Prediction Of Postpartum Hemorrhage Research Articles

Placental T2-weighted MRI-based radiomics-clinical nomogram to predict postpartum hemorrhage of placenta previa

Placental T2-weighted MRI-based radiomics-clinical nomogram to predict postpartum hemorrhage of placenta previa

Prediction of post-delivery hemoglobin levels with machine learning algorithms

Predicting postpartum hemorrhage (PPH) before delivery is crucial for enhancing patient outcomes, enabling timely transfer and implementation of prophylactic therapies. We attempted to utilize machine learning (ML) using basic pre-labor clinical data and laboratory measurements to predict postpartum Hemoglobin (Hb) in non-complicated singleton pregnancies. The local databases of two academic care centers on patient delivery were incorporated into the current study. Patients with preexisting coagulopathy, traumatic cases, and allogenic blood transfusion were excluded from all analyses. The association of pre-delivery variables with 24-h post-delivery hemoglobin level was evaluated using feature selection with Elastic Net regression and Random Forest algorithms. A suite of ML algorithms was employed to predict post-delivery Hb levels. Out of 2051 pregnant women, 1974 were included in the final analysis. After data pre-processing and redundant variable removal, the top predictors selected via feature selection for predicting post-delivery Hb were parity (B: 0.09 [0.05–0.12]), gestational age, pre-delivery hemoglobin (B:0.83 [0.80–0.85]) and fibrinogen levels (B:0.01 [0.01–0.01]), and pre-labor platelet count (B*1000: 0.77 [0.30–1.23]). Among the trained algorithms, artificial neural network provided the most accurate model (Root mean squared error: 0.62), which was subsequently deployed as a web-based calculator: https://predictivecalculators.shinyapps.io/ANN-HB. The current study shows that ML models could be utilized as accurate predictors of indirect measures of PPH and can be readily incorporated into healthcare systems. Further studies with heterogenous population-based samples may further improve the generalizability of these models.

Use of Shock Index, Modified Shock Index, and Age-Adjusted Shock Index for Detection of Postpartum Hemorrhage.

The aim of this study is to evaluate the role of shock index (SI), modified shock index (MSI), and delta shock index (ΔSI) in predicting postpartum hemorrhage (PPH) and adverse maternal outcomes. In this cross-sectional cohort study, a study group consisting of 416 pregnant women who delivered at our hospital and had postpartum hemorrhage was compared with 467 control patients with normal follow-up. SI (pulse/systolic blood pressure), MSI (pulse/mean arterial pressure), ΔSI (input SI - 2nd- or 6th-hour SI) values were calculated. A total of 883 postpartum women were included in the study. The study group had higher peripartum, 2nd-hour, and 6th-hour SI values (p=0.011, p=0.001, p<0.001, respectively). Peripartum MSI values (p=0.004), 2nd-hour MSI values (p<0.001), and 6th-hour MSI values (p<0.001) were significantly lower in the control group than in the PPH group. When the groups were evaluated, the cut-off value of the 2nd-hour SI parameter was>0.8909 (sensitivity 30%, specificity 84%), and the 6th-hour SI parameter was>0.8909 (sensitivity 40%, specificity 80%) for predicting postpartum hemorrhage requiring blood transfusion and surgical intervention. The cut-off value of the 2nd-hour MSI parameter was>1.2 (sensitivity 34%, specificity 82%), and the cut-off value of the 6th-hour MSI parameter was>1.2652 (sensitivity 32%, specificity 90%). The 2nd- and 6th-hour SI and 2nd- and 6th-hour MSI values were significantly higher in patients with postpartum hemorrhage. Values greater than 0.89 for SI and 1.2 for MSI were considered significant for predicting postpartum hemorrhage with maternal impairment.

Machine learning approaches for predicting postpartum hemorrhage: a comprehensive systematic literature review

Postpartum hemorrhage (PPH) represents a significant threat to maternal health, particularly in developing countries, where it remains a leading cause of maternal mortality. Unfortunately, only 60% of pregnant women at high risk for PPH are identified, leaving 40% undetected until they experience PPH. To address this critical issue and ensure timely intervention, leveraging rapidly advancing technology with machine learning (ML) methodologies for maternal health prediction is imperative. This review synthesizes findings from 43 selected research articles, highlighting the predominant ML techniques employed in PPH prediction. Among these, logistic regression (LR), extreme gradient boosting (XGB), random forest (RF), and decision tree (DT) emerge as the most frequently utilized methods. By harnessing the power of ML, we aim to foster technological advancements in the healthcare sector, with a particular focus on maternal health and ultimately contribute to the reduction of maternal mortality rates worldwide.

