102 Use of blood pressure trends to predict severity of hypotensive episodes in neonates
Notice bibliographique
Résumé
Abstract Background Hypotension leading to circulatory insufficiency is a serious morbidity in neonates admitted to Neonatal Intensive Care Unit (NICU). Early recognition, and timely and appropriate treatment are essential to prevent adverse consequences. However, currently there are no early tools to predict episode severity to allow triaging of patients to higher level of care or intervention. Objectives This study aimed to look at trends in vital signs 6 hours prior to use of vasoactive agent and analyzed the relationship between the early trends and episode severity and outcomes. Design/Methods This was a retrospective cohort study at a tertiary care NICU in Southwestern Ontario that included all neonates who needed inotropic support between Jan 1, 2018, to Dec 31, 2022. Data regarding physiological parameters in the 6 hours prior to inotropic agent start were collected. Details of the episodes including number of vasoactive agents, doses, vasoactive inotropic score (VIS) and outcomes such as mortality were collected. The relationship between changes in vital signs (systolic blood pressure [SBP], diastolic blood pressure [DBP], mean blood pressure [MBP], heart rate and oxygen saturation index [OSI]) and episode severity (VIS) and mortality were examined. Repeated measures analysis of variance models was used to examine trends over time between age groups, and logistic regression models were used to examine predictors of dichotomous outcomes. Results 154 neonates were included in the study. 50.6% of cohort were <28 weeks,14.9% were 28-34 weeks and 34.4% were >34 weeks. Baseline characteristics and underlying etiology for hypotension are summarized in Table 1. On analysis of blood pressure values 6 hours prior to initiation of vasoactive agents (6-0hrs), we detected a significant declining trend over time in SBP, DBP and MBP for all neonates (Figure 1). The changes in heart rate and oxygen requirement were not significant in that 6-hour window. The logistic regression analysis showed that, for every 1-unit drop in SBP 6-0hrs, DBP 6-0hrs and MBP 6-0hrs, VIS increased by 0.30 (95%CI=0.50, 0.10), p=0.003; 0.32 (95%CI=0.56, 0.07), p=0.012; and 0.26 (95%CI=0.51, 0.01), p=0.044 respectively. Similar findings were recorded during the period of 3hours prior to initiation of vasoactive agents (3-0hrs) . Assessment of indices of oxygenation demonstrated that for every 1-unit increase in OSI 3-0hrs, VIS increased by 1.07 (95%CI=0.34, 1.80), p=0.005. Vital sign trends did not show a relationship to mortality as shown in Table 2. Conclusion This study shows that blood pressure drops significantly in the 6 hours prior to actual initiation of vasoactive medications. The rate of drop could successfully predict episode severity. Integrating the rate of hourly change in BP within routine vital monitoring algorithms could be clinically useful in managing vulnerable neonates.
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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».