Utility of the END-IT Score to Predict the outcome of Childhood Status Epilepticus: A Retrospective Cohort Study

Nonconvulsive status epilepticus in neurocritical care: A critical reappraisal of outcome prediction scores

OBJECTIVE

Nonconvulsive status epilepticus (NCSE) is a frequent condition in the neurocritical care unit (NCCU) patient population, with high morbidity and mortality. We aimed to assess the validity of available outcome prediction scores for prognostication in an NCCU patient population in relation to their admission reason (NCSE vs. non-NCSE related).

METHODS

All 196 consecutive patients diagnosed with NCSE during the NCCU stay between January 2010 and December 2020 were included. Demographics, Simplified Acute Physiology Score II (SAPS II), NCSE characteristics, and in-hospital and 3-month outcome were extracted from the electronic charts. Status Epilepticus Severity Score (STESS), Epidemiology-Based Mortality Score in Status Epilepticus (EMSE), and encephalitis, NCSE, diazepam resistance, imaging features, and tracheal intubation score (END-IT) were evaluated as previously described. Univariable and multivariable analysis and comparison of sensitivity/specificity/positive and negative predictive values/accuracy were performed.

RESULTS

A total of 30.1% died during the hospital stay, and 63.5% of survivors did not achieve favorable outcome at 3 months after onset of NCSE. Patients admitted primarily due to NCSE had longer NCSE duration and were more likely to be intubated at diagnosis. The receiver operating characteristic (ROC) for SAPS II, EMSE, and STESS when predicting mortality was between .683 and .762. The ROC for SAPS II, EMSE, STESS, and END-IT when predicting 3-month outcome was between .649 and .710. The accuracy in predicting mortality/outcome was low, when considering both proposed cutoffs and optimized cutoffs (estimated using the Youden Index) as well as when adjusting for admission reason.

SIGNIFICANCE

The scores EMSE, STESS, and END-IT perform poorly when predicting outcome of patients with NCSE in an NCCU environment. They should be interpreted cautiously and only in conjunction with other clinical data in this particular patient group.

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.