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Prediction of imminent, severe deterioration of children with parallel circulations using real-time processing of
Craig G Rusin1, Sebastian I Acosta1, Lara S Shekerdemian2
1Department of Pediatrics-Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Tex.
Insights
Sudden death is a risk for infants with hypoplastic left heart syndrome. A new computer algorithm can detect subtle physiologic changes, providing an early warning of deterioration 1-2 hours before critical events.
Area of Science:
- Pediatric Cardiology
- Computational Biology
- Critical Care Medicine
Background:
- Hypoplastic left heart syndrome (HLHS) and similar conditions involve parallel systemic and pulmonary circulation, increasing sudden death risk.
- Acute deterioration in these patients often follows subtle, undetected physiologic changes.
- Early detection of impending clinical events is crucial for timely intervention.
Purpose of the Study:
- To develop a computer algorithm for real-time recognition of deterioration precursors in infants with parallel circulation.
- To provide an automated early warning system for clinical staff.
Main Methods:
- Continuous, high-resolution physiologic data collected from 25 infants with parallel circulation in a cardiovascular intensive care unit.
- Identification of cardiorespiratory deterioration events (requiring CPR or intubation) via chart review.
- Application and optimization of a classification algorithm to identify pre-deterioration physiologic patterns.
Main Results:
- Twenty deterioration events were identified in 13 of the 25 infants.
- The algorithm demonstrated high sensitivity and specificity in detecting impending deterioration.
- The system accurately predicted critical events 1-2 hours before overt clinical signs (ROC AUC = 0.91).
Conclusions:
- Automated, real-time analysis of physiologic data can identify subtle deterioration signs missed by clinicians.
- This algorithm serves as a valuable early warning indicator for critical deterioration in high-risk pediatric cardiac patients.
- The findings support the integration of intelligent systems into critical care for improved patient outcomes.
Objectives:
Sudden death is common in patients with hypoplastic left heart syndrome and comparable lesions with parallel systemic and pulmonary circulation from a common ventricular chamber. It is hypothesized that unforeseen acute deterioration is preceded by subtle changes in physiologic dynamics before overt clinical extremis. Our objective was to develop a computer algorithm to automatically recognize precursors to deterioration in real-time, providing an early warning to care staff.
Methods:
Continuous high-resolution physiologic recordings were obtained from 25 children with parallel systemic and pulmonary circulation who were admitted to the cardiovascular intensive care unit of Texas Children's Hospital between their early neonatal palliation and stage 2 surgical palliation. Instances of cardiorespiratory deterioration (defined as the need for cardiopulmonary resuscitation or endotracheal intubation) were found via a chart review. A classification algorithm was applied to both primary and derived parameters that were significantly associated with deterioration. The algorithm was optimized to discriminate predeterioration physiology from stable physiology.
Results:
Twenty cardiorespiratory deterioration events were identified in 13 of the 25 infants. The resulting algorithm was both sensitive and specific for detecting impending events, 1 to 2 hours in advance of overt extremis (receiver operating characteristic area = 0.91, 95% confidence interval = 0.88-0.94).
Conclusions:
Automated, intelligent analysis of standard physiologic data in real-time can detect signs of clinical deterioration too subtle for the clinician to observe without the aid of a computer. This metric may serve as an early warning indicator of critical deterioration in patients with parallel systemic and pulmonary circulation.
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