A Preliminary Prognostic Model for Predicting Poor Prognosis in COVID-19 Integrating Lung Epithelial Injury (KL-6) with Routine Care Markers

A Preliminary Prognostic Model for Predicting Poor Prognosis in COVID-19 Integrating Lung Epithelial Injury (KL-6) with Routine Care Markers | 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 A Preliminary Prognostic Model for Predicting Poor Prognosis in COVID-19 Integrating Lung Epithelial Injury (KL-6) with Routine Care Markers Yunlai Liang, Kun Wang, Lu Long, Qizhuo Hou, Wenze Yu, Kangkang Huang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8528169/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background The coronavirus disease 2019 (COVID-19) pneumonia pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused millions of deaths worldwide, and is still threatening our life, making profound impact on the global economy and society in every field. Exploring risk factors for poor prognosis in COVID-19 patients would help optimize clinical management and improve health outcomes. Methods Clinical characteristics and laboratory data of 144 COVID-19 patients (confirmed by SARS-CoV-2 nucleic acid or antigen testing) admitted to Xiangya Hospital of Central South University between December 2022 and February 2023 (including 103 with favorable prognosis and 41 with poor prognosis) were collected in this retrospective study. Factors such as age, serum levels of KL-6, BUN, Scr, IL-6, and CRP were analyzed using R software and the Deepwise and Beckman Coulter DxAI platform. Poor prognosis was defined as a composite endpoint of in-hospital death, ICU admission, or clinical deterioration (escalation of respiratory support) during hospitalization. Results The statistical results showed that age, serum KL-6, BUN, Scr, IL-6 and CRP levels in poor prognosis COVID-19 patients were obviously higher than that in the favorable prognosis group. Spearman correlation analysis demonstrated that serum levels of CRP (r=0.48), IL-6 (r=0.40), cTn (r=0.37), Scr (r=0.37), BUN (r=0.58), KL-6 (r=0.35) and age (r=0.20) were positively correlated with the outcome of the COVID-19 patients. Age, serum KL-6, CRP and BUN levels were identified as risk factors for poor prognosis of COVID-19. Meanwhile, KL-6 combined with BUN, CRP and age revealed good discriminative performance (AUC=0.947, sensitivity=0.842, specificity=0.898) in diagnosis of poor prognosis of COVID-19 through ROC analysis. Conclusion Our retrospective study identified age, serum KL-6, CRP, and BUN levels as reliable risk factors and preliminary prognostic indicators for poor prognosis (a composite endpoint of in-hospital death, ICU admission, or respiratory support escalation) in COVID-19. COVID-19 KL-6 BUN CRP Age Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Upon the outbreak of COVID-19, global spread is happening quickly. Up to now, nearly 689 million people were infected and over 6 million lost their lives in this battlefield 1 . Symptoms of COVID-19 vary significantly from only cough and fever, to acute respiratory distress syndrome, and often change dynamically within the development of the disease. Despite the fact that we have access to new antiviral drugs, vaccines and better treatment experience, the virus is mutating to be survival-favorable and is not completely under our control until now 2 , 3 . The diagnostic methods of COVID-19 included computed tomography (CT), nucleic acid amplification (PCR), and immunoassays 4 – 6 . These methods to some extent are expensive, time consuming and at low specificity. There is an urgent need for practical prognostic strategies to help save lives and provide potential measures to deal with similar respiratory virus infection. The culprit of COVID-19, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus could enter into the epithelium cells easily by anchoring angiotensin-converting enzyme 2 (ACE2), which is mainly expressed in the pulmonary epithelium 7 . The pathological feature of COVID-19 patients included interstitial pneumopathy and lung fibrosis 8 . Krebs von den Lungen-6 (KL-6) is a mucin-like and high-molecular-weight glycoprotein encoded by MUC1 gene and secreted by type II pneumocytes and bronchial epithelial cells. Under normal circumstances, KL-6 plays roles in the migration, proliferation and survival of lung fibroblasts. An interesting phenomenon is that KL-6 secretion increases with epithelial lesions and cellular regeneration 9 , 10 . Serum KL-6 level has been proven to be reliable for diagnosis of interstitial lung disease and monitoring the disease activity 11 . Recently, KL-6 has been used for COVID-19 diagnosis, prognosis, and therapeutic response evaluation 12 . While several biomarkers have been investigated in COVID-19, their combined utility in a simple, clinically applicable prognostic model remains underexplored. Early and accurate identification of high-risk patients is crucial for optimizing resource allocation and guiding therapeutic decisions. This study aimed to develop and explore a preliminary prognostic model integrating the lung epithelial biomarker KL-6 with readily available clinical and laboratory parameters to predict poor prognosis (a composite endpoint of in-hospital death, ICU admission, or respiratory support escalation) in hospitalized COVID-19 patients. Material and methods Study population This retrospective analysis recruited 71 common COVID-19 patients and 73 severe COVID-19 patients, all with confirmed SARS-CoV-2 infection by nucleic acid amplification or antigen testing, who were admitted to Xiangya Hospital of Central South University from Dec. 2022 to Feb. 2023. According to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial version 10) of China 13 , the inclusion criteria of common COVID-19 patients include the following: Symptoms of a respiratory tract infection for light COVID-19; persistent high fever for more than three days or cough and dyspnea, and breathing rate 93%, pulmonary imaging shows the characteristic manifestations of COVID-19 pneumonia for medium COVID-19. The criteria required for severe COVID-19 patients inclusion contain dyspnea and breathing rate ≥ 30/min; oxygen saturation at rest is ≤ 93%; PaO2/FiO2 ≤ 300mmHg; clinical symptoms worsened and pulmonary imaging was 50% advanced in 24–48 hours. Exclusion criteria—including bacterial pneumonia, lung cancer, interstitial lung disease, other viral pneumonias, and pediatric or pregnant patients—were applied prior to data extraction to minimize confounding effects. The procedure of patient selection was depicted in a flowchart (Fig. 1 ). This study was conducted in accordance with the Declaration of Helsinki. The ethics committee of Xiangya Hospital of Central South University approved the study and waived the requirement for individual informed consent due to its retrospective nature and the use of anonymized data obtained as part of routine clinical care. Patients were classified as common or severe COVID-19 at admission according to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial version 10) of China. The primary outcome (poor prognosis) reflects dynamic clinical deterioration during hospitalization, which is distinct from baseline severity classification (admission status). Definition of Poor Prognosis Poor prognosis was defined as a composite endpoint comprising any of the following during hospitalization: In-hospital death Admission to the intensive care unit (ICU) Clinical deterioration, defined as an escalation in respiratory support (e.g., from nasal cannula to high-flow oxygen, non-invasive ventilation, or mechanical ventilation) This composite endpoint was chosen to capture clinically significant worsening and was assessed from admission until discharge or death. Data collection This retrospective analysis selected eligible patients from Xiangya Hospital of Central South University. Clinical characteristics including age, gender, Azvudine medication history, length of stay (LOS) and outcome were acquired from the hospital information system (HIS). Laboratory tests including serum Krebs von den Lungen-6 (KL-6), erythrocyte sedimentation rate (ESR), serum total protein (TP), serum albumin (ALB), blood urea nitrogen (BUN), serum creatinine (Scr), serum uric acid (UA), serum cardiac troponin (cTn), serum interleukin-6 (IL-6) and serum C-reactive protein (CRP) were performed and the initial test results of the above factors during the hospitalization were adopted from the laboratory information system (LIS). Serum specimens were detected by using the automatic biochemical analyzer AU5800 (Beckman Coulter, Brea, CA, USA) with the manufacturer’s reagent kits. Detection methods for all indicators were performed in accordance with the relevant guidelines and regulations and internal quality control was performed daily, and external quality assessment was performed as required. Statistical analysis All statistical analyses were conducted by R software for Windows (Version 3.6.1, https://www.r-project.org/ ) and the Deepwise and Beckman Coulter DxAI platform ( https://dxonline.deepwise.com ). Continuous variables were presented as mean ± standard deviation (SD) or median with interquartile range (IQR), and categorical variables were presented as frequencies with percentages. The differences of clinical characteristics and laboratory tests between the favorable prognosis group and the poor prognosis group were compared with Student’s t-test, Mann–Whitney test, or chi-square test. Spearman correlation analysis (exploratory) was used to analyze the association between continuous variables and the binary outcome (poor prognosis), as the outcome is binary. Multiple linear regression was conducted as an exploratory analysis to assess factors associated with the outcome, while multivariable logistic regression was the primary approach for identifying independent risk factors for the binary outcome (poor prognosis). Multivariable logistic regression was implemented to analyze the risk factors for COVID-19 clinical prognosis. Missing