Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort
Abstract Background Preterm birth (PTB) is the leading cause of infant mortality. Risk for PTB is influenced by multiple biological pathways, many of which are poorly understood. Some PTBs result from medically indicated labor following complications from hypertension and/or diabetes, while many oth...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | BMC Pregnancy and Childbirth |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12884-025-07166-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850024803820371968 |
|---|---|
| author | Jean M. Costello Hannah Takasuka Jacquelyn Roger Ophelia Yin Alice Tang Tomiko Oskotsky Marina Sirota John A. Capra |
| author_facet | Jean M. Costello Hannah Takasuka Jacquelyn Roger Ophelia Yin Alice Tang Tomiko Oskotsky Marina Sirota John A. Capra |
| author_sort | Jean M. Costello |
| collection | DOAJ |
| description | Abstract Background Preterm birth (PTB) is the leading cause of infant mortality. Risk for PTB is influenced by multiple biological pathways, many of which are poorly understood. Some PTBs result from medically indicated labor following complications from hypertension and/or diabetes, while many others are spontaneous with unknown causes. Previously, investigation of potential risk factors has been limited by a lack of data on maternal medical history and the difficulty of classifying PTBs as indicated or spontaneous. Here, we leverage electronic health record (EHR) data (patient health information including demographics, diagnoses, and medications) and a supplemental curated pregnancy database to overcome these limitations. Novel associations may provide new insight into the pathophysiology of PTB as well as help identify individuals who would be at risk of PTB. Methods We quantified associations between maternal diagnoses and preterm birth both with and without controlling for maternal age and socioeconomic factors within a University of California, San Francisco (UCSF), EHR cohort with 10,643 births (n term = 9692, n spontaneous_preterm = 449, n indicated_preterm = 418) and maternal pre-conception diagnoses derived from International Classification of Diseases (ICD) 9 and 10 codes. Results Thirty diagnoses significantly and robustly (False Discovery Rate (FDR) < 0.05) associated with indicated PTBs compared to term. We discovered known (hypertension, diabetes, and chronic kidney disease) and less established (blood, cardiac, gynecological, and liver diagnoses) associations. Essential hypertension had the most significant association with indicated PTB (adjusted pBH = 4 × 10–20, adjusted OR = 6 (95% CI 4-8)), and the odds ratios for the significant diagnoses ranged from 2 to 23. The results for indicated PTB largely recapitulated the diagnosis associations with all PTBs. However, no diagnosis significantly associated with spontaneous PTB. Conclusions Our study underscores the limitations of approaches that combine indicated and spontaneous births. When combined, significant associations were almost entirely driven by indicated PTBs, although the spontaneous and indicated groups were of a similar size. Investigating the spontaneous population has the potential to reveal new pathways and understanding of the heterogeneity of PTB. |
| format | Article |
| id | doaj-art-a5d3f82bf42e4a93a41781e9e6c87f0e |
| institution | DOAJ |
| issn | 1471-2393 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj-art-a5d3f82bf42e4a93a41781e9e6c87f0e2025-08-20T03:01:00ZengBMCBMC Pregnancy and Childbirth1471-23932025-02-0125111310.1186/s12884-025-07166-2Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohortJean M. Costello0Hannah Takasuka1Jacquelyn Roger2Ophelia Yin3Alice Tang4Tomiko Oskotsky5Marina Sirota6John A. Capra7Bakar Computational Health Sciences Institute, UCSFGraduate Program in Oral and Craniofacial Sciences, UCSFGraduate Program in Biological and Medical Informatics, UCSFDepartment of Obstetrics, Gynecology & Reproductive Sciences, UCSFGraduate Program in Bioengineering, UCSF and UC BerkeleyBakar Computational Health Sciences Institute, UCSFBakar Computational Health Sciences Institute, UCSFBakar Computational Health Sciences Institute, UCSFAbstract Background Preterm birth (PTB) is the leading cause of infant mortality. Risk for PTB is influenced by multiple biological pathways, many of which are poorly understood. Some PTBs result from medically indicated labor following complications from hypertension and/or diabetes, while many others are spontaneous with unknown causes. Previously, investigation of potential risk factors has been limited by a lack of data on maternal medical history and the difficulty of classifying PTBs as indicated or spontaneous. Here, we leverage electronic health record (EHR) data (patient health information including demographics, diagnoses, and medications) and a supplemental curated pregnancy database to overcome these limitations. Novel associations may provide new insight into the pathophysiology of PTB as well as help identify individuals who would be at risk of PTB. Methods We quantified associations between maternal diagnoses and preterm birth both with and without controlling for maternal age and socioeconomic factors within a University of California, San Francisco (UCSF), EHR cohort with 10,643 births (n term = 9692, n spontaneous_preterm = 449, n indicated_preterm = 418) and maternal pre-conception diagnoses derived from International Classification of Diseases (ICD) 9 and 10 codes. Results Thirty diagnoses significantly and robustly (False Discovery Rate (FDR) < 0.05) associated with indicated PTBs compared to term. We discovered known (hypertension, diabetes, and chronic kidney disease) and less established (blood, cardiac, gynecological, and liver diagnoses) associations. Essential hypertension had the most significant association with indicated PTB (adjusted pBH = 4 × 10–20, adjusted OR = 6 (95% CI 4-8)), and the odds ratios for the significant diagnoses ranged from 2 to 23. The results for indicated PTB largely recapitulated the diagnosis associations with all PTBs. However, no diagnosis significantly associated with spontaneous PTB. Conclusions Our study underscores the limitations of approaches that combine indicated and spontaneous births. When combined, significant associations were almost entirely driven by indicated PTBs, although the spontaneous and indicated groups were of a similar size. Investigating the spontaneous population has the potential to reveal new pathways and understanding of the heterogeneity of PTB.https://doi.org/10.1186/s12884-025-07166-2Spontaneous preterm birthIndicated preterm birthElectronic health recordsDiagnosis associations |
| spellingShingle | Jean M. Costello Hannah Takasuka Jacquelyn Roger Ophelia Yin Alice Tang Tomiko Oskotsky Marina Sirota John A. Capra Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort BMC Pregnancy and Childbirth Spontaneous preterm birth Indicated preterm birth Electronic health records Diagnosis associations |
| title | Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort |
| title_full | Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort |
| title_fullStr | Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort |
| title_full_unstemmed | Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort |
| title_short | Pre-conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped EHR cohort |
| title_sort | pre conception clinical risk factors differ between spontaneous and indicated preterm birth in a densely phenotyped ehr cohort |
| topic | Spontaneous preterm birth Indicated preterm birth Electronic health records Diagnosis associations |
| url | https://doi.org/10.1186/s12884-025-07166-2 |
| work_keys_str_mv | AT jeanmcostello preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT hannahtakasuka preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT jacquelynroger preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT opheliayin preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT alicetang preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT tomikooskotsky preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT marinasirota preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort AT johnacapra preconceptionclinicalriskfactorsdifferbetweenspontaneousandindicatedpretermbirthinadenselyphenotypedehrcohort |