Clinical Characteristics and Predictors of Mortality for Young Adults with Severe COVID-19: A Retrospective Study

Yanjiao Lu Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Zhenli Huang Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Meijia Wang Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Kun Tang Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Shanshan Wang Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Pengfei Gao Henan University of Science and Technology A liated First Hospital Jungang Xie Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Department of Radiology Tao Wang Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology Department of Radiology Jianping Zhao (  zhaojp88@126.com ) Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology


Background
The newly emergent human severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID- 19), resulting in epidemics and pandemics. (1,2) As of April 18th 2020, SARS-CoV-2 has caused more than 2000000 infections and 100000 deaths worldwide. (3) Previous studies have focused on general epidemiological ndings, clinical presentations, and clinical outcomes of patients of COVID-19. (4,5) Accumulating studies have suggested that all ages people are susceptible to SARS-CoV-2 infection, which can result in severe and even fatal respiratory diseases. (6)(7)(8) As reported by Liu et al, clinical features of elderly patients with COVID-19 were signi cantly different from that of younger patients. (6) It has to be noted that elderly patients were with more comorbidities, leading to more complicated pathogenesis in  Would the pathogenesis of COVID-19 be different in young adults, with less comorbidities and more strong host immune? Indeed, there are plenty of severe COVID-19 cases in young adults. However, the characteristics and associated risk factors of non-elderly patients, especially with severe COVID-19, have not been fully elucidated so far. Furthermore, young people as the main social labor force, it is urgent to identify the risk factors associated with mortality of young adults in severe COVID-19.
We intended to investigate the clinical characteristics and provide predictors of mortality for young adults with severe COVID-19.

Study participants and data collection
For this retrospective, non-interventional study, a total of 376 patients with COVID-19 were recruited retrospectively at Tongji Hospital from January 25 to February 15, 2020, and 299 cases were excluded due to ineligible. According to nal outcome, we classi ed 77 young adults with severe COVID-19 into survivor group (37 patients) and non-survivor group (40 patients).
The study was performed in accordance with Tongji Hospital Ethics Committee (TJ-IRB20200353).
Written informed consent was waived by the Ethics Commission owing to the rapid emergence of this infectious disease.
We prospectively collected information of all patients including demographic data, clinical characteristics, laboratory ndings, treatment and outcomes from reviewing medical records. Two researchers individually reviewed the data collection forms and check the collected data. To investigate the risk of inhospital death, all patients were followed from admission to discharge or death (1 to 58 days). The primary outcome was in-hospital death de ned as the case fatality rate.

De nitions
The diagnosis of COVID-19 was established according to the de nition established by World Health Organization (WHO) interim guidance. (9) The clinical classi cations of patients as having severe or not COVID-19 are established based on the 2019 American Thoracic Society / Infectious Disease Society of America guideline, taking into account its global acceptance for severity strati cation of communityacquired pneumonia although lacking of validation in patients with viral pneumonia.(10) The young adults were de ned as people under age of 65 years old.

Primary variables selection in logistic regression model
Univariate and multivariate logistic regression were performed to make out the association of clinical characteristics and laboratory parameters for the risk of death. Taking the total deaths events (n = 40) of our study into account and to avoid over tting in multivariate logistic regression model, four factors were chosen for multivariable logistic analysis on the basis of previous results and clinical constraints. Original researches have shown plasma levels of d-dimer and high sensitivity cardiac troponin I (hs-CTnl) to be higher in severe or critical ill cases, whereas lymphopenia has been less observed in surviving or moderate ill patients with SARS-COV-2 infection. (11)(12)(13) Therefore, we chose lymphocyte count, d-dimer, hs-CTnl, and other variable as the four variables in our multivariable logistic regression model.
We ruled out variables from the multivariable analysis if the differences between-group were not signi cant, if the accuracy was not con rmed (eg, exposure, which was self-reported), if the number of incidences was too small to calculate odds ratios, and if they had collinearity.
According to the level of lymphocyte, d-dimer, hs-CTnI and high sensitivity C-reactive protein (hs-CRP), we classi ed of young adults with severe COVID-19 to subgroups. For each factor, cut points used to de ne a high level were as following: Lymphocyte <0.5 X10 9 /L, d-dimer >21μg/mL, hs-CTnI >15.6pg/ml and hs-CRP >100mg/L. High-risk group indicated elevation in two or more factors, while low-risk group indicated elevation in one or no factors.

Statistical Analysis
We described the categorical variables as frequency rates and percentages, and continuous variables median and interquartile range (IQR) values. Unpaired 2-sided Student's t test was used for continuous variables if the data were normally distributed; if not, Mann-Whitney test was used. The frequencies of categorical variables were compared using χ2 test or Fisher's exact test as appropriate.
All statistical analyses and graphs were generated and plotted using SPSS (version 22.0) and GraphPad Prism version 7.0 software (GraphPad Software Inc). The tests with p value less than 0.05 was considered statistically signi cant.