data were handled by complete-case analysis, as the rate of missing values for key variables was < 5%. No imputation was performed. Results Clinical characteristics of included patients We recruited 71 patients with common COVID-19 and 73 patients with severe COVID-19 patients. Patients were categorized into favorable prognosis (n = 103) and poor prognosis (n = 41) based on the composite endpoint defined above. The demographic and clinical characteristic of the patients were summarized in Table 1 . There was no significant difference in LOS, ESR, TB, CK, Mb, gender and Paxlovid history between the two groups. Age, KL-6, DB, BUN, Scr, UA, cTn, LDH, CK-MB, IL-6, CRP and N% levels in the poor prognosis group were obviously higher than that in the favorable prognosis group. TP and L% levels in the favorable prognosis group were significantly increased compared to that in the poor prognosis group. Table 1 Clinical characteristics of the included patients Variables Favorable prognosis (n = 103) Poor prognosis (n = 41) W/t P Classification c Common 56 (54.4%) 13 (31.7%) 0.01 ** Severe 47 (45.6%) 28 (68.3%) Gender c Female 34 (33.0%) 9 (22.0%) 0.19 Male 69 (67.0%) 32 (78.0%) Age (years) b 71.0 (58.0-82.5) 79.0 (68.0–84.0) 1583.50 0.02 * LOS (days) a 16.41 ± 12.20 15.12 ± 10.70 0.59 0.56 ESR (mm/h) a 59.13 ± 32.31 56.46 ± 33.39 0.34 0.74 KL-6 (U/mL) b 353.30 (233.40-690.45) 865.80 (464.40–1714.00) 1167.50 0.00 ** TP (g/L) b 58.90 (54.60–63.90) 54.60 (49.30–62.60) 2758.50 0.00 ** ALB (g/L) a 31.93 ± 4.51 28.87 ± 4.56 3.66 0.00 ** BUN (mmol/L) b 7.12 (4.81–9.76) 17.29 (11.45–22.14) 535.00 0.00 ** Scr (µmol/L) b 72.30 (57.15–94.20) 159.00 (76.90-250.90) 1115.00 0.00 ** UA (µmol/L) b 226.50 (169.35–314.80) 251.50 (205.40–461.00) 1654 .00 0.04 * cTn (ng/mL) a 166.12 ± 240.01 340.73 ± 305.22 -3.38 0.00 ** IL-6 (pg/mL) b 9.51 (2.78–25.25) 69.77 (14.26-177.75) 657.00 0.00 ** CRP (mg/L) b 21.70 (7.10-56.48) 108.00 (44.40-158.50) 704.500 0.00 ** Nomogram usage: For a single patient, assign points based on their age, KL-6, BUN, and CRP values (e.g., age = 75 years → 40 points, KL-6 = 600 U/mL → 30 points), sum the total points, and project to the bottom axis to obtain the personalized poor prognosis probability. A threshold of 74% (Youden index maximum) is recommended for clinical reference: patients with probability > 74% may require closer monitoring. LOS, length of stay, ESR; erythrocyte sedimentation rate; KL-6, Krebs von den Lungen-6; TP, serum total protein; ALB, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; UA, serum uric acid; cTn, serum cardiac troponin; IL-6, serum interleukin-6; CRP, serum C-reactive protein. * P < 0.05, ** P < 0.01. Correlation analysis between disease outcome and the indictors For inquiry of the correlation among indicators, especially the relationship between the outcome of the patients and the indicators, we conducted spearman correlation analysis and the results were presented in Fig. 2 . It was found that adverse outcome of COVID-19 patients was positively associated with KL-6 (r = 0.35, P < 0.01), BUN (r = 0.58, P < 0.01), Scr (r = 0.37, P < 0.01), cTn (r = 0.37, P < 0.01), CK-MB (r = 0.35, P < 0.01), IL-6 (r = 0.40, P < 0.01), CRP (r = 0.48, P < 0.01) and N% (r = 0.47, P < 0.01), but negatively associated with ALB (r=-0.3, P < 0.01) and L% (r=-0.49, P < 0.01). Impact factors of the poor prognosis of COVID-19 Multiple linear regression analysis was performed to evaluate the impact factors on the outcome of COVID-19 patients, where the outcome was set as the dependent variable, while the independent variables included age, KL-6, TP, ALB, DB, BUN, Scr, UA, cTn, LDH, CKMB, IL-6, CRP, N% and L% (α in = 0.05 and α out = 0.10 with backward selection). Factors of age, KL-6, BUN and CRP were introduced to the standard regression equation as Y = 0.142X 1 + 0.2X 2 + 0.404X 3 + 0.351X 4 , with adjusted R 2 = 0.518 (Y: outcome, X 1 : age, X 2 : KL-6, X 3 : BUN, X 4 : CRP, P < 0.01) (Table 2 ). Table 2 Multiple linear regression analysis of the clinical prognosis of COVID-19 patients Independent Variables b S b b , ІtІ P Age (years) 0.004 0.14 0.002 2.133 0.035 * KL-6 (U/mL) / 0.2 / 3.034 0.003 ** BUN (mmol/L) 0.021 0.40 0.004 5.781 0.000 ** IL-6 (pg/mL) / 0.12 / 1.753 0.082 CRP (mg/L) 0.002 0.35 / 5.046 0.000 ** The dependent variable is outcome and we adopt forward regression for analysis. KL-6, Krebs von den Lungen-6; BUN, blood urea nitrogen; IL-6, serum interleukin-6; CRP, serum C-reactive protein. * P < 0.05, ** P < 0.01. Risk factors for poor prognosis of COVID-19 Risk factors that may affect the outcome of COVID-19 were assessed by multivariable logistic regression. Adverse outcome of COVID-19 was introduced as the dependent variable, while the independent variables were age, KL-6, TP, ALB, BUN, Scr, UA, cTn, IL-6, CRP and N%. The results shown in Table 3 disclosed that age (OR = 1.093, P = 0.034), BUN (OR = 1.169, P = 0.01), CRP (OR = 1.022, P < 0.01) and KL-6 (OR = 1.001, P < 0.01) were the risk factors that may contribute to the adverse outcome of COVID-19. Multivariable logistic regression was implemented with adjustment for baseline disease severity (common vs severe) as a confounding variable to address potential overlap between baseline severity and outcome. Table 3 Analysis of risk factors for clinical prognosis of COVID-19 patients Variables B Sb Waldχ 2 P OR OR 95%CI Age (years) 0.089 0.042 2.117 0.034 * 1.093 1.007–1.187 BUN (mmol/L) 0.156 0.061 2.562 0.01 * 1.169 1.037–1.318 CRP (mg/L) 0.022 0.008 2.765 0.006 ** 1.022 1.006–1.038 KL-6 (U/mL) 0.001 0 2.7 0.007 ** 1.001 1.000-1.002 KL-6, Krebs von den Lungen-6; BUN, blood urea nitrogen; IL-6, serum interleukin-6; CRP, serum C-reactive protein. * P < 0.05, ** P < 0.01. The influence of age, KL-6, BUN and CRP on time to discharge or in-hospital adverse event in COVID-19 patients In order to investigate the influence of KL-6, BUN, CRP and age on the outcome event of COVID-19, we chose the best cut-off of the above four risk factors to conduct survival analysis. The time to discharge or in-hospital adverse event in this study was the LOS of COVID-19 patients from HIS. As shown in Fig. 3 , when the COVID-19 patients’ serum KL-6 levels were > 514.6 U/mL, the median time to discharge or in-hospital adverse event (MTDE) was 24 days, significantly lower than the patients whose serum KL-6 levels were 74.75 mg/L, the MST was 20 days, obviously below those patients with serum CRP 10.33 mmol/L, the MST was 19 days, which notably lower than those patients with BUN < 10.33 mmol/L. The best cut-off of age derived from ROC curve of COVID-19 patients was 77.5 years old. Notably, in this study, no significant difference of MTDE was found in COVID-19 patients between age above 77.5 years old (24 days) and under 77.5 years old (45 days). The efficiency of age, KL-6, BUN and CRP in predicting prognosis of COVID-19 We further explored the value of the above four risk factors to predict the possibility of adverse outcome of COVID-19. The AUC of ROC curve for BUN, KL-6, CRP and age was 0.873 (95% CI: 0.811–0.935, sensitivity: 0.878, specificity: 0.786), 0.724 (95% CI: 0.628–0.820, sensitivity: 0.707, specificity: 0.689), 0.811 (95% CI: 0.732–0.890, sensitivity: 0.632, specificity: 0.827) and 0.625 (95% CI: 0.530–0.720, sensitivity: 0.585, specificity: 0.650), respectively, and the best cut-off was 10.33 mmol/L, 514.6 U/mL, 74.75 mg/L and 77.5 years old, respectively (Fig. 4 a). When we combined the four risk factors to predict the occurrence of adverse outcome of COVID-19, the AUC of ROC curve reached 0.947 (95% CI: 0.911–0.983), while the sensitivity, specificity and Youden Index was 0.842, 0.898 and 0.74, respectively (Fig. 4 b). We included the risk factors of age, KL-6, BUN and CRP as the possible predictors of the adverse outcome of COVID-19 to build a prediction model, which was presented as a nomogram graph in Fig. 4 c. Each level of every variable was assigned a score on points scale. By adding the scores for each of the selected variables, a total score was obtained. Then, the sum score was located on the Total Points scale and vertically projected onto the bottom axis of probability of poor prognosis of COVID-19, and thus, a personalized risk of poor prognosis of COVID-19 could be easily obtained. The threshold probability of the model was 74% based on the maximal Youden’s index, with a sensitivity of 0.842 and specificity of 0.898. In other words, a risk probability of greater than 74% requires further tests to confirm the poor prognosis of COVID-19. Discussion In this study, we discovered that old age, elevated serum levels of KL-6, BUN and CRP were risk factors for COVID-19 to develop to poor prognosis. Meanwhile, we found that combining age, KL-6, BUN, and CRP exhibited good discriminative performance for predicting poor prognosis, providing a preliminary reference for clinical risk stratification. Our results could provide a pattern for clinical staff to assess the prognosis of COVID-19 and draw their attention to give more medical help to the high risk COVID-19 patients. Sensitivity analysis: To address potential overlap between baseline severity and outcome, we re-analyzed 71 common COVID-19 patients (excluding severe cases at admission). Age, KL-6, BUN, and CRP remained independent risk factors for poor prognosis (AUC = 0.923, sensitivity = 0.810, specificity = 0.872), supporting model robustness. Detailed data are available upon request. Serum KL-6 as a biomarker, was widely used in the diagnosis of many interstitial lung diseases 11 ; KL-6 elevation was also found in many respiratory infections diseases such as mycobacterial infections 14 , 15 , viral infection 16 and pneumocystis pneumonia 17 . In the beginning of COVID-19 infection, KL-6 serum levels have been manifested to relate with the diagnosis, prognosis, and therapeutic response evaluation 4 , 12 , 18 , 19 . Some studies have reported that the extent of lung lesions in COVID-19-associated pneumonia has correlation with increased serum KL-6 levels 20 . However, another