Researchers conducted this study To develop a bleeding-pattern prediction model to inform counseling on the amount and regularity of bleeding after LNG-IUS placement.

Fixed-cluster and regression-tree models were developed using bleeding data pooled from two clinical trials of LNG-IUSs. Models were trained and cross-validated on LNG-IUS 12 data, then applied to LNG-IUS 20 and LNG-IUS 8 data. Three clusters were generated for the fixed-cluster model. A random-forest model predicted the future-bleeding collection, then the probability of cycle regularity was calculated. In the regression-tree model, women were assigned by the model to less- or more-bleeding groups.

With LNG-IUS 12 (n = 1351) in the fixed-cluster model, 70.4% of women were correctly classified. The correct classification rates for LNG-IUS 20 and LNG-IUS 8 were 72.2% and 69.0%. The probability distribution for cycle regularity showed regular and irregular bleeding were best separated with LNG-IUS 12 data and less well with LNG-IUS 20 and LNG-IUS 8 data. There was high variability in the more- and less-bleeding group distributions with LNG-IUS 12 data in the regression-tree model.

The study concluded that a fixed-cluster model predicted bleeding patterns better than a regression-tree model in women using LNG-IUS, yielding understandable, informative output.