Prognostic Value of Neutrophil-To-Lymphocyte Ratio, Lactate Dehydrogenase, D-Dimer and CT Score in Patients with COVID-19

Prognostic Value of Neutrophil-To-Lymphocyte Ratio, Lactate Dehydrogenase, D-Dimer and CT Score in Patients with COVID-19 | 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 Prognostic Value of Neutrophil-To-Lymphocyte Ratio, Lactate Dehydrogenase, D-Dimer and CT Score in Patients with COVID-19 Yu-Qing Cai, Xiao-Bin Zhang, Hui-Qing Zeng, Xiao-Jie Wei, Zhen-Yu Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-30959/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: To explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19) in two centers of Hubei, China. Methods: A total of 432 patients with laboratory confirmed COVID-19 were retrospectively enrolled and divided into non-severe and severe groups. The baseline data, laboratory findings, chest computed tomography (CT) results evaluating by CT score on admission, and clinical outcomes were collected and compared. The logistic regression was used to assess the independent relationship between the baseline level of four indicators (NLR, LDH, D-dimer, CT score) on admission and the severity of COVID-19, respectively. Results: Among 432 patients, 125 (28.94%) cases were divided into severe group, the remaining (n = 307, 71.06%) were in non-severe group. In multivariate logistic regression, the high level of NLR, LDH were independent predictor in the early classification of patients with COVID-19 (OR = 2.163; 95%CI = 1.162–4.026; p = 0.015 for NLR > 3.82; OR = 2.298; 95%CI = 1.327–3.979; p = 0.003 for LDH > 246U/L). Furthermore, combining NLR > 3.82 and LDH > 246U/L could increase the sensitivity of diagnosis in severe patients (NLR > 3.82 [50.40%] vs. Combined diagnosis [72.80%]; p = 0.0007; LDH > 246 [59.2%] vs. Combined diagnosis [72.80%]; p < 0.0001). Conclusions: The high levels of NLR and LDH in serum have potential value in the early identification of severe patients with COVID-19. The combination of LDH and NLR can improve the sensitivity of diagnosis. Importance : COVID-19 has been a global pandemic. The mortality rate is range from 3.5-6.0%. In order to predict the risk factors of severity of COVID-19. we explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19) in two centers of Hubei, China. We found that the high levels of NLR and LDH in serum have potential value in the early identification of severe patients with COVID-19. The combination of LDH and NLR can improve the sensitivity of diagnosis. Health Economics & Outcomes Research COVID-19 neutrophil-to-Lymphocyte Ratio lactate dehydrogenase D-dimer CT score Figures Figure 1 Background Since December 2019, coronavirus disease-2019 (COVID-19) caused by a novel coronavirus (SARS-CoV-2) spread rapidly all over the world and has caused a major public health issue [ 1 ]. By the morning of May 11, Beijing time, the number of patients has exceeded 4.1 million [ 2 ]. It is obviously a huge challenge to the global healthcare system[ 3 ]. And the mortality of COVID-19 patients is related to the health-care burden[ 4 ].Therefore, the reasonable distribution of medical resources is particularly important. Early identification of critical patients is critical to the rational allocation of resources and the improvement of patients' prognosis. Meanwhile, according to reports, compared with non-severe patients, the hematological changes of severe patients are more prominent[ 5 ]. Neutrophil-to-Lymphocyte Ratio (NLR), lactate dehydrogenase (LDH), and D-dimer are close with the poor prognosis of COVID-19[ 6 , 7 ].Without other clinical parameters, the computed tomography (CT) evaluation is an independent prognostic factor in patients with COVID-19 [ 8 ]. However, there is little data about the comparison of the above four indicators. Therefore, in this study, we aimed to compare the prediction efficiency of four indicators and evaluate the significance of optimum cutoff. Subsequently, combined diagnosis analysis was performed to evaluate whether it can improve the diagnosis efficiency. Materials And Methods Study Design and Participants From January 20, 2020, to March 30, 2020, a total of 432 patients confirmed COVID-19 by the laboratory in designated treatment hospitals (Optic Valley division of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan and Yichang Third People's Hospital, Hubei Province) were enrolled. The patients were divided into 2 groups based on the seventh edition of the New Coronavirus Pneumonia Diagnosis and Treatment Program published by the Chinese National Health Commission[ 9 ]: the mild and moderate types were classified as non-severe group and the severe and critical were included into severe group. The disease is classified as severe if one of the following items is met: 1) shortness of breath, respiratory rate ≥ 30 beats per min; 2) the oxygen saturation ≤ 93% in a resting state; 3) arterial partial pressure of oxygen (PaO 2 ) / fraction of inspiration O 2 (FiO 2 ) ≤ 300 mmHg (1 mmHg = 0.133 kPa); 4) pulmonary images show that the lesions progressed more than 50% within 24–48 h. The critical should meet one of the following conditions: 1) respiratory failure and need mechanical ventilation; 2) shock; and 3) other organ failures need ICU monitoring and treatment. Date Collection The data of patients’ demographic characteristics, comorbidities, laboratory findings, chest CT results, and clinical outcomes were extracted from electronic medical records. According to the extent of involvement of each lobe, each lobe was scored as 0 (0%), 1 (1–25%), 2 (26–50%), 3 (51–75%), or 4 (76–100%). The total severity score (TSS) is the cumulative score of five lobes (score range 0–20) [ 10 ] [ 11 ]. In order to ensure the accuracy of the data, all data were checked by two physicians, respectively. Statistical Analysis According to the different data distribution, continuous variables were described as mean ± standard or median (Inter-quartile range, IQR), and groups were compared by student’s t-test or Mann-Whitney U test based on the data distribution. Categorical variables were presented as n (%) and analyzed by Pearson’s chi-square. Receiver operator characteristic (ROC) was used to evaluate the efficacy of NLR, LDH, D-dimer and CT score and get the optimum cutoff. Logistic regression was used to access the predictive value for disease risk. The statistical software needed is SPSS version 21 and Medcalc (version 19.1). A value of p < 0.05 was considered statistically significant. Results Baseline, laboratory and imaging characteristics In this retrospective study, a total of 432 patients with COVID-19 were enrolled, including 202(46.5%) women and 230(53%) men, the average age was 52.88 years. Fever (308, 71.3%), cough (270, 62.5%), expectoration (130, 30.1%) and fatigue (128, 29.6%) were the common symptoms. Hypertension (92, 21.3%) was the most common comorbidity. The patients were divided into two groups: severe group (125/432, 28.94%) and non-severe (307/432, 71.06%) group based on the severity of the disease. Comparing with the non-severe group, in terms of the baseline characteristics, the average age of severe group was older (59.60 ± 16.65 years vs 50.14 ± 16.26 years, p < 0.0001). Meanwhile, it is noticed that severe group has higher incidence of comorbidities, such as hypertension ( p < 0.0001), diabetes ( p < 0.0001), and COPD ( p = 0.009). As for the clinical laboratory findings, lower level of lymphocyte ( p < 0.0001) and higher level of white blood cell ( p = 0.023), neutrophil ( p < 0.0001), C-reaction protein ( p < 0.0001), LDH ( p < 0.0001), D-dimer ( p < 0.0001) and NLR ( p < 0.0001) were detected in severe group as compared with non-severe group. Regarding CT results, 96.0% (120/125) patients had bilateral lung involvement, 32% (40/125) consolidation, and 3.2% (4/125) pleural effusion among patients in severe group. There was significant difference in CT score (6.0 [ 4 – 9 ] for severe group vs 6[ 4 – 7 ] for non-severe group, p < 0.0001) between two groups (Table 1 ). Table 1 Baseline, laboratory and imaging characteristics Variable Total (n = 432) Severe group (n = 125) Non-severe Group (n = 307) p value Age(years) 52.88 ± 16.91 59.60 ± 16.65 50.14 ± 16.26 < 0.0001 Gender 0.072 Female-n (%) 202(46.5) 50(40) 152(49.5) Male-n (%) 230(53) 75(60) 155(50.5) Clinical symptom-n (%) Fever 308(71.3) 94(75.2) 214(69.7) 0.252 Fatigue 128(29.6) 46(36.8) 82(26.7) 0.037 Dyspnea 35(8.1) 20(16) 15(4.9) < 0.0001 Pharyngalgia 34(7.9) 12(9.6) 22(7.2) 0.394 Cough 270(62.5) 84(67.2) 186(60.6) 0.198 Chest tightness 47(10.9) 22(17.6) 25(8.1) 0.004 Diarrhea 20(4.6) 6(4.8) 14(4.6) 0.914 Myalgia 46(10.6) 19(15.2) 27(8.8) 0.05 Expectoration 130(30.1) 35(28) 95(30.9) 0.545 Headache 19(4.4) 6(4.8) 13(4.2) 0.795 Poor appetite 53(12.3) 19(15.2) 34(11.1) 0.236 Comorbidities-n(%) Hypertension 92(21.3) 53(42.4) 39(12.7) < 0.0001 Diabetes 56(13) 31(24.8) 25(8.1) < 0.0001 COPD 25(5.8) 13(10.4) 12(3.9) 0.009 Renal insufficiency 9(2.1) 8(6.4) 1(0.3) < 0.0001 Cardiac insufficiency 8(1.9) 7(5.6) 1(0.3) 0.01 Hepatic insufficiency 30(6.9) 16(12.8) 14(4.6) 0.002 Anemia 13(3.0) 7(5.6) 6(2.0) 0.089 Clinical laboratory White blood cell-10^ 9 /L 5.25 ± 2.52 5.76 ± 3.19 5.04 ± 2.16 0.023 Lymphocyte-10^ 9 /L 1.28 ± 0.62 1.04 ± 0.70 1.37 ± 0.55 < 0.0001 Neutrophil-10^ 9 /L (IQR) 2.98(2.11–4.18) 3.41(2.32–5.50) 2.82(2.08–3.77) < 0.0001 CRP-mg/L(IQR) 22.32(9.15–37.7) 45.2(14.85–55.8) 22.32(7.7-22.32) < 0.0001 Platelet-10^ 9 /L 173.02 ± 80.88 163.03 ± 83.74 177.08 ± 79.47 0.102 D-dimer-µg/ml (IQR) 0.55(0.44–0.82) 0.62(0.50–1.42) 0.52(0.42–0.68) < 0.0001 LDH-U/L(IQR) 210(170-267.75) 265(207.5–356) 196(162–235) < 0.0001 NLR (IQR) 2.33(1.51–3.94) 3.84(2.06–7.13) 2.03(1.41-3,25) < 0.0001 CT manifestations CT score (IQR) 6(4-7.75) 6(4–9) 6(4–7) < 0.0001 Bilateral lung involved-n (%) 359(83.1) 120(96) 239(77.9) < 0.0001 Ground glass opacity-n (%) 426(98.6) 124(99.2) 302(98.4) 0.830 Consolidation-n (%) 96(22.2) 40(32) 56(18.2) 0.002 Pleural effusion-n (%) 5(1.2) 4(3.2) 1(0.3) 0.042 Pleural thickening-n (%) 5(1.2) 1(0.8) 4(1.3) 1.000 Abbreviation: COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein, NLR = neutrophil - to- lymphocyte ratio, LDH = lactate dehydrogenase. Predictive value of NLR, LDH, D-dimer and CT score As Table 1 showed, NLR, LDH, D-dimer, and CT score were statistically significantly higher in the severe group. On the basis of receiver operating characteristic (ROC), the area under curve (AUC) was 0.716 for NLR, 0.740 for LDH, 0.650 for D-dimer, and 0.612 for CT score, indicating certain diagnostic value for the severity of disease (Fig. 1 and Table 2 ). In addition, the optimum cutoff from ROC was 3.82, 246 U/L, 0.83 µg/ml, and 7 for NLR, LDH, D-dimer, and CT score, respectively (Table 2 ). Variables Assessment of validity AUC Optimum cutoff Sensitivity Specificity Predictive value Likelihood ratio positive Negative positive negative NLR 