Evidence map›Paper›PMID 39707645›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2024

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

Lan Wang, Aiping Qin, Yihua Yang, Yufu Jin, Qiuyan Huang, Xinyue Huang, Yu Feng, Ting Liang

Abstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 2 pooled it
–field-weighted citation impact
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

2 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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

8 authors.

Lan WangDepartment of Gynecology, Guilin People's Hospital, Guilin, Guangxi, China.ORCID 0000-0002-1564-6416
Aiping QinReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.ORCID 0000-0002-0185-7204
Yihua YangReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.
Yufu JinReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.
Qiuyan HuangReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.
Xinyue HuangReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.
Yu FengReproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.
Ting LiangReproductive Center, Guangxi Medical University First Affiliated Hospital,, Nanning, Guangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, and preterm birth remain debated. This study aims to address these gaps by systematically evaluating the influence of SCH-related clinical factors on pregnancy outcomes using robust analytical techniques. MATERIAL AND METHODS Data from SCH and non-SCH pregnant women were collected and split into training and test datasets. Machine learning classifiers and regression models were used to assess the impact of clinical indices on outcomes such as delivery type, NICU transfer, gestational age, and birth weight. Results were evaluated using ROC and calibration plots. RESULTS (1) SCH women had a significantly higher risk of stillbirth or miscarriage than non-SCH women (P<0.001). Logistic regression and XGB models showed AUCs of 0.858 and 0.916, respectively. Key factors affecting delivery outcomes included the first positive HCG level, hematoma duration, CA125 level, gestational sac diameter, fibrinogen level, and spouse age. (2) 12.7% of successfully delivered SCH newborns required NICU transfer, but clinical indices did not predict NICU need (AUC 0.589 and 0.629). (3) Successfully delivered SCH women had longer gestational ages than those with miscarriage/stillbirth (38.8 vs 10.1 weeks), but indices did not predict preterm/full-term birth (AUCs 0.449 and 0.503). (4) Birth weight was significantly affected by live birth times and gestational age (P<0.05), though the adjusted R-square was 0.226. CONCLUSIONS (1) SCH increases miscarriage or stillbirth risk. (2) the first positive HCG level, the hematoma duration, serum CA125 level, the gestational sac maximum diameter, fibrinogen, and the spouse age highly impacted the delivery outcome. (3) SCH indices do not affect NICU transfer or birth weight. (4) Miscarriage/stillbirth mainly occurs in the first trimester; passing this stage often leads to successful delivery. (5) The birth weight of full-term newborns is significantly higher than that of preterm infants. The clinical indices of SCH pregnant women have no impact on the birth weight of the newborn.

Indexed as

HematomaMachine LearningPregnancy OutcomeAbortion, SpontaneousAdultBirth WeightChorionFemaleGestational AgeHumansInfant, NewbornLogistic ModelsPregnancyPregnancy ComplicationsPremature BirthRisk Factors

Identifiers

PMID39707645
PMCPMC11669258

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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.