research has found no association between the serum KL-6 level and the severity of COVID-19-associated pneumonia 4 , 21 , 22 . In our study, serum KL-6 levels in poor prognosis group (865.800 (464.400–1714.000) U/mL) were significantly elevated compared with the favorable prognosis group (353.300 (233.400-690.450) U/mL). Moreover, our further analysis proved that serum KL-6 level was correlated with the outcome of COVID-19 (r = 0.35) and was a risk factor for poor prognosis of COVID-19. Lung epithelium is the main target of SARS-CoV-2 and infection of the virus facilitates the secretion of proinflammatory cytokines in injured alveolar epithelial cells 23 . Furthermore, the inflammatory response promotes the release of KL-6 into the blood 12 . KL-6 is not only a specific biomarker of lung type Ⅱ pneumocytes 24 , but also identified as a prognostic biomarker for the diagnosis and prediction of the severity of idiopathic pulmonary fibrosis and acute respiratory distress syndrome 25 , 26 . Clinical applicability of KL-6 detection: KL-6 was measured using the Beckman Coulter AU5800 automatic biochemical analyzer, a widely used platform in clinical laboratories with a turnaround time of 4–6 hours (consistent with routine inflammatory biomarkers). The detection cost is comparable to CRP in Chinese clinical settings. However, applicability in resource-limited regions requires further evaluation, and no direct comparison with existing tools (e.g., NEWS2, WHO severity classification) was performed, which limits assessment of incremental value. Older age was found to be associated with higher disease severity in COVID-19 patients in previous studies 27 – 29 . The median age of patients receiving intensive care was higher than the patients who were not admitted to ICU (61 years vs 51 years) 30 . In hospitalized adult patients, the percentage of severe cases ranged from 19.8% to 49% 27,31,32 , but only 2.2% in a pediatric cohort study 33 . Kuo et al. believed that biological aging was an optimal predictor for disease severity after performing biological age evaluation comprised of chronological age and another nine biomarkers including ALB, AST, Scr, CRP, glucose, L%, mean corpuscular volume, red blood cell distribution width and white blood cell count 30 , 34 . In our study, the median age of 79 years in poor prognosis patients is significantly higher than that of 71 years in the favorable prognosis group, which was consistent with previous studies 27 – 29 . Our results confirmed that elderly COVID-19 patients deserve more attention during clinical treatment. It is worth noting that the occurrence of acute kidney injury (AKI) was found to range from 0.5% to 29% in hospitalized COVID-19 patients in China 35 – 37 . A study from New York City reported a 46% incidence of AKI among 3993 hospitalized COVID-19 patients 38 . COVID-19 patients combined with AKI had higher probability of admitted to ICU, progressed to necessitating renal replacement therapy and developed to poor prognosis 37 – 39 . Elevated serum BUN and creatine levels could be indicative of renal damage or AKI of COVID-19 patients. Previous studies revealed that the prevalence of patients with increased BUN and Scr levels among severe cases were 13.1% and 14.4, respectively, which were significantly higher than those in mild cases 37 . In this study, serum BUN and creatinine concentration in poor prognosis group was 17.29 (11.45–22.14) mmol/L and 159.00 (76.90–461.00) µmol/L, respectively, significantly higher than that in the favorable prognosis group, which was for BUN, 7.12 (4.81–9.76) mmol/L, and Scr, 72.30 (57.15–94.20) µmol/L. In addition, our study revealed that elevated BUN level was a risk factor for COVID-19 developing to poor prognosis. Our results indicate that clinical staff should focus more on COVID-19 patients with elevated BUN and Scr. Elevated serum CRP was regarded as a risk factor for disease progression and mortality in severe COVID-19 patients and also an indicator of developing cytokine storm in COVID-19 patients 27 , 40 . A study concluded that CRP cut-off value of 34.6 mg/L presented good performance (sensitivity 82.3%, specificity 73%) in discriminating severe and non-severe COVID-19 patients relative to D-dimer 41 . In a meta-analysis, 20 out of 32 studies found that COVID-19 patients with elevated CRP were at nearly four-fold high risk to develop poor prognosis 42 . Our results showed that the CRP levels in poor prognosis group were significantly high (108.000 (44.400-158.500) mg/L, P < 0.001), compared to that in the favorable prognosis group (21.700 (7.095–56.475) mg/L); furthermore, with the cut-off value of 74.75 mg/L, CRP had good diagnostic efficacy in predicting poor prognosis of COVID-19, and the AUC of ROC curve approached to 0.811, with the sensitivity of 63.20% and the specificity of 82.7%. The results we obtained are slightly different from previous studies 41 , which could be explained by the mutation of the novel coronavirus and the population from different ethnic groups. In summary, age-related immunosenescence, lung epithelial injury reflected by KL-6, renal dysfunction indicated by BUN, and systemic inflammatory response represented by CRP synergistically promote disease progression in COVID-19 patients. This mechanism is consistent with the core pathological process of "multiple organ injury + cytokine storm" reported in existing studies, which also supports the rationale for these four indicators as independent risk factors. In confirming these independent risk factors, we specifically addressed potential confounding factors to enhance the reliability of the results. Regarding key confounding factors: baseline oxygen requirement, comorbidities, and COVID-19 treatment regimens (e.g., Azvudine) were all considered in the study design. During patient selection, severe comorbidities that may affect prognosis (such as interstitial lung disease) were excluded. Additionally, baseline disease severity (common vs. severe type) was further adjusted in the multivariable logistic regression model to reduce potential overlap bias between baseline status and outcome indicators. However, the vaccination status (including vaccination doses and vaccine type) and the infecting SARS-CoV-2 variant information (e.g., whether it was the EG.5 variant) were not systematically recorded in this cohort. Both factors may alter prognosis by influencing viral pathogenicity or host immune responses, thereby limiting the generalizability of the study results and should be supplemented and incorporated into future prospective studies for analysis. Despite these limitations, the combined model integrating age, KL-6, BUN, and CRP still exhibits favorable discriminative performance (AUC = 0.947, sensitivity = 0.842, specificity = 0.898). The combination of age, KL-6, BUN, and CRP in our model captures distinct yet complementary pathophysiological processes: immunosenescence (age), lung epithelial injury (KL-6), renal dysfunction (BUN), and systemic inflammation (CRP). These factors may interact synergistically; for instance, systemic inflammation can exacerbate lung and kidney injury, while pre-existing organ dysfunction in the elderly may amplify the deleterious effects of SARS-CoV-2 infection. Although we did not investigate genetic factors such as TMPRSS2 or FURIN, which influence viral entry, our model focuses on downstream host response markers that are readily measurable in clinical practice and reflect the integrated disease burden. Elevated IL-6 and CRP are hallmarks of cytokine release syndrome not only in COVID-19 but also in other critical illnesses such as sepsis and acute respiratory distress syndrome from other causes. However, the unique combination with KL-6—a specific marker of lung epithelial damage—may enhance specificity for predicting pulmonary complications in viral pneumonias. While our model was derived from a COVID-19 cohort, the pathophysiological principles it embodies (epithelial injury, hyperinflammation, and extra-pulmonary organ dysfunction) suggest potential applicability to other severe coronavirus infections (e.g., MERS) or viral pneumonias, though this requires explicit validation in respective populations. Identifying risk factors for COVID-19 progression to poor prognosis is of great importance for clinician and public health strategies. The novel coronavirus has been constantly mutating since its outbreak, along with the variation of its infectivity and pathogenicity. At present, the main strain of the new coronavirus epidemic is EG.5, which is highly infectious but weak in pathogenicity 43 . What matters is that, in many countries, such as China, elderly people make up a large portion of the population and how to avoid poor prognosis of the elderly infected with COVID-19 is still a tremendous challenge confronting us. Our findings align with growing evidence that biomarkers such as KL-6 reflect lung epithelial injury and systemic inflammation, which are central to COVID-19 pathogenesis. The integration of KL-6 with routine clinical parameters provides a preliminary reference for risk stratification, particularly in resource-limited settings. Our model may thus not only aid in acute-phase risk stratification but also inspire longitudinal studies linking acute biomarker profiles to long-term outcomes. This study has several limitations. First, it is a single-center retrospective analysis without a pre-registered protocol, and no external or formal internal validation (e.g., bootstrapping, cross-validation) was performed, so the model is preliminary and requires further prospective multi-center validation. Second, the sample size is modest, and no power calculation was performed a priori. Third, the composite endpoint of “poor prognosis,” while clinically relevant, may encompass heterogeneous outcomes. Fourth, our cohort consisted exclusively of hospitalized Chinese patients infected with the Omicron variant, which may limit generalizability to other populations or SARS-CoV-2 variants. Nevertheless, our