Predictive model for the risk of postpartum depression: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

SHAO Zhuyan ¹ , ZONG Kejing ¹ , FAN Qingmei ¹ , HAN Qian ¹ , TANG Yan ¹ ,  QIAO Jianhong ²

1. Department of Nursing, Shandong University of Traditional Chinese Medicine, Jinan 250355, P. R. China;
2. Department of Nursing, the First Affiliated Hospital of Shandong First Medical University and Shandong University (Shandong Province Qianfoshan Hospital), Jinan 250014, P. R. China;

Corresponding author：

QIAO Jianhong, Email: 1969144595@qq.com

Keywords：

Postpartum depression; Risk; Prediction models; Systematic review

DOI：

10.7507/1672-2531.202303021

Video：

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Objective To systematically evaluate postpartum depression risk prediction models in order to provide references for the construction, application and optimization of related prediction models. Methods The CNKI, VIP, WanFang Data, PubMed, Web of Science and EMbase were electronically searched to collect studies on predictive model for the risk of postpartum from January 2013 to April 2023. Two reviewers independently screened the literature, extracted data, and assessed the quality of the included studies based on PROBAST tool. Results A total of 10 studies, each study with 1 optimal model were evaluated. Common predictors included prenatal depression, age, smoking history, thyroid hormones and other factors. The area under the curve of the model was greater than 0.7, and the overall applicability was general. Overall high risk of bias and average applicability, mainly due to insufficient number of events in the analysis domain for the response variable, improper handling of missing data, screening of predictors based on univariate analysis, lack of model performance assessment, and consideration of model overfitting. Conclusion The model is still in the development stage. The included model has good predictive performance and can help early identify people with high incidence of postpartum depression. However, the overall applicability of the model needs to be strengthened, a large sample, multi-center prospective clinical study should be carried out to construct the optimal risk prediction model of PPD, in order to identify and prevent PPD as soon as possible.

Citation： SHAO Zhuyan, ZONG Kejing, FAN Qingmei, HAN Qian, TANG Yan, QIAO Jianhong. Predictive model for the risk of postpartum depression: a systematic review. Chinese Journal of Evidence-Based Medicine, 2023, 23(7): 807-813. doi: 10.7507/1672-2531.202303021 Copy

