ArticleInternational journal of women's health2026
Prediction of Pregnancy-Related Complications in Women of Advanced Maternal Age: A Nomogram Based on LASSO and Logistic Regression.
Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The increasing prevalence of advanced maternal age (AMA) is significantly associated with a higher risk of diverse pregnancy-related complications, posing a challenge to maternal and fetal health. However, personalized risk assessment tools specifically designed for this high-risk population remain limited. This study aimed to develop and validate a multi-factor nomogram to predict composite pregnancy complications in AMA women, facilitating early clinical intervention. Objective: To construct and validate a predictive nomogram integrating metabolic, immune-nutritional, and lifestyle indicators to quantify the risk of composite pregnancy complications in women of advanced maternal age (AMA). Methods: A retrospective cohort study was conducted on 2212 AMA women. They were randomly divided into a training set (n = 1548) and a validation set (n = 664). The primary endpoint was the composite pregnancy complication. LASSO regression was used to screen candidate variables, followed by multivariate logistic regression to determine the independent predictors. A nomogram was then constructed to visualize the model. The performance was evaluated through the area under the receiver operating characteristic curve (AUC), calibration plot (Hosmer-Lemeshow test), and decision curve analysis (DCA). Results: Seven independent predictors were identified. Risk factors included history of miscarriage, habitual high-salt/high-fat diet, elevated D-dimer, ALT/AST ratio, non-HDL-C, and importantly, the Triglyceride-Glucose (TyG) index (Calculated based on mmol/L) (OR = 5.817, 95% CI 3.893-8.693). Conversely, a higher CALLY index (OR = 0.754, 95% CI 0.675-0.842) served as a protective factor. The nomogram displayed favorable discrimination in both the training set (AUC = 0.733, 95% CI 0.707-0.758) and validation set (AUC = 0.754, 95% CI 0.709-0.786). Calibration curves demonstrated excellent agreement (P>0.05), and DCA confirmed significant net clinical benefit across threshold probabilities of 0.16-0.93. Conclusion: This study developed a robust nomogram that effectively incorporates metabolic, immune-nutritional, and lifestyle profiles. It serves as a practical screening tool for early risk stratification in the AMA population, facilitating individualized clinical decision-making.
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