0.716 3.82 50.40% 84.04% 56.3 80.6% 3.16 0.59 LDH(U/L) 0.740 246 59.20% 79.15% 53.6% 82.7% 2.84 0.52 D-dimer(μg/ml) 0.650 0.83 37.6% 84.04% 49% 76.8% 2.36 0.74 CT-score 0.612 7 36.8% 79.8% 42.6% 75.6% 1.82 0.79 Abbreviation: ROC= receiver operator characteristic, NLR=neutrophil-to-lymphocyte ratio, LDH=lactate dehydrogenase. Table 2 Area under ROC curve and optimum cutoff We assumed that when the level of NLR, LDH, D-dimer, and CT score on admission exceeded the optimum cutoff, the patients were prone to develop severe or critical types. According to optimum cutoff, the patients were divided into different subgroups. As Table 3 showed, about 25.9% (112/432), 31.9% (138/432), 22.2% (96/432) and 25% (108/432) patient, respectively, had high level of NLR, LDH, D-dimer and CT score on admission. After grouping, the distribution of baseline NLR [63/125 (50.4%) vs 49/307(16%); p < 0.0001], LDH [74/125(59.2%) vs. 64/307(20.8%); p < 0.0001]; D-dimer[47/125 (37.6%) vs 49/307(16%); p < 0.0001] and CT score [46/125 (36.8%) vs 62/307 (20.2%); p 3.82 112(25.9%) 63(50.4%) 49(16%) p 246 138(31.9%) 74(59.2%) 64(20.8%) p 0.83 96(22.2%) 47(37.6%) 49(16%) p 7 108(25%) 46(36.8%) 62(20.2%) p < 0.0001 ≤ 7 324(75%0 79(63.2%) 245(79.8%) Abbreviation: ROC = receiver operator characteristic, NLR = neutrophil-to-lymphocyte ratio, LDH = lactate dehydrogenase. Univariate analysis indicated that the high level of NLR, LDH, D-dimer and CT score positively correlated with the severity of disease (OR = 5.350; 95%CI = 3.361–8.518; p < 0.0001 for NLR; OR = 5.509;95%CI = 3.511–8.646; p < 0.0001 for LDH; OR = 3.173; 95%CI = 1.976–5.094; p < 0.0001 for D-dimer; OR = 2.301; 95%CI = 1.455–3.638; p 3.82, LDH > 246U/L were persisted (OR = 2.163; 95%CI = 1.162–4.026; p = 0.015 for NLR; OR = 2.298;95%CI = 1.327–3.979; p = 0.003 for LDH). While the relationship between D-dimer > 0.83 µg/ml, CT score > 7 and the severity of disease was weakened (OR = 1.209; 95%CI = 0.626–2.334; p = 0.571 for D-dimer; OR = 1.519;95%CI = 0.71–3.247; p = 0.281 for CT score). In addition, fatigue (OR = 1.978;95%CI = 1.127–3.473; p = 0.018), chest tightness (OR = 2.265; 95%CI = 1.011–5.074; p = 0.047), hypertension (OR = 2.534, 95%CI = 1.259–5.099; p = 0.009), CRP (OR = 1.013; 95%CI = 1.003–1.023; p = 0.011), bilateral lung involved(OR = 3.890; 95%CI = 1.356–11.154; p = 0.011) were still positively correlated with the severity of disease (Table 4 ). Table 4 The univariate and multivariable logistic regression Variables Unadjusted Odds ratio (95%CI) p value Adjusted Odds ratio (95%CI) p value NLR 5.350(3.361,8.518) < 0.0001 2.163(1.162,4.026) 0.015 LDH(U/L) 5.509(3.511,8.646) < 0.0001 2.298(1.327,3.979) 0.003 D-dimer(µg/ml) 3.173(1.976,5.094) < 0.0001 1.209(0.626,2.334) 0.571 CT score 2.301(1.455,3.638) < 0.0001 1.519(0.71,3.247) 0.281 Age 1.036(1.022,1.050) < 0.0001 0.994(0.975,1.014) 0.561 Fatigue 1.598(1.026,2.488) 0.038 1.978(1.127,3.473) 0.018 Dyspnea 3.708(1.831,7.509) < 0.0001 1.348(0.507,3.585) 0.55 Chest tightness 2.409(1.302,4.460) 0.005 2.265(1.011,5.074) 0.047 Hypertension 5.058(3.103,8.245) < 0.0001 2.534(1.259,5.099) 0.009 Diabetes 3.720(2.091,6.619) < 0.0001 1.304(0.597,2.848) 0.506 COPD 2.853(1.264,6.441) 0.012 1.019(0.314,3.303) 0.975 Renal insufficiency 20.923(2.589,169.118) 0.004 4.788(0.449,51.025) 0.195 Cardiac insufficiency 18.153(2.210,149.133) 0.007 2.245(0.135,37.251) 0.573 Hepatic insufficiency 3.072(1.451,6.505) 0.003 2.209(0.842,5.792) 0.107 CRP (mg/L) 1.025(1.017,1.033) < 0.0001 1.013(1.003,1.023) 0.011 Bilateral lung involved 6.828(2.683,17.381) < 0.0001 3.890(1.356,11.154) 0.011 Consolidation 2.109(1.312,3.390) 0.002 1.303(0.6,2.829) 0.504 Pleural effusion 10.116(1.119,91.421) 0.039 5.097(0.409,63.513) 0.206 Abbreviation: NLR = neutrophil - to- lymphocyte ratio, LDH = lactate dehydrogenase, COPD = chronic obstructive pulmonary disease, CRP = C-reactive protein. Evaluation of multi-parameter model According to the logistic regression, NLR > 3.82 and LDH > 246U/L were statistically significant risk factors (Table 4 ). And as Table 2 showed, the sensitivity of NLR > 3.82 and LDH > 246U/L in predicting the severity of COVID-19 were 50.40% and 59.20%, respectively. Then, further evaluation was made to judge whether the combined diagnosis model of two indexes can improve the sensitivity of prediction. The Table 5 indicated that the combined diagnosis of NLR > 3.82 and LDH > 246U/L could increase the sensitivity in predicting the severity of disease [NLR > 3.82(50.40%) vs combined diagnosis model (72.80%); p = 0.0007; LDH > 246(59.2%) vs combined diagnosis model (72.80%); p 3.82 0.0007 1 50.40% 84.04% LDH > 246U/L 3.82 and LDH > 246U/L, 0.0007 1 = p value between NLR > 3.82 and combined diagnosis model, 246U/L and combined diagnosis model. Discussion A total of 432 patients with COVID-19 were included in this retrospective study. In the univariate analysis, we found that the high level of NLR, LDH, D-dimer, and CT score have significant correlation with the severity of COVID-19. While after adjusting other statistically significant indexes, the predictive value of NLR > 3.82, LDH > 246U/L were persisted. This indicated that when NLR exceeded the cutoff point, the risk of serious disease increased by 2.163 times. And the risk of LDH over optimum cutoff increased by 2.298 times. While the value of D-dimer > 0.83 µg/ml and CT score > 7 in predicting the severity of disease was weak and could not be recommended as independent predictors. In addition, the risk of severity was also closely related to fatigue, chest tightness, hypertension and CRP. Meanwhile, combining NLR > 3.82 and LDH > 246U/L can improve the sensitivity of disease risk prediction. Immune dysfunction plays an important role in the severity of COVID-19 [ 12 ]. Recent studies have elucidated that neutropenia and lower lymphopenia could be found in the severe group of COVID-19[ 13 , 14 ]. NLR took lymphocyte and neutrophil into account at the same time. Several studies have shown the predictive value of NLR in distinguishing COVID-19 patients with severe and critical types. In a study of the dynamic changes of lymphocyte subsets and cytokine profiles in patients with COVID-19, NLR can be used as a prognostic factor for early identification of severe cases[ 15 ]. A cohort of patients with COVID-19 also proved that, after adjustment of confounding factors, each unit increase in NLR, the risk of in-hospital mortality increases by 8%[ 16 ]. Another study conducted by Yang X et. al, [ 6 ] in 93 patients with COVID-19 demonstrated that NLR can be used as independent indicators for poor clinical outcome and the largest AUC for NLR were 0.841,with specificity (63.6%) and sensitivity (88%). However, limited by sample diversity, the outcome needs further evaluation. In the present study, the predictive value of NLR is consistent with abovementioned studies. Meanwhile, the sample size and diversity were enriched by collecting data from two clinical centers, which will strengthen the reliability of conclusions. The optimum cutoff for NLR was 3.82 and AUC was 0.716. And the sensitivity and specificity of NLR > 3.82 were 50.40% and 84.04%, respectively. Meanwhile, in multivariate logistic regression, NLR > 3.82 can be used as an independent predictor for disease risk (OR = 2.163;95%CI = 1.162–4.026; p = 0.015). The elevation of LDH was one of the most common laboratory abnormalities in patients with COVID-19. Acute lung injury was highly related to LDH[ 17 ]. A systematic literature review and meta-analysis had shown that LDH > 245U/L can predict the progress of COVID-19[ 7 ]. In a study of the risk factors for death in cancer patients with COVID-19, the elevated LDH was closely related to the increase of mortality [ 18 ]. Furthermore, in another retrospective analysis of 120 patients with COVID-19, comparing with mild patients, the severe patients have higher LDH levels (mean 200.8 U/L for mild vs mean 342.8 U/L for severe)[ 19 ]. The predictive value of LDH is further confirmed by our study. Meanwhile, ROC analysis showed that the AUC for LDH was 0.74 and the optimum cutoff was 246 U/L. The sensitivity was 59.2% and the specificity was 79.15%. And the logistic regression indicated that the risk of serious disease increased by 2.298 times when LDH over optimum cutoff (OR = 2.298; 95%CI = 1.327–3.979; p = 0.003). In addition, the sensitivity of disease risk prediction can be improved by combining LDH > 246U/L with NLR > 3.82. (NLR > 3.82 [50.40%] vs. combined diagnosis model [72.80%]; p = 0.0007; LDH > 246 [59.2%] vs. combined diagnosis model [72.80%]; p 3.82 [84.04%] vs. combined diagnosis model [69.71%]; p = 0.0007; LDH > 246[79.15%] vs. combined diagnosis model [69.71%]; p < 0.0001). Moreover, the sensitivity, specificity, and AUC for NLR and LDH are not relatively high enough. Due to the different admission time of patients with COVID-19 and the acute aggravation of some patients in a period of time after admission, the value of admission indicators may be underestimated. However, compared with other articles[ 6 , 15 , 20 ], the sample size and diversity of patients with COVID-19 increase the reliability of the results in this study. Meanwhile, more importantly, the optimum cutoff can indicate the risk of acute aggravation of patients with COVID-19 in the present study. Furthermore, it provides more evidence for the establishment of multi-parameter diagnosis model. Coagulation disorders are more common in severe patients than in light patients [ 21 , 22 ]. A study conducted by Zhang L et al. [ 23 ] had proved that the level of D-dimer ≥ 2.0 µg/mL (fourfold increase) could effectively predict the mortality of patients with COVID-19. While after balancing the confounding factors, the logistic regression showed that D-dimer > 0.83 µg/ml could not be used as an independent predictor of disease risk in this study (OR = 1.209; 95%CI = 0.626–2.334; p = 0.571). In a dynamic study of hematological parameters in patients with COVID-19, the D-dimer of the severe group was higher than the non-severe group on days 1, 7 and 14 ( p < 0.05) [ 24 ]. This suggests that due to different admission times, the ability of D-dimer to predict disease risk may be weakened. In another response to the prognostic value of D-dimer in patients with COVID-19, the predictive value of D-dimer might be affected by other factors, such as hormonotherapy, antibiotic therapy et al. Due to the baseline level of D-dimer varies greatly in patients, the value of dynamic monitoring of D-dimer may be higher in patients with COVID-19[ 25 ]. Further researches are still required to evaluate the significance of D-dimer in evaluating the severity of COVID-19. COVID-19 patients have lung involvement with imaging changes [ 10 , 26 ]. In different stages of the disease, the CT manifestations are different, which are important to the diagnosis and staging of patients [ 27 ]. With the same semi-quantitative scoring