Agnostic identification of plasma biomarkers for postpartum hemorrhage risk

Postpartum hemorrhage is difficult to predict, is associated with significant maternal morbidity, and is the leading cause of maternal mortality worldwide. The identification of maternal biomarkers that can predict increased postpartum hemorrhage risk would enhance clinical care and may uncover mechanisms that lead to postpartum hemorrhage. This retrospective case-control study employed agnostic proteomic profiling of maternal plasma samples to identify differentially abundant proteins in controls and postpartum hemorrhage cases. Maternal plasma samples were procured from a cohort of >60,000 participants in a single institution's perinatal repository. Postpartum hemorrhage was defined as a decrease in hematocrit of ≥10% or receipt of transfusion within 24 hours after delivery. Postpartum hemorrhage cases (n=30) were matched by maternal age and delivery mode (vaginal or cesarean) with controls (n=56). Mass spectrometry was used to identify differentially abundant proteins using integrated peptide peak areas. Statistically significant differences between groups were defined as P<.05 after controlling for multiple comparisons. By study design, cases and controls did not differ in race, ethnicity, gestational age at delivery, blood type, or predelivery platelet count. Cases had slightly but significantly lower predelivery and postdelivery hematocrit and hemoglobin. Mass spectrometry detected 1140 proteins, including 77 proteins for which relative abundance differed significantly between cases and controls (fold change >1.15, P<.05). Of these differentially abundant plasma proteins, most had likely liver or placental origins. Gene ontology term analysis mapped to protein clusters involved in responses to wound healing, stress response, and host immune defense. Significantly differentially abundant proteins with the highest fold change (prostaglandin D2 synthase, periostin, and several serine protease inhibitors) did not correlate with predelivery hematocrit or hemoglobin but identified postpartum hemorrhage cases with logistic regression modeling revealing good-to-excellent area under the operator receiver characteristic curves (0.802-0.874). Incorporating predelivery hemoglobin with these candidate proteins further improved the identification of postpartum hemorrhage cases. Agnostic analysis of maternal plasma samples identified differentially abundant proteins in controls and postpartum hemorrhage cases. Several of these proteins are known to participate in biologically plausible pathways for postpartum hemorrhage risk and have potential value for predicting postpartum hemorrhage. These findings identify candidate protein biomarkers for future validation and mechanistic studies.

Postpartum hemorrhage in electronic health records: risk factors at admission and in-hospital occurrence.

Postpartum hemorrhage (PPH) is the leading cause of maternal death globally. Therefore, prevention strategies have been created. The study aimed to evaluate the occurrence of PPH and its risk factors after implementing a risk stratification at admission in a teaching hospital. A retrospective cohort involving a database of SISMATER® electronic medical record. Classification in low, medium, or high risk for PPH was performed through data filled out by the obstetrician-assistant. PPH frequency was calculated, compared among these groups and associated with the risk factors. The prevalence of PPH was 6.8%, 131 among 1,936 women. Sixty-eight (51.9%) of them occurred in the high-risk group, 30 (22.9%) in the medium-risk and 33 (25.2%) in the low-risk group. The adjusted-odds ratio (OR) for PPH were analyzed using a confidence interval (95% CI) and was significantly higher in who presented multiple pregnancy (OR 2.88, 95% CI 1.28 to 6.49), active bleeding on admission (OR 6.12, 95% CI 1.20 to 4.65), non-cephalic presentation (OR 2.36, 95% CI 1.20 to 4.65), retained placenta (OR 9.39, 95% CI 2.90 to 30.46) and placental abruption (OR 6.95, 95% CI 2.06 to 23.48). Vaginal delivery figured out as a protective factor (OR 0.58, 95% CI 0.34 to 0.98). Prediction of PPH is still a challenge since its unpredictable factor arrangements. The fact that the analysis did not demonstrate a relationship between risk category and frequency of PPH could be attributable to the efficacy of the strategy: Women classified as "high-risk" received adequate medical care, consequently.

The value of fibrinogen combined with D-dimer and neonatal weight in predicting postpartum hemorrhage in vaginal delivery.