1.	Tokumitsu K, Sugawara N, Maruo K, et al. Prevalence of perinatal depression among Japanese women: a meta-analysis. Ann Gen Psychiatry, 2020, 19(1): 41.
2.	Hahn-Holbrook J, Cornwell-Hinrichs T, Anaya I. Economic and health predictors of national postpartum depression prevalence: a systematic review, meta-analysis, and meta-regression of 291 studies from 56 countries. Front Psychiatry, 2017, 8: 248.
3.	庄幼青, 蒋翠婷, 曾丽玲, 等. 产妇心理弹性在产后负性生活事件与产后抑郁间的中介效应. 解放军护理杂志, 2021, 38(12): 18-21.
4.	Oliveira TA, Luzetti G, Rosalem MMA, et al. Screening of perinatal depression using the Edinburgh postpartum depression scale. Rev Bras Ginecol Obstet, 2022, 44(5): 452-457.
5.	林雪梅, 杨建辉, 陈佩珊, 等. 母亲孕期或产后抑郁情绪对子代情绪和行为的影响: Meta分析. 中华实用儿科临床杂志, 2022, 37(4): 284-289.
6.	Farias-Antunez S, Xavier MO, Santos IS. Effect of maternal postpartum depression on offspring's growth. J Affect Disord, 2018, 228: 143-152.
7.	Siu AL, Force USPST, Bibbins-Domingo K, et al. Screening for depression in sdults: US Preventive Services Task Force recommendation Statement. JAMA, 2016, 315(4): 380-7.
8.	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.
9.	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.
10.	Amit G, Girshovitz I, Marcus K, et al. Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth, 2021, 21(1): 630.
11.	Hochman E, Feldman B, Weizman A, et al. Development and validation of a machine learning-based postpartum depression prediction model: a nationwide cohort study. Depress Anxiety, 2021, 38(4): 400-411.
12.	Matsuo S, Ushida T, Emoto R, et al. Machine learning prediction models for postpartum depression: a multicenter study in Japan. J Obstet Gynaecol Res, 2022, 48(7): 1775-1785.
13.	Reps JM, Wilcox M, Mcgee BA, et al. Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy. BMC Pregnancy Childbirth, 2022, 22(1): 442.
14.	Shin D, Lee KJ, Adeluwa T, et al. Machine learning-based predictive modeling of postpartum depression. J Clin Med, 2020, 9(9): 2899.
15.	Yang ST, Yang SQ, Duan KM, et al. The development and application of a prediction model for postpartum depression: optimizing risk assessment and prevention in the clinic. J Affect Disord, 2022, 296: 434-442.
16.	Zhang Y, Wang S, Hermann A, et al. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disord, 2021, 279: 1-8.
17.	蔡飞亚, 况利, 王我, 等. 基于社会心理因素的产后抑郁症模型的建立和评价. 第二军医大学学报, 2017, 38(4): 476-481.
18.	肖美丽, 晏春丽, 付冰, 等. 随机森林算法在产后抑郁风险预测中的应用. 中南大学学报(医学版), 2020, 45(10): 1215-1222.
19.	Munk-Olsen T, Liu X, Madsen KB, et al. Postpartum depression: a developed and validated model predicting individual risk in new mothers. Transl Psychiatry, 2022, 12(1): 419.
20.	Moraes GP, Lorenzo L, Pontes GA, et al. Screening and diagnosing postpartum depression: when and how. Trends Psychiatry Psychother, 2017, 39(1): 54-61.
21.	Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
22.	卢振玲, 裴宇权, 李晖, 等. 围手术期非计划性低体温风险预测模型的系统评价. 中国护理管理, 2022, 22(6): 881-887.
23.	Scoppetta O, Cassiani-Miranda CA, Arocha-Diaz KN, et al. Validity of the patient health questionnaire-2 (PHQ-2) for the detection of depression in primary care in Colombia. J Affect Disord, 2021, 278: 576-582.
24.	吴尧, 谢碧姣, 王丹心, 等. 康复期脑卒中患者跌倒风险预测模型的系统评价. 中华护理杂志, 2022, 57(12): 1440-1446.
25.	谢晓冉, 徐蓉. 糖尿病足发病风险预测模型的系统评价. 中华护理杂志, 2021, 56(1): 124-131.
26.	Upadhyay RP, Chowdhury R, Aslyeh S, et al. Postpartum depression in India: a systematic review and meta-analysis. Bull World Health Organ, 2017, 95(10): 706C-717C.
27.	Pampaka D, Papatheodorou SI, Alseaidan M, et al. Postnatal depressive symptoms in women with and without antenatal depressive symptoms: results from a prospective cohort study. Arch Womens Ment Health, 2019, 22(1): 93-103.
28.	孔令华, 王馨悦, 孙珏, 等. 苏州市某社区产后抑郁流行病学调查及相关危险因素. 四川精神卫生, 2018, 31(2): 152-155.
29.	Oztora S, Arslan A, Caylan A, et al. Postpartum depression and affecting factors in primary care. Niger J Clin Pract, 2019, 22(1): 85-91.
30.	李海滟, 朱贝贝, 陶芳标. 妊娠糖尿病与围生期抑郁关系的研究进展. 现代预防医学, 2021, 48(10): 1802-1805.
31.	Schmidt P, Longoni A, Pinheiro RT, et al. Postpartum depression in maternal thyroidal changes. Thyroid Res, 2022, 15(1): 6.
32.	Wesseloo R, Kamperman AM, Bergink V, et al. Thyroid peroxidase antibodies during early gestation and the subsequent risk of first-onset postpartum depression: a prospective cohort study. J Affect Disord, 2018, 225: 399-403.
33.	祝颖, 陈一丹, 韩怡雯, 等. 产后抑郁症与甲状腺功能关系的研究进展. 中国现代医生, 2019, 57(35): 165-168.
34.	鲍慈青, 乐涛, 孙诗雨, 等. 孕早期和孕晚期促甲状腺激素水平对产妇产后抑郁的影响. 中国妇幼保健, 2022, 37(7): 1163-1166.
35.	王凯璇, 吴鸿雁, 姚欢杰, 等. 18 kDa转位蛋白联合四氢孕酮血浆含量改变对产后抑郁症的预测价值. 河北医药, 2021, 43(22): 3393-3396.
36.	刘嫣, 齐伟静, 胡洁. 人际心理疗法对产后抑郁的治疗效果. 解放军护理杂志, 2018, 35(14): 27-30.

