Progression of Severity in Coronavirus Disease 2019 Patients Before Treatment and a Self-Assessment Scale to Predict Disease Severity

Progression of Severity in Coronavirus Disease 2019 Patients Before Treatment and a Self-Assessment Scale to Predict Disease Severity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Progression of Severity in Coronavirus Disease 2019 Patients Before Treatment and a Self-Assessment Scale to Predict Disease Severity Ye Yao, Jie Tian, Xia Meng, Haidong Kan, Lian Zhou, Weibing Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-96164/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: With the pandemic of Coronavirus Disease 2019, differing case-fatality rates and limited resources have led to adoption of separate management strategies for severe and nonsevere disease. For patients in quarantine in the community, self-assessment of COVID-19 severity risk can guide appropriate medical consultation. Methods: Data from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. T-test and chi-square test were used to investigate the associations of various patient characteristics with disease severity, and multivariable logistic regression models identified strongly correlated variables for inclusion in the scale. Scale accuracy was assessed using receiver operating characteristic analysis. A least absolute shrinkage and selection operator regression cross-validated prediction accuracy. Results: Twelve scale items - age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage - were identified and showed good predictive ability (area under the curve =0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (=0·71). Conclusions: Our COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Health Economics & Outcomes Research Coronavirus Disease 2019 Self-Assessment Scale Severity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Summary Our study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. Introduction Coronavirus Disease 19 (COVID-19) has formed a worldwide pandemic[ 1 ], and its clinical spectrum of disease ranges from mild to critical illness. Most COVID-19 patients present with mild symptoms, such as fever and cough, but a small proportion of patients develop severe pneumonia with progression to life-threatening complications, including acute respiratory distress syndrome, multi-organ failure, and death[ 2 ]. Consequently, the case-fatality rate differs widely between patients with severe and nonsevere disease. According to the most comprehensive report from the Chinese Centre for Disease Control and Prevention, which reviewed 72,314 cases, the average COVID-19 case-fatality rate is 2.3%, but it is as high as 49% in patients with critical illness[ 3 ]. In some countries, such as the United States[ 4 ], the Republic of Korea[ 5 ], and Scotland, differing case-fatality rates and limited resources have led to the adoption of separate management strategies for severe and nonsevere disease. Particularly in regions with high case volume, the need to conserve the number of available intensive care unit beds and ventilators has dictated home quarantine for patients with nonsevere disease, with reserving hospitalization for patients with severe disease[ 6 – 8 ]; however, the patient who suddenly deteriorates while inappropriately quarantined at home will suffer delay in treatment and in turn, worsened prognosis. For patients in quarantine in community, accurate COVID-19 severity self-assessment can guide appropriate and timely medical consultation. During this pandemic, biological and clinical predictors of COVID-19 infection severity will also assist in the judicious allocation of limited resources. Previous research has shown that clinical characteristics, including chronic disease, lymphopenia, and elevated D-dimer level, are associated with the severity of COVID-19[ 9 – 15 ], but most of these studies used univariate analysis with comparatively complicated laboratory parameters. A study among hospitalized adults identified through the U.S. COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) showed that increasing age, male sex, and underlying conditions were associated with higher risk of ICU admission and death[ 16 ], but it failed to include mild patients who were not hospitalized. Wynants et al.[ 17 ] and Urwin et al.[ 18 ] presented information available at that time on prediction models on prognosis of COVID-19, but those models were limited in small sample size and rated at high risk of bias resulted in probably optimistic. Further evidence needs to emerge around the validity of these scores. This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings. Methods Data Sources and Processing COVID-19 patient data for the period January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System, in Wuhan, Hubei Province. The data was obtained by investigations conducted by epidemiology professionals after the patient was diagnosed. The inclusion criterion was a confirmed COVID-19 diagnosis by positive high-throughput sequencing or reverse-transcription polymerase chain reaction assay of nasal and pharyngeal swab specimens. Outcome Measurement The Chinese Diagnosis and Treatment Protocol for COVID-19[ 19 ] defines four levels of COVID-19 disease: mild, ordinary, severe, and critical illness; additionally, asymptomatic infection is recognized. We categorized COVID-19 disease severity, based on these definitions, as “nonsevere” (which grouped asymptomatic, mild, and ordinary disease) versus “severe” (grouping severe and critical illness). Potential Predictive Variables The study variables included demographic factors (age, gender); present/past medical history (illness duration, quarantine status, and presence of hypertension, pulmonary disease, diabetes mellitus, cardio/cerebrovascular disease, chronic liver disease, and chronic kidney disease); clinical symptoms; blood test parameters (white blood cell count, lymphocyte count and percentage, and neutrophil percentage); and imaging findings (abnormal chest computed tomography scan). Statistical Analysis In all, 36 variables were considered as potential predictors of COVID-19 disease severity. T-test and chi-square test were used to compare the differences of each variable in patients with severe and nonsevere disease. The variables that showed significant association with disease severity were then included into logistic regression models for multivariable analysis, to confirm their candidacy for inclusion in the new prediction scale (COVID-19 Severity Self-Assessment Scale). In the logistic modelling, we used the following formulae to calculate the probability and 95% confidence intervals (CIs)[ 20 ]. probability = lower limit of 95%CI = upper limit of 95%CI = Receiver-operator characteristic (ROC) analysis was performed and the area under the curve (AUC) calculated to verify the accuracy of the final prediction scale. We extracted p-values for AUC by conducting permutation analyses. To avoid the influence of potential collinearity among the variables, least absolute shrinkage and selection operator (LASSO) regression analysis was also performed. The statistical analysis was performed using MATLAB software, version 2019b (MathWorks Inc). In this study, a p-value less than 0·01 is statistically significant. Results Demographic Characteristics Data were collected from 45,450 patients. The study population had a mean (standard deviation [SD]) age of 53·44 (16·38) years, and 21,689 (47·7%) patients were men. The mean (SD) illness duration was 10·40 (7·90) days (see Figure E1 in the supplementary online data for the distribution). Among all the patients, 7,798 (17·2%) were considered to have severe disease and 37,652 (82·8%) to have nonsevere disease. Accordingly, 37,654 (82·9%) patients were quarantined at home. Figure 1 shows the distribution of disease severity by age and gender. Both age (r > 0·91, p 0·69, p < 0·0001) correlated positively with disease severity, as seen in Fig. 2 , and this was unaffected by gender (men and women held similar trend with age and illness duration as showed in Figure E2). Patients with severe disease had a mean (SD) age of 60·85 (15·28) years and illness duration of 12·55 (7·93) days after symptom onset, compared with patients with nonsevere disease, who had a mean (SD) age of 51·90 (16·17) years and illness duration of 9·95 (7·82) days (t = 44·85, p < 0·0001 for age; t = 26·62, p < 0·0001 for illness duration). Quarantine rate did not differ significantly according to illness severity (χ 2 = 0·17, p = 0·682) (Table 1 ). Table 1 Demographic Characteristics of the Sample Severe, mean (sd) / n (%) (n = 7798) Nonsevere, mean (sd) / n (%) (n = 37652) t / χ 2 P value Age (years) 60·85 (15·28) 51·90 (16·17) t = 44·85 < 0·0001** Gender (male) 3908 (50·1%) 17781 (47·2%) χ 2 = 21·64 < 0·0001** Illness Duration (days) 12·55 (7·93) 9·95 (7·82) t = 26·62 < 0·0001** Quarantine (yes) 6448 (82·7%) 31206 (82·9%) χ 2 = 0·17 0·682 Abbreviation: sd, standard deviation **: p < 0·0001. Clinical Symptoms and Comorbidities Clinical manifestations were recorded for 