Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study

Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study | 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 Article Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study Haojing Zhang, Lin Kan, Dianzhu Pan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5297867/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 19 You are reading this latest preprint version Abstract Objective: To investigate the risk factors for corona virus disease 2019 (COVID-19) and construct a nomogram prediction model to evaluate the clinical treatment of long COVID. Methods: Clinical data were collected from patients who were diagnosed with COVID-19 and hospitalized at the First Affiliated Hospital of Jinzhou Medical University from December 7, 2022, to February 1, 2023. The prediction model was constructed via a nomogram. External validation was carried out with clinical data from patients at Panjin Central Hospital. Results: In the development cohort and the validation cohort of this study, 60.3% and 59.5% of the patients developed long COVID, respectively. After least absolute shrinkage and selection operator (Lasso) regression, the final variables included in the prediction model were the percentage of lymphocytes, the Charlson comorbidity index (CCI), computed tomography (CT) score, and oxygen requirement. The area under the receiver operating characteristic curve (AUROC) for external validation of the model was 0.794, and the p value of the calibration curve was 0.170. The decision curve analysis indicates that the model performs well. Conclusion: The prediction model developed in this study is useful for assessing the likelihood of developing long COVID in hospitalized patients. long COVID COVID-19 Risk factor Nomogram Forecast Figures Figure 1 Figure 2 Figure 3 Introduction Since the first confirmed case of the novel coronavirus [ 1 ] in the latest mutant strain, JN.1 [ 2 ] , COVID-19 has spread worldwide for more than four years. As of February 1, 2024, COVID-19 has caused more than 770 million confirmed cases and more than 7.03 million deaths globally. With the World Health Organization (WHO) declaring on May 5, 2023, that COVID-19 is no longer a global public health emergency, the global health crisis caused by the novel coronavirus infection has been effectively controlled. However, the chronic effects of acute infection continue to affect many long COVID survivors. After recovering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, patients worldwide have found it difficult to return to baseline health. Feedback from multiple channels [ 3 , 4 ] has led health workers to realize that the impact of COVID-19 on their health is neither temporary nor confined to a single system. They quickly associated new symptoms of discomfort and unexplained physical weakness with COVID-19 sequelae [ 5 ] . The WHO defines this condition as long COVID syndrome [ 6 ] , officially described as symptoms persisting in COVID-19 patients three months after onset or diagnosis in asymptomatic individuals, lasting for at least two months and not attributable to other diagnoses. These symptoms significantly affect patients' daily lives. Long COVID syndrome is prevalent, affecting 9–63% of the COVID-19 population globally, and is six times [ 7 ] more common than other coronaviruses. Prevalence rates by country include China at 49%-76%, Italy at 5%-51%, and the USA at 16%-53%. The incidence among hospitalized patients is notably higher than that among nonhospitalized patients [ 8 ] . With the cycle of "Yang–Yang Kang–Fu Yang", people are profoundly feeling the enduring challenge posed by COVID-19. Studies have indicated that inpatients, outpatients, and patients treated at home report symptoms during the follow-up process, including persistent fatigue, muscle weakness, dyspnea, dry cough, palpitations, anxiety, depression, headache, cognitive impairment, olfactory and taste dysfunction, and decreased immunity [ 9 ] . Long-term novel coronavirus infection can also lead to structural and functional involvement of multiple organs. COVID-19 has broad coverage and a lasting impact on patients. Given the vast population base affected by COVID-19, the potential health resource shortages and economic burdens are challenging to quantify. Consequently, COVID-19 has attracted unprecedented attention. The diagnosis, differentiation, and treatment of long COVID remain pressing issues to address. A significant amount of previous research has focused on the clinical manifestations of long COVID [ 4 ] , with few studies addressing the screening of high-risk groups and early intervention, which are crucial for predicting and managing long COVID. A single-factor prediction model is often insufficient; a multifactorial approach significantly improves the prediction accuracy. This study aims to estimate the probability of long COVID through retrospective data analysis, covering demographics, comorbidities, symptoms, admission details, vaccination status, laboratory parameters, treatment data, and clinical severity, by constructing a nomogram and externally validating the model to assess its predictive performance. Methods 2.1 Study design and population This retrospective study was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Jinzhou Medical University (IRB number: KYLL2024279), and all patients provided written informed consent. Data were collected from 209 patients hospitalized at the First Affiliated Hospital of Jinzhou Medical University for COVID-19 treatment from December 7, 2022, to February 1, 2023, and from 211 patients hospitalized at Panjin Central Hospital for COVID-19 treatment. The cases from the First Affiliated Hospital of Jinzhou Medical University served as the modeling set, and those from Panjin Central Hospital served as the validation set. The inclusion criteria were as follows: age ≥ 18 years; inpatient status; nasal swab reverse transcriptase polymerase chain reaction (RT‒PCR) test results positive for COVID-19; and adherence to the diagnosis and treatment plan for severe and above patients with novel coronavirus infection (tenth edition). The exclusion criteria included patients who died during hospitalization and at follow-up; patients lost to follow-up; patients who lacked data from the first confirmed RT‒PCR test; or patients who lacked two consecutive negative RT‒PCR tests. After screening, 141 datasets were fully included in the modeling set, and 163 were included in the validation set. A flow chart illustrating the patient selection process is detailed in Fig. 1. 2.2 Data collection Demographics (age and sex), past medical history, admission symptoms, vaccination status, laboratory parameters, imaging findings, treatment-related data, and clinical severity data were collected. Past medical history included hypertension, cardiovascular disease (coronary heart disease, atrial fibrillation, pulmonary embolism, rheumatic heart disease, previous myocardial infarction, cardiac insufficiency), diabetes, chronic liver disease (fatty liver, hepatitis B, hepatitis C), chronic lung disease (bronchiectasis, chronic obstructive pulmonary disease, emphysema, tuberculosis, asthma), chronic kidney disease (chronic renal failure, chronic nephritis), rheumatic disease (systemic lupus erythematosus, rheumatoid arthritis, scleroderma), nervous system disorders (cerebral infarction and hemorrhage), and malignancy. Vaccination status was divided into four groups: unvaccinated, partially vaccinated (one dose), fully vaccinated (two doses), and booster vaccinated (three doses or more). The laboratory parameters included white blood cell count, percentage of neutrophils, percentage of lymphocytes, percentage of eosinophils, troponin, alanine aminotransferase, aspartate aminotransferase, albumin, C-reactive protein, D-dimer, creatinine, lactate dehydrogenase, procalcitonin (PCT), and interleukin 6 (IL-6) levels. Imaging results included chest CT and high-resolution CT (HRCT). Treatment-related data included oxygen inhalation status, oxygen inhalation mode, hormone dosage, and the use of antiviral drugs. 2.3 Data wrangling 2.3.1 CCI: In noncirrhotic patients, the following diseases are assigned 1 point each: myocardial infarction (AMI), congestive heart failure, peripheral vascular disease, dementia, cerebrovascular disease without sequelae, connective tissue disease, peptic ulcer disease, diabetes without end-organ damage, chronic lung disease, and mild liver disease without portal hypertension. Diseases assigned 2 points include hemiplegia, moderate to severe chronic kidney disease (CKD), end-stage diabetes, solid tumors without metastases, leukemia, lymphoma, and moderate to severe liver disease. Patients with metastatic tumors and AIDS are given 6 points. The age group scores are added as follows: 0 points for age ≤ 40, 1 point for ages 41–50, 2 points for ages 51–60, 3 points for ages 61–70, and 4 points for ages > 70. The final CCI score is the sum of the disease scores and the corresponding age group score. 2.3.2 CT score: Each lung lobe is scored from 0–5, with 0 indicating no involvement, 1 indicating 75% involvement. The overall CT score is the sum of each lobe score (0–25). CT scores are categorized as follows: 0–5 for category 1, 6–10 for category 2, 11–15 for category 3, 16–20 for category 4, and 21–25 for category 5. 2.3.3 Oxygen requirements: Classified as no oxygen, low-flow oxygen (1–2 L/min), or high-flow oxygen (≥ 3 L/min). 2.3.4 Severity classification: On the basis of WHO guidelines, severity is classified into nonsevere, severe, and critical categories. 2.4 Statistical analysis SPSS (version 26.0) and R language (version 4.0.1) were used for statistical analysis, defining statistical significance as a two-tailed p value < 0.05. Normality tests for continuous variables were performed via the Kolmogorov‒Smirnov test. Independent sample t tests and χ 2 tests were used for normally distributed variables, whereas the Wilcoxon rank-sum test was applied for nonnormally distributed variables. Normally distributed continuous variables are presented as the means ± standard deviations, nonnormally distributed continuous variables are presented as medians (interquartile ranges), and categorical variables are presented as numbers (percentages). Predictor variables were selected via a stepwise forward method on the basis of the Akaike information criterion (AIC) in the modeling set. Receiver operating characteristic (ROC) curves were plotted for the selected continuous variables to calculate cutoffs. The prediction model was then exported as a nomogram. External validation was performed on the validation set, and the model's consistency, discrimination, and clinical usefulness were evaluated through the AUROC, calibration curve, and decision curve analysis. Results Ultimately, 141 patients were included in the modeling set, and 163 patients were included in the validation set. The median ages were 71 and 74 years in the modeling and validation sets, respectively. The median BMI values were 23.93 kg/m 2 and 24.22 kg/m 2 for the modeling and validation sets, respectively (Table 1). The proportions of patients who developed long COVID were 60.3% (85/141) in the modeling set and 59.5% (97/163) in the validation set (Table 2 ). Table 1. Characteristics of patients in the development and validation cohort for this study. Characteristic Development Cohort Validation Cohort Z/t/χ 2 p Number of patients 141(100%) 163(100%) Gender 153.343 <0.001 Male 67（22.0%） 85（28.0%） Female 74（24.3%） 78（25.7%） Age, years 71.00（62.00，80.00） 74.00（63.00，83.00） -1.467 0.142 BMI，kg/m 2 23.93（22.13，26.12） 24.22（21.03，26.04） -0.329 0.742 Length of hospital stay, days 12.00（9.00，15.00） 14.00（10.00，18.00） -3.089 0.002 Self-medication prior to admission 2.035 0.154 Yes 112（36.8%） 118（38.8%） No 29（9.5%） 45（14.8%） CCI -0.825 0.409 0 score 2（0.7%） 3（1.0%） 1 score 10（3.3%） 8（2.6%） 2 score 8（2.6%） 9（3.0%） 3 score 14（4.6%） 17（5.6%） 4 score 27（8.9%） 39（12.8%） 5 score 29（9.5%） 38（12.5%） 6 score 24（7.9%） 32（10.5%） 7 score 20（6.6%） 9（3.0%） 8 score 6（2.0%） 5（1.6%） 9 score 1（0.3%） 3（1.0%） Leucocyte count,×10 9 /L 6.120（4.230，8.165） 6.760（4.940，8.820） -1.771 0.077 Percentage of the neutrophils,% 74.590（65.420，81.665） 77.300（68.500，86.500） -2.317 0.021 Percentage of the lymphocytes,% 15.310（9.970，23.340） 14.600（7.700，21.300） -1.443 0.149 Hemoglobin,g/L 123.300（110.500，135.100） 123.000（113.000，137.000） -0.982 0.326 Platelet count,×10 9 /L 186.600（133.900，271.050） 205.000（149.000，270.000） -0.994 0.320 CRP,mg/L 