A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life
Aim A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA. Methods We reviewed 89 fetuses as an investigation c...
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| Veröffentlicht in: | The journal of obstetrics and gynaecology research 2022-09, Vol.48 (9), p.2304-2313 |
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| container_title | The journal of obstetrics and gynaecology research |
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| creator | Wang, Hui‐Hui Wang, Xi‐Ming Zhu, Mei Liang, Hao Feng, Juan Zhang, Nan Wang, Yue‐Mei Yu, Yong‐Hui Wang, An‐Biao |
| description | Aim
A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA.
Methods
We reviewed 89 fetuses as an investigation cohort with prenatal suspicion for CoA and categorized them into three subgroups: severe CoA: symptomatic CoA and surgery within the first 3 months; mild CoA: surgery within 4 months to 1 year (29); and false‐positive CoA: not requiring surgery (45). Logistic regression was used to create a multiparametric model, and a validation cohort of 86 fetuses with suspected CoA was used to validate the model.
Results
The prediction model had an optimal criterion >0.25 (sensitivity of 97.7%; specificity of 59.1%), and the area under the receiver operator curve was 0.85. The parameters and their cut‐off values were as follows: left common carotid artery to left subclavian artery distance/distal transverse arch (LCCA‐LSCA)/DT Index >1.77 (sensitivity 62%, specificity 88%, 95% confidence interval [CI]: 0.6–0.8), and z‐score of AAo peak Doppler > −1.7 (sensitivity 77%, specificity 56%, 95% CI: 0.6–0.8). The risk assessment demonstrated that fetuses with a model probability >60% should have inpatient observation for a high risk of CoA, whereas fetuses with a model probability |
| doi_str_mv | 10.1111/jog.15341 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2681442809</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2681442809</sourcerecordid><originalsourceid>FETCH-LOGICAL-c4121-8b49a862f29d84a68c304fd1b2d3c2a6cba1203dde0988324650018f5d4fa32a3</originalsourceid><addsrcrecordid>eNp1kE9PwyAchonRuDk9-AUMiRc9bANKW-ptWdzULNlFzw3lz2S2ZUKbZd9ebKcHE7lA4Pm9eXkAuMZogsOabu1mguOI4hMwxJSmY5TGyWk4h6sxQ2kyABfebxHCaYbZORhEcRpTlCVDYGZQlKY2gpdw55Q0ojG2hpWVqoSNhco3puKNgs27gs74D6itg8JyJxreoVZ3b9y6hj_AhbMV1Krh3XSt9oV1NSyNVpfgTPPSq6vjPgJvi8fX-dN4tV4-z2ersaCYhLoFzThLiCaZZJQnTESIaokLIiNBeCIKjgmKpFQoYywiNInDv5iOJdU8Ijwagbs-d-fsZxv655XxQpUlr5VtfU4SFhwRhrKA3v5Bt7Z1dWiXkxQxRuIU00Dd95Rw1nundL5zwYk75Bjl3_7D1Cbv_Af25pjYFpWSv-SP8ABMe2BvSnX4Pyl_WS_7yC9MZY3o</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2708825714</pqid></control><display><type>article</type><title>A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Wang, Hui‐Hui ; Wang, Xi‐Ming ; Zhu, Mei ; Liang, Hao ; Feng, Juan ; Zhang, Nan ; Wang, Yue‐Mei ; Yu, Yong‐Hui ; Wang, An‐Biao</creator><creatorcontrib>Wang, Hui‐Hui ; Wang, Xi‐Ming ; Zhu, Mei ; Liang, Hao ; Feng, Juan ; Zhang, Nan ; Wang, Yue‐Mei ; Yu, Yong‐Hui ; Wang, An‐Biao</creatorcontrib><description>Aim
A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA.
Methods
We reviewed 89 fetuses as an investigation cohort with prenatal suspicion for CoA and categorized them into three subgroups: severe CoA: symptomatic CoA and surgery within the first 3 months; mild CoA: surgery within 4 months to 1 year (29); and false‐positive CoA: not requiring surgery (45). Logistic regression was used to create a multiparametric model, and a validation cohort of 86 fetuses with suspected CoA was used to validate the model.