4,984 patients and yielded 20 clinical symptoms (Table 2 ); among these, the incidence of dyspnea (χ 2 = 24·56, p < 0·0001) and shortness of breath (defined as clinical evidence of altered breathing) (χ 2 = 62·67, p < 0·0001) differed significantly between severe and nonsevere patients. Among 1,326 patients with severe disease, 225 (17·0%) had dyspnea and 296 (22·3%) had shortness of breath; conversely, among the 3,658 patients with nonsevere disease, 425 (11·6%) had dyspnea and 480 (13·1%) had shortness of breath. Comorbid conditions were recorded for 5,062 patients and were found in a higher proportion of patients with severe versus nonsevere disease (Table 3 ). Additionally, the number of comorbidities showed significant association with the severity of COVID-19 (t = 7·96, p 15·14, p < 0·0001). Notably, there was no significant difference in the prevalence of chronic liver (χ 2 = 0·38, p = 0·538) or kidney disease (χ 2 = 2·00, p = 0·157) between patients with severe and nonsevere disease. Table 2 Reported Symptoms of the Participants Symptoms Severe, n (%) (n = 1326) Nonsevere, n (%) (n = 3658) χ 2 P value Fever 1110 (83·7) 2951 (80·7) 5·95 0·015 Vomiting 72 (5·4) 160 (4·4) 2·44 0·118 Dyspnea 225 (17·0) 425 (11·6) 24·56 < 0·0001** Shortness of Breath † 296 (22·3) 480 (13·1) 62·67 < 0·0001** Expectoration 289 (21·8) 715 (19·5) 3·06 0·080 Sore Throat 55 (4·1) 229 (6·3) 8·08 0·004* Headache 129 (9·7) 463 (12·7) 7·97 0·005* Chills 126 (9·5) 372 (10·2) 0·48 0·488 Dry Cough 580 (43·7) 1661 (45·4) 1·09 0·296 Nausea 36 (2·7) 100 (2·7) 0·00 0·971 Runny Nose 19 (1·4) 101 (2·8) 7·31 0·007* Conjunctival Hyperemia 3 (0·2) 7 (0·2) 0·06 0·808 Muscle Soreness 237 (17·9) 658 (18·0) 0·01 0·926 Chest Pain 22 (1·7) 99 (2·7) 4·51 0·034 Chest Tightness 229 (17·3) 551 (15·1) 3·59 0·058 Diarrhea 127 (9·6) 362 (9·9) 0·11 0·738 Abdominal Pain 3 (0·2) 14 (0·4) 0·70 0·402 Nasal Congestion 23 (1·7) 74 (2·0) 0·42 0·515 Fatigue 537 (40·5) 1335 (36·5) 6·65 0·010* Joint Soreness 60 (4·5) 224 (6·1) 4·63 0·031 † clinical evidence of altered breathing *: p < 0·01; **: p < 0·0001. Table 3 Reported Comorbidities of the Participants Comorbidities Severe, n (%) / mean (sd) (n = 1339) Nonsevere, n (%) / mean(sd) (n = 3723) t / χ 2 P value Hypertension 306 (22·9%) 599 (16·1%) χ 2 = 30·69 < 0·0001** Pulmonary Disease 51 (3·8%) 71 (1·9%) χ 2 = 15·14 < 0·0001** Diabetes Mellitus 147 (11·0%) 239 (6·4%) χ 2 = 29·06 < 0·0001** Cardio-cerebrovascular Disease 126 (9·4%) 214 (5·7%) χ 2 = 21·08 < 0·0001** Chronic Liver Disease 14 (1·0%) 32 (0·9%) χ 2 = 0·38 0·538 Chronic Kidney Disease 17 (1·3%) 31 (0·8%) χ 2 = 2·00 0·157 No. of Comorbidities † 0·50 (0·80) 0·32 (0·66) t = 7·96 < 0·0001** † total number of comorbid conditions, including hypertension, pulmonary disease, diabetes mellitus, cardio/cerebrovascular disease, chronic liver disease, and chronic kidney disease, for each subject **: p < 0·0001. Laboratory and Imaging Results Laboratory results were recorded for 2,471 patients. The percentages of neutrophils and lymphocytes were significantly different in patients with severe versus nonsevere disease: in patients with severe disease, neutrophils were higher (t = − 7·53, p < 0·0001) and lymphocytes were lower (t = 4·67, p < 0·0001; Table 4 ). A total of 3,438 patients underwent computed tomography examination, revealing abnormalities in 90·5% of patients with severe disease and 92·2% of patients with nonsevere disease—a nonsignificant difference (χ 2 = 2·16, p = 0·142). Table 4 Reported Laboratory Results of the Participants Indexes Severe, mean(sd) (n = 605) Nonsevere, mean(sd) (n = 1866) t P value WBC (White Blood Cell Count) 5·69 (3·07) 5·37 (2·87) 2·38 0·018 Lx (Lymphocyte Count) 1·33 (3·47) 1·96 (6·19) -2·39 0·017 L (Lymphocyte Percentage, %) 20·21 (12·74) 24·90 (13·50) -7·53 < 0·0001** N (Neutrophil Percentage, %) 67·17 (21·24) 62·90 (18·94) 4·67 < 0·0001** **: p < 0·0001. Predictor Selection Among the 36 variables analyzed, 12 showed statistical differences between the groups of patients with severe and nonsevere disease, indicating their potential predictive value: gender, age, illness duration, dyspnea, shortness of breath, hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage. Of these, four were strong predictors of severe disease (Table 5 ): age (odds ratio [OR] = 1·03; 95%CI: 1·02–1·04; p < 0·0001), illness duration (OR = 1·08; 95%CI: 1·06–1·10; p < 0·0001), shortness of breath (OR = 1·64; 95%CI: 1·26–2·13; p = 0·0002), and lymphocyte percentage (OR = 0·98; 95%CI: 0·97–0·99; p < 0·0001). Table 5 Logistic Regression Model for the Prediction of Severe Disease Variables Odds Ratio 95% CI t P value Age 1·03 1·02,1·04 7·28, < 0·0001** Gender 1·30 1·06,1·58 2·55 0·011 Illness Duration 1·08 1·06,1·10 9·12 < 0·0001** Dyspnea 1·18 0·87,1·61 1·07 0·287 Shortness of Breath † 1·64 1·26,2·13 3·69 0·0002* Hypertension 0·86 0·45,1·67 -0·43 0·664 Pulmonary Disease 1·67 0·73,3·81 1·23 0·220 Diabetes Mellitus 1·29 0·64,2·58 0·71 0·480 Cardio-cerebrovascular Disease 0·86 0·42,1·77 -0·41 0·684 No. of Comorbidities 1·03 0·57,1·85 0·10 0·921 L (Lymphocyte Percentage) 0·98 0·97,0·99 -4·79 < 0·0001** N (Neutrophil Percentage) 1·00 0·99,1·00 -1·17 0·242 † clinical evidence of altered breathing *: p < 0·01; **: p < 0·0001. Effect size of strong predictors at different disease stages To further understand whether the above strong predictors have the same effect in different disease stages, we analyzed effect size of three strong severity predictors (age, shortness of breath, and lymphocyte percentage) at different illness duration. As sample size differed at different stages, we applied Cohen’s d for continuous variables and odds ratio for categorical variables as effect size to describe statistical results. As shown in Fig. 3 , all the three variables were always helpful predictors to find out severe patients with illness duration increasing. The risk factors won’t change at different disease stages. Construction and Performance of the COVID-19 Severity Self-Assessment Scale As described, regression modelling identified 12 variables for inclusion in the prediction scale; however, blood tests cannot be performed by patients doing self-assessment, and the two blood test indicators (neutrophil percentage and lymphocyte percentage) were excluded from the final scale, leaving a 10-item scale. Figure 4 shows the results of the ROC analysis evaluating the accuracy of different model scales. The AUC of the full logistic regression model, with all 12 variables showing strong association to disease severity, was 0·72 (p < 0·0001). Following removal of neutrophil percentage and lymphocyte percentage, the remaining model (the final “COVID-19 Severity Self-Assessment Scale”) showed a similar AUC = 0·71 (p < 0·0001) and further, higher accuracy in older-aged (≥ 65 years) patients (AUC = 0·75, p < 0·0001). The LASSO regression extracted similar results, indicating no confounding collinearity between the variables and cross-validating the prediction accuracy. The final 10-item scale yielded a total score of 100 points, with higher score indicating a higher risk for severe illness. ROC analysis determined the cutoff value of 49·65, with scores above 49·65 predicting high risk. With this score, the final scale can correctly identify 87% patients. Once the predictive variables were determined and the self-assessment scale developed, an online calculator tool was constructed to allow patients access to expedient results ( http://180.167.250.222:10080/COVID-19-Severity-Self-Assessment-Scale.html ; Fig. 5 ). Discussion The study results showed that age, illness duration after onset, shortness of breath, and lymphocyte percentage are key factors in predicting whether COVID-19 infection will advance to severe disease. Notably, quarantine does not change the likelihood of developing a more serious illness. Fever is often thought to be associated with disease severity, such as in influenza, but we found no significant difference in fever rates between patients with severe and nonsevere COVID-19 disease. Previous studies(9, 11) have found comorbid chronic diseases to be correlated with increased disease severity among COVID-19 patients. A study outside Wuhan by Shi et al. also found that hypertension is a risk factor for severe COVID-19(21). Similarly, we found that four chronic diseases were more prevalent in patients with severe disease. While comorbidities showed weaker significance in the multivariate analysis, they were nevertheless included in the scale for higher accuracy of the model. Surprisingly, abnormality on chest computed tomography or X-ray exam did not distinguish patients with severe illness from those with nonsevere illness; however, our data only included the presence or absence of abnormality, and more detailed analysis of the abnormalities, e.g., of lesion size and distribution, might have disclosed different levels of severity—further analysis of the results by specialists is warranted(22, 23). Previously, older age has been reported as an important risk factor in SARS and MERS(24, 25). Our study confirmed that increased