Postpartum hemorrhage risk prediction models: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

GUO Shujie ,  WANG Yanhong , ZHAO Yanan , LIU Dongmei , BI Xiaoxuan , ZHANG Ke , JIANG Jingjing , FENG Yuxuan

School of Nursing, Lanzhou University, Lanzhou 730020, P. R. China;

Corresponding author：

WANG Yanhong, Email: yanhongwang@lzu.edu.cn

Keywords：

Postpartum hemorrhage; Prediction model; Systematic review; Nursing care

DOI：

10.7507/1672-2531.202205069

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically review the performance of postpartum hemorrhage risk prediction models, and to provide references for the future construction and application of effective prediction models. Methods The CNKI, WanFang Data, VIP, CBM, PubMed, EMbase, The Cochrane Library, Web of Science, and CINAHL databases were electronically searched to identify studies reporting risk prediction models for postpartum hemorrhage from database inception to March 20^th, 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias and applicability of the included studies. Results A total of 39 studies containing 58 postpartum hemorrhage risk prediction models were enrolled. The area under the curve of 49 models was over 0.7. All but one of the models had a high risk of bias. Conclusion Models for predicting postpartum hemorrhage risk have good predictive performance. Given the lack of internal and external validation, and the differences in study subjects and outcome indicators, the clinical value of the models needs to be further verified. Prospective cohort studies should be conducted using uniform predictor assessment methods and outcome indicators to develop effective prediction models that can be applied to a wider range of populations.

Citation： GUO Shujie, WANG Yanhong, ZHAO Yanan, LIU Dongmei, BI Xiaoxuan, ZHANG Ke, JIANG Jingjing, FENG Yuxuan. Postpartum hemorrhage risk prediction models: a systematic review. Chinese Journal of Evidence-Based Medicine, 2022, 22(11): 1287-1300. doi: 10.7507/1672-2531.202205069 Copy