Results
The prediction model had an optimal criterion >0.25 (sensitivity of 97.7%; specificity of 59.1%), and the area under the receiver operator curve was 0.85. The parameters and their cut‐off values were as follows: left common carotid artery to left subclavian artery distance/distal transverse arch (LCCA‐LSCA)/DT Index >1.77 (sensitivity 62%, specificity 88%, 95% confidence interval [CI]: 0.6–0.8), and z‐score of AAo peak Doppler > −1.7 (sensitivity 77%, specificity 56%, 95% CI: 0.6–0.8). The risk assessment demonstrated that fetuses with a model probability >60% should have inpatient observation for a high risk of CoA, whereas fetuses with a model probability <15% should not undergo clinical follow‐up.
Conclusion
The probability model performs well in predicting CoA outcomes postnatally and can also improve the accuracy of risk assessment. The objectivity of its parameters may allow its implementation in multicenter studies of fetal cardiology.</description><identifier>ISSN: 1341-8076</identifier><identifier>EISSN: 1447-0756</identifier><identifier>DOI: 10.1111/jog.15341</identifier><identifier>PMID: 35754096</identifier><language>eng</language><publisher>Kyoto, Japan: John Wiley & Sons Australia, Ltd</publisher><subject>Aorta ; Aorta, Thoracic - diagnostic imaging ; Aortic Coarctation - diagnostic imaging ; Aortic Coarctation - surgery ; Carotid artery ; coarctation of the aorta ; congenital heart disease ; Coronary vessels ; Female ; Fetus ; Fetuses ; Humans ; Infant, Newborn ; logistic regression ; Models, Statistical ; prediction model ; Prediction models ; Pregnancy ; Prenatal diagnosis ; Prognosis ; Retrospective Studies ; Risk assessment ; Sensitivity and Specificity ; Surgery ; Ultrasonography, Prenatal</subject><ispartof>The journal of obstetrics and gynaecology research, 2022-09, Vol.48 (9), p.2304-2313</ispartof><rights>2022 The Authors. published by John Wiley & Sons Australia, Ltd on behalf of Japan Society of Obstetrics and Gynecology.</rights><rights>2022 The Authors. Journal of Obstetrics and Gynaecology Research published by John Wiley & Sons Australia, Ltd on behalf of Japan Society of Obstetrics and Gynecology.</rights><rights>2022. This article is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4121-8b49a862f29d84a68c304fd1b2d3c2a6cba1203dde0988324650018f5d4fa32a3</citedby><cites>FETCH-LOGICAL-c4121-8b49a862f29d84a68c304fd1b2d3c2a6cba1203dde0988324650018f5d4fa32a3</cites><orcidid>0000-0001-9093-1922</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fjog.15341$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjog.15341$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35754096$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Hui‐Hui</creatorcontrib><creatorcontrib>Wang, Xi‐Ming</creatorcontrib><creatorcontrib>Zhu, Mei</creatorcontrib><creatorcontrib>Liang, Hao</creatorcontrib><creatorcontrib>Feng, Juan</creatorcontrib><creatorcontrib>Zhang, Nan</creatorcontrib><creatorcontrib>Wang, Yue‐Mei</creatorcontrib><creatorcontrib>Yu, Yong‐Hui</creatorcontrib><creatorcontrib>Wang, An‐Biao</creatorcontrib><title>A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life</title><title>The journal of obstetrics and gynaecology research</title><addtitle>J Obstet Gynaecol Res</addtitle><description>Aim
A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA.
Methods
We reviewed 89 fetuses as an investigation cohort with prenatal suspicion for CoA and categorized them into three subgroups: severe CoA: symptomatic CoA and surgery within the first 3 months; mild CoA: surgery within 4 months to 1 year (29); and false‐positive CoA: not requiring surgery (45). Logistic regression was used to create a multiparametric model, and a validation cohort of 86 fetuses with suspected CoA was used to validate the model.
