Risk Prediction Model for Postpartum Hemorrhage

Overseen By Tracie Baker, CCRP

Age: Any Age

Sex: Female

Trial Phase: Academic

Sponsor: Holly Ende

Disqualifiers: Pre-delivery hysterectomy, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

10000 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This research project aims to enhance the safety of childbirth by using advanced computer models to predict the risk of postpartum hemorrhage (PPH). PPH is a significant concern for mothers during and after delivery. Current risk assessment tools are basic and do not adapt to changing conditions. This study will investigate whether a new and recently validated model for predicting PPH, combined with a provider-facing Best Practice Advisory (BPA) regarding currently recommended strategies triggered by an increased predicted risk, can improve perinatal outcomes. This study will compare the current category based risk assessment tool with a new, enhanced prediction model which calculates risk based on 21 factors, automatically updates as new information becomes available during labor and, if elevated, provides a provider-facing Best Practice Advisory (BPA) recommending consideration of strategies that are institutionally agreed to represent high-quality practice. Investigators hypothesize that the enhanced care approach will result in improved perinatal outcomes. The goal of the study is to improve the wellbeing of mothers during childbirth by harnessing the power of modern technology and data analysis.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your healthcare provider.

Is the Novel PPH Risk Prediction Model safe for humans?

The research articles focus on predicting postpartum hemorrhage risk using various models and data but do not provide specific safety information about the models themselves. These models are tools for prediction and do not involve direct treatment or intervention, so they are generally considered safe to use. ^{1 2 3 4 5}

How does the Novel PPH Risk Prediction Model treatment differ from other treatments for postpartum hemorrhage?

The Novel PPH Risk Prediction Model is unique because it focuses on predicting the risk of postpartum hemorrhage (excessive bleeding after childbirth) using a combination of clinical and biological data, rather than treating the condition after it occurs. This approach aims to identify women at risk before severe bleeding happens, potentially allowing for preventive measures to be taken. ^{2 4 6 7 8}

What data supports the effectiveness of the treatment Novel PPH Risk Prediction Model for predicting postpartum hemorrhage?

The research highlights the development of various prediction models and tools that assess risk factors for postpartum hemorrhage (PPH), suggesting that a reliable risk assessment tool can help optimize interventions and reduce adverse outcomes. This implies that the Novel PPH Risk Prediction Model could be effective in identifying women at risk and improving maternal health by preventing severe PPH. ^{2 4 6 8 9}

Who Is on the Research Team?

Holly Ende, MD

Principal Investigator

Vanderbilt University Medical Center

Are You a Good Fit for This Trial?

This trial is for pregnant individuals at risk of postpartum hemorrhage (PPH). Participants should be giving birth where the study is being conducted. There are no specific inclusion or exclusion criteria provided, but typically participants would need to be of childbearing age and not have conditions that could interfere with the study.

Inclusion Criteria

All vaginal and cesarean deliveries occurring at Vanderbilt University Medical Center

Exclusion Criteria

Patients who are discharged prior to delivery will be excluded from subsequent analysis

I am not planning a hysterectomy before delivery for placenta issues.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Participants are assigned to either the standard care arm or the novel PPH risk prediction model arm. The intervention arm includes additional risk prediction and Best Practice Advisory (BPA) for elevated risk cases.

Duration of labor and delivery

Postpartum Monitoring

Participants are monitored for postpartum outcomes, including morbidity and mortality, up to 30 days postpartum.

30 days

Follow-up

Participants are monitored for safety and effectiveness after treatment, including postpartum hospital discharge outcomes.

2-4 days

What Are the Treatments Tested in This Trial?

Interventions

• Novel PPH Risk Prediction Model

Trial Overview The trial tests a new computer model predicting PPH against current basic tools. It uses 21 factors to assess risk in real-time during labor, providing instant recommendations if there's an increased risk.

How Is the Trial Designed?

Group I: Novel PPH Risk Prediction Model - Comparator Arm B Active Control 1 Intervention

Standard Care with addition of a recently developed, novel PPH risk prediction model.

Group II: Standard Care - Comparator Arm A Active Control 1 Intervention

Standard Care, which includes a category-based risk assessment tool as part of nursing admission workflow. The prophylactic interventions in the intervention are recommended by the tool but not specifically tied to provider-facing clinical decision support.

Who Is Running the Clinical Trial?

Holly Ende

Lead Sponsor

Trials

1

Recruited

10,000+

Vanderbilt University Medical Center

Collaborator

Trials

922

Recruited

939,000+

Published Research Related to This Trial

In a study of 7236 women with post-partum hemorrhage (PPH), severe cases were identified in 18.5% of participants, with several clinical factors like parity and labor induction linked to increased risk.

A prediction model developed to identify severe PPH had a moderate performance (ROC area of 0.63), but its sensitivity (49%) and specificity (70%) were insufficient for routine clinical use, indicating a need for additional data, such as laboratory results, to improve accuracy.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Severity of post-partum hemorrhage after vaginal delivery is not predictable from clinical variables available at the time post-partum hemorrhage is diagnosed. Cortet, M., Maucort-Boulch, D., Deneux-Tharaux, C., et al.[2022]

A prediction model for postpartum hemorrhage (PPH) was developed using data from 5,807 singleton pregnancies, identifying four key risk factors: nulliparity, fetal macrosomia, mode of delivery, and history of PPH.

The model demonstrated good predictive performance with a C-statistic of 0.751, and external validation confirmed its reliability, suggesting that the nomogram could help clinicians assess individual PPH risk and improve management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predicting risk of postpartum haemorrhage during the intrapartum period in a general obstetric population. Maher, GM., McKernan, J., O'Byrne, L., et al.[2022]

In a study of over 3 million births in Norway from 1967 to 2017, postpartum hemorrhage (PPH) occurred in 9.3% of cases, with retained placenta being the most common cause of severe PPH (29.3%).