1.	GBD 2015 Maternal Mortality Collaborators. Global, regional, and national levels of maternal mortality, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016, 388(10053): 1775-1812.
2.	刘兴会, 何镭. 产后出血的预防和处理. 中国实用妇科与产科杂志, 2020, 36(2): 123-126.
3.	Mehrabadi A, Hutcheon JA, Lee L, et al. Epidemiological investigation of a temporal increase in atonic postpartum haemorrhage: a population-based retrospective cohort study. BJOG, 2013, 120(7): 853-862.
4.	Lutomski JE, Byrne BM, Devane D, et al. Increasing trends in atonic postpartum haemorrhage in Ireland: an 11-year population-based cohort study. BJOG, 2012, 119(3): 306-314.
5.	Mehrabadi A, Hutcheon JA, Lee L, et al. Trends in postpartum hemorrhage from 2000 to 2009: a population-based study. BMC Pregnancy Childbirth, 2012, 12: 108.
6.	Kramer MS, Berg C, Abenhaim H, et al. Incidence, risk factors, and temporal trends in severe postpartum hemorrhage. Am J Obstet Gynecol, 2013, 209(5): 449.
7.	Zwart JJ, Richters JM, Ory F, et al. Severe maternal morbidity during pregnancy, delivery and puerperium in the Netherlands: a nationwide population-based study of 371 000 pregnancies. BJOG, 2008, 115(7): 842-850.
8.	Reale SC, Easter SR, Xu X, et al. Trends in postpartum hemorrhage in the United States from 2010 to 2014. Anesth Analg, 2020, 130(5): e119-e122.
9.	石慧峰, 陈练, 王晓霞, 等. 2016—2019年中国严重产后出血的流行病学现状和变化趋势. 中华妇产科杂志, 2021, 56(7): 451-457.
10.	石慧峰, 陈练, 尹韶华, 等. 2016~2020年中国阴道分娩并发症发生现状调查. 实用妇产科杂志, 2022, 38(1): 13-17.
11.	Bienstock JL, Eke AC, Hueppchen NA. Postpartum hemorrhage. N Engl J Med, 2021, 384(17): 1635-1645.
12.	World Health Organization. WHO recommendations for the prevention and treatment of postpartum haemorrhage. Geneva: World Health Organization, 2012.
13.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
14.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
15.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
16.	Chi Z, Zhang S, Wang Y, et al. Research of the assessable method of postpartum hemorrhage. Technol Health Care, 2016, 24(Suppl 2): S465-469.
17.	Goad L, Rockhill K, Schwarz J, et al. Development and validation of a prediction model for postpartum hemorrhage at a single safety net tertiary care center. Am J Obstet Gynecol MFM, 2021, 3(5): 100404.
18.	Pressly MA, Parker RS, Waters JH, et al. Improvements and limitations in developing multivariate models of hemorrhage and transfusion risk for the obstetric population. Transfusion, 2021, 61(2): 423-434.
19.	Qi F, Tian D, Wang Y, et al. Prediction of postpartum hemorrhage by adaptive K-nearest neighbor based on influence factors. 9th International Conference on Information Technology in Medicine and Education (ITME), 2018.
20.	Rubio-Álvarez A, Molina-Alarcón M, Arias-Arias Á, et al. Development and validation of a predictive model for excessive postpartum blood loss: a retrospective, cohort study. Int J Nurs Stud, 2018, 79: 114-121.
21.	Venkatesh KK, Strauss RA, Grotegut CA, et al. Machine learning and statistical models to predict postpartum hemorrhage. Obstet Gynecol, 2020, 135(4): 935-944.
22.	Zheutlin AB, Vieira L, Shewcraft RA, et al. Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records. J Am Med Inform Assoc, 2022, 29(2): 296-305.
23.	梁秋霞, 翁廷松, 卢荔婕, 等. 产妇产后出血的危险因素分析及基于循证医学的预警信息系统构建. 中国性科学, 2021, 30(6): 80-84.
24.	倪胜莲, 曹琳琳, 郭志超, 等. 低风险初产妇产后出血风险预测. 中国生育健康杂志, 2021, 32(2): 117-121.
25.	沈婕, 任青, 林元, 等. 二胎孕妇产后出血的危险因素及预测模型的建立. 中国妇产科临床杂志, 2019, 20(5): 458-459.
26.	赵相娟, 张梅娜, 张涛, 等. 产后出血的影响因素分析及预测. 中国妇产科临床杂志, 2012, 13(2): 108-111.
27.	周彤彤, 俞凯, 袁贞明, 等. 基于LSTM与XGBOOST混合模型的孕妇产后出血预测. 计算机系统应用, 2020, 29(3): 148-154.
28.	朱佳慧. 产后出血危险因素统计分析及风险预测建模. 北京: 北京工业大学, 2020.
29.	Chen C, Liu X, Chen D, et al. A risk model to predict severe postpartum hemorrhage in patients with placenta previa: a single-center retrospective study. Ann Palliat Med, 2019, 8(5): 611-621.
30.	Chen D, Xu J, Ye P, et al. Risk scoring system with MRI for intraoperative massive hemorrhage in placenta previa and accreta. J Magn Reson Imaging, 2020, 51(3): 947-958.