42.400（10.850，69.750） 22.430（6.180，63.910） -1.858 0.063 D-dimer, mg/L 0.320（0.175，0.730） 0.830（0.480，1.420） -8.065 <0.001 Lactate dehydrogenase,U/L 254.240（251.000，254.240） 227.000（191.000，287.000） -2.179 0.029 Albumin,g/L 34.462±4.704 36.902±4.836 -4.443 <0.001 On admission pH 7.42（7.41，7.44） 7.430（7.400，7.450） -0.696 0.487 On admission pCO 2 ,mmHg 35.470（32.900，37.150） 37.000（33.800，40.200） -2.973 0.003 On admission pO 2 ,mmHg 79.630（70.000，85.950） 82.800（70.400，94.000） -2.227 0.026 On admission SO 2 ,% 94.300（93.430，96.300） 96.300（93.900，97.200） -4.152 <0.001 Oxygen demand -0.769 0.442 No oxygen 32（10.5%） 53（17.4%） Low flow oxygen absorption 75（24.7%） 65（21.4%） High flow oxygen absorption 34（11.2%） 45（14.8%） Total CT score -0.139 0.890 0-5 score 19（6.25%） 20（6.6%） 6-10 score 22（7.2%） 34（11.2%） 11-15 score 30（9.9%） 32（10.5%） 16-20 score 44（14.5%） 42（13.8%） 21-25 score 26（8.6%） 35（11.5%） Antiviral drug -6.432 <0.001 Azvudine 43（14.1%） 78（25.7%） Nirmatrelvir/ritonavir 2（0.7%） 7（2.3%） Ganciclovir sodium injection 10（3.3%） 0（0%） Potassium Sodium Dehydroandrographolide Succinate for Injection 1（0.3%） 24（7.9%） Other 0（0%） 12（3.9%） Unused 85（28.0%） 42（13.8%） Vaccination status -5.826 <0.001 Unvaccinated 34（11.2%） 70（23.0%） Inject 1 shot 9（3.0%） 19（6.25%） Inject 2 shot 15（4.9%） 16（5.3%） Inject 3 shot 29（9.5%） 46（15.1%） Booster 11（3.6%） 12（3.9%） Not quite clear 43（14.1%） 0（0%） Continuous variables with a normal distribution are reported as the means ± standard deviations (SDs); nonnormally distributed continuous variables are expressed as medians (interquartile ranges); and categorical variables are reported as numbers (percentages). Abbreviations: BMI, body mass index; CCI, Charlson Comorbidity Index; CRP, C-reactive protein; CT, computed tomography. Table 2 Univariate analysis of patients in the development and validation cohort for this study Characteristic Development Cohort 141(100) Validation Cohort 163(100) Long COVID group occurred No long COVID group occurred p Long COVID group occurred No long COVID group occurred p Number of patients 85(60.3%) 56(39.7%) 97(59.5%) 66(40.5%) Gender 0.834 0.440 Male 41(29.1%) 26(18.4%) 53(32.5%) 32(19.6%) Female 44(31.2%) 30(21.3%) 44(27.0%) 34(20.9%) Age, years 69.86 ± 12.440 69.71 ± 14.402 0.95 76.00(67.50, 83.50) 71.00(59.00, 80.00) 0.029 Body mass index, kg/m 2 23.93(22.27, 25.92) 23.93(21.49, 26.49) 0.752 23.500 ± 4.102 24.036 ± 3.363 0.381 Length of hospital stay, days 12(9, 15.5) 11(8.25, 14) 0.4 15.00(11.00, 18.50) 12.00(9.00, 15.25) 0.012 Self-medication prior to admission 0.291 0.321 Yes 70(49.6%) 42(29.8%) 73(44.8%) 45(27.6%) No 15(10.6%) 14(9.9%) 24(14.7%) 21(12.9%) CCI 0.001 0.001 0 score 1(0.7%) 1(0.7%) 1(0.6%) 2(1.2%) 1 score 0(0%) 10(7.1%) 0(0%) 8(4.9%) 2 score 3(2.1%) 5(3.5%) 3(1.8%) 6(3.7%) 3 score 8(5.7%) 6(4.3%) 10(6.1%) 7(4.3%) 4 score 17(12.1%) 10(7.1%) 23(14.1%) 16(9.8%) 5 score 21(14.9%) 8(5.7%) 28(17.2%) 10(6.1%) 6 score 15(10.6%) 9(6.4%) 17(10.4%) 15(9.3%) 7 score 14(9.9%) 6(4.3%) 7(4.3%) 2(1.2%) 8 score 5(3.5%) 1(0.7%) 5(3.1%) 0(0%) 9 score 1(0.7%) 0(0%) 3(1.8%) 0(0%) Leucocyte count,×10 9 /L 5.890(4.070, 8.080) 6.400(4.823, 8.293) 0.441 6.930(5.140, 8.885) 6.440(4.608, 8.690) 0.369 Percentage of the neutrophils,% 73.431 ± 11.841 71.633 ± 13.539 0.406 77.500(69.850, 86.650) 76.500(60.636, 86.050) 0.168 Percentage of the lymphocytes,% 14.330(8.830, 22.045) 18.495(12.358, 28.318) 0.015 13.000(7.400, 18.600) 16.700(9.275, 29.775) 0.003 Hemoglobin,g/L 123.30(110.05, 134.65) 123.50(111.93, 136.70) 0.747 123.000(113.000, 134.500) 124.500(113.750, 141.750) 0.49 Platelet count,×10 9 /L 173.400(131.700, 271.050) 214.600(140.850, 274.900) 0.304 208.000(151.500, 264.500) 197.500(143.250, 273.000) 0.709 C-reactive protein, mg/L 42.400(9.910, 74.450) 43.250(11.150, 63.725) 0.779 25.460(7.920, 63.850) 16.905(4.400, 64.688) 0.273 D-dimer, mg/L 0.340(0.180, 0.730) 0.305(0.163, 0.695) 0.41 0.870(0.490, 1.435) 0.755(0.479, 1.403) 0.594 Lactate dehydrogenase,U/L 254.240(251.500, 254.24) 254.240(236.000, 254.240) 0.889 229.000(190.000, 304.500) 226.750(191.375, 262.500) 0.425 Albumin,g/L 34.425 ± 4.604 34.519 ± 4.892 0.907 36.334 ± 4.689 37.738 ± 4.962 0.069 On admission pH 7.42(7.41, 7.44) 7.42(7.41, 7.44) 0.767 7.434(7.401, 7.455) 7.427(7.394, 7.450) 0.550 On admission pCO 2 ,mmHg 35.400(32.550, 36.800) 35.470(33.975, 39.025) 0.182 36.893 ± 5.369 37.221 ± 5.180 0.698 On admission pO 2 ,mmHg 79.630(70.650, 85.250) 79.630(69.100, 87.325) 0.666 83.000(70.550, 92.220) 81.150(70.067, 101.260) 0.589 On admission SO 2 ,% 94.60(93.45, 96.30) 93.48(93.15, 96.25) 0.6 96.100(93.950, 97.100) 96.340(93.758, 97.450) 0.635 Oxygen demand 0.011 1.130 No oxygen 27(19.1%) 5(3.5%) 37(22.7%) 16(9.8%) Low flow oxygen absorption 40(28.4%) 35(24.8%) 35(21.5%) 30(18.4%) High flow oxygen absorption 18(12.8%) 16(11.3%) 25(15.3%) 20(12.3%) Total CT score 0.002 0.60 0–5 score 4(2.8%) 15(10.6%) 8(4.9%) 12(7.4%) 6–10 score 12(8.5%) 10(7.1%) 18(11.0%) 16(9.8%) 11–15 score 19(13.5%) 11(7.8%) 20(12.3%) 12(7.4%) 16–20 score 33(23.4%) 11(7.8%) 29(17.8%) 13(8.0%) 21–25 score 17(12.1%) 9(6.4%) 22(13.5%) 13(8.0%) Antiviral drug 0.322 0.082 Azvudine 29(20.6%) 14(9.9%) 50(30.7%) 28(17.2%) Nirmatrelvir/ ritonavir 2(1.4%) 0(0%) 4(2.5%) 3(1.8%) Ganciclovir sodium injection 6(4.3%) 4(2.8%) 0(0%) 0(0%) Potassium SodiumDehydroandrograp-holide Succinate for Injection 0(0%) 1(0.7%) 11(6.7%) 13(8.0%) Other 0(0%) 0(0%) 5(3.1%) 7(4.3%) Unused 48(34.0%) 37(26.2%) 27(16.6%) 15(9.2%) Vaccination status 0.195 0.251 Unvaccinated 25(17.7%) 9(6.4%) 44(27.0%) 26(16.0%) Inject 1 shot 2(1.4%) 7(5.0%) 13(8.0%) 6(3.7%) Inject 2 shot 11(7.8%) 4(2.8%) 11(6.7%) 5(3.1%) Inject 3 shot 18(12.8%) 11(7.8%) 21(12.9%) 25(15.3%) Booster 5(3.5%) 6(4.3%) 8(4.9%) 4(2.5%) Not quite clear 24(17.0%) 19(13.5%) 0(0%) 0(0%) Continuous variables with a normal distribution are reported as the means ± standard deviations (SDs); nonnormally distributed continuous variables are expressed as medians (interquartile ranges); and categorical variables are reported as numbers (percentages). Student’s t test was used to compare the means of two continuous normally distributed variables, and the Mann–Whitney U test was used to determine the means of two continuous nonnormally distributed variables. The chi-square test or Fisher’s exact test was used for categorical variables. Abbreviations: BMI, body mass index; CCI, Charlson Comorbidity Index; CRP, C-reactive protein; CT, computed tomography. In the modeling cohort, patients who developed long COVID had higher CCI scores ( p = 0.001), a greater percentage of lymphocytes ( p = 0.001), greater oxygen demand ( p = 0.011), and higher total CT scores ( p = 0.002) than those who did not develop long COVID (Table 3 ). Table 3 Multivariate binary logistic regression of patients in development cohort. Characteristic β (95% CI) OR(95% CI) p constant −0.202(−1.681,1.266) 0.817(0.186,3.548) Total CT score 0.405(0.057,0.770) 1.499(1.058,2.160) 0.025 CCI 0.264(0.036,0.506) 1.302(1.037,1.658) 0.026 Percentage of the lymphocytes,% −0.039(−0.075,−0.004) 0.962(0.928,0.996) 0.031 Oxygen demand −1.126(−1.791,−0.521) 0.324(0.167,0.594) < 0.001 Area under ROC curve AUROC 95% CI Development Cohort 0.844(0.781,0.907) < 0.001 Validation Cohort 0.794(0.724,0.864) < 0.001 The β coefficient, odds ratio, and 95% confidence interval were analyzed via multivariate binary logistic regression. OR, odds ratio; CI, confidence interval; ROC, receiver operating characteristic curve; AUC area, area under the ROC curve. A multivariate LASSO regression model was employed to construct a predictive model with regression coefficients for the CCI, percentage of lymphocytes, oxygen demand, and total CT score (Fig. 2). On the basis of these results, we developed and validated a nomogram for predicting the probability of the occurrence of long COVID (Table 3 and Fig. 2). Each clinical feature was assigned a specific point value. For example, a long COVID patient with a CCI score of 8 points received 60 points, a CT score of 3 points received 12 points, a lymphocyte percentage of 45 points received 5 points, and an oxygen demand of 2 points received 0 points. This resulted in a total score of 77 points for the patient, suggesting an approximate 40% probability of developing long COVID. This outcome can assist in clinical decision-making plans (Fig. 2). The AUROCs for the modeling and validation sets were 0.844 (0.781, 0.907) and 0.794 (0.724, 0.864), respectively. The cutoff value for risk probability in this model was set at 59.4%, with a sensitivity of 71.4% and specificity of 81.2%. The p value of the unreliability test was 0.077. The p value of the Hosmer and Lemeshow chi-square (H–L chi-square) statistic was 0.170, indicating good calibration. Decision curve analysis demonstrated that the prediction model has clinical utility for the population in the validation set (Fig. 3). Discussion The chronic effects of SARS-CoV-2 infection, including postacute sequelae of SARS-CoV-2 infection (PASC) symptoms and unexplained physical weakness that persists long-term, continue to affect a significant portion of COVID-19 survivors. In this research, we developed a prediction model utilizing clinical data from patients admitted to the First Affiliated Hospital of Jinzhou Medical University for COVID-19 treatment, following China's "Category B and B Management" implementation and full societal reopening. LASSO regression analysis identified crucial factors associated with long COVID, culminating in the selection and external validation of four clinical variables—the CCI, CT score, oxygen requirement, and lymphocyte percentage—using clinical data from Panjin Central Hospital. The CCI, which combines age and comorbidities across all age groups and systemic conditions, was first proposed by Charlson in 1987 [ 10 ] to predict long-term mortality and has since been widely adopted for forecasting long-term prognosis and survival. Given its efficacy, simplicity, and applicability and considering that the occurrence of long COVID is also part of the prognosis of patients with COVID-19, the CCI was introduced in this study for the first time. Statistical findings revealed that a higher CCI was correlated with an increased likelihood of long COVID. While most published studies have focused solely on either age or comorbidities, our review identified fourteen study sets that considered age, with twelve confirming the predictive value of age. This research encompasses data from China, Italy, the United States, the United Kingdom, and other countries [ 4 , 5 , 11 – 14 ] , covering most designated medical centers for treating the novel coronavirus. With respect to comorbidities, determining whether long COVID symptoms originate from preexisting conditions, new symptoms caused by COVID-19, or a combination of both is challenging. Consequently, long COVID definitions emphasize duration over distinguishing between these factors. A 2022 JAMA article [ 12 ] highlighted that an increased number of comorbidities elevates the risk of long COVID. Specifically, a study from Italy noted the importance of an allergic constitution among various comorbidities. Another study in the BMJ from the United States noted that individuals with preexisting interstitial lung disease are more likely to develop severe COVID-19. The ongoing impact of coronavirus on T-cell function leads to immune escape, allowing the virus to persist at low concentrations for extended periods. Data from past coronavirus outbreaks, such as SARS and MERS [ 15 , 16 ] , show that some patients continue to suffer from lung damage after the infection has resolved. The novel coronavirus, characterized by its low virulence and viral load, does not typically cause systemic symptoms but can persist in certain organs, leading to specific clinical symptoms. A recent Nature study [ 17 ] demonstrated that most patients struggle to completely eliminate the virus during the acute phase, with approximately 0.1% − 0.5% of patients harboring the virus for at least 60 days. The continued presence of the virus weakens the immune system's ability to clear it, particularly in individuals with a history of medical conditions, thus increasing the likelihood of long-term effects from the novel coronavirus. Current research often overlooks the simultaneous consideration of age and comorbidities. A nomogram developed by the National Infectious Disease Center team at Fudan University in Shanghai, China [ 11 ] , included five risk factors. Being over 75 years of age, having chronic kidney disease, and suffering from chronic lung disease were identified