Evidence map›Paper›PMID 34890349›Full record

ArticleJMIR mental health2021

Evaluation of Repeated Web-Based Screening for Predicting Postpartum Depression: Prospective Cohort Study.

Kathrin Haßdenteufel, Katrin Lingenfelder, Cornelia E Schwarze, Manuel Feisst, Katharina Brusniak, Lina Maria Matthies, Maren Goetz, Markus Wallwiener, Stephanie Wallwiener

Open access · goldAbstract read
In one paragraph

Article in JMIR mental health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 6 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Kathrin Haßdenteufel *Department of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-0141-7425
Katrin Lingenfelder *Department of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0001-9769-8828
Cornelia E SchwarzeDepartment of Psychology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-1110-4449
Manuel FeisstInstitute of Medical Biometry, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0002-5016-9731
Katharina BrusniakDepartment of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-2005-9432
Lina Maria MatthiesDepartment of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0001-9754-0844
Maren GoetzDepartment of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0002-3558-5542
Markus WallwienerDepartment of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0002-4139-9340
Stephanie WallwienerDepartment of Obstetrics and Gynecology, Heidelberg University, Heidelberg, Germany.ORCID https://orcid.org/0000-0003-2045-650X
Heidelberg University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPostpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage.

objectiveThe aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm.

methodsIn total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy.

resultsDigitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum.

conclusionsThe best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women's personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term.

Indexed as

algorithmEdinburgh Postnatal Depression Scalepostpartum depressionpregnancyscreening

Identifiers

PMID34890349
PMCPMC8709910
OpenAlexW4200443194

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.