Abstract
Background
Alactic base excess (ABE) is a novel biomarker to evaluate the renal capability of handling acid-base disturbances, which has been found to be associated with adverse prognosis of sepsis and shock patients. This study aimed to evaluate the association between ABE and the risk of in-hospital mortality in patients with acute myocardial infarction (AMI).
Methods
This retrospective cohort study collected AMI patients’ clinical data from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The outcome was in-hospital mortality after intensive care unit (ICU) admission. Univariate and multivariate Cox proportional hazards models were performed to assess the association of ABE with in-hospital mortality in AMI patients, with hazard ratios (HRs) and 95% confidence intervals (CI). To further explore the association, subgroup analyses were performed based on age, AKI, eGFR, sepsis, and AMI subtypes.
Results
Of the total 2779 AMI patients, 502 died in hospital. Negative ABE (HR = 1.26, 95%CI: 1.02–1.56) (neutral ABE as reference) was associated with a higher risk of in-hospital mortality in AMI patients, but not in positive ABE (P = 0.378). Subgroup analyses showed that negative ABE was significantly associated with a higher risk of in-hospital mortality in AMI patients aged>65 years (HR = 1.46, 95%CI: 1.13–1.89), with eGFR<60 (HR = 1.35, 95%CI: 1.05–1.74), with AKI (HR = 1.32, 95%CI: 1.06–1.64), with ST-segment elevation acute myocardial infarction (STEMI) subtype (HR = 1.79, 95%CI: 1.18–2.72), and without sepsis (HR = 1.29, 95%CI: 1.01–1.64).
Conclusion
Negative ABE was significantly associated with in-hospital mortality in patients with AMI.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12872-024-04112-6.
Keywords: Acute myocardial infarction, Alactic base excess, In-hospital mortality, MIMIC database
Introduction
Acute myocardial infarction (AMI), a common cause of admission to the intensive care unit (ICU), is myocardial necrosis caused by acute and persistent coronary ischemia and hypoxia [1], which remains a major public problem threatening human life, causing millions of deaths worldwide every year [2]. Despite advances in coronary revascularization, the mortality from AMI remains high [3]. Exploring early biomarkers of mortality in AMI patients is essential for clinicians to identify high-risk patients to intervene timely.
Acid-base disorders are common in the ICU and seriously affect the prognosis of ICU patients [4]. The anion gap (AG) is an available clinical tool for the differential diagnosis of acid-base disorders [5]. Evidence suggests that AG levels were significantly associated with the risk of all-cause mortality in patients with AMI and can be used as a robust and reliable predictor of AMI mortality during follow-up [6, 7]. Alactic base excess (ABE) is a new marker proposed by Gattinoni et al. in 2019 to quantify the role of renal function in acid-base balance [8]. Unlike AG, ABE contributes to quickly distinguishing between metabolic acidosis caused by the accumulation of lactate and that caused by fixed acids other than lactate [fixed acids are acids (e.g., phosphates and sulfate) that cannot be eliminated through the lungs] [8]. Negative ABE suggests impaired renal function (inability to compensate for strong anions), whereas positive ABE may indicate additional processes leading to metabolic alkalosis (e.g., overuse of diuretics and volume contraction) [8, 9]. The current study shows that negative ABE was associated with a high risk of in-hospital mortality in septic patients with and without renal dysfunction [10]. In addition, lower ABE levels were associated with an increased risk of 28-day all-cause mortality in shock patients [11]. To the best of our knowledge, however, the association between ABE level and the risk of in-hospital mortality in patients with AMI remains unclear.
Herein, we assessed the association of ABE level with the risk of in-hospital mortality in AMI patients admitted to ICU. To further explore the association, subgroup analyses were performed based on age, eGFR, AMI subtypes, AKI, and sepsis subgroups.
