Anion gap as a prognostic tool for risk stratification in critically ill patients – a systematic review and meta-analysis

This systematic review and meta-analysis adheres to the “preferred reporting items for systematic reviews and meta-analyses” (PRISMA) standards [14]. A protocol was registered with PROSPERO, registration number CRD42015015249.

We searched the electronic databases of Medline, Embase, Scopus, Medion, The Cochrane Library, Web of Science and regional bibliographic databases including African Index Medicus, Latin America and the Caribbean (LILACS), IndMed, Index Medicus for South East Asia Region (IMSEAR) and Western Pacific Region Index Medicus (WPRIM). In addition, journals specialising in the fields of critical care, anaesthetics, emergency medicine and intensive care medicine were searched electronically. Searches were performed for studies that were conducted on humans and published in English, German or French using the search terms “anion gap”, “unmeasured anions” and “unidentified acids”. The initial search was performed in January 2015 and the search was subsequently updated in May 2016. All search results were initially screened by abstract and title and those considered relevant subsequently underwent full-text screening. To identify further relevant studies, reference lists were reviewed, citation searches were performed and citation alerts were set up for all articles considered relevant after full-text screening.

Studies, conducted in any acute care clinical setting, were eligible for inclusion if they were published within the last 15 years, reported measurement of the observed and/or corrected serum AG in adult patients and mortality defined as “in-hospital mortality”, “in ICU mortality” or, if a time-frame was stated, death within up to 31-days of hospital admission. The latter definition was chosen where both outcomes were reported within a single study. Case studies, case–control studies and studies whose main focus was a hyperglycaemic emergency, poisoning or renal failure were excluded.

Data extraction was performed by a single reviewer, SG. A second reviewer, WS, independently extracted data from 10 % of the studies selected using a random number generator. Corresponding authors were contacted where necessary to discuss missing or unclear data.

Two reviewers (SG and WS) independently graded the methodological quality and risk of bias of included studies using a modified version of the Quality In Prognostic Studies (QUIPS) tool [15]. This tool assesses the risk of bias of prognostic studies in six domains: study participation, study attrition, prognostic factor measurement, outcome measurement, confounding, and statistical analysis and reporting. Each study was rated as being at high, moderate or low risk of bias in each domain. Disagreements were resolved by discussion between the two reviewers.

Area under the ROC curve (AUCs), odds ratios (ORs) and mean differences were pooled in random or fixed effects generic inverse variance models for the observed and corrected AG. The I² test was used to quantify heterogeneity. A fixed-effects model was used where the I² was below 30 %; otherwise, a random-effects model was used. Meta-analysis of ORs and mean differences was undertaken in Review Manager version 5.3 (The Cochrane Collaboration, 2014, Copenhagen), while AUCs were pooled in StatsDirect version 2.8.0 (England: StatsDirect Ltd. 2013); an AUC of ≥0.8 was considered to denote good discriminatory power. Pooled estimates were not presented in forest plots due to high heterogeneity in the meta-analyses of AUC and mean difference; in the results section the pooled estimates are reported together with their respective 95 % confidence intervals (CIs). Subgroup analysis was undertaken to assess whether heterogeneity between studies could be explained by the following study characteristics: patient age, study setting, quantity of intravenous fluids received, determination of the AG before the initiation of hospital-based treatment, the routine use of gelatin-based intravenous fluids in the study country, choice of outcome measure, publication date and overall mortality.

Statistical significance testing for subgroup differences employed the unpaired t-test in GraphPad® QuickCalc Web Calculator (La Jolla California USA) [16]. Probabilities were two-tailed and a probability of less than 0.05 was considered statistically significant. No adjustments were made for multiple comparisons. Sensitivity analysis was undertaken to assess the effect of including retrospective studies and studies at high risk of attrition bias in the meta-analysis. Funnel plots were visually inspected for evidence of publication bias.