age was associated with COVID-19 severity and the severity risk would increase 1·32 times with every 10 years old. Older age has also been mentioned a strong relationship with death in patients with COVID-19(10). Apart from age, we also found that illness duration after onset held a significant association with COVID-19 severity. The risk would increase 2·22 times with every 10 days after illness onset. Thus, we strongly recommend early detection and treatment in healthcare settings for patients with COVID-19, especially aged patients, to reduce the mortality rate. Shortness of breath, a symptom written in the Chinese Protocol on Prevention and Control of COVID-19(19), was confirmed as a risk factor of COVID-19 severity in our study. Patients with shortness of breath held 1·64 times higher severity risk. Lymphocyte percentage, as a common blood test parameter, could be easily extracted in the community healthcare. There are several previous studies which mentioned that lymphocyte was a risk factor of COVID-19 severity(12, 26) with comparatively small sample sizes. In this study, we cross-validated this association with a large sample size and quantified that the severity risk would increase 1·26 times with 10% lymphocyte decreasing. We developed a self-assessment tool to predict the development of severe illness among COVID-19 infected patients in quarantine at home. To our knowledge, a list of risk scores has been established for COVID-19 mortality and severity. For example, a mortality risk prediction score was currently available online and methodologically suitable for use in the community(27). However, it was not developed with COVID-19 patients’ data, and therefore further validation should be required. There are some studies that have developed similar critical illness risk score(28, 29), but they rarely pay attention to the effect of the illness duration on the severity of the disease. The possible reason is that the confirmed was immediately sent to a medical institution as soon as it was discovered, in the case of sufficient medical resources, such as in the late stage of the outbreak in China. We analyzed the data of the patients in the early stage of the outbreak in Wuhan and found that the longer they have been ill, the greater the possibility that the disease will develop into severe. Existing COVID-19 severity prediction models(29–33) showed that complex laboratory indicators, such as direct bilirubin and lactate dehydrogenase, could help us to evaluate disease progression more accurately. On the other hand, results from these indicators relied a lot on the availability of specialized laboratory parameters. Furthermore, hospital visits would increase COVID-19 cross-infection risks. The COVID-19 Severity Self-Assessment Scale we constructed needn’t complex laboratory parameters and offers relatively high accuracy, which makes it convenient and practical for use in home self-assessment and for preliminary rapid screening in community healthcare settings. To the best of our knowledge, this study included the most patients with COVID-19 before treatments in the early stage of the Wuhan outbreak. The lack of medical resources caused by the sudden outbreak prevented patients from receiving timely treatment, which resulted in a longer disease course (Figure E1 in the supplementary online data) that more closely resembled the natural progression of the disease. Despite these strengths, the data used in the scale construction were entirely from China, which could potentially limit the generalizability of the findings and use of the scale in other countries. Conclusions Twelve scale items - age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage - were identified and showed good predictive ability of whether confirmed patients would develop severe disease. After excluding the laboratory parameters, we constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings. Declarations Ethics approval and consent to participate The study was conducted according to the principles of the Declaration of Helsinki, and all data were de-identified to protect patient confidentiality. Ethical approval for this study was obtained from the School of Public Health, Fudan University. Consent for publication Not applicable Availability of data and materials The datasets used and analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Bill & Melinda Gates Foundation [grant number INV-006277]; and the Shanghai Sailing Program [grant number 17YF142600]. Authors’ contributions YY and JT contributed equally. HDK, LZ and WBW contributed equally to the correspondence work. YY and WBW designed the study. YY, XM, HDK, LZ, and WDW collected, analyzed and interpreted the data. YY and XM performed the statistical analysis of the data. YY and JT drafted the manuscript. All authors critically reviewed and approved the final version of the manuscript. Acknowledgments We thank Eleanor Scharf, MSc(A), from Liwen Bianji, Edanz Editing China ( www.liwenbianji.cn/ac ), for editing the English text of a draft of this manuscript. References WHO Coronavirus Disease (COVID-19) Dashborad [ https://covid19.who.int/ ] Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-96164","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":3795098,"identity":"3884bbcc-ac8f-49fc-b379-ce15da8fb433","order_by":0,"name":"Ye Yao","email":"","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ye","middleName":"","lastName":"Yao","suffix":""},{"id":3795099,"identity":"6286774b-a82b-42c0-b0ce-0340d036c15a","order_by":1,"name":"Jie Tian","email":"","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Tian","suffix":""},{"id":3795100,"identity":"27261aff-2534-4f07-a2a8-fde9b592a791","order_by":2,"name":"Xia Meng","email":"","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Meng","suffix":""},{"id":3795101,"identity":"6ae605cb-6470-4f59-8230-bb83ccde36db","order_by":3,"name":"Haidong Kan","email":"","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haidong","middleName":"","lastName":"Kan","suffix":""},{"id":3795102,"identity":"e49c615d-26bf-49e1-b3a8-443663caa134","order_by":4,"name":"Lian Zhou","email":"","orcid":"","institution":"Jiangsu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lian","middleName":"","lastName":"Zhou","suffix":""},{"id":3795103,"identity":"92d9ebe9-782e-4bf4-837c-78a9c9219c1d","order_by":5,"name":"Weibing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsUlEQVRIiWNgGAWjYFACHsYHFQzMYKYEsVqYDc6QqoVNgjQtBjdyj1UcbLOWN2dgPnibh8EujwgteWk3DralG+5sYEu25mFILiaoxex2jtntj22HGTcc4DGT5mE4kNhAjJaCg22H7Tcc4P9GvBYGoJZEoC1sxGmxv/8uWeLAufTkDYfZjC3nGCQT1iLZc/bghwNl1rYbjjc/vPGmwo6wFgQAR40B8epHwSgYBaNgFOABADDOPk7GVn7IAAAAAElFTkSuQmCC","orcid":"","institution":"Fudan University School of Public Health","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Weibing","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2020-10-21 16:04:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-96164/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-96164/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":3207344,"identity":"435650c5-a38a-4bf1-a91b-87d61780246e","added_by":"auto","created_at":"2020-10-26 21:06:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63850,"visible":true,"origin":"","legend":"Distribution of Disease Severity by Age and Gender. \nSeverity Ratio refers to the proportion of patients with severe disease. Age correlated positively with Coronavirus Disease 2019 severity (r\u003e0·91, p\u003c0·0001), and this was unaffected by gender.\nSeverity Ratio refers to the proportion of patients with severe disease.","description":"","filename":"OnlineFigure1.Png","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/b3261fec6be0a0bac37486b6.Png"},{"id":3207345,"identity":"acd0817a-f4ba-43df-87d7-961a8bc875d1","added_by":"auto","created_at":"2020-10-26 21:06:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50990,"visible":true,"origin":"","legend":"Distribution of Illness Severity by Age and Illness Duration.\nSeverity Ratio refers to the proportion of patients with severe disease. This increased with advancing age and illness duration. ","description":"","filename":"OnlineFigure2.Png","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/8f1e805a6481dec4e1dfc3ef.Png"},{"id":3207346,"identity":"75740e4c-f094-4c59-9bb2-12e55d025290","added_by":"auto","created_at":"2020-10-26 21:06:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":74420,"visible":true,"origin":"","legend":"Effect size of strong predictors at different disease stages\nThe effect size (Cohen’s d for continuous variables and odds ratio for categorical variables) of three strong severity predictors (age, shortness of breath, and lymphocyte percentage) with different illness durations. All the three variables could help to find out severe patients at different disease stages.","description":"","filename":"OnlineFigure3.Png","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/e37fe6993d87ad2907e9f64c.Png"},{"id":3207347,"identity":"690cad68-f8f0-4c65-8e6d-df8dc7059eb3","added_by":"auto","created_at":"2020-10-26 21:06:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104611,"visible":true,"origin":"","legend":"ROC Curves for Patients with Severe Disease.