Results
The prediction model had an optimal criterion >0.25 (sensitivity of 97.7%; specificity of 59.1%), and the area under the receiver operator curve was 0.85. The parameters and their cut‐off values were as follows: left common carotid artery to left subclavian artery distance/distal transverse arch (LCCA‐LSCA)/DT Index >1.77 (sensitivity 62%, specificity 88%, 95% confidence interval [CI]: 0.6–0.8), and z‐score of AAo peak Doppler > −1.7 (sensitivity 77%, specificity 56%, 95% CI: 0.6–0.8). The risk assessment demonstrated that fetuses with a model probability >60% should have inpatient observation for a high risk of CoA, whereas fetuses with a model probability <15% should not undergo clinical follow‐up.
Conclusion
The probability model performs well in predicting CoA outcomes postnatally and can also improve the accuracy of risk assessment. The objectivity of its parameters may allow its implementation in multicenter studies of fetal cardiology.</description><subject>Aorta</subject><subject>Aorta, Thoracic - diagnostic imaging</subject><subject>Aortic Coarctation - diagnostic imaging</subject><subject>Aortic Coarctation - surgery</subject><subject>Carotid artery</subject><subject>coarctation of the aorta</subject><subject>congenital heart disease</subject><subject>Coronary vessels</subject><subject>Female</subject><subject>Fetus</subject><subject>Fetuses</subject><subject>Humans</subject><subject>Infant, Newborn</subject><subject>logistic regression</subject><subject>Models, Statistical</subject><subject>prediction model</subject><subject>Prediction models</subject><subject>Pregnancy</subject><subject>Prenatal diagnosis</subject><subject>Prognosis</subject><subject>Retrospective Studies</subject><subject>Risk assessment</subject><subject>Sensitivity and Specificity</subject><subject>Surgery</subject><subject>Ultrasonography, Prenatal</subject><issn>1341-8076</issn><issn>1447-0756</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>EIF</sourceid><recordid>eNp1kE9PwyAchonRuDk9-AUMiRc9bANKW-ptWdzULNlFzw3lz2S2ZUKbZd9ebKcHE7lA4Pm9eXkAuMZogsOabu1mguOI4hMwxJSmY5TGyWk4h6sxQ2kyABfebxHCaYbZORhEcRpTlCVDYGZQlKY2gpdw55Q0ojG2hpWVqoSNhco3puKNgs27gs74D6itg8JyJxreoVZ3b9y6hj_AhbMV1Krh3XSt9oV1NSyNVpfgTPPSq6vjPgJvi8fX-dN4tV4-z2ersaCYhLoFzThLiCaZZJQnTESIaokLIiNBeCIKjgmKpFQoYywiNInDv5iOJdU8Ijwagbs-d-fsZxv655XxQpUlr5VtfU4SFhwRhrKA3v5Bt7Z1dWiXkxQxRuIU00Dd95Rw1nundL5zwYk75Bjl3_7D1Cbv_Af25pjYFpWSv-SP8ABMe2BvSnX4Pyl_WS_7yC9MZY3o</recordid><startdate>202209</startdate><enddate>202209</enddate><creator>Wang, Hui‐Hui</creator><creator>Wang, Xi‐Ming</creator><creator>Zhu, Mei</creator><creator>Liang, Hao</creator><creator>Feng, Juan</creator><creator>Zhang, Nan</creator><creator>Wang, Yue‐Mei</creator><creator>Yu, Yong‐Hui</creator><creator>Wang, An‐Biao</creator><general>John Wiley & Sons Australia, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>7TO</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-9093-1922</orcidid></search><sort><creationdate>202209</creationdate><title>A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life</title><author>Wang, Hui‐Hui ; Wang, Xi‐Ming ; Zhu, Mei ; Liang, Hao ; Feng, Juan ; Zhang, Nan ; Wang, Yue‐Mei ; Yu, Yong‐Hui ; Wang, An‐Biao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4121-8b49a862f29d84a68c304fd1b2d3c2a6cba1203dde0988324650018f5d4fa32a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Aorta</topic><topic>Aorta, Thoracic - diagnostic imaging</topic><topic>Aortic Coarctation - diagnostic imaging</topic><topic>Aortic Coarctation - surgery</topic><topic>Carotid artery</topic><topic>coarctation of the aorta</topic><topic>congenital heart disease</topic><topic>Coronary vessels</topic><topic>Female</topic><topic>Fetus</topic><topic>Fetuses</topic><topic>Humans</topic><topic>Infant, Newborn</topic><topic>logistic regression</topic><topic>Models, Statistical</topic><topic>prediction model</topic><topic>Prediction