The purpose of this study was to explore whether fibrinogen (Fib) can be used as a predictor of postpartum hemorrhage (PPH) in parturients with vaginal delivery, and the value of combining Fib with other indexes to predict postpartum hemorrhage in vaginal delivery. A total of 207 parturients who delivered via vagina were divided into PPH group (n=102) and non-PPH group (n=105). The PPH group was further divided into mildPPH group and severe PPH group. The differences of Fib, platelet (PLT), mean platelet volume (MPV), platelet distribution width (PDW), D-dimer (D-D), hemoglobin (HGB) and neonatal weight (Nw) between the two groups were compared to explore the significance of these indexes in predicting PPH. Fib, PLT and PDW in PPH group were significantly lower than those in non-PPH group, while D-D and Nw in PPH group were significantly higher than those in non-PPH group. In the binary logistic regression model, we found that Fib, D-D and Nw were independently related to PPH. The risk of PPH increased by 9.87 times for every 1 g/L decrease in Fib. The cut-off value of Fib is 4.395 (sensitivity 0.705, specificity 0.922). The AUC value of PPH predicted by Fib combined withD-D and Nw was significantly higher than that of PPH predicted by Fib (p<0.05, 95 % CI 0.00313-0.0587). Fib, D-D and Nw have good predictive value for PPH of vaginal delivery, among which Fib is the best. The combination of three indexes of Fib, D-D and Nw can predict PPH more systematically and comprehensively, and provide a basis for clinical prevention and treatment of PPH.

69 Derivation and validation of an intrapartum algorithm to predict postpartum hemorrhage risk using photoplethysmography data

69 Derivation and validation of an intrapartum algorithm to predict postpartum hemorrhage risk using photoplethysmography data

Diffusion kurtosis and intravoxel incoherent motion in predicting postpartum hemorrhage in patients at high risk for placenta accreta spectrum disorders.

Placenta accreta spectrum (PAS) disorder encompasses a spectrum of pathologies, from placenta accreta to placenta percreta, which is usually associated with postpartum hemorrhage (PPH). This cross-sectional study enrolled 109 patients suspected of having PAS disorders based on previous ultrasound results or clinical risk factors from November 2018 to March 2022 in Sichuan Provincial People's Hospital. Of the 109 patients, 34 had PPH and 75 did not have PPH. Magnetic resonance imaging (MRI) including diffusion-weighted imaging (DWI), intravoxel incoherent motion (IVIM), and diffusion kurtosis imaging (DKI) was performed for each patient and the apparent diffusion coefficient (ADC) from DWI, perfusion fraction (f), pure diffusion coefficient (D), and pseudo-diffusion coefficient (D*) from IVIM, and mean diffusion kurtosis (MK) and mean diffusion coefficient (MD) from DKI were measured and compared. The correlation between the DWI parameters and estimated blood loss (EBL) during surgery was identified using correlation analysis. The diagnostic performance for predicting PPH was compared between the two methods. The amount of bleeding during delivery was positively correlated with D [r=0.331, P<0.001, 95% confidence interval (CI): 0.170 to 0.477], D* (r=0.389, P<0.001, 95% CI: 0.207 to 0.527), f (r=0.222, P=0.02, 95% CI: 0.036 to 0.398), and MD (r=0.277, P=0.003, 95% CI: 0.108 to 0.439), but negatively correlated with MK (r=-0.280, P=0.003, 95% CI: -0.431 to -0.098). In predicting PPH, multivariate analyses showed the independent risk factors were placenta previa and D; the area under the curve (AUC) was 0.795 (95% CI: 0.711 to 0.878) when the two risk factors were combined together. IVIM and DKI parameters are correlated with EBL. The combined use of placenta previa and D are helpful for predicting PPH in patients at high risk of PAS disorders.

Prediction of postpartum hemorrhage (PPH) using machine learning algorithms in a Kenyan population.

