He Yaqiong, Liu Peng, Xie Leyun, Zeng Saizhen, Lin Huashan, Zhang Bing, Liu Jianbin
Department of Radiology, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China.
Department of Pediatrics, Hunan Provincial People's Hospital (The First Affiliated Hospital of Hunan Normal University), Changsha, China.
Front Pediatr. 2022 Jun 27;10:874822. doi: 10.3389/fped.2022.874822. eCollection 2022.
To construct and validate a predictive model for risk factors in children with severe adenoviral pneumonia based on chest low-dose CT imaging and clinical features.
A total of 177 patients with adenoviral pneumonia who underwent low-dose CT examination were collected between January 2019 and August 2019. The assessment criteria for severe pneumonia were divided into mild group ( = 125) and severe group ( = 52). All cases divided into training cohort ( = 125) and validation cohort ( = 52). We constructed a prediction model by drawing a nomogram and verified the predictive efficacy of the model through the ROC curve, calibration curve and decision curve analysis.
The difference was statistically significant ( < 0.05) between the mild adenovirus pneumonia group and the severe adenovirus pneumonia group in gender, age, weight, body temperature, L/N ratio, LDH, ALT, AST, CK-MB, ADV DNA, bronchial inflation sign, emphysema, ground glass sign, bronchial wall thickening, bronchiectasis, pleural effusion, consolidation score, and lobular inflammation score. Multivariate logistic regression analysis showed that gender, LDH value, emphysema, consolidation score, and lobular inflammation score were severe independent risk factors for adenovirus pneumonia in children. Logistic regression was employed to construct clinical model, imaging semantic feature model, and combined model. The AUC values of the training sets of the three models were 0.85 (0.77-0.94), 0.83 (0.75-0.91), and 0.91 (0.85-0.97). The AUC of the validation set was 0.77 (0.64-0.91), 0.83 (0.71-0.94), and 0.85 (0.73-0.96), respectively. The calibration curve fit good of the three models. The clinical decision curve analysis demonstrates the clinical application value of the nomogram prediction model.
The prediction model based on chest low-dose CT image characteristics and clinical characteristics has relatively clear predictive value in distinguishing mild adenovirus pneumonia from severe adenovirus pneumonia in children and might provide a new method for early clinical prediction of the outcome of adenovirus pneumonia in children.
构建并验证基于胸部低剂量CT影像及临床特征的儿童重症腺病毒肺炎危险因素预测模型。
收集2019年1月至2019年8月期间177例行低剂量CT检查的腺病毒肺炎患儿。重症肺炎评估标准分为轻症组（n = 125）和重症组（n = 52）。所有病例分为训练队列（n = 125）和验证队列（n = 52）。通过绘制列线图构建预测模型，并通过ROC曲线、校准曲线及决策曲线分析验证模型的预测效能。
轻症腺病毒肺炎组与重症腺病毒肺炎组在性别、年龄、体重、体温、L/N比值、LDH、ALT、AST、CK-MB、ADV DNA、支气管充气征、肺气肿、磨玻璃征、支气管壁增厚、支气管扩张、胸腔积液、实变评分及小叶炎症评分方面差异有统计学意义（P < 0.05）。多因素logistic回归分析显示，性别、LDH值、肺气肿、实变评分及小叶炎症评分是儿童腺病毒肺炎的重症独立危险因素。采用logistic回归构建临床模型、影像语义特征模型及联合模型。三个模型训练集的AUC值分别为0.85（0.77 - 0.94）、0.83（0.75 - 0.91）和0.91（0.85 - 0.97）。验证集的AUC分别为0.77（0.64 - 0.91）、0.83（0.71 - 0.94）和0.85（0.73 - 0.96）。三个模型校准曲线拟合良好。临床决策曲线分析显示了列线图预测模型的临床应用价值。
基于胸部低剂量CT影像特征及临床特征的预测模型在区分儿童轻症与重症腺病毒肺炎方面具有较明确的预测价值，可能为儿童腺病毒肺炎预后的早期临床预测提供新方法。
