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预测暴发性坏死性小肠结肠炎的列线图。

Nomogram for predicting fulminant necrotizing enterocolitis.

机构信息

Department of Neonatology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.

Branch Center of the Third Affiliated Hospital of Advanced Medical Research Center of Zhengzhou University, Zhengzhou, Henan, China.

出版信息

Pediatr Surg Int. 2023 Mar 20;39(1):154. doi: 10.1007/s00383-023-05435-9.

Abstract

BACKGROUND

Fulminant necrotizing enterocolitis (FNEC) is the most serious subtype of NEC and has a high mortality rate and a high incidence of sequelae. Onset prediction can help in the establishment of a customized treatment strategy. This study aimed to develop and evaluate a predictive nomogram for FNEC.

METHODS

We conducted a retrospective observation to study the clinical data of neonates diagnosed with NEC (Bell stage ≥ IIB). Neonates were divided into the FNEC and NEC groups. A multivariate logistic regression model was used to construct the nomogram model. The performance of the nomogram was assessed using area under the curve, calibration analysis, and decision curve analysis.

RESULTS

A total of 206 neonate cases were included, among which 40 (19.4%) fulfilled the definition of FNEC. The identified predictors were assisted ventilation after NEC onset; shock at NEC onset; feeding volumes before NEC onset; neutrophil counts on the day of NEC onset; and neutrophil, lymphocyte, and monocyte counts on day 1 after NEC onset. The nomogram exhibited good discrimination, with an area under the receiver operating characteristic curve of 0.884 (95% CI 0.825-0.943). The predictive model was well calibrated. Decision curve analysis confirmed the clinical usefulness of this nomogram.

CONCLUSION

A nomogram with a potentially effective application was developed to facilitate the individualized prediction of FNEC, with the hope of providing further direction for the early diagnosis of FNEC and timing of intervention.

摘要

背景

暴发性坏死性小肠结肠炎(FNEC)是最严重的 NEC 亚型,死亡率和后遗症发生率均较高。发病预测有助于制定个性化的治疗策略。本研究旨在开发和评估一种用于 FNEC 的预测列线图。

方法

我们进行了一项回顾性观察研究,以研究诊断为 NEC(Bell 分期≥IIB)的新生儿的临床数据。将新生儿分为 FNEC 组和 NEC 组。使用多变量逻辑回归模型构建列线图模型。通过曲线下面积、校准分析和决策曲线分析评估列线图的性能。

结果

共纳入 206 例新生儿病例,其中 40 例(19.4%)符合 FNEC 的定义。确定的预测因素包括:NEC 发病后辅助通气;NEC 发病时休克;NEC 发病前的喂养量;NEC 发病当天的中性粒细胞计数;以及 NEC 发病后第 1 天的中性粒细胞、淋巴细胞和单核细胞计数。该列线图具有良好的区分度,受试者工作特征曲线下面积为 0.884(95%CI 0.825-0.943)。预测模型具有良好的校准度。决策曲线分析证实了该列线图的临床实用性。

结论

开发了一种具有潜在有效应用的列线图,以促进对 FNEC 的个体化预测,希望为 FNEC 的早期诊断和干预时机提供进一步的指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c59c/10027821/aba4d9d60859/383_2023_5435_Fig1_HTML.jpg

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