ± 24.29 | H = 83.715 | < 0.001 |
| CCI, Mean ± SD | 5.55 ± 2.97 | 5.83 ± 3.14 | 5.46 ± 3.03 | 5.31 ± 2.94 | 5.61 ± 2.74 | H = 79.279 | < 0.001 |
| SOFA, Mean ± SD | 6.55 ± 3.52 | 8.08 ± 3.95 | 6.40 ± 3.34 | 5.91 ± 3.20 | 5.84 ± 3.08 | H = 945.893 | < 0.001 |
| APSIII, Mean ± SD | 52.64 ± 22.61 | 63.62 ± 24.64 | 51.04 ± 21.70 | 47.34 ± 20.66 | 48.69 ± 19.39 | H = 1323.889 | < 0.001 |
| Temperature, °C, Mean ± SD | 36.87 ± 0.52 | 36.83 ± 0.57 | 36.87 ± 0.53 | 36.89 ± 0.50 | 36.88 ± 0.50 | H = 27.498 | < 0.001 |
| Heart rate, bpm, Mean ± SD | 86.95 ± 16.17 | 89.90 ± 17.75 | 86.56 ± 15.96 | 85.65 ± 15.08 | 85.73 ± 15.41 | H = 185.444 | < 0.001 |
| Resp rate, bpm, Mean ± SD | 19.71 ± 4.10 | 20.66 ± 4.55 | 19.52 ± 4.00 | 19.25 ± 3.86 | 19.42 ± 3.81 | H = 266.225 | < 0.001 |
| MBP, mmHg, Mean ± SD | 76.10 ± 9.48 | 75.98 ± 10.09 | 75.91 ± 9.11 | 76.39 ± 9.17 | 76.13 ± 9.51 | H = 9.501 | 0.023 |
| SpO2, Mean ± SD | 97.11 ± 2.00 | 97.05 ± 2.11 | 97.26 ± 1.89 | 97.21 ± 1.90 | 96.91 ± 2.08 | H = 64.387 | < 0.001 |
| Glucose, mg/dL, M(Q1, Q3) | 129.00 (105.00–170.00) | 142.00 (109.00–211.50) | 129.00 (107.00–166.00) | 125.00 (104.00–160.00) | 123.00 (102.00–155.00) | H = 324.008 | < 0.001 |
| Sodium, mEq/L, Mean ± SD | 137.95 ± 5.62 | 136.73 ± 6.72 | 137.95 ± 5.37 | 138.21 ± 4.80 | 138.88 ± 5.21 | H = 353.974 | < 0.001 |
| Potassium, mEq/L, Mean ± SD | 4.40 ± 0.90 | 4.68 ± 1.13 | 4.41 ± 0.85 | 4.29 ± 0.74 | 4.23 ± 0.75 | H = 466.448 | < 0.001 |
| Septic shock, n(%) | χ2 = 643.434 | < 0.001 | |||||
| No | 13,466 (79.95) | 2796 (66.84) | 3366 (80.86) | 3611 (85.59) | 3693 (86.35) | ||
| Yes | 3376 (20.05) | 1387 (33.16) | 797 (19.14) | 608 (14.41) | 584 (13.65) | ||
| CKD, n(%) | χ2 = 181.748 | < 0.001 | |||||
| No | 12,315 (73.12) | 2739 (65.48) | 3075 (73.87) | 3280 (77.74) | 3221 (75.31) | ||
| Yes | 4527 (26.88) | 1444 (34.52) | 1088 (26.13) | 939 (22.26) | 1056 (24.69) | ||
| MV, n(%) | χ2 = 78.048 | < 0.001 | |||||
| No | 7802 (46.32) | 2183 (52.19) | 1851 (44.46) | 1847 (43.78) | 1921 (44.91) | ||
| Yes | 9040 (53.68) | 2000 (47.81) | 2312 (55.54) | 2372 (56.22) | 2356 (55.09) | ||
| RRT, n(%) | χ2 = 234.909 | < 0.001 | |||||
| No | 16,025 (95.15) | 3797 (90.77) | 4005 (96.2) | 4097 (97.11) | 4126 (96.47) | ||
| Yes | 817 (4.85) | 386 (9.23) | 158 (3.8) | 122 (2.89) | 151 (3.53) | ||
| Vasopressor, n(%) | χ2 = 68.782 | < 0.001 | |||||
| No | 8625 (51.21) | 2041 (48.79) | 2038 (48.96) | 2126 (50.39) | 2420 (56.58) | ||
| Yes | 8217 (48.79) | 2142 (51.21) | 2125 (51.04) | 2093 (49.61) | 1857 (43.42) | ||
| 28‐day mortality, n(%) | χ2 = 471.730 | < 0.001 | |||||
