Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery
Background: Postpartum hemorrhage (PPH) remains one of the biggest reasons of maternal morbidity and mortality. Clinical prediction of PPH remains challenging, particularly in the case of a vaginal birth. The purpose of this research is identifying patients at risk for PPH in vagi...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IMR Press
2024-09-01
|
| Series: | Clinical and Experimental Obstetrics & Gynecology |
| Subjects: | |
| Online Access: | https://www.imrpress.com/journal/CEOG/51/10/10.31083/j.ceog5110221 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849308166475481088 |
|---|---|
| author | Yongjuan Bi Yanhua Zhang Li Li Jing Bai Jing Li |
| author_facet | Yongjuan Bi Yanhua Zhang Li Li Jing Bai Jing Li |
| author_sort | Yongjuan Bi |
| collection | DOAJ |
| description | Background: Postpartum hemorrhage (PPH) remains one of the biggest reasons of maternal morbidity and mortality. Clinical prediction of PPH remains challenging, particularly in the case of a vaginal birth. The purpose of this research is identifying patients at risk for PPH in vaginal delivery by using risk factors and predictive models. Methods: 1840 cases who underwent vaginal deliveries at Beijing Ditan Hospital, Capital Medical University between December 2020 to December 2022, which were divided into two groups based on the amount of blood loss (PPH and non-PPH groups). Fourteen risk factors could cause increased risk of PPH, including demographic characteristics and placental anomalies factors. Logistic regression analysis was used to influence the risk factors of PPH in vaginal delivery. According to the results of multivariate logistic regression analysis, a risk prediction model was established, the Hosmer-Lemeshow test was used to assess the model fit. Results: A total of 94 cases presented with PPH in this study, and the incidence of PPH was 5.10% (94/1840). Two items including macrosomia (odds ratio (OR): 2.229, 95% confidence interval (95% CI): 1.062–4.679) and placental anomalies (OR: 4.095, 95% CI: 2.488–6.742) were independent risk factors affecting the occurrence of PPH with vaginal delivery (p < 0.05). Conclusion: The construction of a logistic regression-based model can be used to predict the risk of PPH after vaginal delivery, predictability to be studied further. Clinically, more attention should be paid to vaginal delivery, early identification and screening of high-risk factors for PPH, as well as timely preventive interventions for high-risk groups so as to reduce the risk of PPH. |
| format | Article |
| id | doaj-art-16de7e0bc9604d1a8d50bef5669f75c4 |
| institution | Kabale University |
| issn | 0390-6663 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | IMR Press |
| record_format | Article |
| series | Clinical and Experimental Obstetrics & Gynecology |
| spelling | doaj-art-16de7e0bc9604d1a8d50bef5669f75c42025-08-20T03:54:32ZengIMR PressClinical and Experimental Obstetrics & Gynecology0390-66632024-09-01511022110.31083/j.ceog5110221S0390-6663(24)02441-2Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal DeliveryYongjuan Bi0Yanhua Zhang1Li Li2Jing Bai3Jing Li4Department of Obstetrics, Beijing Ditan Hospital, Capital Medical University, 100015 Beijing, ChinaDepartment of Intensive Care Unit, Beijing Ditan Hospital, Capital Medical University, 100015 Beijing, ChinaDepartment of Obstetrics, Beijing Ditan Hospital, Capital Medical University, 100015 Beijing, ChinaDepartment of Paediatric, Beijing Ditan Hospital, Capital Medical University, 100015 Beijing, ChinaDepartment of Obstetrics, Beijing Ditan Hospital, Capital Medical University, 100015 Beijing, ChinaBackground: Postpartum hemorrhage (PPH) remains one of the biggest reasons of maternal morbidity and mortality. Clinical prediction of PPH remains challenging, particularly in the case of a vaginal birth. The purpose of this research is identifying patients at risk for PPH in vaginal delivery by using risk factors and predictive models. Methods: 1840 cases who underwent vaginal deliveries at Beijing Ditan Hospital, Capital Medical University between December 2020 to December 2022, which were divided into two groups based on the amount of blood loss (PPH and non-PPH groups). Fourteen risk factors could cause increased risk of PPH, including demographic characteristics and placental anomalies factors. Logistic regression analysis was used to influence the risk factors of PPH in vaginal delivery. According to the results of multivariate logistic regression analysis, a risk prediction model was established, the Hosmer-Lemeshow test was used to assess the model fit. Results: A total of 94 cases presented with PPH in this study, and the incidence of PPH was 5.10% (94/1840). Two items including macrosomia (odds ratio (OR): 2.229, 95% confidence interval (95% CI): 1.062–4.679) and placental anomalies (OR: 4.095, 95% CI: 2.488–6.742) were independent risk factors affecting the occurrence of PPH with vaginal delivery (p < 0.05). Conclusion: The construction of a logistic regression-based model can be used to predict the risk of PPH after vaginal delivery, predictability to be studied further. Clinically, more attention should be paid to vaginal delivery, early identification and screening of high-risk factors for PPH, as well as timely preventive interventions for high-risk groups so as to reduce the risk of PPH.https://www.imrpress.com/journal/CEOG/51/10/10.31083/j.ceog5110221postpartum hemorrhagemultivariate logistic regressionprediction modelpregnancy |
| spellingShingle | Yongjuan Bi Yanhua Zhang Li Li Jing Bai Jing Li Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery Clinical and Experimental Obstetrics & Gynecology postpartum hemorrhage multivariate logistic regression prediction model pregnancy |
| title | Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery |
| title_full | Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery |
| title_fullStr | Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery |
| title_full_unstemmed | Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery |
| title_short | Risk Factors and Predictive Models for Postpartum Hemorrhage after Vaginal Delivery |
| title_sort | risk factors and predictive models for postpartum hemorrhage after vaginal delivery |
| topic | postpartum hemorrhage multivariate logistic regression prediction model pregnancy |
| url | https://www.imrpress.com/journal/CEOG/51/10/10.31083/j.ceog5110221 |
| work_keys_str_mv | AT yongjuanbi riskfactorsandpredictivemodelsforpostpartumhemorrhageaftervaginaldelivery AT yanhuazhang riskfactorsandpredictivemodelsforpostpartumhemorrhageaftervaginaldelivery AT lili riskfactorsandpredictivemodelsforpostpartumhemorrhageaftervaginaldelivery AT jingbai riskfactorsandpredictivemodelsforpostpartumhemorrhageaftervaginaldelivery AT jingli riskfactorsandpredictivemodelsforpostpartumhemorrhageaftervaginaldelivery |