The study selection process is summarised in Fig. 1. In total, the search yielded 2688 non-duplicate publications; 2630 articles were excluded after title and abstract screening thus 58 articles were retrieved in full-text. Twenty-nine articles were excluded during full-text screening, leaving 29 studies that were subjected to data extraction. Ten studies were excluded during data extraction and thus 19 studies were included in the systematic review, of which 18 were included in one or more quantitative syntheses.

Table 1 illustrates the characteristics of included studies. A majority of studies were conducted in high income countries [7, 8, 16‐26] while three studies were conducted in middle income countries [27‐29] and one in a low income country [30]. Studies were conducted in the following settings: ICU (10 studies), trauma centre (5 studies), coronary care unit/intensive cardiac care unit (3 studies) and Accident and Emergency department (1 study). Five studies accounted for the effect of intravenous fluids on AG levels: in one study no patient received more than 400 ml of any intravenous fluid before the AG was measured [7], in two studies patients receiving more than 250 ml or 500 ml of intravenous fluids respectively were excluded from the analysis [16, 30]. Two studies stated that the AG was determined before hospital based management, including intravenous fluids, was initiated [21, 29]. The remaining studies did not report on the quantity of intravenous fluids received by their study cohorts. No study reported both in-hospital and a time-frame specific mortality. One study [29] failed to define the outcome measure, reporting it as “mortality”. Sensitivity analysis was carried out to determine whether inclusion of this study affected the pooled effect measure. The risks of bias ratings are displayed in Table 2. Risk of attrition bias and confounding were the most poorly rated domains. There were no disagreements between review authors on data extraction.

Owing to the high heterogeneity identified in the meta-analyses of AUC and mean difference, the pooled effect measures reported in this section should not be interpreted. Overall, three studies reported good discriminatory ability of the AG to predict mortality, of which two studies were included in meta-analysis [7, 16] and one study allowed the calculation of an OR for a specific AG threshold [22]. The former two studies were responsible for a large proportion of the statistical heterogeneity; both studies were conducted in young patients in the same trauma centre and only patients receiving less than a specified volume of intravenous fluids were included in the analysis. The latter study was conducted in patients with ST-elevation myocardial infarction undergoing percutaneous coronary intervention. The remaining 16 studies reported poor to moderate ability of the AG to predict mortality.

Nine studies reported AUCs for the observed AG (Fig. 2). Meta-analysis yielded a summary AUC of 0.72 (95 % CI 0.59 to 0.86). Heterogeneity was very high (I² = 99 %) but reduced to I² = 68 % when the two studies by Kaplan and Kellum were excluded from the analysis [7, 16]. Six studies reported AUCs for the corrected AG (Additional file 1: Figure S1). The summary AUC was estimated as 0.67 (95 % CI 0.62 to 0.71) and heterogeneity was high (I² = 67 %).

Six studies reported ORs derived by logistic regression modelling for the observed AG (Fig. 3); five reported univariate logistic regression ORs whilst one study reported an OR adjusted for age. The summary OR was 1.08 (95 % CI 1.06 to 1.11); results were homogenous (I² = 0 %) but it should be noted that the two studies by Kaplan and Kellum were not included in this analysis as neither study reported the OR. Four studies reported ORs derived by univariate logistic regression for the corrected AG (Additional file 2: Figure S2). The summary OR was 1.10 (95 % CI 1.07 to 1.13) and heterogeneity was very low (I² = 5 %). Data reported in the study of Lazzeri and colleagues allowed the calculation of an OR for a specified AG positivity threshold. This yielded an OR of 2.8 (95 % CI 1.5 to 5.5) for an AG positivity threshold of 11 mEq/L [22].