system, a multi-center paired cohort study conducted by J. Liu et al. [ 28 ] showed that CT changes are obvious in acute exacerbation of COVID-19, accompanied by an increase of CT score. This indicated that elevated CT score may predict the poor outcome. Another retrospective single-center study indicated that the CT score has a high diagnostic value in patients with severe COVID-19. ROC analysis showed that AUC for CT score was 0.918. The optimum cutoff of CT score was 7.5. The sensitivity was 82.6% and the specificity was 100% [ 11 ]. However, the study only analyzes imaging, without combined with clinical data. And significant differences in the number of patients between severe-critical patients and non-severe groups also affect the accuracy of the results. While in present study, after combining the clinical data, the CT score can’t be used as an independent predictor of disease risk (OR = 1.519; 95%CI = 0.71–3.247; p = 0.281). A study by Zhang B et. al[ 29 ], demonstrated that the severity of lung abnormalities evaluated by CT score might be associated with laboratory parameters. Therefore, due to correlation between CT score and laboratory parameters, the ability to independently predict disease risk of CT score may be attenuated. Additional investigations are warranted to assess whether CT score can be an independent predictor of disease risk. There are some limitations in this study. First, owing to different severity of patients and different medical resources, the time from onset to admission might not be representative, which might affect the level of four parameters on admission. Meanwhile, the representativeness of CT score and D-dimer may also be affected by different admission times. Second, other clinical data and test results are not included in the analysis, which may cause bias, weakening the reliability of the results. Third, to a certain degree, the CT score as a semi-quantitative evaluation method was subjective. Conclusion As independent factors, the levels of NLR, LDH in serum have a significant correlation with the severity of COVID-19. We suggested that NLR and LDH could be recommended as predictors for evaluating the severity of COVID-19. Abbreviations COVID-19: coronavirus disease-2019 NLR: neutrophil-to-Lymphocyte Ratio LDH: lactate dehydrogenase CT: computed tomography PaO 2 : partial pressure of oxygen FiO 2 : fraction of inspiration O 2 TSS: total severity score IQR: inter-quartile range ROC: receiver operator characteristic Declarations Ethics approval and consent to participate: The study was approved by the Ethics Committee of Zhongshan Hospital, Xiamen University and Second affiliated Hospital of Fujian Medical University. Consent for publication: No applicable. Availability of data and material: All data generated or analyzed during this study are included in this published article. Competing interests: The authors declare that they have no conflict of interest. Funding: This work was supported by Grant 2018-2-65 for Youth Research Fund from Fujian Provincial Health Bureau, and Grant 2018J01393 for Fund from Natural Science Foundation of Fujian Province, China. Authors' contributions: Conception and design: Y-Q Cai, X-B Zhang, and H-Q Zeng. Collection and assembly of data: Y-Q Cai, X-B Zhang, X-J Wei, Z-Y Zhang, L-D Chen, M-H Wang, W-Z Yao, Q-F Huang, Z-Q Ye. Data analysis and interpretation: Y-Q Cai, X-B Zhang, and H-Q Zeng. Manuscript writing: All authors. Final approval of manuscript: All authors. Acknowledgments: Not applicable. References Lai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges. Int J Antimicrob Agents 2020, 55: 105924. The diagnosis of the novel coronavirus pneumonia exceeds 4.1 million British and French gradually "unsealed" [ https://baijiahao.baidu.com/s?id=1666376262699977033&wfr=spider&for=pc ] Verelst F, Kuylen E, Beutels P: Indications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020. Euro Surveill 2020, 25 . Ji Y, Ma Z, Peppelenbosch MP, Pan Q: Potential association between COVID-19 mortality and health-care resource availability. The Lancet Global Health 2020, 8 . Velavan TP, Meyer CG: Mild versus severe COVID-19: laboratory markers. Int J Infect Dis 2020. Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, et al: Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med 2020. Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu S, et al: Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. Journal of Infection 2020. Colombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, Silva M, Sverzellati N, Michieletti E: Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia. Radiology 2020 : 201433. Diagnosis and Treatment of Pneumonia of New Coronavirus Infection (Trial Version 7) [ http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml ] Chung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, et al: CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020, 295: 202-207. Li K, Fang Y, Li W, Pan C, Qin P, Zhong Y, Liu X, Huang M, Liao Y, Li S: CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). Eur Radiol 2020. Giamarellos-Bourboulis EJ, Netea MG, Rovina N, Akinosoglou K, Antoniadou A, Antonakos N, Damoraki G, Gkavogianni T, Adami M-E, Katsaounou P, et al: Complex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure. Cell Host & Microbe 2020. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, Xie C, Ma K, Shang K, Wang W, Tian DS: Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis 2020. Jiang M, Guo Y, Luo Q, Huang Z, Zhao R, Liu S, Le A, Li J, Wan L: T cell subset counts in peripheral blood can be used as discriminatory biomarkers for diagnosis and severity prediction of COVID-19. J Infect Dis 2020. Liu J, Li S, Liu J, Liang B, Wang X, Wang H, Li W, Tong Q, Yi J, Zhao L, et al: Longitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients. EBioMedicine 2020, 55 . Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HHX, Luo M, Chen L, Zhao Y: Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. Journal of Infection 2020. Liu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, Wang Z, Li J, Li J, Feng C, et al: Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury. Sci China Life Sci 2020, 63: 364-374. Mehta V, Goel S, Kabarriti R, Cole D, Goldfinger M, Acuna-Villaorduna A, Pradhan K, Thota R, Reissman S, Sparano JA, et al: Case Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System. Cancer Discov 2020. Zhang R, Ouyang H, Fu L, Wang S, Han J, Huang K, Jia M, Song Q, Fu Z: CT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city. Eur Radiol 2020. Xia X, Wen M, Zhan S, He J, Chen W: [An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19]. Nan Fang Yi Ke Da Xue Xue Bao 2020, 40: 333-336. Tang N, Li D, Wang X, Sun Z: Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. Journal of Thrombosis and Haemostasis 2020, 18: 844-847. Terpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, Psaltopoulou T, Gerotziafas G, Dimopoulos MA: Hematological findings and complications of COVID-19. Am J Hematol 2020. Zhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, Zhang Z: D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost 2020. Fu J, Kong J, Wang W, Wu M, Yao L, Wang Z, Jin J, Wu D, Yu X: The clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: A retrospective study in Suzhou China. Thromb Res 2020, 192: 3-8. Zhang L: Response to "uncertainties on the prognostic value of D-dimers in COVID-19 patients". J Thromb Haemost 2020. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, et al: Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. The Lancet 2020, 395: 507-513. Li M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, Liu H: Coronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease. Academic Radiology 2020, 27: 603-608. Liu J, Chen T, Yang H, Cai Y, Yu Q, Chen J, Chen Z, Shang QL, Ma C, Chen X, Xiao E: Clinical and radiological changes of hospitalised patients with COVID-19 pneumonia from disease onset to acute exacerbation: a multicentre paired cohort study. Eur Radiol 2020. Zhang B, Zhang J, Chen H, Chen L, Chen Q, Li M, Chen Z, You J, Yang K, Zhang S: Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters. Eur J Nucl Med Mol Imaging 2020. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-30959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":609583,"identity":"2d0e2d9a-af8e-4428-bb9b-2e598af6819b","order_by":0,"name":"Yu-Qing Cai","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yu-Qing","middleName":"","lastName":"Cai","suffix":""},{"id":609584,"identity":"ef89237e-695c-4c04-940f-816a7917d749","order_by":1,"name":"Xiao-Bin Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYDACCRgpwdgAZNhARHlI0JJGtBY44zBhLfKzm589/NpmIc8v3dz24OeO84lrZyQwPnjbxiBvjkML45xj5sYyZyQMZ8452G7Ye+a2sdmNBGbDuW0MhjsbsGthlkgwk5aokGDccCOxTZqx7bYcUAubNG8bQ4LBAexa2CTSv0lLGEjYQ7Wc4wFqYf+NTwuPRI6Z5IcKiUSolgNgW5jxaZGQyCmTZjgjkQz0S5tkb1uysdmZh82Sc85JGG7AoUV+Rvo2yZ9tdbb90u3PJH622SVuO5588MObMht5XLaAgwAtFsBxKoFNJULJD7zSo2AUjIJRMOIBAJOnV7v3GfKEAAAAAElFTkSuQmCC","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xiao-Bin","middleName":"","lastName":"Zhang","suffix":""},{"id":609585,"identity":"c4f66b0e-8f2a-4081-9c56-08ab158ba0d5","order_by":2,"name":"Hui-Qing Zeng","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui-Qing","middleName":"","lastName":"Zeng","suffix":""},{"id":609586,"identity":"4a4d27c8-dd5a-4f36-b746-5402bcbb7412","order_by":3,"name":"Xiao-Jie Wei","email":"","orcid":"","institution":"Third Hospital of Fujian Provincine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao-Jie","middleName":"","lastName":"Wei","suffix":""},{"id":609587,"identity":"997d8138-cdf6-4aa6-996e-4643e614a0bc","order_by":4,"name":"Zhen-Yu Zhang","email":"","orcid":"","institution":"Zhangzhou Municipal Hospital of Fujian Province and Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhen-Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":609588,"identity":"cfbe83ab-6c40-4894-afa9-23ba333153b5","order_by":5,"name":"Li-Da Chen","email":"","orcid":"","institution":"Zhangzhou Municipal Hospital of Fujian Province and Zhangzhou Affiliated Hospital of Fujian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li-Da","middleName":"","lastName":"Chen","suffix":""},{"id":609589,"identity":"308ffa14-87f2-4435-9aef-397d51462d8c","order_by":6,"name":"Ming-Hui Wang","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming-Hui","middleName":"","lastName":"Wang","suffix":""},{"id":609590,"identity":"2d596534-e4a4-4441-978a-d0435d4f329a","order_by":7,"name":"Wen-Zhen Yao","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen-Zhen","middleName":"","lastName":"Yao","suffix":""},{"id":609591,"identity":"365298d9-adc6-4b78-b501-a67c9b6735c3","order_by":8,"name":"Qiu-Fen Huang","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiu-Fen","middleName":"","lastName":"Huang","suffix":""},{"id":609592,"identity":"bd18c644-7624-4004-9469-80667fde5a57","order_by":9,"name":"Zhang-Qiang Ye","email":"","orcid":"","institution":"Zhongshan Hospital Xiamen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhang-Qiang","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2020-05-22 04:30:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-30959/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-30959/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":1213141,"identity":"422e45f5-2b9b-425f-90b7-fe27d90cb354","added_by":"auto","created_at":"2020-05-29 21:22:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1866293,"visible":true,"origin":"","legend":"ROC analysis of NLR, LDH, D-dimer and CT score in disease risk prediction (A. NLR; B.LDH; C. D-dimer; D. CT score).","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-30959/v1/Fig1.jpg"},{"id":13533754,"identity":"b093a6bd-ca18-4506-9a80-69d1b0c707f0","added_by":"auto","created_at":"2021-09-17 01:22:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125670,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-30959/v1/79d90a06-d1a5-43c2-b72f-398ef6b3bdef.