Evidence map›Paper›PMID 34233644›Full record

ArticleBMC pregnancy and childbirth2021

Reliability of last menstrual period recall, an early ultrasound and a Smartphone App in predicting date of delivery and classification of preterm and post-term births.

Linda Majola, Samantha Budhram, Vani Govender, Megeshinee Naidoo, Zukiswa Godlwana, Carl Lombard, Dhayendre Moodley

Open access · goldAbstract readEvaluation Study
In one paragraph

Article in BMC pregnancy and childbirth, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.1field-weighted citation impact, top 12% 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

8 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

7 authors at 3 institutions in 1 country.

Linda MajolaDepartment of Obstetrics and Gynaecology, School of Clinical Medicine, University of KwaZulu-Natal, 719 Umbilo Road, Congella, 4013, South Africa.
Samantha BudhramDepartment of Obstetrics and Gynaecology, School of Clinical Medicine, University of KwaZulu-Natal, 719 Umbilo Road, Congella, 4013, South Africa.
Vani GovenderCentre for the AIDS Programme of Research in South Africa, 719 Umbilo Road, Congella, 4013, South Africa.
Megeshinee NaidooCentre for the AIDS Programme of Research in South Africa, 719 Umbilo Road, Congella, 4013, South Africa.
Zukiswa GodlwanaCentre for the AIDS Programme of Research in South Africa, 719 Umbilo Road, Congella, 4013, South Africa.
Carl LombardBiostatistics Unit, South African Medical Research Council, Tygerberg, South Africa.
Dhayendre MoodleyDepartment of Obstetrics and Gynaecology, School of Clinical Medicine, University of KwaZulu-Natal, 719 Umbilo Road, Congella, 4013, South Africa. moodleyd1@ukzn.ac.za.
Centre for the AIDS Programme of Research in South Africa · ZAUniversity of KwaZulu-Natal · ZAStellenbosch University · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA reliable expected date of delivery (EDD) is important for pregnant women in planning for a safe delivery and critical for management of obstetric emergencies. We compared the accuracy of LMP recall, an early ultrasound (EUS) and a Smartphone App in predicting the EDD in South African pregnant women. We further evaluated the rates of preterm and post-term births based on using the different measures.

methodsThis is a retrospective sub-study of pregnant women enrolled in a randomized controlled trial between October 2017-December 2019. EDD and gestational age (GA) at delivery were calculated from EUS, LMP and Smartphone App. Data were analysed using SPSS version 25. A Bland-Altman plot was constructed to determine the limits of agreement between LMP and EUS.

resultsThree hundred twenty-five pregnant women who delivered at term (≥ 37 weeks by EUS) and without pregnancy complications were included in this analysis. Women had an EUS at a mean GA of 16 weeks and 3 days). The mean difference between LMP dating and EUS is 0.8 days with the limits of agreement 31.4-30.3 days (Concordance Correlation Co-efficient 0.835; 95%CI 0.802, 0.867). The mean(SD) of the marginal time distribution of the two methods differ significantly (p = 0.00187). EDDs were < 14 days of the actual date of delivery (ADD) for 287 (88.3%;95%CI 84.4-91.4), 279 (85.9%;95%CI 81.6-89.2) and 215 (66.2%;95%CI 60.9-71.1) women for EUS, Smartphone App and LMP respectively but overall agreement between EUS and LMP was only 46.5% using a five category scale for EDD-ADD with a kappa of .22. EUS 14-24 weeks and EUS < 14 weeks predicted EDDs < 14 days of ADD in 88.1% and 79.3% of women respectively. The proportion of births classified as preterm (< 37 weeks) was 9.9% (95%CI 7.1-13.6) by LMP and 0.3% (95%CI 0.1-1.7) by Smartphone App. The proportion of post-term (> 42 weeks gestation) births was 11.4% (95%CI 8.4-15.3), 1.9% (95%CI 0.9-3.9) and 3.4% (95%CI 1.9-5.9) by LMP, EUS and Smartphone respectively.

conclusionsEUS and Smartphone App were the most accurate to estimate the EDD in pregnant women. LMP-based dating resulted in misclassification of a significantly greater number of preterm and post-term deliveries compared to EUS and the Smartphone App.

Indexed as

Mobile ApplicationsAdultDelivery, ObstetricFemaleHumansInfant, NewbornMenstruationMental RecallPredictive Value of TestsPregnancyPregnancy, ProlongedPremature BirthReproducibility of ResultsRetrospective StudiesSmartphoneStatistics as TopicACOG smartphone appEstimated Date of DeliveryLast normal menstrual period recallPreterm birth misclassificationUltrasound dating

Identifiers

PMID34233644
PMCPMC8265063
OpenAlexW3182179586

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.