Mean difference was the most frequently reported effect measure with ten studies reporting it for the observed AG (Fig. 4). The summary mean difference was 3.55 mEq/L (95 % CI 1.08 to 6.02). Heterogeneity was high (I² = 97 %); however, excluding the study by Kaplan and Kellum [7] completely eliminated heterogeneity (I² = 0 %). The mean difference for corrected AG was reported by three studies (Additional file 3: Figure S3) and the summary mean difference was estimated as 3.25 mEq/L (95 % CI 1.53 to 4.96) with homogeneous results (I² = 0 %).

Sensitivity analysis showed that including retrospective studies and studies at risk of attrition bias did not affect the summary AUC. Including prospective studies only (six studies) yielded a summary AUC of 0.73 (95 % CI 0.69 to 0.78). Similarly, excluding studies rated at high risk of attrition bias (four studies) yielded a summary AUC of 0.75 (95 % CI 0.54 to 0.96). Excluding the study by Dondorp and colleagues [31] with an undefined outcome measure (“mortality”) yielded an AUC of 0.72 (95 % CI 0.56 to 0.88).

AUC of observed AG was chosen for subgroup analysis; results are shown in Table 3. The quantity of intravenous fluids given to a patient had the strongest influence on the summary AUC. Studies excluding patients who received more than a specified volume of intravenous fluids [7, 16] reported a significantly higher summary AUC than studies not excluding patients for this reason (P = 0.0008), but heterogeneity remained high in both subgroups. For the subsequent analysis, studies restricting intravenous fluids [7, 16] and studies measuring the AG before initiation of hospital-based management [21, 29] were combined in a subgroup and compared to studies that did not account in any way for the effect of intravenous fluids on the AG. The former subgroup yielded a significantly higher summary AUC than the latter subgroup (P <0.0001). Heterogeneity remained high in the former subgroup (I² = 98 %), though results in the latter subgroup were homogenous (I² = 0 %). The summary AUC of studies conducted in countries where gelatin-based resuscitation fluids are routinely used [8, 19, 28] is not significantly different to that of studies conducted in countries where gelatins are not routinely used [13, 24] (P = 0.33). Studies in which intravenous fluids were restricted or in which the AG was measured before initiation of hospital-based treatment were not included in the latter comparison. The observed AG appears to be a slightly better predictor of mortality among younger patients (P = 0.011) and those admitted to trauma centre settings (P = 0.0235). Subgroup analysis showed no significant difference between studies reporting in-hospital mortality and those reporting a time-framed mortality (P = 0.65); similarly, no significant difference was found between studies in which overall mortality was below 30 % and above 30 % (P = 0.89) or between studies published before and after the year 2005 (P = 0.43); and heterogeneity remained high in all subgroups. Funnel plots did not show evidence of publication bias (Additional file 4: Figure S4 and Additional file 5: Figure S5).

The methodological quality of primary studies was generally poor. Most studies were rated at moderate or high risk of attrition bias and sampling bias as a result of failure to quantify missing outcome or prognostic data, especially in retrospective studies, where case notes with missing information are less likely to have been available. This may have affected our results where information was missing in a non-random manner. Similarly, risk of confounding was moderate to high in the majority of studies. Only one study explored the influence of age on AG levels, and stratification was not employed by any study; the risk of confounding affecting the review outcome is therefore high. Several studies did not report relevant effect measures, such as OR, AUC or mean difference or failed to provide confidence intervals, leading to exclusion from this review. Furthermore, the overall severity of illness in the study cohort was sometimes not quantified by means of an accepted disease severity score, such as the Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) score or Injury Severity Score (ISS) in trauma patients. No studies reported short-term mortality outcomes, which may have been more appropriate as naturally the prognosis in critical care patients is heavily influenced by clinical interventions undertaken during the inpatient stay. Shapiro and colleagues found that a single lactate level drawn on admission has good discriminatory power to predict 3-day mortality (AUC = 0.8) but poor discriminatory power to predict 28-day mortality (AUC = 0.67) [36]. Lastly, few studies stated the types of intravenous fluids used and no study included the quantity of intravenous fluid used as a variable in multivariate analysis.