model exhibited favorable discriminative performance in this single-center cohort and provides preliminary evidence for the value of integrating KL-6 with routine parameters. Prospective multi-center validation with a larger sample size is mandatory before clinical application. Declarations Consent to participate and publish: Written informed consent was waived by the ethics committee due to the retrospective nature of the study. Patient data were anonymized and maintained with confidentiality. Funding: This study was supported by several Natural Science Foundation of Hunan Province (2023JJ40949, 2023JJ40971, 2023JJ30965, 2023JJ40962). Competing interests: The authors have no relevant financial or non-financial interests to disclose. Ethics approval: Ethical approval was waived by the ethics committee of Xiangya Hospital of Central South University in view of the retrospective nature of the study and all the procedures being performed were part of the routine care. Clinical trial number: not applicable. Data and/or Code availability: The datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. Our statistical analyses were conducted by R software for Windows (Version 3.6.1, https://www.r-project.org/) and the Deepwise and Beckman Coulter DxAI platform (https://dxonline.deepwise.com). Authors’ Contributions: KH wrote the main manuscript draft, KH and YL designed this manuscript, YL analyzed the original data, KW and QH prepared figures, LL and YZ prepared Tables. BY amended the first draft and approved the final version. All authors reviewed the manuscript. All authors agree to participate in and publish this article. Acknowledgements: No. References WHO. WHO Coronavirus (COVID-19) Dashboard. 2023). Drożdżal S, et al. An update on drugs with therapeutic potential for SARS-CoV-2 (COVID-19) treatment. Drug Resist Updat. 2021;59:100794. https://doi.org/10.1016/j.drup.2021.100794 . Nyberg T, et al. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study. Lancet (London England). 2022;399:1303–12. https://doi.org/10.1016/s0140-6736(22)00462-7 . Frix AN, et al. Could KL-6 levels in COVID-19 help to predict lung disease? Respir Res. 2020;21:309. https://doi.org/10.1186/s12931-020-01560-4 . Schoneveld L, et al. YKL-40 as a new promising prognostic marker of severity in COVID infection. Crit Care. 2021;25:66. https://doi.org/10.1186/s13054-020-03383-7 . Defêche J et al. In-Depth Longitudinal Comparison of Clinical Specimens to Detect SARS-CoV-2. Pathogens 10 (2021). https://doi.org/10.3390/pathogens10111362 Hoffmann M, et al. SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell. 2020;181:271–e280278. https://doi.org/10.1016/j.cell.2020.02.052 . Venkataraman T, Coleman CM, Frieman MB. Overactive Epidermal Growth Factor Receptor Signaling Leads to Increased Fibrosis after Severe Acute Respiratory Syndrome Coronavirus Infection. J Virol. 2017;91. https://doi.org/10.1128/jvi.00182-17 . Ohnishi H, et al. Circulating KL-6 levels in patients with drug induced pneumonitis. Thorax. 2003;58:872–5. https://doi.org/10.1136/thorax.58.10.872 . Ishizaka A, et al. Elevation of KL-6, a lung epithelial cell marker, in plasma and epithelial lining fluid in acute respiratory distress syndrome. Am J Physiol Lung Cell Mol Physiol. 2004;286:L1088–1094. https://doi.org/10.1152/ajplung.00420.2002 . Ishikawa N, Hattori N, Yokoyama A, Kohno N. Utility of KL-6/MUC1 in the clinical management of interstitial lung diseases. Respir Investig. 2012;50:3–13. https://doi.org/10.1016/j.resinv.2012.02.001 . Cambier M, et al. Increased KL-6 levels in moderate to severe COVID-19 infection. PLoS ONE. 2022;17:e0273107. https://doi.org/10.1371/journal.pone.0273107 . China NHC. o. t. P. s. R. o. Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial Version 10). Clinical Practice and Education in General Medicine , 1–7 (2023). Inoue Y, et al. Evaluation of serum KL-6 levels in patients with pulmonary tuberculosis. Tuber Lung Dis. 1995;76:230–3. https://doi.org/10.1016/s0962-8479(05)80010-3 . Asakura T, et al. Serum Krebs von den Lungen-6 level in the disease progression and treatment of Mycobacterium avium complex lung disease. Respirology. 2021;26:112–9. https://doi.org/10.1111/resp.13886 . Kawasaki Y, et al. Serum KL-6 levels as a biomarker of lung injury in respiratory syncytial virus bronchiolitis. J Med Virol. 2009;81:2104–8. https://doi.org/10.1002/jmv.21634 . Urabe N, et al. Serial change in serum biomarkers during treatment of Non-HIV Pneumocystis pneumonia. J Infect Chemother. 2019;25:936–42. https://doi.org/10.1016/j.jiac.2019.05.007 . Awano N, et al. Serum KL-6 level is a useful biomarker for evaluating the severity of coronavirus disease 2019. Respir Investig. 2020;58:440–7. https://doi.org/10.1016/j.resinv.2020.07.004 . d'Alessandro M, et al. Peripheral biomarkers' panel for severe COVID-19 patients. J Med Virol. 2021;93:1230–2. https://doi.org/10.1002/jmv.26577 . Xue M, et al. Exploration and correlation analysis of changes in Krebs von den Lungen-6 levels in COVID-19 patients with different types in China. Biosci Trends. 2020;14:290–6. https://doi.org/10.5582/bst.2020.03197 . Arnold DT, et al. Krebs von den Lungen 6 (KL-6) as a marker for disease severity and persistent radiological abnormalities following COVID-19 infection at 12 weeks. PLoS ONE. 2021;16:e0249607. https://doi.org/10.1371/journal.pone.0249607 . Castellví I, et al. Krebs von den Lungen-6 glycoprotein circulating levels are not useful as prognostic marker in COVID-19 pneumonia: A large prospective cohort study. Front Med (Lausanne). 2022;9:973918. https://doi.org/10.3389/fmed.2022.973918 . Ziegler CGK, et al. SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues. Cell. 2020;181:1016–e10351019. https://doi.org/10.1016/j.cell.2020.04.035 . Kohno N, et al. KL-6, a mucin-like glycoprotein, in bronchoalveolar lavage fluid from patients with interstitial lung disease. Am Rev Respir Dis. 1993;148:637–42. https://doi.org/10.1164/ajrccm/148.3.637 . Sato H, et al. KL-6 levels are elevated in plasma from patients with acute respiratory distress syndrome. Eur Respir J. 2004;23:142–5. https://doi.org/10.1183/09031936.03.00070303 . Kondo T, et al. KL-6 concentration in pulmonary epithelial lining fluid is a useful prognostic indicator in patients with acute respiratory distress syndrome. Respir Res. 2011;12:32. https://doi.org/10.1186/1465-9921-12-32 . Zhang JJ, et al. Clinical, radiological, and laboratory characteristics and risk factors for severity and mortality of 289 hospitalized COVID-19 patients. Allergy. 2021;76:533–50. https://doi.org/10.1111/all.14496 . Wolff D, Nee S, Hickey NS, Marschollek M. Risk factors for Covid-19 severity and fatality: a structured literature review. Infection. 2021;49:15–28. https://doi.org/10.1007/s15010-020-01509-1 . Zhang J, et al. Risk factors for disease severity, unimprovement, and mortality in COVID-19 patients in Wuhan, China. Clin Microbiol Infect. 2020;26:767–72. https://doi.org/10.1016/j.cmi.2020.04.012 . Gao Yd, et al. Risk factors for severe and critically ill COVID-19 patients: A review. Allergy. 2020;76:428–55. https://doi.org/10.1111/all.14657 . Zhang K, et al. Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell. 2020;181:1423–e14331411. https://doi.org/10.1016/j.cell.2020.04.045 . Ye Z, Zhang Y, Wang Y, Huang Z, Song B. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol. 2020;30:4381–9. https://doi.org/10.1007/s00330-020-06801-0 . Du H, et al. Clinical characteristics of 182 pediatric COVID-19 patients with different severities and allergic status. Allergy. 2021;76:510–32. https://doi.org/10.1111/all.14452 . Kuo CL et al. COVID-19 severity is predicted by earlier evidence of accelerated aging. medRxiv (2020). https://doi.org/10.1101/2020.07.10.20147777 Zhou F, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet (London England). 2020;395:1054–62. https://doi.org/10.1016/s0140-6736(20)30566-3 . Jacot D, Greub G, Jaton K, Opota O. Viral load of SARS-CoV-2 across patients and compared to other respiratory viruses. Microbes Infect. 2020;22:617–21. https://doi.org/10.1016/j.micinf.2020.08.004 . Cheng Y, et al. Kidney disease is associated with in-hospital death of patients with COVID-19. Kidney Int. 2020;97:829–38. https://doi.org/10.1016/j.kint.2020.03.005 . Chan L, et al. AKI in Hospitalized Patients with COVID-19. J Am Soc Nephrol. 2021;32:151–60. https://doi.org/10.1681/asn.2020050615 . Azam TU, et al. Soluble Urokinase Receptor (SuPAR) in COVID-19-Related AKI. J Am Soc Nephrol. 2020;31:2725–35. https://doi.org/10.1681/asn.2020060829 . Azkur AK, et al. Immune response to SARS-CoV-2 and mechanisms of immunopathological changes in COVID-19. Allergy. 2020;75:1564–81. https://doi.org/10.1111/all.14364 . Soraya GV, Ulhaq ZS. Crucial laboratory parameters in COVID-19 diagnosis and prognosis: An updated meta-analysis. Med Clin (Barc). 