Prediction of the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics
Purpose To predict the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics. Methods Gut microbiota and clinical data from 180 subjects (120 for training set and 60 for validation) attending the West China Hospital (WCH) were collected between June 2018 an...
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| Published in | World journal of urology Vol. 40; no. 1; pp. 221 - 227 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.01.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0724-4983 1433-8726 1433-8726 |
| DOI | 10.1007/s00345-021-03801-7 |
Cover
| Summary: | Purpose
To predict the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics.
Methods
Gut microbiota and clinical data from 180 subjects (120 for training set and 60 for validation) attending the West China Hospital (WCH) were collected between June 2018 and January 2021. Based on the gut microbiota and clinical data from 120 subjects (66 non-kidney stone individuals and 54 kidney stone patients), we evaluated eight machine learning methods to predict the occurrence of calcium oxalate kidney stones.
Results
With fivefold cross-validation, the random forest method produced the best area under the curve (AUC) of 0.94. We further applied random forest to an independent validation dataset with 60 samples (34 non-kidney stone individuals and 26 kidney stone patients), which yielded an AUC of 0.88.
Conclusion
Our results demonstrated that clinical data combined with gut microbiota characteristics may help predict the occurrence of kidney stones. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ISSN: | 0724-4983 1433-8726 1433-8726 |
| DOI: | 10.1007/s00345-021-03801-7 |