Cardiorespiratory signal analysis in extremely preterm infants during the peri-extubation period
Notice bibliographique
Résumé
Background: Mechanical ventilation (MV) is common among extremely preterm (EPT) infants and prediction/prevention of extubation failure remains an enigma. One controversial approach is the use of an endotracheal continuous positive airway pressure (ETT-CPAP) trial while observing stability of vital signs. After extubation, infants are treated with some type of non- invasive respiratory support: nasal CPAP (NCPAP), high flow nasal cannula (HFNC), nasal intermittent positive pressure ventilation (NIPPV), and non-invasive neurally adjusted ventilatory assist (NIV-NAVA). Despite many trials, due to significant methodological issues, the superiority of a particular type is not entirely clear, although there are some encouraging findings with patient-synchronized ventilation through NIPPV and NIV-NAVA. In the neonatal intensive care unit (NICU) vital signs are continuously monitored and these signals are available for analyses of cardiorespiratory behavior. Investigations include analysis of heart rate variability (HRV), diaphragmatic activity, and respiratory variability (RV). While these measures are typically used in research settings, there is growing evidence for its clinical usefulness in providing more precise and individualized information.Objectives: The overall aim of this thesis is to investigate cardiorespiratory behavior of EPT infants during the peri-extubation period. The specific objectives are to evaluate the effects of an ETT-CPAP trial prior to extubation, and different types of non-invasive respiratory support provided after extubation on the cardiorespiratory behavior. The utility of cardiorespiratory behavior measures for extubation outcomes was also investigated.Methods: A series of prospective crossover studies were designed and performed. All studies were done in the Montreal Children’s Hospital NICU and included EPT infants with birth weight ≤1250 grams undergoing their first elective extubation attempt. The first study investigated and compared measures of cardiorespiratory behaviour between a period of MV and a 5min ETT- CPAP trial, during the pre-extubation period. The other studies were performed shortly after extubation, a period of transition and clinical instability. The following comparisons were done: NCPAP vs HFNC, and NCPAP, NIPPV, vs NIV-NAVA. Cardiorespiratory signals included electrocardiogram (ECG) and electrical activity of the diaphragm (Edi). ECGs were analyzed to extract several HRV parameters. Edi signals were analyzed to extract diaphragmatic activity parameters, including measures of respiratory effort and timing, and RV. Extubation failure was defined as reintubation within 7 days of extubation.Results: The analysis of these signals during the peri-extubation period yielded important findings related to cardiorespiratory behavior of EPT infants. The use of a 5min ETT-CPAP prior to extubation was associated with increased respiratory efforts and variability of these efforts but more consistent breathing timing. Shortly after extubation, HRV parameters were increased during HFNC when compared to NCPAP, but only in a subgroup of infants successfully extubated. This finding raised the possibility that inability to increase HRV when provided with less support may be a sign of imminent failure. NIV-NAVA and NIPPV were associated with lower respiratory efforts and higher RV when compared to NCPAP. Interestingly, RV parameters demonstrated strong accuracies (0.75-1.00) to predict extubation outcomes both pre- and post-extubation.Conclusion: Analysis of individual cardiorespiratory behavior during the peri-extubation period demonstrated potential value in evaluate respiratory efforts and predict impending extubation success or failure. The exploratory investigations of numerous cardiorespiratory parameters in this thesis should encourage performance of larger studies and assist in their design planning. The lack of standardized guidelines for cardiorespiratory signal analysis is a concern
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».