ArticlePediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies2020
Performance of a Clinical Decision Support Tool to Identify PICU Patients at High Risk for Clinical Deterioration.
Article in Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed.
- P-WIN deployment: real-time personalized machine learning to predict critical deterioration in a pediatric intensive care unit.JAMIA open · 2026Article
- Clinical decision support for potentially sedating drugs in palliative care: a mixed-methods pilot study.BMC palliative care · 2026Article
- Validation of a hospital-wide risk stratification model for predicting code events among critically ill children.Resuscitation plus · 2026Article
- Machine Learning-Based Prediction of Critical Deterioration in the PICU.Critical care explorations · 2026Article
- Machine Learning for Predicting Critical Events Among Hospitalized Children.JAMA network open · 2025Article
- Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies · 2025Article
- A Novel Situational Awareness Scoring System in Pediatric Cardiac Intensive Care Unit Patients.Journal of pediatric intensive care · 2024Article
- Usability Testing of Situation Awareness Clinical Decision Support in the Intensive Care Unit.Applied clinical informatics · 2024Article
- A New Neuroanesthetic Protocol of Rendering Specialized Care in Treating Degenerative Lumbar Spine Diseases in High-Risk Patients: Prospective Analysis of the Results.Sovremennye tekhnologii v meditsine · 2024Article
- Use of design thinking and human factors approach to improve situation awareness in the pediatric intensive care unit.Journal of hospital medicine · 2023Article
- Low Inadequate Oxygen Delivery Index is Associated with Decreased Cardiac Arrest Risk in High-Risk Pediatric ICU Patients.Critical care explorations · 2022Article
- Optimizing Situation Awareness to Reduce Emergency Transfers in Hospitalized Children.Pediatrics · 2021Article
- Pediatric In-Hospital Cardiac Arrest and Cardiopulmonary Resuscitation in the United States: A Review.JAMA pediatrics · 2021Review
- Deep Learning Model to Predict Serious Infection Among Children With Central Venous Lines.Frontiers in pediatrics · 2021Article
- Characteristics of Hot and Cold Debriefs for In-hospital Cardiac Arrest in the Pediatric Intensive Care Unit: A Mixed-methods Analysis.Pediatric quality & safetyArticle
- Sustainability of a PICU Situation Awareness Intervention: A Qualitative Study.Pediatric quality & safetyArticle
- Contextual Factors Affecting Implementation of In-hospital Pediatric CPR Quality Improvement Interventions in a Resuscitation Collaborative.Pediatric quality & safetyArticle
- Building an Adaptable Pediatric Intensive Care Unit Simulation Portfolio: Advancing Efficiency, Flexibility, and Team-based Training.Pediatric quality & safetyArticle
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Abstract
objectivesTo evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.
designRetrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.
settingTwo quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center. PATIENTS: The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.
interventionsWe replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration. MEASUREMENTS AND MAIN
resultsThe primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.
conclusionsPerformance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.
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