Prevalence of Iron Deficiency in Pregnancy: A Single Centre Canadian Study
Notice bibliographique
Résumé
Abstract Background Iron deficiency (ID) is the most common and widespread nutritional deficiency in both developing and developed countries (WHO, 2001; Mei et al., 2011). Women of child bearing age are at the highest risk, but this risk increases even more during pregnancy. The expansion of blood volume, growth of the fetus and placenta increase demand for iron to approximately 5.0mg/day by the third trimester (Met et al., 2011). Common symptoms of ID during pregnancy include fatigue, shortness of breath, and difficulty concentrating (WHO, 2001). Poor prenatal iron status is associated with diminished cognitive performance, language ability, and motor functions in the child (Tamura et al, 2002). For the mother, it is associated with risk of blood transfusion and post-partum depression. Despite international recommendations and guidelines on the management of ID in pregnancy, it remains a problem of epidemic proportions and is often unrecognized and left untreated. To increase awareness of ID, a quality improvement project, IRON Deficiency project in Pregnancy: Maternal Iron Optimization (IRON MOM) was implemented January 1st, 2017 at St. Michael's Hospital (SMH), in Toronto, Canada. Phase 1 of the project involved adapting lab requisitions and workflow in the obstetrics clinic to incorporate routine measurement of ferritin in week 12, 16 and 28 of pregnancy. As part of the IRON MOM, laboratory requisitions were modified to include ferritin as part of routine screening for all pregnant women. Objective The primary objective of this study was to assess the prevalence of ID in pregnant women consistently screened for ID after the implementation of the IRON MOM quality improvement project at a tertiary hospital in Toronto, Canada. Methods Administrative laboratory data was collected from the electronic medical record system at SMH, Toronto, Canada between January 1 and December 31, 2017. Suboptimal iron stores was defined as serum ferritin between 30-50μg/L. ID was defined as serum ferritin between 15-29μg/L, and severe ID was defined as <15μg/L. Significant anemia was defined as hemoglobin levels <100 g/L. Descriptive statistics were used to calculate proportions. SAS version 9.4 was used to perform the analyses. Results In 2017, 2400 ferritin tests were completed on pregnant women at our institution. A total of 76.8% (1844/2400) of tests demonstrated iron deficiency with a ferritin <30μg/L. Of those, 30.2% (726/2400) had ferritin between 15-29μg/L, and 46.6% (1118/2400) were severely iron deficient with a ferritin <15μg/L (Figure 1). 3282 hemoglobin checks, at delivery, occurred in this same one-year period and 10.5% (345/3282) were significantly anemic (<100 g/L). Of those, 6.2% (204/3282) had hemoglobin levels between 90-99g/L, 2.6% (85/3282) had hemoglobin levels between 80-89g/L, and 1.7% (56/3282) had hemoglobin levels <80g/L. Conclusion We found an extremely high prevalence of ID in our pregnant patient population. This confirms that ID remains an underappreciated problem, even at a tertiary care centre. Our findings highlight a tremendous gap in awareness, which demands strategies to improve knowledge translation. Future directions include the simplification and digitization of IRON MOM to empower pregnant patients to advocate for their care. Figure 1. Figure 1. Disclosures Lausman: Ferring: Other: gave a talk.
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,007 |
| Études des sciences et des technologies | 0,005 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».