<![CDATA[AbstractPreeclampsia is one of the leading causes of maternal morbidity and mortality in Indonesia and is frequently underdiagnosed, particularly in primary healthcare facilities such as community health centers (Puskesmas). Delayed early identification increases the risk of severe complications for both mothers and infants. Currently, Antenatal Care (ANC) services at Puskesmas rely on routine vital sign monitoring without an integrated scoring system that could provide early warning of preeclampsia risk. This study aimed to develop and validate an Early Warning Score (EWS) model based on vital signs and clinical symptoms for early detection of preeclampsia in primary healthcare settings. A prospective cohort design was employed, involving 65 third-trimester pregnant women who were followed until delivery. Data collected included blood pressure, heart rate, respiratory rate, and symptoms such as headache, blurred vision, epigastric pain, and edema. Bivariate analysis using the Chi-Square test was conducted to identify variables associated with preeclampsia, followed by multivariate logistic regression to determine significant predictors for model construction. Model validity was assessed using the ROC curve for discrimination, the Hosmer–Lemeshow test for calibration, and Decision Curve Analysis (DCA) for clinical utility. The incidence of preeclampsia among respondents was 12.3% (8 out of 65 participants). Three significant predictors were identified: systolic blood pressure ≥140 mmHg (OR 5.21; p=0.003), headache (OR 6.45; p=0.002), and blurred vision (OR 4.78; p=0.015). The developed EWS model demonstrated excellent discrimination (AUROC 0.86) and satisfactory calibration (Hosmer–Lemeshow p=0.521). DCA results indicated meaningful net clinical benefit at probability thresholds of 10–30%. In conclusion, the proposed EWS model is accurate, well-calibrated, and clinically beneficial, and is recommended as a screening tool for early detection of preeclampsia in Puskesmas to improve ANC services and reduce maternal complications]]>
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