Establishing a perinatal red blood cell transfusion risk evaluation model for obstetric patients: a retrospective cohort study

Xing, Zhun; He, Yanjing; Ji, Chao; Xu, Chang; Zhang, Wen; Li, Yunhui; Tan, Xiangqian; Zhao, Ping; Wang, Qiushi; Zheng, Liqiang

doi:10.1111/trf.15208

Cited by 9 publications

(16 citation statements)

References 24 publications

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“…In line with the findings from other investigators, we witnessed an expected increased risk of transfusion with antepartum hemorrhage, specifically in women with placenta previa and placental abruptions. 6,14,20,21 Additionally, placenta previa was recognized as one of the risk factors for PPH and peripartum hysterectomy as, alongside placental implantation, it can result in a delayed placental expulsion which can lead to contraction failure causing severe bleeding. 21 Furthermore, in conformity with Spiegelman's work, we also observed that the use of anticoagulants during the antepartum period is a major risk factor for an increased rate of transfusion.…”

Section: Resultsmentioning

confidence: 99%

“…6,14,20,21 Additionally, placenta previa was recognized as one of the risk factors for PPH and peripartum hysterectomy as, alongside placental implantation, it can result in a delayed placental expulsion which can lead to contraction failure causing severe bleeding. 21 Furthermore, in conformity with Spiegelman's work, we also observed that the use of anticoagulants during the antepartum period is a major risk factor for an increased rate of transfusion. 4 Preoperative anemia was one of the biggest risk factors for transfusion.…”

Section: Resultsmentioning

confidence: 99%

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Risk Factors for Blood Transfusion during Caesarean Section

Shaikh¹,

Shaikh²,

Nasim³

et al. 2022

PJMHS

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Objective: To assess the risk factors and evaluate the association of blood transfusion with neonatal outcomes. Study Design: Prospective study Place and Duration of Study: Department of Obstetrics & Gynaecology, Lady Willingdon Hospital, Khairpur Mir’s Pakistan from 1st June 2021 to 31st December 2021. Methodology: Nine hundred and fifty seven women who underwent C-sections were enrolled. The maternal socio-demographics and comorbidities, obstetrics and operative factors, indications for C-section, and neonatal outcomes were recorded. Results: The results revealed that high maternal weight and age, preoperative anemia, placenta previa or abruption, antepartum hemorrhage, third trimester bleeding, multiparity, assisted reproductive technology (ART), prophylactic anticoagulation, and prolonged labor were significantly associated with transfusion during C-section. However, prior uterine scar or atony, previous C-sections, multiple births, diabetes, fibroids, Human immunodeficiency virus (HIV), type of C-section, the cadre of the lead surgeon, surgery type, failed induction or instrumentation, fetal distress, malpresentation, and adhesions were not significantly associated with blood transfusion. There was no significant difference in birth weights of neonates from the two groups. Conclusion: Healthcare professionals should stay cautious when dealing with parturients exhibiting such risk factors. Timely arrangement and transfusion of blood in these women can help reduce maternal mortality. Key words: Caesarean section, Blood transfusion, Risk factors, Postpartum hemorrhage, Antepartum hemorrhage

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Risk Factors for Blood Transfusion during Caesarean Section

Shaikh¹,

Shaikh²,

Nasim³

et al. 2022

PJMHS

View full text Add to dashboard Cite

show abstract

“…This study involved more candidate predictors that may lead to perinatal blood transfusions than other studies, such as whether live in local, whether have permanent job, the number of previous deliveries, the number of abortions, the number of previous cesarean deliveries, anesthesia, emergency situations, fibroid levels, coagulation disorders, predelivery oxytocin use, and being hepatitis B virus carrier or abnormal liver function [ 23 ]. Besides, we established a risk prediction model of perinatal blood transfusions who underwent cesarean sections, using a nomogram.…”

Section: Discussionmentioning

confidence: 99%

A risk prediction model of perinatal blood transfusion for patients who underwent cesarean section: a case control study

