Analysis of risk factors progression of preterm delivery using electronic health records
<h3>Background</h3><p dir="ltr">Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and red...
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2022
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| _version_ | 1864513519691497472 |
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| author | Zeineb Safi (18281719) |
| author2 | Neethu Venugopal (18281722) Haytham Ali (6896447) Michel Makhlouf (15740711) Faisal Farooq (13134579) Sabri Boughorbel (846228) |
| author2_role | author author author author author |
| author_facet | Zeineb Safi (18281719) Neethu Venugopal (18281722) Haytham Ali (6896447) Michel Makhlouf (15740711) Faisal Farooq (13134579) Sabri Boughorbel (846228) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zeineb Safi (18281719) Neethu Venugopal (18281722) Haytham Ali (6896447) Michel Makhlouf (15740711) Faisal Farooq (13134579) Sabri Boughorbel (846228) |
| dc.date.none.fl_str_mv | 2022-08-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1186/s13040-022-00298-7 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Analysis_of_risk_factors_progression_of_preterm_delivery_using_electronic_health_records/25516147 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biological sciences Genetics Mathematical sciences Mathematical physics Preterm Pregnancy EHR Epidemiology Risk factors Progression Temporal analysis Precision medicine Predictive models |
| dc.title.none.fl_str_mv | Analysis of risk factors progression of preterm delivery using electronic health records |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR).</p><h3>Results</h3><p dir="ltr">The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation.</p><h3>Conclusions</h3><p dir="ltr">The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.</p><h2>Other Information</h2><p dir="ltr">Published in: BioData Mining<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13040-022-00298-7" target="_blank">https://dx.doi.org/10.1186/s13040-022-00298-7</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_afaad24a69cbf8c5653dd6200a262df7 |
| identifier_str_mv | 10.1186/s13040-022-00298-7 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25516147 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Analysis of risk factors progression of preterm delivery using electronic health recordsZeineb Safi (18281719)Neethu Venugopal (18281722)Haytham Ali (6896447)Michel Makhlouf (15740711)Faisal Farooq (13134579)Sabri Boughorbel (846228)Biological sciencesGeneticsMathematical sciencesMathematical physicsPretermPregnancyEHREpidemiologyRisk factorsProgressionTemporal analysisPrecision medicinePredictive models<h3>Background</h3><p dir="ltr">Preterm deliveries have many negative health implications on both mother and child. Identifying the population level factors that increase the risk of preterm deliveries is an important step in the direction of mitigating the impact and reducing the frequency of occurrence of preterm deliveries. The purpose of this work is to identify preterm delivery risk factors and their progression throughout the pregnancy from a large collection of Electronic Health Records (EHR).</p><h3>Results</h3><p dir="ltr">The study cohort includes about 60,000 deliveries in the USA with the complete medical history from EHR for diagnoses, medications and procedures. We propose a temporal analysis of risk factors by estimating and comparing risk ratios and variable importance at different time points prior to the delivery event. We selected the following time points before delivery: 0, 12 and 24 week(s) of gestation. We did so by conducting a retrospective cohort study of patient history for a selected set of mothers who delivered preterm and a control group of mothers that delivered full-term. We analyzed the extracted data using logistic regression and random forests models. The results of our analyses showed that the highest risk ratio and variable importance corresponds to history of previous preterm delivery. Other risk factors were identified, some of which are consistent with those that are reported in the literature, others need further investigation.</p><h3>Conclusions</h3><p dir="ltr">The comparative analysis of the risk factors at different time points showed that risk factors in the early pregnancy related to patient history and chronic condition, while the risk factors in late pregnancy are specific to the current pregnancy. Our analysis unifies several previously reported studies on preterm risk factors. It also gives important insights on the changes of risk factors in the course of pregnancy. The code used for data analysis will be made available on github.</p><h2>Other Information</h2><p dir="ltr">Published in: BioData Mining<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1186/s13040-022-00298-7" target="_blank">https://dx.doi.org/10.1186/s13040-022-00298-7</a></p>2022-08-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1186/s13040-022-00298-7https://figshare.com/articles/journal_contribution/Analysis_of_risk_factors_progression_of_preterm_delivery_using_electronic_health_records/25516147CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/255161472022-08-17T03:00:00Z |
| spellingShingle | Analysis of risk factors progression of preterm delivery using electronic health records Zeineb Safi (18281719) Biological sciences Genetics Mathematical sciences Mathematical physics Preterm Pregnancy EHR Epidemiology Risk factors Progression Temporal analysis Precision medicine Predictive models |
| status_str | publishedVersion |
| title | Analysis of risk factors progression of preterm delivery using electronic health records |
| title_full | Analysis of risk factors progression of preterm delivery using electronic health records |
| title_fullStr | Analysis of risk factors progression of preterm delivery using electronic health records |
| title_full_unstemmed | Analysis of risk factors progression of preterm delivery using electronic health records |
| title_short | Analysis of risk factors progression of preterm delivery using electronic health records |
| title_sort | Analysis of risk factors progression of preterm delivery using electronic health records |
| topic | Biological sciences Genetics Mathematical sciences Mathematical physics Preterm Pregnancy EHR Epidemiology Risk factors Progression Temporal analysis Precision medicine Predictive models |