An exploration of potential risk factors for gastroschisis using decision tree learning
Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests. Data were from th...
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| Veröffentlicht in: | Annals of epidemiology 2024-12, Vol.101, p.19-26 |
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| creator | Petersen, Julie M. Gradus, Jaimie L. Werler, Martha M. Parker, Samantha E. |
| description | Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests.
Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.
The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index |
| doi_str_mv | 10.1016/j.annepidem.2024.12.004 |
| format | Article |
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Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.
The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index < 30 kg/m2 (aOR 3.3, 95 % CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education.
Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.
•Gastroschisis is a serious congenital anomaly from unknown causes.•Potential to identify new possible risk factors using decision tree learning.•Known risk factors (young maternal age, lower body-mass-index) confirmed.•Some modifiable risk factors (substance use, use of oral contraceptives or NSAIDs) suggested in prior research.•Novel factors (e.g., certain aspects of diet) warrant further investigation.</description><identifier>ISSN: 1047-2797</identifier><identifier>ISSN: 1873-2585</identifier><identifier>EISSN: 1873-2585</identifier><identifier>DOI: 10.1016/j.annepidem.2024.12.004</identifier><identifier>PMID: 39657869</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Congenital abnormalities ; Decision trees ; Gastroschisis ; Machine learning ; Random forest ; Risk factors</subject><ispartof>Annals of epidemiology, 2024-12, Vol.101, p.19-26</ispartof><rights>2024</rights><rights>Copyright © 2024. Published by Elsevier Inc.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0001-7845-4545 ; 0000-0003-1459-5327 ; 0000-0003-3392-6814 ; 0000-0002-7193-077X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.annepidem.2024.12.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39657869$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Petersen, Julie M.</creatorcontrib><creatorcontrib>Gradus, Jaimie L.</creatorcontrib><creatorcontrib>Werler, Martha M.</creatorcontrib><creatorcontrib>Parker, Samantha E.</creatorcontrib><title>An exploration of potential risk factors for gastroschisis using decision tree learning</title><title>Annals of epidemiology</title><addtitle>Ann Epidemiol</addtitle><description>Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests.
Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.
The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index < 30 kg/m2 (aOR 3.3, 95 % CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education.
Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.
•Gastroschisis is a serious congenital anomaly from unknown causes.•Potential to identify new possible risk factors using decision tree learning.•Known risk factors (young maternal age, lower body-mass-index) confirmed.•Some modifiable risk factors (substance use, use of oral contraceptives or NSAIDs) suggested in prior research.•Novel factors (e.g., certain aspects of diet) warrant further investigation.</description><subject>Congenital abnormalities</subject><subject>Decision trees</subject><subject>Gastroschisis</subject><subject>Machine learning</subject><subject>Random forest</subject><subject>Risk factors</subject><issn>1047-2797</issn><issn>1873-2585</issn><issn>1873-2585</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNo9kUtPxCAQgInRuOvjLyhHL61AKdDjZuMrMfGi8Ugona6sXajQNfrvxezqaR75ZjKZD6FLSkpKqLhel8Z7GF0Hm5IRxkvKSkL4AZpTJauC1ao-zDnhsmCykTN0ktKaECKVZMdoVjWilko0c_S68Bi-xiFEM7ngcejxGCbwkzMDji69497YKcSE-xDxyqQphmTfXHIJb5PzK9yBzVUenSIAHsBEn9tn6Kg3Q4LzfTxFL7c3z8v74vHp7mG5eCyAUt4UAJxQK3oqBFGSKy6gNcywNp_KiVC1srKtetvzXpmOKWtq1VCuSG0qQ1lbnaKr3d4xho8tpElvXLIwDMZD2CZdUS4Eo03NMnqxR7ftBjo9Rrcx8Vv_PSMDix0A-eBPB1En68Bb6FwEO-kuOE2J_hWg1_pfgP4VoCnTWUD1A7tkez8</recordid><startdate>20241208</startdate><enddate>20241208</enddate><creator>Petersen, Julie M.</creator><creator>Gradus, Jaimie L.</creator><creator>Werler, Martha M.