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003ePrognostic Value of Neutrophil-To-Lymphocyte Ratio, Lactate Dehydrogenase, D-Dimer and CT Score in Patients with COVID-19\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eSince December 2019, coronavirus disease-2019 (COVID-19) caused by a novel coronavirus (SARS-CoV-2) spread rapidly all over the world and has caused a major public health issue [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. By the morning of May 11, Beijing time, the number of patients has exceeded 4.1\u0026nbsp;million [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is obviously a huge challenge to the global healthcare system[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. And the mortality of COVID-19 patients is related to the health-care burden[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].Therefore, the reasonable distribution of medical resources is particularly important. Early identification of critical patients is critical to the rational allocation of resources and the improvement of patients' prognosis.\u003c/p\u003e \u003cp\u003eMeanwhile, according to reports, compared with non-severe patients, the hematological changes of severe patients are more prominent[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Neutrophil-to-Lymphocyte Ratio (NLR), lactate dehydrogenase (LDH), and D-dimer are close with the poor prognosis of COVID-19[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].Without other clinical parameters, the computed tomography (CT) evaluation is an independent prognostic factor in patients with COVID-19 [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, there is little data about the comparison of the above four indicators. Therefore, in this study, we aimed to compare the prediction efficiency of four indicators and evaluate the significance of optimum cutoff. Subsequently, combined diagnosis analysis was performed to evaluate whether it can improve the diagnosis efficiency.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eFrom January 20, 2020, to March 30, 2020, a total of 432 patients confirmed COVID-19 by the laboratory in designated treatment hospitals (Optic Valley division of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan and Yichang Third People's Hospital, Hubei Province) were enrolled. The patients were divided into 2 groups based on the seventh edition of the New Coronavirus Pneumonia Diagnosis and Treatment Program published by the Chinese National Health Commission[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]: the mild and moderate types were classified as non-severe group and the severe and critical were included into severe group. The disease is classified as severe if one of the following items is met: 1) shortness of breath, respiratory rate\u0026thinsp;\u0026ge;\u0026thinsp;30 beats per min; 2) the oxygen saturation\u0026thinsp;\u0026le;\u0026thinsp;93% in a resting state; 3) arterial partial pressure of oxygen (PaO\u003csub\u003e2\u003c/sub\u003e) / fraction of inspiration O\u003csub\u003e2\u003c/sub\u003e (FiO\u003csub\u003e2\u003c/sub\u003e)\u0026thinsp;\u0026le;\u0026thinsp;300\u0026nbsp;mmHg (1\u0026nbsp;mmHg\u0026thinsp;=\u0026thinsp;0.133\u0026nbsp;kPa); 4) pulmonary images show that the lesions progressed more than 50% within 24\u0026ndash;48\u0026nbsp;h. The critical should meet one of the following conditions: 1) respiratory failure and need mechanical ventilation; 2) shock; and 3) other organ failures need ICU monitoring and treatment.\u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eDate Collection \u003c/h2\u003e\n\u003cp\u003eThe data of patients\u0026rsquo; demographic characteristics, comorbidities, laboratory findings, chest CT results, and clinical outcomes were extracted from electronic medical records. According to the extent of involvement of each lobe, each lobe was scored as 0 (0%), 1 (1\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%), or 4 (76\u0026ndash;100%). The total severity score (TSS) is the cumulative score of five lobes (score range 0\u0026ndash;20) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In order to ensure the accuracy of the data, all data were checked by two physicians, respectively.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAccording to the different data distribution, continuous variables were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard or median (Inter-quartile range, IQR), and groups were compared by student\u0026rsquo;s t-test or Mann-Whitney U test based on the data distribution. Categorical variables were presented as n (%) and analyzed by Pearson\u0026rsquo;s chi-square. Receiver operator characteristic (ROC) was used to evaluate the efficacy of NLR, LDH, D-dimer and CT score and get the optimum cutoff. Logistic regression was used to access the predictive value for disease risk. The statistical software needed is SPSS version 21 and Medcalc (version 19.1). A value of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBaseline, laboratory and imaging characteristics\u003c/h2\u003e \u003cp\u003eIn this retrospective study, a total of 432 patients with COVID-19 were enrolled, including 202(46.5%) women and 230(53%) men, the average age was 52.88\u0026nbsp;years. Fever (308, 71.3%), cough (270, 62.5%), expectoration (130, 30.1%) and fatigue (128, 29.6%) were the common symptoms. Hypertension (92, 21.3%) was the most common comorbidity.\u003c/p\u003e \u003cp\u003eThe patients were divided into two groups: severe group (125/432, 28.94%) and non-severe (307/432, 71.06%) group based on the severity of the disease. Comparing with the non-severe group, in terms of the baseline characteristics, the average age of severe group was older (59.60\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65\u0026nbsp;years \u003cem\u003evs\u003c/em\u003e 50.14\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Meanwhile, it is noticed that severe group has higher incidence of comorbidities, such as hypertension (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), diabetes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), and COPD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). As for the clinical laboratory findings, lower level of lymphocyte (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and higher level of white blood cell (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023), neutrophil (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), C-reaction protein (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), LDH (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), D-dimer (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and NLR (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were detected in severe group as compared with non-severe group. Regarding CT results, 96.0% (120/125) patients had bilateral lung involvement, 32% (40/125) consolidation, and 3.2% (4/125) pleural effusion among patients in severe group. There was significant difference in CT score (6.0 [\u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] for severe group \u003cem\u003evs\u003c/em\u003e 6[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] for non-severe group, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) between two groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline, laboratory and imaging characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;432)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;125)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-severe\u003c/p\u003e \u003cp\u003eGroup (n\u0026thinsp;=\u0026thinsp;307)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.60\u0026thinsp;\u0026plusmn;\u0026thinsp;16.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.14\u0026thinsp;\u0026plusmn;\u0026thinsp;16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152(49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230(53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75(60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155(50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical symptom-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e308(71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94(75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214(69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128(29.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharyngalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34(7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e270(62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84(67.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186(60.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest tightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47(10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiarrhea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyalgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpectoration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130(30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95(30.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeadache\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor appetite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities-n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(21.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(42.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39(12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56(13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(5.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(6.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClinical laboratory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell-10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.76\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocyte-10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil-10^\u003csup\u003e9\u003c/sup\u003e/L (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98(2.11\u0026ndash;4.