急性胆源性胰腺炎持续器官功能衰竭动态提名图的建立与验证:一项回顾性研究。

IF 4.2 2区 医学 Q2 IMMUNOLOGY Journal of Inflammation Research Pub Date : 2024-11-08 eCollection Date: 2024-01-01 DOI:10.2147/JIR.S489044
Kaier Gu, Qianchun Wang
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引用次数: 0

摘要

目的:本研究的目的是利用入院 24 小时内观察到的指标,创建急性胆源性胰腺炎(ABP)患者发生持续性器官衰竭(POF)的预测模型。早期发现高危 POF 患者对临床决策至关重要:收集温州医科大学附属第一医院2016年1月1日至2024年1月1日期间确诊的ABP患者入院24小时内的临床数据和实验室指标,并进行回顾性分析。采用最小绝对收缩和选择操作数(LASSO)回归法和多元逻辑回归(逐步回归)法来确定构建预测模型的变量。利用曲线下面积(AUC)、校准曲线和决策曲线分析(DCA)对预测模型的性能进行了评估。并与其他评分系统进行了比较,如 SIRS、BISAP、APACHE II、CTSI 和 MCTSI。此外,还创建了一个基于网络的计算器,以简化计算过程:在 324 例 ABP 患者中,有 25 例出现 POF。初步筛选确定了 18 个变量;通过 LASSO 回归和多变量逻辑回归分析,BMI、Hb、ALB、Ca 和 LIP 等五个变量被确定为 POF 的独立预测因子。根据这些因素建立预测模型,绘制提名图。与其他评分系统相比,AUC 的接收者操作特征曲线分析表明其值明显更高。校准曲线和 DCA 表明,建立的模型预测 POF 的准确性更高,临床决策的净效益也更高。利用该预测模型开发了一个网络计算器:结论:已建立的预测模型包含五个风险指标,具有很高的鉴别力和准确性,有助于早期识别有发生 POF 风险的 ABP 患者。这对指导临床决策具有重要价值。
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Establishment and Validation of a Dynamic Nomogram for Persistent Organ Failure in Acute Biliary Pancreatitis: A Retrospective Study.

Purpose: The objective of this study was to create a predictive model for the onset of persistent organ failure (POF) in individuals suffering from acute biliary pancreatitis (ABP) by utilizing indicators observed within 24 hours of hospital admission. Early detection of high-risk POF patients is crucial for clinical decision-making.

Patients and methods: Clinical data and laboratory indicators within 24 hours of admission from ABP patients diagnosed at The First Affiliated Hospital of Wenzhou Medical University between January 1, 2016, and January 1, 2024 were collected and retrospectively analyzed. The Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression (stepwise regression) methods were employed to identify variables for constructing the prediction model. The prediction model's performance was evaluated using the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). It was compared with other scoring systems such as SIRS, BISAP, APACHE II, CTSI, and MCTSI. Additionally, a web-based calculator was created to simplify the calculation process.

Results: Out of 324 ABP patients, 25 developed POF. Initial screening identified 18 variables; through LASSO regression and multivariable logistic regression analysis, five variables including BMI, Hb, ALB, Ca, and LIP were determined as independent predictors of POF. According to these factors to build prediction model, draw the nomogram. The AUC's receiver operating characteristic curve analysis demonstrated a significantly higher value in comparison to other scoring systems. Calibration curve and DCA show that the established model to predict the accuracy of POF is higher, clinical decision of net benefit is also higher. A network calculator utilizing this predictive model was developed.

Conclusion: A predictive model incorporating five risk indicators has been established exhibiting high discriminatory power and accuracy which aids in early identification of ABP patients at risk for developing POF. This holds significant value in guiding clinical decision-making.

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来源期刊
Journal of Inflammation Research
Journal of Inflammation Research Immunology and Microbiology-Immunology
CiteScore
6.10
自引率
2.20%
发文量
658
审稿时长
16 weeks
期刊介绍: An international, peer-reviewed, open access, online journal that welcomes laboratory and clinical findings on the molecular basis, cell biology and pharmacology of inflammation.
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