Methods
Study design and population
This retrospective cohort study extracted AMI patients’ data from the Medical Information Mart for Intensive Care (MIMIC)-IV (https://mimic.mit.edu/docs/iv/) database. The database is a large, publicly available clinical database that covers comprehensive information for each patient admitted to the ICU at the Tertiary Academic Medical Center in Boston, MA, USA from 2008 to 2019. The MIMIC-IV database was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). The informed consents were waived due to the patient’s de-identification data. The inclusion criteria were: (1) age ≥ 18 years old; (2) patients diagnosed as AMI [12]; (3) patients with ICU stay more than 1 day. The exclusion criteria were: (1) missing data on pH, bicarbonate, and lactate; (2) missing survival information.
Data extraction and definitions
The following variable information was extracted, including age, gender, race, insurance status, sepsis (sepsis was diagnosed according to the sepsis-3 criteria [13]), acute kidney injury (AKI) (AKI was determined according to the criteria proposed by the Kidney Disease Improving Global Outcomes [14]), AMI subtypes [ST-segment elevation acute myocardial infarction (STEMI), non-STEMI and unknown] [12], 24-h urine output, weight, heart rate, systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse oximetry-derived oxygen saturation (SpO2), Glasgow coma scale (GCS), Charlson comorbidity index (CCI), white blood cell (WBC), platelet, anemia, red blood cell distribution width (RDW), estimated glomerular filtration rate (eGFR), international normalized ratio (INR), glucose, magnesium, sodium, potassium, chloride, ventilation, vasopressor, renal replacement therapy (RRT), percutaneous coronary intervention (PCI), coronary artery bypass graft (CABG), and alteplase.
ABE definition
ABE (mmol/L) = standard base excess (mmol/L) + lactate (mmol/L) [8]. ABE levels were divided into three categories according to the sextiles [8]: (1) negative ABE [<-2.81 mmol/L (< the second sextiles)], (2) neutral ABE [≥-2.81 and < 2.644 mmol/L (≥ the second sextiles and < the fifth sextiles)], (3) positive ABE [≥ 2.644 mmol/L (≥ the fifth sextiles)].
Outcome and follow-up
The outcome was in-hospital mortality after ICU admission. The start time of follow-up was the time of first admission to ICU, and the end time was the time of in-hospital death or discharge. The median follow-up time was 6.95 (4.83, 11.73) days.
Statistical analysis
Continuous variables with normal distribution were presented as mean ± standard deviation (SD), and compared between groups using the Student’s t test. Continuous variables with skewed distribution were presented as medians and quartiles [M (Q1, Q3)] and performed using Mann-Whitney U-test. Categorical variables were represented as number and percentage [n (%)], and the Chi-square (χ2) test was used to compare differences between groups.
Missing values were carried out by the random forest interpolation. Sensitivity analyses were performed on the data before and after imputation (Supplement Table S1). All confounding factors were screened via a univariate Cox proportional hazard analysis (Supplement Table S2). Univariate and multivariate Cox proportional hazard models were used to evaluate the association between ABE and in-hospital mortality in patients with AMI, and hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. Model 1 was a crude model. Model 2 was adjusted for age, sepsis, AKI, AMI subtypes, 24-hour urine output, heart rate, SpO2, GCS, CCI, WBC, RDW, eGFR, INR, glucose, magnesium, sodium, chloride, vasopressor, RRT, CABG, and alteplase. A restricted cubic spline (RCS) curve is one of the commonly used methods for assessing nonlinear relationships, which enables correction analysis between continuous exposures and outcomes. The non-linear association between ABE values and in-hospital mortality was evaluated by the RCS curve. Subgroup analyses were performed based on age, AKI, eGFR, AMI subtypes, and sepsis to further explore the associations. All statistical analyses were performed by SAS 9.4 (SAS Institute Inc., Cary, NC, USA). Forest plots were drawn using R version 4.2.2 (Institute for Statistics and Mathematics, Vienna, Austria). The P < 0.05 was regarded as statistically significant.
