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Back to the Basics or Back to the Future? The Art and Science of Predicting Clinical Deterioration in Hospitalized Children.

Unauthorized reproduction of this article is prohibited Pediatric Critical Care Medicine www.pccmjournal.org 839 clinical decline early and facilitate interventions to prevent harm (2, 3). These measures, including critical deterioration events and emergency transfers, describe patients who require lifesustaining intervention in a defined time period following ICU transfer (2, 3). Both measures have been associated with adverse patient-level outcomes, including increased length of stay and in-hospital mortality (2, 4). Detecting which patients are at risk for these events, however, represents its own unique challenge. Structured processes to enhance situation awareness, Pediatric Early Warning Score (PEWS) systems, and, more recently, automated clinical prediction models in the electronic health record (EHR) have attempted to enhance the early recognition of clinical decline with varying levels of success (3, 5–7). Balancing and integrating the value, feasibility, and efficiency of the tried-andtrue clinician assessment at the bedside with the automated and powerful data in the EHR have proven challenging in a health system with ever-increasing acuity and complexity. In this issue of Pediatric Critical Care Medicine, Mayampurath et al (8) describe an automated vital sign–based model to predict clinical deterioration in hospitalized children. In this study, the primary outcome of clinical deterioration was defined as any ward to ICU transfer; the secondary outcome was one of the aforementioned proximal measures to arrest critical deterioration events (2). The vital sign model was compared with a modified Bedside PEWS (BedsidePEWS), in which subjective components of the BedsidePEWS were removed (9). Key findings include that the vital sign model outperformed the modified BedsidePEWS in predicting ICU transfers at 12 and 24 hours before transfer, and outperformed the modified BedsidePEWS in predicting critical deterioration events at multiple time points from 6 to 24 hours before transfer. When considering feasibility and efficiency, there are multiple clear advantages to this model. It can be integrated into the EHR and uses existing vital sign data without dependence on external input. It can also provide automated and continuous scoring. This new prediction model also provides unique superior features when compared with previously described models. It is applicable to the duration of a hospital stay given the continuous acquisition of new vital sign data, and it is more generalizable to a pediatric population given the lack of matching to control patients during the derivation phase (6, 10). This prediction model certainly appeals to our desire as clinicians to leverage technology, big data, and machine learning to improve care for our patients as we move into an increasingly automated and technology-dependent future. But what about going back to the basics? Medical educators strive to continue to emphasize the critical importance of the history and bedside clinical assessments in an age Nobel prize–winning author Toni Morrison famously said, “All important things are hard.” This certainly seems to be the case for defining, detecting, and mitigating clinical deterioration in children. Fortunately, pediatric cardiopulmonary arrest, particularly outside of the ICU, is rare (1). Nevertheless, unrecognized deterioration can have devastating consequences for patients and families and lead to significant morbidity and potential mortality. Over the past decade, more frequent proximal measures of deterioration have been used as assessment metrics for initiatives that aim to recognize