Literature DB >> 32417316

Dyspnea rather than fever is a risk factor for predicting mortality in patients with COVID-19.

Li Shi1, Ying Wang1, Yadong Wang2, Guangcai Duan1, Haiyan Yang3.   

Abstract

Entities:  

Keywords:  COVID-19; Dyspnea; Fever; Mortality; Risk factor

Mesh:

Year:  2020        PMID: 32417316      PMCID: PMC7228739          DOI: 10.1016/j.jinf.2020.05.013

Source DB:  PubMed          Journal:  J Infect        ISSN: 0163-4453            Impact factor:   6.072


× No keyword cloud information.
Dear Editor, Recently, the paper titled “Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis” was published in the Journal of Infection in April 2020. The results from Zheng et al. indicated that fever was negatively associated with the progression of COVID-19 such as severe illness and death (OR = 0.56, 95% CI [0.38–0.82], P = 0.003) and shortness of breath/dyspnea was positively associated with the progression of COVID-19 such as severe illness and death (OR = 4.16, 95% CI [3.13–5.53], P < 0.00001), which suggests that COVID-19 patients with fever may have a lower risk to develop to severe and critical disease outcomes and COVID-19 patients with dyspnea may have a higher risk to develop to severe and critical disease outcomes. However, Fu et al. observed that there was no statistically significant association between fever or shortness of breath and the severity of patients with COVID-19. To unambiguously identify the risk factors for predicting mortality in patients with COVID-19, we carry out a meta-analysis to evaluate whether fever and dyspnea (not included shortness of breath) were associated with the risk of mortality in COVID-19 patients. This meta-analysis was carried out based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline. Li Shi and Ying Wang systematically searched the electronic databases, including Web of Science, Chinese National Knowledge Infrastructure (CNKI) and PubMed. These search engines were utilized to capture available literature by using the following three groups of keywords: “coronavirus 2019, 2019-nCoV, SARS-CoV-2, COVID-19″, “outcome, mortality” and “clinical”. The last search was conducted on May 4, 2020. Only articles reporting the number of COVID-19 patients with clinical symptoms of fever or dyspnea in the survival group and non-survival group were identified as eligible articles. All calculations were implemented with Stata 11.2 software. The pooled odds ratio (OR) with the corresponding 95% confidence interval (CI) was used to evaluate the risk of mortality in COVID-19 patients with fever or dyspnea. The robustness of the results was appraised by performing a sensitivity analysis. Both Begg's test and Egger's test were applied to evaluate publication bias. , After selecting 1589 articles, 15 articles were finally obtained for this meta-analysis. As displayed in Table 1 , data on 2851 COVID-19 patients (2114 survivors and 737 non-survivors) were available in these articles. The sample size ranged from 27 to 663. Most of the articles were performed in China, with the exception of one in the UK.
Table 1

Characteristics of the included studies.

