PDF(Pubmed)
Abstract：
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), led to high morbidity and mortality rates worldwide. It is known that some patients, initially hospitalized in general wards, deteriorate over time and require advanced respiratory support (ARS). This study aimed to identify key risk factors predicting the need for ARS in patients during the pandemic\'s early months.
METHODS: In this retrospective study, we included patients admitted within the first three months of the pandemic who were diagnosed with COVID-19 via reverse transcription polymerase chain reaction (RT-PCR). The patients who required ARS or invasive mechanical ventilation at admission were excluded. Data on demographics, comorbidities, symptoms, vital signs, and laboratory parameters were collected. Statistical analyses, including multivariate logistic regression and receiver operating characteristic (ROC) curve analysis, were performed to identify independent predictors of ARS and determine the cut-off point.
RESULTS: Among 162 patients, 32.1% required ARS. Key differences between ARS and non-ARS groups included age, body mass index (BMI), coronary artery disease prevalence, neutrophil count, C-reactive protein (CRP), ferritin, D-dimer, troponin T levels, neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation response index (SIRI), and symptom-to-admission time. Multivariate analysis revealed that age, elevated CRP levels, elevated ferritin levels, and SIRI were significant predictors for ARS. The ROC curve for SIRI showed an area under the curve (AUC) of 0.785, with a cut-off value of 1.915.
CONCLUSIONS: Age, CRP levels, ferritin levels, and SIRI are crucial predictors of the need for ARS in COVID-19 patients. The early identification of high-risk patients is essential for timely interventions and resource optimization, particularly during the early stages of pandemics. These insights may assist in optimizing strategies for future respiratory health crisis management.
METHODS: In this retrospective study, we included patients admitted within the first three months of the pandemic who were diagnosed with COVID-19 via reverse transcription polymerase chain reaction (RT-PCR). The patients who required ARS or invasive mechanical ventilation at admission were excluded. Data on demographics, comorbidities, symptoms, vital signs, and laboratory parameters were collected. Statistical analyses, including multivariate logistic regression and receiver operating characteristic (ROC) curve analysis, were performed to identify independent predictors of ARS and determine the cut-off point.
RESULTS: Among 162 patients, 32.1% required ARS. Key differences between ARS and non-ARS groups included age, body mass index (BMI), coronary artery disease prevalence, neutrophil count, C-reactive protein (CRP), ferritin, D-dimer, troponin T levels, neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation response index (SIRI), and symptom-to-admission time. Multivariate analysis revealed that age, elevated CRP levels, elevated ferritin levels, and SIRI were significant predictors for ARS. The ROC curve for SIRI showed an area under the curve (AUC) of 0.785, with a cut-off value of 1.915.
CONCLUSIONS: Age, CRP levels, ferritin levels, and SIRI are crucial predictors of the need for ARS in COVID-19 patients. The early identification of high-risk patients is essential for timely interventions and resource optimization, particularly during the early stages of pandemics. These insights may assist in optimizing strategies for future respiratory health crisis management.
摘要：
背景：2019年冠状病毒病（COVID-19）大流行，由严重急性呼吸综合征冠状病毒2（SARS-CoV-2）引起，导致了世界范围内的高发病率和高死亡率。众所周知，有些病人，最初在普通病房住院，随着时间的推移而恶化，需要高级呼吸支持（ARS）。本研究旨在确定预测大流行早期患者需要ARS的关键风险因素。
方法：在这项回顾性研究中，我们纳入了大流行前3个月内通过逆转录聚合酶链反应(RT-PCR)诊断为COVID-19的患者.入院时需要ARS或有创机械通气的患者被排除在外。人口统计数据，合并症,症状,生命体征,并收集实验室参数。统计分析，包括多变量逻辑回归和受试者工作特征(ROC)曲线分析，进行了识别ARS的独立预测因子并确定截止点。
结果：在162名患者中，32.1%需要ARS。ARS和非ARS组之间的主要差异包括年龄，体重指数（BMI），冠状动脉疾病患病率，中性粒细胞计数,C反应蛋白(CRP),铁蛋白,D-二聚体,肌钙蛋白T水平,中性粒细胞与淋巴细胞比率（NLR），全身免疫炎症反应指数（SIRI），和症状到入院时间。多变量分析表明，年龄，CRP水平升高，铁蛋白水平升高，和SIRI是ARS的重要预测因子。SIRI的ROC曲线显示曲线下面积(AUC)为0.785,截断值为1.915。
结论：年龄，CRP水平,铁蛋白水平,和SIRI是COVID-19患者需要ARS的关键预测因子。及早发现高危患者,对及时干预和优化资源至关重要,特别是在大流行的早期阶段。这些见解可能有助于优化未来呼吸健康危机管理策略。
方法：在这项回顾性研究中，我们纳入了大流行前3个月内通过逆转录聚合酶链反应(RT-PCR)诊断为COVID-19的患者.入院时需要ARS或有创机械通气的患者被排除在外。人口统计数据，合并症,症状,生命体征,并收集实验室参数。统计分析，包括多变量逻辑回归和受试者工作特征(ROC)曲线分析，进行了识别ARS的独立预测因子并确定截止点。
结果：在162名患者中，32.1%需要ARS。ARS和非ARS组之间的主要差异包括年龄，体重指数（BMI），冠状动脉疾病患病率，中性粒细胞计数,C反应蛋白(CRP),铁蛋白,D-二聚体,肌钙蛋白T水平,中性粒细胞与淋巴细胞比率（NLR），全身免疫炎症反应指数（SIRI），和症状到入院时间。多变量分析表明，年龄，CRP水平升高，铁蛋白水平升高，和SIRI是ARS的重要预测因子。SIRI的ROC曲线显示曲线下面积(AUC)为0.785,截断值为1.915。
结论：年龄，CRP水平,铁蛋白水平,和SIRI是COVID-19患者需要ARS的关键预测因子。及早发现高危患者,对及时干预和优化资源至关重要,特别是在大流行的早期阶段。这些见解可能有助于优化未来呼吸健康危机管理策略。
