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分析与 2019 年新型冠状病毒病住院患者疾病结局相关的因素。

Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus disease.

机构信息

Department of Respiratory and Critical Care Medicine, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China.

Department of Respiratory Intensive Care Unit, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei 430014, China.

出版信息

Chin Med J (Engl). 2020 May 5;133(9):1032-1038. doi: 10.1097/CM9.0000000000000775.

Abstract

BACKGROUND

Since early December 2019, the 2019 novel coronavirus disease (COVID-19) has caused pneumonia epidemic in Wuhan, Hubei province of China. This study aimed to investigate the factors affecting the progression of pneumonia in COVID-19 patients. Associated results will be used to evaluate the prognosis and to find the optimal treatment regimens for COVID-19 pneumonia.

METHODS

Patients tested positive for the COVID-19 based on nucleic acid detection were included in this study. Patients were admitted to 3 tertiary hospitals in Wuhan between December 30, 2019, and January 15, 2020. Individual data, laboratory indices, imaging characteristics, and clinical data were collected, and statistical analysis was performed. Based on clinical typing results, the patients were divided into a progression group or an improvement/stabilization group. Continuous variables were analyzed using independent samples t-test or Mann-Whitney U test. Categorical variables were analyzed using Chi-squared test or Fisher's exact test. Logistic regression analysis was performed to explore the risk factors for disease progression.

RESULTS

Seventy-eight patients with COVID-19-induced pneumonia met the inclusion criteria and were included in this study. Efficacy evaluation at 2 weeks after hospitalization indicated that 11 patients (14.1%) had deteriorated, and 67 patients (85.9%) had improved/stabilized. The patients in the progression group were significantly older than those in the disease improvement/stabilization group (66 [51, 70] vs. 37 [32, 41] years, U = 4.932, P = 0.001). The progression group had a significantly higher proportion of patients with a history of smoking than the improvement/stabilization group (27.3% vs. 3.0%, χ = 9.291, P = 0.018). For all the 78 patients, fever was the most common initial symptom, and the maximum body temperature at admission was significantly higher in the progression group than in the improvement/stabilization group (38.2 [37.8, 38.6] vs. 37.5 [37.0, 38.4]°C, U = 2.057, P = 0.027). Moreover, the proportion of patients with respiratory failure (54.5% vs. 20.9%, χ = 5.611, P = 0.028) and respiratory rate (34 [18, 48] vs. 24 [16, 60] breaths/min, U = 4.030, P = 0.004) were significantly higher in the progression group than in the improvement/stabilization group. C-reactive protein was significantly elevated in the progression group compared to the improvement/stabilization group (38.9 [14.3, 64.8] vs. 10.6 [1.9, 33.1] mg/L, U = 1.315, P = 0.024). Albumin was significantly lower in the progression group than in the improvement/stabilization group (36.62 ± 6.60 vs. 41.27 ± 4.55 g/L, U = 2.843, P = 0.006). Patients in the progression group were more likely to receive high-level respiratory support than in the improvement/stabilization group (χ = 16.01, P = 0.001). Multivariate logistic analysis indicated that age (odds ratio [OR], 8.546; 95% confidence interval [CI]: 1.628-44.864; P = 0.011), history of smoking (OR, 14.285; 95% CI: 1.577-25.000; P = 0.018), maximum body temperature at admission (OR, 8.999; 95% CI: 1.036-78.147, P = 0.046), respiratory failure (OR, 8.772, 95% CI: 1.942-40.000; P = 0.016), albumin (OR, 7.353, 95% CI: 1.098-50.000; P = 0.003), and C-reactive protein (OR, 10.530; 95% CI: 1.224-34.701, P = 0.028) were risk factors for disease progression.

CONCLUSIONS

Several factors that led to the progression of COVID-19 pneumonia were identified, including age, history of smoking, maximum body temperature at admission, respiratory failure, albumin, and C-reactive protein. These results can be used to further enhance the ability of management of COVID-19 pneumonia.