Results
Demographics and baseline characteristic of young adults with severe COVID-19 From 25 Jan 2020 to 15 Feb 2020, 376 patients were admitted to Tongji hospital with con rmed COVID-19, of whom 299 were considered ineligible. 77 young adults with severe COVID-19 were included in this study (Supplement Figure 1). Baseline characteristics of patients were divided into groups by survival or non-survival (Table 1). Different from all-age populations, there were no signi cant difference in age and sex among young adults with severe COVID-19 (Supplement table 1). Patients in non-survivor group were with faster heart rate than survival group. Other characteristics such as exposure history, smoking history, comorbidities, respiratory rate, percutaneous oxygen saturation, blood pressure showed no signi cance between two groups.  Table 1 Demographics and baseline characteristic of young adults with severe COVID-19. Similar to the results reported in previous studies, we pointed out that the top four symptoms included fever (94%), cough (77%), dyspnea (66%), fatigue (55%) in hospital among all-age population ( Table 1,  Supplement table 2). (1,11) Except for dyspnea that were more often present in non-survivor group than survivor group (83% vs. 49%), other symptoms were comparable in the two groups. But in all-age patients, incidence of unconscious and dizziness were higher in non-survivors than that of survivors.

Laboratory ndings
The non-survivors had more white blood cells and neutrophils counts than that of the survivors, may result from the presence of secondary bacterial infection as indicated by higher concentrations of hs-CRP and procalcitonin (Table 2, Supplement table 3). As expected, the non-survivors had reduced lymphocytes. Compared with survivors, those in non-survivor group underwent susceptible to abnormalities of liver, kidney and coagulation function, suggested by elevation of albumin or creatinine, and dysregulation of d-dimer. The non-survivors had experienced more frequently and severe heart injury, as all laboratory heart function parameters including hs-CTnl, myoglobin, and N-terminal pro-brain natriuretic peptide (NT-proBNP), were all signi cantly increased. The similar results had been shown in allage patients.   Table 2 Laboratory examinations of young adults with severe COVID-19.

Treatment and outcomes
More than half non-survivors experienced mechanical ventilation and ICU admission (  Treatment and outcomes of young adults with severe COVID-19.

Predictors of mortality
For all demographic data, clinical symptoms, and laboratory ndings shown in Table 1 and Table 2, we initially evaluated every variable that demonstrated statistical signi cance with p < 0.05 in difference between non-survivor and survivor groups using univariate logistic regression analysis (Table 4).Slightly different from all-age population, white blood cell, neutrophil granulocyte, lymphocyte, prothrombin time, d-dimer, albumin, direct-bilirubin, urea nitrogen, procalcitonin, hs-CRP, NT-proBNP and hs-CTnl were associated with the risk of mortality (Supplement Abbreviation: OR, odds ratio; PT, prothrombin time; hs-CRP, high sensitivity C-reactive protein; NT-proBNP, N-terminal pro-brain natriuretic peptide; hs-CTnI, hypersensitive cardiac troponin I. Table 4 Univariate logistic regression analysis of mortality risk factors for young adults with severe COVID-19.
Multivariate logistic regression analyses were applied to assess the independent prognostic effect of related factors ( Abbreviation: OR, odds ratio; PT, prothrombin time; hs-CRP, high sensitivity C-reactive protein; NT-proBNP, N-terminal pro-brain natriuretic peptide; CTnI, hypersensitive cardiac troponin I. Table 5 Multivariate logistic regression analysis of mortality risk factors for young adults with severe COVID-19. According to the level of four variables in mode 5, we classi ed of young adults with severe COVID-19 to low-risk and high-risk subgroups. The cumulative survival rate of low-risk group was much higher than that of high-risk group (Figure 2). The same predictive effect of four factors were shown in all-age participants with severe COVID-19.

Discussion
This retrospective study reported clinical characteristics and identi ed several predictors for mortality of young adults with severe COVID-19. In particular, lymphocyte count less than 0.5 x10 9 /L, d-dimer level greater than 21μg/mL, hs-CTnI degree higher than 15.6pg/ml and hs-CRP level higher than 100mg/L were correlated with higher odds of on-admission mortality. Furthermore, we con rmed the markedly reduction of survival probability during the course of disease in severely ill young patients with high risk.
Formerly, elder age has been announced as an independent predictor of mortality in COVID-19

Conclusions
In conclusion, lymphopenia, elevated level of d-dimer, hs-CTnI and hs-CRP were independent predictors of mortality in young adults with severe COVID-19. Earlier con rmation, more intensive observation and appropriate treatment should be considered in high-risk young patients.

Declarations
Ethics approval and consent to participate: The study was performed in accordance with Tongji Hospital Ethics Committee (TJ-IRB20200353). Written informed consent was waived by the Ethics Commission owing to the rapid emergence of this infectious disease.
YJL, ZLH, MJW, KT, SSW and PFG contributed equally to the study and shared rst authorship. JPZ and TW designed the study, had full access to all data in the study and take responsibility for the integrity of data and the accuracy of the data analysis. YJL, ZLH, MJW, KT, SSW and PFG contributed to patient recruitment, data collection, data analysis, data interpretation, literature search, and writing of the manuscript. All authors contributed to data acquisition, data analysis, or data interpretation, and reviewed and approved the nal version of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. JPZ is the guarantor.