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新型冠状病毒肺炎患者的临床特征及死亡决定因素:印度南部一家三级医疗中心的回顾性分析横断面研究

Clinical Profile and Determinants of Mortality in Patients With COVID-19: A Retrospective Analytical Cross-Sectional Study in a Tertiary Care Center in South India.

作者信息

Kurien Surupa S, David Regi S, Chellappan Ajitha K, Varma Ravi P, Pillai Padmakumar R, Yadev Induprabha

机构信息

Department of Pathology, Government Medical College & Hospital, Thiruvananthapuram, IND.

Department of Internal Medicine, Government Medical College & Hospital, Thiruvananthapuram, IND.

出版信息

Cureus. 2022 Mar 12;14(3):e23103. doi: 10.7759/cureus.23103. eCollection 2022 Mar.

Abstract

Introduction The COVID-19 pandemic gained ground in India, starting from a few cases and spreading to the whole country; eventually becoming the second-most affected country worldwide. Here, we present the clinical and laboratory profile and the risk factors associated with mortality in COVID-19. The study comes from Kerala, a region that reported the first case in India. Kerala has the second-highest case burden in the country but also has managed to keep the case fatality rate down below the national average. Methodology This is a single-center retrospective cross-sectional study on 391 laboratory-confirmed COVID-19 positive inpatients between September 2020 and October 2020. Hematological parameters, coagulation parameters, liver function tests (LFT), and renal function tests (RFT) results were collected and compared among survivors and non-survivors to identify predictive biomarkers of mortality. Results The mean age of all patients was 53.2 years (SD 17.0). On bivariate analyses, the mean values of total leukocyte count (TLC), absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), D-dimer at admission, prothrombin time international normalized ratio (PT INR), blood urea nitrogen (BUN), and creatinine were significantly higher in non-survivors than in survivors: mean (SD) 11.9 (7.6) vs 7.5 (4.2) (x10/L), 10.5 (7.4) vs 5.3 (4.1) (x10/L), 11.6 (13.5) vs 3.4 (3.5), 185 (117) vs 48 (85) (mg/L), 829.4 (551.2) vs 323.6 (374.1) (ng/ml), 905.5 (589.1) vs 485.1 (353.9) (U/L), 4.01 (3.53) vs 1.29 (2.08) (µg/ml), 1.21 (0.42) vs 0.99 (0.18), 105.1 (91.4) vs 33.6 (31.0) (mg/dl), 3.6 (4.1) vs 1.1 (1.6) (mg/dl), respectively, p < 0.001. Absolute lymphocyte count, serum albumin, and albumin/globulin (A/G) ratio were lower in non-survivors than in survivors (mean (SD) 1.3 (1.0) vs 2.0 (0.9) (x10/L), p < 0.001; 3.0 (0.7) vs 3.8 (2.1) (g/dl), p 0.005; 0.9 (0.3) vs 1.2 (0.4), p < 0.001). Multivariate analysis identified ANC, D-dimer at admission, CRP, and BUN as independent prognostic factors associated with mortality. Conclusion Several accessible tests like TLC, ANC, NLR, and BUN can be used in low-resource settings to assess severity in patients with COVID-19. In addition, ANC, D-dimer at admission, CRP, and BUN can be used as independent predictors of in-patient mortality in COVID-19 patients in hospital settings.

摘要

引言

新冠疫情在印度蔓延,最初仅有少数病例,随后扩散至全国;最终印度成为全球受影响第二严重的国家。在此,我们呈现新冠患者的临床和实验室特征以及与死亡率相关的风险因素。本研究来自喀拉拉邦,该地区报告了印度的首例病例。喀拉拉邦是印度病例负担第二高的地区,但成功将病死率控制在全国平均水平以下。

方法

这是一项单中心回顾性横断面研究,研究对象为2020年9月至2020年10月期间391例实验室确诊的新冠住院患者。收集并比较幸存者和非幸存者的血液学参数、凝血参数、肝功能检查(LFT)和肾功能检查(RFT)结果,以确定死亡率的预测生物标志物。

结果

所有患者的平均年龄为53.2岁(标准差17.0)。在双变量分析中,非幸存者入院时的总白细胞计数(TLC)、绝对中性粒细胞计数(ANC)、中性粒细胞与淋巴细胞比值(NLR)、C反应蛋白(CRP)、铁蛋白、乳酸脱氢酶(LDH)、D-二聚体、凝血酶原时间国际标准化比值(PT INR)、血尿素氮(BUN)和肌酐的平均值显著高于幸存者:平均值(标准差)分别为11.9(7.6)对7.5(4.2)(×10⁹/L)、10.5(7.4)对5.3(4.1)(×10⁹/L)、11.6(13.5)对3.4(3.5)、185(117)对48(85)(mg/L)、829.4(551.2)对323.6(374.1)(ng/ml)、905.5(589.1)对485.1(353.9)(U/L)、4.01(3.53)对1.29(2.08)(μg/ml)、1.21(0.42)对0.99(0.18)、105.1(91.4)对33.6(31.0)(mg/dl)、3.6(4.1)对1.1(1.6)(mg/dl),p<0.001。非幸存者的绝对淋巴细胞计数、血清白蛋白和白蛋白/球蛋白(A/G)比值低于幸存者(平均值(标准差)分别为1.3(1.0)对2.0(0.9)(×10⁹/L),p<0.001;3.0(0.7)对3.8(2.1)(g/dl),p=0.005;0.9(0.3)对1.2(0.4),p<0.001)。多变量分析确定ANC、入院时的D-二聚体、CRP和BUN为与死亡率相关的独立预后因素。

结论

在资源匮乏的环境中,可使用一些易于获得的检查,如TLC、ANC、NLR和BUN来评估新冠患者的病情严重程度。此外,ANC、入院时的D-二聚体、CRP和BUN可作为医院环境中新冠患者住院死亡率的独立预测指标。

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