摘要

背景

自 2019 年 12 月初以来,新型冠状病毒疾病(COVID-19)已在中国湖北省武汉市引发肺炎疫情。本研究旨在探讨影响 COVID-19 患者肺炎进展的因素。相关结果将用于评估预后,并寻找 COVID-19 肺炎的最佳治疗方案。

方法

纳入基于核酸检测结果呈 COVID-19 阳性的患者。患者于 2019 年 12 月 30 日至 2019 年 1 月 15 日期间被收入武汉的 3 家三级医院。收集个体数据、实验室指标、影像学特征和临床数据,并进行统计分析。根据临床分型结果,将患者分为进展组或改善/稳定组。连续变量采用独立样本 t 检验或 Mann-Whitney U 检验进行分析。分类变量采用卡方检验或 Fisher 确切检验进行分析。采用 logistic 回归分析探讨疾病进展的危险因素。

结果

符合纳入标准的 78 例 COVID-19 诱导性肺炎患者纳入本研究。住院 2 周后的疗效评估显示,11 例(14.1%)患者病情恶化,67 例(85.9%)患者改善/稳定。进展组患者的年龄明显大于改善/稳定组(66[51,70]岁 vs. 37[32,41]岁,U=4.932,P=0.001)。进展组有吸烟史的患者比例明显高于改善/稳定组(27.3% vs. 3.0%,χ²=9.291,P=0.018)。所有 78 例患者中,发热是最常见的初始症状,进展组患者入院时的最高体温明显高于改善/稳定组(38.2[37.8,38.6]℃ vs. 37.5[37.0,38.4]℃,U=2.057,P=0.027)。此外,进展组呼吸衰竭(54.5% vs. 20.9%,χ²=5.611,P=0.028)和呼吸频率(34[18,48]次/min vs. 24[16,60]次/min,U=4.030,P=0.004)的比例明显高于改善/稳定组。与改善/稳定组相比,进展组的 C 反应蛋白明显升高(38.9[14.3,64.8]mg/L vs. 10.6[1.9,33.1]mg/L,U=1.315,P=0.024)。与改善/稳定组相比,进展组的白蛋白明显降低(36.62±6.60g/L vs. 41.27±4.55g/L,U=2.843,P=0.006)。进展组患者比改善/稳定组更有可能接受高级别呼吸支持(χ²=16.01,P=0.001)。多变量 logistic 分析表明,年龄(比值比[OR],8.546;95%置信区间[CI]:1.628-44.864;P=0.011)、吸烟史(OR,14.285;95%CI:1.577-25.000;P=0.018)、入院时最高体温(OR,8.999;95%CI:1.036-78.147,P=0.046)、呼吸衰竭(OR,8.772,95%CI:1.942-40.000;P=0.016)、白蛋白(OR,7.353,95%CI:1.098-50.000;P=0.003)和 C 反应蛋白(OR,10.530;95%CI:1.224-34.701,P=0.028)是疾病进展的危险因素。

结论

确定了导致 COVID-19 肺炎进展的几个因素,包括年龄、吸烟史、入院时最高体温、呼吸衰竭、白蛋白和 C 反应蛋白。这些结果可用于进一步提高 COVID-19 肺炎的管理能力。

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本文引用的文献

1
Identification of a novel coronavirus causing severe pneumonia in human: a descriptive study.
Chin Med J (Engl). 2020 May 5;133(9):1015-1024. doi: 10.1097/CM9.0000000000000722.
2
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.
Lancet. 2020 Feb 15;395(10223):497-506. doi: 10.1016/S0140-6736(20)30183-5. Epub 2020 Jan 24.
3
A Novel Coronavirus from Patients with Pneumonia in China, 2019.
N Engl J Med. 2020 Feb 20;382(8):727-733. doi: 10.1056/NEJMoa2001017. Epub 2020 Jan 24.
4
Emerging coronaviruses: Genome structure, replication, and pathogenesis.
J Med Virol. 2020 Apr;92(4):418-423. doi: 10.1002/jmv.25681. Epub 2020 Feb 7.
6
Recent advances in the detection of respiratory virus infection in humans.
J Med Virol. 2020 Apr;92(4):408-417. doi: 10.1002/jmv.25674. Epub 2020 Feb 4.
8
Severe Acute Respiratory Syndrome: Historical, Epidemiologic, and Clinical Features.
Infect Dis Clin North Am. 2019 Dec;33(4):869-889. doi: 10.1016/j.idc.2019.07.001.
9
Complemented Palindromic Small RNAs First Discovered from SARS Coronavirus.
Genes (Basel). 2018 Sep 5;9(9):442. doi: 10.3390/genes9090442.
10
Predictive factors of depressive symptoms of elderly patients with cancer receiving first-line chemotherapy.
Psychooncology. 2017 Jan;26(1):15-21. doi: 10.1002/pon.4090. Epub 2016 Feb 23.

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