31.	Dang X, Zhang L, Bao Y, et al. Developing and validating nomogram to predict severe postpartum hemorrhage in women with placenta previa undergoing cesarean delivery: a multicenter retrospective case-control study. Front Med (Lausanne), 2022, 8: 789529.
32.	Kang J, Kim HS, Lee EB, et al. Prediction model for massive transfusion in placenta previa during cesarean section. Yonsei Med J, 2020, 61(2): 154-160.
33.	Kong CW, To WWK. Risk factors for severe postpartum haemorrhage during caesarean section for placenta praevia. J Obstet Gynaecol, 2020, 40(4): 479-484.
34.	Lee JY, Ahn EH, Kang S, et al. Scoring model to predict massive post-partum bleeding in pregnancies with placenta previa: a retrospective cohort study. J Obstet Gynaecol Res, 2018, 44(1): 54-60.
35.	陈诚. 前置胎盘相关严重产后出血风险预警模型的构建与临床应用. 重庆: 中国人民解放军陆军军医大学, 2019.
36.	费爱梅, 周晔, 阮吉明. 凶险性前置胎盘患者产后出血风险预测模型分析. 中国妇幼保健, 2021, 36(5): 1000-1003.
37.	Kim JW, Lee YK, Chin JH, et al. Development of a scoring system to predict massive postpartum transfusion in placenta previa totalis. J Anesth, 2017, 31(4): 593-600.
38.	Ahmadzia HK, Phillips JM, James AH, et al. Predicting peripartum blood transfusion in women undergoing cesarean delivery: a risk prediction model. PLoS One, 2018, 13(12): e0208417.
39.	Chen B, Zhang L, Wang D, et al. Nomogram to predict postpartum hemorrhage in cesarean delivery for women with scarred uterus: a retrospective cohort study in China. J Obstet Gynaecol Res, 2020, 46(9): 1772-1782.
40.	Du L, Feng L, Bi S, et al. Probability of severe postpartum hemorrhage in repeat cesarean deliveries: a multicenter retrospective study in China. Sci Rep, 2021, 11(1): 8434.
41.	张征, 闫秋菊. 列线图预测模型评估二次剖宫产产妇产后出血危险因素. 中国计划生育学杂志, 2022, 30(1): 84-88.
42.	Akazawa M, Hashimoto K, Katsuhiko N, et al. Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth. Sci Rep, 2021, 11(1): 22620.
43.	Liu C, Xu Y, Li J, et al. Development and validation of a predictive model for severe postpartum hemorrhage in women undergoing vaginal delivery: a retrospective cohort study. Int J Gynaecol Obstet, 2022, 157(2): 353-358.
44.	Liu J, Wang C, Yan R, et al. Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve. Arch Gynecol Obstet, 2022, 306(4): 1015-1025.
45.	王南, 刘雅静, 窦本芝. 产妇阴道分娩产后出血危险因素分析及预测模型的构建. 湖南师范大学学报(医学版), 2021, 18(4): 36-40.
46.	Albright CM, Spillane TE, Hughes BL, et al. A regression model for prediction of cesarean-associated blood transfusion. Am J Perinatol, 2019, 36(9): 879-885.
47.	Dunkerton SE, Jeve YB, Walkinshaw N, et al. Predicting postpartum hemorrhage (PPH) during cesarean delivery using the leicester PPH predict tool: a retrospective cohort study. Am J Perinatol, 2018, 35(2): 163-169.
48.	陈宁静, 陈春榕, 刘江英. Logistic回归及决策树模型在高龄剖宫产产妇产后出血影响因素分析中的应用. 右江民族医学院学报, 2020, 42(6): 754-758.
49.	Wu Q, Yao K, Liu Z, et al. Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study. EBioMedicine, 2019, 50: 355-365.
50.	Blitz MJ, Yukhayev A, Pachtman SL, et al. Twin pregnancy and risk of postpartum hemorrhage. J Matern Fetal Neonatal Med, 2020, 33(22): 3740-3745.
51.	Huang QS, Zhu XL, Qu QY, et al. Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: development and validation of the MONITOR model in a nationwide multicenter study. Am J Hematol, 2021, 96(5): 561-570.
52.	Koopmans CM, vander Tuuk K, Groen H, et al. Prediction of postpartum hemorrhage in women with gestational hypertension or mild preeclampsia at term. Acta Obstet Gynecol Scand, 2014, 93(4): 399-407.
53.	Wei X, Wei J, Wang S. Risk factors for postpartum hemorrhage in patients with retained placenta: building a predict model. J Perinat Med, 2022, 50(5): 601-607.
54.	Xing Z, He Y, Ji C, et al. Establishing a perinatal red blood cell transfusion risk evaluation model for obstetric patients: a retrospective cohort study. Transfusion, 2019, 59(5): 1667-1674.
55.	中华医学会妇产科学分会产科学组. 产后出血预防与处理指南(2014). 中华妇产科杂志, 2014, 49(9): 641-646.
56.	Dang X, Xiong G, Fan C, et al. Systematic external evaluation of four preoperative risk prediction models for severe postpartum hemorrhage in patients with placenta previa: a multicenter retrospective study. J Gynecol Obstet Hum Reprod, 2022, 51(4): 102333.
57.	Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med, 2016, 375(13): 1216-1219.

