A Composite Risk Scoring Model for Predicting Adverse Perinatal Outcomes in Patients with Pre-Eclampsia- A Pilot Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Accurate early identification of pregnancies at high risk for adverse fetal outcomes (small-for-gestational-age [SGA], preterm birth, stillbirth) enables targeted interventions. The study aims to pilot the development and internal validation of a point-based Fetal Risk Score (rFRS₅) incorporating ten routine maternal parameters, and to compare its performance against a simpler four-item score (FRS red). Methodology: In this single-centre, retrospective cohort study of 118 pregnant women, we assigned points based on clinically meaningful ranges for age, parity, BMI, blood pressure, 24-hour proteinuria, haemoglobin, platelets, ALT, AST, and LDH. We computed rFRS₅ (0-19 points) and FRS_red (0-6 points). Discrimination was assessed using the ROC AUC (5-fold cross-validation), and calibration was evaluated using the Brier score and calibration curves. Optimal thresholds were determined by sensitivity/specificity trade-offs. Variables for score development were selected a priori based on clinical relevance and published evidence. Internal validation was performed using five-fold cross-validation and calibration methods. Result: rFRS₅ achieved ROC AUC 0.80 and Brier score 0.219; FRS red achieved ROC AUC 0.82 and Brier score 0.220. For rule-out (sensitivity 100 %), rFRS₅ < 2 and FRS red < 2 both provided NPV 100 %. For rule-in, FRS red ≥ 6 yielded specificity 94 % and PPV 50 %, outperforming rFRS₅ (specificity 68 %, PPV 40 %). Conclusion: In this pilot study, both scores effectively stratify fetal risk, with the simpler FRS red offering superior rule-in performance in resource-limited settings. Larger, prospective studies are warranted to confirm these findings.
Source: PubMed
Authors: gothwal, meenakshi, Babu, Adhibha, Singh, Pratibha, Yadav, Garima, Asati, Swati, Gupta, Anubhav
Institution: All India Institute of Medical Sciences Jodhpur
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4415764573
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