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Observational Study
. 2021 Jun 29;77(25):3184-3192.
doi: 10.1016/j.jacc.2021.04.072.

Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Craig G Rusin  1 , Sebastian I Acosta  2 , Eric L Vu  3 , Mubbasheer Ahmed  4 , Kennith M Brady  3 , Daniel J Penny  2
Affiliations
Observational Study

Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle

Craig G Rusin et al. J Am Coll Cardiol. .

Abstract

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.

Keywords: arrest prediction; clinical deterioration; data mining; forecasting; prediction algorithm; single-ventricle physiology.

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Conflict of interest statement

Funding Support and Author Disclosures This research was supported in part by the National Institutes of Health (1R56HL131574 and 1R01HL142994) and the American Heart Association (16BGIA27490024). Dr. Rusin is a cofounder of Medical Informatics Corp.; no funding was provided by the company to support this work. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Comment in

  • Prediction of Cardiac Arrest: A Dream or Reality?
    Ravishankar C, Naim MY. Ravishankar C, et al. J Am Coll Cardiol. 2021 Jun 29;77(25):3193-3194. doi: 10.1016/j.jacc.2021.04.074. J Am Coll Cardiol. 2021. PMID: 34167644 Free PMC article. No abstract available.

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