Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort
Objective To develop risk prediction models for singleton preterm birth (PTB)
Gespeichert in:
| Veröffentlicht in: | Journal of perinatology 2021-09, Vol.41 (9), p.2173-2181 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2181 |
|---|---|
| container_issue | 9 |
| container_start_page | 2173 |
| container_title | Journal of perinatology |
| container_volume | 41 |
| creator | Arabi Belaghi, Reza Beyene, Joseph McDonald, Sarah D. |
| description | Objective
To develop risk prediction models for singleton preterm birth (PTB) |
| doi_str_mv | 10.1038/s41372-021-01109-3 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_journals_2572355100</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A675425251</galeid><sourcerecordid>A675425251</sourcerecordid><originalsourceid>FETCH-LOGICAL-c473t-208a1a08292d004403c784faf97d3a69df06ad6a194006d8b162d5d730ee8edf3</originalsourceid><addsrcrecordid>eNp9kk1v1DAQhiMEokvhD3BAlpAqLinjrzg5Viu-pEpc4Gx543HWrRMvdlLEjZ9eL7vQFiHkg6WZ5301M3qr6iWFcwq8fZsF5YrVwGgNlEJX80fVigrV1FIK_rhagRK8brloTqpnOV8B7JvqaXXCBaWsa-Sq-rkOfvK9CST5fE3GaDFk4mIiu4QzppFsfJq3JGDOZN6aibCWfEe8zsRM9l6Zs2M5OjJgns3s40SW7KeBGBJMGpAUxxTzDvvZ3yDp4zam-Xn1xJmQ8cXxP62-vn_3Zf2xvvz84dP64rLuheJzzaA11EDLOmYBhADeq1Y44zpluWk666AxtjG0EwCNbTe0YVZaxQGxRev4afXm4LtL8dtSBtSjzz2GYCaMS9ZMCpBUia4r6Ou_0Ku4pKlMVyjFuJQU4I4aTEDtJxfnZPq9qb5olBRMMkkLdf4PqjyLo-_jhM6X-gPB2T3BFk2YtzmGZX_O_BBkB7AvN80Jnd4lP5r0Q1PQ-3zoQz50yYf-lQ_Ni-jVcbVlM6L9I_kdiALwA5BLaxow3e3-H9tbg43Cxg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2572355100</pqid></control><display><type>article</type><title>Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort</title><source>MEDLINE</source><source>SpringerLink Journals</source><creator>Arabi Belaghi, Reza ; Beyene, Joseph ; McDonald, Sarah D.</creator><creatorcontrib>Arabi Belaghi, Reza ; Beyene, Joseph ; McDonald, Sarah D.</creatorcontrib><description><![CDATA[Objective
To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks.
Methods
Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks.
Results
During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5–73.6%) and 73.4% (68.6–78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5–72.3%) and 75.5% (72.3–78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7–75.1%) and 78.2% (95% CI: 75.8–80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes.
Conclusions
Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.]]></description><identifier>ISSN: 0743-8346</identifier><identifier>EISSN: 1476-5543</identifier><identifier>DOI: 10.1038/s41372-021-01109-3</identifier><identifier>PMID: 34112965</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>631/136/1425 ; 692/308/174 ; 9/10 ; 96/10 ; Birth ; Calibration ; Cohort Studies ; Female ; Gestation ; Humans ; Infant, Newborn ; Medicine ; Medicine & Public Health ; Pediatric Surgery ; Pediatrics ; Prediction models ; Pregnancy ; Pregnancy Trimester, First ; Pregnancy Trimester, Second ; Premature birth ; Premature Birth - epidemiology ; Regression analysis ; Regression models ; Retrospective Studies ; Risk Factors ; Statistics</subject><ispartof>Journal of perinatology, 2021-09, Vol.41 (9), p.2173-2181</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2021</rights><rights>2021. The Author(s), under exclusive licence to Springer Nature America, Inc.</rights><rights>COPYRIGHT 2021 Nature Publishing Group</rights><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c473t-208a1a08292d004403c784faf97d3a69df06ad6a194006d8b162d5d730ee8edf3</citedby><cites>FETCH-LOGICAL-c473t-208a1a08292d004403c784faf97d3a69df06ad6a194006d8b162d5d730ee8edf3</cites><orcidid>0000-0002-6989-9267 ; 0000-0002-4461-2178</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/s41372-021-01109-3$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/s41372-021-01109-3$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34112965$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Arabi Belaghi, Reza</creatorcontrib><creatorcontrib>Beyene, Joseph</creatorcontrib><creatorcontrib>McDonald, Sarah D.</creatorcontrib><title>Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort</title><title>Journal of perinatology</title><addtitle>J Perinatol</addtitle><addtitle>J Perinatol</addtitle><description><![CDATA[Objective
To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks.