| No | 12,959 (76.94) | 2717 (64.95) | 3285 (78.91) | 3505 (83.08) | 3452 (80.71) | ||
| Yes | 3883 (23.06) | 1466 (35.05) | 878 (21.09) | 714 (16.92) | 825 (19.29) | ||
| 180‐day mortality, n(%) | χ2 = 391.866 | < 0.001 | |||||
| No | 11,291 (67.04) | 2298 (54.94) | 2932 (70.43) | 3111 (73.74) | 2950 (68.97) | ||
| Yes | 5551 (32.96) | 1885 (45.06) | 1231 (29.57) | 1108 (26.26) | 1327 (31.03) |
Note: Participants were categorized into quartiles based on serum SBE levels as follows: Quartile 1 (SBE ≤ −5.42; range: −29.7 to −5.42), Quartile 2 (−5.42 < SBE ≤ −1.96), Quartile 3 (−1.96 < SBE ≤ 1.04), Quartile 4 (SBE > 1.04; range: 1.04–25.7). M: Median; Q1: 1st Quartile; Q3: 3rd Quartile; χ2: Chi‐squared test; H: Kruskal–Wallis H test; CVICU: Cardiovascular Intensive Care Unit; SpO2: peripheral capillary oxygen saturation.
Abbreviations: APSIII, Acute Physiology Score III; CCI, Charlson Comorbidity Index; CCU, Coronary Care Unit; CKD, chronic kidney disease; ICU, intensive care unit; MBP, mean blood pressure; MICU, Medical Intensive Care Unit; MV, mechanical ventilation; RRT, renal replacement therapy; SD, standard deviation; SICU, Surgical Intensive Care Unit; SOFA, Sequential Organ Failure Assessment.
3.2. Primary and Secondary Outcomes
Patients in the Q1 group, representing the lowest SBE levels, exhibited a 28‐day mortality rate of 35.05%, which was significantly higher than that of the other groups. The lowest rate was observed in the Q3 group (16.92%). A similar pattern was noted for 180‐day mortality, with Q1 at 45.06% and Q3 at 26.26% (both p < 0.001).
The application of a Cox proportional hazards regression model revealed a statistically significant association between the higher quartiles and a reduced risk of both 28‐day and 180‐day all‐cause mortality, when compared with the lowest SBE quartile (Q1). These findings are outlined in Table 2. Following adjustment for confounding variables, including age, gender, race, Charlson Comorbidity Index (CCI), SOFA score, Acute Physiology Score III (APSIII) score, mechanical ventilation, RRT, vasopressor use, hypertension, diabetes, septic shock, and CKD, the adjusted HRs for 28‐day mortality in Q2, Q3, and Q4 were determined to be 0. The results of the study indicated that the prevalence of the condition increased with age, with an odds ratio of 81 (95% CI: 0.74–0.88), 0.73 (95% CI: 0.66–0.80), and 0.81 (95% CI: 0.74–0.88), respectively. A similar pattern was observed for 180‐day mortality. The findings indicate an independent inverse association between higher SBE levels and reduced short‐ and long‐term mortality risks, with all differences reaching statistical significance.