AuthorLocationCaseNon-survival patients
Survival patients
nAge, yearsMaleFeverDyspneanAge, yearsMaleFeverDyspnea
Yang X et al. P: 32105632China523264.6 ± 11.221 (65.6)31 (96.9)21 (65.6)2051.9 ± 12.914 (70.0)20 (100.0)12 (60.0)
Zhou F et al. P: 32171076China1915469.0 (63.0–76.0)38 (70.4)51 (94.4)NR13752.0 (45.0–58.0)81 (59.1)129 (94.2)NR
Cao J et al. P: 32239127China1021772 (63–81)13 (76.5)12 (70.6)NR8553 (47–66)40 (47.1)61 (71.8)NR
Ruan Q et al. P: 32253449China1506867 (15–81)49 (72.1)59 (86.8)59 (86.8)8250 (44–81)53 (64.6)68 (82.9)51 (62.2)
Deng Y et al. P: 32209890China22510969 (62–74)73 (67.0)95 (87.2)77 (70.6)11640 (33–57)51 (44.0)94 (81.0)22 (19.0)
Zhang J et al. P: 32304745China6632567.1 (61–78)15 (60.0)19 (76.0)11 (44.0)63859.1 (43–68)306 (48.0)508 (79.6)150 (23.5)
Wu C et al. P: 32167524China844468.5 (59.3–75.0)29 (65.9)39 (88.6)29 (65.9)4050.0 (40.3–56.8)31 (77.5)39 (97.5)21 (52.5)
Chen T et al. P: 32217556China27411368.0 (62.0–77.0)83 (73.5)104 (92.0)70 (61.9)16151.0 (37.0–66.0)88 (54.7)145 (90.0)50 (31.1)
Wang L et al. P: 32240670China3396576 (70–83)39 (60.0)56 (87.5)38 (59.4)27468 (64–74)127 (46.4)255 (93.4)100 (36.6)
Yuan M et al. P: 32191764China271068 (63–73)4 (40.0)6 (60.0)10 (100.0)1755 (35–60)8 (47.1)15 (88.2)1 (5.9)
Leung C et al. P: 32353398China1548975 (67–81)53 (59.6)44 (67.7)*25 (40.3)*6568 (66–74)36 (55.4)58 (90.6)*4 (6.7)*
Wang D et al. P: 32354360China1071973.0 (64.0–81.0)16 (84.2)19 (100.0)15 (78.9)8844.5 (35.0–58.8)41 (46.6)85 (96.6)20 (22.7)
Yan Y et al. P: 32345579China483970.5 ± 10.130 (76.9)36 (92.3)30 (76.9)964.7 ± 7.33 (33.3)7 (77.8)3 (33.3)
Wang K et al. P: 32361723China2961965.6 ± 12.611 (57.9)10 (52.6)NR27746.0 ± 14.4129 (46.6)203 (74.9)*NR
441469.0 ± 13.410 (71.4)12 (100.0)*NR3048.8 ± 14.214 (46.7)27 (90.0)NR
Tomlins J et al. P: 32353384UK952077 (72–85)12 (60.0)12 (60.0)NR7574 (56–82)48 (64.0)56 (74.7)NR

All values are n (%), median (IQR), or mean±SD. P, PMID.

data missing for patients; NR, not reported.

Characteristics of the included studies. All values are n (%), median (IQR), or mean±SD. P, PMID. data missing for patients; NR, not reported. We found that dyspnea was significantly associated with higher mortality in COVID-19 patients on the basis of 11 studies with 2091 cases (OR = 4.34, 95% CI [2.68–7.05], P < 0.001; I = 69.2%, P < 0.001, random-effects model) (Fig. 1 A). However, we did not observe a significant association between fever and the risk of mortality in patients with COVID-19 on the basis of 15 studies with 2818 cases (OR = 0.74, 95% CI [0.50–1.09], P = 0.127; I = 38.0%, P = 0.062, random-effects model) (Fig. 1B). As presented in sensitivity analysis, none of the individual studies significantly effected the overall OR, which proved the robustness of our results (Figs. 1C and D). No evidence of publication bias was provided by Begg's test (dyspnea: P = 0.350 and fever: P = 0.964, respectively) and Egger's test (dyspnea: P = 0.294 and fever: P = 0.854, respectively).
Fig. 1

The pooled odds ratio (OR) with the corresponding 95% confidence interval (CI) on the relationship between dyspnea (A) and fever (B) and the risk of mortality in COVID-19 patients. Sensitivity analysis for evaluating the relationship between dyspnea (C) and fever (D) and the risk of mortality in COVID-19 patients.

The pooled odds ratio (OR) with the corresponding 95% confidence interval (CI) on the relationship between dyspnea (A) and fever (B) and the risk of mortality in COVID-19 patients. Sensitivity analysis for evaluating the relationship between dyspnea (C) and fever (D) and the risk of mortality in COVID-19 patients. To our knowledge, the most common clinical symptoms were fever, cough, fatigue and dyspnea in COVID-19 patients.5., 6., 7. Zheng et al. demonstrated that the proportion of fever was significantly lower in critical/mortal group compared with the non-critical group, which suggests that fever may protect COVID-19 patients from developing to severe and critical disease outcomes. Fu et al. reported that the prevalence of fever in critical group was slightly higher than that in the non-severe group (80.8%, 95% CI [41.1–100.0]) vs. (71.2%, 95% CI [23.8–99.9]), but the difference was not statistically significant. Our present study showed that fever was not significantly associated with the risk of mortality in COVID-19 patients. In addition, our study suggested that dyspnea was positively associated with the risk of mortality in COVID-19 patients. Taken together, dyspnea, rather than fever, is recommended as an indicator of poor outcome in COVID-19 patients, further well-designed studies with larger sample sizes are needed to validate the findings of our current study.

