Lactate, Base Excess, and the Pediatric Index of Mortality: Exploratory Study of an International, Multicenter Dataset

Prognostic Markers in Pediatric Acute Liver Failure

Introduction

Acute liver failure (ALF), although rare in children, is a complex progressive pathology, with multisystem involvement and high mortality. Isolated variables or those included in prognostic scores have been studied, to optimize organ allocation. However, its validation is challenging. This study aimed to assess the accuracy of several biomarkers and scores as predictors of prognosis in pediatric ALF (PALF).

Methods

An observational study with retrospective data collection, including all cases of ALF, was defined according to the criteria of the Pediatric Acute Liver Failure Study Group, admitted to a pediatric intensive care unit (PICU) for 28 years. Two groups were defined: spontaneous recovery (SR) and non-SR (NSR) - submitted to liver transplantation (LT) or death at PICU discharge.

Results

Fifty-nine patients were included, with a median age of 24 months, and 54% were female. The most frequent etiologies were metabolic (25.4%) and infectious (18.6%); 32.2% were undetermined. SR occurred in 21 patients (35.6%). In NSR group (N = 38, 64.4%), 25 required LT (42.4%) and 19 died (32.2%), 6 (15.7%) of whom after LT. The accuracy to predict NSR was acceptable for lactate at admission (AUC 0.72; 95% CI: 0.57-0.86; p = 0.006), ammonia peak (AUC 0.72; 95% CI: 0.58-0.86; p = 0.006), and INR peak (AUC 0.70; 95% CI: 0.56-0.85; p = 0.01). The cut-off value for lactate at admission was 1.95 mmol/L (sensitivity 78.4% and specificity 61.9%), ammonia peak was 64 μmol/L (sensitivity 100% and specificity 38.1%), and INR peak was 4.8 (sensitivity 61.1% and specificity 76.2%). Lactate on admission was shown to be an independent predictor of NSR on logistic regression model. Two prognostic scores had acceptable discrimination for NSR, LIU (AUC 0.73; 95% CI: 0.59-0.87; p = 0.004) and PRISM (AUC 0.71; 95% CI: 0.56-0.86; p = 0.03). In our study, the PALF delta score (PALF-ds) had lower discrimination capacity (AUC 0.63; 95% CI: 0.47-0.78; p = 0.11).

Conclusions

The lactate at admission, an easily obtained parameter, had a similar capacity than the more complex scores, LIU and PRISM, to predict NSR. The prognostic value in our population of the promising dynamic score, PALF-ds, was lower than expected.

Editor's Choice Articles for September

The September 2023 issue and this year has already proven to be important for improving our understanding of pediatric acute respiratory distress syndrome (PARDS); Pediatric Critical Care Medicine (PCCM) has published 16 articles so far. Therefore, my three Editor's Choice articles this month highlight yet more PCCM material about PARDS by covering the use of noninvasive ventilation (NIV), the trajectory in cytokine profile during illness, and a new look at lung mechanics. The PCCM Connections for Readers give us the opportunity to focus on some clinical biomarkers of severity and mortality risk during critical illness.

Serum lactate and the mortality of critically ill patients in the emergency department: A retrospective study

Serum lactate levels have been widely studied as a prognostic marker in critically ill patients, particularly those in the intensive care unit. However, it remains unknown whether the serum lactate levels affect the mortality rate of critically ill patients admitted to hospital. To investigate this hypothesis, the vital signs and blood gas analysis data of 1,393 critically ill patients who visited the Emergency Department of Affiliated Kunshan Hospital of Jiangsu University (Kunshan, China) between January and December 2021 were collected. Patients were divided into two groups, 30-day survival group and a 30-day death group, and logistic regression analysis was used to investigate the association between vital signs, laboratory results and mortality rates of critically ill patients. A total of 1,393 critically ill patients was enrolled in the present study, with a male-to-female ratio of 1.17:1.00, a mean age of 67.72±19.29 years and a mortality rate of 11.6%. The multivariate logistic regression analysis revealed that increased serum lactate levels were an independent risk factor for mortality rate of critically ill patients [Odds ratio (OR)=1.50, 95% confidence interval (95% CI): 1.40-1.62]. The critical cut-off value for the serum lactate levels was identified as 2.35 mmol/l. In addition, OR values of age, heart rate, systolic blood pressure, transcutaneous oxygen saturation (SpO₂) and hemoglobin were 1.02, 1.01, 0.99, 0.96 and 0.99, respectively (95% CI: 1.01-1.04, 1.00-1.02, 0.98-0.99, 0.94-0.98 and 0.98-1.00, respectively). The logistic regression model was found to be of value in terms of identifying the mortality rate of patients and the area under the receiver operating characteristic curve was 0.894 (95% CI: 0.863-0.925; P<0.001). In conclusion, the present study showed that high serum lactate levels in critically ill patients upon admission to hospital are associated with higher 30-day mortality rate.

Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study

PURPOSE

Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU).

METHODS

Population-based study of children < 16 years requiring ICU admission in Queensland, Australia, between 1997 and 2019. Failure to meet the National Minimum Standard (NMS) in the National Assessment Program-Literacy and Numeracy (NAPLAN) assessment during primary and secondary school was the primary outcome. Routine ICU information was used to train machine learning classifiers. Models were trained, validated and tested using stratified nested cross-validation.

RESULTS

13,957 childhood ICU survivors with 37,200 corresponding NAPLAN tests after a median follow-up duration of 6 years were included. 14.7%, 17%, 15.6% and 16.6% failed to meet NMS in school grades 3, 5, 7 and 9. The model demonstrated an Area Under the Receiver Operating Characteristic curve (AUROC) of 0.8 (standard deviation SD, 0.01), with 51% specificity to reach 85% sensitivity [relative Area Under the Precision Recall Curve (rel-AUPRC) 3.42, SD 0.06]. Socio-economic status, illness severity, and neurological, congenital, and genetic disorders contributed most to the predictions. In children with no comorbidities admitted between 2009 and 2019, the model achieved a AUROC of 0.77 (SD 0.03) and a rel-AUPRC of 3.31 (SD 0.42).

CONCLUSIONS

A machine learning model using data available at time of ICU discharge predicted failure to meet minimum educational requirements at school age. Implementation of this prediction tool could assist in prioritizing patients for follow-up and targeting of rehabilitative measures.

The pyruvate dehydrogenase complex: Life's essential, vulnerable and druggable energy homeostat

Found in all organisms, pyruvate dehydrogenase complexes (PDC) are the keystones of prokaryotic and eukaryotic energy metabolism. In eukaryotic organisms these multi-component megacomplexes provide a crucial mechanistic link between cytoplasmic glycolysis and the mitochondrial tricarboxylic acid (TCA) cycle. As a consequence, PDCs also influence the metabolism of branched chain amino acids, lipids and, ultimately, oxidative phosphorylation (OXPHOS). PDC activity is an essential determinant of the metabolic and bioenergetic flexibility of metazoan organisms in adapting to changes in development, nutrient availability and various stresses that challenge maintenance of homeostasis. This canonical role of the PDC has been extensively probed over the past decades by multidisciplinary investigations into its causal association with diverse physiological and pathological conditions, the latter making the PDC an increasingly viable therapeutic target. Here we review the biology of the remarkable PDC and its emerging importance in the pathobiology and treatment of diverse congenital and acquired disorders of metabolic integration.