PDF(Pubmed)
Abstract：
UNASSIGNED: Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD.
UNASSIGNED: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68).
UNASSIGNED: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets.
UNASSIGNED: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.
UNASSIGNED: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68).
UNASSIGNED: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets.
UNASSIGNED: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.
摘要：
■川崎病休克综合征（KDSS）是川崎病（KD）的重要表现。近年来,logistic回归预测模型已被广泛用于预测各种疾病的发生概率。本研究旨在探讨KD患儿的临床特征，建立并验证预测KD患儿KDSS的个性化logistic回归模型。
■对2021年1月至2023年12月期间诊断为KDSS并住院的儿童的临床资料进行回顾性分析。通过逻辑回归和套索回归分析选择最佳预测因子。建立训练集（n=162）的逻辑回归模型来预测KDSS的发生。进一步通过逻辑回归进行模型预测。使用受试者工作特性曲线来评估逻辑回归模型的性能。我们通过使用1000bootstrap重采样程序可视化校准曲线来构建列线图模型。使用独立的验证集(n=68)验证模型。
■在单变量分析中，在KDSS和KD组之间存在显着差异的24个变量中，进一步的logistic和Lasso回归分析发现，五个变量与KDSS独立相关：皮疹，脑钠肽，血清Na,血清P,和天冬氨酸转氨酶.建立了训练集的逻辑回归模型（受试者工作特性曲线下的面积，0.979；敏感性=96.2%；特异性=97.2%）。校准曲线显示逻辑回归模型的预测值与训练集和验证集中的实际观察值之间具有良好的一致性。
■在这里我们建立了一个可行且高度准确的逻辑回归模型来预测KDSS的发生，这将使其能够早期识别。
■对2021年1月至2023年12月期间诊断为KDSS并住院的儿童的临床资料进行回顾性分析。通过逻辑回归和套索回归分析选择最佳预测因子。建立训练集（n=162）的逻辑回归模型来预测KDSS的发生。进一步通过逻辑回归进行模型预测。使用受试者工作特性曲线来评估逻辑回归模型的性能。我们通过使用1000bootstrap重采样程序可视化校准曲线来构建列线图模型。使用独立的验证集(n=68)验证模型。
■在单变量分析中，在KDSS和KD组之间存在显着差异的24个变量中，进一步的logistic和Lasso回归分析发现，五个变量与KDSS独立相关：皮疹，脑钠肽，血清Na,血清P,和天冬氨酸转氨酶.建立了训练集的逻辑回归模型（受试者工作特性曲线下的面积，0.979；敏感性=96.2%；特异性=97.2%）。校准曲线显示逻辑回归模型的预测值与训练集和验证集中的实际观察值之间具有良好的一致性。
■在这里我们建立了一个可行且高度准确的逻辑回归模型来预测KDSS的发生，这将使其能够早期识别。