1. GBD 2015 Maternal Mortality Collaborators. Global, regional, and national levels of maternal mortality, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet, 2016, 388(10053): 1775-1812.
2. 刘兴会, 何镭. 产后出血的预防和处理. 中国实用妇科与产科杂志, 2020, 36(2): 123-126.
3. Mehrabadi A, Hutcheon JA, Lee L, et al. Epidemiological investigation of a temporal increase in atonic postpartum haemorrhage: a population-based retrospective cohort study. BJOG, 2013, 120(7): 853-862.
4. Lutomski JE, Byrne BM, Devane D, et al. Increasing trends in atonic postpartum haemorrhage in Ireland: an 11-year population-based cohort study. BJOG, 2012, 119(3): 306-314.
5. Mehrabadi A, Hutcheon JA, Lee L, et al. Trends in postpartum hemorrhage from 2000 to 2009: a population-based study. BMC Pregnancy Childbirth, 2012, 12: 108.
6. Kramer MS, Berg C, Abenhaim H, et al. Incidence, risk factors, and temporal trends in severe postpartum hemorrhage. Am J Obstet Gynecol, 2013, 209(5): 449.
7. Zwart JJ, Richters JM, Ory F, et al. Severe maternal morbidity during pregnancy, delivery and puerperium in the Netherlands: a nationwide population-based study of 371 000 pregnancies. BJOG, 2008, 115(7): 842-850.
8. Reale SC, Easter SR, Xu X, et al. Trends in postpartum hemorrhage in the United States from 2010 to 2014. Anesth Analg, 2020, 130(5): e119-e122.
9. 石慧峰, 陈练, 王晓霞, 等. 2016—2019年中国严重产后出血的流行病学现状和变化趋势. 中华妇产科杂志, 2021, 56(7): 451-457.
10. 石慧峰, 陈练, 尹韶华, 等. 2016~2020年中国阴道分娩并发症发生现状调查. 实用妇产科杂志, 2022, 38(1): 13-17.
11. Bienstock JL, Eke AC, Hueppchen NA. Postpartum hemorrhage. N Engl J Med, 2021, 384(17): 1635-1645.
12. World Health Organization. WHO recommendations for the prevention and treatment of postpartum haemorrhage. Geneva: World Health Organization, 2012.
13. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
14. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
15. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
16. Chi Z, Zhang S, Wang Y, et al. Research of the assessable method of postpartum hemorrhage. Technol Health Care, 2016, 24(Suppl 2): S465-469.
17. Goad L, Rockhill K, Schwarz J, et al. Development and validation of a prediction model for postpartum hemorrhage at a single safety net tertiary care center. Am J Obstet Gynecol MFM, 2021, 3(5): 100404.
18. Pressly MA, Parker RS, Waters JH, et al. Improvements and limitations in developing multivariate models of hemorrhage and transfusion risk for the obstetric population. Transfusion, 2021, 61(2): 423-434.
19. Qi F, Tian D, Wang Y, et al. Prediction of postpartum hemorrhage by adaptive K-nearest neighbor based on influence factors. 9th International Conference on Information Technology in Medicine and Education (ITME), 2018.
20. Rubio-Álvarez A, Molina-Alarcón M, Arias-Arias Á, et al. Development and validation of a predictive model for excessive postpartum blood loss: a retrospective, cohort study. Int J Nurs Stud, 2018, 79: 114-121.