Postpartum hemorrhage (PPH) is a significant cause of maternal mortality worldwide, particularly in low- and middle-income countries. It is essential to develop effective prediction models to identify women at risk of PPH and implement appropriate interventions to reduce maternal morbidity and mortality. This study aims to predict the occurrence of postpartum hemorrhage using machine learning models based on antenatal, intrapartum, and postnatal visit data obtained from the Kenya Antenatal and Postnatal Care Research Collective cohort. Four machine learning models - logistic regression, naïve Bayes, decision tree, and random forest - were constructed using 67% training data (1,056/1,576). The training data was further split into 67% for model building and 33% cross validation. Once the models are built, the remaining 33% (520/1,576) independent test data was used for external validation to confirm the models' performance. Models were fine-tuned using feature selection through extra tree classifier technique. Model performance was assessed using accuracy, sensitivity, and area under the curve (AUC) of the receiver operating characteristics (ROC) curve. The naïve Bayes model performed best with 0.95 accuracy, 0.97 specificity, and 0.76 AUC. Seven factors (anemia, limited prenatal care, hemoglobin concentrations, signs of pallor at intrapartum, intrapartum systolic blood pressure, intrapartum diastolic blood pressure, and intrapartum respiratory rate) were associated with PPH prediction in Kenyan population. This study demonstrates the potential of machine learning models in predicting PPH in the Kenyan population. Future studies with larger datasets and more PPH cases should be conducted to improve prediction performance of machine learning model. Such prediction algorithms would immensely help to construct a personalized obstetric path for each pregnant patient, improve resource allocation, and reduce maternal mortality and morbidity.

Nomogram to predict postpartum hemorrhage in cesarean delivery for twin pregnancies: a retrospective cohort study in China.

Postpartum hemorrhage (PPH) is the most common cause of maternal morbidity and mortality worldwide. A reliable risk assessment tool for PPH could optimize available interventions to reduce adverse maternal outcomes. The objective of this study was to explore a nomogram predicting the risk of postpartum hemorrhage after cesarean delivery for twin pregnancies. This single-center retrospective cohort study conducted twin pregnancies who underwent cesarean delivery between January 2014 and July 2021. Propensity score matching at baseline was used to match PPH (blood loss ≥1000 mL) and non-PPH group (blood loss <1000 mL). A nomogram was developed to predict the risk of PPH in cesarean delivery for twin pregnancies. The receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA) were, respectively, used to evaluate the discrimination, calibration, and clinical utility of the prediction models. After propensity score matching, 186 twin pregnancies in the PPH group were matched with 186 controls in the non-PPH group. Seven independent prognostic variables, including antepartum albumin, assisted reproductive technology, hypertensive disorders of pregnancy, placenta previa, placenta accrete spectrum, intrapartum cesarean delivered, and estimated weights of twins, were used to build the nomogram. Based on the performance of the model, it appears that a good calibration (Hosmer-Lemeshow χ2 = 4.84, P > 0.05), an excellent predictive ability (area under the curve: 0.778, 95% CI: 0.732-0.825), and a good positive net benefit in the predictive model have been achieved. The nomogram was first generated to predict PPH in cesarean delivery for twin pregnancies, which could help clinicians to provide a reference for the preoperative surgical plan, choose optimal treatments, optimize healthcare resources, and thereby reduce the associated adverse maternal outcomes.

Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach

Early detection of postpartum hemorrhage risk factors by healthcare providers during pregnancy and the postpartum period may allow healthcare providers to act to prevent it. Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk for postpartum hemorrhage is necessary. This study used a traditional analytical approach and a machine learning model to predict postpartum hemorrhage. Women who gave birth at the Khaleej-e-Fars Hospital in Bandar Abbas, Iran, were evaluated retrospectively between January 1, 2020, and January 1, 2022. These pregnant women were divided into 2 groups, namely those who had postpartum hemorrhage and those who did not. We used 2 approaches for the analysis. At the first level, we used the traditional analysis methods. Demographic factors, maternal comorbidities, and obstetrical factors were compared between the 2 groups. A bivariate logistic regression analysis of the risk factors for postpartum hemorrhage was done to estimate the crude odds ratios and their 95% confidence intervals. In the second level, we used machine learning approaches to predict postpartum hemorrhage. Of the 8888 deliveries, we identified 163 women with recorded postpartum hemorrhage, giving a frequency of 1.8%. According to a traditional analysis, factors associated with an increased risk for postpartum hemorrhage in a bivariate logistic regression analysis were living in a rural area (odds ratio, 1.41; 95% confidence interval, 1.08-1.98); primiparity (odds ratio, 3.16; 95% confidence interval, 1.90-4.75); mild to moderate anemia (odds ratio, 5.94; 95% confidence interval 2.81-8.34); severe anemia (odds ratio, 6.01; 95% confidence interval 3.89-11.09); abnormal placentation (odds ratio, 7.66; 95% confidence interval, 2.81-17.34); fetal macrosomia (odds ratio, 8.14; 95% confidence interval, 1.02-14.47); shoulder dystocia (odds ratio, 7.88; 95% confidence interval, 1.07-13.99); vacuum delivery (odds ratio, 2.01; 95% confidence interval, 1.15-5.98); cesarean delivery (odds ratio, 1.86; 95% confidence interval, 1.12-3.79); and general anesthesia during cesarean delivery (odds ratio, 7.66; 95 % confidence interval, 3.11-9.36). According to machine learning analysis, the top 5 algorithms were XGBoost regression (area under the receiver operating characteristic curve of 99%), XGBoost classification (area under the receiver operating characteristic curve of 98%), LightGBM (area under the receiver operating characteristic curve of 94%), random forest regression (area under the receiver operating characteristic curve of 86%), and linear regression (area under the receiver operating characteristic curve of 78%). However, after considering all performance parameters, the XGBoost classification was found to be the best model to predict postpartum hemorrhage. The importance of the variables in the linear regression model, similar to traditional analysis methods, revealed that macrosomia, general anesthesia, anemia, shoulder dystocia, and abnormal placentation were considered to be weighted factors, whereas XGBoost classification considered living residency, parity, cesarean delivery, education, and induced labor to be weighted factors. Risk factors for postpartum hemorrhage can be identified using traditional statistical analysis and a machine learning model. Machine learning models were a credible approach for improving postpartum hemorrhage prediction with high accuracy. More research should be conducted to analyze appropriate variables and prepare big data to determine the best model.

