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Table 3 Prediction results using final simplified logistic regression models with predictors at varied stages of pregnancy

From: Development and validation of prediction models for gestational diabetes treatment modality using supervised machine learning: a population-based cohort study

  Cross-validated AUC (95% CI) Integrated calibration index Calibrated AUC (95% CI)
Discovery set Validation set
Level 1a 0.632 (0.623–0.640) 0.609 (0.587–0.632) 0.073 0.609 (0.587–0.632)
Levels 1–2b 0.648 (0.640–0.656) 0.621 (0.599–0.643) 0.075 0.621 (0.599–0.643)
Levels 1–3c 0.770 (0.764–0.775) 0.746 (0.730–0.763) 0.072 0.752 (0.734–0.77)
Levels 1–4d 0.825 (0.820–0.830) 0.798 (0.783–0.813) 0.038 0.802 (0.786–0.818)
  1. AUC, area under the receiver operating characteristic curve; CI, confidence interval; GDM, gestational diabetes
  2. aPredictors included history of GDM, pre-pregnancy obesity, and prediabetes before pregnancy
  3. bPredictors included history of GDM, pre-pregnancy obesity, glucose levels at 50-g, 1-h glucose challenge test for GDM screening (≥ 200 mg/dL), and prediabetes before pregnancy, in addition to three pairwise interactions between the first three predictors
  4. cPredictors included fasting glucose value at 100-g, 3-h oral glucose tolerance test, gestational week at GDM diagnosis (continuous), and GDM diagnosis by Carpenter-Coustan criteria (versus by fasting hyperglycemia)
  5. dPredictors included gestational week at GDM diagnosis (continuous), fasting glucose value at 100-g, 3-h oral glucose tolerance test, self-monitored glycemic control status at fasting, number of fasting self-monitored blood glucose measurements, and an interaction term between last two variables