as the most significant risk factors. This highlights the particular importance of age and comorbidities in predicting the risk of severe COVID-19 in the Chinese population. The CT score, initially utilized to assess the prognosis of the interstitial pneumonia index [ 18 ] , has been adopted for its quantitative analysis, high accuracy, and ability to provide rapid diagnostics. Many research teams have adapted and revised it [ 19 ] as a tool for real-time assessment of the severity of lung involvement in COVID-19 patients. Compared with other indicators of disease progression, the majority of research teams [ 20 ] recognize the CT score as the most accurate index reflecting disease progression, with more severe COVID-19 cases being more likely to develop into long COVID [ 13 ] . This study highlights the CT score as the optimal indicator of disease progression. For the first time, the CT score was applied to COVID-19 patients and included in the final prediction model through LASSO regression analysis. In the longitudinal follow-up of patients with COVID-19, persistent imaging changes are often observed, which are closely correlated with the peak CT score during the acute phase [ 21 ] . Current research primarily describes these persistent imaging changes, with pulmonary fibrosis-like changes being the most frequently observed. A follow-up study of the initial set of COVID-19 patients from Wuhan Jinyintan Hospital [ 21 ] statistically identified a CT score exceeding 18 during the acute phase of COVID-19 as an independent risk factor for developing pulmonary fibrosis-like changes during follow-up. These persistent imaging changes correlate with long-standing clinical symptoms, and long COVID can be considered an umbrella term encompassing these clinical manifestations. According to the novel coronavirus infection diagnosis and treatment plan (10th version), severe and critical patients are more focused on supportive treatments than ordinary and mild patients are. Respiratory system support therapy, specifically oxygen therapy, is a primary treatment strategy. In a multicenter study including 2,433 cases [ 13 ] , 71.6% of hospitalized patients received oxygen therapy, and 0.9% underwent mechanical ventilation. These studies have established a connection between oxygen therapy, mechanical ventilation, and the persistence of fatigue, which is one of the most common long COVID symptoms [ 4 ] . Our research focused on long COVID among hospitalized patients with acute SARS-CoV-2 infection, indicating that symptomatic oxygen support is provided on the basis of the severity of the patient's condition. Our findings suggest that a lower oxygen requirement increases the likelihood of developing long COVID. This conclusion may be attributed to two factors. First, under conditions of insufficient blood oxygen saturation, oxygen is preferentially supplied to vital organs such as the brain, heart, and lungs. Other organs may remain hypoxic, leading to long-term tissue decompensation, chronic cellular damage, and the persistence of symptoms and discomfort after discharge, which are recognized as clinical manifestations of long COVID. Second, for patients with severe conditions, timely high-flow oxygen support is often provided. A significant proportion of these patients might die before discharge or during follow-up; thus, they were excluded from this study, introducing bias. Therefore, the incidence of long COVID appears to be lower among recipients of high-flow oxygen therapy. On the other hand, prolonged oxygen therapy can cause lung damage. Several post-COVID-19 follow-up studies have revealed [ 21 ] interstitial lung lesions in the CT scans of patients with long COVID. It remains unclear whether this damage is attributable to COVID-19 itself or to the side effects of oxygen therapy received during hospitalization. This ambiguity highlights the need for further research to fully understand the relationship between oxygen therapy and long COVID. The association between oxygen requirements and long COVID essentially reflects a mismatch between the oxygen demand of the body and the administration of oxygen therapy, which is highly subjective. This study, which is based on clinical data from two medical centers, has limitations, and the relationship between oxygen therapy and long COVID warrants additional investigation. The percentage of lymphocytes in the blood is a quick indicator used to identify the source of infection, with lymphocyte percentages < 20% indicating viral infection. A lower lymphocyte percentage is associated with a greater likelihood of viral infection. Research has indicated that adults infected with COVID-19 exhibit lymphopenia, increased platelet counts, and elevated lactate dehydrogenase levels, with a decrease in lymphocyte count in severe cases indicating more severe disease and a greater risk of mortality [ 22 ] . In this study, the lymphocyte percentage was included as an indicator of COVID-19 severity. This metric, derived from a routine blood test available to all patients, holds significant potential for widespread application. This research utilized data from the first major outbreak to develop a prediction model for long COVID syndrome in patients with severe COVID-19, offering a scientific basis for early identification with clinical relevance. Nonetheless, this study has several limitations: (1) This was a multicenter retrospective study, and further large-scale multicenter prospective studies are needed for validation. (2) An assessment of lung involvement in long COVID severity is lacking, including further subgroup analysis predictions. (3) It does not include data from intensive care units, particularly from mechanically ventilated patients. Conclusion The prediction model developed in this study identified the CCI, CT score, oxygen requirement, and lymphocyte percentage as significant factors associated with long COVID. These factors could serve as risk factors for the early detection and identification of long COVID in clinical settings. Abbreviations COVID-19 Corona Virus Disease 2019 CCI Charlson Comorbidity Indexc CT Computed Tomography Lasso Least absolute shrinkage and selection operator AUROC Area Under the Receiver Operating Characteristic WHO World Health Organization SARS-CoV-2 severe acute respiratory syndrome coronavirus 2 IRB institutional Review Board RT-PCR reverse transcriptase polymerase chain reaction PCT procalcitonin IL-6 Interleukin 6 HRCT High Resolution CT MI myocardial infarction CKD chronic kidney disease AIDS AcquiredImmune Deficiency Syndrome AIC Akaike information criterion ROC Receiver operating characteristic H–L Hosmer and Lemeshow PASC post-acute sequelae of SARS-CoV-2 infection Declarations Ethics approval and consent to participate Ethical approval (KYLL2024279) was provided by the Institutional Research and Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University. Informed consent was obtained from all eligible subjects. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors have no competing interests to declare. Funding None Authors' contributions ZHJ contributed to data collection and writing.KL provided technical support and conducted data analysis.Both ZHJ and KL made equal contributions to this paper. PDZ designed this study and reviewed manuscript. Acknowledgments We thank Dr. Pan of the First Affiliated Hospital of Jinzhou Medical University for technical support and review of the manuscript. We also sincerely thank the First Affiliated Hospital of Jinzhou Medical University for providing data support and coordination. References Chen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507–13. Looi MK. Covid-19: WHO adds JN.1 as new variant of interest. BMJ. 2023;383:2975. Wu X, Liu X, Zhou Y, et al. 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalization: a prospective study. Lancet Respir Med. 2021;9(7):747–54. Morin L, Savale L, Pham T, et al. Four-Month Clinical Status of a Cohort of Patients After Hospitalization for COVID-19. JAMA. 2021;325(15):1525–34. Al-Aly Z, Xie Y, Bowe B. High-dimensional characterization of postacute sequelae of COVID-19. Nature. 2021;594(7862):259–64. Tanno LK, Casale T, Demoly P. Coronavirus Disease (COVID)-19: World Health Organization Definitions and Coding to Support the Allergy Community and Health Professionals. J Allergy Clin Immunol Pract. 2020. 8(7). Lippi G, Sanchis-Gomar F, Henry BM. COVID-19 and its long-term sequelae: what do we know in 2023. Pol Arch Intern Med. 2023;133(4):16402. [pii]. Raman B, Bluemke DA, Lüscher TF, Neubauer S. Long COVID: postacute sequelae of COVID-19 with a cardiovascular focus. Eur Heart J. 2022;43(11):1157–72. Zhang H, Huang C, Gu X, et al. 3-year outcomes of discharged survivors of COVID-19 following the SARS-CoV-2 omicron (B.1.1.529) wave in 2022 in China: a longitudinal cohort study. Lancet Respir Med. 2024;12(1):55–66. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83. Guo Y, Guo Y, Zhang Y, et al. Factors affecting prolonged SARS-CoV-2 infection and development and validation of predictive nomograms. J Med Virol. 2023;95(2):e28550. Azzolini E, Levi R, Sarti R, et al. Association Between BNT162b2 Vaccination and Long COVID After Infections Not Requiring Hospitalization in Health Care Workers. JAMA. 2022;328(7):676–8. Zhang X, Wang F, Shen Y, et al. Symptoms and Health Outcomes Among Survivors of COVID-19 Infection 1 Year After Discharge From Hospitals in Wuhan, China. JAMA Netw Open. 2021;4(9):e2127403. Daugherty SE, Guo Y, Heath K, et al. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2021;373:n1098. Zhao YM, Shang YM, Song WB, et al. Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. EClinicalMedicine. 2020;25:100463. Shah AS, Wong AW, Hague CJ, et al. A prospective study of 12-week respiratory outcomes in COVID-19-related hospitalizations. Thorax. 2021;76(4):402–4. Ghafari M, Hall M, Golubchik T, et al. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature. 2024;626(8001):1094–101. Ichikado K, Suga M, Müller NL, et al. Acute interstitial pneumonia: comparison of high-resolution computed tomography findings between survivors and nonsurvivors. Am J Respir Crit Care Med. 2002;165(11):1551–6. Pan F, Ye T, Sun P, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295(3):715–21. Yazdi NA, Ghadery AH, SeyedAlinaghi S, et al. Predictors of the chest CT score in COVID-19 patients: a cross-sectional study. Virol J. 2021;18(1):225. Han X, Fan Y, Alwalid O, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021;299(1):E177–86. Jiang SQ, Huang QF, Xie WM, Lv C, Quan XQ. The association between severe COVID-19 and low platelet count: evidence from 31 observational studies involving 7613 participants. Br J Hematol. 2020;190(1):e29–33. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 16 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 22 Jan, 2026 Reviewers agreed at journal 07 Jan, 2026 Reviewers agreed at journal 21 Dec, 2025 Reviews received at journal 11 Dec, 2024 Reviewers agreed at journal 02 Dec, 2024 Reviewers invited by journal 27 Nov, 2024 Editor invited by journal 25 Oct, 2024 Editor assigned by journal 25 Oct, 2024 Submission checks completed at journal 25 Oct, 2024 First submitted to journal 20 Oct, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5297867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":370860799,"identity":"70977038-fe57-468c-963a-1b5b8307611a","order_by":0,"name":"Haojing Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haojing","middleName":"","lastName":"Zhang","suffix":""},{"id":370860804,"identity":"2d951cec-bfc4-4042-b689-fb33305f0eae","order_by":1,"name":"Lin Kan","email":"","orcid":"","institution":"The First Affiliated Hospital of Jinzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lin","middleName":"","lastName":"Kan","suffix":""},{"id":370860805,"identity":"7ff2ea69-7907-4999-a7c4-93185525cb3f","order_by":2,"name":"Dianzhu Pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYJACiQQgYcDAfODAhx+kaWFLPDizh1gtDGAtPMaHOdiIUG7OfvbgjQc1d+zNJXI+HGbgYZDnFzuAX4tlT16yRcKxZ8yWM3I3HC6wYDCcOTsBvxaDAzlmEglsh9kMbgC1zOBhSDC4TUjL+TdALf8O8xjcyHlwmIeNGC03gLYkth2WADIYiNNiOeONsUVi32EDgzPPDICBLEHYL+b8OYY3f3w7bG9wPPnxhw8/bOT5pQk5DI0vgV85Ni2jYBSMglEwCjABAO1dSBSrKQXvAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of Jinzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dianzhu","middleName":"","lastName":"Pan","suffix":""}],"badges":[],"createdAt":"2024-10-20 10:23:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5297867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5297867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68699120,"identity":"b8a4c655-6282-4272-bd05-a3ec140b4009","added_by":"auto","created_at":"2024-11-11 07:09:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":237905,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5297867/v1/032981c564db524ea1a5b467.jpg"},{"id":68699112,"identity":"5b62269b-270e-429d-82c7-9bf8e5620944","added_by":"auto","created_at":"2024-11-11 07:09:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":322802,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5297867/v1/7233b2cd7ac05eb1aad0ed97.jpg"},{"id":68699113,"identity":"a80fbc5e-63eb-41a8-9203-0dd83be4e501","added_by":"auto","created_at":"2024-11-11 07:09:48","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":292981,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5297867/v1/9b3d41d5b0d28d2619d4eb4a.jpg"},{"id":68700991,"identity":"55a389dc-c71c-4cbd-a38d-542235e58a83","added_by":"auto","created_at":"2024-11-11 07:26:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5297867/v1/04e02703-9f1b-4b53-9cad-ff4417815363.