Retraction of: The Significance of Thromboelastogram in Predicting Postpartum Hemorrhage and Guiding Blood Transfusion.

This retracts the article DOI: 10.7754/Clin.Lab.2021.210317.

Distribution and performance of shock index to predict postpartum hemorrhage after vaginal delivery

The shock index (SI) – heart rate (.min-1) divided by systolic blood pressure (mmHg) – has emerged as a promising tool to predict postpartum hemorrhage (PPH) after delivery but has been poorly assessed in the general population of parturients. This study aimed to describe the distribution of SI during the two first hours after vaginal delivery, and to evaluate the performances of SI measured at 15 and at 30 minutes in predicting the occurrence of PPH. Data came from a multicenter, randomized controlled trial aimed to compare prophylactic administration of tranexamic acid versus placebo in addition to prophylactic oxytocin for the prevention of PPH in women with a singleton live fetus ≥ 35 weeks, born vaginally (TRAAP trial). SI was systematically measured at 0, 15, 30, 45, 60 and 120 minutes after delivery. We selected the measurement at 15 and at 30 minutes as predictors for PPH. Primary and secondary outcomes were respectively quantitative blood loss ≥ 1000 mL (QBL≥1000) and provider-assessed clinically significant PPH (cPPH). For each outcome, we calculated the area under the receiver operating characteristic curve (AUROC) values of each predictor. For each analysis, women who already met the PPH-related outcome to be predicted before the SI measurement were excluded to take into account the temporal sequence between predictors and outcomes. Among the source population of 3891 women, the prevalence of QBL≥1000 and cPPH was respectively 2.7% (104/3839) and 9.1% (354/3891). The distributions of SI at 15 and at 30 minutes after delivery were similar with median value of 0.73 and 97th percentile of 1.11 for both (Table 1). The AUROC values of SI measured at 15 minutes to predict QBL≥1000 and cPPH were respectively 0.66 (95% confidence interval (95%CI) 0.60-0.73) and 0.56 (95%CI 0.52-0.60) (Table 2). The AUROC values of SI measured at 30 minutes to predict QBL≥1000 and cPPH were respectively 0.68 (95%CI 0.61-0.75) and 0.49 (95%CI 0.43-0.55) (Table 2). SI measured at 15 and at 30 minutes after vaginal delivery was a poor predictor of QBL≥1000 and of cPPH.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Natural language processing of admission notes to predict postpartum hemorrhage