\nA ROC curve was generated for all 12 variables (i.e., 10 self-assessment variables and 2 blood test parameters) that showed strong correlation with disease severity (blue line, AUC=0·72, p\u003c0·0001). Then, separate ROC curves were generated for the 10 variables in the final self-assessment scale (green line, AUC=0·71, p\u003c0·0001) and for the same 10 variables, in the older-aged (not less than 65 years old) patients (red line, AUC=0·75, p\u003c0·0001).\nAbbreviations: ROC, receiver operating characteristic; AUC, area under the (ROC) curve.","description":"","filename":"OnlineFigure4.Png","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/cd30f5862d01c1fa50e83811.Png"},{"id":3207348,"identity":"13df0654-8154-4861-bffa-965037910483","added_by":"auto","created_at":"2020-10-26 21:06:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108076,"visible":true,"origin":"","legend":"The Online Coronavirus Disease 2019 Severity Self-Assessment Scale. \nThe scale is available at: http://180.167.250.222:10080/COVID-19-Severity-Self-Assessment-Scale.html \nAbbreviation: COVID-19, Coronavirus Disease 2019\nNote: Shortness of breath is defined here as clinical evidence of altered breathing. ","description":"","filename":"OnlineFigure5.Png","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/f14cd772db81221bbcfe0b7e.Png"},{"id":13605995,"identity":"aba39ed9-72a0-4e30-8e84-219f9ab8df3c","added_by":"auto","created_at":"2021-09-17 06:05:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2340956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/d2eeacdc-66ed-4ca4-8ecf-cd0771197c50.pdf"},{"id":3207349,"identity":"148219fd-d477-4f97-a86e-287983df7fd9","added_by":"auto","created_at":"2020-10-26 21:06:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":252060,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-96164/v1/91c3086cbe6d6eec8c7bf98f.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eProgression of Severity in Coronavirus Disease 2019 Patients Before Treatment and a Self-Assessment Scale to Predict Disease Severity\u003c/p\u003e","fulltext":[{"header":"Summary","content":"\u003cp\u003eOur study constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance.\u003c/p\u003e"},{"header":"Introduction","content":" \u003cp\u003eCoronavirus Disease 19 (COVID-19) has formed a worldwide pandemic[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and its clinical spectrum of disease ranges from mild to critical illness. Most COVID-19 patients present with mild symptoms, such as fever and cough, but a small proportion of patients develop severe pneumonia with progression to life-threatening complications, including acute respiratory distress syndrome, multi-organ failure, and death[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, the case-fatality rate differs widely between patients with severe and nonsevere disease. According to the most comprehensive report from the Chinese Centre for Disease Control and Prevention, which reviewed 72,314 cases, the average COVID-19 case-fatality rate is 2.3%, but it is as high as 49% in patients with critical illness[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn some countries, such as the United States[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], the Republic of Korea[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and Scotland, differing case-fatality rates and limited resources have led to the adoption of separate management strategies for severe and nonsevere disease. Particularly in regions with high case volume, the need to conserve the number of available intensive care unit beds and ventilators has dictated home quarantine for patients with nonsevere disease, with reserving hospitalization for patients with severe disease[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]; however, the patient who suddenly deteriorates while inappropriately quarantined at home will suffer delay in treatment and in turn, worsened prognosis. For patients in quarantine in community, accurate COVID-19 severity self-assessment can guide appropriate and timely medical consultation. During this pandemic, biological and clinical predictors of COVID-19 infection severity will also assist in the judicious allocation of limited resources.\u003c/p\u003e \u003cp\u003ePrevious research has shown that clinical characteristics, including chronic disease, lymphopenia, and elevated D-dimer level, are associated with the severity of COVID-19[\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], but most of these studies used univariate analysis with comparatively complicated laboratory parameters. A study among hospitalized adults identified through the U.S. COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) showed that increasing age, male sex, and underlying conditions were associated with higher risk of ICU admission and death[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], but it failed to include mild patients who were not hospitalized. Wynants et al.[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and Urwin et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] presented information available at that time on prediction models on prognosis of COVID-19, but those models were limited in small sample size and rated at high risk of bias resulted in probably optimistic. Further evidence needs to emerge around the validity of these scores. This study aims to further investigate the association of COVID-19 disease severity with numerous patient characteristics, and to develop a convenient severity prediction scale for use in self-assessment at home or in preliminary screening in community healthcare settings.\u003c/p\u003e "},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eData Sources and Processing\u003c/h2\u003e\n\u003cp\u003eCOVID-19 patient data for the period January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System, in Wuhan, Hubei Province. The data was obtained by investigations conducted by epidemiology professionals after the patient was diagnosed. The inclusion criterion was a confirmed COVID-19 diagnosis by positive high-throughput sequencing or reverse-transcription polymerase chain reaction assay of nasal and pharyngeal swab specimens.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eOutcome Measurement\u003c/h2\u003e\n\u003cp\u003eThe Chinese Diagnosis and Treatment Protocol for COVID-19[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e] defines four levels of COVID-19 disease: mild, ordinary, severe, and critical illness; additionally, asymptomatic infection is recognized. We categorized COVID-19 disease severity, based on these definitions, as \u0026ldquo;nonsevere\u0026rdquo; (which grouped asymptomatic, mild, and ordinary disease) versus \u0026ldquo;severe\u0026rdquo; (grouping severe and critical illness).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003ePotential Predictive Variables\u003c/h2\u003e\n\u003cp\u003eThe study variables included demographic factors (age, gender); present/past medical history (illness duration, quarantine status, and presence of hypertension, pulmonary disease, diabetes mellitus, cardio/cerebrovascular disease, chronic liver disease, and chronic kidney disease); clinical symptoms; blood test parameters (white blood cell count, lymphocyte count and percentage, and neutrophil percentage); and imaging findings (abnormal chest computed tomography scan).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eIn all, 36 variables were considered as potential predictors of COVID-19 disease severity. T-test and chi-square test were used to compare the differences of each variable in patients with severe and nonsevere disease. The variables that showed significant association with disease severity were then included into logistic regression models for multivariable analysis, to confirm their candidacy for inclusion in the new prediction scale (COVID-19 Severity Self-Assessment Scale). In the logistic modelling, we used the following formulae to calculate the probability and 95% confidence intervals (CIs)[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cp\u003eprobability = \u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1603725783.png\" alt=\"\" /\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003elower limit of 95%CI = \u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1603725833.png\" alt=\"\" /\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003cli\u003e\n\u003cp\u003eupper limit of 95%CI = \u003cimg src=\"https://myfiles.space/user_files/58893_b39df98f09c4a4bb/58893_custom_files/img1603725875.png\" alt=\"\" /\u003e\u003c/p\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eReceiver-operator characteristic (ROC) analysis was performed and the area under the curve (AUC) calculated to verify the accuracy of the final prediction scale. We extracted p-values for AUC by conducting permutation analyses. To avoid the influence of potential collinearity among the variables, least absolute shrinkage and selection operator (LASSO) regression analysis was also performed.