Incidence and risk factors for severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia at preterm and term gestation: a population-based study
Notice bibliographique
Résumé
BACKGROUND: The majority of previous studies on severe preeclampsia, eclampsia, and hemolysis, elevated liver enzymes, and low platelet count syndrome were hospital-based or included a relatively small number of women. Large, population-based studies examining gestational age-specific incidence patterns and risk factors for these severe pregnancy complications are lacking. OBJECTIVE: This study aimed to assess the gestational age-specific incidence rates and risk factors for severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia. STUDY DESIGN: We carried out a retrospective, population-based cohort study that included all women with a singleton hospital birth in Canada (excluding Quebec) from 2012 to 2016 (N=1,078,323). Data on the primary outcomes (ie, severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia) were obtained from delivery hospitalization records abstracted by the Canadian Institute for Health Information. A Cox regression was used to assess independent risk factors (eg, maternal age and chronic comorbidity) for each primary outcome and to assess differences in the effects at preterm vs term gestation (<37 vs ≥37 weeks). RESULTS: The rates of severe preeclampsia (n=2533), hemolysis, elevated liver enzymes, and low platelet count syndrome (n=2663), and eclampsia (n=465) were 2.35, 2.47, and 0.43 per 1000 singleton pregnancies, respectively. The cumulative incidence of term-onset severe preeclampsia was lower than that of preterm-onset severe preeclampsia (0.87 vs 1.54 per 1000; rate ratio, 0.57; 95% confidence intervals, 0.53-0.62), the rates of hemolysis, elevated liver enzymes, and low platelet count syndrome were similar (1.32 vs 1.23 per 1000; rate ratio, 0.93; 95% confidence interval, 0.86-1.00), and the preterm-onset eclampsia rate was lower than the term-onset rate (0.12 vs 0.33 per 1000; rate ratio, 2.64; 95% confidence interval, 2.16-3.23). For each primary outcome, chronic comorbidity and congenital anomalies were stronger risk factors for preterm- vs term-onset disease. Younger mothers (aged <25 years) were at higher risk for severe preeclampsia at term and for eclampsia at all gestational ages, whereas older mothers (aged ≥35 years) had elevated risks for severe preeclampsia and hemolysis, elevated liver enzymes, and low platelet count syndrome. Regardless of gestational age, nulliparity was a risk factor for all outcomes, whereas socioeconomic status was inversely associated with severe preeclampsia. CONCLUSION: The risk for severe preeclampsia declined at term, eclampsia risk increased at term, and hemolysis, elevated liver enzymes, and low platelet count syndrome risk was similar for preterm and term gestation. Young maternal age was associated with an increased risk for eclampsia and term-onset severe preeclampsia. Prepregnancy comorbidity and fetal congenital anomalies were more strongly associated with severe preeclampsia, hemolysis, elevated liver enzymes, and low platelet count syndrome, and eclampsia at preterm gestation.
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 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 ».