Annals of translational medicine · 2021
Preoperatively predicting the pathological types of acute appendicitis using machine learning based on peripheral blood biomarkers and clinical features: a retrospective study.
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Kang Chun-Bo, Li Xiao-Wei, Hou Shi-Yang, Chi Xiao-Qian, Shan Hai-Feng, Zhang Qi-Jun, Li Xu-Bin, Zhang Jie, Liu Tie-Jun
Аннотация
BACKGROUND: This study aimed to establish machine learning models for preoperative prediction of the pathological types of acute appendicitis.
METHODS: Based on histopathology, 136 patients with acute appendicitis were included and divided into three types: acute simple appendicitis (SA, n=8), acute purulent appendicitis (PA, n=104), and acute gangrenous or perforated appendicitis (GPA, n=24). Patients with SA/PA and PA/GPA were divided into training (70%) and testing (30%) sets. Statistically significant features (P<0.05) for pathology prediction were selected by univariate analysis. According to clinical and laboratory data, machine learning logistic regression (LR) models were built. Area under receiver operating characteristic curve (AUC) was used for model assessment.
RESULTS: Nausea and vomiting, abdominal pain time, neutrophils (NE), CD4+ T cell, helper T cell, B lymphocyte, natural killer (NK) cell counts, and CD4+/CD8+ ratio were selected features for the SA/PA group (P<0.05). Nausea and vomiting, abdominal pain time, the highest temperature, CD8+ T cell, procalcitonin (PCT), and C-reactive protein (CRP) were selected features for the PA/GPA group (P<0.05). By using LR models, the blood markers can distinguish SA and PA (training AUC =0.904, testing AUC =0.910). To introduce additional clinical features, the AUC for the testing set increased to 0.926. In the PA/GPA prediction model, AUC with blood biomarkers was 0.834 for the training and 0.821 for the testing set. Combining with clinical features, the AUC for the testing set increased to 0.854.
CONCLUSIONS: Peripheral blood biomarkers can predict the pathological type of SA from PA and GPA. Introducing clinical symptoms could further improve the prediction performance.
METHODS: Based on histopathology, 136 patients with acute appendicitis were included and divided into three types: acute simple appendicitis (SA, n=8), acute purulent appendicitis (PA, n=104), and acute gangrenous or perforated appendicitis (GPA, n=24). Patients with SA/PA and PA/GPA were divided into training (70%) and testing (30%) sets. Statistically significant features (P<0.05) for pathology prediction were selected by univariate analysis. According to clinical and laboratory data, machine learning logistic regression (LR) models were built. Area under receiver operating characteristic curve (AUC) was used for model assessment.
RESULTS: Nausea and vomiting, abdominal pain time, neutrophils (NE), CD4+ T cell, helper T cell, B lymphocyte, natural killer (NK) cell counts, and CD4+/CD8+ ratio were selected features for the SA/PA group (P<0.05). Nausea and vomiting, abdominal pain time, the highest temperature, CD8+ T cell, procalcitonin (PCT), and C-reactive protein (CRP) were selected features for the PA/GPA group (P<0.05). By using LR models, the blood markers can distinguish SA and PA (training AUC =0.904, testing AUC =0.910). To introduce additional clinical features, the AUC for the testing set increased to 0.926. In the PA/GPA prediction model, AUC with blood biomarkers was 0.834 for the training and 0.821 for the testing set. Combining with clinical features, the AUC for the testing set increased to 0.854.
CONCLUSIONS: Peripheral blood biomarkers can predict the pathological type of SA from PA and GPA. Introducing clinical symptoms could further improve the prediction performance.