Contributors

LS, YDW, and HYY designed the study. LS and YW screened the literature and extracted the data. LS performed the meta-analysis and wrote the manuscript. HYY, YDW and GCD provided guidance and reviewed the manuscript. All authors have read and agreed the final manuscript.

Declaration of Competing Interest

All authors report that they have no potential conflict of interest.
  7 in total

1.  Bias in meta-analysis detected by a simple, graphical test.

Authors:  M Egger; G Davey Smith; M Schneider; C Minder
Journal:  BMJ       Date:  1997-09-13

2.  Operating characteristics of a rank correlation test for publication bias.

Authors:  C B Begg; M Mazumdar
Journal:  Biometrics       Date:  1994-12       Impact factor: 2.571

3.  Prevalence and severity of corona virus disease 2019 (COVID-19): A systematic review and meta-analysis.

Authors:  Yong Hu; Jiazhong Sun; Zhe Dai; Haohua Deng; Xin Li; Qi Huang; Yuwen Wu; Li Sun; Yancheng Xu
Journal:  J Clin Virol       Date:  2020-04-14       Impact factor: 3.168

4.  Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis.

Authors:  Alfonso J Rodriguez-Morales; Jaime A Cardona-Ospina; Estefanía Gutiérrez-Ocampo; Rhuvi Villamizar-Peña; Yeimer Holguin-Rivera; Juan Pablo Escalera-Antezana; Lucia Elena Alvarado-Arnez; D Katterine Bonilla-Aldana; Carlos Franco-Paredes; Andrés F Henao-Martinez; Alberto Paniz-Mondolfi; Guillermo J Lagos-Grisales; Eduardo Ramírez-Vallejo; Jose A Suárez; Lysien I Zambrano; Wilmer E Villamil-Gómez; Graciela J Balbin-Ramon; Ali A Rabaan; Harapan Harapan; Kuldeep Dhama; Hiroshi Nishiura; Hiromitsu Kataoka; Tauseef Ahmad; Ranjit Sah
Journal:  Travel Med Infect Dis       Date:  2020-03-13       Impact factor: 6.211

5.  Clinical characteristics of hospitalized patients with SARS-CoV-2 infection: A single arm meta-analysis.

Authors:  Pengfei Sun; Shuyan Qie; Zongjian Liu; Jizhen Ren; Kun Li; Jianing Xi
Journal:  J Med Virol       Date:  2020-03-11       Impact factor: 20.693

6.  Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis.

Authors:  Zhaohai Zheng; Fang Peng; Buyun Xu; Jingjing Zhao; Huahua Liu; Jiahao Peng; Qingsong Li; Chongfu Jiang; Yan Zhou; Shuqing Liu; Chunji Ye; Peng Zhang; Yangbo Xing; Hangyuan Guo; Weiliang Tang
Journal:  J Infect       Date:  2020-04-23       Impact factor: 6.072

7.  Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis.

Authors:  Leiwen Fu; Bingyi Wang; Tanwei Yuan; Xiaoting Chen; Yunlong Ao; Thomas Fitzpatrick; Peiyang Li; Yiguo Zhou; Yi-Fan Lin; Qibin Duan; Ganfeng Luo; Song Fan; Yong Lu; Anping Feng; Yuewei Zhan; Bowen Liang; Weiping Cai; Lin Zhang; Xiangjun Du; Linghua Li; Yuelong Shu; Huachun Zou
Journal:  J Infect       Date:  2020-04-10       Impact factor: 6.072

  7 in total
  19 in total

1.  Comparison of COVID-19 characteristics in Egyptian patients according to their Toll-Like Receptor-4 (Asp299Gly) polymorphism.