Methods
Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks.
Results
During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5–73.6%) and 73.4% (68.6–78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5–72.3%) and 75.5% (72.3–78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7–75.1%) and 78.2% (95% CI: 75.8–80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes.
Conclusions
Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.]]></description><subject>631/136/1425</subject><subject>692/308/174</subject><subject>9/10</subject><subject>96/10</subject><subject>Birth</subject><subject>Calibration</subject><subject>Cohort Studies</subject><subject>Female</subject><subject>Gestation</subject><subject>Humans</subject><subject>Infant, Newborn</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Pediatric Surgery</subject><subject>Pediatrics</subject><subject>Prediction models</subject><subject>Pregnancy</subject><subject>Pregnancy Trimester, First</subject><subject>Pregnancy Trimester, Second</subject><subject>Premature birth</subject><subject>Premature Birth - epidemiology</subject><subject>Regression analysis</subject><subject>Regression models</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Statistics</subject><issn>0743-8346</issn><issn>1476-5543</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kk1v1DAQhiMEokvhD3BAlpAqLinjrzg5Viu-pEpc4Gx543HWrRMvdlLEjZ9eL7vQFiHkg6WZ5301M3qr6iWFcwq8fZsF5YrVwGgNlEJX80fVigrV1FIK_rhagRK8brloTqpnOV8B7JvqaXXCBaWsa-Sq-rkOfvK9CST5fE3GaDFk4mIiu4QzppFsfJq3JGDOZN6aibCWfEe8zsRM9l6Zs2M5OjJgns3s40SW7KeBGBJMGpAUxxTzDvvZ3yDp4zam-Xn1xJmQ8cXxP62-vn_3Zf2xvvz84dP64rLuheJzzaA11EDLOmYBhADeq1Y44zpluWk666AxtjG0EwCNbTe0YVZaxQGxRev4afXm4LtL8dtSBtSjzz2GYCaMS9ZMCpBUia4r6Ou_0Ku4pKlMVyjFuJQU4I4aTEDtJxfnZPq9qb5olBRMMkkLdf4PqjyLo-_jhM6X-gPB2T3BFk2YtzmGZX_O_BBkB7AvN80Jnd4lP5r0Q1PQ-3zoQz50yYf-lQ_Ni-jVcbVlM6L9I_kdiALwA5BLaxow3e3-H9tbg43Cxg</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Arabi Belaghi, Reza</creator><creator>Beyene, Joseph</creator><creator>McDonald, Sarah D.</creator><general>Nature Publishing Group US</general><general>Nature Publishing Group</general><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>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7RV</scope><scope>7T5</scope><scope>7T7</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AN0</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6989-9267</orcidid><orcidid>https://orcid.org/0000-0002-4461-2178</orcidid></search><sort><creationdate>20210901</creationdate><title>Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort</title><author>Arabi Belaghi, Reza ; Beyene, Joseph ; McDonald, Sarah D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c473t-208a1a08292d004403c784faf97d3a69df06ad6a194006d8b162d5d730ee8edf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>631/136/1425</topic><topic>692/308/174</topic><topic>9/10</topic><topic>96/10</topic><topic>Birth</topic><topic>Calibration</topic><topic>Cohort Studies</topic><topic>Female</topic><topic>Gestation</topic><topic>Humans</topic><topic>Infant, Newborn</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Pediatric Surgery</topic><topic>Pediatrics</topic><topic>Prediction models</topic><topic>Pregnancy</topic><topic>Pregnancy Trimester, First</topic><topic>Pregnancy Trimester, Second</topic><topic>Premature birth</topic><topic>Premature Birth - epidemiology</topic><topic>Regression analysis</topic><topic>Regression models</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Arabi Belaghi, Reza</creatorcontrib><creatorcontrib>Beyene, Joseph</creatorcontrib><creatorcontrib>McDonald, Sarah