Artificial intelligence is being integrated into practice to support clinical decision-making. The objective of this work was to determine if natural language processing (NLP) of admission notes can predict postpartum hemorrhage (PPH) during the delivery admission, a common obstetric complication leading to severe maternal morbidity. This is a retrospective study of individuals admitted for delivery between July 2016 and December 2020 at a single academic hospital. The outcome of interest was postpartum hemorrhage, defined as estimated blood loss ≥1000 mL. Admission notes were prepared for text processing by excluding stop words, punctuation, terms that occurred very frequently ( >70%) and infrequently (< 1%). The ‘bag of words’ approach with a LASSO model was used to determine if notes were predictive of PPH. Single word vocabularies were trained in 75% of the data and model performance examined in the withheld 25% (test set). Models were then externally validated in a separate hospital within the same health system. Areas under the receiver operating characteristic curve (AUC) were calculated to assess for model discrimination, and calibration plots were generated. The analysis included 12,590 patients in the derivation group and 20,775 patients in the external validation group. The rates of PPH were 8.2% in the test set and 8.7% in the validation set, respectively. AUCs were 0.74 (95% CI 0.71, 0.77) and 0.67 (95% CI 0.66, 0.69) in the test and validation set. The calibration plots revealed a group of patients at significantly higher risk of PPH (those in the top decile of predicted risk compared to others): rates of PPH 27.7% vs 6.2% and 23.0% vs 7.1% in the test and validation sets, respectively (p< 0.001). NLP of EHR documentation generated during routine clinical care identified individuals at highest risk for PPH. Next steps involve comparing predictions to clinician assessment and piloting the model in clinical practice. If successful, this type of tool could ultimately be incorporated to provide real-time information to care teams.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Role of variation in platelet indices in pregnancy-related pathological complications and the prediction of postpartum hemorrhage: a case-control study

Pregnancy-related pathological complications (PPCs) increase the risk of postpartum hemorrhage (PPH), Platelet activation and destruction are expected outcomes of PPCs. This study sought to compare the platelet indices of non-pregnant (NP) child-bearing aged women, healthy pregnant (HP) women, and women with PPCs, and investigate the use of these indices in PPH prediction. This retrospective clinical study included 260 NP child-bearing aged women and 119 pregnant women. Of the 119 pregnant patients, 69 had HPs and 50 suffered from PPCs. Further, 50 patients delivered with PPH. We compared the platelet counts (PCs), mean platelet volumes (MPVs), platelet distribution widths (PDWs), plateletcrits (Pcts), MPV ratios (PC/MPV and Pct/MPV), alpha angles (angles), and maximum amplitudes (MAs) of the patients using Sysmex XN10 hematology analyzer and TEG 5000 Hemostasis analyzer system, respectively. With the exception of PDW, there were significant differences in the platelet parameters of the NP, HP, and PPC patients (P<0.05). The intergroup comparison results showed that the NP patients differed significantly from the HP and PPC patients in terms of age, MA, PC, Pct, and Pct/MPV (P<0.0125). Further, the HP and PPC patients differed significantly in terms of Pct, MPV, PC/MPV, and Pct/MPV (P<0.0125). Additionally, the univariate analysis showed that in the PPC patients, low MPV values were strongly related to PPH [odds ratio (OR) =0.012, P=0.003; OR =0.331, P=0.047]. Women with PPCs had significantly lower PC, Pct, PC/MPV and Pct/MPV values, but significantly higher MA and MPV values. PPHs were strongly related to PPC and low MPV values. A timely accurate diagnosis and evaluating MPV values may be useful in the prediction of PPH.

Risk assessment tools to predict postpartum hemorrhage.

Postpartum hemorrhage (PPH) is a leading cause of maternal morbidity and mortality, and accurate risk assessments may allow providers to anticipate and prevent serious hemorrhage-related adverse events. Multiple category-based tools have been developed by national societies through expert consensus, and these tools assign low, medium, or high risk of hemorrhage based on a review of each patient's risk factors. Validation studies of these tools show varying performance, with a wide range of positive and negative predictive values. Risk prediction models for PPH have been developed and studied, and these models offer the advantage of more nuanced and individualized prediction. However, there are no published studies demonstrating external validation or successful clinical use of such models. Future work should include refinement of these models, study of best practices for implementation, and ultimately linkage of prediction to improved patient outcomes.

