Bowen Li Xiaoliang Liu Shuran Shao Ping Wu Mei Wu Lei Liu Yimin Hua Hongyu Duan ^* Kaiyu Zhou ^* Chuan Wang ^*

• West China Second University Hospital, Sichuan University, Chengdu, China

Background: Kawasaki disease (KD) is characterized as an acute febrile inflammatory disorder, which may potentially escalate into a more severe condition termed Kawasaki disease shock syndrome (KDSS). The objective of this research is to understand the clinical attributes of KDSS and to explore the predictive significance of coagulation profiles in the incidence of KDSS. Method: Patients with Kawasaki disease (KD) were prospectively enrolled and divided into the KDSS group (n=29) and the non-KDSS group (n=494). Multivariate logistic regression analysis was used to ascertain the relationship between coagulation profiles and KDSS. Furthermore, ROC curve analysis was conducted to evaluate the predictive value of the coagulation profile for the occurrence of KDSS. Result: Among the KDSS patients, the median age was higher and cervical lymph node involvement was greater compared to the non-KDSS group. Additionally, pericardial effusion, valve regurgitation, cardiac enlargement, coronary artery lesions (CALs), and Intravenous immunoglobulin (IVIG) resistance were significantly more frequent in the KDSS group than in non-KDSS group. Notably, Prothrombin time (PT) , activated partial thromboplastin time (APTT), D-dimer, and fibrin degradation products (FDP) were significantly elevated in the KDSS group compared to the non-KDSS group. Conversely, total thrombin time (TT), fibrinogen, and antithrombin III (ATIII) activity were significantly reduced. Multivariate logistic regression analysis revealed that PT, APTT, D-dimer, and ATIII were independent risk factors for predicting KDSS occurrence. ROC curve analysis established critical values for PT, D-dimer, FDP, and ATIII as 13.45 s, 2.03 mg/L, 7.45 μg/mL, and 77.5%, respectively. Sensitivity for predicting KDSS occurrence was 76%, 79%, 83%, and 76%, while specificity was 51%, 72%, 63%, and 80%, respectively. When we performed a combined ROC curve analysis of the four indicators, we found that its predictive sensitivity was much higher. Moreover, the Delong test results showed that the AUC of the combined analysis was significantly higher than that of the individual analyses. Conclusion: Characteristic features of KDSS include older age, a greater likelihood of experiencing pericardial effusion, valve regurgitation, cardiac enlargement, CALs, and IVIG resistance. KD patients with a hypercoagulable state during the acute phase are at a higher risk of developing KDSS.