Analyzing Continuous Physiologic Data to Find Hemodynamic Signatures Associated With New Brain Injury After Congenital Heart Surgery

Antenatal diagnosis, neonatal brain volumes, and neurodevelopment in transposition of the great arteries

AIM

To examine whether antenatal diagnosis modifies relationships between neonatal brain volumes and 18-month neurodevelopmental outcomes in children with transposition of the great arteries (TGA).

METHOD

In a retrospective cohort of 139 children with TGA (77 antenatally diagnosed), we obtained total brain volumes (TBVs) on pre- (n = 102) and postoperative (n = 112) magnetic resonance imaging. Eighteen-month neurodevelopmental outcomes were assessed using the Bayley Scales of Infant and Toddler Development, Third Edition. Generalized estimating equations with interaction terms were used to determine whether antenatal diagnosis modified associations between TBVs and neurodevelopmental outcomes accounting for postmenstrual age at scan, brain injury, and ventricular septal defect.

RESULTS

Infants with postnatal diagnosis had more preoperative hypotension (35% vs 14%, p = 0.004). The interactions between antenatal diagnosis and TBVs were significantly related to cognitive (p = 0.003) outcomes. Specifically, smaller TBVs were associated with lower cognitive scores in infants diagnosed postnatally; this association was attenuated in those diagnosed antenatally.

INTERPRETATION

Antenatal diagnosis modifies associations between neonatal brain volume and 18-month cognitive outcome in infants with TGA. These findings suggest that antenatal diagnosis may be neuroprotective, possibly through improved preoperative clinical status. These data highlight the need to improve antenatal diagnosis rates.

WHAT THIS PAPER ADDS

Antenatal diagnosis of transposition of the great arteries modified relationships between neonatal brain volume and neurodevelopment. Smaller brain volumes related to poorer cognitive scores with postnatal diagnosis only. There was more preoperative hypotension in the postnatal diagnosis group.

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.