Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia
Abstract Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-02-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-55627-z |
| _version_ | 1797274636935757824 |
|---|---|
| author | Eskeziaw Abebe Kassahun Seifu Hagos Gebreyesus Kokeb Tesfamariam Bilal Shikur Endris Meselech Assegid Roro Yalemwork Getnet Hamid Yimam Hassen Nele Brusselaers Samuel Coenen |
| author_facet | Eskeziaw Abebe Kassahun Seifu Hagos Gebreyesus Kokeb Tesfamariam Bilal Shikur Endris Meselech Assegid Roro Yalemwork Getnet Hamid Yimam Hassen Nele Brusselaers Samuel Coenen |
| author_sort | Eskeziaw Abebe Kassahun |
| collection | DOAJ |
| description | Abstract Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable and routinely collected characteristics of pregnant women in the primary care setting. We used a logistic regression model to develop a model based on the data collected from 481 pregnant women. Model accuracy was evaluated through discrimination (measured by the area under the Receiver Operating Characteristic curve; AUC) and calibration (via calibration graphs and the Hosmer–Lemeshow goodness of fit test). Internal validation was performed using a bootstrapping technique. A simplified risk score was developed, and the cut-off point was determined using the “Youden index” to classify pregnant women into high or low risk for preterm birth. The incidence of preterm birth was 19.5% (95% CI:16.2, 23.3) of pregnancies. The final prediction model incorporated mid-upper arm circumference, gravidity, history of abortion, antenatal care, comorbidity, intimate partner violence, and anemia as predictors of preeclampsia. The AUC of the model was 0.687 (95% CI: 0.62, 0.75). The calibration plot demonstrated a good calibration with a p-value of 0.713 for the Hosmer–Lemeshow goodness of fit test. The model can identify pregnant women at high risk of preterm birth. It is applicable in daily clinical practice and could contribute to the improvement of the health of women and newborns in primary care settings with limited resources. Healthcare providers in rural areas could use this prediction model to improve clinical decision-making and reduce obstetrics complications. |
| first_indexed | 2024-03-07T15:01:09Z |
| format | Article |
| id | doaj.art-2e420d42e1b94676a0b464070804e573 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-07T15:01:09Z |
| publishDate | 2024-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-2e420d42e1b94676a0b464070804e5732024-03-05T19:06:15ZengNature PortfolioScientific Reports2045-23222024-02-0114111210.1038/s41598-024-55627-zDevelopment and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural EthiopiaEskeziaw Abebe Kassahun0Seifu Hagos Gebreyesus1Kokeb Tesfamariam2Bilal Shikur Endris3Meselech Assegid Roro4Yalemwork Getnet5Hamid Yimam Hassen6Nele Brusselaers7Samuel Coenen8Department of Family Medicine & Population Health, Faculty of Medicine and Health Sciences, University of AntwerpDepartmentof of Nutrition and Dietetics, School of Public Health, Addis Ababa UniversityDepartment of Food Technology, Safety, and Health, Faculty of Bioscience Engineering, Ghent UniversityDepartmentof of Nutrition and Dietetics, School of Public Health, Addis Ababa UniversityDepartment of Reproductive Health and Health Service Management, School of Public Health, Addis Ababa UniversityDepartmentof of Nutrition and Dietetics, School of Public Health, Addis Ababa UniversityDepartment of Family Medicine & Population Health, Faculty of Medicine and Health Sciences, University of AntwerpGlobal Health Institute, Department of Family Medicine & Population Health, Antwerp UniversityCentre for General Practice, Department of Family Medicine & Population Health, Faculty of Medicine and Health Sciences, University of AntwerpAbstract Preterm birth is one of the most common obstetric complications in low- and middle-income countries, where access to advanced diagnostic tests and imaging is limited. Therefore, we developed and validated a simplified risk prediction tool to predict preterm birth based on easily applicable and routinely collected characteristics of pregnant women in the primary care setting. We used a logistic regression model to develop a model based on the data collected from 481 pregnant women. Model accuracy was evaluated through discrimination (measured by the area under the Receiver Operating Characteristic curve; AUC) and calibration (via calibration graphs and the Hosmer–Lemeshow goodness of fit test). Internal validation was performed using a bootstrapping technique. A simplified risk score was developed, and the cut-off point was determined using the “Youden index” to classify pregnant women into high or low risk for preterm birth. The incidence of preterm birth was 19.5% (95% CI:16.2, 23.3) of pregnancies. The final prediction model incorporated mid-upper arm circumference, gravidity, history of abortion, antenatal care, comorbidity, intimate partner violence, and anemia as predictors of preeclampsia. The AUC of the model was 0.687 (95% CI: 0.62, 0.75). The calibration plot demonstrated a good calibration with a p-value of 0.713 for the Hosmer–Lemeshow goodness of fit test. The model can identify pregnant women at high risk of preterm birth. It is applicable in daily clinical practice and could contribute to the improvement of the health of women and newborns in primary care settings with limited resources. Healthcare providers in rural areas could use this prediction model to improve clinical decision-making and reduce obstetrics complications.https://doi.org/10.1038/s41598-024-55627-zPrediction modelPreterm birthRisk scorePregnant womenEthiopia |
| spellingShingle | Eskeziaw Abebe Kassahun Seifu Hagos Gebreyesus Kokeb Tesfamariam Bilal Shikur Endris Meselech Assegid Roro Yalemwork Getnet Hamid Yimam Hassen Nele Brusselaers Samuel Coenen Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia Scientific Reports Prediction model Preterm birth Risk score Pregnant women Ethiopia |
| title | Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia |
| title_full | Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia |
| title_fullStr | Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia |
| title_full_unstemmed | Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia |
| title_short | Development and validation of a simplified risk prediction model for preterm birth: a prospective cohort study in rural Ethiopia |
| title_sort | development and validation of a simplified risk prediction model for preterm birth a prospective cohort study in rural ethiopia |
| topic | Prediction model Preterm birth Risk score Pregnant women Ethiopia |
| url | https://doi.org/10.1038/s41598-024-55627-z |
| work_keys_str_mv | AT eskeziawabebekassahun developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT seifuhagosgebreyesus developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT kokebtesfamariam developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT bilalshikurendris developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT meselechassegidroro developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT yalemworkgetnet developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT hamidyimamhassen developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT nelebrusselaers developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia AT samuelcoenen developmentandvalidationofasimplifiedriskpredictionmodelforpretermbirthaprospectivecohortstudyinruralethiopia |