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

Abstract

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 prior to overt clinical extremis. Our objective is 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 CVICU of Texas Children’s Hospital between their early neonatal palliation and stage 2 surgical palliation. Instances of cardiorespiratory deterioration (defined as the need for CPR 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 pre-deterioration 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, one to two hours in advance of overt extremis (ROC Area = 0.91, 95% CI = 0.88–0.94). Conclusion 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.

Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit

“…Advanced technologies using statistical methods and AI can be used to develop predictive models by detecting patterns in electronically recorded data that are more significant than the raw data itself. While a multivariate regression analysis may identify single vital signs parameters that are predictors of decompensation, predictive models incorporate many physiologic vital signs to contrast normal physiology to physiology that is displayed prior to the event of interest, such as a clinical decompensation (52) or sepsis (53).…”

“…A randomized controlled trial demonstrated a significant reduction in mortality due to sepsis in low birthweight infants monitored with this predictive system (53,56). The use of predictive models to develop a risk index was applied to patients with congenital heart disease by Rusin and colleagues (52). High resolution continuous physiologic data from single ventricle patients with parallel circulations was utilized to build a classification model based on differences in physiology between patients nearing a clinical decompensation compared to stable patients.…”

“…High resolution continuous physiologic data from single ventricle patients with parallel circulations was utilized to build a classification model based on differences in physiology between patients nearing a clinical decompensation compared to stable patients. As the goal of this model was to detect deterioration before obvious clinical signs developed, the hour of data immediately preceding the deterioration was not used in the classification model, as it is generally expected that obvious clinical signs of deterioration were present at that time (52). A risk index was created that measures similarity between a patients' current physiology and the physiology that was observed in infants who were approaching a clinical decompensation (52).…”

“…As the goal of this model was to detect deterioration before obvious clinical signs developed, the hour of data immediately preceding the deterioration was not used in the classification model, as it is generally expected that obvious clinical signs of deterioration were present at that time (52). A risk index was created that measures similarity between a patients' current physiology and the physiology that was observed in infants who were approaching a clinical decompensation (52). This process is unique and beneficial in its ability to continuously and in real-time calculate the risk of deterioration for a certain patient.…”

“…This process is unique and beneficial in its ability to continuously and in real-time calculate the risk of deterioration for a certain patient. This study also indicates the ability of predictive models to discriminate the minor physiologic differences that are present in predecompensation periods, such as alterations in ST segment variability, which is a variable that is not easily evaluated from most bedside monitoring equipment (52). Although data was collected prospectively, the model was created retrospectively, therefore it is difficult to determine when clinicians recognized deteriorating patients and responded with interventions, such as non-invasive positive pressure ventilation or escalation of inotropic support (52).…”