1. Tokumitsu K, Sugawara N, Maruo K, et al. Prevalence of perinatal depression among Japanese women: a meta-analysis. Ann Gen Psychiatry, 2020, 19(1): 41.
2. Hahn-Holbrook J, Cornwell-Hinrichs T, Anaya I. Economic and health predictors of national postpartum depression prevalence: a systematic review, meta-analysis, and meta-regression of 291 studies from 56 countries. Front Psychiatry, 2017, 8: 248.
3. 庄幼青, 蒋翠婷, 曾丽玲, 等. 产妇心理弹性在产后负性生活事件与产后抑郁间的中介效应. 解放军护理杂志, 2021, 38(12): 18-21.
4. Oliveira TA, Luzetti G, Rosalem MMA, et al. Screening of perinatal depression using the Edinburgh postpartum depression scale. Rev Bras Ginecol Obstet, 2022, 44(5): 452-457.
5. 林雪梅, 杨建辉, 陈佩珊, 等. 母亲孕期或产后抑郁情绪对子代情绪和行为的影响: Meta分析. 中华实用儿科临床杂志, 2022, 37(4): 284-289.
6. Farias-Antunez S, Xavier MO, Santos IS. Effect of maternal postpartum depression on offspring's growth. J Affect Disord, 2018, 228: 143-152.
7. Siu AL, Force USPST, Bibbins-Domingo K, et al. Screening for depression in sdults: US Preventive Services Task Force recommendation Statement. JAMA, 2016, 315(4): 380-7.
8. 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.
9. 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.
10. Amit G, Girshovitz I, Marcus K, et al. Estimation of postpartum depression risk from electronic health records using machine learning. BMC Pregnancy Childbirth, 2021, 21(1): 630.
11. Hochman E, Feldman B, Weizman A, et al. Development and validation of a machine learning-based postpartum depression prediction model: a nationwide cohort study. Depress Anxiety, 2021, 38(4): 400-411.
12. Matsuo S, Ushida T, Emoto R, et al. Machine learning prediction models for postpartum depression: a multicenter study in Japan. J Obstet Gynaecol Res, 2022, 48(7): 1775-1785.
13. Reps JM, Wilcox M, Mcgee BA, et al. Development of multivariable models to predict perinatal depression before and after delivery using patient reported survey responses at weeks 4-10 of pregnancy. BMC Pregnancy Childbirth, 2022, 22(1): 442.
14. Shin D, Lee KJ, Adeluwa T, et al. Machine learning-based predictive modeling of postpartum depression. J Clin Med, 2020, 9(9): 2899.
15. Yang ST, Yang SQ, Duan KM, et al. The development and application of a prediction model for postpartum depression: optimizing risk assessment and prevention in the clinic. J Affect Disord, 2022, 296: 434-442.
16. Zhang Y, Wang S, Hermann A, et al. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disord, 2021, 279: 1-8.