</creator><creator>Parker, Samantha E.</creator><general>Elsevier Inc</general><scope>NPM</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7845-4545</orcidid><orcidid>https://orcid.org/0000-0003-1459-5327</orcidid><orcidid>https://orcid.org/0000-0003-3392-6814</orcidid><orcidid>https://orcid.org/0000-0002-7193-077X</orcidid></search><sort><creationdate>20241208</creationdate><title>An exploration of potential risk factors for gastroschisis using decision tree learning</title><author>Petersen, Julie M. ; Gradus, Jaimie L. ; Werler, Martha M. ; Parker, Samantha E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-e1149-ee401c6f1660874846eba2a2b007406858c7b3fcf4f8ad28ca58914805a3a12b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Congenital abnormalities</topic><topic>Decision trees</topic><topic>Gastroschisis</topic><topic>Machine learning</topic><topic>Random forest</topic><topic>Risk factors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Petersen, Julie M.</creatorcontrib><creatorcontrib>Gradus, Jaimie L.</creatorcontrib><creatorcontrib>Werler, Martha M.</creatorcontrib><creatorcontrib>Parker, Samantha E.</creatorcontrib><collection>PubMed</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Petersen, Julie M.</au><au>Gradus, Jaimie L.</au><au>Werler, Martha M.</au><au>Parker, Samantha E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An exploration of potential risk factors for gastroschisis using decision tree learning</atitle><jtitle>Annals of epidemiology</jtitle><addtitle>Ann Epidemiol</addtitle><date>2024-12-08</date><risdate>2024</risdate><volume>101</volume><spage>19</spage><epage>26</epage><pages>19-26</pages><issn>1047-2797</issn><issn>1873-2585</issn><eissn>1873-2585</eissn><abstract>Despite a wealth of research, the etiology of the abdominal wall defect gastroschisis remains largely unknown. The strongest known risk factor is young maternal age. Our objective was to conduct a hypothesis-generating analysis regarding gastroschisis etiology using random forests.
Data were from the Slone Birth Defects Study (case-control, United States and Canada, 1998–2015). Cases were gastroschisis-affected pregnancies (n = 273); controls were live-born infants, frequency-matched by center (n = 2591). Potential risk factor data were ascertained via standardized interviews. We calculated adjusted odds ratios (aOR) and 95 % confidence intervals (CIs) using targeted maximum likelihood estimation.
The strongest associations were observed with young maternal age (aOR 3.4, 95 % CI 2.9, 4.0) and prepregnancy body-mass-index < 30 kg/m2 (aOR 3.3, 95 % CI 2.4, 4.5). More moderate increased odds were observed for parents not in a relationship, non-Black maternal race, young paternal age, marijuana use, cigarette smoking, alcohol intake, lower parity, oral contraceptive use, nonsteroidal anti-inflammatory drug use, daily fast food/processed foods intake, lower poly- or monounsaturated fat, higher total fat, and lower parental education.
Our research provides support for established risk factors and suggested novel factors (e.g., certain aspects of diet), which warrant further investigation.
•Gastroschisis is a serious congenital anomaly from unknown causes.•Potential to identify new possible risk factors using decision tree learning.•Known risk factors (young maternal age, lower body-mass-index) confirmed.•Some modifiable risk factors (substance use, use of oral contraceptives or NSAIDs) suggested in prior research.•Novel factors (e.g., certain aspects of diet) warrant further investigation.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>39657869</pmid><doi>10.1016/j.annepidem.2024.12.004</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-7845-4545</orcidid><orcidid>https://orcid.org/0000-0003-1459-5327</orcidid><orcidid>https://orcid.org/0000-0003-3392-6814</orcidid><orcidid>https://orcid.org/0000-0002-7193-077X</orcidid></addata></record> |
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| source | Elsevier ScienceDirect Journals Complete |
| subjects | Congenital abnormalities Decision trees Gastroschisis Machine learning Random forest Risk factors |
| title | An exploration of potential risk factors for gastroschisis using decision tree learning |
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