21. Venkatesh KK, Strauss RA, Grotegut CA, et al. Machine learning and statistical models to predict postpartum hemorrhage. Obstet Gynecol, 2020, 135(4): 935-944.
22. Zheutlin AB, Vieira L, Shewcraft RA, et al. Improving postpartum hemorrhage risk prediction using longitudinal electronic medical records. J Am Med Inform Assoc, 2022, 29(2): 296-305.
23. 梁秋霞, 翁廷松, 卢荔婕, 等. 产妇产后出血的危险因素分析及基于循证医学的预警信息系统构建. 中国性科学, 2021, 30(6): 80-84.
24. 倪胜莲, 曹琳琳, 郭志超, 等. 低风险初产妇产后出血风险预测. 中国生育健康杂志, 2021, 32(2): 117-121.
25. 沈婕, 任青, 林元, 等. 二胎孕妇产后出血的危险因素及预测模型的建立. 中国妇产科临床杂志, 2019, 20(5): 458-459.
26. 赵相娟, 张梅娜, 张涛, 等. 产后出血的影响因素分析及预测. 中国妇产科临床杂志, 2012, 13(2): 108-111.
27. 周彤彤, 俞凯, 袁贞明, 等. 基于LSTM与XGBOOST混合模型的孕妇产后出血预测. 计算机系统应用, 2020, 29(3): 148-154.
28. 朱佳慧. 产后出血危险因素统计分析及风险预测建模. 北京: 北京工业大学, 2020.
29. Chen C, Liu X, Chen D, et al. A risk model to predict severe postpartum hemorrhage in patients with placenta previa: a single-center retrospective study. Ann Palliat Med, 2019, 8(5): 611-621.
30. Chen D, Xu J, Ye P, et al. Risk scoring system with MRI for intraoperative massive hemorrhage in placenta previa and accreta. J Magn Reson Imaging, 2020, 51(3): 947-958.
31. Dang X, Zhang L, Bao Y, et al. Developing and validating nomogram to predict severe postpartum hemorrhage in women with placenta previa undergoing cesarean delivery: a multicenter retrospective case-control study. Front Med (Lausanne), 2022, 8: 789529.
32. Kang J, Kim HS, Lee EB, et al. Prediction model for massive transfusion in placenta previa during cesarean section. Yonsei Med J, 2020, 61(2): 154-160.
33. Kong CW, To WWK. Risk factors for severe postpartum haemorrhage during caesarean section for placenta praevia. J Obstet Gynaecol, 2020, 40(4): 479-484.
34. Lee JY, Ahn EH, Kang S, et al. Scoring model to predict massive post-partum bleeding in pregnancies with placenta previa: a retrospective cohort study. J Obstet Gynaecol Res, 2018, 44(1): 54-60.
35. 陈诚. 前置胎盘相关严重产后出血风险预警模型的构建与临床应用. 重庆: 中国人民解放军陆军军医大学, 2019.
36. 费爱梅, 周晔, 阮吉明. 凶险性前置胎盘患者产后出血风险预测模型分析. 中国妇幼保健, 2021, 36(5): 1000-1003.
37. Kim JW, Lee YK, Chin JH, et al. Development of a scoring system to predict massive postpartum transfusion in placenta previa totalis. J Anesth, 2017, 31(4): 593-600.
38. Ahmadzia HK, Phillips JM, James AH, et al. Predicting peripartum blood transfusion in women undergoing cesarean delivery: a risk prediction model. PLoS One, 2018, 13(12): e0208417.
39. Chen B, Zhang L, Wang D, et al. Nomogram to predict postpartum hemorrhage in cesarean delivery for women with scarred uterus: a retrospective cohort study in China. J Obstet Gynaecol Res, 2020, 46(9): 1772-1782.
40. Du L, Feng L, Bi S, et al. Probability of severe postpartum hemorrhage in repeat cesarean deliveries: a multicenter retrospective study in China. Sci Rep, 2021, 11(1): 8434.
41. 张征, 闫秋菊. 列线图预测模型评估二次剖宫产产妇产后出血危险因素. 中国计划生育学杂志, 2022, 30(1): 84-88.