Results
Patients’ characteristics
According to the inclusion and exclusion criteria, 2,779 AMI patients were included in this study, of which 502 died in hospital. The flowchart of the patient screening was shown in Fig. 1. Patients who died in the hospital were older (71.45 vs. 68.24 years), with a higher proportion of females (40.44% vs. 33.47%), compared with patients in the survival group. There were statistical differences between the two groups with respect to 24 h urine output, heart rate, DBP, SpO2, GCS, CCI, WBC, platelet, RDW, eGFR, INR, glucose, magnesium, sodium, and chloride (P < 0.05 for all). The ventilation, vasopressor, RRT, and CABG accounted for 98.01%, 84.66%, 25.90% and 4.58% of the treatment management of patients with AMI, respectively. In addition, the proportion of patients with STEMI in-hospital mortality group was significantly higher than that in the survival group (26.89 vs. 15.72%, P < 0.001). The median ABE value for all patients was − 0.90 (-3.96, 1.59). Table 1 shows the characteristics of AMI patients in the two groups. Moreover, there was a significant difference in ABE values between patients with and without sepsis regardless of whether ABE was used as a continuous [-2.49 (-5.73, 0.95) vs. -0.74 (-3.71, 1.64); P < 0.001] or categorial variable (P < 0.001) (Supplement Table S3).
Fig. 1.
The flowchart of acute myocardial infarction (AMI) patients screening
Table 1.
Characteristics of patients with acute myocardial infarction (AMI)
| Variables | Total (n = 2779) | Survival (n = 2277) | In-hospital mortality (n = 502) | Statistics | P |
|---|---|---|---|---|---|
| Age, years, Mean ± SD | 68.82 ± 12.54 | 68.24 ± 12.40 | 71.45 ± 12.88 | t=-5.22 | < 0.001 |
| Gender, n (%) | χ2 = 8.824 | 0.003 | |||
| Female | 965 (34.72) | 762 (33.47) | 203 (40.44) | ||
| Male | 1814 (65.28) | 1515 (66.53) | 299 (59.56) | ||
| Race, n (%) | χ2 = 1.546 | 0.672 | |||
| White | 1784 (64.20) | 1473 (64.69) | 311 (61.95) | ||
| Black | 192 (6.91) | 153 (6.72) | 39 (7.77) | ||
| Asian | 59 (2.12) | 48 (2.11) | 11 (2.19) | ||
| Others | 744 (26.77) | 603 (26.48) | 141 (28.09) | ||
| Insurance status, n (%) | χ2 = 5.236 | 0.073 | |||
| Medicare | 1494 (53.76) | 1201 (52.74) | 293 (58.37) | ||
| Medicaid | 125 (4.50) | 105 (4.61) | 20 (3.98) | ||
| Others | 1160 (41.74) | 971 (42.64) | 189 (37.65) | ||
| Sepsis, n (%) | χ2 = 68.082 | < 0.001 | |||
| No | 2432 (87.51) | 2048 (89.94) | 384 (76.49) | ||
| Yes | 347 (12.49) | 229 (10.06) | 118 (23.51) | ||
| AKI, n (%) | χ2 = 50.906 | < 0.001 | |||
| Yes | 2344 (84.35) | 1868 (82.04) | 476 (94.82) | ||
| No | 435 (15.65) | 409 (17.96) | 26 (5.18) | ||
| AMI subtypes, n (%) | χ2 = 45.810 | < 0.001 | |||
| NSTEMI | 2201 (79.20) | 1859 (81.64) | 342 (68.13) | ||