The study found that the sex of the newborn affects PPH risk, with male infants associated with a lower risk of PPH, particularly for retained placenta and uterine atony, while a history of cesarean section significantly increased the risk of PPH due to dystocia.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Risk factors and recurrence of cause-specific postpartum hemorrhage: A population-based study. Linde, LE., Rasmussen, S., Moster, D., et al.[2022]

A new nomogram was developed to predict the risk of postpartum hemorrhage (PPH) in twin pregnancies undergoing cesarean delivery, based on a study of 372 matched cases (186 with PPH and 186 without) from January 2014 to July 2021.

The nomogram demonstrated good predictive ability with an area under the curve of 0.778, which can help clinicians optimize surgical plans and treatments to reduce maternal morbidity and mortality associated with PPH.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Nomogram to predict postpartum hemorrhage in cesarean delivery for twin pregnancies: a retrospective cohort study in China. Zhang, Y., Chen, L., Zhou, W., et al.[2023]

In a study of 270 women who delivered vaginally, the average blood loss within 2 hours postpartum was 427.49 mL, with 31% experiencing blood loss greater than 500 mL, indicating a significant risk of postpartum hemorrhage (PPH).

The use of forceps during delivery was strongly associated with increased blood loss (odds ratio of 9.5), while factors like previous anemia and episiotomy were linked to even higher blood loss (>1,000 mL), highlighting the need for careful monitoring and prompt intervention in these cases.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Risk Factors for Postpartum Hemorrhage and its Severe Forms with Blood Loss Evaluated Objectively - A Prospective Cohort Study. Borovac-Pinheiro, A., Ribeiro, FM., Pacagnella, RC.[2023]

Using machine learning and statistical models, researchers successfully predicted the risk of postpartum hemorrhage in women at labor admission, analyzing data from 152,279 births, with 7,279 cases (4.8%) of postpartum hemorrhage identified.

The extreme gradient boosting model showed the highest accuracy in predicting postpartum hemorrhage (C statistic: 0.93), indicating its potential to help healthcare providers prepare for and manage at-risk patients effectively.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Machine Learning and Statistical Models to Predict Postpartum Hemorrhage. Venkatesh, KK., Strauss, RA., Grotegut, CA., et al.[2022]

A study of 43,657 deliveries identified four levels of postpartum hemorrhage based on treatment interventions, showing that higher levels of hemorrhage were linked to greater drops in hematocrit, indicating more severe blood loss.

The use of uterotonics to manage postpartum hemorrhage increased significantly over the study period, and while severe maternal morbidity remained stable, the risk of hysterectomy decreased, suggesting improved management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Framework for Improving Characterization of Obstetric Hemorrhage Using Informatics Data. Goffman, D., Friedman, AM., Sheen, JJ., et al.[2023]

A nationwide study involving 432 pregnant women with immune thrombocytopenia (ITP) developed the MONITOR prediction model, which accurately identifies the risk of postpartum hemorrhage (PPH) using seven key clinical factors.

The MONITOR model demonstrated high accuracy in predicting PPH, with an area under the curve (AUC) of 0.868 in internal validation, indicating it can significantly improve clinical care and resource allocation for at-risk patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: Development and validation of the MONITOR model in a nationwide multicenter study. Huang, QS., Zhu, XL., Qu, QY., et al.[2021]

A predictive model for postpartum hemorrhage (PPH) was developed using data from 2742 vaginal deliveries, identifying eight key risk factors, including pre-eclampsia and antepartum bleeding, which can help healthcare providers assess risk.

The model demonstrated good discriminant power in both the derivation cohort (AUROC 0.69) and an external validation cohort (AUROC 0.66), suggesting it can effectively identify women at risk for PPH and improve preventive care strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development and Validation of a Predictive Tool for Postpartum Hemorrhage after Vaginal Delivery: A Prospective Cohort Study. Bihan, L., Nowak, E., Anouilh, F., et al.[2023]

Citations

1. Australia pubmed.ncbi.nlm.nih.gov

Severity of post-partum hemorrhage after vaginal delivery is not predictable from clinical variables available at the time post-partum hemorrhage is diagnosed. [2022]

2. Ireland pubmed.ncbi.nlm.nih.gov

Predicting risk of postpartum haemorrhage during the intrapartum period in a general obstetric population. [2022]

3. United States pubmed.ncbi.nlm.nih.gov

Risk factors and recurrence of cause-specific postpartum hemorrhage: A population-based study. [2022]

4. Switzerland pubmed.ncbi.nlm.nih.gov

Nomogram to predict postpartum hemorrhage in cesarean delivery for twin pregnancies: a retrospective cohort study in China. [2023]

5. Brazil pubmed.ncbi.nlm.nih.gov

Risk Factors for Postpartum Hemorrhage and its Severe Forms with Blood Loss Evaluated Objectively - A Prospective Cohort Study. [2023]

6. United States pubmed.ncbi.nlm.nih.gov

Machine Learning and Statistical Models to Predict Postpartum Hemorrhage. [2022]

7. United States pubmed.ncbi.nlm.nih.gov

A Framework for Improving Characterization of Obstetric Hemorrhage Using Informatics Data. [2023]

8. United States pubmed.ncbi.nlm.nih.gov

Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: Development and validation of the MONITOR model in a nationwide multicenter study. [2021]

9. Switzerland pubmed.ncbi.nlm.nih.gov

Development and Validation of a Predictive Tool for Postpartum Hemorrhage after Vaginal Delivery: A Prospective Cohort Study. [2023]