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSince the first confirmed case of the novel coronavirus \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e in the latest mutant strain, JN.1 \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e, COVID-19 has spread worldwide for more than four years. As of February 1, 2024, COVID-19 has caused more than 770\u0026nbsp;million confirmed cases and more than 7.03\u0026nbsp;million deaths globally. With the World Health Organization (WHO) declaring on May 5, 2023, that COVID-19 is no longer a global public health emergency, the global health crisis caused by the novel coronavirus infection has been effectively controlled. However, the chronic effects of acute infection continue to affect many long COVID survivors.\u003c/p\u003e \u003cp\u003eAfter recovering from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, patients worldwide have found it difficult to return to baseline health. Feedback from multiple channels \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e has led health workers to realize that the impact of COVID-19 on their health is neither temporary nor confined to a single system. They quickly associated new symptoms of discomfort and unexplained physical weakness with COVID-19 sequelae \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. The WHO defines this condition as long COVID syndrome \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e, officially described as symptoms persisting in COVID-19 patients three months after onset or diagnosis in asymptomatic individuals, lasting for at least two months and not attributable to other diagnoses. These symptoms significantly affect patients' daily lives.\u003c/p\u003e \u003cp\u003eLong COVID syndrome is prevalent, affecting 9–63% of the COVID-19 population globally, and is six times \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e more common than other coronaviruses. Prevalence rates by country include China at 49%-76%, Italy at 5%-51%, and the USA at 16%-53%. The incidence among hospitalized patients is notably higher than that among nonhospitalized patients \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. With the cycle of \"Yang–Yang Kang–Fu Yang\", people are profoundly feeling the enduring challenge posed by COVID-19.\u003c/p\u003e \u003cp\u003eStudies have indicated that inpatients, outpatients, and patients treated at home report symptoms during the follow-up process, including persistent fatigue, muscle weakness, dyspnea, dry cough, palpitations, anxiety, depression, headache, cognitive impairment, olfactory and taste dysfunction, and decreased immunity \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Long-term novel coronavirus infection can also lead to structural and functional involvement of multiple organs.\u003c/p\u003e \u003cp\u003eCOVID-19 has broad coverage and a lasting impact on patients. Given the vast population base affected by COVID-19, the potential health resource shortages and economic burdens are challenging to quantify. Consequently, COVID-19 has attracted unprecedented attention. The diagnosis, differentiation, and treatment of long COVID remain pressing issues to address.\u003c/p\u003e \u003cp\u003eA significant amount of previous research has focused on the clinical manifestations of long COVID \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e, with few studies addressing the screening of high-risk groups and early intervention, which are crucial for predicting and managing long COVID. A single-factor prediction model is often insufficient; a multifactorial approach significantly improves the prediction accuracy. This study aims to estimate the probability of long COVID through retrospective data analysis, covering demographics, comorbidities, symptoms, admission details, vaccination status, laboratory parameters, treatment data, and clinical severity, by constructing a nomogram and externally validating the model to assess its predictive performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e2.1 Study design and population\u003c/p\u003e\u003cp\u003e This retrospective study was approved by the Institutional Review Board (IRB) of the First Affiliated Hospital of Jinzhou Medical University (IRB number: KYLL2024279), and all patients provided written informed consent. Data were collected from 209 patients hospitalized at the First Affiliated Hospital of Jinzhou Medical University for COVID-19 treatment from December 7, 2022, to February 1, 2023, and from 211 patients hospitalized at Panjin Central Hospital for COVID-19 treatment. The cases from the First Affiliated Hospital of Jinzhou Medical University served as the modeling set, and those from Panjin Central Hospital served as the validation set. The inclusion criteria were as follows: age ≥ 18 years; inpatient status; nasal swab reverse transcriptase polymerase chain reaction (RT‒PCR) test results positive for COVID-19; and adherence to the diagnosis and treatment plan for severe and above patients with novel coronavirus infection (tenth edition). The exclusion criteria included patients who died during hospitalization and at follow-up; patients lost to follow-up; patients who lacked data from the first confirmed RT‒PCR test; or patients who lacked two consecutive negative RT‒PCR tests. After screening, 141 datasets were fully included in the modeling set, and 163 were included in the validation set. A flow chart illustrating the patient selection process is detailed in Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e2.2 Data collection\u003c/p\u003e\u003cp\u003eDemographics (age and sex), past medical history, admission symptoms, vaccination status, laboratory parameters, imaging findings, treatment-related data, and clinical severity data were collected.\u003c/p\u003e\u003cp\u003ePast medical history included hypertension, cardiovascular disease (coronary heart disease, atrial fibrillation, pulmonary embolism, rheumatic heart disease, previous myocardial infarction, cardiac insufficiency), diabetes, chronic liver disease (fatty liver, hepatitis B, hepatitis C), chronic lung disease (bronchiectasis, chronic obstructive pulmonary disease, emphysema, tuberculosis, asthma), chronic kidney disease (chronic renal failure, chronic nephritis), rheumatic disease (systemic lupus erythematosus, rheumatoid arthritis, scleroderma), nervous system disorders (cerebral infarction and hemorrhage), and malignancy. Vaccination status was divided into four groups: unvaccinated, partially vaccinated (one dose), fully vaccinated (two doses), and booster vaccinated (three doses or more). The laboratory parameters included white blood cell count, percentage of neutrophils, percentage of lymphocytes, percentage of eosinophils, troponin, alanine aminotransferase, aspartate aminotransferase, albumin, C-reactive protein, D-dimer, creatinine, lactate dehydrogenase, procalcitonin (PCT), and interleukin 6 (IL-6) levels. Imaging results included chest CT and high-resolution CT (HRCT). Treatment-related data included oxygen inhalation status, oxygen inhalation mode, hormone dosage, and the use of antiviral drugs.\u003c/p\u003e\u003cp\u003e2.3 Data wrangling\u003c/p\u003e\u003cp\u003e2.3.1 CCI: In noncirrhotic patients, the following diseases are assigned 1 point each: myocardial infarction (AMI), congestive heart failure, peripheral vascular disease, dementia, cerebrovascular disease without sequelae, connective tissue disease, peptic ulcer disease, diabetes without end-organ damage, chronic lung disease, and mild liver disease without portal hypertension. Diseases assigned 2 points include hemiplegia, moderate to severe chronic kidney disease (CKD), end-stage diabetes, solid tumors without metastases, leukemia, lymphoma, and moderate to severe liver disease. Patients with metastatic tumors and AIDS are given 6 points. The age group scores are added as follows: 0 points for age ≤ 40, 1 point for ages 41–50, 2 points for ages 51–60, 3 points for ages 61–70, and 4 points for ages \u0026gt; 70. The final CCI score is the sum of the disease scores and the corresponding age group score.\u003c/p\u003e\u003cp\u003e2.3.2 CT score: Each lung lobe is scored from 0–5, with 0 indicating no involvement, 1 indicating \u0026lt; 5%, 2 indicating 5–25%, 3 indicating 26–50%, 4 indicating 51–75%, and 5 indicating \u0026gt; 75% involvement. The overall CT score is the sum of each lobe score (0–25). CT scores are categorized as follows: 0–5 for category 1, 6–10 for category 2, 11–15 for category 3, 16–20 for category 4, and 21–25 for category 5.\u003c/p\u003e\u003cp\u003e2.3.3 Oxygen requirements: Classified as no oxygen, low-flow oxygen (1–2 L/min), or high-flow oxygen (≥ 3 L/min).\u003c/p\u003e\u003cp\u003e 2.3.4 Severity classification: On the basis of WHO guidelines, severity is classified into nonsevere, severe, and critical categories.\u003c/p\u003e\u003cp\u003e2.4 Statistical analysis\u003c/p\u003e\u003cp\u003eSPSS (version 26.0) and R language (version 4.0.1) were used for statistical analysis, defining statistical significance as a two-tailed \u003cem\u003ep\u003c/em\u003e value \u0026lt; 0.05.\u003c/p\u003e\u003cp\u003eNormality tests for continuous variables were performed via the Kolmogorov‒Smirnov test. Independent sample t tests and χ\u003csup\u003e2\u003c/sup\u003e tests were used for normally distributed variables, whereas the Wilcoxon rank-sum test was applied for nonnormally distributed variables. Normally distributed continuous variables are presented as the means ± standard deviations, nonnormally distributed continuous variables are presented as medians (interquartile ranges), and categorical variables are presented as numbers (percentages).\u003c/p\u003e\u003cp\u003ePredictor variables were selected via a stepwise forward method on the basis of the Akaike information criterion (AIC) in the modeling set. Receiver operating characteristic (ROC) curves were plotted for the selected continuous variables to calculate cutoffs. The prediction model was then exported as a nomogram. External validation was performed on the validation set, and the model's consistency, discrimination, and clinical usefulness were evaluated through the AUROC, calibration curve, and decision curve analysis.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUltimately, 141 patients were included in the modeling set, and 163 patients were included in the validation set. The median ages were 71 and 74 years in the modeling and validation sets, respectively. The median BMI values were 23.93 kg/m\u003csup\u003e2\u003c/sup\u003e and 24.22 kg/m\u003csup\u003e2\u003c/sup\u003e for the modeling and validation sets, respectively (Table\u0026nbsp;1). The proportions of patients who developed long COVID were 60.3% (85/141) in the modeling set and 59.5% (97/163) in the validation set (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"540\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 540px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Characteristics of patients in the development and validation cohort for this study.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eDevelopment Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003eValidation Cohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003eZ/t/\u0026chi;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNumber of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e141(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e163(100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e153.