Automated prediction of cardiorespiratory deterioration in patients with single-ventricle parallel circulation: A multicenter validation study.

Objectives: Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes. Methods: A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm. Results: Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927). Conclusions: Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.

Mathematical analysis of hemoglobin target in univentricular parallel circulation.

OBJECTIVE: The hemoglobin threshold for a decision to transfuse red blood cells in univentricular patients with parallel circulation is unclear. A pediatric expertise initiative put forth a "weak recommendation" for avoiding reflexive transfusion beyond a hemoglobin of 9 g/dL. We have created a mathematical model to assess the impact of hemoglobin thresholds in patients with parallel circulation. METHODS: A univentricular circulation was mathematically modeled. We examined the impact on oxygen extraction ratios and systemic and venous oxygen saturations by varying hemoglobin levels, pulmonary to systemic blood flow ratios, and total cardiac output. RESULTS: Applying a total cardiac index of 6 L/m2/min, oxygen consumption of 150 mL/min/m2, and a Qp/Qs ∼ 1, we found a hemoglobin level of 9 g/dL would lead to severe arterial (arterial oxygen saturation <70%) and venous (systemic venous oxygen saturation <40%) hypoxemia. To operate above the critical oxygen economy boundary (systemic venous oxygen saturation ∼40%) and maintain arterial oxygen saturation >70% would require either increasing the cardiac index to âˆ¼ 9 L/m2/min or increasing the hemoglobin to greater than 13 g/dL. Further, we found a greater improvement in arterial and venous saturation arises when hemoglobin is augmented from levels below 12 g/dL. CONCLUSIONS: Based on our model, a hemoglobin level of 9 g/dL would require a constricted set of features to sustain arterial saturations >70% and systemic venous saturations >40% and would risk unfavorable oxygen economy with elevations in oxygen consumption. Further prospective clinical studies are needed to delineate the impact of restrictive transfusion practices in univentricular circulation.

Automated Prediction of Cardiorespiratory Deterioration in Patients With Single Ventricle.

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.

Prediction of imminent, severe deterioration of children with parallel circulations using real-time processing of physiologic data.

OBJECTIVES: Sudden death is common in patients with hypoplastic left heart syndrome and comparable lesions with parallel systemic and pulmonary circulation from a common ventricular chamber. It is hypothesized that unforeseen acute deterioration is preceded by subtle changes in physiologic dynamics before overt clinical extremis. Our objective was to develop a computer algorithm to automatically recognize precursors to deterioration in real-time, providing an early warning to care staff. METHODS: Continuous high-resolution physiologic recordings were obtained from 25 children with parallel systemic and pulmonary circulation who were admitted to the cardiovascular intensive care unit of Texas Children's Hospital between their early neonatal palliation and stage 2 surgical palliation. Instances of cardiorespiratory deterioration (defined as the need for cardiopulmonary resuscitation or endotracheal intubation) were found via a chart review. A classification algorithm was applied to both primary and derived parameters that were significantly associated with deterioration. The algorithm was optimized to discriminate predeterioration physiology from stable physiology. RESULTS: Twenty cardiorespiratory deterioration events were identified in 13 of the 25 infants. The resulting algorithm was both sensitive and specific for detecting impending events, 1 to 2 hours in advance of overt extremis (receiver operating characteristic area = 0.91, 95% confidence interval = 0.88-0.94). CONCLUSIONS: Automated, intelligent analysis of standard physiologic data in real-time can detect signs of clinical deterioration too subtle for the clinician to observe without the aid of a computer. This metric may serve as an early warning indicator of critical deterioration in patients with parallel systemic and pulmonary circulation.