D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nursing & Allied Health Database</collection><collection>Immunology Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>British Nursing Database</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Nursing & Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of perinatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Arabi Belaghi, Reza</au><au>Beyene, Joseph</au><au>McDonald, Sarah D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort</atitle><jtitle>Journal of perinatology</jtitle><stitle>J Perinatol</stitle><addtitle>J Perinatol</addtitle><date>2021-09-01</date><risdate>2021</risdate><volume>41</volume><issue>9</issue><spage>2173</spage><epage>2181</epage><pages>2173-2181</pages><issn>0743-8346</issn><eissn>1476-5543</eissn><abstract><![CDATA[Objective
To develop risk prediction models for singleton preterm birth (PTB) < 28 weeks and <32 weeks.
Methods
Using a retrospective cohort of 267,226 singleton births in Ontario hospitals, we included variables from the first and second trimester in multivariable logistic regression models to predict overall and spontaneous PTB < 28 weeks and <32 weeks.
Results
During the first trimester, the area under the curve (AUC) for prediction of PTB < 28 weeks for nulliparous and multiparous women was 68.5% (95% CI: 63.5–73.6%) and 73.4% (68.6–78.2%), respectively, while for PTB < 32 weeks it was 68.9% (65.5–72.3%) and 75.5% (72.3–78.7%), respectively. AUCs for second-trimester models were 72.4% (95% CI: 69.7–75.1%) and 78.2% (95% CI: 75.8–80.5%), respectively, in nulliparous and multiparous women. Predicted probabilities were well-calibrated within a wide range around expected base prevalence for the study outcomes.
Conclusions
Our prediction models generated acceptable AUCs for PTB < 28 weeks and <32 weeks with good calibration during the first and second trimester.]]></abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>34112965</pmid><doi>10.1038/s41372-021-01109-3</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-6989-9267</orcidid><orcidid>https://orcid.org/0000-0002-4461-2178</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0743-8346 |
| ispartof | Journal of perinatology, 2021-09, Vol.41 (9), p.2173-2181 |
| issn | 0743-8346 1476-5543 |
| language | eng |
| recordid | cdi_proquest_journals_2572355100 |
| source | MEDLINE; SpringerLink Journals |
| subjects | 631/136/1425 692/308/174 9/10 96/10 Birth Calibration Cohort Studies Female Gestation Humans Infant, Newborn Medicine Medicine & Public Health Pediatric Surgery Pediatrics Prediction models Pregnancy Pregnancy Trimester, First Pregnancy Trimester, Second Premature birth Premature Birth - epidemiology Regression analysis Regression models Retrospective Studies Risk Factors Statistics |
| title | Clinical risk models for preterm birth less than 28 weeks and less than 32 weeks of gestation using a large retrospective cohort |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-21T07%3A01%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Clinical%20risk%20models%20for%20preterm%20birth%20less%20than%2028%20weeks%20and%20less%20than%2032%20weeks%20of%20gestation%20using%20a%20large%20retrospective%20cohort&rft.jtitle=Journal%20of%20perinatology&rft.au=Arabi%20Belaghi,%20Reza&rft.date=2021-09-01&rft.volume=41&rft.issue=9&rft.spage=2173&rft.epage=2181&rft.pages=2173-2181&rft.issn=0743-8346&rft.eissn=1476-5543&rft_id=info:doi/10.1038/s41372-021-01109-3&rft_dat=%3Cgale_proqu%3EA675425251%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2572355100&rft_id=info:pmid/34112965&rft_galeid=A675425251&rfr_iscdi=true |