{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T18:25:40Z","timestamp":1787250340345,"version":"build-2736575974"},"reference-count":46,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Preeclampsia remains a leading cause of maternal and perinatal morbidity and mortality globally, with high incidence and case fatality rates in low\u2010 and middle\u2010income countries (LMICs). Early identification of at\u2010risk women is critical, yet many predictive models require laboratory or imaging resources that are unavailable in resource\u2010limited settings. This study aimed to develop a points\u2010based clinical triage tool for preeclampsia risk stratification using routinely collected antenatal care (ANC) data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A prospective cohort study of 703 pregnant women attending ANC was conducted, with retrospective extraction of baseline clinical data. Maternal sociodemographic characteristics, obstetric history, blood pressure (BP) measurements, and proteinuria screening results were analysed. Multivariable logistic regression was used to identify independent predictors of preeclampsia, and model performance was evaluated using discrimination, calibration, and internal bootstrapping. A weighted scoring system was derived from regression coefficients, and receiver operating characteristic (ROC) analysis was used to determine optimal cut\u2010off values for risk stratification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A five\u2010component clinical triage tool for risk stratification of preeclampsia was developed. The tool incorporates maternal age (\u2265\u200935 years), nulliparity, elevated blood pressure (\u2265\u2009140\/90\u2009mmHg), family history of preeclampsia, and dipstick proteinuria (\u2265\u2009+\u20091), generating a total risk score ranging from 0 to 18. Based on the risk score, women were categorised into low risk (0\u201310 points), moderate risk (11\u201315 points), and high risk (\u2265\u200916 points) groups. The incidence of preeclampsia increased across these categories, from 5.1% in the low\u2010risk group to 30.2% among women classified as high risk. At the high\u2010risk threshold (\u2265\u200916 points), the tool demonstrated good discriminatory performance (AUC\u2009=\u20090.83), with a sensitivity of 78.3% and a specificity of 83.3% for identifying women who subsequently developed preeclampsia.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study demonstrates that a simple, points\u2010based clinical triage tool using routinely collected antenatal data has potential for stratifying preeclampsia risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.71916","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T20:44:22Z","timestamp":1773002662000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a Triage Tool for Stratification and Referral of Women at Risk of Preeclampsia in Low\u2010Resource Antenatal Settings: A Prospective Cohort Study"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9787-6575","authenticated-orcid":false,"given":"Bismark","family":"Opoku Mensah","sequence":"first","affiliation":[{"name":"Department of Biological sciences University of Worcester Worcester UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ernestina Obenewaa","family":"Anim","sequence":"additional","affiliation":[{"name":"Department of Nursing All Nations University Koforidua Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abena Serwaa","family":"Adjei","sequence":"additional","affiliation":[{"name":"Department of Population and Reproductive Health KNUST School of Public Health Kumasi Ghana"},{"name":"Department of Public Health Komfo Anokye Teaching Hospital Kumasi Ghana"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1097\/FM9.0000000000000096"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bpobgyn.2024.102525"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(24)00560-6"},{"key":"e_1_2_12_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12884-017-1575-2"},{"key":"e_1_2_12_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/puh2.154"},{"key":"e_1_2_12_7_1","doi-asserted-by":"publisher","DOI":"10.12659\/MSM.944104"},{"key":"e_1_2_12_8_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13148-024-01798-5"},{"key":"e_1_2_12_9_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-021-07055-2"},{"key":"e_1_2_12_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2024.1337803"},{"key":"e_1_2_12_11_1","doi-asserted-by":"publisher","DOI":"10.3390\/bios10100133"},{"key":"e_1_2_12_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-021-07249-8"},{"key":"e_1_2_12_13_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0291036"},{"key":"e_1_2_12_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0001790"},{"key":"e_1_2_12_15_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00612-x"},{"key":"e_1_2_12_16_1","doi-asserted-by":"publisher","DOI":"10.1161\/HYPERTENSIONAHA.123.21053"},{"key":"e_1_2_12_17_1","unstructured":"L.AhenkorahMetabolic Syndrome Oxidative Stress and Putative Risk Factors Amongst Ghanaian Women Presenting with Pregnancy\u2010induced Hypertension 2009."},{"key":"e_1_2_12_18_1","first-page":"11","volume-title":"Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating","author":"Steyerberg E. 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