| Autor: |
Takao, Marina Mayumi Vendrame, Carvalho, Luiz Sérgio Fernandes, Silva, Paula Garcia Pereira, Pereira, Maisa Moraes, Viana, Ana Carolina, da Silva, Marcos Tadeu Nolasco, Riccetto, Adriana Gut Lopes |
| Předmět: |
|
| Zdroj: |
International Archives of Allergy & Immunology; 2022, Vol. 183 Issue 11, p1226-1230, 5p |
| Abstrakt: |
Background: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system. Objectives: The study aimed to develop and compare risk prediction model to measure the individual chance of a confirmed diagnosis of IEI in children at risk for this disorder. Method: Clinical and laboratory data of 128 individuals were used to derive machine learning (ML) and logistic regression risk prediction models, to measure the individual chance of a confirmed diagnosis of IEI in children with suspected disorder, according to previous general pediatrician/clinician judgement. Their performances were compared. Results: Statistically significant variables were mainly leucopenia, neutropenia, lymphopenia, and low levels of immunoglobulins A/G/M. ML models performed better. Conclusion: The enhanced predictive power provided by ML models could be a resource to track IEI, providing better healthcare outcomes. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
|
