Prediction of high-flow nasal cannula failure in patients with sepsis by a preliminary risk stratification model based on the ROX index.

IF 3.2 3区 医学 Q2 INFECTIOUS DISEASES
Yuanwen Ye, Feifei Li, Liangen Lin, Linglong Chen, Baohua Yang, Xiaowu Wei
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引用次数: 0

Abstract

Background: High-Flow Nasal Cannula (HFNC) carries the risk of delayed intubation and invasive mechanical ventilation (IMV), often associated with adverse outcomes. Therefore, it is crucial to predict the risk of HFNC failure as early as possible.

Objective: To explore the value of the Respiratory Rate-Oxygenation (ROX) index and a Preliminary Risk Stratification Model based on the ROX Index for prediction of HFNC failure in patients with sepsis.

Methods: This retrospective study was based on the Medical Information Mart for Intensive Care (MIMIC) database. Independent risk factors for HFNC failure were explored using Least Absolute Shrinkage and Selection Operator (LASSO) and multivariable logistic regression analyses, with discrimination shown using Receiver Operating Characteristic Curves (ROC). To evaluate the discriminatory ability of the ROX index at different time points, we performed Landmark Analysis. The relationship between the ROX index and the risk of HFNC failure was confirmed through logistic proportional hazards model. Kaplan-Meier (K-M) curves were employed to illustrate the association between the ROX index and the likelihood of HFNC failure. Additional subgroup analyses were conducted to further substantiate the robustness of the findings. Finally, a Preliminary risk stratification model for predicting HFNC failure was established based on the ROX index, with ROC curves used to demonstrate its discriminatory power.

Results: A total of 316 sepsis patients who met the criteria were included, of whom 116 patients failed HFNC and required intubation and IMV. LASSO and multivariable logistic analyses demonstrated the ROX index as an independent risk factor for HFNC failure, with an area under the ROC curve of 0.721 (0.658-0.784) after 12 h of HFNC use, indicating good predictive value. Logistic proportional hazards model and K-M analysis demonstrated that patients in the low ROX group (based on the established cutoff value) exhibited a significantly elevated risk of failure of HFNC therapy. This finding was consistent with the results of subgroup analyses. Based on the ROX index, a Preliminary risk stratification model with a total score of approximately 16 points was established by combining Glasgow Coma Scale (GCS), Body Mass Index (BMI), and flow rate, and the area under the ROC curve was 0.801 (0.7516-0.8509), indicating better predictive value.

Conclusion: The ROX index has good predictive value for HFNC failure in sepsis patients, and the Preliminary risk stratification model established based on the ROX index shows better predictive potential, but requires prospective external validation.

基于ROX指数的初步风险分层模型预测脓毒症患者高流量鼻插管失败
背景:高流量鼻插管(HFNC)具有延迟插管和有创机械通气(IMV)的风险,通常与不良后果相关。因此,尽早预测HFNC的失效风险至关重要。目的:探讨呼吸速率-氧合(ROX)指数及基于ROX指数的初步风险分层模型对脓毒症患者HFNC衰竭的预测价值。方法:本回顾性研究基于重症监护医学信息市场(MIMIC)数据库。使用最小绝对收缩和选择算子(LASSO)和多变量逻辑回归分析探讨HFNC失败的独立危险因素,并使用受试者工作特征曲线(ROC)显示区别。为了评估ROX指数在不同时间点的区分能力,我们进行了里程碑分析。通过logistic比例风险模型验证ROX指数与HFNC失效风险之间的关系。Kaplan-Meier (K-M)曲线用于说明ROX指数与HFNC失败可能性之间的关系。进行了额外的亚组分析以进一步证实研究结果的稳健性。最后,基于ROX指数建立了预测HFNC失效的初步风险分层模型,并利用ROC曲线表明其判别能力。结果:共纳入316例符合标准的脓毒症患者,其中116例HFNC失败,需要插管和IMV。LASSO和多变量logistic分析表明,ROX指数是HFNC失效的独立危险因素,使用12 h后ROC曲线下面积为0.721(0.658-0.784),具有较好的预测价值。Logistic比例风险模型和K-M分析表明,低ROX组(基于已建立的临界值)患者HFNC治疗失败的风险显著升高。这一发现与亚组分析的结果一致。以ROX指数为基础,结合Glasgow Coma Scale (GCS)、Body Mass index (BMI)、flow rate建立总分约为16分的初步风险分层模型,ROC曲线下面积为0.801(0.7516-0.8509),具有较好的预测价值。结论:ROX指数对败血症患者HFNC衰竭有较好的预测价值,基于ROX指数建立的初步风险分层模型具有较好的预测潜力,但需要前瞻性的外部验证。
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来源期刊
BMC Infectious Diseases
BMC Infectious Diseases 医学-传染病学
CiteScore
6.50
自引率
0.00%
发文量
860
审稿时长
3.3 months
期刊介绍: BMC Infectious Diseases is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of infectious and sexually transmitted diseases in humans, as well as related molecular genetics, pathophysiology, and epidemiology.
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