TABLE 2.
Cox regression modeling results for the primary and secondary outcomes.
| Variable | Unadjusted analysis | Adjusted analysis | ||
|---|---|---|---|---|
| HR (95% CI) | p value | aHR (95% CI) | p value | |
| Primary outcome | ||||
| Quartile 1 | Reference | Reference | ||
| Quartile 2 | 0.53 (0.49, 0.58) | < 0.001 ∗ | 0.81 (0.74, 0.88) | < 0.001 ∗ |
| Quartile 3 | 0.42 (0.38, 0.45) | < 0.001 ∗ | 0.73 (0.66, 0.80) | < 0.001 ∗ |
| Quartile 4 | 0.48 (0.44, 0.52) | < 0.001 ∗ | 0.81 (0.74, 0.88) | < 0.001 ∗ |
| Secondary outcome | ||||
| Quartile 1 | Reference | Reference | ||
| Quartile 2 | 0.56 (0.52, 0.60) | < 0.001 ∗ | 0.81 (0.75, 0.87) | < 0.001 ∗ |
| Quartile 3 | 0.48 (0.45, 0.52) | < 0.001 ∗ | 0.80 (0.74, 0.86) | < 0.001 ∗ |
| Quartile 4 | 0.58 (0.54, 0.62) | < 0.001 ∗ | 0.91 (0.85, 0.98) | 0.018 ∗ |
Note: Participants were categorized into quartiles based on serum SBE levels as follows: Quartile 1 (SBE ≤ −5.42; range: −29.7 to −5.42), Quartile 2 (−5.42 < SBE ≤ −1.96), Quartile 3 (−1.96 < SBE ≤ 1.04), Quartile 4 (SBE > 1.04; range: 1.04–25.7). Cox proportional hazards regression models were used to calculate hazard ratios (HRs) with 95% confidence intervals (CIs). All models were adjusted for age, sex, race, Charlson Comorbidity Index (CCI), Sequential Organ Failure Assessment (SOFA), Acute Physiology Score III (APSIII), mechanical ventilation, renal replacement therapy, vasopressor use, hypertension, diabetes, septic shock, and chronic kidney disease.
Abbreviations: aHRs, adjusted hazard ratios; CIs, confidence intervals; HR, hazard ratio.
∗Significant difference between patients in four groups (p < 0.05).
KM survival curves further demonstrated significant differences in survival across SBE quartile groups, with patients in the Q1 group exhibiting markedly lower survival rates during both the 28‐day and 180‐day follow‐up periods (Figure 1, log‐rank test p < 0.0001). Furthermore, RCS analysis (Figure 2) revealed a U‐shaped nonlinear association between SBE and mortality risk, indicating that increases in SBE within a certain range may offer a protective effect by reducing the risk of death.
FIGURE 1.
Kaplan–Meier survival curves between groups. Abbreviations: SBE: standard base excess; Q1: Quartile 1; Q2: Quartile 2; Q3: Quartile 3; Q4: Quartile 4. Note: (A) Kaplan–Meier survival curves (28 day). (B) Kaplan–Meier survival curves (180 day). The log‐rank test indicates a statistically significant difference in survival across the quartiles (p < 0.0001).
FIGURE 2.
Restricted cubic spline analysis. Note: (A) Restricted cubic spline curves (28 day). (B) Restricted cubic spline curves (180 day). Models were adjusted for age, sex, race, Charlson Comorbidity Index (CCI), sequential organ failure assessment (SOFA), Acute Physiology Score III (APSIII), mechanical ventilation, renal replacement therapy, vasopressor use, hypertension, diabetes, septic shock, and chronic kidney disease.
3.3. Subgroup Analyses
In subgroup analyses, the inverse correlation between SBE and mortality risk remained consistent across most strata (Table 3 and Supporting Table 2). Higher SBE levels were associated with a lower mortality risk. Interaction analyses identified significant interactions with age, sex, and SOFA score for both the primary and secondary outcomes.