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e67（22.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e85（28.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e74（24.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e78（25.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e71.00（62.00，80.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e74.00（63.00，83.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-1.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eBMI，kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e23.93（22.13，26.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e24.22（21.03，26.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eLength of hospital stay, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e12.00（9.00，15.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e14.00（10.00，18.00）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-3.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSelf-medication prior to admission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e2.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e112（36.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e118（38.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29（9.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e45（14.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eCCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e0 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2（0.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e3（1.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e1 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10（3.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e8（2.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e2 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e8（2.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e9（3.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e3 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e14（4.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e17（5.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e4 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e27（8.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e39（12.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e5 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29（9.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e38（12.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e6 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e24（7.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e32（10.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e7 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e20（6.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e9（3.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e8 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6（2.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e5（1.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e9 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1（0.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e3（1.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eLeucocyte count,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e6.120（4.230，8.165）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e6.760（4.940，8.820）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-1.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003ePercentage of the neutrophils,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e74.590（65.420，81.665）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e77.300（68.500，86.500）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-2.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003ePercentage of the lymphocytes,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e15.310（9.970，23.340）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e14.600（7.700，21.300）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-1.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eHemoglobin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e123.300（110.500，135.100）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e123.000（113.000，137.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.326\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003ePlatelet count,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e186.600（133.900，271.050）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e205.000（149.000，270.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.320\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eCRP,mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e42.400（10.850，69.750）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e22.430（6.180，63.910）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-1.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eD-dimer, mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0.320（0.175，0.730）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0.830（0.480，1.420）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-8.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eLactate dehydrogenase,U/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e254.240（251.000，254.240）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e227.000（191.000，287.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-2.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAlbumin,g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e34.462\u0026plusmn;4.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e36.902\u0026plusmn;4.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-4.443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOn admission pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e7.42（7.41，7.44）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e7.430（7.400，7.450）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOn admission pCO\u003csub\u003e2\u003c/sub\u003e,mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e35.470（32.900，37.150）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e37.000（33.800，40.200）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-2.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOn admission pO\u003csub\u003e2\u003c/sub\u003e,mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e79.630（70.000，85.950）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e82.800（70.400，94.000）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-2.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOn admission SO\u003csub\u003e2\u003c/sub\u003e,%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e94.300（93.430，96.300）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e96.300（93.900，97.200）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-4.152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOxygen demand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.442\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNo oxygen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e32（10.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e53（17.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eLow flow oxygen absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e75（24.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e65（21.4%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eHigh flow oxygen absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e34（11.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e45（14.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eTotal CT score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e0-5 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e19（6.25%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e20（6.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e6-10 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e22（7.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e34（11.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e11-15 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e30（9.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e32（10.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e16-20 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e44（14.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e42（13.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003e21-25 score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e26（8.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e35（11.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAntiviral drug\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-6.432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"59\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eAzvudine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e43（14.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e78（25.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNirmatrelvir/ritonavir\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e2（0.7%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e7（2.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eGanciclovir sodium injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e10（3.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0（0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003ePotassium\u0026nbsp;Sodium\u003c/p\u003e\n \u003cp\u003eDehydroandrographolide\u003c/p\u003e\n \u003cp\u003eSuccinate\u0026nbsp;for\u0026nbsp;Injection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e1（0.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e24（7.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e0（0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e12（3.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eUnused\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e85（28.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e42（13.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eVaccination status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e-5.826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eUnvaccinated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e34（11.2%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e70（23.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eInject 1 shot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e9（3.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e19（6.25%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eInject 2 shot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e15（4.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e16（5.3%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eInject 3 shot\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e29（9.5%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e46（15.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eBooster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e11（3.6%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e12（3.9%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 146px;\"\u003e\n \u003cp\u003eNot quite clear\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e43（14.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 137px;\"\u003e\n \u003cp\u003e0（0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eContinuous variables with a normal distribution are reported as the means \u0026plusmn; standard deviations (SDs); nonnormally distributed continuous variables are expressed as medians (interquartile ranges); and categorical variables are reported as numbers (percentages). Abbreviations: BMI, body mass index; CCI, Charlson Comorbidity Index; CRP, C-reactive protein; CT, computed tomography.\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis of patients in the development and validation cohort for this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eDevelopment Cohort 141(100)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eValidation Cohort 163(100)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLong COVID group occurred\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo long COVID group occurred\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLong COVID group occurred\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo long COVID group occurred\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97(59.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e66(40.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(29.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53(32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32(19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34(20.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\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\u003e69.86\u0026thinsp;\u0026plusmn;\u0026thinsp;12.