Comparison of a new predictive model with other critical scores for predicting in-hospital mortality among children with pneumonia-related bacteremia

Value of cardiac enzyme spectrum for the risk assessment of mortality in critically ill children: a single-centre retrospective study

OBJECTIVES

Identifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effects of different types of cardiac enzyme spectrum within 24 hours of admission on the short-term prognosis of patients in paediatric intensive care units.

DESIGN

A retrospective study.

SETTING

A single-centre, tertiary care hospital in China, with patient data from 2010 to 2018.

PARTICIPANTS

A total of 4343 critically ill children were enrolled.

INTERVENTION

None.

PRIMARY AND SECONDARY OUTCOME MEASURES

The main outcome measure was in-hospital mortality, which was defined as death from any cause during hospitalisation. The secondary outcome was 30-day mortality, intensive care unit (ICU) length of stay (LOS) and total LOS.

RESULTS

Using the local polynomial regression fitting method, an approximately linear increase in in-hospital mortality was detected for creatine kinase (CK), creatine kinase MB (CK-MB), aspartate aminotransferase (AST) and lactate dehydrogenase (LDH). Among the different types of cardiac enzyme spectrum, LDH had the highest area under the curve value (0.729), followed by AST (0.701), CK-MB (0.613) and CK (0.557). The Kaplan‒Meier analysis showed that the patients in the high LDH group had higher 30-day mortality. The multivariate logistic regression revealed that high LDH was independently associated with in-hospital mortality (OR 2.45, 95% CI 1.84 to 3.24). After propensity score matching (PSM) and sensitivity analysis, the results remained consistent.

CONCLUSIONS

LDH is a reliable outcome predictor in critically ill children, including those with various comorbidities.

Development and validation of a nomogram to predict the risk of sepsis-associated encephalopathy for septic patients in PICU: a multicenter retrospective cohort study

BACKGROUND

Patients with sepsis-associated encephalopathy (SAE) have higher mortality rates and longer ICU stays. Predictors of SAE are yet to be identified. We aimed to establish an effective and simple-to-use nomogram for the individual prediction of SAE in patients with sepsis admitted to pediatric intensive care unit (PICU) in order to prevent early onset of SAE.

METHODS

In this retrospective multicenter study, we screened 790 patients with sepsis admitted to the PICU of three hospitals in Shandong, China. Least absolute shrinkage and selection operator regression was used for variable selection and regularization in the training cohort. The selected variables were used to construct a nomogram to predict the risk of SAE in patients with sepsis in the PICU. The nomogram performance was assessed using discrimination and calibration.

RESULTS

From January 2017 to May 2022, 613 patients with sepsis from three centers were eligible for inclusion in the final study. The training cohort consisted of 251 patients, and the two independent validation cohorts consisted of 193 and 169 patients. Overall, 237 (38.7%) patients developed SAE. The morbidity of SAE in patients with sepsis is associated with the respiratory rate, blood urea nitrogen, activated partial thromboplastin time, arterial partial pressure of carbon dioxide, and pediatric critical illness score. We generated a nomogram for the early identification of SAE in the training cohort (area under curve [AUC] 0.82, 95% confidence interval [CI] 0.76-0.88, sensitivity 65.6%, specificity 88.8%) and validation cohort (validation cohort 1: AUC 0.80, 95% CI 0.74-0.86, sensitivity 75.0%, specificity 74.3%; validation cohort 2: AUC 0.81, 95% CI 0.73-0.88, sensitivity 69.1%, specificity 83.3%). Calibration plots for the nomogram showed excellent agreement between SAE probabilities of the observed and predicted values. Decision curve analysis indicated that the nomogram conferred a high net clinical benefit.

CONCLUSIONS

The novel nomogram and online calculator showed performance in predicting the morbidity of SAE in patients with sepsis admitted to the PICU, thereby potentially assisting clinicians in the early detection and intervention of SAE.

Predicting Clinical Deterioration and Mortality at Differing Stages During Hospitalization: A Systematic Review of Risk Prediction Models in Children in Low- and Middle-Income Countries

OBJECTIVE

To determine which risk prediction model best predicts clinical deterioration in children at different stages of hospital admission in low- and middle-income countries.

METHODS

For this systematic review, Embase and MEDLINE databases were searched, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The key search terms were "development or validation study with risk-prediction model" AND "deterioration or mortality" AND "age 0-18 years" AND "hospital-setting: emergency department (ED), pediatric ward (PW), or pediatric intensive care unit (PICU)" AND "low- and middle-income countries." The Prediction Model Risk of Bias Assessment Tool was used by two independent authors. Forest plots were used to plot area under the curve according to hospital setting. Risk prediction models used in two or more studies were included in a meta-analysis.

RESULTS

We screened 9486 articles and selected 78 publications, including 67 unique predictive models comprising 1.5 million children. The best performing models individually were signs of inflammation in children that can kill (SICK) (ED), pediatric early warning signs resource limited settings (PEWS-RL) (PW), and Pediatric Index of Mortality (PIM) 3 as well as pediatric sequential organ failure assessment (pSOFA) (PICU). Best performing models after meta-analysis were SICK (ED), pSOFA and Pediatric Early Death Index for Africa (PEDIA)-immediate score (PW), and pediatric logistic organ dysfunction (PELOD) (PICU). There was a high risk of bias in all studies.

CONCLUSIONS

We identified risk prediction models that best estimate deterioration, although these risk prediction models are not routinely used in low- and middle-income countries. Future studies should focus on large scale external validation with strict methodological criteria of multiple risk prediction models as well as study the barriers in the way of implementation.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews: Prospero ID: CRD42021210489.