Decreased tissue oxygenation in newborns with congenital heart defects: a case-control study

Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development

BACKGROUND

Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.

OBJECTIVE

This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.

METHODS

Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO₂) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.

RESULTS

A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.

CONCLUSIONS

In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.

Thinking Out of the (Big) Box: A Wearable Near-Infrared Spectroscopy Monitor for the Helicopter Emergency Medical Service

OBJECTIVE

In prehospital helicopter emergency medical service (HEMS) settings, the medical team has limited monitoring options, usually restricted to systemic variables. Regional tissue oxygenation (rO₂) can be assessed by near-infrared spectroscopy (NIRS), but clinical NIRS monitors are unpractical ("big boxes" and additional cables) in HEMS. As an alternative, we identified a wearable, athlete training NIRS device (Moxy; Idiag, Fehraltorf, Switzerland) and hypothesized that it would be applicable in our HEMS setting.

METHODS

This feasibility study was performed at the Dutch HEMS Lifeliner 1. The Moxy sensor was tested in-flight and on ground. We tested various anatomic measurement spots, and multiple conditions and interventions were imposed to track rO₂.

RESULTS

The rO₂ measurements with the wearable Moxy NIRS device are both feasible and practical in an HEMS setting. Multiple conditions and interventions were tested successfully (eg, tourniquet placement [rO₂↓], muscle compression [rO₂↓], reperfusion [rO₂↑], oxygen administration [rO₂↑], hyperemia [rO₂↑], and venous congestion [rO₂↓]).

CONCLUSION

Our results suggest that rO₂ measurements with the wearable Moxy NIRS device are both feasible and practical in HEMS, and Moxy allows the tracking of simulated pathophysiologic effects on rO₂. Future studies will have to verify our preliminary data and elucidate if and how wearable NIRS monitoring may support treatment in HEMS and improve patient outcome.

Blood Glucose and Lactate Levels and Cerebral Oxygenation in Preterm and Term Neonates-A Systematic Qualitative Review of the Literature

Background: Cerebral oxygenation monitored non-invasively by near-infrared spectroscopy (NIRS) is of increasing interest in neonatal care. Cerebral oxygenation is determined by cerebral oxygen delivery and cerebral oxygen consumption. Oxygen delivery as well as oxygen consumption might be influenced by metabolic parameters like blood glucose and lactate. Objective: The aim of the present systematic qualitative review is therefore to identify and summarize all studies, which describe cerebral oxygenation measured with NIRS and blood glucose and/or blood lactate levels in neonates. Data sources: A systematic search of Ovid Embase and PubMed was performed. Search terms included near-infrared spectroscopy, fractional tissue oxygen extraction, cerebral tissue oxygen saturation, regional cerebral tissue oxygen saturation, oxygenation, term, and preterm neonates, cesarean delivery, transition, after-birth, newborn, vaginal delivery, cesarean delivery, baby, neonatal transition, metabolism, lactate, glucose, and blood glucose level. Study selection/data synthesis: Studies analyzing cerebral oxygenation and blood glucose and/or blood lactate levels in neonates were included. Animal studies, duplicates, or studies in non-English language were excluded. Results: Twenty-five studies were identified that describe blood glucose and/or blood lactate levels as primary or secondary outcome parameters with additional measured cerebral oxygenation by NIRS in neonates. Twelve studies were included with blood glucose measurements: four described an association between blood glucose levels and cerebral oxygenation, two show no association, and six do not report on possible associations. Eighteen studies were included with lactate measurements: one describe an association between lactate levels and cerebral oxygenation, while three show no association and 14 do not report on possible associations. Discussion: The influence of blood glucose and blood lactate levels on the cerebral oxygenation in neonates is still controversial. However, there seems to be an association between cerebral oxygenation and the metabolic parameter blood glucose and lactate, which need further investigation.