Abstract
Kawasaki disease is the leading cause of pediatric acquired heart disease. Coronary artery abnormalities are the main complication of Kawasaki disease. Kawasaki disease patients with intravenous immunoglobulin resistance are at a greater risk of developing coronary artery abnormalities. Several scoring models have been established to predict resistance to intravenous immunoglobulin, but clinicians usually do not apply those models in patients because of their poor performance. To find a better model, we retrospectively collected data including 753 observations and 82 variables. A total of 644 observations were included in the analysis, and 124 of the patients observed were intravenous immunoglobulin resistant (19.25%). We considered 7 different linear and nonlinear machine learning algorithms, including logistic regression (L1 and L1 regularized), decision tree, random forest, AdaBoost, gradient boosting machine (GBM), and lightGBM, to predict the class of intravenous immunoglobulin resistance (binary classification). Data from patients who were discharged before Sep 2018 were included in the training set (n = 497), while all the data collected after 9/1/2018 were included in the test set (n = 147). We used the area under the ROC curve, accuracy, sensitivity, and specificity to evaluate the performances of each model. The gradient GBM had the best performance (area under the ROC curve 0.7423, accuracy 0.8844, sensitivity 0.3043, specificity 0.9919). Additionally, the feature importance was evaluated with SHapley Additive exPlanation (SHAP) values, and the clinical utility was assessed with decision curve analysis. We also compared our model with the Kobayashi score, Egami score, Formosa score and Kawamura score. Our machine learning model outperformed all of the aforementioned four scoring models. Our study demonstrates a novel and robust machine learning method to predict intravenous immunoglobulin resistance in Kawasaki disease patients. We believe this approach could be implemented in an electronic health record system as a form of clinical decision support in the near future.
Highlights
Kawasaki disease (KD) is a self-limited systemic vasculitis that predominantly affects children under 5 years old
We diagnosed incomplete KD when patients had 5 days of fever and 2 or 3 compatible clinical criteria and had creactive protein (CRP) 30 mmol/L or erythrocyte sedimentation rate (ESR) 40 mm/h; we evaluated coronary artery abnormalities (CAAs) based on echocardiography or a set of suspicious laboratory criteria according to the guidelines
We found that the ensemble-based gradient boosting machine (GBM) algorithm achieved the optimal Area under the ROC curve (AUC) (0.7423) on the testing set, suggesting that the ensemblebased GBM algorithm performed the best
Summary
Kawasaki disease (KD) is a self-limited systemic vasculitis that predominantly affects children under 5 years old. Tomisaku Kawasaki first reported on KD in 1967. Fifty years after the first report of KD, the cause of the disease remains unknown. The incidence of KD varies from 3.4 to 218.6 cases per 100,000 children. The incidence in certain Asian countries (Japan, Korean, China) is significantly higher than that in Western countries. The incidence worldwide has exhibited an increasing trend in the last few decades [1]. Clinical features of KD include persistent fever, cervical lymphadenopathy and mucocutaneous changes. Most clinical features resolve in 4 weeks even without treatment. KD is still the leading cause of pediatric acquired heart disease because of its main complication, coronary artery abnormalities (CAAs). CAAs contribute the most to the mortality of KD patients [1]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
