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.

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.

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%.

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.

During off-pump coronary bypass grafting, surgical manipulation and dislocation of the heart may cause cardiovascular instability. Monitoring of cardiac output facilitates intraoperative haemodynamic management but pulmonary artery catheters are often considered too invasive. Pulse contour analysis and transoesophageal echocardiography could serve as alternatives, but there is controversy about their accuracies. We validated pulse contour analysis using a standard radial arterial catheter (PulseCO™) and aortic Doppler flowmetry with transoesophageal echocardiography in patients undergoing off-pump coronary bypass surgery. Pulmonary arterial thermodilution served as the reference technique.

In 20 patients undergoing off-pump coronary bypass, cardiac output was measured with bolus thermodilution (COTD), pulse contour analysis (COPC), and transoesophageal echocardiography (COecho) at fixed time intervals during the procedure. Data were compared using linear regression and Bland–Altman analysis. At the end of the procedure, dobutamine was infused at a rate of 2.5 μg kg−1 min−1 in six patients to study the agreement between methods in quantifying changes in cardiac output.

Comparison between COPC and COTD showed a bias ± limits of agreement of −0.03 ± 1.30 L min−1 (mean error 29%). Doppler echocardiography was not always feasible when the heart was displaced from the oesophagus and had lower accuracy: bias ± limits of agreement vs. COTD was 0.45 ± 1.93 (mean error 43%). Increases in cardiac output induced by dobutamine were well quantified both by pulse contour analysis (COPC = 0.76 × COTD + 0.58; r2 = 0.65) and Doppler, although the latter tended to overestimate these changes (COecho = 1.58 × COTD − 0.13; r2 = 0.53).

Calibrated pulse contour analysis using the PulseCO system is an acceptable technique to measure cardiac output non-invasively in off-pump coronary bypass patients. Doppler echocardiography performs less well and is not always feasible with transoesophageal echocardiography when the heart is displaced.