TABLE 3.
Subgroup analysis results in primary outcome.
| Subgroups | SBE | HR (95% CI) | p value | p For interaction |
|---|---|---|---|---|
| Age | ||||
| < 67 | Quartile 1 | Reference | ||
| Quartile 2 | 0.80 (0.69–0.92) | 0.002 ∗ | 0.492 | |
| Quartile 3 | 0.69 (0.59–0.81) | < 0.001 ∗ | 0.233 | |
| Quartile 4 | 0.71 (0.61–0.83) | < 0.001 ∗ | 0.012 | |
| ≥ 67 | Quartile 1 | Reference | ||
| Quartile 2 | 0.84 (0.75–0.94) | 0.002 ∗ | 0.492 | |
| Quartile 3 | 0.77 (0.69–0.87) | < 0.001 ∗ | 0.233 | |
| Quartile 4 | 0.89 (0.80–1.00) | 0.043 ∗ | 0.012 | |
| Sex | ||||
| Female | Quartile 1 | Reference | ||
| Quartile 2 | 0.80 (0.70–0.91) | 0.001 ∗ | 0.670 | |
| Quartile 3 | 0.77 (0.66–0.88) | < 0.001 ∗ | 0.109 | |
| Quartile 4 | 0.81 (0.71–0.92) | 0.002 ∗ | 0.380 | |
| Male | Quartile 1 | Reference | ||
| Quartile 2 | 0.81 (0.73–0.91) | < 0.001 ∗ | 0.670 | |
| Quartile 3 | 0.70 (0.62–0.80) | < 0.001 ∗ | 0.109 | |
| Quartile 4 | 0.80 (0.71–0.91) | < 0.001 ∗ | 0.380 | |
| Race | ||||
| White | Quartile 1 | Reference | ||
| Quartile 2 | 0.76 (0.68–0.84) | < 0.001 ∗ | 0.010 | |
| Quartile 3 | 0.75 (0.66–0.84) | < 0.001 ∗ | 0.982 | |
| Quartile 4 | 0.82 (0.74–0.92) | 0.001 ∗ | 0.919 | |
| Others | Quartile 1 | Reference | ||
| Quartile 2 | 0.88 (0.77–1.01) | 0.640 | 0.010 | |
| Quartile 3 | 0.68 (0.58–0.79) | < 0.001 ∗ | 0.982 | |
| Quartile 4 | 0.76 (0.66–0.88) | < 0.001 ∗ | 0.919 | |
| SOFA | ||||
| < 10 | Quartile 1 | Reference | ||
| Quartile 2 | 0.86 (0.77–0.96) | 0.008 ∗ | 0.186 | |
| Quartile 3 | 0.77 (0.69–0.87) | < 0.001 ∗ | 0.241 | |
| Quartile 4 | 0.86 (0.77–0.96) | 0.007 ∗ | 0.220 | |
| ≥ 10 | Quartile 1 | Reference | ||
| Quartile 2 | 0.75 (0.65–0.86) | < 0.001 ∗ | 0.186 | |
| Quartile 3 | 0.66 (0.56–0.77) | < 0.001 ∗ | 0.241 | |
| Quartile 4 | 0.73 (0.62–0.85) | < 0.001 ∗ | 0.220 | |
Note: Participants were categorized into quartiles based on serum SBE levels as follows: Quartile 1 (SBE ≤ −5.42; range: −29.7 to −5.42), Quartile 2 (−5.42 < SBE ≤ −1.96), Quartile 3 (−1.96 < SBE ≤ 1.04), Quartile 4 (SBE > 1.04; range: 1.04–25.7).
Abbreviations: CIs, confidence intervals; HR, hazard ratio; SBE, standard base excess; SOFA, sequential organ failure assessment.
∗Significant difference between patients in four groups (p < 0.05).
