Abstract
BACKGROUND: This study aimed to identify risk factors for adverse pregnancy outcomes (APO) in women with isolated maternal hypothyroxinemia (IMH) and to develop a nomogram for predicting APO risk during routine antenatal visits. METHODS: Data from 1254 IMH pregnancies, collected between January 2016 and December 2018 at the International Peace Maternal and Child Health Hospital (IPMCH) in Shanghai, China, were analyzed. APO, the primary outcome, included preterm birth (PTB), macrosomia, gestational diabetes mellitus (GDM), and hypertensive disorders of pregnancy (HDP). Multivariable logistic regression analyses identified risk factors for APO in IMH, and the least absolute shrinkage and selection operator (LASSO) regression algorithm was applied for feature selection, with cross-validation determining the optimal tuning parameter (λ). A nomogram based on the multivariable logistic regression model was developed to estimate APO risk, validated using 500 bootstrap resampling and a 2019 cohort of 745 women from the same institution. Model performance was assessed using discrimination and calibration metrics. RESULTS: Among 57 features, maternal age, elevated diastolic blood pressure, a family history of hypertension or diabetes, pre-conception BMI, fasting plasma triglycerides, fasting plasma glucose, HbA1c, and monocyte counts in the first trimester were identified via LASSO regression as significant risk factors, with pre-conception BMI being the most prominent. The area under the receiver operating characteristic curve (ROC-AUC), sensitivity, specificity, PPV, NPV and F1 score were 0.73 (95% CI: 0.70-0.76), 62.09%, 72.11%, 59.03%, 74.60% and 60.49% respectively in the training cohort, and 0.72 (95% CI: 0.68-0.76), 60.01%, 72.65%, 56.15%, 75.12% and 58.01% respectively in the validation cohort. Calibration plots demonstrated good consistency between predicted and observed APO incidences, and decision curve analyses indicated a positive net clinical benefit across cohorts. CONCLUSIONS: This study presented a robust early-pregnancy predictive model for APO in IMH women, offering clinicians a valuable tool for risk assessment and the early identification of high-risk pregnancies.