| STEMI | 493 (17.74) | 358 (15.72) | 135 (26.89) | ||
| Unknown | 85 (3.06) | 60 (2.64) | 25 (4.98) | ||
| 24-h urine output, mL, M (Q1, Q3) | 1510.00 (890.00, 2275.00) | 1635.00 (1045.00, 2395.00) | 871.50 (385.00, 1575.00) | Z=-14.690 | < 0.001 |
| Weight, kg, Mean ± SD | 82.63 ± 22.13 | 82.99 ± 21.80 | 81.03 ± 23.53 | t = 1.71 | 0.088 |
| Heart rate, bmp, Mean ± SD | 87.45 ± 18.15 | 86.27 ± 17.30 | 92.77 ± 20.78 | t=-6.52 | < 0.001 |
| SBP, mmHg, Mean ± SD | 118.60 ± 23.62 | 118.64 ± 23.39 | 118.42 ± 24.65 | t = 0.19 | 0.852 |
| DBP, mmHg, Mean ± SD | 64.32 ± 17.93 | 63.96 ± 17.54 | 65.96 ± 19.50 | t=-2.12 | 0.034 |
| SpO2, %, Mean ± SD | 97.15 ± 4.70 | 97.46 ± 4.37 | 95.74 ± 5.77 | t = 6.29 | < 0.001 |
| GCS, M (Q1, Q3) | 14.00 (10.00, 15.00) | 14.00 (11.00, 15.00) | 11.00 (4.00, 15.00) | Z=-8.669 | < 0.001 |
| CCI, M (Q1, Q3) | 4.00 (2.00, 5.00) | 3.00 (2.00, 5.00) | 5.00 (3.00, 6.00) | Z = 9.135 | < 0.001 |
| WBC, K/uL, M (Q1, Q3) | 12.90 (9.50, 17.50) | 12.70 (9.40, 16.90) | 14.35 (10.50, 19.40) | Z = 5.126 | < 0.001 |
| Platelet, K/uL, M (Q1, Q3) | 182.00 (132.00, 244.00) | 179.00 (132.00, 238.00) | 198.00 (131.00, 278.00) | Z = 2.998 | 0.003 |
| Anemia, n (%) | χ2 = 0.046 | 0.830 | |||
| Yes | 2224 (80.03) | 1824 (80.11) | 400 (79.68) | ||
| No | 555 (19.97) | 453 (19.89) | 102 (20.32) | ||
| RDW, %, Mean ± SD | 14.71 ± 2.11 | 14.51 ± 1.97 | 15.64 ± 2.44 | t=-9.75 | < 0.001 |
| eGFR, ml/min/1.73m2, M (Q1, Q3) | 65.39 (39.14, 89.31) | 70.90 (44.53, 91.79) | 42.83 (27.12, 59.54) | Z=-13.771 | < 0.001 |
| INR, M (Q1, Q3) | 1.40 (1.20, 1.60) | 1.30 (1.20, 1.50) | 1.40 (1.20, 1.90) | Z = 6.432 | < 0.001 |
| Glucose, mg/dL, M (Q1, Q3) | 148.00 (119.00, 195.00) | 144.00 (118.00, 188.00) | 172.50 (127.00, 239.00) | Z = 6.848 | < 0.001 |
| Magnesium, mg/dL, Mean ± SD | 2.19 ± 0.53 | 2.22 ± 0.55 | 2.06 ± 0.42 | t = 7.24 | < 0.001 |
| Sodium, mEq/L, Mean ± SD | 136.68 ± 4.97 | 136.43 ± 4.63 | 137.78 ± 6.16 | t=-4.64 | < 0.001 |
| Potassium, mEq/L, Mean ± SD | 4.47 ± 0.83 | 4.48 ± 0.81 | 4.43 ± 0.92 | t = 1.07 | 0.287 |
| Chloride, mEq/L, Mean ± SD | 103.83 ± 6.40 | 104.10 ± 6.02 | 102.62 ± 7.79 | t = 3.99 | < 0.001 |
| Ventilation, n (%) | χ2 = 4.558 | 0.033 | |||
| Yes | 2679 (96.40) | 2187 (96.05) | 492 (98.01) | ||
| No | 100 (3.60) | 90 (3.95) | 10 (1.99) | ||
| Vasopressor, n (%) | χ2 = 54.803 | < 0.001 | |||
| Yes | 1976 (71.10) | 1551 (68.12) | 425 (84.66) | ||
| No | 803 (28.90) | 726 (31.88) | 77 (15.34) | ||
| RRT, n (%) | χ2 = 106.496 | < 0.001 | |||
| Yes | 340 (12.23) | 210 (9.22) | 130 (25.90) | ||
| No | 2439 (87.77) | 2067 (90.78) | 372 (74.10) | ||
| PCI, n (%) | χ2 = 1.838 | 0.175 | |||
| Yes | 390 (14.03) | 310 (13.61) | 80 (15.94) | ||
| No | 2389 (85.97) | 1967 (86.39) | 422 (84.06) | ||
| CABG, n (%) | χ2 = 323.803 | < 0.001 | |||