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.71\u0026thinsp;\u0026plusmn;\u0026thinsp;14.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.00(67.50, 83.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.00(59.00, 80.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.93(22.27, 25.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.93(21.49, 26.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.500\u0026thinsp;\u0026plusmn;\u0026thinsp;4.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.036\u0026thinsp;\u0026plusmn;\u0026thinsp;3.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of hospital stay, days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(9, 15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(8.25, 14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.00(11.00, 18.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.00(9.00, 15.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-medication prior to admission\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70(49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42(29.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e73(44.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45(27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24(14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21(12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23(14.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21(14.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28(17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10(6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17(10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2(1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucocyte count,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.890(4.070, 8.080)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.400(4.823, 8.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.930(5.140, 8.885)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.440(4.608, 8.690)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of the neutrophils,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.431\u0026thinsp;\u0026plusmn;\u0026thinsp;11.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.633\u0026thinsp;\u0026plusmn;\u0026thinsp;13.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e77.500(69.850, 86.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.500(60.636, 86.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of the lymphocytes,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.330(8.830, 22.045)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.495(12.358, 28.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.000(7.400, 18.600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.700(9.275, 29.775)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin,g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e123.30(110.05, 134.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.50(111.93, 136.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.000(113.000, 134.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.500(113.750, 141.750)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count,\u0026times;10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173.400(131.700, 271.050)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214.600(140.850, 274.900)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e208.000(151.500, 264.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197.500(143.250, 273.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.400(9.910, 74.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.250(11.150, 63.725)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.460(7.920, 63.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.905(4.400, 64.688)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-dimer, mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.340(0.180, 0.730)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.305(0.163, 0.695)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.870(0.490, 1.435)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.755(0.479, 1.403)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate dehydrogenase,U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e254.240(251.500, 254.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e254.240(236.000, 254.240)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229.000(190.000, 304.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e226.750(191.375, 262.500)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.425\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin,g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.425\u0026thinsp;\u0026plusmn;\u0026thinsp;4.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.519\u0026thinsp;\u0026plusmn;\u0026thinsp;4.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.334\u0026thinsp;\u0026plusmn;\u0026thinsp;4.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.738\u0026thinsp;\u0026plusmn;\u0026thinsp;4.962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn admission pH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.42(7.41, 7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.42(7.41, 7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.434(7.401, 7.455)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.427(7.394, 7.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn admission pCO\u003csub\u003e2\u003c/sub\u003e,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.400(32.550, 36.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.470(33.975, 39.025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.893\u0026thinsp;\u0026plusmn;\u0026thinsp;5.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.221\u0026thinsp;\u0026plusmn;\u0026thinsp;5.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn admission pO\u003csub\u003e2\u003c/sub\u003e,mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79.630(70.650, 85.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e79.630(69.100, 87.325)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.000(70.550, 92.220)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.150(70.067, 101.260)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOn admission SO\u003csub\u003e2\u003c/sub\u003e,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.60(93.45, 96.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.48(93.15, 96.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.100(93.950, 97.100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.340(93.758, 97.450)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen demand\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.130\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo oxygen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow flow oxygen absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35(24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35(21.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30(18.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh flow oxygen absorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25(15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20(12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal CT 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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;5 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u0026ndash;10 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12(8.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u0026ndash;15 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19(13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20(12.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12(7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u0026ndash;20 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33(23.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29(17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u0026ndash;25 score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntiviral drug\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAzvudine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50(30.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28(17.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNirmatrelvir/\u003c/p\u003e \u003cp\u003eritonavir\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3(1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGanciclovir sodium injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u0026nbsp;SodiumDehydroandrograp-holide\u0026nbsp;Succinate\u0026nbsp;for\u0026nbsp;Injection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnused\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(34.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27(16.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15(9.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVaccination status\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnvaccinated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(17.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44(27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26(16.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInject 1 shot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2(1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(5.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6(3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInject 2 shot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInject 3 shot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(12.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21(12.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25(15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBooster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot quite clear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24(17.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19(13.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0(0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eContinuous variables with a normal distribution are reported as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SDs); nonnormally distributed continuous variables are expressed as medians (interquartile ranges); and categorical variables are reported as numbers (percentages). Student\u0026rsquo;s t test was used to compare the means of two continuous normally distributed variables, and the Mann\u0026ndash;Whitney U test was used to determine the means of two continuous nonnormally distributed variables. The chi-square test or Fisher\u0026rsquo;s exact test was used for categorical variables. Abbreviations: BMI, body mass index; CCI, Charlson Comorbidity Index; CRP, C-reactive protein; CT, computed tomography.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the modeling cohort, patients who developed long COVID had higher CCI scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), a greater percentage of lymphocytes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), greater oxygen demand (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011), and higher total CT scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) than those who did not develop long COVID (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate binary logistic regression of patients in development cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR(95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003econstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.202(\u0026minus;1.681,1.266)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.817(0.186,3.548)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal CT score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.405(0.057,0.770)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.499(1.058,2.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.264(0.036,0.506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.302(1.037,1.658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercentage of the lymphocytes,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.039(\u0026minus;0.075,\u0026minus;0.004)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.962(0.928,0.996)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOxygen demand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.126(\u0026minus;1.791,\u0026minus;0.