Association between serum anion gap and all-cause mortality in patients with acute myocardial infarction: A retrospective study based on MIMIC-IV database

Lu, Jianhong; Zhong, Lei; Yuan, Meng; Min, Jie; Xu, Yin

doi:10.1016/j.heliyon.2023.e17397

Cited by 2 publications

(5 citation statements)

References 23 publications

(23 reference statements)

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“…Many previous studies have used the anion gap to predict mortality rates for specific patient groups or designated hospital departments [ 3 , 7 – 16 ]. These studies have demonstrated associations in populations such as patients with cardiogenic shock, cardiac disease, post–cardiac arrest complications, disseminated intravascular coagulation, postoperative status, and advanced kidney disease.…”

Section: Discussionmentioning

confidence: 99%

“…Many previous studies have used the anion gap to predict mortality rates for specific patient groups or designated hospital departments [3,[7][8][9][10][11][12][13][14][15][16]. These studies have demonstrated associations in populations such as patients with cardiogenic shock, cardiac disease, post-cardiac…”

Section: Discussionmentioning

confidence: 99%

“…Some studies have explored the relationship between the anion gap and mortality in ICU units caring for specific categories of patients with specific types of diseases [3,[7][8][9][10][11][12][13][14][15][16]. However, few studies have investigated the relationship between the anion gap, including the corrected anion gap, and mortality in ICU patients not restricted to particular diseases or hospital departments.…”

Section: Introductionmentioning

confidence: 99%

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Dynamics of serum anion gaps with in-hospital mortality: Analysis of the multi-open databases

Yang,

Jo,

Lee

et al. 2024

PLoS ONE

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Background Few studies have investigated the relationship between the anion gap, including the corrected anion gap, and patient mortality in intensive care units (ICUs) without restricting the analysis to specific diseases or medical specialties. Our primary objective was to investigate the association between the anion gap and ICU mortality using multiple open-access databases. Methods We identified 4229 subjects from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, whose entries were from between 2008 and 2019. For each patient, the anion gap and corrected anion gap were calculated, and the study sample was divided into tertile groups (T) according to these levels. The association between the anion gap and in-hospital mortality was assessed using hazard ratios (HRs) and 95% confidence intervals (CIs) derived from a multivariable-adjusted Cox proportional hazards model. Besides MIMIC-IV, we also incorporated study samples from two other databases (MIMIC-III and electronic ICU) to calculate summary HRs using a random-effects meta-analysis. Results Within MIMIC-IV, 1015 patients (24%) died during an average follow-up period of 15.5 days. The fully adjusted HRs and 95% CIs for T2 and T3, relative to T1, were 1.31 (95% CI 1.08–1.58) and 1.54 (95% CI 1.24–1.90), respectively. When grouped by corrected anion gap, the results remained statistically significant. In the meta-analysis, the summary HRs and 95% CIs for T2 and T3 were 1.24 (95% CI 1.08–1.43) and 1.55 (95% CI 1.33–1.82), respectively. Conclusions Both the anion gap and corrected anion gap were associated with in-hospital mortality regardless of specific diseases or medical specialties.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamics of serum anion gaps with in-hospital mortality: Analysis of the multi-open databases

Yang,

Jo,

Lee

et al. 2024

PLoS ONE

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“…Although many studies have explored the prognostic value of AG in various diseases, including acute pancreatitis, acute myocardial infarction, congestive heart failure, and sepsis, they have not compared the distinct impacts of single AG vs. ACAG (7)(8)(9)(10)(11). AG levels are often underestimated in cases of hypoalbuminemia, which commonly occurs during hospitalizations of aSAH patients (34).…”

Section: Discussionmentioning

confidence: 99%

“…The AG has been considered a marker of tissue hypoperfusion and has been confirmed to be associated with mortality due to various diseases, including acute pancreatitis, acute myocardial infarction, congestive heart failure, and sepsis (7)(8)(9)(10)(11). However, AG levels can be underestimated with hypoalbuminemia.…”

Section: Introductionmentioning

confidence: 99%

A prognostic model incorporating the albumin-corrected anion gap in patients with aneurysmal subarachnoid hemorrhage

Wang,

Rong,

et al. 2024

Front. Neurol.

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BackgroundAneurysmal subarachnoid hemorrhage (aSAH) patients typically have poor prognoses. The anion gap (AG) has been proven to correlate with mortality in various critically ill patients. However, hypoalbuminemia can lead to underestimations of the true anion gap levels. This study was conducted to verify the prognostic value of single AG and albumin-corrected anion gap (ACAG) among aSAH patients.MethodsSignificant factors in the univariate logistic regression analysis were included in the multivariate logistic regression analysis to explore the risk factors for mortality in aSAH patients and to confirm the independent relationship between ACAG and mortality. The restricted cubic spline (RCS) was used to visually show the relationship between ACAG level and mortality risk of aSAH patients. The predictive model for mortality was developed by incorporating significant factors into the multivariate logistic regression analysis. The prognostic value of ACAG and the developed model was evaluated by calculating the area under the receiver operating characteristics curve (AUC).ResultsAmong 710 aSAH patients, a 30-day mortality was observed in 20.3% of the cases. A positive relationship was demonstrated between the ACAG level and mortality in aSAH patients using the RCS curve. The multivariate logistic regression analysis helped discover that only six factors were finally and independently related to mortality of aSAH patients after adjusting for confounding effects, including the Hunt–Hess scale score (p = 0.006), surgical options (p < 0.001), white blood cell count (p < 0.001), serum chloride levels (p = 0.023), ACAG (p = 0.039), and delayed cerebral ischemia (p < 0.001). The AUC values for the AG, albumin, and ACAG in predicting mortality among aSAH patients were 0.606, 0.536, and 0.617, respectively. A logistic regression model, which includes the Hunt–Hess scale score, surgical options, white blood cell count, serum chloride levels, ACAG, and delayed cerebral ischemia, achieved an AUC of 0.911 for predicting mortality.ConclusionThe ACAG is an effective prognostic marker for aSAH patients. A prognostic model incorporating ACAG could help clinicians evaluate the risk of poor outcomes among aSAH patients, thereby facilitating the development of personalized therapeutic strategies.

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Association between serum anion gap and all-cause mortality in patients with acute myocardial infarction: A retrospective study based on MIMIC-IV database

Cited by 2 publications

References 23 publications

Dynamics of serum anion gaps with in-hospital mortality: Analysis of the multi-open databases

Dynamics of serum anion gaps with in-hospital mortality: Analysis of the multi-open databases

A prognostic model incorporating the albumin-corrected anion gap in patients with aneurysmal subarachnoid hemorrhage

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