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老年失代偿期肝硬化患者自发性细菌性腹膜炎的预测因素及列线图

Predictive Factors and Nomogram for Spontaneous Bacterial Peritonitis in Decompensated Cirrhosis Among the Elderly.

作者信息

Yan Fang, Peng Xiaoxia, Yang Xingyao, Yuan Li, Zheng Xiaomei, Yang Yongxue

机构信息

Geriatric Diseases Institute of Chengdu, Department of Geriatrics, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, People's Republic of China.

Center for Medicine Research and Translation, Chengdu Fifth People's Hospital (The Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine), Chengdu, People's Republic of China.

出版信息

J Inflamm Res. 2024 Dec 12;17:10901-10911. doi: 10.2147/JIR.S484629. eCollection 2024.

DOI:10.2147/JIR.S484629
PMID:39687773
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11648538/
Abstract

BACKGROUND AND AIMS

Spontaneous bacterial peritonitis (SBP) represents a significant complication in the decompensated phase of cirrhosis. The challenges in treating SBP and the associated mortality rates are markedly elevated in elderly individuals. Timely detection and intervention for SBP are imperative. We aimed to develop a predictive tool for the occurrence of SBP in elderly individuals with decompensated cirrhosis (DC).

METHODS

Elderly patients diagnosed with DC were enrolled from Chengdu Fifth People's Hospital in China, spanning from January 1, 2015, to September 31, 2023. Among the patients, 337 were assigned to the training cohort, while 145 were designated to the validation cohort. A multivariate logistic regression analysis was performed to identify significant predictors and to develop a nomogram for predicting the occurrence of SBP. To evaluate the model's discrimination and calibration, a bootstrap method with 1000 resamples was utilized.

RESULTS

Findings from the multivariate logistic regression analysis indicated that constipation (odds ratio [OR] 2.09, 95% confidence interval [CI] 1.25-3.49, =0.005), ascites (OR 2.84, 95% CI 1.64-4.92, <0.001), Child-Pugh-Turcotte (CPT) score (OR 4.80, 95% CI 1.69-13.60, =0.003), and high sensitivity C-reactive protein (hs-CRP) (OR 2.96, 95% CI 1.54-5.45, =0.001) were significant independent predictors for the occurrence of SBP in elderly individuals with DC. The generated nomogram showed an area under the curve of 0.779 for the training cohort and 0.817 for the validation cohort. The nomogram's calibration curve nearly matched the perfect diagonal line, and decision curve analysis showed an improved net benefit for the model. Subsequent validation further corroborated the reliability of the predictive nomogram.

CONCLUSION

In conclusion, the nomogram, incorporating variables such as constipation, ascites, CPT score, and hs-CRP, effectively predicted the occurrence of SBP in elderly patients with DC, underscoring its substantial clinical applicability.

摘要

背景与目的

自发性细菌性腹膜炎(SBP)是肝硬化失代偿期的一种严重并发症。在老年患者中,治疗SBP的挑战及相关死亡率显著升高。及时检测和干预SBP至关重要。我们旨在开发一种预测工具,用于预测失代偿期肝硬化(DC)老年患者发生SBP的风险。

方法

选取2015年1月1日至2023年9月31日在中国成都市第五人民医院确诊为DC的老年患者。其中,337例患者被分配到训练队列,145例患者被分配到验证队列。进行多因素逻辑回归分析,以确定显著的预测因素,并制定预测SBP发生的列线图。为评估模型的辨别力和校准度,采用了1000次重采样的自助法。

结果

多因素逻辑回归分析结果表明,便秘(比值比[OR] 2.09,95%置信区间[CI] 1.25 - 3.49,P = 0.005)、腹水(OR 2.84,95% CI 1.64 - 4.92,P < 0.001)、Child-Pugh-Turcotte(CPT)评分(OR 4.80,95% CI 1.69 - 13.60,P = 0.003)和高敏C反应蛋白(hs-CRP)(OR 2.96,95% CI 1.54 - 5.45,P = 0.001)是DC老年患者发生SBP的显著独立预测因素。生成的列线图在训练队列中的曲线下面积为0.779,在验证队列中的曲线下面积为0.817。列线图的校准曲线几乎与理想对角线匹配,决策曲线分析显示该模型的净效益有所提高。后续验证进一步证实了预测列线图的可靠性。

结论

总之,该列线图纳入了便秘、腹水、CPT评分和hs-CRP等变量,有效预测了DC老年患者发生SBP的风险,突出了其显著的临床适用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/f2d1d5938e24/JIR-17-10901-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/c1f487060044/JIR-17-10901-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/b76efd132924/JIR-17-10901-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/1af7a4b38899/JIR-17-10901-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/f2d1d5938e24/JIR-17-10901-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/c1f487060044/JIR-17-10901-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/b76efd132924/JIR-17-10901-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/1af7a4b38899/JIR-17-10901-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c334/11648538/f2d1d5938e24/JIR-17-10901-g0004.jpg

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