2020;155:143–51. https://doi.org/10.1016/j.medcli.2020.05.017 . Malik P, et al. Biomarkers and outcomes of COVID-19 hospitalisations: systematic review and meta-analysis. BMJ Evid Based Med. 2021;26:107–8. https://doi.org/10.1136/bmjebm-2020-111536 . Abbasi J. What to Know About EG.5, the Latest SARS-CoV-2 Variant of Interest. JAMA. 2023. https://doi.org/10.1001/jama.2023.16498 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 31 Jan, 2026 Reviewers invited by journal 29 Jan, 2026 Editor invited by journal 08 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 06 Jan, 2026 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-8528169","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":583515728,"identity":"c418afc0-4815-42e4-9390-a55230adb9da","order_by":0,"name":"Yunlai Liang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Yunlai","middleName":"","lastName":"Liang","suffix":""},{"id":583515734,"identity":"3bccdcac-4bfa-4b10-baf9-fdb1c8984bcb","order_by":1,"name":"Kun Wang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Kun","middleName":"","lastName":"Wang","suffix":""},{"id":583515736,"identity":"01693c05-dc48-47ea-a2c2-20dab9ec444e","order_by":2,"name":"Lu Long","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Long","suffix":""},{"id":583515737,"identity":"6f3813e4-4af8-47d5-9ae9-45169d4bd9df","order_by":3,"name":"Qizhuo Hou","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Qizhuo","middleName":"","lastName":"Hou","suffix":""},{"id":583515741,"identity":"a96781d7-dc9a-4493-bcd5-1204751569ac","order_by":4,"name":"Wenze Yu","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Wenze","middleName":"","lastName":"Yu","suffix":""},{"id":583515743,"identity":"a626247b-0eb9-444e-b904-4827f8499218","order_by":5,"name":"Kangkang Huang","email":"","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":false,"prefix":"","firstName":"Kangkang","middleName":"","lastName":"Huang","suffix":""},{"id":583515754,"identity":"b2989b73-e506-4fc0-b6c5-f58030e491d0","order_by":6,"name":"Bin Yi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYJCCwwwGDHJs7O0HSNNizMdzJoF4LcxAnDhPwsGAOOUGN3IMDxcU3Elvk2BIYPhRsY2wFskZOQaHZxg8y22TbjzA2HPmNmEt/BJALTwGh3PbZA4kMDO2EaGFDaolnU0iwYA4LTBbEojXItnzrADol8OGbcBAPkiUXwyOJ2/+XPDnsLx8e/vBBz8qiNDCIJCBiI4DRKgHAv7jD4hTOApGwSgYBSMXAABYJD0EBq0AswAAAABJRU5ErkJggg==","orcid":"","institution":"Xiangya Hospital Central South University","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yi","suffix":""}],"badges":[],"createdAt":"2026-01-06 07:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8528169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8528169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101880833,"identity":"5670c276-5009-48e5-9d56-f04d403e5d96","added_by":"auto","created_at":"2026-02-04 15:06:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101180,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow chart depicting patient selection\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8528169/v1/4a4fff4dbefe6a0a1949b760.jpeg"},{"id":101881089,"identity":"03141d09-11c5-4e03-a826-27eb0cc016ca","added_by":"auto","created_at":"2026-02-04 15:09:30","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":181644,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations between the outcome of COVID-19 and other indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpearman correlation analysis (exploratory) heatmap: Red indicates positive correlation, blue indicates negative correlation; correlation coefficient r ranges from -1 to 1.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8528169/v1/d2ee2df0be17a47791fdccdc.jpg"},{"id":101787414,"identity":"4b5e2076-e361-43f1-9a7e-914e20847439","added_by":"auto","created_at":"2026-02-03 15:48:42","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":77772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe influence of serum KL-6, CRP, BUN and age on the survival of COVID-19 patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea, the MTDE of KL-6 encoded=1 COVID-19 patients was 24, and no in-hospital adverse events observed when KL-6 encoded=0, \u003cem\u003eP\u003c/em\u003e=0.01. b, the MST of CRP encoded =1 COVID-19 patients was 20, and when CRP encoded=0, the MST was 45, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. c, the MST of BUN encoded =1 COVID-19 patients was 19, and no exit when BUN encoded=0, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001. d, the MTDE of age encoded =1 COVID-19 patients was 24, and when CRP encoded=0, the MTDE was 45, \u003cem\u003eP\u003c/em\u003e=0.15.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8528169/v1/d8a73fc9179ed506cb1de392.jpeg"},{"id":101787411,"identity":"21484708-8908-4cc1-b7a2-bfaf9dcebccc","added_by":"auto","created_at":"2026-02-03 15:48:41","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":48162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe efficiency of KL-6, BUN, CRP and age to predict the prognosis of COVID-19 patients.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ea, the AUC of BUN, KL-6, CRP and age to predict poor prognosis of COVID-19 patients were 0.873, 0.724, 0.811 and 0.625, respectively. b, when combining KL-6, BUN, CRP and age to predict poor prognosis of COVID-19 patients, the AUC, sensitivity and specificity reached 0.947, 0.842 and 0.898, respectively. c, Nomogram for predicting the probability of poor prognosis in COVID-19 patients based on age, KL-6, BUN, and CRP. Nomogram usage: For a single patient, assign points based on their age, KL-6, BUN, and CRP values (e.g., age=75 years → 40 points, KL-6=600 U/mL → 30 points), sum the total points, and project to the bottom axis to obtain the personalized poor prognosis probability. A threshold of 74% (Youden index maximum) is recommended for clinical reference: patients with probability \u0026gt;74% may require closer monitoring.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8528169/v1/c54ea2f228b21fb75ad7ca25.jpeg"},{"id":102298518,"identity":"16a8419c-96f4-42ce-8863-9b53d45bb7c1","added_by":"auto","created_at":"2026-02-10 10:42:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1411905,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8528169/v1/34a56624-eaf5-4e7c-ade2-1cc44a081626.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Preliminary Prognostic Model for Predicting Poor Prognosis in COVID-19 Integrating Lung Epithelial Injury (KL-6) with Routine Care Markers","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUpon the outbreak of COVID-19, global spread is happening quickly. Up to now, nearly 689\u0026nbsp;million people were infected and over 6\u0026nbsp;million lost their lives in this battlefield\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Symptoms of COVID-19 vary significantly from only cough and fever, to acute respiratory distress syndrome, and often change dynamically within the development of the disease. Despite the fact that we have access to new antiviral drugs, vaccines and better treatment experience, the virus is mutating to be survival-favorable and is not completely under our control until now\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The diagnostic methods of COVID-19 included computed tomography (CT), nucleic acid amplification (PCR), and immunoassays\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. These methods to some extent are expensive, time consuming and at low specificity. There is an urgent need for practical prognostic strategies to help save lives and provide potential measures to deal with similar respiratory virus infection.\u003c/p\u003e \u003cp\u003eThe culprit of COVID-19, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus could enter into the epithelium cells easily by anchoring angiotensin-converting enzyme 2 (ACE2), which is mainly expressed in the pulmonary epithelium\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. The pathological feature of COVID-19 patients included interstitial pneumopathy and lung fibrosis\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Krebs von den Lungen-6 (KL-6) is a mucin-like and high-molecular-weight glycoprotein encoded by MUC1 gene and secreted by type II pneumocytes and bronchial epithelial cells. Under normal circumstances, KL-6 plays roles in the migration, proliferation and survival of lung fibroblasts. An interesting phenomenon is that KL-6 secretion increases with epithelial lesions and cellular regeneration\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Serum KL-6 level has been proven to be reliable for diagnosis of interstitial lung disease and monitoring the disease activity\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Recently, KL-6 has been used for COVID-19 diagnosis, prognosis, and therapeutic response evaluation\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile several biomarkers have been investigated in COVID-19, their combined utility in a simple, clinically applicable prognostic model remains underexplored. Early and accurate identification of high-risk patients is crucial for optimizing resource allocation and guiding therapeutic decisions. This study aimed to develop and explore a preliminary prognostic model integrating the lung epithelial biomarker KL-6 with readily available clinical and laboratory parameters to predict poor prognosis (a composite endpoint of in-hospital death, ICU admission, or respiratory support escalation) in hospitalized COVID-19 patients.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis retrospective analysis recruited 71 common COVID-19 patients and 73 severe COVID-19 patients, all with confirmed SARS-CoV-2 infection by nucleic acid amplification or antigen testing, who were admitted to Xiangya Hospital of Central South University from Dec. 2022 to Feb. 2023. According to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial version 10) of China\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, the inclusion criteria of common COVID-19 patients include the following: Symptoms of a respiratory tract infection for light COVID-19; persistent high fever for more than three days or cough and dyspnea, and breathing rate\u0026thinsp;\u0026lt;\u0026thinsp;30/min, oxygen saturation at rest is \u0026gt;\u0026thinsp;93%, pulmonary imaging shows the characteristic manifestations of COVID-19 pneumonia for medium COVID-19. The criteria required for severe COVID-19 patients inclusion contain dyspnea and breathing rate\u0026thinsp;\u0026ge;\u0026thinsp;30/min; oxygen saturation at rest is \u0026le;\u0026thinsp;93%; PaO2/FiO2\u0026thinsp;\u0026le;\u0026thinsp;300mmHg; clinical symptoms worsened and pulmonary imaging was 50% advanced in 24\u0026ndash;48 hours. Exclusion criteria\u0026mdash;including bacterial pneumonia, lung cancer, interstitial lung disease, other viral pneumonias, and pediatric or pregnant patients\u0026mdash;were applied prior to data extraction to minimize confounding effects. The procedure of patient selection was depicted in a flowchart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was conducted in accordance with the Declaration of Helsinki. The ethics committee of Xiangya Hospital of Central South University approved the study and waived the requirement for individual informed consent due to its retrospective nature and the use of anonymized data obtained as part of routine clinical care. Patients were classified as common or severe COVID-19 at admission according to the Diagnosis and Treatment Protocol for Novel Coronavirus Infection (Trial version 10) of China. The primary outcome (poor prognosis) reflects dynamic clinical deterioration during hospitalization, which is distinct from baseline severity classification (admission status).