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Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data

Published online by Cambridge University Press:  09 September 2019

Sanjukta N. Bose Open the ORCID record for Sanjukta N. Bose [Opens in a new window] ,
Adam Verigan ,
Jade Hanson ,
Luis M. Ahumada ,
Sharon R. Ghazarian ,
Neil A. Goldenberg ,
Arabela Stock  and
Jeffrey P. Jacobs
Sanjukta N. Bose
Affiliation:
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
Adam Verigan
Affiliation:
Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA
Jade Hanson
Affiliation:
Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA Johns Hopkins All Children’s Clinical and Translational Research Organization and All Children’s Research Institute, St. Petersburg, FL, USA
Luis M. Ahumada
Affiliation:
Johns Hopkins All Children’s Health Informatics Core, St. Petersburg, FL, USA
Sharon R. Ghazarian
Affiliation:
Johns Hopkins All Children’s Health Informatics Core, St. Petersburg, FL, USA
Neil A. Goldenberg
Affiliation:
Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA Johns Hopkins All Children’s Clinical and Translational Research Organization and All Children’s Research Institute, St. Petersburg, FL, USA Departments of Pediatrics and Medicine, Divisions of Hematology, Johns Hopkins School of Medicine, Baltimore, MD, USA
Arabela Stock
Affiliation:
Division of Cardiology and Critical Care, New York Presbyterian Weill-Cornell Medical Center, NY, USA
Jeffrey P. Jacobs*
Affiliation:
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital, St. Petersburg, FL, USA Johns Hopkins All Children’s Clinical and Translational Research Organization and All Children’s Research Institute, St. Petersburg, FL, USA Johns Hopkins All Children’s Health Informatics Core, St. Petersburg, FL, USA Departments of Pediatrics and Medicine, Divisions of Hematology, Johns Hopkins School of Medicine, Baltimore, MD, USA Division of Cardiology and Critical Care, New York Presbyterian Weill-Cornell Medical Center, NY, USA
*
Author for correspondence: J. P. Jacobs, MD, 2021 Brightwaters Blvd., Saint Petersburg, Florida 33704. Tel: (727) 235-3100; Fax: (727) 551-0404; E-mail: [email protected]
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Abstract

Objective:

To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.

Methods:

We performed a single-institution retrospective cohort study (11 January 2013–16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children’s hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model’s discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.

Results:

The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.

Conclusions:

Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.

Keywords

Cardiac arrestcardiothoracic surgeryneonateinfantintensive carephysiologic monitoring
Type
Original Article
Information
Cardiology in the Young , Volume 29 , Issue 11 , November 2019 , pp. 1340 - 1348
Copyright
© Cambridge University Press 2019 

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Footnotes

‡

The original version of this article was published with one incorrect author name. A notice detailing this has been published and the error rectified in the online and print PDF and HTML copies.

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