Chen Yang, Wang Zhiyong, Xiao Dong, Zeng Hongwu, Ma Xiaopeng
Shenzhen Children's Hospital, Shenzhen, China.
College of Medicine, Shantou University, Shantou, China.
Front Pediatr. 2021 Nov 18;9:763125. doi: 10.3389/fped.2021.763125. eCollection 2021.
There is a lack of assessment methods of acute appendicitis in little children. The purpose of this study was to develop and internally validate a nomogram for predicting the severity of acute appendicitis of young children (<3 years old). We develop a prediction model based on a training dataset of 121 patients (<3 years old) with acute appendicitis. Admission information was collected between January 2010 and January 2021, which contained demographic characteristic, laboratory examinations, treatment and pathology type, etc. Logistic regression analysis was used to identify independent risk factors and establish the predictive model. C-index and calibration curves were applied to evaluate the performance of the nomogram. Then corrected C-index was calculated to conduct internal verification by using the bootstrapping validation. Decision curve analysis determined clinical application of the prediction model. Predictors contained in the prediction nomogram included weight for age, onset time (from developing symptoms to hospital), admission temperature, leukocyte count, neutrophil ratio, and total bilirubin. Logistic regression analysis showed that weight for age (X1) < -2.32 SD ( = 0.046), onset time (X2) > 2.5 days ( = 0.044), admission temperature (X3) > 38.5°C ( = 0.009), leukocyte count (X4) > 12.185109/L ( = 0.045), neutrophil ratio (X5) > 68.7% ( = 0.029), and total bilirubin (X6) > 9.05 μmol/L ( = 0.035) were found to be significant for predicting the severity of appendicitis. The logistic regression equation was logit () = -0.149X1 + 0.51X2 + 1.734X3 + 0.238X4 + 0.061X5 + 0.098X6 - 75.229. C-index of nomogram was calculated at 0.8948 (95% Cl: 0.8332-0.9567) and it still was 0.8867 through bootstrapping validation. Decision curve analysis showed that when the threshold probability ranged from 14 to 88%, there is a net benefit of using this prediction model for severity of appendicitis in little children. This novel nomogram incorporating the weight for age, onset time, admission temperature, leukocyte count, neutrophil ratio, and total bilirubin could be conveniently used to estimate the severity of appendicitis of young children <3 years old) and determine appropriate treatment options in time.
小儿急性阑尾炎缺乏评估方法。本研究的目的是开发并内部验证一个用于预测幼儿（<3岁）急性阑尾炎严重程度的列线图。我们基于121例<3岁急性阑尾炎患儿的训练数据集开发了一个预测模型。收集了2010年1月至2021年1月期间的入院信息，包括人口统计学特征、实验室检查、治疗及病理类型等。采用逻辑回归分析确定独立危险因素并建立预测模型。应用C指数和校准曲线评估列线图的性能。然后通过自举验证计算校正C指数进行内部验证。决策曲线分析确定了预测模型的临床应用。预测列线图中的预测因素包括年龄别体重、发病时间（从出现症状到入院）、入院体温、白细胞计数、中性粒细胞比例和总胆红素。逻辑回归分析显示，年龄别体重（X1）<-2.32标准差（P=0.046）、发病时间（X2）>2.5天（P=0.044）、入院体温（X3）>38.5℃（P=0.009）、白细胞计数（X4）>12.18×10⁹/L（P=0.045）、中性粒细胞比例（X5）>68.7%（P=0.029）和总胆红素（X6）>9.05μmol/L（P=0.035）对预测阑尾炎严重程度具有显著意义。逻辑回归方程为logit（P）=-0.149X1+0.51X2+1.734X3+0.238X4+0.061X5+0.098X6-75.229。列线图的C指数计算为0.8948（95%CI：0.8332-0.9567），通过自举验证后仍为0.8867。决策曲线分析表明，当阈值概率在14%至88%之间时，使用该预测模型评估小儿阑尾炎严重程度有净效益。这种包含年龄别体重、发病时间、入院体温、白细胞计数、中性粒细胞比例和总胆红素的新型列线图可方便地用于估计<3岁幼儿阑尾炎的严重程度，并及时确定合适的治疗方案。