Specifically, elevated SBE was associated with a more substantial reduction in mortality among patients younger than 67 years, whereas the protective effect in the Q4 group was diminished in those aged ≥ 67 years (p for interaction = 0.012 for 28‐day mortality; p = 0.011 for 180‐day mortality). In the analysis based on sex, no significant interaction was observed for 28‐day mortality (p for interaction = 0.380), whereas a borderline significant interaction was observed for 180‐day mortality (p for interaction = 0.049). Furthermore, patients with SOFA scores of 10 or more experienced a greater survival benefit from higher SBE levels, with the Q4 group showing a significant reduction in 180‐day mortality (p for interaction = 0.013), whereas no significant interaction was observed for 28‐day mortality (p for interaction = 0.220).
4. Discussion
The present study utilized a large‐scale critical care database to systematically evaluate the association between SBE at ICU admission and both short‐term and long‐term mortality risks in patients with SA‐AKI. The findings indicated a significant correlation between SBE levels and both 28‐day and 180‐day mortality, with the correlation persisting even after multivariable adjustment. Furthermore, the RBC model indicated a nonlinear relationship between SBE and mortality risk, suggesting that the association between SBE levels and mortality was not simply linear across the observed range. This finding highlights the potential prognostic relevance of SBE as a continuous marker of metabolic acid–base disturbances in patients with SA‐AKI.
SBE is a pivotal indicator for evaluating metabolic acid–base status, reflecting the balance of buffering alkali in the blood independent of respiratory influence [17]. A reduced SBE is indicative of metabolic acidosis, which is frequently associated with pathological states such as impaired tissue perfusion, lactic acid accumulation, renal dysfunction, and heightened inflammatory responses in critically ill patients [18, 19]. In patients with SA‐AKI, metabolic acidosis may result from multiple mechanisms, including lactic acidosis caused by tissue hypoperfusion and uremic acidosis due to impaired renal acid excretion and accumulation of unmeasured anions. As a high‐risk population, patients with SA‐AKI frequently exhibit profound metabolic derangements and internal environment disturbances [20]. Furthermore, alkalosis has been demonstrated to disrupt tissue oxygenation and impair ion channel function, thereby compromising cellular metabolism [21]. These findings emphasize the significance of maintaining SBE within an optimal range to preserve homeostasis and support organ function. The study also revealed that the prognostic value of SBE was more pronounced in specific subgroups, particularly among younger patients, those with elevated SOFA scores, and male patients. The possible explanations for this finding include greater metabolic resilience and heightened sensitivity to acid–base disturbances observed in younger individuals and the higher prevalence of metabolic acidosis in patients with severe multiorgan dysfunction, as reflected by elevated SOFA scores. Furthermore, biological sex is recognized as a risk factor for AKI, potentially influenced by hormonal differences between males and females [22]. The subgroup findings emphasize the clinical significance of enhanced dynamic monitoring of SBE in high‐risk populations to facilitate timely intervention and optimize outcomes.
Of particular significance is the fact that, in its capacity as a reversible and modifiable physiological marker, SBE holds a clinical value not only in the context of risk stratification but also in guiding therapeutic interventions. The extant evidence would appear to suggest that timely recognition and correction of metabolic acidosis can enhance organ perfusion, potentially mitigating the progression of AKI and preventing multiorgan dysfunction [23]. In cases of markedly reduced SBE, clinicians should evaluate and address potential causes of metabolic acidosis, such as impaired perfusion and lactate accumulation [19, 24, 25]. Nursing interventions are equally vital, encompassing increased frequency of blood gas monitoring, prompt evaluation of acid–base status, coordination with medical orders for individualized fluid and nutritional management, and continuous monitoring of renal function to facilitate early detection and intervention.
