Impact of Baseline Kidney Function on the Rate of Progressive Kidney Disease After Pregnancy: A Population-Based Cohort Study Research Protocol
Notice bibliographique
Résumé
Background: Better data are necessary to determine whether baseline level of kidney function affects the rate of progressive kidney disease following pregnancy. Objective: The objective was to determine whether the baseline (pre-pregnancy) estimated glomerular filtration rate (eGFR) modifies the association between becoming pregnant and the subsequent rate of progressive kidney disease. Design: Population-based cohort study using provincial administrative health care databases in Ontario and Alberta, Canada. Setting: The sample will be accrued from April 1, 2007, to March 31, 2023, in Ontario and from April 1, 2012, to March 31, 2023, in Alberta. Follow-up for study outcomes will occur until March 31, 2024. Participants: The pregnant group will include adult female residents of Ontario or Alberta with a record of a pregnancy of 20 to 46 weeks’ gestation during the accrual period, and the non-pregnant group will include adult female residents with no prior record of pregnancy. The cohort entry dates in those in the pregnant group will be the estimated date of conception; the entry dates for those in the non-pregnant group will be randomly assigned following the distribution of dates in the pregnant group. To be eligible, individuals must be between 18 and 45 years old at cohort entry. They require at least 1 serum creatinine measurement within 2 years before entry and should not have received maintenance dialysis or a prior kidney transplant. Both groups will be categorized into one of 3 levels of baseline eGFR (≥60, 45–59, and <45 mL/min per 1.73 m 2 ). Inverse probability of treatment weighting on a propensity score will be used to balance the pregnant and non-pregnant groups on baseline characteristics (including age, proteinuria, hypertension, and diabetes) within the 3 categories of baseline eGFR. Measurements: The primary outcome, progressive kidney disease, will be defined as a composite of a persistent ≥40% drop in eGFR from the baseline value, a new persistent eGFR <15 mL/min per 1.73 m 2 , receipt of maintenance dialysis, or receipt of a kidney transplant. The secondary outcomes will be the components of the primary composite outcome examined separately and the annualized change in eGFR in mL/min per 1.73 m 2 from baseline. Methods: We will test for statistical interaction to determine whether the baseline category of eGFR modifies the rate of long-term progressive kidney disease after pregnancy. We hypothesize that a statistical interaction will be present. We will present weighted cause-specific hazard ratios (HRs) and cumulative incidence function (CIF) curves for up to 10 years of follow-up for the pregnant and non-pregnant groups stratified by each eGFR category. We will perform additional pre-specified analyses to confirm whether the findings are robust and examine associations that account for baseline proteinuria. Results: Based on a feasibility analysis using ICES data in Ontario, we expect the cohort to include over 400 000 pregnant females and 1.2 million non-pregnant females. This includes at least 395 000 pregnant females with baseline eGFR ≥60 mL/min/1.73 m 2 , 300 with eGFR 45 to 59 mL/min/1.73 m 2 , and 110 with eGFR <45 mL/min/1.73 m 2 . The median follow-up is anticipated to be 5 years (range = 1-17 years) with minimal loss to follow-up. Limitations: Measures of kidney function will be obtained as part of routine care (not according to a research schedule). Measures of baseline proteinuria are frequently missing from routine care data, even in up to 15% of those with an eGFR <45 mL/min per 1.73 m 2 . Conclusion: This study will investigate whether the level of baseline eGFR modifies the rate of progressive kidney disease after pregnancy and will estimate the cumulative incidence of progressive kidney disease in pregnant and non-pregnant females across 3 categories of baseline eGFR.
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 | 0,040 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,005 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,005 |
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 ».