PMID- 29113339
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220311
IS  - 1949-2553 (Electronic)
IS  - 1949-2553 (Linking)
VI  - 8
IP  - 46
DP  - 2017 Oct 6
TI  - A new model to predict intravenous immunoglobin-resistant Kawasaki disease.
PG  - 80722-80729
LID - 10.18632/oncotarget.21083 [doi]
AB  - OBJECTIVES: To clarify the independent risk factors and construct predictive 
      model for intravenous immunoglobin (IVIG)-resistant KD (IVIGRKD). RESULTS: The 
      ratio of male to female in the overall samples was 1.62:1 and the incidence of 
      IVIGR was 17.9%. Multivariate regression analysis showed that the OR (95% CI) 
      values of fever duration >/= 7 days, delayed diagnosis, gamma-glutamyl transferase 
      >/= 25 U/L, serum sodium </= 135 mmol/L, neutrophil-to-lymphocyte ratio >/= 2.8 and 
      platelets </= 350 x 10(9)/L were 2.94 (2.17-4.00), 1.64 (1.07-2.53), 1.38 
      (1.07-1.79), 1.68 (1.30-2.19), 1.58 (1.22-2.06) and 1.39 (1.08-1.80), 
      respectively. Based on these OR values, a new predictive model was established 
      with an AUC of 0.685, a sensitivity of 60.7% and a specificity of 66.5%, and 
      showed superiority to formerly reported models. Further analysis of patients </= 6 
      months old gave rise to improved predictions for IVIGRKD with an AUC of 0.746 
      relative the new model for the total samples. MATERIALS AND METHODS: A total of 
      2,126 KD cases were enrolled in this study. Clinical indicators showing 
      significant differences were screened using univariate analysis, and the 
      independent risk factors were further elucidated using multivariate regression 
      analysis. A new model was constructed, and the predictive ability was evaluated 
      with the area under the curve (AUC) value and the sensitivity and specificity by 
      using the receiver operating characteristic (ROC) curve. CONCLUSIONS: The new 
      model for predicting IVIGRKD in this study is superior to those reported 
      previously, and further analysis of patients with IVIGRKD younger than 6 months 
      old allowed optimization of the predictive model.
FAU - Hua, Wang
AU  - Hua W
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Sun, Yameng
AU  - Sun Y
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Wang, Ying
AU  - Wang Y
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Fu, Songling
AU  - Fu S
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Wang, Wei
AU  - Wang W
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Xie, Chunhong
AU  - Xie C
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Zhang, Yiying
AU  - Zhang Y
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
FAU - Gong, Fangqi
AU  - Gong F
AD  - Children's Hospital, Zhejiang University School of Medicine, Hangzhou 310052, PR 
      China.
LA  - eng
PT  - Journal Article
DEP - 20170919
PL  - United States
TA  - Oncotarget
JT  - Oncotarget
JID - 101532965
PMC - PMC5655234
OTO - NOTNLM
OT  - IVIG resistant
OT  - Kawasaki disease
OT  - independent risk factors
OT  - predictive model
COIS- CONFLICTS OF INTEREST The authors declare no conflicts of interest.
EDAT- 2017/11/09 06:00
MHDA- 2017/11/09 06:01
PMCR- 2017/10/06
CRDT- 2017/11/09 06:00
PHST- 2017/07/11 00:00 [received]
PHST- 2017/09/04 00:00 [accepted]
PHST- 2017/11/09 06:00 [entrez]
PHST- 2017/11/09 06:00 [pubmed]
PHST- 2017/11/09 06:01 [medline]
PHST- 2017/10/06 00:00 [pmc-release]
AID - 21083 [pii]
AID - 10.18632/oncotarget.21083 [doi]
PST - epublish
SO  - Oncotarget. 2017 Sep 19;8(46):80722-80729. doi: 10.18632/oncotarget.21083. 
      eCollection 2017 Oct 6.