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.