AbstractResearch on the severity and prognosis of sepsis with or without progressive delirium is relatively insufficient. We constructed a prediction model of the risk factors for 28-day mortality in patients who developed sepsis or sepsis-associated delirium. The modeling group of patients diagnosed with Sepsis-3 and patients with progressive delirium of related indicators were selected from the MIMIC-IV database.

摘要关于脓毒症伴或不伴进行性谵妄的严重程度和预后的研究相对不足。我们构建了脓毒症或脓毒症相关deli妄患者28天死亡率危险因素的预测模型。从MIMIC-IV数据库中选择诊断为脓毒症-3的患者和相关指标进行性谵妄患者的建模组。

Relevant independent risk factors were determined and integrated into the prediction model. Receiver operating characteristic (ROC) curves and the Hosmer–Lemeshow (HL) test were used to evaluate the prediction accuracy and goodness-of-fit of the model. Relevant indicators of patients with sepsis or progressive delirium admitted to the intensive care unit (ICU) of a 3A hospital in Xinjiang were collected and included in the verification group for comparative analysis and clinical validation of the prediction model.

确定相关的独立风险因素并将其整合到预测模型中。受试者工作特征（ROC）曲线和Hosmer-Lemeshow（HL）检验用于评估模型的预测准确性和拟合优度。收集新疆某三甲医院重症监护病房（ICU）脓毒症或进行性谵妄患者的相关指标，并将其纳入验证组，对预测模型进行比较分析和临床验证。

The total length of stay in the ICU, hemoglobin levels, albumin levels, activated partial thrombin time, and total bilirubin level were the five independent risk factors in constructing a prediction model. The area under the ROC curve of the predictive model (0.904) and the HL test result (χ2 = 8.518) indicate a good fit.

ICU总住院时间，血红蛋白水平，白蛋白水平，活化部分凝血酶时间和总胆红素水平是构建预测模型的五个独立危险因素。预测模型的ROC曲线下面积（0.904）和HL检验结果（χ2=8.518）表明拟合良好。

This model is valuable for clinical diagnosis and treatment and auxiliary clinical decision-making..

该模型对临床诊断和治疗以及辅助临床决策具有重要价值。。

IntroductionSepsis is a life-threatening organ dysfunction caused by the dysregulated response of an organism to infection1. Sepsis and septic shock impact millions of people worldwide yearly, killing between one in three and one in six of those it affects. Sepsis-associated delirium (SAD) is a cerebral dysfunction that occurs during sepsis.

引言脓毒症是一种威胁生命的器官功能障碍，由生物体对感染的反应失调引起1。脓毒症和感染性休克每年影响全球数百万人，其中三分之一至六分之一的人死亡。脓毒症相关deli妄（SAD）是脓毒症期间发生的脑功能障碍。

SAD is among the most serious complications of sepsis and is an independent risk factor for death in patients with sepsis. Its pathophysiological mechanism is complex, with no standard, unified diagnosis or treatment method at present2. Early and accurate identification of high-risk patients and timely adoption of effective therapeutic measures are of great importance to improve patient prognosis.

SAD是脓毒症最严重的并发症之一，是脓毒症患者死亡的独立危险因素。其病理生理机制复杂，目前尚无标准，统一的诊断或治疗方法2。早期准确地识别高危患者并及时采取有效的治疗措施对改善患者预后至关重要。

Currently, the acute physiology and chronic health evaluation II (APACHE II) and sequential organ failure assessment (SOFA) scores are most often applied in clinical practice to assess the condition of patients with sepsis and to evaluate the physiological function of organs, respectively3. The most recent clinical practice guidelines on delirium from the Society of Critical Care Medicine recommend regular assessment of delirium using validated tools, such as the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) or the Intensive Care Delirium Screening Checklist2.

目前，急性生理学和慢性健康评估II（APACHE II）和序贯器官衰竭评估（SOFA）评分在临床实践中最常用于评估脓毒症患者的病情和评估器官的生理功能3。重症监护医学会最近关于deli妄的临床实践指南建议使用经过验证的工具定期评估deli妄，例如重症监护病房（CAM-ICU）的混淆评估方法或重症监护deli妄筛查清单2。

However, early assessment of SAD progress and prediction model sensitivity in patients with sepsis require further research. Risk prediction models have been widely applied in medical research to predict future onset. Although not an integral part of treatment, risk prediction is essential to improve clinical decision-making4.

