Background

Daycare and school closures prompted by shelter-in-place orders may have increased opportunities for unintentional ingestions among young children.

Objectives

We examined emergency department (ED) presentations for toxic exposures among young children during the COVID-19 pandemic in the San Francisco Bay Area, which had some of the strictest and most prolonged shelter-in-place policies in the United States.

Methods

We performed a retrospective cross-sectional study of children 0 to 5 years of age who presented with an ED International Statistical Classification of Diseases and Related Health Problems, Tenth Revision diagnosis code of toxic exposure within a tertiary care hospital system between March 16, 2016 and March 15, 2021. We considered the period after March 16, 2020 to represent the pandemic.

Results

During the pandemic, the absolute number of poisonings among young children remained stable. Overall, ED encounters within this cohort decreased by 55%, which doubled the relative toxic exposure rate per 1000 ED encounters from 4.99 (95% confidence interval [CI] 3.19-5.90) to 9.79 (95% CI 8.09-11.49). Rates of admission, severe medical complications, operating room case requests, and length of stay were not significantly different. Shelter-in-place was associated with significantly higher odds of cannabis ingestion (odds ratio = 2.70, 95% CI 1.60-4.49).

Conclusion

Despite dramatic decreases in overall ED patient volumes, the absolute number and severity of toxic exposures were similar during the pandemic compared with previous years. © 2022 Elsevier Inc.

Objective

The Pediatric Emergency Care Applied Research Network (PECARN) has developed a clinical-decision instrument (CDI) to identify children at very low risk of intra-abdominal injury. However, the CDI has not been externally validated. We sought to vet the PECARN CDI with the Predictability Computability Stability (PCS) data science framework, potentially increasing its chance of a successful external validation.

Materials & methods

We performed a secondary analysis of two prospectively collected datasets: PECARN (12,044 children from 20 emergency departments) and an independent external validation dataset from the Pediatric Surgical Research Collaborative (PedSRC; 2,188 children from 14 emergency departments). We used PCS to reanalyze the original PECARN CDI along with new interpretable PCS CDIs developed using the PECARN dataset. External validation was then measured on the PedSRC dataset.

Results

Three predictor variables (abdominal wall trauma, Glasgow Coma Scale Score <14, and abdominal tenderness) were found to be stable. A CDI using only these three variables would achieve lower sensitivity than the original PECARN CDI with seven variables on internal PECARN validation but achieve the same performance on external PedSRC validation (sensitivity 96.8% and specificity 44%). Using only these variables, we developed a PCS CDI which had a lower sensitivity than the original PECARN CDI on internal PECARN validation but performed the same on external PedSRC validation (sensitivity 96.8% and specificity 44%).

Conclusion

The PCS data science framework vetted the PECARN CDI and its constituent predictor variables prior to external validation. We found that the 3 stable predictor variables represented all of the PECARN CDI's predictive performance on independent external validation. The PCS framework offers a less resource-intensive method than prospective validation to vet CDIs before external validation. We also found that the PECARN CDI will generalize well to new populations and should be prospectively externally validated. The PCS framework offers a potential strategy to increase the chance of a successful (costly) prospective validation.