Serum anion gap at admission as a predictor of mortality in the pediatric intensive care unit

Role of Albumin-corrected Anion Gap and Lactate Clearance in Predicting Mortality in Pediatric Intensive Care Patients

Background

Identifying mortality risk in critically ill children is central to diagnostic and treatment practices. For this purpose, scoring systems, such as the Pediatric Index of Mortality 3 (PIM 3), have been proposed; however, the role of biochemical markers, such as albumin-corrected anion gap (cAG) and lactate clearance (LC), in predicting mortality in pediatric intensive care unit (PICU) patients is yet to be explored.

Aims

To evaluate the predictive value of the cAG and LC for mortality in pediatric patients admitted to a PICU.

Study Design

Retrospective single-center cohort study.

Methods

Clinical and laboratory data from the time of PICU admission were collected, and patients were classified into based on their 0- and 6-hour of admission lactate levels into an LC(+) group (patients with normal or decreasing lactate levels) or an LC(−) group (increasing lactate levels). LC and cAG levels were compared using the Mann-Whitney U test and Student’s t-test, respectively. Additionally, multiple logistic regression analysis was performed to evaluate the effect of LC and cAG on mortality.

Results

We included 825 patients in the study; the mortality rate was 8.6%. The absence of LC [adjusted odds ratio (AOR) =4.735; 95% confidence interval (CI): 2.163-10.367; p < 0.001], cAG (AOR =1.064; 95% CI: 1.010-1.122; p = 0.019) and PIM 3 (AOR = 1.871; 95% CI: 1.553-2.254; p < 0.001) were independent risk factors for mortality. Using the receiver operating characteristic curve analysis of PIM 3 as a predictor of mortality, area under the curve values of 0.832 (95% CI: 0.805-0.857; p < 0.001) for the original score and 0.858 for a revised PIM 3 score (based on the β coefficients obtained for cAG and LC; 95% CI 0.832-0.881; p < 0.001) were obtained, which was significantly different (p = 0.027).

Conclusion

A cAG value > 18 at the time of PICU admission high lactate levels which do not decrease within 6 hours of hospitalization are associated with an increased risk of mortality. The revised PIM 3 score, which includes cAG and LC, is a better predictor of mortality than the classical PIM 3 score.

Serum anion gap is associated with mortality in intensive care unit patients with diastolic heart failure

Serum anion gap (AG) is closely related to mortality in critically ill patients with several diseases. We aimed to determine the relationship between serum AG levels and 28-day intensive care unit (ICU) mortality in patients with diastolic heart failure (DHF). This cohort study enrolled critically ill patients with DHF from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Serum AG levels were calculated using the traditional and albumin-adjusted methods. Multivariate Cox proportional hazards regression and restricted cubic spline curves were used to determine the correlation between serum AG levels and 28-day ICU mortality. We used receiver operating characteristic (ROC) curves and area under the curve (AUC) to compare the ability of traditional and albumin-adjusted AG to predict mortality. Overall, 3290 patients were included. Multivariate analysis showed an association of high levels of traditional (hazard ratio [HR], 1.48; 95% confidence interval [CI], 1.1-1.98, p = 0.009) and albumin-adjusted AG (HR, 1.36; 95% CI, 1.02-1.79, p = 0.033) with higher risk of 28-day ICU mortality. Restricted cubic spline curves indicated a linear relationship between AG level and 28-day ICU mortality. Comparison of the ROC curves revealed that albumin-adjusted AG had a greater ability to predict 28-day ICU mortality compared with traditional AG (AUCs of 0.569 [95% CI, 0.536-0.601] and 0.619 [95% CI, 0.588-0.649], respectively). In ICU patients with DHF, higher levels of traditional and albumin-adjusted AG were associated with higher 28-day ICU mortality. Albumin-adjusted AG exhibited greater predictive ability for mortality compared with traditional AG.

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

Background

Although previous studies have reported that many biomarkers can determine the prognosis of patients with acute myocardial infarction (AMI), serum anion gap (AG) has not been well studied. We aimed to investigate the association between serum AG and mortality in patients with AMI.

