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重症新型冠状病毒肺炎中真菌感染相关情况及结局:一项全国性病例对照研究

Fungal infection-related conditions and outcomes in severe COVID-19: a nationwide case-control study.

作者信息

Maeshima Katsuya, Yamamoto Ryo, Matsumura Kazuki, Kaito Daiki, Homma Koichiro, Yamakawa Kazuma, Tagami Takashi, Hayakawa Mineji, Ogura Takayuki, Hirayama Atsushi, Yasunaga Hideo, Sasaki Junichi

机构信息

Department of Emergency and Critical Care Medicine, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.

Department of Emergency and Critical Care Medicine, Osaka Medical and Pharmaceutical University, Osaka, Japan.

出版信息

BMC Infect Dis. 2024 Dec 18;24(1):1435. doi: 10.1186/s12879-024-10317-z.

Abstract

BACKGROUND

Fungal infections are significant complications of severe coronavirus disease 2019 (COVID-19). Although various risk factors for poor outcomes in patients with COVID-19 have been identified, clinical and treatment factors associated with fungal infections in patients with severe COVID-19 remain unclear. This study aimed to elucidate clinical factors associated with fungal infections during severe COVID-19 treatment.

METHODS

This was a post hoc analysis of the J-RECOVER study, a multicenter retrospective observational study involving patients with COVID-19 who required admission at 66 hospitals between January and September 2020. Inclusion criteria were ages ≥ 18 years, COVID-19 diagnosis with reverse-transcription polymerase chain reaction, and treatment with mechanical ventilation (MV). Patients who received antifungal drugs before MV were excluded. Potential predictors were identified through univariate analysis of patient and treatment characteristics between patients with- and those without fungal infection, which was defined as antifungal agent use for ≥ 5 days. To account for facility-specific data clustering, generalized estimating equations (GEE) were employed as adjusted analyses to calculate the relative risks of potentially associated factors. Two sensitivity analyses were performed with modified definitions for the two groups: patients who received antifungal drugs for ≤ 4 days were excluded, and fungal infection was re-defined as antifungal drug use for ≥ 14 days.

RESULTS

Among 4,915 patients in the J-RECOVER study, 559 adults with COVID-19 who required MV were included. Fungal infections occurred in 57 (10.2%) patients. Univariate analyses identified age, age ≥ 65 years, D-dimer level, remdesivir use, steroid use, and duration of steroid therapy as potential predictors of fungal infections. Multivariate analysis using GEE on these six factors revealed that only the duration of steroid use was significantly associated with an increased risk of fungal infection (odds ratio [OR] for a day increase: 1.01; 95% confidence interval [CI]: 1.00-1.01; p < 0.001). The two sensitivity analyses similarly showed that the duration of steroid use was associated with fungal infection (odds ratio for a day increase: 1.01; 95% CI: 1.00-1.01; p < 0.001 for both).

CONCLUSIONS

In patients with severe COVID-19 requiring MV, each additional day of steroid use was associated with prolonged use of antifungal medications for ≥ 5 days.

摘要

背景

真菌感染是2019年冠状病毒病(COVID-19)严重病例的重要并发症。尽管已确定COVID-19患者预后不良的各种风险因素,但重症COVID-19患者中与真菌感染相关的临床和治疗因素仍不清楚。本研究旨在阐明重症COVID-19治疗期间与真菌感染相关的临床因素。

方法

这是一项对J-RECOVER研究的事后分析,J-RECOVER研究是一项多中心回顾性观察性研究,纳入了2020年1月至9月期间在66家医院需要住院治疗的COVID-19患者。纳入标准为年龄≥18岁、经逆转录聚合酶链反应确诊为COVID-19且接受机械通气(MV)治疗。在MV治疗前接受抗真菌药物治疗的患者被排除。通过对有真菌感染和无真菌感染患者的患者及治疗特征进行单因素分析来确定潜在预测因素,真菌感染定义为使用抗真菌药物≥5天。为了考虑特定机构的数据聚类情况,采用广义估计方程(GEE)进行校正分析,以计算潜在相关因素的相对风险。对两组采用修改后的定义进行了两项敏感性分析:排除接受抗真菌药物治疗≤4天的患者,并将真菌感染重新定义为使用抗真菌药物≥14天。

结果

在J-RECOVER研究的4915例患者中,纳入了559例需要MV的成年COVID-19患者。57例(10.2%)患者发生真菌感染。单因素分析确定年龄、年龄≥65岁、D-二聚体水平、使用瑞德西韦、使用类固醇以及类固醇治疗持续时间为真菌感染的潜在预测因素。对这六个因素使用GEE进行多变量分析显示,只有类固醇使用持续时间与真菌感染风险增加显著相关(每天增加的比值比[OR]:1.01;95%置信区间[CI]:1.00-1.01;p<0.001)。两项敏感性分析同样显示,类固醇使用持续时间与真菌感染相关(每天增加的比值比:1.01;95%CI:1.00-1.01;两项分析的p均<0.001)。

结论

在需要MV的重症COVID-19患者中,类固醇使用每增加一天,抗真菌药物使用≥5天的时间就会延长。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ccaa/11653589/3d7fe202ba10/12879_2024_10317_Fig1_HTML.jpg

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