A review of the extant literature reveals a notable gap in research on SBE across diverse patient populations. The majority of extant studies have focused on blood gas parameters, including BE, lactate, and pH, as components of critical care scoring systems [8, 26, 27]. These systems are utilized to evaluate the relationship between acid–base balance and mortality risk. For instance, a correlation has been demonstrated between reductions in BE and an increased mortality rate in surgical critical care, sepsis, and postcardiac surgery settings [28–30]. However, research into the use of SBE as an independent prognostic indicator, particularly in populations with kidney injury, remains limited. This study extends the clinical utility of SBE by quantifying its predictive value for outcomes in SA‐AKI and confirming its nonlinear association with mortality, thereby contributing to the refinement of prognostic assessment in critically ill patients.
Notwithstanding the significant clinical ramifications of this study, it is imperative to acknowledge its several limitations. Firstly, it should be noted that this was a single‐center, retrospective observational study. Despite extensive adjustments being made for potential confounders, the possibility of residual confounding cannot be excluded, and unmeasured variables may still influence the observed association between SBE and patient outcomes. In particular, serum lactate was not included in the multivariable models due to its high missing rate (30.91%), which may have limited the assessment of the independent prognostic value of SBE. Additionally, excluding patients with ICU stays shorter than 24 h may have reduced the representation of early mortality cases with severe metabolic acidosis and potentially attenuated the prognostic association between markedly reduced SBE and outcomes. Secondly, SBE was assessed on only one occasion during the early phase of ICU admission, which limited the ability to capture its dynamic trajectory during the progression of SA‐AKI. Serial measurements of SBE and evaluation of its longitudinal changes may provide additional prognostic insights and should be explored in future studies. Thirdly, as a composite indicator influenced by various pathological processes, SBE is not specific to SA‐AKI and may also be reduced in conditions such as multiorgan dysfunction, lactic acidosis, or other acidotic states. This necessitates further investigation into its specificity. Additionally, although the MIMIC database offers high‐quality, structured data, it originates from a single U.S. institution, and differences in racial composition, healthcare resources, and treatment practices may limit the generalizability of the findings to other settings. It is evident that further multicenter, prospective studies are required in order to validate the applicability and robustness of SBE as a prognostic marker in a variety of clinical environments.
5. Conclusion
In summary, SBE, as a marker of metabolic acid–base balance, independently predicts both short‐ and long‐term mortality in patients with SA‐AKI, with a particularly strong prognostic value in subgroups such as younger and critically ill individuals. This study builds on extant evidence and supports the incorporation of SBE into clinical assessment tools to enhance disease severity evaluation and guide personalized treatment strategies. The incorporation of SBE thus offers valuable insights into the optimized management of critically ill patients.
Nomenclature
- 95% CIs
Confidence intervals
- ADQI
Acute disease quality initiative
- APSIII
Acute Physiology Score III
- BE
Base excess
- CCI
Charlson Comorbidity Index
- CKD
Chronic kidney disease
- DOD
Date of death
- ESKD
End‐stage renal disease
- HRs
Hazard ratios
- ICU
Intensive care unit
- KDIGO
Kidney disease: Improving global outcomes
- MIMIC
Medical Information Mart for Intensive Care IV
- RCS
Restricted cubic spline
- RRT
Renal replacement therapy
- SA‐AKI
Sepsis‐associated acute kidney injury
- SBE
Standard base excess
- SD
Standard deviation
- SOFA
Sequential organ failure assessment
- SQL
Structured query language
- STROBE
Strengthening the Reporting of Observational Studies in Epidemiology
Author Contributions
Mingming Li and Yuxia Li contributed equally to this work.
Mingming Li: conceptualization (lead), data curation (lead), formal analysis (lead), and writing–original draft (equal).
Yuxia Li: conceptualization (lead), data curation (lead), formal analysis (lead), and writing–original draft (equal).
Jingru Lan: data curation (supporting), methodology (equal), and validation (equal).
Mei Feng: data curation (supporting), methodology (equal), and validation (equal).
Jun Lyu: conceptualization (supporting), resources (lead), supervision (lead), and writing–review and editing (equal).
Xuling Li: conceptualization (supporting), methodology (supporting), validation (lead), supervision (equal), and writing–review and editing (lead).
