| 入院24 h内首次血常规参数对儿童危重症的预测价值 |
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| 引用本文： | 杭航,赵武,孙琦,郭启秀,马晓倩,郑子凡.入院24 h内首次血常规参数对儿童危重症的预测价值[J].中华全科医学,2023,21(2):190-194. |
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| 作者姓名： | 杭航 赵武 孙琦 郭启秀 马晓倩 郑子凡 |
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| 作者单位： | 蚌埠医学院第一附属医院儿科，安徽 蚌埠 233004 |
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| 基金项目： | 安徽省重点研究与开发计划项目2022e07020035 |
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| 摘 要： | 目的 探讨入院24 h内首次血常规参数与儿童危重症的相关性，并建立ROC曲线和列线图模型以评价其对儿童危重症的预测价值。 方法 以2015年4月—2019年12月蚌埠医学院第一附属医院儿童重症监护室入院24 h内行血常规检查并完成小儿危重病例评分的患儿为研究对象。将患儿随机分为训练队列和验证队列，训练队列患儿变量与危重症的相关性采用logistic回归分析。采用受试者工作特征曲线分析变量对2个队列危重症的预测效能，采用R语言构建训练队列列线图预测模型评估危重症的发生概率。 结果 共纳入496例患儿，男283例，女213例，中位年龄2.0(0.57，5.88)岁。训练队列347例，验证队列149例。白细胞计数(WBC)、红细胞分布宽度CV(RDW-CV)及网织红细胞百分比(RET%)与训练队列患儿危重症显著相关(均P < 0.05)，WBC+RDW-CV+RET%联合指标预测训练队列及验证队列危重症的曲线下面积分别为0.644和0.711，在最佳截断值为0.357和0.290时，联合指标预测2个队列危重症的灵敏度分别为46.4%和79.6%，特异度分别为80.0%和60.0%。以训练队列WBC、RDW-CV及RET%构建列线图模型，一致性指数、校准曲线、决策曲线和临床影响曲线分析表明列线图可预测儿童危重症。 结论 入院24 h内首次WBC+RDW-CV+RET%对儿童危重症具有较好的预测效能，以WBC、RDW-CV及RET%构建的列线图可预测儿童危重症的发生概率。
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| 关 键 词： | 危重症 血细胞计数 受试者工作特征曲线 列线图 儿童 |
| 收稿时间： | 2022-11-19 |
Predictive value of the first blood routine parameters within 24 hours of admission for critical illness in children |
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| Affiliation: | Department of Pediatrics, the First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui 233004, China |
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| Abstract: | Objective To explore the correlation between the first blood routine parameters within 24 hours of admission and critical illness in children, and establish ROC curve and a nomogram model to evaluate their predictive value for critical illness in children. Methods Patients who underwent blood routine test and completed pediatric critical illness score within 24 hours of admission in the Pediatric Intensive Care Unit of the First Affiliated Hospital of Bengbu Medical College from April 2015 to December 2019 were enrolled. The patients were randomly divided into a training cohort and a validation cohort. The correlation between the variables of the training cohort and critical illness was analyzed by logistic regression analysis. The predictive performances of variables for critical illness in the two cohorts were analyzed by receiver operating characteristic curve. The nomogram prediction model of the training cohort was constructed using R language to assess the occurrence probability of critical illness. Results Among 496 patients, there were 283 males and 213 females, with a median age of 2.0 (0.57, 5.88) years. There were 347 patients in the training cohort, and 149 patients in the validation cohort. White cell count (WBC), red cell distribution width-coefficient of variation (RDW-CV), and reticulocyte percentage (RET%) were significantly associated with critical illness in children in the training cohort (all P < 0.05). The receiver operating characteristic curve analysis showed that the areas under the curve of WBC+RDW-CV+RET% combined index for predicting critical illness in the training cohort and the validation cohort were 0.644 and 0.711, respectively. When the optimal cut-off values of 0.357 for the training cohort and 0.290 for the validation cohort were used, sensitivities of the combined index for predicting critical illness were 46.4% and 79.6%, and specificities were 80.0% and 60.0%, respectively. The nomogram prediction model was constructed using WBC, RDW-CV, and RET% of the patients in the training cohort. The concordance index, calibration curve, decision curve, and clinical impact curve analyses indicated that this nomogram could be used to predict critical illness in children. Conclusion The first WBC+RDW-CV+RET% combined index within 24 hours of admission has a good predictive performance for critical illness in children. The nomogram constructed by WBC, RDW-CV, and RET% can be used to predict the occurrence probability of critical illness in children. |
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