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.41(2.32\u0026ndash;5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.82(2.08\u0026ndash;3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP-mg/L(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.32(9.15\u0026ndash;37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.2(14.85\u0026ndash;55.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.32(7.7-22.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet-10^\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.02\u0026thinsp;\u0026plusmn;\u0026thinsp;80.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163.03\u0026thinsp;\u0026plusmn;\u0026thinsp;83.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e177.08\u0026thinsp;\u0026plusmn;\u0026thinsp;79.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer-\u0026micro;g/ml (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.55(0.44\u0026ndash;0.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.62(0.50\u0026ndash;1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52(0.42\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH-U/L(IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e210(170-267.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e265(207.5\u0026ndash;356)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e196(162\u0026ndash;235)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.33(1.51\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84(2.06\u0026ndash;7.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.03(1.41-3,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT manifestations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(4-7.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(4\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral lung involved-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359(83.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120(96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239(77.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGround glass opacity-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426(98.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124(99.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302(98.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsolidation-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(22.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40(32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural effusion-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural thickening-n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease, CRP\u0026thinsp;=\u0026thinsp;C-reactive protein, NLR\u0026thinsp;=\u0026thinsp;neutrophil - to- lymphocyte ratio, LDH\u0026thinsp;=\u0026thinsp;lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003ePredictive value of NLR, LDH, D-dimer and CT score\u003c/h2\u003e\n\u003cp\u003eAs Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed, NLR, LDH, D-dimer, and CT score were statistically significantly higher in the severe group. On the basis of receiver operating characteristic (ROC), the area under curve (AUC) was 0.716 for NLR, 0.740 for LDH, 0.650 for D-dimer, and 0.612 for CT score, indicating certain diagnostic value for the severity of disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, the optimum cutoff from ROC was 3.82, 246\u0026nbsp;U/L, 0.83\u0026nbsp;\u0026micro;g/ml, and 7 for NLR, LDH, D-dimer, and CT score, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" width=\"100\"\u003e\n\u003cp\u003eVariables\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"8\" width=\"564\"\u003e\n\u003cp\u003eAssessment of validity\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"50\"\u003e\n\u003cp\u003eAUC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"76\"\u003e\n\u003cp\u003eOptimum cutoff\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"83\"\u003e\n\u003cp\u003eSensitivity\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" width=\"84\"\u003e\n\u003cp\u003eSpecificity\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"137\"\u003e\n\u003cp\u003ePredictive value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"134\"\u003e\n\u003cp\u003eLikelihood ratio\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003eNegative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003epositive\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003enegative\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eNLR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e0.716\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e3.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e50.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e84.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e56.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e80.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e3.16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eLDH(U/L)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e0.740\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e246\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e59.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e79.15%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e53.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e82.7%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e2.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.52\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eD-dimer(\u0026mu;g/ml)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e0.650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e37.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e84.04%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e49%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e76.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e2.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"100\"\u003e\n\u003cp\u003eCT-score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003e0.612\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"76\"\u003e\n\u003cp\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e36.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"84\"\u003e\n\u003cp\u003e79.8%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e42.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"72\"\u003e\n\u003cp\u003e75.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"65\"\u003e\n\u003cp\u003e1.82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"69\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" width=\"0%\"\u003e\n\u003cp\u003eAbbreviation: ROC= receiver operator characteristic, NLR=neutrophil-to-lymphocyte ratio, LDH=lactate dehydrogenase.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eArea under ROC curve and optimum cutoff\u003c/p\u003e\n\u003c/div\u003e\n \u003cp\u003eWe assumed that when the level of NLR, LDH, D-dimer, and CT score on admission exceeded the optimum cutoff, the patients were prone to develop severe or critical types. According to optimum cutoff, the patients were divided into different subgroups.\u003c/p\u003e \u003cp\u003eAs Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e showed, about 25.9% (112/432), 31.9% (138/432), 22.2% (96/432) and 25% (108/432) patient, respectively, had high level of NLR, LDH, D-dimer and CT score on admission. After grouping, the distribution of baseline NLR [63/125 (50.4%) \u003cem\u003evs\u003c/em\u003e 49/307(16%); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001], LDH [74/125(59.2%) \u003cem\u003evs.\u003c/em\u003e 64/307(20.8%); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001]; D-dimer[47/125 (37.6%) \u003cem\u003evs\u003c/em\u003e 49/307(16%); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] and CT score [46/125 (36.8%) \u003cem\u003evs\u003c/em\u003e 62/307 (20.2%); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001] over optimum cutoff in two groups were significantly significant (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline after grouping\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;432\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSevere group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;125\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon -severe group\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;307\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63(50.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e320(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62(49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138(31.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74(59.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e64(20.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e294(68.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51(40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e243(79.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer(\u0026micro;g/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96(22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49(16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336(77.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e258(84%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46(36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62(20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324(75%0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79(63.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245(79.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: ROC\u0026thinsp;=\u0026thinsp;receiver operator characteristic, NLR\u0026thinsp;=\u0026thinsp;neutrophil-to-lymphocyte ratio, LDH\u0026thinsp;=\u0026thinsp;lactate dehydrogenase.