All authors were actively involved in drafting the manuscript and critically revising it for important intellectual content. Jun Lyu and Xuling Li serve as corresponding authors, responsible for communication and ensuring the integrity of the work.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors.
Disclosure
All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.
Ethics Statement
The Medical Information Mart for Intensive Care IV (MIMIC‐IV) database was supported by grants from the National Institute of Biomedical Imaging and Bioengineering (NIBIB) of the National Institutes of Health (NIH) under award numbers R01‐EB001659 (2003–2013) and R01‐EB017205 (2014–2018) and approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). The present study is conducted in strict accordance with the Strengthening the Reporting of Observational Studies in Epidemiology guidelines and adheres to the ethical standards set forth in the Declaration of Helsinki.
Consent
Data extracted from the MIMIC‐IV database do not require individual informed consent because the research data are publicly available, and all patient data are deidentified.
Conflicts of Interest
The authors declare no conflicts of interest.
Supporting Information
Additional supporting information can be found online in the Supporting Information section.
Supporting information
Supporting Information Supporting Table 1: Definition/codes of the covariates. Supporting Table 2: Subgroup analysis results in secondary outcome. Supporting Figure 1: Inclusion and exclusion criteria for this study. Abbreviations: ICU: intensive care unit; MIMIC‐IV: Medical Information Mart for Intensive Care IV. Note: Participants were categorized into quartiles based on serum SBE levels as follows: Quartile 1 (SBE ≤ −5.42; range: −29.7 to −5.42), Quartile 2 (−5.42 < SBE ≤ −1.96), Quartile 3 (−1.96 < SBE ≤ 1.04), Quartile 4 (SBE > 1.04; range: 1.04–25.7). Supporting Figure 2: Proportion of missing data.
Acknowledgments
The authors would like to thank the team of the Laboratory for Computational Physiology from the Massachusetts Institute of Technology for the development and maintenance of the MIMIC‐IV databases.
Declaration of Generative AI and AI-Assisted Technologies in the Writing Process. During the preparation of this manuscript, the authors used DeepL for language polishing to improve readability and grammar. This affected the writing and editing stages of manuscript preparation. After using this tool, the authors reviewed and edited the content thoroughly and take full responsibility for the final version of the manuscript.
Li, Mingming , Li, Yuxia , Lan, Jingru , Feng, Mei , Lyu, Jun , Li, Xuling , Prognostic Value of Standard Base Excess in Sepsis‐Associated Acute Kidney Injury: A Retrospective Cohort Study, International Journal of Nephrology, 2026, 8851195, 11 pages, 2026. 10.1155/ijne/8851195
Academic Editor: Tej K. Mattoo
Contributor Information
Jun Lyu, Email: lyujun2020@jnu.edu.cn.
Xuling Li, Email: lixuling11@jnu.edu.cn.
Tej K. Mattoo, Email: tmattoo@med.wayne.edu
Data Availability Statement
The data were available on the MIMIC‐IV website at https://mimic.physionet.org/. The data in this article can be reasonably applied to the corresponding author.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supporting Information Supporting Table 1: Definition/codes of the covariates. Supporting Table 2: Subgroup analysis results in secondary outcome. Supporting Figure 1: Inclusion and exclusion criteria for this study. Abbreviations: ICU: intensive care unit; MIMIC‐IV: Medical Information Mart for Intensive Care IV. Note: Participants were categorized into quartiles based on serum SBE levels as follows: Quartile 1 (SBE ≤ −5.42; range: −29.7 to −5.42), Quartile 2 (−5.42 < SBE ≤ −1.96), Quartile 3 (−1.96 < SBE ≤ 1.04), Quartile 4 (SBE > 1.04; range: 1.04–25.7). Supporting Figure 2: Proportion of missing data.
Data Availability Statement
The data were available on the MIMIC‐IV website at https://mimic.physionet.org/. The data in this article can be reasonably applied to the corresponding author.