The Pediatric infectious disease journal · 2017
Novel Risk Assessment Tool for Immunoglobulin Resistance in Kawasaki Disease: Application Using a Random Forest Classifier.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Takeuchi Masato, Inuzuka Ryo, Hayashi Taiyu, Shindo Takahiro, Hirata Yoichiro, Shimizu Nobutaka, Inatomi Jun, Yokoyama Yoshiki, Namai Yoshiyuki, Oda Yoichiro, Takamizawa Masaru, Kagawa Jiro, Harita Yutaka, Oka Akira
Аннотация
BACKGROUND: Resistance to intravenous immunoglobulin (IVIG) therapy is a risk factor for coronary lesions in patients with Kawasaki disease (KD). Risk-adjusted initial therapy may improve coronary outcome in KD, but identification of high risk patients remains a challenge. This study aimed to develop a new risk assessment tool for IVIG resistance using advanced statistical techniques.
METHODS: Data were retrospectively collected from KD patients receiving IVIG therapy, including demographic characteristics, signs and symptoms of KD and laboratory results. A random forest (RF) classifier, a tree-based machine learning technique, was applied to these data. The correlation between each variable and risk of IVIG resistance was estimated.
RESULTS: Data were obtained from 767 patients with KD, including 170 (22.1%) who were refractory to initial IVIG therapy. The predictive tool based on the RF algorithm had an area under the receiver operating characteristic curve of 0.916, a sensitivity of 79.7% and a specificity of 87.3%. Its misclassification rate in the general patient population was estimated to be 15.5%. RF also identified markers related to IVIG resistance such as abnormal liver markers and percentage neutrophils, displaying relationships between these markers and predicted risk.
CONCLUSIONS: The RF classifier reliably identified KD patients at high risk for IVIG resistance, presenting clinical markers relevant to treatment failure. Evaluation in other patient populations is required to determine whether this risk assessment tool relying on RF has clinical value.
METHODS: Data were retrospectively collected from KD patients receiving IVIG therapy, including demographic characteristics, signs and symptoms of KD and laboratory results. A random forest (RF) classifier, a tree-based machine learning technique, was applied to these data. The correlation between each variable and risk of IVIG resistance was estimated.
RESULTS: Data were obtained from 767 patients with KD, including 170 (22.1%) who were refractory to initial IVIG therapy. The predictive tool based on the RF algorithm had an area under the receiver operating characteristic curve of 0.916, a sensitivity of 79.7% and a specificity of 87.3%. Its misclassification rate in the general patient population was estimated to be 15.5%. RF also identified markers related to IVIG resistance such as abnormal liver markers and percentage neutrophils, displaying relationships between these markers and predicted risk.
CONCLUSIONS: The RF classifier reliably identified KD patients at high risk for IVIG resistance, presenting clinical markers relevant to treatment failure. Evaluation in other patient populations is required to determine whether this risk assessment tool relying on RF has clinical value.