Clinical severity prediction in children with osteogenesis imperfecta caused by COL1A1/2 defects
Summary Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of...
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Veröffentlicht in: | Osteoporosis international 2022-06, Vol.33 (6), p.1373-1384 |
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Sprache: | eng |
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Zusammenfassung: | Summary
Osteogenesis imperfecta (OI) is a genetic disease with an estimated prevalence of 1 in 13,500 and 1 in 9700. The classification into subtypes of OI is important for prognosis and management. In this study, we established a clinical severity prediction model depending on multiple features of variants in
COL1A1
/
2
genes.
Introduction
Ninety percent of OI cases are caused by pathogenic variants in the
COL1A1
/
COL1A2
gene. The Sillence classification describes four OI types with variable clinical features ranging from mild symptoms to lethal and progressively deforming symptoms.
Methods
We established a prediction model of the clinical severity of OI based on the random forest model with a training set obtained from the Human Gene Mutation Database, including 790 records of the
COL1A1/COL1A2
genes. The features used in the prediction model were respectively based on variant-type features only, and the optimized features.
Results
With the training set, the prediction results showed that the area under the receiver operating characteristic curve (AUC) for predicting lethal to severe OI or mild/moderate OI was 0.767 and 0.902, respectively, when using variant-type features only and optimized features for
COL1A1
defects, 0.545 and 0.731, respectively, for
COL1A2
defects. For the 17 patients from our hospital, prediction accuracy for the patient with the
COL1A1
and
COL1A2
defects was 76.5% (95% CI: 50.1–93.2%) and 88.2% (95% CI: 63.6–98.5%), respectively.
Conclusion
We established an OI severity prediction model depending on multiple features of the specific variants in
COL1A1
/
2
genes, with a prediction accuracy of 76–88%. This prediction algorithm is a promising alternative that could prove to be valuable in clinical practice. |
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ISSN: | 0937-941X 1433-2965 |
DOI: | 10.1007/s00198-021-06263-0 |