Prognostic Factors for Predicting COVID-19 Severity and Mortality

Q3 Medicine
A. Jahangirimehr, Azam Honarmandpour, Azam Khalighi, Marzieh Najafi, M. Kalantar, Elham Abdolahi Shahvali, A. Hemmatipour, Sahel Heydarheydari
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引用次数: 1

Abstract

Background: COVID-19 has become a serious health problem worldwide. Objectives: The current study investigated the prognostic factors associated with demographical parameters, clinical and vital signs, and laboratory results for predicting severity and mortality in patients infected with COVID-19. Methods: This retrospective analysis was conducted on the medical records of 372 COVID-19-positive patients hospitalized at the Khatam al-Anbiya Hospital, Shoushtar, Iran, from Sep 2020 to Sep 2021. The association of demographic parameters, clinical and vital signs, and laboratory results with severity and patients' outcomes (survival/mortality) was studied. The patients were divided into the non-severe group (n = 275) and the severe group (n = 97). COVID-19 disease severity was determined based on the severity of pulmonary involvement using CT chest images. The collected data were analyzed using IBM SPSS software for Windows (version 18). Logistic regression analysis was employed using the Forward LR method to predict COVID-19 severity and mortality. Results: The rates of mortality and the severe form of the disease were 87.1% (n = 324) and 12.9% (n = 48), respectively. A prognostic value was observed in predicting COVID-19 severity and mortality for some clinical and vital signs (diabetes (P < 0.001, P = 0.019), hypertension (P = 0.024, P = 0.012), pulmonary diseases (P = 0.038, P < 0.001), and anosmia (P = 0.043, P = 0.044) and paraclinical parameters (FBS (P = 0.014, P = 0.045), BUN (P = 0.045, 0.001), Cr (P = 0.027, P = 0.047), Neut (P = 0.002, P = 0.005), and SpO2 (P = 0.014, P = 0.001)). Cardiovascular disorders (P = 0.037), fever (P = 0.008), and dyspnea (P = 0.020) were also effective at predicting disease-related mortality. Multiple logistic regression analyses showed that diabetes disease, the place of residence, PCO2, and BUN with R2 = 0.18, and age, pulmonary diseases, and BUN with R2 = 0.21 were involved in predicting the severity and mortality, respectively. Conclusions: It seems that in addition to the BUN, diabetes and pulmonary diseases play a more significant role in predicting the severity and mortality due to COVID-19, respectively.
预测COVID-19严重程度和死亡率的预后因素
背景:COVID-19已成为全球严重的健康问题。目的:本研究探讨与人口统计学参数、临床和生命体征以及实验室结果相关的预测COVID-19患者严重程度和死亡率的预后因素。方法:回顾性分析2020年9月至2021年9月在伊朗舒什塔尔Khatam al-Anbiya医院住院的372例covid -19阳性患者的病历。研究了人口统计学参数、临床和生命体征以及实验室结果与严重程度和患者结局(生存/死亡率)的关系。将患者分为非重症组(n = 275)和重症组(n = 97)。根据CT胸部图像肺部受累程度确定COVID-19疾病严重程度。使用IBM SPSS for Windows (version 18)软件对收集的数据进行分析。采用Forward LR方法进行Logistic回归分析,预测COVID-19严重程度和死亡率。结果:本组病死率为87.1% (n = 324),重症为12.9% (n = 48)。观察预后价值预测COVID-19严重程度和死亡率对一些临床和生命体征(糖尿病(P < 0.001, P = 0.019),高血压(P = 0.024, P = 0.012),肺疾病(P = 0.038, P < 0.001),和嗅觉缺失症(P = 0.043, P = 0.044)和paraclinical参数(的边后卫(P = 0.014, P = 0.045),面包(P = 0.045, 0.001), Cr (P = 0.027, P = 0.047),中性粒细胞(P = 0.002, P = 0.005),和动脉血氧饱和度(P = 0.014, P = 0.001)。心血管疾病(P = 0.037)、发热(P = 0.008)和呼吸困难(P = 0.020)也是预测疾病相关死亡率的有效指标。多元logistic回归分析显示,糖尿病、居住地、PCO2、BUN (R2 = 0.18)和年龄、肺部疾病、BUN (R2 = 0.21)分别与病情严重程度和死亡率相关。结论:除BUN外,糖尿病和肺部疾病在预测COVID-19严重程度和死亡率方面的作用更为显著。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Shiraz E Medical Journal
Shiraz E Medical Journal Medicine-Medicine (all)
CiteScore
1.00
自引率
0.00%
发文量
63
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