| Yes | 1118 (40.23) | 1095 (48.09) | 23 (4.58) | ||
| No | 1661 (59.77) | 1182 (51.91) | 479 (95.42) | ||
| Alteplase, n (%) | χ2 = 2.442 | 0.118 | |||
| Yes | 259 (9.32) | 203 (8.92) | 56 (11.16) | ||
| No | 2520 (90.68) | 2074 (91.08) | 446 (88.84) | ||
| ABE, M (Q1, Q3) | -0.90 (-3.96, 1.59) | -0.61 (-3.41, 1.82) | -3.02 (-5.87, 0.19) | Z=-8.879 | < 0.001 |
Note: t, t-test; Z, Wilcoxon-Mann-Whitney test; χ2, Chi-square test; SD, standard deviation; M, median; Q1,1st quartile; Q3, 3st quartile;
AKI, acute kidney injury; NSTEMI, non-ST segment elevation myocardial infarction; STEMI, ST-segment elevation acute myocardial infarction; SBP, systolic blood pressure; DBP, diastolic blood pressure; SpO2, oxygen saturation; GCS, Glasgow coma scale; CCI, Charlson comorbidity index; WBC, white blood cell; RDW, red blood cell distribution width; eGFR, estimated glomerular filtration rate; INR, international normalized ratio; PCI, percutaneous coronary intervention; CABG, coronary artery bypass graft; ABE, alactic base excess
Associations of ABE with in-hospital mortality in AMI patients
The Cox proportional hazards models showed that high ABE values (continuous) (HR = 0.98, 95%CI: 0.96–0.99) may be associated with a reduced risk of in-hospital mortality in patients with AMI. When ABE was analyzed as a categorical variable, negative ABE (neutral ABE as reference) was correlated with a higher risk of in-hospital mortality (HR = 1.26, 95%CI: 1.02–1.56) in AMI patients, but not in positive ABE (P = 0.378) (Table 2). The RCS curve presented a non-linear association between ABE values and in-hospital mortality, similar to a curved “V” shape (Fig. 2). The RCS curve between ABE values and in-hospital mortality may explain the inconsistency in the results of ABE values as continuous and categorical variables.
Table 2.
Association between ABE and in-hospital mortality in patients with AMI
| Variables | Model 1 | Model 2 | ||
|---|---|---|---|---|
| HR (95%CI) | P | HR (95%CI) | P | |
| ABE (continuous) | 0.96 (0.94–0.98) | < 0.001 | 0.98 (0.96–0.99) | 0.025 |
| ABE | ||||
| Neutral | Ref | Ref | ||
| Negative | 1.79 (1.47–2.17) | < 0.001 | 1.26 (1.02–1.56) | 0.033 |
| Positive | 1.11 (0.83–1.47) | 0.483 | 0.87 (0.65–1.18) | 0.378 |
Note: ABE, alactic base excess; AMI, acute myocardial infarction; Ref, reference; HR, hazard ratio; CI, confidence interval; negative ABE, < the second sextiles of ABE; neutral ABE, ≥ the second sextiles and < the fifth sextiles of ABE; positive ABE, ≥ the fifth sextiles of ABE;
Model 1: univariate Cox model;
Model 2: multivariate Cox model adjusting for age, sepsis, AKI, AMI subtypes, 24 h urine output, heart rate, SpO2, GCS, CCI, WBC, RDW, eGFR, INR, glucose, magnesium, sodium, chloride, vasopressor, RRT, CABG, and alteplase
Fig. 2.
