Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Craig G. Rusin*, Sebastian I. Acosta, Eric L. Vu, Mubbasheer Ahmed, Kennith M. Brady, Daniel J. Penny

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)3184-3192
Number of pages9
JournalJournal of the American College of Cardiology
Issue number25
StatePublished - Jun 29 2021


  • arrest prediction
  • clinical deterioration
  • data mining
  • forecasting
  • prediction algorithm
  • single-ventricle physiology

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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