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of Poor Prognosis\u003c/h3\u003e\n\u003cp\u003ePoor prognosis was defined as a composite endpoint comprising any of the following during hospitalization:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn-hospital death\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAdmission to the intensive care unit (ICU)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eClinical deterioration, defined as an escalation in respiratory support (e.g., from nasal cannula to high-flow oxygen, non-invasive ventilation, or mechanical ventilation)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis composite endpoint was chosen to capture clinically significant worsening and was assessed from admission until discharge or death.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThis retrospective analysis selected eligible patients from Xiangya Hospital of Central South University. Clinical characteristics including age, gender, Azvudine medication history, length of stay (LOS) and outcome were acquired from the hospital information system (HIS). Laboratory tests including serum Krebs von den Lungen-6 (KL-6), erythrocyte sedimentation rate (ESR), serum total protein (TP), serum albumin (ALB), blood urea nitrogen (BUN), serum creatinine (Scr), serum uric acid (UA), serum cardiac troponin (cTn), serum interleukin-6 (IL-6) and serum C-reactive protein (CRP) were performed and the initial test results of the above factors during the hospitalization were adopted from the laboratory information system (LIS). Serum specimens were detected by using the automatic biochemical analyzer AU5800 (Beckman Coulter, Brea, CA, USA) with the manufacturer\u0026rsquo;s reagent kits. Detection methods for all indicators were performed in accordance with the relevant guidelines and regulations and internal quality control was performed daily, and external quality assessment was performed as required.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted by R software for Windows (Version 3.6.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Deepwise and Beckman Coulter DxAI platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dxonline.deepwise.com\u003c/span\u003e\u003cspan address=\"https://dxonline.deepwise.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Continuous variables were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median with interquartile range (IQR), and categorical variables were presented as frequencies with percentages. The differences of clinical characteristics and laboratory tests between the favorable prognosis group and the poor prognosis group were compared with Student\u0026rsquo;s t-test, Mann\u0026ndash;Whitney test, or chi-square test. Spearman correlation analysis (exploratory) was used to analyze the association between continuous variables and the binary outcome (poor prognosis), as the outcome is binary. Multiple linear regression was conducted as an exploratory analysis to assess factors associated with the outcome, while multivariable logistic regression was the primary approach for identifying independent risk factors for the binary outcome (poor prognosis). Multivariable logistic regression was implemented to analyze the risk factors for COVID-19 clinical prognosis. Missing data were handled by complete-case analysis, as the rate of missing values for key variables was \u0026lt;\u0026thinsp;5%. No imputation was performed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eClinical characteristics of included patients\u003c/h2\u003e \u003cp\u003eWe recruited 71 patients with common COVID-19 and 73 patients with severe COVID-19 patients. Patients were categorized into favorable prognosis (n\u0026thinsp;=\u0026thinsp;103) and poor prognosis (n\u0026thinsp;=\u0026thinsp;41) based on the composite endpoint defined above. The demographic and clinical characteristic of the patients were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There was no significant difference in LOS, ESR, TB, CK, Mb, gender and Paxlovid history between the two groups. Age, KL-6, DB, BUN, Scr, UA, cTn, LDH, CK-MB, IL-6, CRP and N% levels in the poor prognosis group were obviously higher than that in the favorable prognosis group. TP and L% levels in the favorable prognosis group were significantly increased compared to that in the poor prognosis group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of the included patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFavorable prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;103)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eW/t\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eClassification \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56 (54.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.01 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28 (68.3%)\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\u003eGender \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34 (33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9 (22.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.19\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69 (67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32 (78.0%)\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\u003eAge (years)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71.0 (58.0-82.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79.0 (68.0\u0026ndash;84.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1583.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLOS (days)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.41\u0026thinsp;\u0026plusmn;\u0026thinsp;12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.12\u0026thinsp;\u0026plusmn;\u0026thinsp;10.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESR (mm/h)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.13\u0026thinsp;\u0026plusmn;\u0026thinsp;32.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56.46\u0026thinsp;\u0026plusmn;\u0026thinsp;33.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKL-6 (U/mL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e353.30 (233.40-690.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e865.80 (464.40\u0026ndash;1714.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1167.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTP (g/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.90 (54.60\u0026ndash;63.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.60 (49.30\u0026ndash;62.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2758.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALB (g/L) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.93\u0026thinsp;\u0026plusmn;\u0026thinsp;4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.87\u0026thinsp;\u0026plusmn;\u0026thinsp;4.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.12 (4.81\u0026ndash;9.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.29 (11.45\u0026ndash;22.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e535.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr (\u0026micro;mol/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.30 (57.15\u0026ndash;94.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.00 (76.90-250.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1115.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e226.50 (169.35\u0026ndash;314.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e251.50 (205.40\u0026ndash;461.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1654 .00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecTn (ng/mL)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166.12\u0026thinsp;\u0026plusmn;\u0026thinsp;240.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340.73\u0026thinsp;\u0026plusmn;\u0026thinsp;305.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.51 (2.78\u0026ndash;25.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69.77 (14.26-177.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e657.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.70 (7.10-56.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108.00 (44.40-158.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e704.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNomogram usage: For a single patient, assign points based on their age, KL-6, BUN, and CRP values (e.g., age\u0026thinsp;=\u0026thinsp;75 years \u0026rarr; 40 points, KL-6\u0026thinsp;=\u0026thinsp;600 U/mL \u0026rarr; 30 points), sum the total points, and project to the bottom axis to obtain the personalized poor prognosis probability. A threshold of 74% (Youden index maximum) is recommended for clinical reference: patients with probability\u0026thinsp;\u0026gt;\u0026thinsp;74% may require closer monitoring. LOS, length of stay, ESR; erythrocyte sedimentation rate; KL-6, Krebs von den Lungen-6; TP, serum total protein; ALB, albumin; BUN, blood urea nitrogen; Scr, serum creatinine; UA, serum uric acid; cTn, serum cardiac troponin; IL-6, serum interleukin-6; CRP, serum C-reactive protein. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis between disease outcome and the indictors\u003c/h3\u003e\n\u003cp\u003eFor inquiry of the correlation among indicators, especially the relationship between the outcome of the patients and the indicators, we conducted spearman correlation analysis and the results were presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It was found that adverse outcome of COVID-19 patients was positively associated with KL-6 (r\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), BUN (r\u0026thinsp;=\u0026thinsp;0.58, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Scr (r\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), cTn (r\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CK-MB (r\u0026thinsp;=\u0026thinsp;0.35, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), IL-6 (r\u0026thinsp;=\u0026thinsp;0.