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Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle
Craig G Rusin1, Sebastian I Acosta1, Eric L Vu2
1Department of Pediatrics-Cardiology, Baylor College of Medicine, Texas Children's Hospital, Houston, Texas, USA.
Insights
This study developed an algorithm to predict cardiorespiratory deterioration in single-ventricle physiology patients. The algorithm provides 1-2 hours of warning, improving patient safety during interstage hospitalization.
Area of Science:
- Pediatric Cardiology
- Biomedical Engineering
- Critical Care Medicine
Background:
- Children with single-ventricle physiology face high cardiorespiratory deterioration risks between surgeries.
- Early detection of deterioration is crucial for timely intervention and improved outcomes.
Purpose of the Study:
- To develop and validate a real-time computer algorithm for recognizing cardiorespiratory deterioration precursors.
- To provide advanced warning for critical events in single-ventricle physiology patients during interstage hospitalization.
Main Methods:
- Retrospective analysis of prospectively collected physiological data (ECG, PPG) from 238 patients.
- Trained a logistic regression model to identify pre-deterioration physiological dynamics.
- Validated the algorithm on 50% of the data, defining deterioration as cardiac arrest or unplanned intubation.
Main Results:
- The algorithm achieved high sensitivity and specificity in detecting impending deterioration (ROC AUC: 0.958).
- It successfully predicted events 1-2 hours in advance.
- The algorithm generated a low rate of false alarms (1 per patient per day).
Conclusions:
- The developed algorithm offers 1-2 hours of advanced warning for 62% of cardiorespiratory deterioration events.
- This tool can significantly enhance patient safety for single-ventricle physiology children during the interstage period.
Background:
Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first and second stage palliation surgeries.
Objectives:
The objective of this study is to develop and validate a real-time computer algorithm that can automatically recognize physiological precursors of cardiorespiratory deterioration in children with single-ventricle physiology during their interstage hospitalization.
Methods:
A retrospective study was conducted from prospectively collected physiological data of subjects with single-ventricle physiology. Deterioration events were defined as a cardiac arrest requiring cardiopulmonary resuscitation or an unplanned intubation. Physiological metrics were derived from the electrocardiogram (heart rate, heart rate variability, ST-segment elevation, and ST-segment variability) and the photoplethysmogram (peripheral oxygen saturation and pleth variability index). A logistic regression model was trained to separate the physiological dynamics of the pre-deterioration phase from all other data generated by study subjects. Data were split 50/50 into model training and validation sets to enable independent model validation.
Results:
Our cohort consisted of 238 subjects admitted to the cardiac intensive care unit and stepdown units of Texas Children's Hospital over a period of 6 years. Approximately 300,000 h of high-resolution physiological waveform and vital sign data were collected using the Sickbay software platform (Medical Informatics Corp., Houston, Texas). A total of 112 cardiorespiratory deterioration events were observed. Seventy-two of the subjects experienced at least 1 deterioration event. The risk index metric generated by our optimized algorithm was found to be both sensitive and specific for detecting impending events 1 to 2 h in advance of overt extremis (receiver-operating characteristic curve area: 0.958; 95% confidence interval: 0.950 to 0.965).
Conclusions:
Our algorithm can provide 1 to 2 h of advanced warning for 62% of all cardiorespiratory deterioration events in children with single-ventricle physiology during their interstage period, with only 1 alarm being generated at the bedside per patient per day.
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