\u003c/p\u003e\n\u003cp\u003eThe statistical analysis was performed using MATLAB software, version 2019b (MathWorks Inc). In this study, a p-value less than 0\u0026middot;01 is statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eDemographic Characteristics\u003c/h2\u003e\n\u003cp\u003eData were collected from 45,450 patients. The study population had a mean (standard deviation [SD]) age of 53\u0026middot;44 (16\u0026middot;38) years, and 21,689 (47\u0026middot;7%) patients were men. The mean (SD) illness duration was 10\u0026middot;40 (7\u0026middot;90) days (see Figure E1 in the supplementary online data for the distribution). Among all the patients, 7,798 (17\u0026middot;2%) were considered to have severe disease and 37,652 (82\u0026middot;8%) to have nonsevere disease. Accordingly, 37,654 (82\u0026middot;9%) patients were quarantined at home.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the distribution of disease severity by age and gender. Both age (r\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;91, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) and illness duration (r\u0026thinsp;\u0026gt;\u0026thinsp;0\u0026middot;69, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) correlated positively with disease severity, as seen in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, and this was unaffected by gender (men and women held similar trend with age and illness duration as showed in Figure E2). Patients with severe disease had a mean (SD) age of 60\u0026middot;85 (15\u0026middot;28) years and illness duration of 12\u0026middot;55 (7\u0026middot;93) days after symptom onset, compared with patients with nonsevere disease, who had a mean (SD) age of 51\u0026middot;90 (16\u0026middot;17) years and illness duration of 9\u0026middot;95 (7\u0026middot;82) days (t\u0026thinsp;=\u0026thinsp;44\u0026middot;85, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001 for age; t\u0026thinsp;=\u0026thinsp;26\u0026middot;62, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001 for illness duration). Quarantine rate did not differ significantly according to illness severity (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;17, p\u0026thinsp;=\u0026thinsp;0\u0026middot;682) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDemographic Characteristics of the Sample\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere,\u003c/p\u003e\n\u003cp\u003emean (sd) / n (%)\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7798)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonsevere,\u003c/p\u003e\n\u003cp\u003emean (sd) / n (%)\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37652)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et / \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60\u0026middot;85 (15\u0026middot;28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51\u0026middot;90 (16\u0026middot;17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;44\u0026middot;85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender (male)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3908 (50\u0026middot;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17781 (47\u0026middot;2%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;21\u0026middot;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIllness Duration (days)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u0026middot;55 (7\u0026middot;93)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u0026middot;95 (7\u0026middot;82)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;26\u0026middot;62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuarantine (yes)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6448 (82\u0026middot;7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31206 (82\u0026middot;9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;682\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003eAbbreviation: sd, standard deviation\n\u003cp\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eClinical Symptoms and Comorbidities\u003c/h2\u003e\n\u003cp\u003eClinical manifestations were recorded for 4,984 patients and yielded 20 clinical symptoms (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e); among these, the incidence of dyspnea (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24\u0026middot;56, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) and shortness of breath (defined as clinical evidence of altered breathing) (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;62\u0026middot;67, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) differed significantly between severe and nonsevere patients. Among 1,326 patients with severe disease, 225 (17\u0026middot;0%) had dyspnea and 296 (22\u0026middot;3%) had shortness of breath; conversely, among the 3,658 patients with nonsevere disease, 425 (11\u0026middot;6%) had dyspnea and 480 (13\u0026middot;1%) had shortness of breath.\u003c/p\u003e\n\u003cp\u003eComorbid conditions were recorded for 5,062 patients and were found in a higher proportion of patients with severe versus nonsevere disease (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the number of comorbidities showed significant association with the severity of COVID-19 (t\u0026thinsp;=\u0026thinsp;7\u0026middot;96, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001). Patients with hypertension, pulmonary disease, diabetes mellitus, and cardio/cerebrovascular disease were more likely to develop severe disease (t\u0026thinsp;\u0026gt;\u0026thinsp;15\u0026middot;14, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001). Notably, there was no significant difference in the prevalence of chronic liver (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;38, p\u0026thinsp;=\u0026thinsp;0\u0026middot;538) or kidney disease (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2\u0026middot;00, p\u0026thinsp;=\u0026thinsp;0\u0026middot;157) between patients with severe and nonsevere disease.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReported Symptoms of the Participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSymptoms\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere, n (%) (n\u0026thinsp;=\u0026thinsp;1326)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonsevere, n (%) (n\u0026thinsp;=\u0026thinsp;3658)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1110 (83\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2951 (80\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026middot;95\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;015\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVomiting\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e72 (5\u0026middot;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e160 (4\u0026middot;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026middot;44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;118\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDyspnea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e225 (17\u0026middot;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e425 (11\u0026middot;6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u0026middot;56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShortness of Breath\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e296 (22\u0026middot;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e480 (13\u0026middot;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62\u0026middot;67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExpectoration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e289 (21\u0026middot;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e715 (19\u0026middot;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026middot;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;080\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSore Throat\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e55 (4\u0026middot;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229 (6\u0026middot;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8\u0026middot;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;004*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeadache\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e129 (9\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e463 (12\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u0026middot;97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;005*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChills\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (9\u0026middot;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e372 (10\u0026middot;2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;488\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDry Cough\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e580 (43\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1661 (45\u0026middot;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;296\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNausea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36 (2\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (2\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;971\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRunny Nose\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (1\u0026middot;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101 (2\u0026middot;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u0026middot;31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;007*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConjunctival Hyperemia\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0\u0026middot;2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (0\u0026middot;2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;808\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuscle Soreness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e237 (17\u0026middot;9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e658 (18\u0026middot;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;01\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;926\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChest Pain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (1\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99 (2\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026middot;51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;034\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChest Tightness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e229 (17\u0026middot;3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e551 (15\u0026middot;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026middot;59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;058\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiarrhea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e127 (9\u0026middot;6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e362 (9\u0026middot;9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;738\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAbdominal Pain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0\u0026middot;2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (0\u0026middot;4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;402\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNasal Congestion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (1\u0026middot;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74 (2\u0026middot;0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;515\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFatigue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e537 (40\u0026middot;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1335 (36\u0026middot;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6\u0026middot;65\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;010*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJoint Soreness\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e60 (4\u0026middot;5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e224 (6\u0026middot;1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026middot;63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;031\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eclinical evidence of altered breathing\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e*: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;01; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReported Comorbidities of the Participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComorbidities\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere,\u003c/p\u003e\n\u003cp\u003en (%) / mean (sd)\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1339)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonsevere,\u003c/p\u003e\n\u003cp\u003en (%) / mean(sd)\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3723)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et / \u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e306 (22\u0026middot;9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e599 (16\u0026middot;1%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;30\u0026middot;69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePulmonary Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51 (3\u0026middot;8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71 (1\u0026middot;9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15\u0026middot;14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e147 (11\u0026middot;0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e239 (6\u0026middot;4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;29\u0026middot;06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardio-cerebrovascular Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e126 (9\u0026middot;4%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e214 (5\u0026middot;7%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;21\u0026middot;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Liver Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (1\u0026middot;0%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32 (0\u0026middot;9%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0\u0026middot;38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;538\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (1\u0026middot;3%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31 (0\u0026middot;8%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2\u0026middot;00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;157\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of Comorbidities\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;50 (0\u0026middot;80)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;32 (0\u0026middot;66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003et\u0026thinsp;=\u0026thinsp;7\u0026middot;96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003etotal number of comorbid conditions, including hypertension, pulmonary disease, diabetes mellitus, cardio/cerebrovascular disease, chronic liver disease, and chronic kidney disease, for each subject\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003eLaboratory and Imaging Results\u003c/h2\u003e\n\u003cp\u003eLaboratory results were recorded for 2,471 patients. The percentages of neutrophils and lymphocytes were significantly different in patients with severe versus nonsevere disease: in patients with severe disease, neutrophils were higher (t\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;7\u0026middot;53, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) and lymphocytes were lower (t\u0026thinsp;=\u0026thinsp;4\u0026middot;67, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001; Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA total of 3,438 patients underwent computed tomography examination, revealing abnormalities in 90\u0026middot;5% of patients with severe disease and 92\u0026middot;2% of patients with nonsevere disease\u0026mdash;a nonsignificant difference (\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2\u0026middot;16, p\u0026thinsp;=\u0026thinsp;0\u0026middot;142).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eReported Laboratory Results of the Participants\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eIndexes\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSevere, mean(sd) (n\u0026thinsp;=\u0026thinsp;605)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNonsevere, mean(sd) (n\u0026thinsp;=\u0026thinsp;1866)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWBC (White Blood Cell Count)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026middot;69 (3\u0026middot;07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5\u0026middot;37 (2\u0026middot;87)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026middot;38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;018\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLx (Lymphocyte Count)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;33 (3\u0026middot;47)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;96 (6\u0026middot;19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-2\u0026middot;39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;017\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL (Lymphocyte Percentage, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u0026middot;21 (12\u0026middot;74)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u0026middot;90 (13\u0026middot;50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-7\u0026middot;53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN (Neutrophil Percentage, %)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67\u0026middot;17 (21\u0026middot;24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62\u0026middot;90 (18\u0026middot;94)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4\u0026middot;67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e**: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003ePredictor Selection\u003c/h2\u003e\n\u003cp\u003eAmong the 36 variables analyzed, 