Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.

<h4>Objective</h4>As the first model in predicting the failure of early medical abortion (EMA) was inefficient, this study aims to develop and validate a risk assessment model for predicting the failure of EMAs more accurately in a clinical setting.<h4>Methods</h4>The derivat...

Full description

Saved in:
Bibliographic Details
Main Authors: An-Hao Liu, Bin Xu, Xiu-Wen Li, Yue-Wen Yu, Hui-Xin Guan, Xiao-Lu Sun, Yan-Zhen Lin, Li-Li Zhang, Xian-Di Zhuo, Jia Li, Wen-Qun Chen, Wen-Feng Hu, Ming-Zhu Ye, Xiu-Min Huang, Xun Chen
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2024-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0315025
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832589912356945920
author An-Hao Liu
Bin Xu
Xiu-Wen Li
Yue-Wen Yu
Hui-Xin Guan
Xiao-Lu Sun
Yan-Zhen Lin
Li-Li Zhang
Xian-Di Zhuo
Jia Li
Wen-Qun Chen
Wen-Feng Hu
Ming-Zhu Ye
Xiu-Min Huang
Xun Chen
author_facet An-Hao Liu
Bin Xu
Xiu-Wen Li
Yue-Wen Yu
Hui-Xin Guan
Xiao-Lu Sun
Yan-Zhen Lin
Li-Li Zhang
Xian-Di Zhuo
Jia Li
Wen-Qun Chen
Wen-Feng Hu
Ming-Zhu Ye
Xiu-Min Huang
Xun Chen
author_sort An-Hao Liu
collection DOAJ
description <h4>Objective</h4>As the first model in predicting the failure of early medical abortion (EMA) was inefficient, this study aims to develop and validate a risk assessment model for predicting the failure of EMAs more accurately in a clinical setting.<h4>Methods</h4>The derivation cohort was obtained from a comprehensive systematic review and meta-analysis. The clinically significant risk factors were identified and combined with their corresponding odds ratios to establish a risk assessment model. The risk factors were assigned scores based on their respective weightings. The model's performance was evaluated by an external validation cohort obtained from a tertiary hospital. The outcome was defined as the incidence of EMA failure.<h4>Results</h4>A total of 126,420 patients who had undergone medical abortions were included in the systematic review and meta-analysis, and the pooled failure rate was 6.7%. The final risk factors consisted of gestational age, maternal age, parity, previous termination of pregnancy, marital status, type of residence, and differences between gestational age calculated using the last menstrual period and that measured via ultrasound. The risk factors were assigned scores based on their respective weightings, with a maximum score of 19. The clinical prediction model exhibited a good discrimination, as validated by external verification (402 patients) with an area under the curve of 0.857 (95% confidence interval 0.804-0.910). The optimal cutoff value was determined to be 13.5 points, yielding a sensitivity of 83.3% and specificity of 75.4%.<h4>Conclusion</h4>This study effectively establishes a simple risk assessment model including seven routinely available clinical parameters for predicting EMA failure. In preliminary validation, this model demonstrates good performance in terms of predictive efficiency, accuracy, calibration, and clinical benefit. However, more research and validation are warranted for future application.<h4>Trial registration number</h4>CRD42023485388.
format Article
id doaj-art-6b3b3345089947e9a3a72ac3a786bece
institution Kabale University
issn 1932-6203
language English
publishDate 2024-01-01
publisher Public Library of Science (PLoS)
record_format Article
series PLoS ONE
spelling doaj-art-6b3b3345089947e9a3a72ac3a786bece2025-01-24T05:31:06ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-011912e031502510.1371/journal.pone.0315025Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.An-Hao LiuBin XuXiu-Wen LiYue-Wen YuHui-Xin GuanXiao-Lu SunYan-Zhen LinLi-Li ZhangXian-Di ZhuoJia LiWen-Qun ChenWen-Feng HuMing-Zhu YeXiu-Min HuangXun Chen<h4>Objective</h4>As the first model in predicting the failure of early medical abortion (EMA) was inefficient, this study aims to develop and validate a risk assessment model for predicting the failure of EMAs more accurately in a clinical setting.<h4>Methods</h4>The derivation cohort was obtained from a comprehensive systematic review and meta-analysis. The clinically significant risk factors were identified and combined with their corresponding odds ratios to establish a risk assessment model. The risk factors were assigned scores based on their respective weightings. The model's performance was evaluated by an external validation cohort obtained from a tertiary hospital. The outcome was defined as the incidence of EMA failure.<h4>Results</h4>A total of 126,420 patients who had undergone medical abortions were included in the systematic review and meta-analysis, and the pooled failure rate was 6.7%. The final risk factors consisted of gestational age, maternal age, parity, previous termination of pregnancy, marital status, type of residence, and differences between gestational age calculated using the last menstrual period and that measured via ultrasound. The risk factors were assigned scores based on their respective weightings, with a maximum score of 19. The clinical prediction model exhibited a good discrimination, as validated by external verification (402 patients) with an area under the curve of 0.857 (95% confidence interval 0.804-0.910). The optimal cutoff value was determined to be 13.5 points, yielding a sensitivity of 83.3% and specificity of 75.4%.<h4>Conclusion</h4>This study effectively establishes a simple risk assessment model including seven routinely available clinical parameters for predicting EMA failure. In preliminary validation, this model demonstrates good performance in terms of predictive efficiency, accuracy, calibration, and clinical benefit. However, more research and validation are warranted for future application.<h4>Trial registration number</h4>CRD42023485388.https://doi.org/10.1371/journal.pone.0315025
spellingShingle An-Hao Liu
Bin Xu
Xiu-Wen Li
Yue-Wen Yu
Hui-Xin Guan
Xiao-Lu Sun
Yan-Zhen Lin
Li-Li Zhang
Xian-Di Zhuo
Jia Li
Wen-Qun Chen
Wen-Feng Hu
Ming-Zhu Ye
Xiu-Min Huang
Xun Chen
Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
PLoS ONE
title Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
title_full Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
title_fullStr Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
title_full_unstemmed Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
title_short Development and validation of a risk assessment model for predicting the failure of early medical abortions: A clinical prediction model study based on a systematic review and meta-analysis.
title_sort development and validation of a risk assessment model for predicting the failure of early medical abortions a clinical prediction model study based on a systematic review and meta analysis
url https://doi.org/10.1371/journal.pone.0315025
work_keys_str_mv AT anhaoliu developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT binxu developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT xiuwenli developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT yuewenyu developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT huixinguan developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT xiaolusun developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT yanzhenlin developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT lilizhang developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT xiandizhuo developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT jiali developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT wenqunchen developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT wenfenghu developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT mingzhuye developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT xiuminhuang developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis
AT xunchen developmentandvalidationofariskassessmentmodelforpredictingthefailureofearlymedicalabortionsaclinicalpredictionmodelstudybasedonasystematicreviewandmetaanalysis