Methods

Adult patients first admitted to the ICU and diagnosed with AMI from 2008 to 2019 in the MIMIC-IV database were included. Patients were divided into the survival and non-survival groups based on 30-day and 90-day outcomes. According to the AG value (15.12 mmol/L) with a hazard ratio of 1 in the restricted cubic spline (RCS) analysis, patients were further divided into high and low AG groups. The Kaplan-Meier survival curve was plotted, and all-cause mortality was compared between the high and low groups using the log-rank test. Multivariate Cox regression analysis and RCS analysis were constructed to assess the relationship between AG and recent all-cause mortality in patients with AMI.

Results

4446 patients were enrolled. The 30-day and 90-day mortality rates in the high AG group (25.53%, 31.75%) were higher than that in the low AG group (9.73%, 14.01%, P < 0.001) independently. The Kaplan-Meier curve showed that the 30-day and 90-day cumulative survival rates were lower in the high AG group than that in the low AG group (P < 0.001). RCS analysis showed that there was a non-linear relationship between AG and the risk of 90-day all-cause mortality in patients with AMI (χ2 = 18.680 P < 0.001). When AG was 15.12 mmol/L, its HR was about 1. Multivariable Cox regression analysis confirmed that increased AG was associated with higher 30-day and 90-day mortality.

Conclusion

Elevated serum AG (≥15.12 mmol/L) is an independent predictor for short-term mortality in patients with AMI, and it may provide a basis for clinicians to identify patients with poor prognosis as early as possible.

Association between albumin corrected anion gap and 30-day all-cause mortality of critically ill patients with acute myocardial infarction: a retrospective analysis based on the MIMIC-IV database

BACKGROUND

The anion gap (AG) has been linked to the prognosis of many cardiovascular disorders. However, the correlation between albumin-corrected anion gap (ACAG) and 30 d all-cause mortality of intensive care patients with acute myocardial infarction (AMI) is unclear. Furthermore, owing to the lack of studies, it is also unknown whether ACAG is more accurate than AG in predicting the mortality of AMI.

METHODS

The Medical Information Mart for Intensive Care IV (MIMIC IV) dataset was used to provide patient data in this retrospective cohort study. ACAG is computed using the formulae: [4.4-{albumin (g/dl)}] × 2.5 + AG. The primary outcome was 30 d all-cause mortality intensive care patients with AMI. To explore the prognostic worthiness of ACAG, the receiver operating characteristic curve, smooth curve fitting, Cox regression model, and Kaplan survival analysis was performed.

RESULTS

We enrolled 2,160 patients in this study. ACAG had a better predictive value for 30 d all-cause mortality than AG, with an area under the curve of 0.66. The association between ACAG levels and overall mortality was nonlinear. In our model, after correcting for confounding factors, the ACAG was the independent predictor for 30 d all-cause mortality (HR 1.75, 95%CI 1.24, 2.47). ACAG K-M estimator curve analyses revealed that the group with ACAG ≥ 21.75 mmol/l had poor survival rate than the other group.

CONCLUSIONS

High serum ACAG levels were a significant risk factor for 30 d all-cause mortality in critically ill patients with AMI. ACAG concentration and 30 d all-cause mortality had a nonlinear relationship. ACAG had better predictive value in identifying 30 d all-cause mortality of patients with AMI in ICU than the AG.

Metabolic Acidosis in Children: A Literature Review

Metabolic acidosis is characterised by a primary decrease in the serum bicarbonate concentration, a secondary decrease in the arterial partial pressure of CO­2, and a reduction in blood pH. Metabolic acidosis, acute or chronic, may have deleterious effects on cellular function and cause increased morbidity and mortality. A systematic review of the available literature was performed to identify data on the prevalence, manifestations, cause, outcomes, and treatment of metabolic acidosis in children. Online databases (Ovid Medline, Embase, and PubMed), commercial search engines (including Google), and chapters on metabolic acidosis in the standard textbooks of paediatrics and medicine were reviewed. Systematic approach to acute metabolic acidosis starts with proper history taking and examination. This is followed by assessment of acid-base parameters, including pH, partial pressure of CO2, and bicarbonate concentration in arterial blood. Blood gas is needed to differentiate primary metabolic acidosis from compensated respiratory alkalosis. Once the diagnosis of a metabolic acidosis has been confirmed, serum electrolyte values are used to determine the serum anion gap. The various causes of increased and normal anion gap metabolic acidosis have been discussed in the article. The main aim of treatment in metabolic acidosis is to reverse the primary pathophysiology. In acute metabolic acidosis, sodium bicarbonate therapy is not beneficial due to potential complications and is reserved for specific situations. Base therapy is used in chronic metabolic acidosis where it ameliorates many of its untoward effects. Other modalities of treatment of metabolic acidosis include peritoneal or haemodialysis and tris-hydroxymethyl aminomethane.