12 showed statistical differences between the groups of patients with severe and nonsevere disease, indicating their potential predictive value: gender, age, illness duration, dyspnea, shortness of breath, hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage. Of these, four were strong predictors of severe disease (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e): age (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1\u0026middot;03; 95%CI: 1\u0026middot;02\u0026ndash;1\u0026middot;04; p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001), illness duration (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;08; 95%CI: 1\u0026middot;06\u0026ndash;1\u0026middot;10; p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001), shortness of breath (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;64; 95%CI: 1\u0026middot;26\u0026ndash;2\u0026middot;13; p\u0026thinsp;=\u0026thinsp;0\u0026middot;0002), and lymphocyte percentage (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;98; 95%CI: 0\u0026middot;97\u0026ndash;0\u0026middot;99; p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eLogistic Regression Model for the Prediction of Severe Disease\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e95% CI\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003et\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;02,1\u0026middot;04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7\u0026middot;28,\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;06,1\u0026middot;58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u0026middot;55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;011\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIllness Duration\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;06,1\u0026middot;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9\u0026middot;12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDyspnea\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;87,1\u0026middot;61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;07\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;287\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShortness of Breath\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;26,2\u0026middot;13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u0026middot;69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;0002*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;45,1\u0026middot;67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0\u0026middot;43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;664\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePulmonary Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;67\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;73,3\u0026middot;81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;220\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;64,2\u0026middot;58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;71\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;480\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCardio-cerebrovascular Disease\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;86\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;42,1\u0026middot;77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-0\u0026middot;41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;684\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo. of Comorbidities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;03\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;57,1\u0026middot;85\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;921\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eL (Lymphocyte Percentage)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;97,0\u0026middot;99\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-4\u0026middot;79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0\u0026middot;0001**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eN (Neutrophil Percentage)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u0026middot;00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;99,1\u0026middot;00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-1\u0026middot;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u0026middot;242\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e\u003csup\u003e\u0026dagger;\u003c/sup\u003eclinical evidence of altered breathing\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\"\u003e*: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;01; **: p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eEffect size of strong predictors at different disease stages\u003c/h2\u003e\n\u003cp\u003eTo further understand whether the above strong predictors have the same effect in different disease stages, we analyzed effect size of three strong severity predictors (age, shortness of breath, and lymphocyte percentage) at different illness duration. As sample size differed at different stages, we applied Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for continuous variables and odds ratio for categorical variables as effect size to describe statistical results. As shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, all the three variables were always helpful predictors to find out severe patients with illness duration increasing. The risk factors won\u0026rsquo;t change at different disease stages.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eConstruction and Performance of the COVID-19 Severity Self-Assessment Scale\u003c/h2\u003e\n\u003cp\u003eAs described, regression modelling identified 12 variables for inclusion in the prediction scale; however, blood tests cannot be performed by patients doing self-assessment, and the two blood test indicators (neutrophil percentage and lymphocyte percentage) were excluded from the final scale, leaving a 10-item scale.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of the ROC analysis evaluating the accuracy of different model scales. The AUC of the full logistic regression model, with all 12 variables showing strong association to disease severity, was 0\u0026middot;72 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001). Following removal of neutrophil percentage and lymphocyte percentage, the remaining model (the final \u0026ldquo;COVID-19 Severity Self-Assessment Scale\u0026rdquo;) showed a similar AUC\u0026thinsp;=\u0026thinsp;0\u0026middot;71 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001) and further, higher accuracy in older-aged (\u0026ge;\u0026thinsp;65\u0026nbsp;years) patients (AUC\u0026thinsp;=\u0026thinsp;0\u0026middot;75, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;0001). The LASSO regression extracted similar results, indicating no confounding collinearity between the variables and cross-validating the prediction accuracy.\u003c/p\u003e\n\u003cp\u003eThe final 10-item scale yielded a total score of 100 points, with higher score indicating a higher risk for severe illness. ROC analysis determined the cutoff value of 49\u0026middot;65, with scores above 49\u0026middot;65 predicting high risk. With this score, the final scale can correctly identify 87% patients.\u003c/p\u003e\n\u003cp\u003eOnce the predictive variables were determined and the self-assessment scale developed, an online calculator tool was constructed to allow patients access to expedient results (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://180.167.250.222:10080/COVID-19-Severity-Self-Assessment-Scale.html\u003c/span\u003e\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":" \u003cp\u003eThe study results showed that age, illness duration after onset, shortness of breath, and lymphocyte percentage are key factors in predicting whether COVID-19 infection will advance to severe disease. Notably, quarantine does not change the likelihood of developing a more serious illness. Fever is often thought to be associated with disease severity, such as in influenza, but we found no significant difference in fever rates between patients with severe and nonsevere COVID-19 disease. Previous studies(9, 11) have found comorbid chronic diseases to be correlated with increased disease severity among COVID-19 patients. A study outside Wuhan by Shi et al. also found that hypertension is a risk factor for severe COVID-19(21). Similarly, we found that four chronic diseases were more prevalent in patients with severe disease. While comorbidities showed weaker significance in the multivariate analysis, they were nevertheless included in the scale for higher accuracy of the model. Surprisingly, abnormality on chest computed tomography or X-ray exam did not distinguish patients with severe illness from those with nonsevere illness; however, our data only included the presence or absence of abnormality, and more detailed analysis of the abnormalities, e.g., of lesion size and distribution, might have disclosed different levels of severity\u0026mdash;further analysis of the results by specialists is warranted(22, 23).