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUnivariate analysis indicated that the high level of NLR, LDH, D-dimer and CT score positively correlated with the severity of disease (OR\u0026thinsp;=\u0026thinsp;5.350; 95%CI\u0026thinsp;=\u0026thinsp;3.361\u0026ndash;8.518; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001 for NLR; OR\u0026thinsp;=\u0026thinsp;5.509;95%CI\u0026thinsp;=\u0026thinsp;3.511\u0026ndash;8.646;\u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001 for LDH; OR\u0026thinsp;=\u0026thinsp;3.173; 95%CI\u0026thinsp;=\u0026thinsp;1.976\u0026ndash;5.094; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001 for D-dimer; OR\u0026thinsp;=\u0026thinsp;2.301; 95%CI\u0026thinsp;=\u0026thinsp;1.455\u0026ndash;3.638; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001 for CT score). However, after adjusting other statistically significant index, the predictive value of NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82, LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L were persisted (OR\u0026thinsp;=\u0026thinsp;2.163; 95%CI\u0026thinsp;=\u0026thinsp;1.162\u0026ndash;4.026; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.015 for NLR; OR\u0026thinsp;=\u0026thinsp;2.298;95%CI\u0026thinsp;=\u0026thinsp;1.327\u0026ndash;3.979; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003 for LDH). While the relationship between D-dimer\u0026thinsp;\u0026gt;\u0026thinsp;0.83\u0026nbsp;\u0026micro;g/ml, CT score\u0026thinsp;\u0026gt;\u0026thinsp;7 and the severity of disease was weakened (OR\u0026thinsp;=\u0026thinsp;1.209; 95%CI\u0026thinsp;=\u0026thinsp;0.626\u0026ndash;2.334; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.571 for D-dimer; OR\u0026thinsp;=\u0026thinsp;1.519;95%CI\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;3.247; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.281 for CT score). In addition, fatigue (OR\u0026thinsp;=\u0026thinsp;1.978;95%CI\u0026thinsp;=\u0026thinsp;1.127\u0026ndash;3.473; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.018), chest tightness (OR\u0026thinsp;=\u0026thinsp;2.265; 95%CI\u0026thinsp;=\u0026thinsp;1.011\u0026ndash;5.074; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.047), hypertension (OR\u0026thinsp;=\u0026thinsp;2.534, 95%CI\u0026thinsp;=\u0026thinsp;1.259\u0026ndash;5.099; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.009), CRP (OR\u0026thinsp;=\u0026thinsp;1.013; 95%CI\u0026thinsp;=\u0026thinsp;1.003\u0026ndash;1.023; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.011), bilateral lung involved(OR\u0026thinsp;=\u0026thinsp;3.890; 95%CI\u0026thinsp;=\u0026thinsp;1.356\u0026ndash;11.154;\u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.011) were still positively correlated with the severity of disease (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe univariate and multivariable logistic regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnadjusted Odds ratio\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusted Odds ratio\u003c/p\u003e \u003cp\u003e(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.350(3.361,8.518)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.163(1.162,4.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.509(3.511,8.646)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.298(1.327,3.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer(\u0026micro;g/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.173(1.976,5.094)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.209(0.626,2.334)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.571\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.301(1.455,3.638)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.519(0.71,3.247)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.036(1.022,1.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994(0.975,1.014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.598(1.026,2.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.978(1.127,3.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.708(1.831,7.509)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.348(0.507,3.585)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChest tightness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.409(1.302,4.460)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.265(1.011,5.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.058(3.103,8.245)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.534(1.259,5.099)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.720(2.091,6.619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.304(0.597,2.848)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.853(1.264,6.441)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.019(0.314,3.303)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.923(2.589,169.118)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.788(0.449,51.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiac insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.153(2.210,149.133)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.245(0.135,37.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHepatic insufficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.072(1.451,6.505)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.209(0.842,5.792)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.025(1.017,1.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.013(1.003,1.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral lung involved\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.828(2.683,17.381)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.890(1.356,11.154)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsolidation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.109(1.312,3.390)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.303(0.6,2.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePleural effusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.116(1.119,91.421)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.097(0.409,63.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviation: NLR\u0026thinsp;=\u0026thinsp;neutrophil - to- lymphocyte ratio, LDH\u0026thinsp;=\u0026thinsp;lactate dehydrogenase, COPD\u0026thinsp;=\u0026thinsp;chronic obstructive pulmonary disease, CRP\u0026thinsp;=\u0026thinsp;C-reactive protein.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003ch2\u003eEvaluation of multi-parameter model\u003c/h2\u003e\n\u003cp\u003eAccording to the logistic regression, NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L were statistically significant risk factors (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). And as Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e showed, the sensitivity of NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L in predicting the severity of COVID-19 were 50.40% and 59.20%, respectively. Then, further evaluation was made to judge whether the combined diagnosis model of two indexes can improve the sensitivity of prediction.\u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicated that the combined diagnosis of NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L could increase the sensitivity in predicting the severity of disease [NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82(50.40%) \u003cem\u003evs\u003c/em\u003e combined diagnosis model (72.80%); \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007; LDH\u0026thinsp;\u0026gt;\u0026thinsp;246(59.2%) \u003cem\u003evs\u003c/em\u003e combined diagnosis model (72.80%); \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of univariate and combined diagnosis model\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0007\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.04%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined diagnosis model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.71%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviation: NLR\u0026thinsp;=\u0026thinsp;neutrophil - to- lymphocyte ratio, LDH\u0026thinsp;=\u0026thinsp;lactate dehydrogenase, combined diagnosis model\u0026thinsp;=\u0026thinsp;NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L, 0.0007\u003csup\u003e1\u003c/sup\u003e=\u003cem\u003ep\u003c/em\u003e value between NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and combined diagnosis model, \u0026lt;\u0026thinsp;0.0001\u003csup\u003e2=\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e value between LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L and combined diagnosis model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eA total of 432 patients with COVID-19 were included in this retrospective study. In the univariate analysis, we found that the high level of NLR, LDH, D-dimer, and CT score have significant correlation with the severity of COVID-19. While after adjusting other statistically significant indexes, the predictive value of NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82, LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L were persisted. This indicated that when NLR exceeded the cutoff point, the risk of serious disease increased by 2.163 times. And the risk of LDH over optimum cutoff increased by 2.298 times. While the value of D-dimer\u0026thinsp;\u0026gt;\u0026thinsp;0.83\u0026nbsp;\u0026micro;g/ml and CT score\u0026thinsp;\u0026gt;\u0026thinsp;7 in predicting the severity of disease was weak and could not be recommended as independent predictors. In addition, the risk of severity was also closely related to fatigue, chest tightness, hypertension and CRP. Meanwhile, combining NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 and LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L can improve the sensitivity of disease risk prediction.\u003c/p\u003e \u003cp\u003eImmune dysfunction plays an important role in the severity of COVID-19 [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have elucidated that neutropenia and lower lymphopenia could be found in the severe group of COVID-19[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. NLR took lymphocyte and neutrophil into account at the same time. Several studies have shown the predictive value of NLR in distinguishing COVID-19 patients with severe and critical types. In a study of the dynamic changes of lymphocyte subsets and cytokine profiles in patients with COVID-19, NLR can be used as a prognostic factor for early identification of severe cases[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. A cohort of patients with COVID-19 also proved that, after adjustment of confounding factors, each unit increase in NLR, the risk of in-hospital mortality increases by 8%[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Another study conducted by Yang X et. al, [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] in 93 patients with COVID-19 demonstrated that NLR can be used as independent indicators for poor clinical outcome and the largest AUC for NLR were 0.841,with specificity (63.6%) and sensitivity (88%). However, limited by sample diversity, the outcome needs further evaluation. In the present study, the predictive value of NLR is consistent with abovementioned studies. Meanwhile, the sample size and diversity were enriched by collecting data from two clinical centers, which will strengthen the reliability of conclusions. The optimum cutoff for NLR was 3.82 and AUC was 0.716. And the sensitivity and specificity of NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 were 50.40% and 84.04%, respectively. Meanwhile, in multivariate logistic regression, NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 can be used as an independent predictor for disease risk (OR\u0026thinsp;=\u0026thinsp;2.163;95%CI\u0026thinsp;=\u0026thinsp;1.162\u0026ndash;4.026; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.015).