Initial clinical and laboratory parameters triaging non-pharmaceutical acute pediatric poisoned patients for potential adverse outcomes: a three-year retrospective study

Identifying high-risk pediatric patients with non-pharmaceutical poisoning is crucial to avoid prospective complications and decrease the evident hospital economic burden. Although the preventive strategies have been well studied, determining the early predictors for poor outcomes remains limited. Therefore, this study focused on the initial clinical and laboratory parameters as a triage of non-pharmaceutical poisoned children for potential adverse outcomes taking the causative substance effects into account. This retrospective cohort study included pediatric patients admitted to Tanta University Poison Control Center from January 2018 to December 2020. Sociodemographic, toxicological, clinical, and laboratory data were retrieved from the patient's files. Adverse outcomes were categorized into mortality, complications, and intensive care unit (ICU) admission. Out of enrolled 1,234 pediatric patients, preschool children constituted the highest percentage of the patients investigated (45.06%), with a female predominance (53.2%). The main non-pharmaceutical agents included pesticides (62.6%), corrosives (19%), and hydrocarbons (8.8%), mainly associated with adverse consequences. The significant determinants for adverse outcomes were pulse, respiratory rate, serum bicarbonate (HCO3), Glasgow Coma Scale, O2 saturation, Poisoning Severity Score (PSS), white blood cells (WBCs), and random blood sugar. The cutoffs of serum HCO3 < 17.55 mmol/l, WBCs >8,650 cells/microliter, and PSS > 2 points were the best discriminators for mortality, complications, and ICU admission, respectively. Thus, monitoring these predictors is essential to prioritize and triage pediatric patients who require high-quality care and follow-up, particularly in aluminum phosphide, sulfuric acid, and benzene intoxications.

Association of serum anion gap and risk of long-term mortality in patients following coronary artery bypass grafting: A propensity score matching study

BACKGROUND

The present study aimed to explore the relationship between serum anion gap (AG) and long-term mortality in patients undergoing coronary artery bypass grafting (CABG).

METHODS

Clinical variables were extracted among patients undergoing CABG from Medical Information Mart for Intensive Care III (MIMIC III) database. The primary outcome was 4-year mortality following CABG. An optimal cut-off value of AG was determined by the receiver operating characteristic (ROC) curve. The Kaplan-Meier (K-M) analysis and multivariate Cox hazard analysis were performed to investigate the prognostic value of AG in long-term mortality after CABG. To eliminate the bias between different groups, propensity score matching (PSM) was conducted to validate the findings.

RESULTS

The optimal cut-off value of AG was 17.00 mmol/L. Then a total of 3162 eligible patients enrolled in this study were divided into a high AG group (≥17.00, n = 1022) and a low AG group (<17.00, n = 2,140). A lower survival rate was identified in the high AG group based on the K-M curve (p < .001). Compared with patients in the low AG group, patients in the high AG group had an increased risk of long-term mortality [1-year mortality: hazard ratio, HR: 2.309, 95% CI (1.672-3.187), p < .001; 2-year mortality: HR: 1.813, 95% CI (1.401-2.346), p < .001; 3- year mortality: HR: 1.667, 95% CI (1.341-2.097), p < .001; 4-year mortality: HR: 1.710, 95% CI (1.401-2.087), p < .001] according to multivariate Cox hazard analysis. And further validation of above results was consistent in the matched cohort after PSM.

CONCLUSIONS

The AG is an independent predictive factor for long-term all-cause mortality in patients following CABG, where a high AG value is associated with an increased mortality.

Predictive value of the serum anion gap for 28-day in-hospital all-cause mortality in sepsis patients with acute kidney injury: a retrospective analysis of the MIMIC-IV database

Background

The kidney is one of the most vulnerable organs in sepsis patients, which mainly manifests as sepsis-associated acute kidney injury (SA-AKI). The case fatality rate of SA-AKI is high, and thus, predicting the risk of SA-AKI-related death is hugely significant. Anion gap (AG) is an important indicator in critical illness patients. The present study aimed to analyze the predictive value of the AG for the short-term prognosis of SA-AKI patients.