Oxytocin receptor single nucleotide polymorphism predicts atony-related postpartum hemorrhage

BackgroundPostpartum hemorrhage remains a key contributor to overall maternal morbidity in the United States. Current clinical assessment methods used to predict postpartum hemorrhage are unable to prospectively identify about 40% of hemorrhage cases. Oxytocin is a first-line pharmaceutical for preventing and treating postpartum hemorrhage, which acts through oxytocin receptors on uterine myocytes. Existing research indicates that oxytocin function is subject to variation, influenced in part by differences in the DNA sequence within the oxytocin receptor gene. One variant, rs53576, has been shown to be associated with variable responses to exogenous oxytocin when administered during psychological research studies. How this variant may influence myometrial oxytocin response in the setting of third stage labor has not been studied. We tested for differences in the frequency of the oxytocin receptor genotype at rs53576 in relationship to the severity of blood loss among a sample of individuals who experienced vaginal birth.MethodsA case–control prospective design was used to enroll 119 postpartum participants who underwent vaginal birth who were at least 37 weeks of gestation. Cases were defined by either a 1000 mL or greater blood loss or instances of heavier bleeding where parturients were given additional uterotonic treatment due to uterine atony. Controls were matched to cases on primiparity and labor induction status. Genotype was measured from a maternal blood sample obtained during the 2nd postpartum month from 95 participants. Statistical analysis included bivariate tests and generalized linear and Poisson regression modeling.ResultsThe distribution of the genotype across the sample of 95 participants was 40% GG (n = 38), 50.5% AG (n = 48) and 9.5% AA (n = 9). Blood loss of 1000 mL or greater occurred at a rate of 7.9% for GG, 12.5% for AG and 55.6% for AA participants (p = 0.005). Multivariable models demonstrated A-carriers (versus GG) had 275.2 mL higher blood loss (95% CI 96.9–453.4, p < 0.01) controlling for parity, intrapartum oxytocin, self-reported ancestry, active management of third stage or genital tract lacerations. Furthermore, A-carrier individuals had a 79% higher risk for needing at least one second-line treatment (RR = 1.79, 95% CI = 1.08–2.95) controlling for covariates. Interaction models revealed that A-carriers who required no oxytocin for labor stimulation experienced 371.4 mL greater blood loss (95% CI 196.6–546.2 mL).ConclusionsWe provide evidence of a risk allele in the oxytocin receptor gene that may be involved in the development of postpartum hemorrhage among participants undergoing vaginal birth, particularly among those with fewer risk factors. The findings, if reproducible, could be useful in studying pharmacogenomic strategies for predicting, preventing or treating postpartum hemorrhage.

Postpartum Hemorrhage

Postpartum hemorrhage (PPH) is a major cause of maternal morbidity and mortality. The most common cause of maternal death in Korea is complications related to the postpartum period (obstetric embolism), accounting for 37.5%, while complications of labor and delivery (uterine atony, PPH, etc.) were the second most common cause, accouning for 21.9%. The effective treatment of PPH often requires multidisciplinary intervention. When managing PPH, multiple interventions (medical, mechanical, invasive, and nonsurgical and surgical procedures), which require different levels of skill and technical expertise, may be used to control bleeding. The healthcare provider needs to begin resuscitative efforts quickly, establish the cause of the hemorrhage, and possibly enroll the assistance of other care providers, such as an obstetrician, anesthetist or radiologist. Avoiding delays in diagnosis and treatment will have a significantly impact on sequelae and chances of survival. This review discusses the causes, identification, management, prevention and prediction of PPH.

Serum Fibrinogen Assay in Prediction of Postpartum Hemorrhage and its Role in Treatment

Background: PPH is postpartum haemorrhage. It is uncommon and severe.1 to 5% of neonates are affected with PPH, the incidence of which varies by age 20 to 30 and socioeconomic level. During pregnancy, fibrinogen levels in plasma increase. Blood loss results in coagulopathy and low fibrinogen concentrations. Premature PPH is associated with severe bleeding and transfusions. FC is produced from human plasma without the need for cross-matching or freezing. Postpartum haemorrhage must coagulate. Problems with coagulation induce PPH. Fluids, blood, and ergotamine are agents that induce uterine contractions. Aim: The aim of the present work was to study the role of serum fibrinogen level as a predictor for postpartum hemorrhage and its role in the treatment. Subjects and Methods: This Prospective observational study was conducted in the obstetrics and gynecology unit of Benha University Hospitals and Tokh Hospital. This study included 50 patients in 3rd trimester (34-37 weeks). The duration of the study ranged from 6-12 months. Results: There was significant difference regarding Logistic regression with odds ratios and 95% confidence intervals (CI) predicting Bleeding severity. Conclusion: The fibrinogen level at PPH diagnosis is a marker of the risk of aggravation and should serve as an alert to clinicians.