Authors:  Sara I Taha; Aalaa K Shata; Eman M El-Sehsah; Manar F Mohamed; Nouran M Moustafa; Mariam K Youssef
Journal:  Infez Med       Date:  2022-03-01

2.  Failing the frail: The need to broaden the COVID-19 case definition for geriatric patients.

Authors:  Clare Hunt; Flora Olcott; George Williams; Terrence Chan
Journal:  Clin Med (Lond)       Date:  2021-10-12       Impact factor: 2.659

3.  Characteristics, Outcomes and Indicators of Severity for COVID-19 Among Sample of ESNA Quarantine Hospital's Patients, Egypt: A Retrospective Study.

Authors:  Ali A Ghweil; Mohammed H Hassan; Ashraf Khodeary; Ahmed Okasha Mohamed; Haggagy Mansour Mohammed; Ahmed Alyan Abdelazez; Heba Ahmed Osman; Shamardan Ezzeldin S Bazeed
Journal:  Infect Drug Resist       Date:  2020-07-17       Impact factor: 4.003

4.  Risk factors for predicting mortality of COVID-19 patients: A systematic review and meta-analysis.

Authors:  Lan Yang; Jing Jin; Wenxin Luo; Yuncui Gan; Bojiang Chen; Weimin Li
Journal:  PLoS One       Date:  2020-11-30       Impact factor: 3.240

5.  Development and validation of prognostic scoring system for COVID-19 severity in South India.

Authors:  Vishnu Shankar; Pearlsy Grace Rajan; Yuvaraj Krishnamoorthy; Damal Kandadai Sriram; Melvin George; S Melina I Sahay; B Jagan Nathan
Journal:  Ir J Med Sci       Date:  2022-01-07       Impact factor: 2.089

Review 6.  Association of HScore Parameters with Severe COVID-19: A Systematic Review and Meta-Analysis.

Authors:  Mohammad Hossein Kazemi; Bentolhoda Kuhestani Dehaghi; Elham Roshandel; Hossein Bonakchi; Sayeh Parkhideh; Mahshid Mehdizadeh; Abbas Hajifathali
Journal:  Iran J Med Sci       Date:  2021-09

7.  Role of targeted therapies in rheumatic patients on COVID-19 outcomes: results from the COVIDSER study.

Authors:  Jose María Álvaro Gracia; Carlos Sanchez-Piedra; Javier Manero; María Ester Ruiz-Lucea; Laura López-Vives; Cristina Bohorquez; Julia Martinez-Barrio; Gema Bonilla; Paloma Vela; María Jesús García-Villanueva; María Teresa Navío-Marco; Marina Pavía; María Galindo; Celia Erausquin; Miguel A Gonzalez-Gay; Inigo Rua-Figueroa; Jose M Pego-Reigosa; Isabel Castrejon; Jesús T Sanchez-Costa; Enrique González-Dávila; Federico Diaz-Gonzalez
Journal:  RMD Open       Date:  2021-12

8.  Obesity in patients with COVID-19: a systematic review and meta-analysis.

Authors:  Yi Huang; Yao Lu; Yan-Mei Huang; Min Wang; Wei Ling; Yi Sui; Hai-Lu Zhao
Journal:  Metabolism       Date:  2020-09-28       Impact factor: 8.694

9.  Description, Health Care Utilization, and Outcomes for Home Health Care (HHC) COVID-19 Patients Early in the Pandemic: A Comparison to the General HHC Population.

Authors:  Tami M Videon; Robert J Rosati; Steven H Landers
Journal:  Home Health Care Manag Pract       Date:  2021-11

10.  Clinical characteristics and clinical predictors of mortality in hospitalised patients of COVID 19 : An Indian study.

Authors:  K V Padmaprakash; Vasu Vardhan; Sandeep Thareja; J Muthukrishnan; Nishant Raman; Kuldeep Kumar Ashta; Sandeep Rana; Kislay Kishore; Dheeraj Nauhwaar
Journal:  Med J Armed Forces India       Date:  2021-07-26
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.