Methods

SA-AKI patient data from the Medical Information Mart for Intensive Care (MIMIC-IV) database were collected retrospectively. Hospitalized septic patients who meet the inclusion criteria were included in the final analysis. All laboratory test parameters only included the data generated within the first 24 hours after the patient entered the intensive care unit (ICU) and the extreme value. Univariate and multivariate logistic regression analyses were performed to analyze the risk factors related to the death of SA-AKI patients within 28 days during hospitalization in the ICU.

Results

A total of 3,684 SA-AKI patients were included, including 3,305 patients with low AG (<18 mmol/L) and 379 patients with high AG (≥18 mmol/L). Among these patients, 497 cases (13.5%) died during hospitalization, including 376 cases (11.4%) in the low AG group and 121 cases (31.9%) in the high AG group. Multivariate logistic regression analysis showed that elevated AG increased the risk of death in SA-AKI patients within 28 days during hospitalization in the ICU (odds ratio =1.2, 95% confidence interval: 1.2-1.3). Further analysis showed that the risk of death of SA-AKI patients within 28 days during hospitalization in the ICU was increased when AG ≥14 mmol/L. The relationship between AG level and the risk of death of SA-AKI patients during hospitalization was S-shaped.

Conclusions

In clinical practice, AG levels can serve as a valuable predictor of the death risk of SA-AKI patients during hospitalization.

Normal Anion Gap Metabolic Acidosis in Pediatric Acute Diarrhea: A Menace or an Innocent Bystander?

How to cite this article: Bhowmick R. Normal Anion Gap Metabolic Acidosis in Pediatric Acute Diarrhea: A Menace or an Innocent Bystander? Indian J Crit Care Med 2022;26(12):1235-1236.

Association of Plasma Anion Gap with 28-Day Inhospital Mortality and 1-Year Mortality of Patients with Alcohol Use Disorder at ICU Admission: A Retrospective Cohort Study

Background

Alcohol use disorder (AUD) is common in critically ill patients. Plasma anion gap (AG) was known as a feasible parameter and was associated with outcomes of various diseases. This study is intended to explore whether AG is related to 28-day inhospital mortality and 1-year mortality of critically ill patients with AUD.

Method

We extracted data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The association of plasma AG with 28-day inhospital mortality and 1-year mortality of critically ill AUD patients was assessed using Cox proportional hazard regression models and stratification analyses, allowing AG as a time-varying covariate in the models. To evaluate the accuracy of AG in predicting different endpoints, receiver operator characteristic (ROC) curves were used.

Result

Among the 3993 critically ill patients with AUD, AG was positively associated with 28-day inhospital mortality and 1-year mortality after adjusting confounders (p < 0.001 for all). Compared with lower AG (<12 mmol/L), patients in different groups (12 ≤ AG < 14 mmol/L, 14 ≤ AG < 17 mmol/L, 17 ≤ AG < 20 mmol/L, and AG ≥ 20 mmol/L) had different HRs (95% CIs) for 28-day inhospital mortality (1.105, (0.906, 1.347); 1.171, (0.981, 1.398); 1.320, (1.108, 1.573); and 1.487, (1.254, 1.763), respectively) and 1-year mortality (1.037 (0.898, 1.196); 1.091 (0.955, 1.246); 1.201 (1.052, 1.371); and 1.3093 (1.149, 1.492), respectively).

Conclusion

Increased AG is associated with greater 28-day inhospital mortality and 1-year mortality. The effect of AG on all-cause mortality is linear in critically ill AUD patients.

Development of a model for predicting mortality of breast cancer admitted to Intensive Care Unit

Background

There is still not a mortality prediction model built for breast cancer admitted to intensive care unit (ICU).

Objectives

We aimed to build a prognostic model with comprehensive data achieved from eICU database.

Methods

Outcome was defined as all-cause in-hospital mortality. Least absolute shrinkage and selection operator (LASSO) was conducted to select important variables which were then taken into logistic regression to build the model. Bootstrap method was then conducted for internal validation.