\u003c/p\u003e \u003cp\u003ePreviously, older age has been reported as an important risk factor in SARS and MERS(24, 25). Our study confirmed that increased age was associated with COVID-19 severity and the severity risk would increase 1\u0026middot;32 times with every 10\u0026nbsp;years old. Older age has also been mentioned a strong relationship with death in patients with COVID-19(10). Apart from age, we also found that illness duration after onset held a significant association with COVID-19 severity. The risk would increase 2\u0026middot;22 times with every 10\u0026nbsp;days after illness onset. Thus, we strongly recommend early detection and treatment in healthcare settings for patients with COVID-19, especially aged patients, to reduce the mortality rate. Shortness of breath, a symptom written in the Chinese Protocol on Prevention and Control of COVID-19(19), was confirmed as a risk factor of COVID-19 severity in our study. Patients with shortness of breath held 1\u0026middot;64 times higher severity risk. Lymphocyte percentage, as a common blood test parameter, could be easily extracted in the community healthcare. There are several previous studies which mentioned that lymphocyte was a risk factor of COVID-19 severity(12, 26) with comparatively small sample sizes. In this study, we cross-validated this association with a large sample size and quantified that the severity risk would increase 1\u0026middot;26 times with 10% lymphocyte decreasing.\u003c/p\u003e \u003cp\u003eWe developed a self-assessment tool to predict the development of severe illness among COVID-19 infected patients in quarantine at home. To our knowledge, a list of risk scores has been established for COVID-19 mortality and severity. For example, a mortality risk prediction score was currently available online and methodologically suitable for use in the community(27). However, it was not developed with COVID-19 patients\u0026rsquo; data, and therefore further validation should be required. There are some studies that have developed similar critical illness risk score(28, 29), but they rarely pay attention to the effect of the illness duration on the severity of the disease. The possible reason is that the confirmed was immediately sent to a medical institution as soon as it was discovered, in the case of sufficient medical resources, such as in the late stage of the outbreak in China. We analyzed the data of the patients in the early stage of the outbreak in Wuhan and found that the longer they have been ill, the greater the possibility that the disease will develop into severe. Existing COVID-19 severity prediction models(29\u0026ndash;33) showed that complex laboratory indicators, such as direct bilirubin and lactate dehydrogenase, could help us to evaluate disease progression more accurately. On the other hand, results from these indicators relied a lot on the availability of specialized laboratory parameters. Furthermore, hospital visits would increase COVID-19 cross-infection risks. The COVID-19 Severity Self-Assessment Scale we constructed needn\u0026rsquo;t complex laboratory parameters and offers relatively high accuracy, which makes it convenient and practical for use in home self-assessment and for preliminary rapid screening in community healthcare settings.\u003c/p\u003e \u003cp\u003eTo the best of our knowledge, this study included the most patients with COVID-19 before treatments in the early stage of the Wuhan outbreak. The lack of medical resources caused by the sudden outbreak prevented patients from receiving timely treatment, which resulted in a longer disease course (Figure E1 in the supplementary online data) that more closely resembled the natural progression of the disease. Despite these strengths, the data used in the scale construction were entirely from China, which could potentially limit the generalizability of the findings and use of the scale in other countries.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eTwelve scale items - age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage - were identified and showed good predictive ability of whether confirmed patients would develop severe disease. After excluding the laboratory parameters, we constructed a COVID-19 severity self-assessment scale that can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted according to the principles of the Declaration of Helsinki, and all data were de-identified to protect patient confidentiality. Ethical approval for this study was obtained from the School of Public Health, Fudan University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Bill \u0026amp; Melinda Gates Foundation [grant number INV-006277]; and the Shanghai Sailing Program [grant number 17YF142600].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYY and JT contributed equally. HDK, LZ and WBW contributed equally to the correspondence work. YY and WBW designed the study. YY, XM, HDK, LZ, and WDW collected, analyzed and interpreted the data. YY and XM performed the statistical analysis of the data. YY and JT drafted the manuscript. All authors critically reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Eleanor Scharf, MSc(A), from Liwen Bianji, Edanz Editing China (\u003ca href=\"http://www.liwenbianji.cn/ac\"\u003ewww.liwenbianji.cn/ac\u003c/a\u003e), for editing the English text of a draft of this manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eWHO Coronavirus Disease (COVID-19) Dashborad \u003c/strong\u003e[\u003ca href=\"https://covid19.who.int/\"\u003ehttps://covid19.who.int/\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eHuang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, Zhang L, Fan G, Xu J, Gu X, et al: \u003cstrong\u003eClinical features of patients infected with 2019 novel coronavirus in Wuhan, China.\u003c/strong\u003e \u003cem\u003eLancet \u003c/em\u003e2020, 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\u003c/em\u003e2020\u003cstrong\u003e:\u003c/strong\u003e2020.2003.2028.20045989.\u003c/li\u003e\n\u003cli\u003eZhou Y, Yang Z, Guo Y, Geng S, Gao S, Ye S, Hu Y, Wang Y: \u003cstrong\u003eA New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China.\u003c/strong\u003e \u003cem\u003emedRxiv \u003c/em\u003e2020\u003cstrong\u003e:\u003c/strong\u003e2020.2003.2024.20042119.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Coronavirus Disease 2019, Self-Assessment Scale, Severity","lastPublishedDoi":"10.21203/rs.3.rs-96164/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-96164/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e With the pandemic of Coronavirus Disease 2019, differing case-fatality rates and limited resources have led to adoption of separate management strategies for severe and nonsevere disease. For patients in quarantine in the community, self-assessment of COVID-19 severity risk can guide appropriate medical consultation. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eData from 45,450 patients infected with COVID-19 from January 1 to February 27, 2020 were extracted from the municipal Notifiable Disease Report System in Wuhan, China. T-test and chi-square test were used to investigate the associations of various patient characteristics with disease severity, and multivariable logistic regression models identified strongly correlated variables for inclusion in the scale. Scale accuracy was assessed using receiver operating characteristic analysis. A least absolute shrinkage and selection operator regression cross-validated prediction accuracy.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTwelve scale items - age, gender, illness duration, dyspnea, shortness of breath (clinical evidence of altered breathing), hypertension, pulmonary disease, diabetes, cardio/cerebrovascular disease, number of comorbidities, neutrophil percentage, and lymphocyte percentage - were identified and showed good predictive ability (area under the curve =0·72). After excluding the community healthcare laboratory parameters, the remaining model (the final self-assessment scale) showed similar area under the curve (=0·71).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur COVID-19 severity self-assessment scale can be used by patients in the community to predict their risk of developing severe illness and the need for further medical assistance. The tool is also practical for use in preliminary screening in community healthcare settings.\u003c/p\u003e","manuscriptTitle":"Progression of Severity in Coronavirus Disease 2019 Patients Before Treatment and a Self-Assessment Scale to Predict Disease Severity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-10-26 21:06:18","doi":"10.21203/rs.3.rs-96164/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1e2f0bd2-16b0-4e48-9a7e-d9a5dde06cf4","owner":[],"postedDate":"October 26th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":886981,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2020-11-19T19:07:44+00:00","versionOfRecord":[],"versionCreatedAt":"2020-10-26 21:06:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-96164","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-96164","identity":"rs-96164","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}