Time-Surrogate Variables Enhance the Association Between Cardiotocographic Features and Intrapartum Hypoxic-Ischemic Encephalopathy
Notice bibliographique
Résumé
Abstract Background Interruptions to the flow of oxygenated blood to the fetal brain during labor can lead to hypoxic-ischemic encephalopathy (HIE). Timely preventive interventions for suspected hypoxemia are crucial to avoid progression to neurological injury. Prior research has used features from fetal heart rate (FHR) and uterine activity (UA) signals to predict adverse fetal outcomes. However, these systems focused only on the end of labor, when preventive measures are unlikely effective. Previously, we demonstrated that accounting for proximity to birth improved the association between FHR and UA features and the outcome group. Although proximity to birth cannot be known prospectively, other time-surrogate variables (TSVs) may be as useful. Methods We analyzed intrapartum data from 152,761 vaginal births, comprising 150,813 with healthy outcomes, 1,793 with perinatal acidosis, and 155 with confirmed HIE. Classical and novel FHR features were extracted across labor durations of up to 72 hours. This dataset represents the largest cohort of intrapartum FHR and UA signals to date, both in participant count and signal length. We evaluated four alternative TSVs to evaluate their ability to enhance the association of CTG features and the development of HIE. To do so, we applied information-theoretic methods to quantify their contribution to the association of fetal outcomes with FHR and UA features. Findings Our results show that the cumulative contraction duration and the time from labor onset (TLO) were the most effective TSVs for strengthening the association FHR, UA, and fetal outcome. However, it is more practical to track TLO in clinical settings than a continuous contraction monitoring. Conclusion TLO is the most suitable TSV for prospective intrapartum CTG evaluation. Incorporating it may substantially enhance the performance of automated systems for early detection of intrapartum HIE. Author summary Our study addressed a significant challenge in the development of classifiers to detect fetuses at high risk of developing hypoxic-ischemic encephalopathy (HIE) during labor. Most previous classifiers have concentrated on the end of labor, overlooking the dynamic evolution of FHR and UA patterns. This limits their clinical utility, as preventive interventions late in labor are unlikely to avert adverse fetal outcomes. To address this gap, we evaluated several TSVs that capture the progression of FHR and UA patterns and could support prospective, clinically usable intrapartum classifiers. Analyzing data from over 150,000 births, we found that time since labor onset was the most informative and practical TSV for enhancing the associations between FHR and UA features and the development of HIE during labor. Incorporating this variable would enable classifiers to learn the temporal dynamics of FHR and UA features, potentially improving early identification of fetuses at elevated risk for HIE and supporting more timely, effective interventions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».