Results

448 patients were included in this study and 79 (17.6%) died in hospital. Only 5 items were included in the model and the area under the curve (AUC) was 0.844 (95% confidence interval [CI]: 0.804-0.884). Calibration curve and Brier score (0.111, 95% CI: 0.090-0.127) showed good calibration of the model. After internal validation, corrected AUC and Brier score were 0.834 and 0.116. Decision curve analysis (DCA) also showed effective clinical use of the model. The model can be easily assessed on website of https://breastcancer123.shinyapps.io/BreastCancerICU/.

Conclusions

The model derived in this study can provide an accurate prognosis for breast cancer admitted to ICU easily, which can help better clinical management.

Association between anion gap and mortality of aortic aneurysm in intensive care unit after open surgery

Background

There has not been a well-accepted prognostic model to predict the mortality of aortic aneurysm patients in intensive care unit after open surgery repair. Otherwise, our previous study found that anion gap was a prognosis factor for aortic aneurysm patients. Therefore, we wanted to investigate the relationship between anion gap and mortality of aortic aneurysm patients in intensive care unit after open surgery repair.

Methods

From Medical Information Mart for Intensive Care III, data of aortic aneurysm patients in intensive care unit after open surgery were enrolled. The primary clinical outcome was defined as death in intensive care unit. Univariate analysis was conducted to compare the baseline data in different groups stratified by clinical outcome or by anion gap level. Restricted cubic spline was drawn to find out the association between anion gap level and mortality. Subgroup analysis was then conducted to show the association in different level and was presented as frost plot. Multivariate regression models were built based on anion gap and were adjusted by admission information, severity score, complication, operation and laboratory indicators. Receiver operating characteristic curves were drawn to compare the prognosis ability of anion gap and simplified acute physiology score II. Decision curve analysis was finally conducted to indicate the net benefit of the models.

Results

A total of 405 aortic aneurysm patients were enrolled in this study and the in-intensive-care-unit (in-ICU) mortality was 6.9%. Univariate analysis showed that elevated anion gap was associated with high mortality (P value < 0.001), and restricted cubic spline analysis showed the positive correlation between anion gap and mortality. Receiver operating characteristic curve showed that the mortality predictive ability of anion gap approached that of simplified acute physiology score II and even performed better in predicting in-hospital mortality (P value < 0.05). Moreover, models based on anion gap showed that 1 mEq/L increase of anion gap improved up to 42.3% (95% confidence interval 28.5–59.8%) risk of death.

Conclusions

The level of serum anion gap was an important prognosis factor for aortic aneurysm mortality in intensive care unit after open surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02263-4.

Association Between Anion Gap and Mortality in Critically Ill Patients with Cardiogenic Shock

Background

No epidemiological study has determined the association between the anion gap (AG) and all-cause mortality in critically ill patients with cardiogenic shock (CS). This study was conducted to clarify the relationship between the AG and mortality in CS.

Methods

We extracted clinical data from the public database, MIMIC-III V1.4, by using a generalized additive model to identify the nonlinear relationship between the AG and the 30-day mortality in 1248 intensive care unit patients. Cox proportional hazard models were used to assess the association between the AG and the 30-day, 90-day, and 365-day mortality in CS.

Results

The AG and 30-day all-cause mortality showed a nonlinear relationship, indicated by a J-shaped curve. In the multivariate analysis, after adjusting for potential confounders, a high AG was associated with an increased risk of 30-day, 90-day, and 365-day all-cause mortality in patients with CS compared with patients who had low AG (hazard ratio [95% confidence interval] 1.62 [1.14-2.30]; 1.35 [1.04-1.84]; and 1.38 [1.03-1.84], respectively). Similar results were shown in Model I (adjusted for age, sex and ethnicity) and in Model II (fully adjusting for age, ethnicity, sex, acute kidney injury stage, CHF, renal disease, stroke, malignancy, respiratory failure, pneumonia, sodium, potassium, chloride, BUN, PT, WBC, pH, creatinine, albumin, glucose, bicarbonate, vasopressor use, diastolic blood pressure, respiration rate, temperature, the Elixhauser Comorbidity Index, SOFA score and SAPSII score).

Conclusion

The relationship between the AG and 30-day all-cause mortality followed a J-shaped curve. Higher AG was associated with an increased risk of 30-day, 90-day, and 365-day all-cause mortality in critically ill patients with CS.

