Assessment of global tissue perfusion and oxygenation in neonates and infants after open-heart surgery

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children

Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes existing evidence on novel approaches to continuous cardiorespiratory monitoring in hospitalized children. We aimed to identify opportunities for the development of monitoring technology and the use of machine learning to analyze continuous physiological data to improve the outcomes of hospitalized children. We included original research articles published on or after January 1, 2001, involving novel approaches to collect and use continuous cardiorespiratory physiological data in hospitalized children. OVID Medline, PubMed, and Embase databases were searched. We screened 2909 articles and performed full-text extraction of 105 articles. We identified 58 articles describing novel devices or approaches, which were generally small and single-center. In addition, we identified 47 articles that described the use of continuous physiological data in prediction models, but only 7 integrated multidimensional data (e.g., demographics, laboratory results). We identified three areas for development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using continuous cardiorespiratory data. IMPACT: We performed a comprehensive scoping review of novel approaches to capture and use continuous cardiorespiratory physiological data for monitoring, diagnosis, providing care, and predicting events in hospitalized infants and children, from novel devices to machine learning-based prediction models. We identified three key areas for future development: (1) further validation of promising novel devices; (2) more studies of models integrating multidimensional data with continuous cardiorespiratory data; and (3) further dissemination, implementation, and validation of prediction models using cardiorespiratory data.

Transfusion Decision Making in Pediatric Critical Illness

Transfusion decision making (TDM) in the critically ill requires consideration of: (1) anemia tolerance, which is linked to active pathology and to physiologic reserve, (2) differences in donor RBC physiology from that of native RBCs, and (3) relative risk from anemia-attributable oxygen delivery failure vs hazards of transfusion, itself. Current approaches to TDM (e.g. hemoglobin thresholds) do not: (1) differentiate between patients with similar anemia, but dissimilar pathology/physiology, and (2) guide transfusion timing and amount to efficacy-based goals (other than resolution of hemoglobin thresholds). Here, we explore approaches to TDM that address the above gaps.