Adverse Events During Apnea Testing for the Determination of Death by Neurologic Criteria: A Single-Center, Retrospective Pediatric Cohort.

OBJECTIVES: To report the prevalence of adverse events in children undergoing apnea testing as part of the determination of death by neurologic criteria (DNC). DESIGN: Single-center, retrospective study. SETTING: Academic children's hospital that is a Level I Trauma Center. PATIENTS: All children who underwent apnea testing to determine DNC from July 2013 to June 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We abstracted the medical history, blood gases, ventilator settings, blood pressures, vasoactive infusions, intracranial pressures, chest radiographs, and echocardiograms for all apnea tests as well as any ancillary test. Adverse events were defined as hypotension, hypoxia, pneumothorax, arrhythmia, intracranial hypertension, and cardiac arrest. Fifty-eight patients had 105 apnea tests. Adverse events occurred in 21 of 105 apnea tests (20%), the most common being hypotension (15/105 [14%]) and hypoxia (4/105 [4%]). Five of 21 apnea tests (24%) with adverse events were terminated prematurely (three for hypoxia, one for hypotension, and one for both hypoxia and hypotension) but the patients did not require persistent escalation in care. In the other 16 of 21 apnea tests (76%) with adverse events, clinical changes were transient and managed by titrating vasoactive infusions or completing the apnea test. CONCLUSIONS: In our center, 20% of all apnea tests were associated with adverse events. Only 5% of all apnea tests required premature termination and the remaining 15% were completed and the adverse events resolved with medical care.

The Risk of Coding Racism into Pediatric Sepsis Care: The Necessity of Antiracism in Machine Learning.

Machine learning holds the possibility of improving racial health inequalities by compensating for human bias and structural racism. However, unanticipated racial biases may enter during model design, training, or implementation and perpetuate or worsen racial inequalities if ignored. Pre-existing racial health inequalities could be codified into medical care by machine learning without clinicians being aware. To illustrate the importance of a commitment to antiracism at all stages of machine learning, we examine machine learning in predicting severe sepsis in Black children, focusing on the impacts of structural racism that may be perpetuated by machine learning and difficult to discover. To move toward antiracist machine learning, we recommend partnering with ethicists and experts in model development, enrolling representative samples for training, including socioeconomic inputs with proximate causal associations to racial inequalities, reporting outcomes by race, and committing to equitable models that narrow inequality gaps or at least have equal benefit.