Noninvasive Real-Time Mortality Prediction in Intensive Care Units Based on Gradient Boosting Method: Model Development and Validation Study

BACKGROUND

Monitoring critically ill patients in intensive care units (ICUs) in real time is vitally important. Although scoring systems are most often used in risk prediction of mortality, they are usually not highly precise, and the clinical data are often simply weighted. This method is inefficient and time-consuming in the clinical setting.

OBJECTIVE

The objective of this study was to integrate all medical data and noninvasively predict the real-time mortality of ICU patients using a gradient boosting method. Specifically, our goal was to predict mortality using a noninvasive method to minimize the discomfort to patients.

METHODS

In this study, we established five models to predict mortality in real time based on different features. According to the monitoring, laboratory, and scoring data, we constructed the feature engineering. The five real-time mortality prediction models were RMM (based on monitoring features), RMA (based on monitoring features and the Acute Physiology and Chronic Health Evaluation [APACHE]), RMS (based on monitoring features and Sequential Organ Failure Assessment [SOFA]), RMML (based on monitoring and laboratory features), and RM (based on all monitoring, laboratory, and scoring features). All models were built using LightGBM and tested with XGBoost. We then compared the performance of all models, with particular focus on the noninvasive method, the RMM model.

RESULTS

After extensive experiments, the area under the curve of the RMM model was 0.8264, which was superior to that of the RMA and RMS models. Therefore, predicting mortality using the noninvasive method was both efficient and practical, as it eliminated the need for extra physical interventions on patients, such as the drawing of blood. In addition, we explored the top nine features relevant to real-time mortality prediction: invasive mean blood pressure, heart rate, invasive systolic blood pressure, oxygen concentration, oxygen saturation, balance of input and output, total input, invasive diastolic blood pressure, and noninvasive mean blood pressure. These nine features should be given more focus in routine clinical practice.

CONCLUSIONS

The results of this study may be helpful in real-time mortality prediction in patients in the ICU, especially the noninvasive method. It is efficient and favorable to patients, which offers a strong practical significance.

The Relationship Between the Serum Anion Gap and All-Cause Mortality in Acute Pancreatitis: An Analysis of the MIMIC-III Database

Purpose

We aimed to investigate the relationship between the serum anion gap (AG) and all-cause mortality in patients with acute pancreatitis (AP) in intensive care units (ICUs).

Patients and Methods

In this retrospective cohort analysis, data of patients with AP were extracted from the Medical Information Mart for Intensive Care database (version III). We collected the maximum serum AG value within the first 24 hours of ICU admission. The main outcome was 90-day all-cause mortality. A multivariate Cox proportional hazard regression model was used to examine the association between the serum AG and mortality. The restricted cubic spline curve was used to confirm a non-linear relationship between serum AG values and mortality.

Results

Of the 279 patients included in the study, 87 (31.18%) died. The serum AG value was positively associated with 90-day all-cause mortality (hazard ratio [HR] 1.08, 95% confidence interval [CI] 1.02-1.14), after adjusting for age, sex, alcohol consumption, congestive heart failure, diabetes mellitus, hypertension, eGFR, albumin, and the SOFA score. There was a non-linear relationship between serum AG values and mortality after adjusting for potential confounders. We used a two-piecewise regression model to obtain a threshold inflection point value of 13.8 mmol/L. The HR and the 95% CI on the left inflection point were 0. 82 (0.61-1.09; p = 0.1719), and on the right inflection point were 1.15 (1.08-1.23; p < 0.0001).

Conclusion

The relationship between all-cause mortality in patients with acute pancreatitis and serum AG values was non-linear. All-cause mortality and serum AG values were positively correlated when the serum AG value was >13.8 mmol/L.