Wang

Hong

2022

BMC Pregnancy Childbirth

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Background Severe obstetric hemorrhage is a leading cause of severe maternal morbidity. A perinatal blood transfusion is the key factor in the treatment of severe obstetric hemorrhage. Our aim is to identify patients with a high risk of perinatal blood transfusions before Cesarean Section, which can promote the effectiveness of the treatment of severe obstetric hemorrhage, as well as improve obstetric preparations. Methods This study retrospectively analyzed the data of 71 perinatal blood transfusion patients and 170 controls, who were both underwent Cesarean Section from July 2018 to September 2019. These data were included in the training set to build the risk prediction model of needing blood transfusion. Additionally, the data of 148 patients with the same protocol from October 2019 to May 2020 were included in the validation set for model validation. A multivariable logistic regression model was used. A risk prediction nomogram was formulated per the results of the multivariate analysis. Results The strongest risk factors for perinatal blood transfusions included preeclampsia (OR = 6.876, 95% CI: 2.226–23.964), abnormal placentation (OR = 5.480, 95% CI: 2.478–12.591), maternal age (OR = 1.087, 95% CI: 1.016–1.166), predelivery hemoglobin (OR = 0.973, 95% CI: 0.948–0.998) and predelivery fibrinogen (OR = 0.479, 95% CI: 0.290–0.759). A risk prediction model of perinatal blood transfusions for cesarean sections was developed (AUC = 0.819; sensitivity: 0.735; specificity: 0.848; critical value: 0.287). Conclusions The risk prediction model can identify the perinatal blood transfusions before Cesarean Section. With the nomogram, the model can be further quantified and visualized, and clinical decision-making can subsequently be further simplified and promoted.

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“…Most RBCs transfusion predictive models are based on traditional statistical methods, with these binary models roughly predicting the risk for transfusion (41)(42)(43)(44). In this study, we first used the XGBoost algorithm to predict the need for perioperative RBCs transfusion (ternary classification).…”

Section: Discussionmentioning

confidence: 99%

Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China

et al. 2021

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Background: Predicting the perioperative requirement for red blood cells (RBCs) transfusion in patients with the pelvic fracture may be challenging. In this study, we constructed a perioperative RBCs transfusion predictive model (ternary classifications) based on a machine learning algorithm.Materials and Methods: This study included perioperative adult patients with pelvic trauma hospitalized across six Chinese centers between September 2012 and June 2019. An extreme gradient boosting (XGBoost) algorithm was used to predict the need for perioperative RBCs transfusion, with data being split into training test (80%), which was subjected to 5-fold cross-validation, and test set (20%). The ability of the predictive transfusion model was compared with blood preparation based on surgeons' experience and other predictive models, including random forest, gradient boosting decision tree, K-nearest neighbor, logistic regression, and Gaussian naïve Bayes classifier models. Data of 33 patients from one of the hospitals were prospectively collected for model validation.Results: Among 510 patients, 192 (37.65%) have not received any perioperative RBCs transfusion, 127 (24.90%) received less-transfusion (RBCs < 4U), and 191 (37.45%) received more-transfusion (RBCs ≥ 4U). Machine learning-based transfusion predictive model produced the best performance with the accuracy of 83.34%, and Kappa coefficient of 0.7967 compared with other methods (blood preparation based on surgeons' experience with the accuracy of 65.94%, and Kappa coefficient of 0.5704; the random forest method with an accuracy of 82.35%, and Kappa coefficient of 0.7858; the gradient boosting decision tree with an accuracy of 79.41%, and Kappa coefficient of 0.7742; the K-nearest neighbor with an accuracy of 53.92%, and Kappa coefficient of 0.3341). In the prospective dataset, it also had a food performance with accuracy 81.82%.Conclusion: This multicenter retrospective cohort study described the construction of an accurate model that could predict perioperative RBCs transfusion in patients with pelvic fractures.

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Establishing a perinatal red blood cell transfusion risk evaluation model for obstetric patients: a retrospective cohort study

Cited by 9 publications

References 24 publications

Risk Factors for Blood Transfusion during Caesarean Section

Risk Factors for Blood Transfusion during Caesarean Section

A risk prediction model of perinatal blood transfusion for patients who underwent cesarean section: a case control study

Ability of a Machine Learning Algorithm to Predict the Need for Perioperative Red Blood Cells Transfusion in Pelvic Fracture Patients: A Multicenter Cohort Study in China

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