Evidence map›Paper›PMID 40016602›Full record

ArticleClinical rheumatology2025

A nomogram for predicting the adverse pregnancy outcomes of systemic lupus erythematosus: a single-center study.

Wei Kong, Xin Zhang, Linyu Geng, Chen Chen, Yue Sun, Xue Xu, Shengnan Zhao, Ziyi Jin, Yang Huang, Dandan Wang and 3 more

Abstract read
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In one paragraph

Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. 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

13 authors.

Wei Kong *Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Xin Zhang *Department of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Linyu GengDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Chen ChenDepartment of Clinical Nutrition, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Yue SunDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Xue XuDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Shengnan ZhaoDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Ziyi JinDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Yang HuangCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, 29 Jiangjun Ave., Nanjing, Jiangsu, 211106, China.
Dandan WangDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Jun LiangDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China.
Yun ZhuDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China. zhuyunjs@126.com.ORCID http://orcid.org/0009-0004-4413-9044
Lingyun SunDepartment of Rheumatology and Immunology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, No. 321 Zhongshan Road, Nanjing, Jiangsu, 210008, China. lingyunsun@nju.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAs systemic lupus erythematosus (SLE) primarily impacts women of childbearing age, a considerable number of patients have fertility needs. However, the risk of experiencing adverse pregnancy outcomes (APOs) was higher in these patients. Our study aimed to construct a predictive model to assess the risks for APOs of SLE.

methodWe retrospectively analyzed the data of pregnant SLE patients hospitalized at Nanjing Drum Tower Hospital from August 2010 to April 2023. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to explore the risk factors for APOs, and a nomogram was established. Afterward, the efficacy of the nomogram was evaluated by analyzing the areas under the curves (AUCs) of Receiver Operating Characteristic (ROC), calibration curves, and Decision Curve Analysis (DCA).

resultsOur study involved 259 pregnant patients with a median age of 29.00 years, and identified 129 cases of APOs, including preterm birth, low birth weight, congenital anomalies, stillbirth/miscarriage, and fetal distress. Through LASSO regression analysis, nine optimal features were selected as risk factors, including age, lupus nephritis, antepartum body mass index, antinuclear antibody, anti-U1RNP/Sm antibody, anti-ribosomal P protein antibody, platelet, albumin levels, SLEDAI scores, diabetes mellitus, rash, and the use of aspirin therapy. These factors were integrated into a predictive nomogram model, which showed good predictive accuracy, with AUC values of 0.870 and 0.830 in training and validation groups, respectively. The calibration curves and DCA also confirmed the good performance of the model.

conclusionsWe developed a tool to predict APOs in SLE patients, offering personalized risk assessments and clinical decision support. As the data used to build the predictive model was obtained from a single center, the tool is currently best suited for application within our center. Further validation in diverse populations is needed to expand its generalizability. Key Points • Our study revealed the independent predictors for APOs of SLE through LASSO regression analysis. • We developed a nomogram to predict APOs in SLE based on the results of LASSO regression analysis. • The predictive model may aid clinical decision-making, enabling timely interventions to reduce the incidence of APOs.

Indexed as

Lupus Erythematosus, SystemicNomogramsPregnancy ComplicationsPregnancy OutcomeAdultFemaleHumansPregnancyRetrospective StudiesRisk AssessmentRisk FactorsROC CurveYoung AdultAdverse pregnancy outcomesNomogramPredictive modelSystemic lupus erythematosus

Identifiers

PMID40016602

What OpenQuestion holds

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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.