17. 蔡飞亚, 况利, 王我, 等. 基于社会心理因素的产后抑郁症模型的建立和评价. 第二军医大学学报, 2017, 38(4): 476-481.
18. 肖美丽, 晏春丽, 付冰, 等. 随机森林算法在产后抑郁风险预测中的应用. 中南大学学报(医学版), 2020, 45(10): 1215-1222.
19. Munk-Olsen T, Liu X, Madsen KB, et al. Postpartum depression: a developed and validated model predicting individual risk in new mothers. Transl Psychiatry, 2022, 12(1): 419.
20. Moraes GP, Lorenzo L, Pontes GA, et al. Screening and diagnosing postpartum depression: when and how. Trends Psychiatry Psychother, 2017, 39(1): 54-61.
21. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol, 2016, 76: 175-182.
22. 卢振玲, 裴宇权, 李晖, 等. 围手术期非计划性低体温风险预测模型的系统评价. 中国护理管理, 2022, 22(6): 881-887.
23. Scoppetta O, Cassiani-Miranda CA, Arocha-Diaz KN, et al. Validity of the patient health questionnaire-2 (PHQ-2) for the detection of depression in primary care in Colombia. J Affect Disord, 2021, 278: 576-582.
24. 吴尧, 谢碧姣, 王丹心, 等. 康复期脑卒中患者跌倒风险预测模型的系统评价. 中华护理杂志, 2022, 57(12): 1440-1446.
25. 谢晓冉, 徐蓉. 糖尿病足发病风险预测模型的系统评价. 中华护理杂志, 2021, 56(1): 124-131.
26. Upadhyay RP, Chowdhury R, Aslyeh S, et al. Postpartum depression in India: a systematic review and meta-analysis. Bull World Health Organ, 2017, 95(10): 706C-717C.
27. Pampaka D, Papatheodorou SI, Alseaidan M, et al. Postnatal depressive symptoms in women with and without antenatal depressive symptoms: results from a prospective cohort study. Arch Womens Ment Health, 2019, 22(1): 93-103.
28. 孔令华, 王馨悦, 孙珏, 等. 苏州市某社区产后抑郁流行病学调查及相关危险因素. 四川精神卫生, 2018, 31(2): 152-155.
29. Oztora S, Arslan A, Caylan A, et al. Postpartum depression and affecting factors in primary care. Niger J Clin Pract, 2019, 22(1): 85-91.
30. 李海滟, 朱贝贝, 陶芳标. 妊娠糖尿病与围生期抑郁关系的研究进展. 现代预防医学, 2021, 48(10): 1802-1805.
31. Schmidt P, Longoni A, Pinheiro RT, et al. Postpartum depression in maternal thyroidal changes. Thyroid Res, 2022, 15(1): 6.
32. Wesseloo R, Kamperman AM, Bergink V, et al. Thyroid peroxidase antibodies during early gestation and the subsequent risk of first-onset postpartum depression: a prospective cohort study. J Affect Disord, 2018, 225: 399-403.
33. 祝颖, 陈一丹, 韩怡雯, 等. 产后抑郁症与甲状腺功能关系的研究进展. 中国现代医生, 2019, 57(35): 165-168.
34. 鲍慈青, 乐涛, 孙诗雨, 等. 孕早期和孕晚期促甲状腺激素水平对产妇产后抑郁的影响. 中国妇幼保健, 2022, 37(7): 1163-1166.
35. 王凯璇, 吴鸿雁, 姚欢杰, 等. 18 kDa转位蛋白联合四氢孕酮血浆含量改变对产后抑郁症的预测价值. 河北医药, 2021, 43(22): 3393-3396.
36. 刘嫣, 齐伟静, 胡洁. 人际心理疗法对产后抑郁的治疗效果. 解放军护理杂志, 2018, 35(14): 27-30.

Chinese Journal of Evidence-Based Medicine

Predictive model for the risk of postpartum depression: a systematic review

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