Predicting Clinical Deterioration in Mechanically Ventilated Children using High-Frequency Physiologic Data - PROJECT SUMMARY Significance: Clinical deterioration in the PICU, as defined by new or progressive organ dysfunction, occurs in up to 5% of critically ill children during their first week of admission. Importantly, patients with progressive organ dysfunction have a mortality rate as high as 50%, with over 20% of survivors experiencing moderate to severe disability. Earlier recognition of patients at high risk of clinical deterioration is critically needed to inform treatment decisions and personalize health care delivery, which would lead to improved patient outcomes. However, timely prediction is challenging due to limitations in existing models. The few models that try to predict clinical deterioration do not leverage metrics from high-frequency physiological monitor data, such as heart rate variability (HRV), despite evidence that these data are highly predictive of deterioration. For the proposed K23 award, I will examine clinical deterioration in the context of new or worsening cardiovascular and/or respiratory (cardiorespiratory) dysfunction in mechanically ventilated children. My central hypothesis is that a longitudinal, multivariable prediction model of clinical deterioration that incorporates high-frequency physiological monitor data and clinical variables will accurately identify children at risk of new or worsening cardiorespiratory dysfunction and that this model will be useful and acceptable to clinicians. Aim 1. Derive, validate, and compare longitudinal prediction models of new or worsening cardiorespiratory dysfunction in critically ill children requiring mechanical ventilation. Aim 2. Design for the implementation of a clinical deterioration model to improve use in practice. Innovation: Our innovative approach includes (1) high-frequency physiological data, (2) advanced machine learning applications, and (3) a user-centered design approach to optimize the implementation plan. Approach: Aim 1: I will derive and compare machine learning-based prediction models using physiological and EHR-based clinical data in a single center. I will then perform internal and external validation of the best performing model. Aim 2: Using user-centered design principles with critical care clinicians, I will elicit the ideal packaging and implementation plan for incorporating a clinical deterioration model into practice. Then, I will lead iterative usability testing of the design and implementation plan to assess acceptability, usefulness, and desirability. This approach will identify the ideal packaging and implementation plan for the clinicians who will ultimately use the model. Anticipated Outcomes and Public Heath Relevance: This study will provide the foundation to: (1) develop accurate prediction models of deterioration that leverage continuous physiological measures and clinical variables; (2) implement useful prediction models at the bedside to support clinical decision making; and (3) design intervention trials to test the impact on patient outcomes.