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.324(0.167,0.594)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArea under ROC curve\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAUROC 95% CI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevelopment Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.844(0.781,0.907)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eValidation Cohort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.794(0.724,0.864)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eThe β coefficient, odds ratio, and 95% confidence interval were analyzed via multivariate binary logistic\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eregression. OR, odds ratio; CI, confidence interval; ROC, receiver operating characteristic curve; AUC\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003earea, area under the ROC curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eA multivariate LASSO regression model was employed to construct a predictive model with regression coefficients for the CCI, percentage of lymphocytes, oxygen demand, and total CT score (Fig.\u0026nbsp;2). On the basis of these results, we developed and validated a nomogram for predicting the probability of the occurrence of long COVID (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach clinical feature was assigned a specific point value. For example, a long COVID patient with a CCI score of 8 points received 60 points, a CT score of 3 points received 12 points, a lymphocyte percentage of 45 points received 5 points, and an oxygen demand of 2 points received 0 points. This resulted in a total score of 77 points for the patient, suggesting an approximate 40% probability of developing long COVID. This outcome can assist in clinical decision-making plans (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eThe AUROCs for the modeling and validation sets were 0.844 (0.781, 0.907) and 0.794 (0.724, 0.864), respectively. The cutoff value for risk probability in this model was set at 59.4%, with a sensitivity of 71.4% and specificity of 81.2%. The \u003cem\u003ep\u003c/em\u003e value of the unreliability test was 0.077. The \u003cem\u003ep\u003c/em\u003e value of the Hosmer and Lemeshow chi-square (H\u0026ndash;L chi-square) statistic was 0.170, indicating good calibration. Decision curve analysis demonstrated that the prediction model has clinical utility for the population in the validation set (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe chronic effects of SARS-CoV-2 infection, including postacute sequelae of SARS-CoV-2 infection (PASC) symptoms and unexplained physical weakness that persists long-term, continue to affect a significant portion of COVID-19 survivors. In this research, we developed a prediction model utilizing clinical data from patients admitted to the First Affiliated Hospital of Jinzhou Medical University for COVID-19 treatment, following China's \"Category B and B Management\" implementation and full societal reopening. LASSO regression analysis identified crucial factors associated with long COVID, culminating in the selection and external validation of four clinical variables\u0026mdash;the CCI, CT score, oxygen requirement, and lymphocyte percentage\u0026mdash;using clinical data from Panjin Central Hospital.\u003c/p\u003e \u003cp\u003eThe CCI, which combines age and comorbidities across all age groups and systemic conditions, was first proposed by Charlson in 1987\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e to predict long-term mortality and has since been widely adopted for forecasting long-term prognosis and survival. Given its efficacy, simplicity, and applicability and considering that the occurrence of long COVID is also part of the prognosis of patients with COVID-19, the CCI was introduced in this study for the first time. Statistical findings revealed that a higher CCI was correlated with an increased likelihood of long COVID.\u003c/p\u003e \u003cp\u003eWhile most published studies have focused solely on either age or comorbidities, our review identified fourteen study sets that considered age, with twelve confirming the predictive value of age. This research encompasses data from China, Italy, the United States, the United Kingdom, and other countries\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, covering most designated medical centers for treating the novel coronavirus.\u003c/p\u003e \u003cp\u003eWith respect to comorbidities, determining whether long COVID symptoms originate from preexisting conditions, new symptoms caused by COVID-19, or a combination of both is challenging. Consequently, long COVID definitions emphasize duration over distinguishing between these factors. A 2022 JAMA article \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e highlighted that an increased number of comorbidities elevates the risk of long COVID. Specifically, a study from Italy noted the importance of an allergic constitution among various comorbidities. Another study in the BMJ from the United States noted that individuals with preexisting interstitial lung disease are more likely to develop severe COVID-19.\u003c/p\u003e \u003cp\u003eThe ongoing impact of coronavirus on T-cell function leads to immune escape, allowing the virus to persist at low concentrations for extended periods. Data from past coronavirus outbreaks, such as SARS and MERS \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, show that some patients continue to suffer from lung damage after the infection has resolved. The novel coronavirus, characterized by its low virulence and viral load, does not typically cause systemic symptoms but can persist in certain organs, leading to specific clinical symptoms. A recent Nature study \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e demonstrated that most patients struggle to completely eliminate the virus during the acute phase, with approximately 0.1% \u0026minus;\u0026thinsp;0.5% of patients harboring the virus for at least 60 days. The continued presence of the virus weakens the immune system's ability to clear it, particularly in individuals with a history of medical conditions, thus increasing the likelihood of long-term effects from the novel coronavirus.\u003c/p\u003e \u003cp\u003eCurrent research often overlooks the simultaneous consideration of age and comorbidities. A nomogram developed by the National Infectious Disease Center team at Fudan University in Shanghai, China \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, included five risk factors. Being over 75 years of age, having chronic kidney disease, and suffering from chronic lung disease were identified as the most significant risk factors. This highlights the particular importance of age and comorbidities in predicting the risk of severe COVID-19 in the Chinese population.\u003c/p\u003e \u003cp\u003eThe CT score, initially utilized to assess the prognosis of the interstitial pneumonia index \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e, has been adopted for its quantitative analysis, high accuracy, and ability to provide rapid diagnostics. Many research teams have adapted and revised it \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e as a tool for real-time assessment of the severity of lung involvement in COVID-19 patients. Compared with other indicators of disease progression, the majority of research teams \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e recognize the CT score as the most accurate index reflecting disease progression, with more severe COVID-19 cases being more likely to develop into long COVID \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study highlights the CT score as the optimal indicator of disease progression. For the first time, the CT score was applied to COVID-19 patients and included in the final prediction model through LASSO regression analysis. In the longitudinal follow-up of patients with COVID-19, persistent imaging changes are often observed, which are closely correlated with the peak CT score during the acute phase \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Current research primarily describes these persistent imaging changes, with pulmonary fibrosis-like changes being the most frequently observed. A follow-up study of the initial set of COVID-19 patients from Wuhan Jinyintan Hospital\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e statistically identified a CT score exceeding 18 during the acute phase of COVID-19 as an independent risk factor for developing pulmonary fibrosis-like changes during follow-up. These persistent imaging changes correlate with long-standing clinical symptoms, and long COVID can be considered an umbrella term encompassing these clinical manifestations.\u003c/p\u003e \u003cp\u003eAccording to the novel coronavirus infection diagnosis and treatment plan (10th version), severe and critical patients are more focused on supportive treatments than ordinary and mild patients are. Respiratory system support therapy, specifically oxygen therapy, is a primary treatment strategy. In a multicenter study including 2,433 cases \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e, 71.6% of hospitalized patients received oxygen therapy, and 0.9% underwent mechanical ventilation. These studies have established a connection between oxygen therapy, mechanical ventilation, and the persistence of fatigue, which is one of the most common long COVID symptoms \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Our research focused on long COVID among hospitalized patients with acute SARS-CoV-2 infection, indicating that symptomatic oxygen support is provided on the basis of the severity of the patient's condition. Our findings suggest that a lower oxygen requirement increases the likelihood of developing long COVID. This conclusion may be attributed to two factors. First, under conditions of insufficient blood oxygen saturation, oxygen is preferentially supplied to vital organs such as the brain, heart, and lungs. Other organs may remain hypoxic, leading to long-term tissue decompensation, chronic cellular damage, and the persistence of symptoms and discomfort after discharge, which are recognized as clinical manifestations of long COVID. Second, for patients with severe conditions, timely high-flow oxygen support is often provided. A significant proportion of these patients might die before discharge or during follow-up; thus, they were excluded from this study, introducing bias. Therefore, the incidence of long COVID appears to be lower among recipients of high-flow oxygen therapy. On the other hand, prolonged oxygen therapy can cause lung damage. Several post-COVID-19 follow-up studies have revealed \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e interstitial lung lesions in the CT scans of patients with long COVID. It remains unclear whether this damage is attributable to COVID-19 itself or to the side effects of oxygen therapy received during hospitalization. This ambiguity highlights the need for further research to fully understand the relationship between oxygen therapy and long COVID. The association between oxygen requirements and long COVID essentially reflects a mismatch between the oxygen demand of the body and the administration of oxygen therapy, which is highly subjective. This study, which is based on clinical data from two medical centers, has limitations, and the relationship between oxygen therapy and long COVID warrants additional investigation.\u003c/p\u003e \u003cp\u003eThe percentage of lymphocytes in the blood is a quick indicator used to identify the source of infection, with lymphocyte percentages\u0026thinsp;\u0026lt;\u0026thinsp;20% indicating viral infection. A lower lymphocyte percentage is associated with a greater likelihood of viral infection. Research has indicated that adults infected with COVID-19 exhibit lymphopenia, increased platelet counts, and elevated lactate dehydrogenase levels, with a decrease in lymphocyte count in severe cases indicating more severe disease and a greater risk of mortality \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In this study, the lymphocyte percentage was included as an indicator of COVID-19 severity. This metric, derived from a routine blood test available to all patients, holds significant potential for widespread application.