Diagnostic significance of noncoding RNAs in kawasaki disease: A systematic review and meta-analysis

Zhong, Xiaoling; Jia, Xufeng; Wang, Hui; Chen, Guihua; Li, Hongxia; Li, Pingping; Yang, Taoyi; Xie, Jiang

doi:10.3389/fped.2022.1071434

Cited by 6 publications

(1 citation statement)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[ 29 ] Our previous research indicated that ncRNAs, especially miRNAs, have large potentials in differentiating KD-CAL from KD-NCAL, acute KD patients from convalescent KD patients and intravenous immunoglobulin-resistant KD from intravenous immunoglobulin-responsive KD patients. [ 30 ] With the literature updating, our research aimed to further explore the diagnostic performance of ncRNAs, including miRNAs, lncRNAs, and circRNAs, in KD patients, which could lead to more convincing conclusions and provide new clues for further research.…”

Section: Introductionmentioning

confidence: 99%

Association of noncoding RNAs with Kawasaki disease: A meta-analysis based on the current evidences

Zhong,

Wang,

Jia

et al. 2023

Medicine

Self Cite

View full text Add to dashboard Cite

Background: In recent years, many studies have focused on the relationship between noncoding RNAs (ncRNAs) and Kawasaki disease (KD). Studies have indicated that ncRNAs are associated with the occurrence and development of KD. Thus, we performed a systematic review and meta-analysis to investigate the diagnostic value of ncRNAs in KD patients. Methods: We searched the PubMed, Web of Science, Embase and Cochrane Library, China National Knowledge Infrastructure, VIP, China Biology Medicine disc databases, and Wanfang databases until August 25, 2023 and screened all eligible studies focusing on the diagnostic performance of ncRNAs in KD patients. Results: In total, 535 articles were found, and 28 articles were included in this systematic review and meta-analysis. The calculated area under the curve value was 0.880 (95% confidence intervals, 0.840–0.900). The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.790, 0.830, 4.610, and 0.260, respectively. The pooled diagnostic odds ratio was 17.890 (95% confidence intervals, 13.110–24.420), indicating a relatively good diagnostic performance of the ncRNAs for detecting KD. In addition, the diagnostic value of micro RNAs in KD was better than that of long noncoding RNAs and circular noncoding RNAs. A subgroup analysis by specimen indicated a better diagnostic value of ncRNAs in plasma and platelet than serum. The diagnostic accuracy of ncRNAs was better in febrile controls than in healthy control groups, indicating a relatively good accuracy in distinguishing KD patients from febrile diseases. Conclusions: This systematic review and meta-analysis demonstrated that ncRNAs could be used as novel biomarkers for detecting KD. More studies should be conducted in the future to verify the diagnostic values of ncRNAs in KD.

show abstract

Section: Introductionmentioning

confidence: 99%

Association of noncoding RNAs with Kawasaki disease: A meta-analysis based on the current evidences

Zhong,

Wang,

Jia

et al. 2023

Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

Biomarkers and Related Factors for the Diagnosis, Risk of Coronary Artery Lesions, and Resistance to Intravenous Immunoglobulin in Kawasaki Disease: An Umbrella Review of Meta-Analyses

Kim,

Son,

Lee

et al. 2024

Pediatr Cardiol

View full text Add to dashboard Cite

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Zhu,

Yang,

Wang

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background Kawasaki disease (KD) is an acute pediatric vasculitis that can lead to coronary artery aneurysms and severe cardiovascular complications, often presenting with obvious fever in the early stages. In current clinical practice, distinguishing KD from other febrile illnesses remains a significant challenge. In recent years, some researchers have explored the potential of machine learning (ML) methods for the differential diagnosis of KD versus other febrile illnesses, as well as for predicting coronary artery lesions (CALs) in people with KD. However, there is still a lack of systematic evidence to validate their effectiveness. Therefore, we have conducted the first systematic review and meta-analysis to evaluate the accuracy of ML in differentiating KD from other febrile illnesses and in predicting CALs in people with KD, so as to provide evidence-based support for the application of ML in the diagnosis and treatment of KD. Objective This study aimed to summarize the accuracy of ML in differentiating KD from other febrile illnesses and predicting CALs in people with KD. Methods PubMed, Cochrane Library, Embase, and Web of Science were systematically searched until September 26, 2023. The risk of bias in the included original studies was appraised using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Stata (version 15.0; StataCorp) was used for the statistical analysis. Results A total of 29 studies were incorporated. Of them, 20 used ML to differentiate KD from other febrile illnesses. These studies involved a total of 103,882 participants, including 12,541 people with KD. In the validation set, the pooled concordance index, sensitivity, and specificity were 0.898 (95% CI 0.874-0.922), 0.91 (95% CI 0.83-0.95), and 0.86 (95% CI 0.80-0.90), respectively. Meanwhile, 9 studies used ML for early prediction of the risk of CALs in children with KD. These studies involved a total of 6503 people with KD, of whom 986 had CALs. The pooled concordance index in the validation set was 0.787 (95% CI 0.738-0.835). Conclusions The diagnostic and predictive factors used in the studies we included were primarily derived from common clinical data. The ML models constructed based on these clinical data demonstrated promising effectiveness in differentiating KD from other febrile illnesses and in predicting coronary artery lesions. Therefore, in future research, we can explore the use of ML methods to identify more efficient predictors and develop tools that can be applied on a broader scale for the differentiation of KD and the prediction of CALs.

show abstract

Diagnostic significance of noncoding RNAs in kawasaki disease: A systematic review and meta-analysis

Cited by 6 publications

References 37 publications

Association of noncoding RNAs with Kawasaki disease: A meta-analysis based on the current evidences

Association of noncoding RNAs with Kawasaki disease: A meta-analysis based on the current evidences

Biomarkers and Related Factors for the Diagnosis, Risk of Coronary Artery Lesions, and Resistance to Intravenous Immunoglobulin in Kawasaki Disease: An Umbrella Review of Meta-Analyses

Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis

Contact Info

Product

Resources

About