Extending curve matching with flexible hyperparameter selection to predict response to long-acting PEGylated growth hormone treatment in growth hormone deficiency children: method development and validation.

PURPOSE: Curve matching can predict the height trajectories of children by analyzing longitudinal growth data. We extended the method to improve the prediction of response to long-acting growth hormone treatment in children with growth hormone deficiency (GHD).

METHODS

We analyzed data from a previous real-world study with a 36-month treatment of PEGylated recombinant human growth hormone (PEG-rhGH). The matching database comprises height measures imputed using the broken stick method. For curve matching, we proposed a flexible hyperparameter selection approach to determining the number of similar patients.

RESULTS

The matching database included 681 patients, with an average of 12.20 ± 2.09 height measurements per patient. Our approach demonstrated significantly improved prediction accuracy compared with the previous approach using a fixed number of similar patients (mean squared errors of 0.0412 ± 0.1156 vs. 0.564 ± 0.1639, 0.851 ± 0.2627, and 0.1077 ± 0.2960 for 5, 10, and 15 similar patients, respectively, all P < 0.05). The optimal prediction scenario was having four height measurements within the first six months and predicting height trajectories from there on.

CONCLUSION

By extending curve matching with flexible hyperparameter selection, we accurately predicted the response to long-acting PEG-rhGH in the GHD children included in this study.