Differences in Sensitivity Between the Japanese and Z Score Criteria for Detecting Coronary Artery Abnormalities Resulting from Kawasaki Disease.
Ryusuke AeYoshihide ShibataTohru KobayashiKoki KosamiMasanari KuwabaraNobuko MakinoYuri MatsubaraTeppei SasaharaHiroya MasudaYosikazu NakamuraPublished in: Pediatric cardiology (2022)
No studies have assessed differences between the Japanese and Z score criteria in the echocardiographic detection sensitivity of coronary artery (CA) abnormalities using large-scale data containing samples from multiple facilities engaged in daily clinical practices of Kawasaki disease (KD). We analyzed data from the 25th Japanese nationwide KD survey, which identified 30,415 patients from 1357 hospitals throughout Japan during 2017-2018. Hospitals were classified according to their use of Z score criteria. We assessed differences in hospital and patient background factors and compared the prevalence of CA abnormalities among groups using the Z score criteria. Multivariable logistic regression analyses were performed to evaluate differences in the detection sensitivity for CA abnormalities. The Z score criteria were more likely to be utilized in larger hospitals with more pediatricians and cardiologists. Even after controlling for potential confounders, detection sensitivities by the Z score criteria were significantly higher than by the Japanese criteria in patients with CA dilatations (adjusted odds ratio (95% confidence interval) 1.77 (1.56-2.01)) and aneurysms (1.62 (1.17-2.24)). No significant difference was found in patients with giant CA aneurysms. Compared with the Japanese criteria, the Z score criteria were significantly more sensitive for detecting patients with CA dilatations regardless of age, and for those with CA aneurysms only in patients aged ≤ 1 year. Our results indicate that differences in the detection sensitivity for CA abnormalities between the Z score and the Japanese criteria were dependent on the CA size and patient age.
Keyphrases
- coronary artery
- healthcare
- end stage renal disease
- ejection fraction
- protein kinase
- chronic kidney disease
- case report
- pulmonary artery
- heart failure
- emergency department
- primary care
- physical activity
- machine learning
- real time pcr
- big data
- risk assessment
- patient reported outcomes
- electronic health record
- mitral valve
- left ventricular
- deep learning
- quantum dots
- adverse drug
- patient reported
- acute care