Restricted cubic spline (RCS) curve for the association between ABE values and in-hospital mortality in patients with AMI. ABE, alactic base excess; AMI, acute myocardial infarction
Associations between ABE and in-hospital mortality in different subgroups
To further explore the association, subgroup analyses were performed based on age, eGFR, AMI subtypes, AKI, and sepsis subgroups (Fig. 3). Among age subgroups, negative ABE (neutral ABE as reference) was associated with an increased risk of in-hospital mortality (HR = 1.46, 95%CI: 1.13–1.89) in patients ≥ 65 years of age, but not in patients < 65 years of age (P = 0.787). For patients with different eGFR levels, negative ABE was linked to a higher risk of in-hospital mortality (HR = 1.35, 95%CI: 1.05–1.74) in patients with eGFR < 60, but not in patients with eGFR ≥ 60 (P = 0.674). In patients with and without AKI, negative ABE was associated with a higher risk of in-hospital mortality (HR = 1.32, 95%CI: 1.06–1.64) in patients with AKI, but not in patients without AKI (P = 0.154). Among patients with different AMI subtypes, negative ABE was linked to an increased risk of in-hospital mortality (HR = 1.79, 95%CI: 1.18–2.72) in patients with STEMI subtype, but not in patients with NSTEMI subtype (P = 0.322). Among sepsis subgroups, negative ABE was associated with a higher risk of in-hospital mortality (HR = 1.29, 95%CI: 1.01–1.64) in patients without sepsis, but not in patients with sepsis (P = 0.791).
Fig. 3.
Associations between ABE and in-hospital mortality among AMI patients based on age, AKI, eGFR, AMI subtype, and sepsis subgroups. ABE, alactic base excess; AMI, acute myocardial infarction; AKI, acute kidney injury; eGFR, estimated glomerular filtration rate
Discussion
This study aims to investigate the effect of ABE level on the risk of in-hospital mortality in AMI patients. We demonstrated that the negative ABE was associated with high in-hospital mortality in AMI patients after adjusting for the potential confounding factors. Further subgroup analyses showed that the negative ABE was significantly associated with a higher risk of in-hospital mortality in AMI patients aged ≥ 65 years, with eGFR levels < 60, with AKI, with STEMI subtype, and without sepsis.
ABE is a marker of the possible accumulation of plasma acids excreted by the kidney, and it indirectly reflects the renal capability of handling the disturbance to acid–base equilibrium [8]. Negative ABE suggests impaired renal function (inability to compensate for strong anions), whereas positive ABE may indicate additional processes leading to metabolic alkalosis, usually caused by diuretics or volume contraction [8, 9]. Gattinoni et al. showed that ABE measurement provides a potentially useful way to improve the management of critically ill patients [8]. Wernly et al. [15] demonstrated for the first time that ABE, which combined BE and lactate, had a higher predictive value for ICU mortality in septic patients. Smuszkiewicz et al. [11] demonstrated that ABE < − 3.63 mmol/L can predict 28-day ICU mortality risk in patients with patients. Furthermore, Cantos et al. [10] demonstrated that negative ABE ( < − 3 mmol/L) was an independent predictor of in-hospital mortality in septic patients with and without renal dysfunction. Similarly, to other studies, our investigation confirms that the negative ABE (<-2.81 mmol/L) was associated with high in-hospital mortality in AMI patients after adjusting for the potential confounding factors.
Our study also examined the association of ABE with in-hospital mortality in specific subpopulations. Previous studies have reported that AMI is common in the elderly and is a leading cause of morbidity and mortality in the elderly, consistent with our findings [16]. Studies have shown that hyperlactataemia accompanies acidaemia if renal function deteriorates [8, 17]. In our study, negative ABE was significantly associated with high in-hospital mortality in AMI patients with eGFR<60 and with AKI. The eGFR < 60 mL/min/1.73m2 and/or proteinuria > 0.150 g/24 h suggests that patients with chronic kidney disease (CKD) [17]. Zhou et al. found that STEMI patients were prone to metabolic acidosis caused by AKI, and is associated with worse short- and long-term cardiovascular outcomes and mortality [18], consistent with our findings.