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), CRP (r\u0026thinsp;=\u0026thinsp;0.48, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and N% (r\u0026thinsp;=\u0026thinsp;0.47, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but negatively associated with ALB (r=-0.3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and L% (r=-0.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eImpact factors of the poor prognosis of COVID-19\u003c/h3\u003e\n\u003cp\u003eMultiple linear regression analysis was performed to evaluate the impact factors on the outcome of COVID-19 patients, where the outcome was set as the dependent variable, while the independent variables included age, KL-6, TP, ALB, DB, BUN, Scr, UA, cTn, LDH, CKMB, IL-6, CRP, N% and L% (α in =\u0026thinsp;0.05 and α out =\u0026thinsp;0.10 with backward selection). Factors of age, KL-6, BUN and CRP were introduced to the standard regression equation as Y\u0026thinsp;=\u0026thinsp;0.142X\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.2X\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.404X\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;0.351X\u003csub\u003e4\u003c/sub\u003e, with adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.518 (Y: outcome, X\u003csub\u003e1\u003c/sub\u003e: age, X\u003csub\u003e2\u003c/sub\u003e: KL-6, X\u003csub\u003e3\u003c/sub\u003e: BUN, X\u003csub\u003e4\u003c/sub\u003e: CRP, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple linear regression analysis of the clinical prognosis of COVID-19 patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent\u003c/p\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eb\u003csup\u003e,\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eІtІ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035 \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKL-6 (U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.003 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL-6 (pg/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000 \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eThe dependent variable is outcome and we adopt forward regression for analysis. KL-6, Krebs von den Lungen-6; BUN, blood urea nitrogen; IL-6, serum interleukin-6; CRP, serum C-reactive protein. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for poor prognosis of COVID-19\u003c/h2\u003e \u003cp\u003eRisk factors that may affect the outcome of COVID-19 were assessed by multivariable logistic regression. Adverse outcome of COVID-19 was introduced as the dependent variable, while the independent variables were age, KL-6, TP, ALB, BUN, Scr, UA, cTn, IL-6, CRP and N%. The results shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e disclosed that age (OR\u0026thinsp;=\u0026thinsp;1.093, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), BUN (OR\u0026thinsp;=\u0026thinsp;1.169, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), CRP (OR\u0026thinsp;=\u0026thinsp;1.022, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and KL-6 (OR\u0026thinsp;=\u0026thinsp;1.001, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were the risk factors that may contribute to the adverse outcome of COVID-19. Multivariable logistic regression was implemented with adjustment for baseline disease severity (common vs severe) as a confounding variable to address potential overlap between baseline severity and outcome.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of risk factors for clinical prognosis of COVID-19 patients\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOR 95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.007\u0026ndash;1.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.037\u0026ndash;1.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.006\u0026ndash;1.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKL-6 (U/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000-1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eKL-6, Krebs von den Lungen-6; BUN, blood urea nitrogen; IL-6, serum interleukin-6; CRP, serum C-reactive protein. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe influence of age, KL-6, BUN and CRP on time to discharge or in-hospital adverse event in COVID-19 patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn order to investigate the influence of KL-6, BUN, CRP and age on the outcome event of COVID-19, we chose the best cut-off of the above four risk factors to conduct survival analysis. The time to discharge or in-hospital adverse event in this study was the LOS of COVID-19 patients from HIS. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, when the COVID-19 patients\u0026rsquo; serum KL-6 levels were \u0026gt;\u0026thinsp;514.6 U/mL, the median time to discharge or in-hospital adverse event (MTDE) was 24 days, significantly lower than the patients whose serum KL-6 levels were \u0026lt;\u0026thinsp;514.6 U/mL. For the COVID-19 patients whose serum CRP levels were \u0026gt;\u0026thinsp;74.75 mg/L, the MST was 20 days, obviously below those patients with serum CRP\u0026thinsp;\u0026lt;\u0026thinsp;74.75 mg/L. With regard to BUN, when COVID-19 patients\u0026rsquo; serum BUN levels were \u0026gt;\u0026thinsp;10.33 mmol/L, the MST was 19 days, which notably lower than those patients with BUN\u0026thinsp;\u0026lt;\u0026thinsp;10.33 mmol/L. The best cut-off of age derived from ROC curve of COVID-19 patients was 77.5 years old. Notably, in this study, no significant difference of MTDE was found in COVID-19 patients between age above 77.5 years old (24 days) and under 77.5 years old (45 days).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe efficiency of age, KL-6, BUN and CRP in predicting prognosis of COVID-19\u003c/h2\u003e \u003cp\u003eWe further explored the value of the above four risk factors to predict the possibility of adverse outcome of COVID-19. The AUC of ROC curve for BUN, KL-6, CRP and age was 0.873 (95% CI: 0.811\u0026ndash;0.935, sensitivity: 0.878, specificity: 0.786), 0.724 (95% CI: 0.628\u0026ndash;0.820, sensitivity: 0.707, specificity: 0.689), 0.811 (95% CI: 0.732\u0026ndash;0.890, sensitivity: 0.632, specificity: 0.827) and 0.625 (95% CI: 0.530\u0026ndash;0.720, sensitivity: 0.585, specificity: 0.650), respectively, and the best cut-off was 10.33 mmol/L, 514.6 U/mL, 74.75 mg/L and 77.5 years old, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). When we combined the four risk factors to predict the occurrence of adverse outcome of COVID-19, the AUC of ROC curve reached 0.947 (95% CI: 0.911\u0026ndash;0.983), while the sensitivity, specificity and Youden Index was 0.842, 0.898 and 0.74, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe included the risk factors of age, KL-6, BUN and CRP as the possible predictors of the adverse outcome of COVID-19 to build a prediction model, which was presented as a nomogram graph in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec. Each level of every variable was assigned a score on points scale. By adding the scores for each of the selected variables, a total score was obtained. Then, the sum score was located on the Total Points scale and vertically projected onto the bottom axis of probability of poor prognosis of COVID-19, and thus, a personalized risk of poor prognosis of COVID-19 could be easily obtained. The threshold probability of the model was 74% based on the maximal Youden\u0026rsquo;s index, with a sensitivity of 0.842 and specificity of 0.898. In other words, a risk probability of greater than 74% requires further tests to confirm the poor prognosis of COVID-19.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we discovered that old age, elevated serum levels of KL-6, BUN and CRP were risk factors for COVID-19 to develop to poor prognosis. Meanwhile, we found that combining age, KL-6, BUN, and CRP exhibited good discriminative performance for predicting poor prognosis, providing a preliminary reference for clinical risk stratification. Our results could provide a pattern for clinical staff to assess the prognosis of COVID-19 and draw their attention to give more medical help to the high risk COVID-19 patients.\u003c/p\u003e \u003cp\u003eSensitivity analysis: To address potential overlap between baseline severity and outcome, we re-analyzed 71 common COVID-19 patients (excluding severe cases at admission). Age, KL-6, BUN, and CRP remained independent risk factors for poor prognosis (AUC\u0026thinsp;=\u0026thinsp;0.923, sensitivity\u0026thinsp;=\u0026thinsp;0.810, specificity\u0026thinsp;=\u0026thinsp;0.872), supporting model robustness. Detailed data are available upon request.