Development of risk prediction models for preterm delivery in a rural setting in Ethiopia

Background Preterm birth complications are the leading causes of death among children under five years. A key practical challenge, however, is the inability to accurately identify pregnancies that are at high risk of preterm delivery, especially in resource-limited settings where there is limited availability of biomarkers assessment. Methods We evaluated whether risk of preterm delivery can be predicted using available data from a pregnancy and birth cohort in Amhara region, Ethiopia. All participants were enrolled in the cohort between December 2018 and March 2020. The study outcome was preterm delivery, defined as any delivery occurring before week 37 of gestation regardless of vital status of the fetus or neonate. A range of sociodemographic, clinical, environmental, and pregnancy-related factors were considered as potential inputs. Cox and accelerated failure time models, and decision tree ensembles were used to predict risk of preterm delivery. Model discrimination was estimated using the area-under-the-curve (AUC). Additionally, the conditional distributions of cervical length (CL) and fetal fibronectin (FFN) were simulated to ascertain whether those factors could improve model performance. Results A total of 2493 pregnancies were included. Of those, 138 women were censored due to loss-to-follow-up before delivery. Overall, predictive performance of models was poor. The AUC was highest for the tree ensemble classifier (0.60, 95%CI [0.57, 0.63]). When models were calibrated so that 90% of women who experienced a preterm delivery were classified as high risk, at least 75% of those classified as high risk did not experience the outcome. The simulation of CL and FFN distributions did not significantly improve models’ performance. Conclusions Prediction of preterm delivery remains a major challenge. In resource-limited settings, predicting high-risk deliveries would not only save lives, but also inform resource allocation. It may not be possible to accurately predict risk of preterm delivery without investing in novel technologies to identify genetic factors, immunological biomarkers or the expression of specific proteins. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work has been supported by the Bill & Melinda Gates Foundation (grants INV-010382 and INV-003612 to Dr Chan). The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Saint Paul's Hospital Millennium Medical college (Addis Ababa, Ethiopia), and Harvard T.H. Chan School of Public Health (Boston, United Stated) Ethics Review Boards gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data are available upon reasonable request. Data use is governed by the Birhan Data Access Committee (DAC) and follows Birhan's data sharing policy. All researchers who wish to access Birhan data can complete a Birhan data request form and submit it for decision by the Birhan DAC. Datasets will only be provided with deidentified data to maintain confidentiality of study participants.