\u003c/p\u003e \u003cp\u003eThe elevation of LDH was one of the most common laboratory abnormalities in patients with COVID-19. Acute lung injury was highly related to LDH[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A systematic literature review and meta-analysis had shown that LDH\u0026thinsp;\u0026gt;\u0026thinsp;245U/L can predict the progress of COVID-19[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In a study of the risk factors for death in cancer patients with COVID-19, the elevated LDH was closely related to the increase of mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, in another retrospective analysis of 120 patients with COVID-19, comparing with mild patients, the severe patients have higher LDH levels (mean 200.8\u0026nbsp;U/L for mild \u003cem\u003evs\u003c/em\u003e mean 342.8\u0026nbsp;U/L for severe)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The predictive value of LDH is further confirmed by our study. Meanwhile, ROC analysis showed that the AUC for LDH was 0.74 and the optimum cutoff was 246\u0026nbsp;U/L. The sensitivity was 59.2% and the specificity was 79.15%. And the logistic regression indicated that the risk of serious disease increased by 2.298 times when LDH over optimum cutoff (OR\u0026thinsp;=\u0026thinsp;2.298; 95%CI\u0026thinsp;=\u0026thinsp;1.327\u0026ndash;3.979; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.003). In addition, the sensitivity of disease risk prediction can be improved by combining LDH\u0026thinsp;\u0026gt;\u0026thinsp;246U/L with NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82. (NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 [50.40%] \u003cem\u003evs.\u003c/em\u003e combined diagnosis model [72.80%]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007; LDH\u0026thinsp;\u0026gt;\u0026thinsp;246 [59.2%] \u003cem\u003evs.\u003c/em\u003e combined diagnosis model [72.80%]; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001). However, the specificity were decreased (NLR\u0026thinsp;\u0026gt;\u0026thinsp;3.82 [84.04%] \u003cem\u003evs.\u003c/em\u003e combined diagnosis model [69.71%]; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0007; LDH\u0026thinsp;\u0026gt;\u0026thinsp;246[79.15%] \u003cem\u003evs.\u003c/em\u003e combined diagnosis model [69.71%]; \u003cem\u003ep\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eMoreover, the sensitivity, specificity, and AUC for NLR and LDH are not relatively high enough. Due to the different admission time of patients with COVID-19 and the acute aggravation of some patients in a period of time after admission, the value of admission indicators may be underestimated. However, compared with other articles[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the sample size and diversity of patients with COVID-19 increase the reliability of the results in this study. Meanwhile, more importantly, the optimum cutoff can indicate the risk of acute aggravation of patients with COVID-19 in the present study. Furthermore, it provides more evidence for the establishment of multi-parameter diagnosis model.\u003c/p\u003e \u003cp\u003eCoagulation disorders are more common in severe patients than in light patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A study conducted by Zhang L \u003cem\u003eet al.\u003c/em\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] had proved that the level of D-dimer\u0026thinsp;\u0026ge;\u0026thinsp;2.0\u0026nbsp;\u0026micro;g/mL (fourfold increase) could effectively predict the mortality of patients with COVID-19. While after balancing the confounding factors, the logistic regression showed that D-dimer\u0026thinsp;\u0026gt;\u0026thinsp;0.83\u0026nbsp;\u0026micro;g/ml could not be used as an independent predictor of disease risk in this study (OR\u0026thinsp;=\u0026thinsp;1.209; 95%CI\u0026thinsp;=\u0026thinsp;0.626\u0026ndash;2.334; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.571). In a dynamic study of hematological parameters in patients with COVID-19, the D-dimer of the severe group was higher than the non-severe group on days 1, 7 and 14 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This suggests that due to different admission times, the ability of D-dimer to predict disease risk may be weakened. In another response to the prognostic value of D-dimer in patients with COVID-19, the predictive value of D-dimer might be affected by other factors, such as hormonotherapy, antibiotic therapy et al. Due to the baseline level of D-dimer varies greatly in patients, the value of dynamic monitoring of D-dimer may be higher in patients with COVID-19[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Further researches are still required to evaluate the significance of D-dimer in evaluating the severity of COVID-19.\u003c/p\u003e \u003cp\u003eCOVID-19 patients have lung involvement with imaging changes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. In different stages of the disease, the CT manifestations are different, which are important to the diagnosis and staging of patients [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. With the same semi-quantitative scoring system, a multi-center paired cohort study conducted by J. Liu \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] showed that CT changes are obvious in acute exacerbation of COVID-19, accompanied by an increase of CT score. This indicated that elevated CT score may predict the poor outcome. Another retrospective single-center study indicated that the CT score has a high diagnostic value in patients with severe COVID-19. ROC analysis showed that AUC for CT score was 0.918. The optimum cutoff of CT score was 7.5. The sensitivity was 82.6% and the specificity was 100% [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the study only analyzes imaging, without combined with clinical data. And significant differences in the number of patients between severe-critical patients and non-severe groups also affect the accuracy of the results. While in present study, after combining the clinical data, the CT score can\u0026rsquo;t be used as an independent predictor of disease risk (OR\u0026thinsp;=\u0026thinsp;1.519; 95%CI\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;3.247; \u003cem\u003ep\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.281). A study by Zhang B et. al[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], demonstrated that the severity of lung abnormalities evaluated by CT score might be associated with laboratory parameters. Therefore, due to correlation between CT score and laboratory parameters, the ability to independently predict disease risk of CT score may be attenuated. Additional investigations are warranted to assess whether CT score can be an independent predictor of disease risk.\u003c/p\u003e \u003cp\u003eThere are some limitations in this study. First, owing to different severity of patients and different medical resources, the time from onset to admission might not be representative, which might affect the level of four parameters on admission. Meanwhile, the representativeness of CT score and D-dimer may also be affected by different admission times. Second, other clinical data and test results are not included in the analysis, which may cause bias, weakening the reliability of the results. Third, to a certain degree, the CT score as a semi-quantitative evaluation method was subjective.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eAs independent factors, the levels of NLR, LDH in serum have a significant correlation with the severity of COVID-19. We suggested that NLR and LDH could be recommended as predictors for evaluating the severity of COVID-19.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eCOVID-19: coronavirus disease-2019\u003c/p\u003e\n\u003cp\u003eNLR: neutrophil-to-Lymphocyte Ratio\u003c/p\u003e\n\u003cp\u003eLDH: lactate dehydrogenase\u003c/p\u003e\n\u003cp\u003eCT: computed tomography\u003c/p\u003e\n\u003cp\u003ePaO\u003csub\u003e2\u003c/sub\u003e: partial pressure of oxygen\u003c/p\u003e\n\u003cp\u003eFiO\u003csub\u003e2\u003c/sub\u003e: fraction of inspiration O\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eTSS: total severity score\u003c/p\u003e\n\u003cp\u003eIQR: inter-quartile range\u003c/p\u003e\n\u003cp\u003eROC: receiver operator characteristic\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003ch2\u003eEthics approval and consent to participate:\u003c/h2\u003e \u003cp\u003eThe study was approved by the Ethics Committee of Zhongshan Hospital, Xiamen University and Second affiliated Hospital of Fujian Medical University.\u003c/p\u003e \u003c/p\u003e \u003ch2\u003eConsent for publication:\u003c/h2\u003e \u003cp\u003eNo applicable.\u003c/p\u003e \u003ch2\u003eAvailability of data and material:\u003c/h2\u003e \u003cp\u003eAll data generated or analyzed during this study are included in this published article.\u003c/p\u003e \u003ch2\u003eCompeting interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by Grant 2018-2-65 for Youth Research Fund from Fujian Provincial Health Bureau, and Grant 2018J01393 for Fund from Natural Science Foundation of Fujian Province, China.\u003c/p\u003e \u003ch2\u003eAuthors' contributions:\u003c/h2\u003e \u003cp\u003eConception and design: Y-Q Cai, X-B Zhang, and H-Q Zeng. Collection and assembly of data: Y-Q Cai, X-B Zhang, X-J Wei, Z-Y Zhang, L-D Chen, M-H Wang, W-Z Yao, Q-F Huang, Z-Q Ye. Data analysis and interpretation: Y-Q Cai, X-B Zhang, and H-Q Zeng. Manuscript writing: All authors. Final approval of manuscript: All authors.\u003c/p\u003e \u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLai CC, Shih TP, Ko WC, Tang HJ, Hsueh PR: \u003cstrong\u003eSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and coronavirus disease-2019 (COVID-19): The epidemic and the challenges.\u003c/strong\u003e \u003cem\u003eInt J Antimicrob Agents \u003c/em\u003e2020, \u003cstrong\u003e55:\u003c/strong\u003e105924.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eThe diagnosis of the novel coronavirus pneumonia exceeds 4.1 million British and French gradually \"unsealed\" \u003c/strong\u003e[\u003ca href=\"https://baijiahao.baidu.com/s?id=1666376262699977033\u0026amp;wfr=spider\u0026amp;for=pc\"\u003ehttps://baijiahao.baidu.com/s?id=1666376262699977033\u0026amp;wfr=spider\u0026amp;for=pc\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eVerelst F, Kuylen E, Beutels P: \u003cstrong\u003eIndications for healthcare surge capacity in European countries facing an exponential increase in coronavirus disease (COVID-19) cases, March 2020.\u003c/strong\u003e \u003cem\u003eEuro Surveill \u003c/em\u003e2020, \u003cstrong\u003e25\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eJi Y, Ma Z, Peppelenbosch MP, Pan Q: \u003cstrong\u003ePotential association between COVID-19 mortality and health-care resource availability.\u003c/strong\u003e \u003cem\u003eThe Lancet Global Health \u003c/em\u003e2020, \u003cstrong\u003e8\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eVelavan TP, Meyer CG: \u003cstrong\u003eMild versus severe COVID-19: laboratory markers.