\u003c/p\u003e \u003cp\u003eThis research utilized data from the first major outbreak to develop a prediction model for long COVID syndrome in patients with severe COVID-19, offering a scientific basis for early identification with clinical relevance. Nonetheless, this study has several limitations: (1) This was a multicenter retrospective study, and further large-scale multicenter prospective studies are needed for validation. (2) An assessment of lung involvement in long COVID severity is lacking, including further subgroup analysis predictions. (3) It does not include data from intensive care units, particularly from mechanically ventilated patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe prediction model developed in this study identified the CCI, CT score, oxygen requirement, and lymphocyte percentage as significant factors associated with long COVID. These factors could serve as risk factors for the early detection and identification of long COVID in clinical settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003eCOVID-19\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eCorona Virus Disease 2019\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eCCI\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eCharlson Comorbidity Indexc\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eCT\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eComputed Tomography\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eLasso\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eLeast absolute shrinkage and selection operator\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAUROC\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eArea Under the Receiver Operating Characteristic\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eWHO\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eWorld Health Organization\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eSARS-CoV-2\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003esevere acute respiratory syndrome coronavirus 2\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIRB\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003einstitutional Review Board\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eRT-PCR\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003ereverse transcriptase polymerase chain reaction\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003ePCT\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eprocalcitonin\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eIL-6\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eInterleukin 6\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eHRCT\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eHigh Resolution CT\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eMI\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003emyocardial infarction\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eCKD\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003echronic kidney disease\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAIDS\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eAcquiredImmune Deficiency Syndrome\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eAIC\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eAkaike information criterion\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eROC\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eReceiver operating characteristic\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003eH\u0026ndash;L\u0026nbsp;\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003eHosmer\u0026nbsp;and\u0026nbsp;Lemeshow\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003ePASC\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003epost-acute sequelae of SARS-CoV-2 infection\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval (KYLL2024279) was provided by the Institutional Research and Ethics Committee of the First Affiliated Hospital of Jinzhou Medical University. Informed consent was obtained from all eligible subjects. The study protocol conformed to the ethical guidelines of the 1975 Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eZHJ contributed to data collection and writing.KL provided technical support and conducted data analysis.Both ZHJ and KL made equal contributions to this paper.\u003c/p\u003e\n\u003cp\u003ePDZ designed this study and reviewed manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Pan of the First Affiliated Hospital of Jinzhou Medical University for technical support and review of the manuscript. We also sincerely thank the First Affiliated Hospital of Jinzhou Medical University for providing data support and coordination.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChen N, Zhou M, Dong X, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395(10223):507\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLooi MK. Covid-19: WHO adds JN.1 as new variant of interest. BMJ. 2023;383:2975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu X, Liu X, Zhou Y, et al. 3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalization: a prospective study. Lancet Respir Med. 2021;9(7):747\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorin L, Savale L, Pham T, et al. Four-Month Clinical Status of a Cohort of Patients After Hospitalization for COVID-19. JAMA. 2021;325(15):1525\u0026ndash;34.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl-Aly Z, Xie Y, Bowe B. High-dimensional characterization of postacute sequelae of COVID-19. Nature. 2021;594(7862):259\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanno LK, Casale T, Demoly P. Coronavirus Disease (COVID)-19: World Health Organization Definitions and Coding to Support the Allergy Community and Health Professionals. J Allergy Clin Immunol Pract. 2020. 8(7).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Sanchis-Gomar F, Henry BM. COVID-19 and its long-term sequelae: what do we know in 2023. Pol Arch Intern Med. 2023;133(4):16402. [pii].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaman B, Bluemke DA, L\u0026uuml;scher TF, Neubauer S. Long COVID: postacute sequelae of COVID-19 with a cardiovascular focus. Eur Heart J. 2022;43(11):1157\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Huang C, Gu X, et al. 3-year outcomes of discharged survivors of COVID-19 following the SARS-CoV-2 omicron (B.1.1.529) wave in 2022 in China: a longitudinal cohort study. Lancet Respir Med. 2024;12(1):55\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Guo Y, Zhang Y, et al. Factors affecting prolonged SARS-CoV-2 infection and development and validation of predictive nomograms. J Med Virol. 2023;95(2):e28550.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzzolini E, Levi R, Sarti R, et al. Association Between BNT162b2 Vaccination and Long COVID After Infections Not Requiring Hospitalization in Health Care Workers. JAMA. 2022;328(7):676\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wang F, Shen Y, et al. Symptoms and Health Outcomes Among Survivors of COVID-19 Infection 1 Year After Discharge From Hospitals in Wuhan, China. JAMA Netw Open. 2021;4(9):e2127403.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaugherty SE, Guo Y, Heath K, et al. Risk of clinical sequelae after the acute phase of SARS-CoV-2 infection: retrospective cohort study. BMJ. 2021;373:n1098.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao YM, Shang YM, Song WB, et al. Follow-up study of the pulmonary function and related physiological characteristics of COVID-19 survivors three months after recovery. EClinicalMedicine. 2020;25:100463.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah AS, Wong AW, Hague CJ, et al. A prospective study of 12-week respiratory outcomes in COVID-19-related hospitalizations. Thorax. 2021;76(4):402\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhafari M, Hall M, Golubchik T, et al. Prevalence of persistent SARS-CoV-2 in a large community surveillance study. Nature. 2024;626(8001):1094\u0026ndash;101.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIchikado K, Suga M, M\u0026uuml;ller NL, et al. Acute interstitial pneumonia: comparison of high-resolution computed tomography findings between survivors and nonsurvivors. Am J Respir Crit Care Med. 2002;165(11):1551\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan F, Ye T, Sun P, et al. Time Course of Lung Changes at Chest CT during Recovery from Coronavirus Disease 2019 (COVID-19). Radiology. 2020;295(3):715\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazdi NA, Ghadery AH, SeyedAlinaghi S, et al. Predictors of the chest CT score in COVID-19 patients: a cross-sectional study. Virol J. 2021;18(1):225.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Fan Y, Alwalid O, et al. Six-month Follow-up Chest CT Findings after Severe COVID-19 Pneumonia. Radiology. 2021;299(1):E177\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang SQ, Huang QF, Xie WM, Lv C, Quan XQ. The association between severe COVID-19 and low platelet count: evidence from 31 observational studies involving 7613 participants. Br J Hematol. 2020;190(1):e29\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"long COVID, COVID-19, Risk factor, Nomogram, Forecast","lastPublishedDoi":"10.21203/rs.3.rs-5297867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5297867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e To investigate the risk factors for corona virus disease 2019 (COVID-19) and construct a nomogram prediction model to evaluate the clinical treatment of long COVID.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Clinical data were collected from patients who were diagnosed with COVID-19 and hospitalized at the First Affiliated Hospital of Jinzhou Medical University from December 7, 2022, to February 1, 2023. The prediction model was constructed via a nomogram. External validation was carried out with clinical data from patients at Panjin Central Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e In the development cohort and the validation cohort of this study, 60.3% and 59.5% of the patients developed long COVID, respectively. After least absolute shrinkage and selection operator (Lasso) regression, the final variables included in the prediction model were the percentage of lymphocytes, the Charlson comorbidity index (CCI), computed tomography (CT) score, and oxygen requirement. The area under the receiver operating characteristic curve (AUROC) for external validation of the model was 0.794, and the \u003cem\u003ep\u003c/em\u003e value of the calibration curve was 0.170. The decision curve analysis indicates that the model performs well.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe prediction model developed in this study is useful for assessing the likelihood of developing long COVID in hospitalized patients.\u003c/p\u003e","manuscriptTitle":"Building and externally validating a prediction model for long COVID in severe and critical COVID-19 patients: A multicenter cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 07:08:51","doi":"10.21203/rs.3.rs-5297867/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-16T06:00:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T19:49:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2026-03-10T17:21:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T21:09:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T11:14:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"28292410387815156036920438606149635824","date":"2026-02-16T21:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26115639229701371953401704998521841852","date":"2026-02-16T20:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"143184502956480586141506806226872230232","date":"2026-02-15T11:52:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"88811978658550714566416165458520225735","date":"2026-01-28T09:17:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287680637661357752356455585050375877022","date":"2026-01-23T01:41:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"241887504717157317502982458533191476914","date":"2026-01-07T11:40:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60275145185850489211037685441684363657","date":"2025-12-21T10:03:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-11T07:57:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"340134533843632650726310159223748072039","date":"2024-12-02T09:50:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-27T08:18:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-25T13:02:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-25T11:28:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-25T11:28:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-10-20T10:18:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"85fb0298-73ee-4a16-a7c0-b6c8b3f8ae68","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-20T04:39:15+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 07:08:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5297867","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5297867","identity":"rs-5297867","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}