然而，脓毒症患者SAD进展和预测模型敏感性的早期评估需要进一步研究。风险预测模型已广泛应用于医学研究中，以预测未来的发病。虽然不是治疗的组成部分，但风险预测对于改善临床决策至关重要4。

Moreover, risk prediction models are used to predict the likelihood or risk of the onset of a particular outcome or event in an individual .

此外，风险预测模型用于预测个体发生特定结果或事件的可能性或风险。

Data availability

数据可用性

The MIMIC-IV database is available from https://mimic.physionet.org. The raw data were extracted using structure query language (SQL) and PostgreSQL, as well as using Excel 2019 and IBM SPSS Statistics for Windows (version 26.0) for data entry and analysis, respectively.

MIMIC-IV数据库可从https://mimic.physionet.org.使用结构查询语言（SQL）和PostgreSQL提取原始数据，并分别使用Excel 2019和IBM SPSS Statistics for Windows（版本26.0）进行数据输入和分析。

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Download referencesFundingThis study was funded by the 2022 Critical Care Medicine Runze Fund of the Wu Jieping Medical Foundation (320.6750.2023–02-3).Author informationAuthors and AffiliationsXinjiang Medical University, Urumqi, 830000, ChinaLi Zhang, Yanjie Yang, Hu Peng & Ling YangSchool of Nursing, Xinjiang Medical University, Urumqi, 830000, ChinaLi Zhang, Yanjie Yang, Hu Peng & Ling YangDepartment of Nursing, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, ChinaLi ZhangCentre for Critical Care Medicine, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, Xinjiang, ChinaXiang Li & Xiangyou YuDepartment of Traumatology and Orthopaedics, the First Affiliated Hospital of Xinjiang Medical University, Urumqi, 830054, ChinaJinyong HuangAuthorsLi ZhangView author publicationsYou can also search for this author in.

下载参考文献资助本研究由吴洁平医学基金会2022年重症医学Runze基金资助（320.6750.2023–02-3）。作者信息作者和附属机构新疆医科大学，乌鲁木齐，830000，ChinaLi Zhang，Yanjie Yang，Hu Peng＆Ling Yang新疆医科大学护理学院，乌鲁木齐，830000，ChinaLi Zhang，Yanjie Yang，Hu Peng＆Ling Yang新疆医科大学第一附属医院护理科，乌鲁木齐，830054，ChinaLi Zhang新疆医科大学第一附属医院重症医学中心，乌鲁木齐，830054 ations您也可以在中搜索此作者。

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PubMed Google ScholarHu PengView作者出版物您也可以在

PubMed Google ScholarLing YangView author publicationsYou can also search for this author in

PubMed谷歌学术YangView作者出版物您也可以在

PubMed Google ScholarXiangyou YuView author publicationsYou can also search for this author in

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PubMed Google ScholarContributionsL.Z. designed, researched and wrote the manuscript, X.L. retrieved and collated the data, J.H. assisted in translating and collating the literature, Y.Y., H.P. and L.Y analyzed the statistical data, and X.Y. the supervisor, guided the overall research plan and process.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献l。Z、 设计，研究和撰写手稿，X.L.检索和整理数据，J.H.协助翻译和整理文献，Y.Y.，H.P.和L.Y分析统计数据，X.Y.主管指导整体研究计划和过程。对应作者对应。

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Reprints and permissionsAbout this articleCite this articleZhang, L., Li, X., Huang, J. et al. Predictive model of risk factors for 28-day mortality in patients with sepsis or sepsis-associated delirium based on the MIMIC-IV database.

转载和许可本文引用本文Zhang，L.，Li，X.，Huang，J。等人。基于MIMIC-IV数据库的脓毒症或脓毒症相关deli妄患者28天死亡率危险因素预测模型。

Sci Rep 14, 18751 (2024). https://doi.org/10.1038/s41598-024-69332-4Download citationReceived: 28 October 2023Accepted: 02 August 2024Published: 13 August 2024DOI: https://doi.org/10.1038/s41598-024-69332-4Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1418751（2024）。https://doi.org/10.1038/s41598-024-69332-4Download引文接收日期：2023年10月28日接收日期：2024年8月2日发布日期：2024年8月13日OI：https://doi.org/10.1038/s41598-024-69332-4Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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KeywordsSepsis with deliriumRisk factorsPrediction modelMIMIC- IV database

关键词谵妄风险因素预测模型Mimic-IV数据库

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