\u003c/strong\u003e \u003cem\u003eInt J Infect Dis \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eYang X, Yu Y, Xu J, Shu H, Xia J, Liu H, Wu Y, Zhang L, Yu Z, Fang M, et al: \u003cstrong\u003eClinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study.\u003c/strong\u003e \u003cem\u003eLancet Respir Med \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eZheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J, Li Q, Jiang C, Zhou Y, Liu S, et al: \u003cstrong\u003eRisk factors of critical \u0026amp; mortal COVID-19 cases: A systematic literature review and meta-analysis.\u003c/strong\u003e \u003cem\u003eJournal of Infection \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eColombi D, Bodini FC, Petrini M, Maffi G, Morelli N, Milanese G, Silva M, Sverzellati N, Michieletti E: \u003cstrong\u003eWell-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia.\u003c/strong\u003e \u003cem\u003eRadiology \u003c/em\u003e2020\u003cstrong\u003e:\u003c/strong\u003e201433.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eDiagnosis and Treatment of Pneumonia of New Coronavirus Infection (Trial Version 7) \u003c/strong\u003e[\u003ca href=\"http://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml\"\u003ehttp://www.nhc.gov.cn/yzygj/s7653p/202003/46c9294a7dfe4cef80dc7f5912eb1989.shtml\u003c/a\u003e]\u003c/li\u003e\n\u003cli\u003eChung M, Bernheim A, Mei X, Zhang N, Huang M, Zeng X, Cui J, Xu W, Yang Y, Fayad ZA, et al: \u003cstrong\u003eCT Imaging Features of 2019 Novel Coronavirus (2019-nCoV).\u003c/strong\u003e \u003cem\u003eRadiology \u003c/em\u003e2020, \u003cstrong\u003e295:\u003c/strong\u003e202-207.\u003c/li\u003e\n\u003cli\u003eLi K, Fang Y, Li W, Pan C, Qin P, Zhong Y, Liu X, Huang M, Liao Y, Li S: \u003cstrong\u003eCT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19).\u003c/strong\u003e \u003cem\u003eEur Radiol \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eGiamarellos-Bourboulis EJ, Netea MG, Rovina N, Akinosoglou K, Antoniadou A, Antonakos N, Damoraki G, Gkavogianni T, Adami M-E, Katsaounou P, et al: \u003cstrong\u003eComplex Immune Dysregulation in COVID-19 Patients with Severe Respiratory Failure.\u003c/strong\u003e \u003cem\u003eCell Host \u0026amp; Microbe \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eQin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, Xie C, Ma K, Shang K, Wang W, Tian DS: \u003cstrong\u003eDysregulation of immune response in patients with COVID-19 in Wuhan, China.\u003c/strong\u003e \u003cem\u003eClin Infect Dis \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eJiang M, Guo Y, Luo Q, Huang Z, Zhao R, Liu S, Le A, Li J, Wan L: \u003cstrong\u003eT cell subset counts in peripheral blood can be used as discriminatory biomarkers for diagnosis and severity prediction of COVID-19.\u003c/strong\u003e \u003cem\u003eJ Infect Dis \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eLiu J, Li S, Liu J, Liang B, Wang X, Wang H, Li W, Tong Q, Yi J, Zhao L, et al: \u003cstrong\u003eLongitudinal characteristics of lymphocyte responses and cytokine profiles in the peripheral blood of SARS-CoV-2 infected patients.\u003c/strong\u003e \u003cem\u003eEBioMedicine \u003c/em\u003e2020, \u003cstrong\u003e55\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eLiu Y, Du X, Chen J, Jin Y, Peng L, Wang HHX, Luo M, Chen L, Zhao Y: \u003cstrong\u003eNeutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19.\u003c/strong\u003e \u003cem\u003eJournal of Infection \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eLiu Y, Yang Y, Zhang C, Huang F, Wang F, Yuan J, Wang Z, Li J, Li J, Feng C, et al: \u003cstrong\u003eClinical and biochemical indexes from 2019-nCoV infected patients linked to viral loads and lung injury.\u003c/strong\u003e \u003cem\u003eSci China Life Sci \u003c/em\u003e2020, \u003cstrong\u003e63:\u003c/strong\u003e364-374.\u003c/li\u003e\n\u003cli\u003eMehta V, Goel S, Kabarriti R, Cole D, Goldfinger M, Acuna-Villaorduna A, Pradhan K, Thota R, Reissman S, Sparano JA, et al: \u003cstrong\u003eCase Fatality Rate of Cancer Patients with COVID-19 in a New York Hospital System.\u003c/strong\u003e \u003cem\u003eCancer Discov \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eZhang R, Ouyang H, Fu L, Wang S, Han J, Huang K, Jia M, Song Q, Fu Z: \u003cstrong\u003eCT features of SARS-CoV-2 pneumonia according to clinical presentation: a retrospective analysis of 120 consecutive patients from Wuhan city.\u003c/strong\u003e \u003cem\u003eEur Radiol \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eXia X, Wen M, Zhan S, He J, Chen W: \u003cstrong\u003e[An increased neutrophil/lymphocyte ratio is an early warning signal of severe COVID-19].\u003c/strong\u003e \u003cem\u003eNan Fang Yi Ke Da Xue Xue Bao \u003c/em\u003e2020, \u003cstrong\u003e40:\u003c/strong\u003e333-336.\u003c/li\u003e\n\u003cli\u003eTang N, Li D, Wang X, Sun Z: \u003cstrong\u003eAbnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia.\u003c/strong\u003e \u003cem\u003eJournal of Thrombosis and Haemostasis \u003c/em\u003e2020, \u003cstrong\u003e18:\u003c/strong\u003e844-847.\u003c/li\u003e\n\u003cli\u003eTerpos E, Ntanasis-Stathopoulos I, Elalamy I, Kastritis E, Sergentanis TN, Politou M, Psaltopoulou T, Gerotziafas G, Dimopoulos MA: \u003cstrong\u003eHematological findings and complications of COVID-19.\u003c/strong\u003e \u003cem\u003eAm J Hematol \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eZhang L, Yan X, Fan Q, Liu H, Liu X, Liu Z, Zhang Z: \u003cstrong\u003eD-dimer levels on admission to predict in-hospital mortality in patients with Covid-19.\u003c/strong\u003e \u003cem\u003eJ Thromb Haemost \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eFu J, Kong J, Wang W, Wu M, Yao L, Wang Z, Jin J, Wu D, Yu X: \u003cstrong\u003eThe clinical implication of dynamic neutrophil to lymphocyte ratio and D-dimer in COVID-19: A retrospective study in Suzhou China.\u003c/strong\u003e \u003cem\u003eThromb Res \u003c/em\u003e2020, \u003cstrong\u003e192:\u003c/strong\u003e3-8.\u003c/li\u003e\n\u003cli\u003eZhang L: \u003cstrong\u003eResponse to \"uncertainties on the prognostic value of D-dimers in COVID-19 patients\".\u003c/strong\u003e \u003cem\u003eJ Thromb Haemost \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eChen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, et al: \u003cstrong\u003eEpidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study.\u003c/strong\u003e \u003cem\u003eThe Lancet \u003c/em\u003e2020, \u003cstrong\u003e395:\u003c/strong\u003e507-513.\u003c/li\u003e\n\u003cli\u003eLi M, Lei P, Zeng B, Li Z, Yu P, Fan B, Wang C, Li Z, Zhou J, Hu S, Liu H: \u003cstrong\u003eCoronavirus Disease (COVID-19): Spectrum of CT Findings and Temporal Progression of the Disease.\u003c/strong\u003e \u003cem\u003eAcademic Radiology \u003c/em\u003e2020, \u003cstrong\u003e27:\u003c/strong\u003e603-608.\u003c/li\u003e\n\u003cli\u003eLiu J, Chen T, Yang H, Cai Y, Yu Q, Chen J, Chen Z, Shang QL, Ma C, Chen X, Xiao E: \u003cstrong\u003eClinical and radiological changes of hospitalised patients with COVID-19 pneumonia from disease onset to acute exacerbation: a multicentre paired cohort study.\u003c/strong\u003e \u003cem\u003eEur Radiol \u003c/em\u003e2020.\u003c/li\u003e\n\u003cli\u003eZhang B, Zhang J, Chen H, Chen L, Chen Q, Li M, Chen Z, You J, Yang K, Zhang S: \u003cstrong\u003eNovel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters.\u003c/strong\u003e \u003cem\u003eEur J Nucl Med Mol Imaging \u003c/em\u003e2020.\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":"COVID-19, neutrophil-to-Lymphocyte Ratio, lactate dehydrogenase, D-dimer, CT score","lastPublishedDoi":"10.21203/rs.3.rs-30959/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-30959/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eTo explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19) in two centers of Hubei, China.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA total of 432 patients with laboratory confirmed COVID-19 were retrospectively enrolled and divided into non-severe and severe groups. The baseline data, laboratory findings, chest computed tomography (CT) results evaluating by CT score on admission, and clinical outcomes were collected and compared. The logistic regression was used to assess the independent relationship between the baseline level of four indicators (NLR, LDH, D-dimer, CT score) on admission and the severity of COVID-19, respectively.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eAmong 432 patients, 125 (28.94%) cases were divided into severe group, the remaining (n = 307, 71.06%) were in non-severe group. In multivariate logistic regression, the high level of NLR, LDH were independent predictor in the early classification of patients with COVID-19 (OR = 2.163; 95%CI = 1.162–4.026; \u003cem\u003ep =\u003c/em\u003e 0.015 for NLR \u0026gt; 3.82; OR = 2.298; 95%CI = 1.327–3.979; \u003cem\u003ep =\u003c/em\u003e 0.003 for LDH \u0026gt; 246U/L). Furthermore, combining NLR \u0026gt; 3.82 and LDH \u0026gt; 246U/L could increase the sensitivity of diagnosis in severe patients (NLR \u0026gt; 3.82 [50.40%] \u003cem\u003evs.\u003c/em\u003e Combined diagnosis [72.80%]; \u003cem\u003ep\u003c/em\u003e = 0.0007; LDH \u0026gt; 246 [59.2%] \u003cem\u003evs.\u003c/em\u003e Combined diagnosis [72.80%]; \u003cem\u003ep \u0026lt;\u003c/em\u003e 0.0001).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe high levels of NLR and LDH in serum have potential value in the early identification of severe patients with COVID-19. The combination of LDH and NLR can improve the sensitivity of diagnosis.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eImportance\u003c/strong\u003e: COVID-19 has been a global pandemic. The mortality rate is range from 3.5-6.0%. In order to predict the risk factors of severity of COVID-19. we explore the significance of neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), D-dimer and CT score in evaluating the severity and prognosis of coronavirus disease – 2019 (COVID-19) in two centers of Hubei, China. We found that the high levels of NLR and LDH in serum have potential value in the early identification of severe patients with COVID-19. The combination of LDH and NLR can improve the sensitivity of diagnosis.\u003c/p\u003e","manuscriptTitle":"Prognostic Value of Neutrophil-To-Lymphocyte Ratio, Lactate Dehydrogenase, D-Dimer and CT Score in Patients with COVID-19","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-05-29 21:22:38","doi":"10.21203/rs.3.rs-30959/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":"4bad242d-ba3b-4593-95a0-3d9b22b18188","owner":[],"postedDate":"May 29th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":109769,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2020-05-29T21:22:38+00:00","versionOfRecord":[],"versionCreatedAt":"2020-05-29 21:22:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-30959","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-30959","identity":"rs-30959","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}