Risks and trends of red blood cell transfusion in obstetric patients: a retrospective study of 45,213 deliveries using administrative data

Shehata, Nadine; Chassé, Michaël; Colas, Jo Ann; Murphy, Malia S. Q.; Forster, Alan J.; Malinowski, Ann Kinga; Ducharme, Robin; Fergusson, Dean; Tinmouth, Alan; Wilson, Kumanan

doi:10.1111/trf.14184

Cited by 16 publications

(15 citation statements)

References 33 publications

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“…The peripartum transfusion rate at our institution (0.9%) was comparable to that in other high resource settings [19,20]. Interestingly, 44% of transfused women received just one unit of red blood cells, suggesting that many of the transfusions given were potentially avoidable with diligent prevention of iron deficiency anemia prior to delivery.…”

Section: Discussionsupporting

confidence: 60%

“…Transfusion data were collected from the blood bank information system. Based on the literature and biologic plausibility, risk factors for and laboratory measures associated with iron deficiency, anemia and transfusion were established [19,20]. Demographic variables collected included age, ethnicity, multiple gestation, social work involvement, history of heavy menstrual bleeding, history of chronic malabsorption (e.g.…”

Section: Methodsmentioning

confidence: 99%

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The contribution of iron deficiency to the risk of peripartum transfusion: a retrospective case control study

VanderMeulen

Strauss

Lin

et al. 2020

BMC Pregnancy Childbirth

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Background: Iron deficiency in pregnancy is associated with inferior maternal and fetal outcomes. Postpartum depression, prematurity, intrauterine growth restriction, impaired childhood cognition and transfusion are all sequelae of maternal iron deficiency anemia. Transfusion to women of childbearing age has important consequences including increasing the risk of hemolytic disease of the fetus and newborn with future pregnancies. The relative contribution of iron deficiency to transfusion rates in the peripartum period is unknown. This study aimed to identify the prevalence of iron deficiency and anemia in pregnant women that received peripartum transfusions relative to age-matched non-transfused controls. Methods:We performed a retrospective case-control study of all women that were transfused in the peripartum period from January, 2014 to July, 2018. Cases were compared to the next age matched control to deliver at our institution. The primary objective was to determine the proportion of patients with iron deficiency in pregnancy or anemia in pregnancy in cases and controls. Charts were reviewed for predisposing risk factors for iron deficiency, laboratory measures of iron deficiency and anemia, iron supplementation history and maternal and fetal outcomes. Factors associated with peripartum transfusion were analyzed using a multivariate logistic regression.Results: 169 of 18, 294 (0.9%) women were transfused in the peripartum period and 64 (44%) of those transfused received 1 unit. Iron deficiency or anemia were present in 103 (71%) transfused women and 74 (51%) control women in pregnancy (OR 2.34, 95% CI: 3.7-18.0). Multivariate analysis identified social work involvement (adjusted OR 4.1, 95% CI: 1.8-10.1), intravenous iron supplementation in pregnancy (adjusted OR 3.8, 95% CI: 1.2-17.4) and delivery by unscheduled cesarean section (adjusted OR 2.8, 95% CI: 1.3-6.2) as significant predictors of peripartum transfusion.(Continued on next page)

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Section: Discussionsupporting

confidence: 60%

Section: Methodsmentioning

confidence: 99%

The contribution of iron deficiency to the risk of peripartum transfusion: a retrospective case control study

VanderMeulen

Strauss

Lin

et al. 2020

BMC Pregnancy Childbirth

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show abstract

“…Obstetric transfusion was also reduced during the study period and compares favorably to data of other registries, particularly if we have in mind that more than half of women admitted for delivery during the study period (1685 ± 198 vs. 1089 ± 35) were Moroccan women not following our recommendations for assessment, prevention, and treatment in pregnancy.…”

Section: Discussionsupporting

confidence: 74%

“…41 This iron complex has been in use for years with proven efficacy and safety in a diversity of conditions, and it may be convenient for inpatients with an IV line or frequent hospital visitors as dialysis patients. 42 Obstetric transfusion was also reduced during the study period and compares favorably to data of other registries, 43 particularly if we have in mind that more than half of women admitted for delivery during the study period (1685 6 198 vs. 1089 6 35) were Moroccan women not following our recommendations for assessment, prevention, and treatment in pregnancy.…”

Section: Discussionsupporting

confidence: 68%

Red blood cell transfusion after a global strategy for early detection and treatment of iron deficiency anemia: three‐year results of a prospective observational study

Nieves¹,

Cobos²,

Gonzalez³

et al. 2018

Transfusion

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“…The common nonlinear ones include support vector machine (SVM) [18], random forest (RF) [19], back propagation neural network (BP) [20], K nearest neighbors (KNN) [21], and XGBoost [22]. These models have been used to predict the pre-surgery transfusion volume or other predictive studies, for example, the random forest algorithm was applied to build the transfusion risk prediction model for the patients undergoing percutaneous coronary intervention [23] and regression model was applied to find the risks of the RBCs' transfusion in obstetric patients [24]. However, the current trained models are for special types of patients or not suitable for predicting the required PBV of the RBCs in surgical patients.…”

Section: Introductionmentioning

confidence: 99%

Predicting the required pre-surgery blood volume in surgical patients based on machine learning

Han

Sun

et al. 2019

Preprint

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Precisely predicting the required pre-surgery blood volume (PBV) in surgical patients is a formidable challenge in China. Inaccurate estimation is associate with excessive costs, postponed surgeries and adverse outcome after surgery due to in sufficient supply or inventory. This study aimed to predict required PBV based on machine learning techniques. 181,027 medical documents over 6 years were cleaned and finally obtained 92,057 blood transfusion records. The blood transfusion and surgery related factors of perioperative patients, surgeons experience volumes and the actual volumes of transfused RBCs were extracted. 6 machine learning algorithms were used to build prediction models.The surgery patients received allogenic RBCs or without transfusion, had total volume less than 10 units, or had the latest laboratory examinations of pre-surgery within 7 days were included, providing 118,823 data points. 39 predictive factors related to the RBCs transfusion were identified. Random forest model was selected to predict the required PBV of RBCs with 72.9% accuracy and strikingly improved the accuracy by 30.4% compared with surgeons experience, where 90% of data was used for training. We tested and demonstrated that both the data-driven models and the random forest model achieved higher accuracy than surgeons experience. Furthermore, we developed a computational tool, PTRBC, to precisely estimate the required PBV in surgical patients and we believe this tool will find more applications in assisting clinician decisions, not only confined to making accurate pre-surgery blood requirement predicting.

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Risks and trends of red blood cell transfusion in obstetric patients: a retrospective study of 45,213 deliveries using administrative data

Cited by 16 publications

References 33 publications

The contribution of iron deficiency to the risk of peripartum transfusion: a retrospective case control study

The contribution of iron deficiency to the risk of peripartum transfusion: a retrospective case control study

Red blood cell transfusion after a global strategy for early detection and treatment of iron deficiency anemia: three‐year results of a prospective observational study

Predicting the required pre-surgery blood volume in surgical patients based on machine learning

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