Relation between Baseline Total Serum Cortisol Level and Outcome in Pediatric Intensive Care Unit

Elevated cortisol level is an component of the stress response. However, some patients have low cortisol levels; a condition termed: critical illness-related corticosteroid insufficiency (CIRCI). Basal cortisol levels during PICU admission may be related to outcome. This prospective cohort study aimed to assess basal total serum cortisol levels and their relation to outcome in PICU. The study included 81 children over 6 months. Total serum cortisol was assessed using an early morning sample. The severity of illness was assessed using the PRISM-III score. Outcome measures included mechanical ventilation duration, use of inotropic support, length of stay, mortality. Comparison between patients' subgroups according to total serum cortisol levels revealed significantly higher PRISM-III score in patients with total serum cortisol levels. In addition, those patients had a significantly higher mortality rate when compared with patients with low and normal total serum cortisol levels. Multivariate logistic regression analysis recognized high total serum cortisol level and PRISM-III score as significant predictors of mortality. We concluded that PRISM-III score and elevated total serum cortisol levels are significant predictors of mortality in the PICU. Although CIRCI is prevalent in this population, it wasn't associated with an increased mortality rate.

A New Insight Into Missing Data in Intensive Care Unit Patient Profiles: Observational Study

Background

The data missing from patient profiles in intensive care units (ICUs) are substantial and unavoidable. However, this incompleteness is not always random or because of imperfections in the data collection process.

Objective

This study aimed to investigate the potential hidden information in data missing from electronic health records (EHRs) in an ICU and examine whether the presence or missingness of a variable itself can convey information about the patient health status.

Methods

Daily retrieval of laboratory test (LT) measurements from the Medical Information Mart for Intensive Care III database was set as our reference for defining complete patient profiles. Missingness indicators were introduced as a way of representing presence or absence of the LTs in a patient profile. Thereafter, various feature selection methods (filter and embedded feature selection methods) were used to examine the predictive power of missingness indicators. Finally, a set of well-known prediction models (logistic regression [LR], decision tree, and random forest) were used to evaluate whether the absence status itself of a variable recording can provide predictive power. We also examined the utility of missingness indicators in improving predictive performance when used with observed laboratory measurements as model input. The outcome of interest was in-hospital mortality and mortality at 30 days after ICU discharge.

Results

Regardless of mortality type or ICU day, more than 40% of the predictors selected by feature selection methods were missingness indicators. Notably, employing missingness indicators as the only predictors achieved reasonable mortality prediction on all days and for all mortality types (for instance, in 30-day mortality prediction with LR, we achieved area under the curve of the receiver operating characteristic [AUROC] of 0.6836±0.012). Including indicators with observed measurements in the prediction models also improved the AUROC; the maximum improvement was 0.0426. Indicators also improved the AUROC for Simplified Acute Physiology Score II model—a well-known ICU severity of illness score—confirming the additive information of the indicators (AUROC of 0.8045±0.0109 for 30-day mortality prediction for LR).

Conclusions

Our study demonstrated that the presence or absence of LT measurements is informative and can be considered a potential predictor of in-hospital and 30-day mortality. The comparative analysis of prediction models also showed statistically significant prediction improvement when indicators were included. Moreover, missing data might reflect the opinions of examining clinicians. Therefore, the absence of measurements can be informative in ICUs and has predictive power beyond the measured data themselves. This initial case study shows promise for more in-depth analysis of missing data and its informativeness in ICUs. Future studies are needed to generalize these results.

Serum anion gap on admission predicts intensive care unit mortality in patients with aortic aneurysm

It has been widely reported that the serum anion gap is significantly associated with mortality in intensive care unit (ICU); however, it remains unknown whether the association is present in aortic aneurysm (AA) patients. The present study aimed to investigate the association between the admission serum anion gap and ICU mortality in AA patients. Data extracted from a publicly accessible clinical database using a modifiable data mining technique were analyzed retrospectively, mainly by employing multivariable logistic regression analysis. The primary study outcome was ICU mortality. A total of 273 patient records were analyzed. The ICU mortality was 8.79% (24/273). The median serum anion gap was significantly higher in non-survivors [17.50 mEq/l, interquartile range (IQR) 15.75-22.50 mEq/l] compared with survivors [13.00 mEq/l, IQR 11.00-15.00 mEq/l, P<0.001]. Multivariate analysis resulted in identification of a clear association between admission serum anion gap and ICU mortality in AA patients [odds ratio (OR) 1.38 per 1 mEq/l increase, 95% confidence interval (CI) 1.08-1.76]. The area under the receiver operating characteristic curve showed an outstanding discrimination ability in predicting ICU mortality (area under curve 0.8513, 95% CI 0.7698-0.9328). In conclusion, admission serum anion gap may serve as a strong predictor of ICU mortality for AA patients.