Evidence map›Paper›PMID 39044159›Full record

ArticleBMC pregnancy and childbirth2024

Construction and application of fetal loss risk model in systemic lupus erythematosus patients with mild disease severity.

Yanran Chen, Yanjuan Chen, Bo Li, Wengyi Xu, Peipei Lei, Hongyang Liu, Dongzhou Liu, Xiaoping Hong

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC pregnancy and childbirth, 2024. 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
–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

1 citing paper in PubMed.

  1. Review
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.

Yanran ChenThe Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China.
Yanjuan ChenDepartment of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
Bo LiDepartment of Rheumatology and Immunology, Shenzhen Longhua people's Hospital, Shenzhen, China.
Wengyi XuDepartment of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
Peipei LeiDepartment of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
Hongyang LiuDepartment of Rheumatology and Immunology, Shenzhen People's Hospital, The Second Clinical Medical College, Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, China.
Dongzhou LiuThe Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China. liu_dz2001@sina.com.ORCID http://orcid.org/0000-0003-3766-6562
Xiaoping HongThe Second Clinical Medical College, Jinan University (Shenzhen People's Hospital), Shenzhen, China. hong_xiaoping@hotmail.com.ORCID http://orcid.org/0000-0002-2062-8394

Funding

Shenzhen People's Hospital "Five Threes" Incubation Program No.SYWGSLCYJ202203The National Natural Science foundation of China No.81971464
6 · The paper itself

Abstract

backgroundThis dynamic nomogram model was developed to predict the probability of fetal loss in pregnant patients with systemic lupus erythematosus (SLE) with mild disease severity before conception.

methodsAn analysis was conducted on 314 pregnancy records of patients with SLE who were hospitalized between January 2015 and January 2022 at Shenzhen People's Hospital, and the Longhua Branch of Shenzhen People's Hospital. Data from the Longhua Branch of the Shenzhen People's Hospital were utilized as an independent external validation cohort. The nomogram, a widely used statistical visualization tool to predict disease onset, progression, prognosis, and survival, was created after feature selection using multivariate logistic regression analysis. To evaluate the model prediction performance, we employed the receiver operating characteristic curve, calibration curve, and decision curve analysis.

resultsLupus nephritis, complement 3, immunoglobulin G, serum albumin, C-reactive protein, and hydroxychloroquine were all included in the nomogram model. The model demonstrated good calibration and discriminatory power, with an area under the curve of 0.867 (95% confidence interval: 0.787-0.947). According to decision curve analysis, the nomogram model exhibited clinical importance when the probability of fetal loss in patients with SLE ranged between 10 and 70%. The predictive ability of the model was demonstrated through external validation.

conclusionThe predictive nomogram approach may facilitate precise management of pregnant patients with SLE with mild disease severity before conception.

Indexed as

Lupus Erythematosus, SystemicNomogramsPregnancy ComplicationsSeverity of Illness IndexAbortion, SpontaneousAdultChinaComplement C3C-Reactive ProteinFemaleFetal DeathHumansHydroxychloroquineLogistic ModelsPregnancyRetrospective StudiesComplement C3C-Reactive ProteinHydroxychloroquineFetal lossMild disease severityPrediction nomogramPregnancy outcomeSystemic lupus erythematosus

Identifiers

PMID39044159
PMCPMC11264457

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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.