The mechanism by which ABE was associated with high in-hospital mortality in AMI patients may involve the lactates and base excess. The kidney is an important organ system involved in the regulation of acid-base balance, which mainly promotes acid-base balance by maintaining bicarbonate homeostasis and excreta acid [19]. Lactate is a product of glycolysis, whose increased concentration causes metabolic acidosis (i.e., a process that results in excess of negative strong ions) [20–22]. Metabolic acidosis can lead to a decrease in intracellular and extracellular pH, which further aggravates cardiac ischemic injury, and is an important cause of death [23]. The base excess represents an increase or decrease of alkali reserves in plasma, and can also reflect acid-base status [24]. Luo et al. showed that when BE was low, the 28-day and 90-day all-cause mortality was strongly associated with acidemia in patients with AMI [25]. ABE may provide a better understanding of the relationship between hyperlactatemia and acidemia, which can help to quickly distinguish between metabolic acidosis secondary to lactate and metabolic acidosis caused by the accumulation of fixed acids [8].
Taken together, this study is the first to explore the association between ABE and the risk of in-hospital mortality in AMI patients. We demonstrate that ABE measured upon admission (<-2.81 mmol/L) and indicating the presence of severe acidosis originating from non-lactic organic acids was significantly associated with high in-hospital mortality in AMI patients. ABE is calculated from base excess and lactate concentration [8], which is inexpensive and easily available. Clinicians can assess ABE level at admission to help identify the mechanism of acid-base disorders that may affect the prognosis of AMI, and provide some reference for the treatment decision and prognosis improvement of AMI patients.
The present study has several limitations. First, this study is a retrospective cohort study, which inevitably has a certain selection bias. Second, in this study, we tried to consider the indicators that affect the prognosis of AMI, but there are still other confounding factors that cannot be obtained, such as AMI infarction degree. Third, the patients in this study were from the ICU, which reflects the prognostic value of ABE in patients with severe AMI, and its prognostic value in hospitalized patients needs to be further verified. Fourth, this study only analyzed the effect of ABE values at admission on in-hospital mortality in AMI patients, and the effect of dynamic changes of ABE on the prognosis of AMI patients needs to be further explored.
Conclusions
Negative ABE (<-2.81 mmol/L) was significantly associated with in-hospital mortality in patients with AMI. Clinically, negative ABE can be used to detect AMI patients associated with metabolic acidosis caused by renal insufficiency.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Acknowledgements
CZ designed the study and wrote the manuscript. CZ and QW collected, analyzed and interpreted the data. CZ critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.
Abbreviations
- AMI
acute myocardial infarction
- ICU
intensive care unit
- AG
anion gap
- ABE
alactic base excess
- MIMIC
the Medical Information Mart for Intensive Care
- AKI
acute kidney injury
- STEMI
ST-segment elevation acute myocardial infarction
- SBP
systolic blood pressure
- DBP
diastolic blood pressure
- SpO2
pulse oximetry-derived oxygen saturation
- GCS
Glasgow coma scale
- CCI
Charlson comorbidity index
- WBC
white blood cell
- RDW
red blood cell distribution width
- eGFR
estimated glomerular filtration rate
- INR
international normalized ratio
- RRT
renal replacement therapy
- PCI
percutaneous coronary intervention
- CABG
coronary artery bypass graft
- RCS
restricted cubic spline
Author contributions
CZ designed the study and wrote the manuscript.CZ and QW collected, analyzed and interpreted the data. CZ critically reviewed, edited and approved the manuscript. All authors read and approved the final manuscript.
Funding
Not applicable.
Data availability
This retrospective cohort study extracted AMI patients’ data from the Medical Information Mart for Intensive Care (MIMIC)-IV (https://mimic.mit.edu/docs/iv/) database.
Declarations
Ethics approval and consent to participate
The requirement of ethical approval for this study was waived by the Institutional Review Board of Zibo Central Hospital, because the data was accessed from MIMIC-IV (a publicly available database). The need for written informed consent was waived by the Institutional Review Board of Zibo Central Hospital due to the retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
This retrospective cohort study extracted AMI patients’ data from the Medical Information Mart for Intensive Care (MIMIC)-IV (https://mimic.mit.edu/docs/iv/) database.