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.	莊幼青, 蔣翠婷, 曾麗玲, 等. 產婦心理彈性在產后負性生活事件與產后抑郁間的中介效應. 解放軍護理雜志, 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, 楊建輝, 陳佩珊, 等. 母親孕期或產后抑郁情緒對子代情緒和行為的影響: Meta分析. 中華實用兒科臨床雜志, 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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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.	蔡飛亞, 況利, 王我, 等. 基于社會心理因素的產后抑郁癥模型的建立和評價. 第二軍醫大學學報, 2017, 38(4): 476-481.
18.	肖美麗, 晏春麗, 付冰, 等. 隨機森林算法在產后抑郁風險預測中的應用. 中南大學學報(醫學版), 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.	盧振玲, 裴宇權, 李暉, 等. 圍手術期非計劃性低體溫風險預測模型的系統評價. 中國護理管理, 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.	吳堯, 謝碧姣, 王丹心, 等. 康復期腦卒中患者跌倒風險預測模型的系統評價. 中華護理雜志, 2022, 57(12): 1440-1446.
25.	謝曉冉, 徐蓉. 糖尿病足發病風險預測模型的系統評價. 中華護理雜志, 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.	孔令華, 王馨悅, 孫玨, 等. 蘇州市某社區產后抑郁流行病學調查及相關危險因素. 四川精神衛生, 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, 朱貝貝, 陶芳標. 妊娠糖尿病與圍生期抑郁關系的研究進展. 現代預防醫學, 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.	祝穎, 陳一丹, 韓怡雯, 等. 產后抑郁癥與甲狀腺功能關系的研究進展. 中國現代醫生, 2019, 57(35): 165-168.
34.	鮑慈青, 樂濤, 孫詩雨, 等. 孕早期和孕晚期促甲狀腺激素水平對產婦產后抑郁的影響. 中國婦幼保健, 2022, 37(7): 1163-1166.
35.	王凱璇, 吳鴻雁, 姚歡杰, 等. 18 kDa轉位蛋白聯合四氫孕酮血漿含量改變對產后抑郁癥的預測價值. 河北醫藥, 2021, 43(22): 3393-3396.
36.	劉嫣, 齊偉靜, 胡潔. 人際心理療法對產后抑郁的治療效果. 解放軍護理雜志, 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. 莊幼青, 蔣翠婷, 曾麗玲, 等. 產婦心理彈性在產后負性生活事件與產后抑郁間的中介效應. 解放軍護理雜志, 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, 楊建輝, 陳佩珊, 等. 母親孕期或產后抑郁情緒對子代情緒和行為的影響: Meta分析. 中華實用兒科臨床雜志, 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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. 蔡飛亞, 況利, 王我, 等. 基于社會心理因素的產后抑郁癥模型的建立和評價. 第二軍醫大學學報, 2017, 38(4): 476-481.
18. 肖美麗, 晏春麗, 付冰, 等. 隨機森林算法在產后抑郁風險預測中的應用. 中南大學學報(醫學版), 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. 盧振玲, 裴宇權, 李暉, 等. 圍手術期非計劃性低體溫風險預測模型的系統評價. 中國護理管理, 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. 吳堯, 謝碧姣, 王丹心, 等. 康復期腦卒中患者跌倒風險預測模型的系統評價. 中華護理雜志, 2022, 57(12): 1440-1446.
25. 謝曉冉, 徐蓉. 糖尿病足發病風險預測模型的系統評價. 中華護理雜志, 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. 孔令華, 王馨悅, 孫玨, 等. 蘇州市某社區產后抑郁流行病學調查及相關危險因素. 四川精神衛生, 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, 朱貝貝, 陶芳標. 妊娠糖尿病與圍生期抑郁關系的研究進展. 現代預防醫學, 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. 祝穎, 陳一丹, 韓怡雯, 等. 產后抑郁癥與甲狀腺功能關系的研究進展. 中國現代醫生, 2019, 57(35): 165-168.
34. 鮑慈青, 樂濤, 孫詩雨, 等. 孕早期和孕晚期促甲狀腺激素水平對產婦產后抑郁的影響. 中國婦幼保健, 2022, 37(7): 1163-1166.
35. 王凱璇, 吳鴻雁, 姚歡杰, 等. 18 kDa轉位蛋白聯合四氫孕酮血漿含量改變對產后抑郁癥的預測價值. 河北醫藥, 2021, 43(22): 3393-3396.
36. 劉嫣, 齊偉靜, 胡潔. 人際心理療法對產后抑郁的治療效果. 解放軍護理雜志, 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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