models</topic><topic>Pregnancy</topic><topic>Prenatal diagnosis</topic><topic>Prognosis</topic><topic>Retrospective Studies</topic><topic>Risk assessment</topic><topic>Sensitivity and Specificity</topic><topic>Surgery</topic><topic>Ultrasonography, Prenatal</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Hui‐Hui</creatorcontrib><creatorcontrib>Wang, Xi‐Ming</creatorcontrib><creatorcontrib>Zhu, Mei</creatorcontrib><creatorcontrib>Liang, Hao</creatorcontrib><creatorcontrib>Feng, Juan</creatorcontrib><creatorcontrib>Zhang, Nan</creatorcontrib><creatorcontrib>Wang, Yue‐Mei</creatorcontrib><creatorcontrib>Yu, Yong‐Hui</creatorcontrib><creatorcontrib>Wang, An‐Biao</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>The journal of obstetrics and gynaecology research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Hui‐Hui</au><au>Wang, Xi‐Ming</au><au>Zhu, Mei</au><au>Liang, Hao</au><au>Feng, Juan</au><au>Zhang, Nan</au><au>Wang, Yue‐Mei</au><au>Yu, Yong‐Hui</au><au>Wang, An‐Biao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life</atitle><jtitle>The journal of obstetrics and gynaecology research</jtitle><addtitle>J Obstet Gynaecol Res</addtitle><date>2022-09</date><risdate>2022</risdate><volume>48</volume><issue>9</issue><spage>2304</spage><epage>2313</epage><pages>2304-2313</pages><issn>1341-8076</issn><eissn>1447-0756</eissn><abstract>Aim
A prenatal diagnosis of coarctation of the aorta (CoA) is challenging. This study aimed to develop a coarctation probability model incorporating prenatal cardiac sonographic markers to estimate the probability of an antenatal diagnosis of CoA.
Methods
We reviewed 89 fetuses as an investigation cohort with prenatal suspicion for CoA and categorized them into three subgroups: severe CoA: symptomatic CoA and surgery within the first 3 months; mild CoA: surgery within 4 months to 1 year (29); and false‐positive CoA: not requiring surgery (45). Logistic regression was used to create a multiparametric model, and a validation cohort of 86 fetuses with suspected CoA was used to validate the model.
Results
The prediction model had an optimal criterion >0.25 (sensitivity of 97.7%; specificity of 59.1%), and the area under the receiver operator curve was 0.85. The parameters and their cut‐off values were as follows: left common carotid artery to left subclavian artery distance/distal transverse arch (LCCA‐LSCA)/DT Index >1.77 (sensitivity 62%, specificity 88%, 95% confidence interval [CI]: 0.6–0.8), and z‐score of AAo peak Doppler > −1.7 (sensitivity 77%, specificity 56%, 95% CI: 0.6–0.8). The risk assessment demonstrated that fetuses with a model probability >60% should have inpatient observation for a high risk of CoA, whereas fetuses with a model probability <15% should not undergo clinical follow‐up.
Conclusion
The probability model performs well in predicting CoA outcomes postnatally and can also improve the accuracy of risk assessment. The objectivity of its parameters may allow its implementation in multicenter studies of fetal cardiology.</abstract><cop>Kyoto, Japan</cop><pub>John Wiley & Sons Australia, Ltd</pub><pmid>35754096</pmid><doi>10.1111/jog.15341</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-9093-1922</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Aorta Aorta, Thoracic - diagnostic imaging Aortic Coarctation - diagnostic imaging Aortic Coarctation - surgery Carotid artery coarctation of the aorta congenital heart disease Coronary vessels Female Fetus Fetuses Humans Infant, Newborn logistic regression Models, Statistical prediction model Prediction models Pregnancy Prenatal diagnosis Prognosis Retrospective Studies Risk assessment Sensitivity and Specificity Surgery Ultrasonography, Prenatal |
| title | A clinical prediction model to estimate the risk for coarctation of the aorta: From fetal to newborn life |
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