42. Akazawa M, Hashimoto K, Katsuhiko N, et al. Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth. Sci Rep, 2021, 11(1): 22620.
43. Liu C, Xu Y, Li J, et al. Development and validation of a predictive model for severe postpartum hemorrhage in women undergoing vaginal delivery: a retrospective cohort study. Int J Gynaecol Obstet, 2022, 157(2): 353-358.
44. Liu J, Wang C, Yan R, et al. Machine learning-based prediction of postpartum hemorrhage after vaginal delivery: combining bleeding high risk factors and uterine contraction curve. Arch Gynecol Obstet, 2022, 306(4): 1015-1025.
45. 王南, 刘雅静, 窦本芝. 产妇阴道分娩产后出血危险因素分析及预测模型的构建. 湖南师范大学学报(医学版), 2021, 18(4): 36-40.
46. Albright CM, Spillane TE, Hughes BL, et al. A regression model for prediction of cesarean-associated blood transfusion. Am J Perinatol, 2019, 36(9): 879-885.
47. Dunkerton SE, Jeve YB, Walkinshaw N, et al. Predicting postpartum hemorrhage (PPH) during cesarean delivery using the leicester PPH predict tool: a retrospective cohort study. Am J Perinatol, 2018, 35(2): 163-169.
48. 陈宁静, 陈春榕, 刘江英. Logistic回归及决策树模型在高龄剖宫产产妇产后出血影响因素分析中的应用. 右江民族医学院学报, 2020, 42(6): 754-758.
49. Wu Q, Yao K, Liu Z, et al. Radiomics analysis of placenta on T2WI facilitates prediction of postpartum haemorrhage: a multicentre study. EBioMedicine, 2019, 50: 355-365.
50. Blitz MJ, Yukhayev A, Pachtman SL, et al. Twin pregnancy and risk of postpartum hemorrhage. J Matern Fetal Neonatal Med, 2020, 33(22): 3740-3745.
51. Huang QS, Zhu XL, Qu QY, et al. Prediction of postpartum hemorrhage in pregnant women with immune thrombocytopenia: development and validation of the MONITOR model in a nationwide multicenter study. Am J Hematol, 2021, 96(5): 561-570.
52. Koopmans CM, vander Tuuk K, Groen H, et al. Prediction of postpartum hemorrhage in women with gestational hypertension or mild preeclampsia at term. Acta Obstet Gynecol Scand, 2014, 93(4): 399-407.
53. Wei X, Wei J, Wang S. Risk factors for postpartum hemorrhage in patients with retained placenta: building a predict model. J Perinat Med, 2022, 50(5): 601-607.
54. Xing Z, He Y, Ji C, et al. Establishing a perinatal red blood cell transfusion risk evaluation model for obstetric patients: a retrospective cohort study. Transfusion, 2019, 59(5): 1667-1674.
55. 中华医学会妇产科学分会产科学组. 产后出血预防与处理指南(2014). 中华妇产科杂志, 2014, 49(9): 641-646.
56. Dang X, Xiong G, Fan C, et al. Systematic external evaluation of four preoperative risk prediction models for severe postpartum hemorrhage in patients with placenta previa: a multicenter retrospective study. J Gynecol Obstet Hum Reprod, 2022, 51(4): 102333.
57. Obermeyer Z, Emanuel EJ. Predicting the future - big data, machine learning, and clinical medicine. N Engl J Med, 2016, 375(13): 1216-1219.

Chinese Journal of Evidence-Based Medicine

Postpartum hemorrhage risk prediction models: a systematic review

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