开发并验证用于预测肝硬化重症监护室患者院内死亡率的提名图。

IF 2.5 Q2 GASTROENTEROLOGY & HEPATOLOGY
Xiao-Wei Tang, Wen-Sen Ren, Shu Huang, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü
{"title":"开发并验证用于预测肝硬化重症监护室患者院内死亡率的提名图。","authors":"Xiao-Wei Tang, Wen-Sen Ren, Shu Huang, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü","doi":"10.4254/wjh.v16.i4.625","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.</p><p><strong>Aim: </strong>To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.</p><p><strong>Methods: </strong>We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.</p><p><strong>Results: </strong>Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.</p><p><strong>Conclusion: </strong>The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.</p>","PeriodicalId":23687,"journal":{"name":"World Journal of Hepatology","volume":null,"pages":null},"PeriodicalIF":2.5000,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056901/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis.\",\"authors\":\"Xiao-Wei Tang, Wen-Sen Ren, Shu Huang, Kang Zou, Huan Xu, Xiao-Min Shi, Wei Zhang, Lei Shi, Mu-Han Lü\",\"doi\":\"10.4254/wjh.v16.i4.625\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.</p><p><strong>Aim: </strong>To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.</p><p><strong>Methods: </strong>We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.</p><p><strong>Results: </strong>Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.</p><p><strong>Conclusion: </strong>The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.</p>\",\"PeriodicalId\":23687,\"journal\":{\"name\":\"World Journal of Hepatology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2024-04-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11056901/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"World Journal of Hepatology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4254/wjh.v16.i4.625\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GASTROENTEROLOGY & HEPATOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"World Journal of Hepatology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4254/wjh.v16.i4.625","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GASTROENTEROLOGY & HEPATOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:入住重症监护病房(ICU)的肝硬化患者死亡率很高:目的:建立并验证预测重症监护病房肝硬化患者院内死亡率的提名图:方法:我们从重症监护医学信息市场IV(MIMIC-IV)和电子ICU(eICU)合作研究数据库(eICU-CRD)中提取了肝硬化患者的人口统计学、病因学、生命体征、实验室检查、合并症、并发症、治疗和严重程度评分数据。预测因子的选择和模型的建立基于 MIMIC-IV 数据集。通过最小绝对缩减和选择算子分析选出的变量通过多元回归分析进一步筛选,以获得最终的预测因子。最终预测因子被纳入多元逻辑回归模型,并用于构建提名图。最后,我们使用 eICU-CRD 进行了外部验证。接收者操作特征曲线下面积(AUC)、决策曲线和校准曲线用于评估模型的有效性:风险因素包括平均呼吸频率、平均收缩压、平均心率、白细胞、国际标准化比值、总胆红素、年龄、有创通气、使用血管加压器、急性肾损伤最大分期和器官功能衰竭顺序评估评分,这些因素都被纳入了多变量逻辑回归。该模型在 MIMIC-IV 和 eICU-CRD 数据库中的 AUC 分别为 0.864 和 0.808。校准曲线也证实了该模型的预测能力,而决策曲线则证实了其临床价值:结论:提名图在预测院内死亡率方面具有很高的准确性。结论:该提名图在预测院内死亡率方面具有很高的准确性,改进所包含的预测因子有助于改善患者的预后。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a nomogram for predicting in-hospital mortality of intensive care unit patients with liver cirrhosis.

Background: Liver cirrhosis patients admitted to intensive care unit (ICU) have a high mortality rate.

Aim: To establish and validate a nomogram for predicting in-hospital mortality of ICU patients with liver cirrhosis.

Methods: We extracted demographic, etiological, vital sign, laboratory test, comorbidity, complication, treatment, and severity score data of liver cirrhosis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic ICU (eICU) collaborative research database (eICU-CRD). Predictor selection and model building were based on the MIMIC-IV dataset. The variables selected through least absolute shrinkage and selection operator analysis were further screened through multivariate regression analysis to obtain final predictors. The final predictors were included in the multivariate logistic regression model, which was used to construct a nomogram. Finally, we conducted external validation using the eICU-CRD. The area under the receiver operating characteristic curve (AUC), decision curve, and calibration curve were used to assess the efficacy of the models.

Results: Risk factors, including the mean respiratory rate, mean systolic blood pressure, mean heart rate, white blood cells, international normalized ratio, total bilirubin, age, invasive ventilation, vasopressor use, maximum stage of acute kidney injury, and sequential organ failure assessment score, were included in the multivariate logistic regression. The model achieved AUCs of 0.864 and 0.808 in the MIMIC-IV and eICU-CRD databases, respectively. The calibration curve also confirmed the predictive ability of the model, while the decision curve confirmed its clinical value.

Conclusion: The nomogram has high accuracy in predicting in-hospital mortality. Improving the included predictors may help improve the prognosis of patients.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
World Journal of Hepatology
World Journal of Hepatology GASTROENTEROLOGY & HEPATOLOGY-
CiteScore
4.10
自引率
4.20%
发文量
172
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信