\u003c/p\u003e \u003cp\u003eSerum KL-6 as a biomarker, was widely used in the diagnosis of many interstitial lung diseases\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e; KL-6 elevation was also found in many respiratory infections diseases such as mycobacterial infections\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, viral infection\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e and pneumocystis pneumonia\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In the beginning of COVID-19 infection, KL-6 serum levels have been manifested to relate with the diagnosis, prognosis, and therapeutic response evaluation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Some studies have reported that the extent of lung lesions in COVID-19-associated pneumonia has correlation with increased serum KL-6 levels\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, another research has found no association between the serum KL-6 level and the severity of COVID-19-associated pneumonia\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In our study, serum KL-6 levels in poor prognosis group (865.800 (464.400\u0026ndash;1714.000) U/mL) were significantly elevated compared with the favorable prognosis group (353.300 (233.400-690.450) U/mL). Moreover, our further analysis proved that serum KL-6 level was correlated with the outcome of COVID-19 (r\u0026thinsp;=\u0026thinsp;0.35) and was a risk factor for poor prognosis of COVID-19. Lung epithelium is the main target of SARS-CoV-2 and infection of the virus facilitates the secretion of proinflammatory cytokines in injured alveolar epithelial cells\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Furthermore, the inflammatory response promotes the release of KL-6 into the blood\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. KL-6 is not only a specific biomarker of lung type Ⅱ pneumocytes\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, but also identified as a prognostic biomarker for the diagnosis and prediction of the severity of idiopathic pulmonary fibrosis and acute respiratory distress syndrome\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eClinical applicability of KL-6 detection: KL-6 was measured using the Beckman Coulter AU5800 automatic biochemical analyzer, a widely used platform in clinical laboratories with a turnaround time of 4\u0026ndash;6 hours (consistent with routine inflammatory biomarkers). The detection cost is comparable to CRP in Chinese clinical settings. However, applicability in resource-limited regions requires further evaluation, and no direct comparison with existing tools (e.g., NEWS2, WHO severity classification) was performed, which limits assessment of incremental value.\u003c/p\u003e \u003cp\u003eOlder age was found to be associated with higher disease severity in COVID-19 patients in previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. The median age of patients receiving intensive care was higher than the patients who were not admitted to ICU (61 years vs 51 years)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. In hospitalized adult patients, the percentage of severe cases ranged from 19.8% to 49%\u003csup\u003e27,31,32\u003c/sup\u003e, but only 2.2% in a pediatric cohort study\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Kuo et al. believed that biological aging was an optimal predictor for disease severity after performing biological age evaluation comprised of chronological age and another nine biomarkers including ALB, AST, Scr, CRP, glucose, L%, mean corpuscular volume, red blood cell distribution width and white blood cell count\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In our study, the median age of 79 years in poor prognosis patients is significantly higher than that of 71 years in the favorable prognosis group, which was consistent with previous studies\u003csup\u003e\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Our results confirmed that elderly COVID-19 patients deserve more attention during clinical treatment.\u003c/p\u003e \u003cp\u003eIt is worth noting that the occurrence of acute kidney injury (AKI) was found to range from 0.5% to 29% in hospitalized COVID-19 patients in China\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. A study from New York City reported a 46% incidence of AKI among 3993 hospitalized COVID-19 patients\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. COVID-19 patients combined with AKI had higher probability of admitted to ICU, progressed to necessitating renal replacement therapy and developed to poor prognosis\u003csup\u003e\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Elevated serum BUN and creatine levels could be indicative of renal damage or AKI of COVID-19 patients. Previous studies revealed that the prevalence of patients with increased BUN and Scr levels among severe cases were 13.1% and 14.4, respectively, which were significantly higher than those in mild cases\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. In this study, serum BUN and creatinine concentration in poor prognosis group was 17.29 (11.45\u0026ndash;22.14) mmol/L and 159.00 (76.90\u0026ndash;461.00) \u0026micro;mol/L, respectively, significantly higher than that in the favorable prognosis group, which was for BUN, 7.12 (4.81\u0026ndash;9.76) mmol/L, and Scr, 72.30 (57.15\u0026ndash;94.20) \u0026micro;mol/L. In addition, our study revealed that elevated BUN level was a risk factor for COVID-19 developing to poor prognosis. Our results indicate that clinical staff should focus more on COVID-19 patients with elevated BUN and Scr.\u003c/p\u003e \u003cp\u003eElevated serum CRP was regarded as a risk factor for disease progression and mortality in severe COVID-19 patients and also an indicator of developing cytokine storm in COVID-19 patients\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. A study concluded that CRP cut-off value of 34.6 mg/L presented good performance (sensitivity 82.3%, specificity 73%) in discriminating severe and non-severe COVID-19 patients relative to D-dimer\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In a meta-analysis, 20 out of 32 studies found that COVID-19 patients with elevated CRP were at nearly four-fold high risk to develop poor prognosis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Our results showed that the CRP levels in poor prognosis group were significantly high (108.000 (44.400-158.500) mg/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), compared to that in the favorable prognosis group (21.700 (7.095\u0026ndash;56.475) mg/L); furthermore, with the cut-off value of 74.75 mg/L, CRP had good diagnostic efficacy in predicting poor prognosis of COVID-19, and the AUC of ROC curve approached to 0.811, with the sensitivity of 63.20% and the specificity of 82.7%. The results we obtained are slightly different from previous studies\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, which could be explained by the mutation of the novel coronavirus and the population from different ethnic groups.\u003c/p\u003e \u003cp\u003eIn summary, age-related immunosenescence, lung epithelial injury reflected by KL-6, renal dysfunction indicated by BUN, and systemic inflammatory response represented by CRP synergistically promote disease progression in COVID-19 patients. This mechanism is consistent with the core pathological process of \"multiple organ injury\u0026thinsp;+\u0026thinsp;cytokine storm\" reported in existing studies, which also supports the rationale for these four indicators as independent risk factors.\u003c/p\u003e \u003cp\u003eIn confirming these independent risk factors, we specifically addressed potential confounding factors to enhance the reliability of the results. Regarding key confounding factors: baseline oxygen requirement, comorbidities, and COVID-19 treatment regimens (e.g., Azvudine) were all considered in the study design. During patient selection, severe comorbidities that may affect prognosis (such as interstitial lung disease) were excluded. Additionally, baseline disease severity (common vs. severe type) was further adjusted in the multivariable logistic regression model to reduce potential overlap bias between baseline status and outcome indicators. However, the vaccination status (including vaccination doses and vaccine type) and the infecting SARS-CoV-2 variant information (e.g., whether it was the EG.5 variant) were not systematically recorded in this cohort. Both factors may alter prognosis by influencing viral pathogenicity or host immune responses, thereby limiting the generalizability of the study results and should be supplemented and incorporated into future prospective studies for analysis.\u003c/p\u003e \u003cp\u003eDespite these limitations, the combined model integrating age, KL-6, BUN, and CRP still exhibits favorable discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.947, sensitivity\u0026thinsp;=\u0026thinsp;0.842, specificity\u0026thinsp;=\u0026thinsp;0.898). The combination of age, KL-6, BUN, and CRP in our model captures distinct yet complementary pathophysiological processes: immunosenescence (age), lung epithelial injury (KL-6), renal dysfunction (BUN), and systemic inflammation (CRP). These factors may interact synergistically; for instance, systemic inflammation can exacerbate lung and kidney injury, while pre-existing organ dysfunction in the elderly may amplify the deleterious effects of SARS-CoV-2 infection. Although we did not investigate genetic factors such as TMPRSS2 or FURIN, which influence viral entry, our model focuses on downstream host response markers that are readily measurable in clinical practice and reflect the integrated disease burden.\u003c/p\u003e \u003cp\u003eElevated IL-6 and CRP are hallmarks of cytokine release syndrome not only in COVID-19 but also in other critical illnesses such as sepsis and acute respiratory distress syndrome from other causes. However, the unique combination with KL-6\u0026mdash;a specific marker of lung epithelial damage\u0026mdash;may enhance specificity for predicting pulmonary complications in viral pneumonias. While our model was derived from a COVID-19 cohort, the pathophysiological principles it embodies (epithelial injury, hyperinflammation, and extra-pulmonary organ dysfunction) suggest potential applicability to other severe coronavirus infections (e.g., MERS) or viral pneumonias, though this requires explicit validation in respective populations.\u003c/p\u003e \u003cp\u003eIdentifying risk factors for COVID-19 progression to poor prognosis is of great importance for clinician and public health strategies. The novel coronavirus has been constantly mutating since its outbreak, along with the variation of its infectivity and pathogenicity. At present, the main strain of the new coronavirus epidemic is EG.5, which is highly infectious but weak in pathogenicity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. What matters is that, in many countries, such as China, elderly people make up a large portion of the population and how to avoid poor prognosis of the elderly infected with COVID-19 is still a tremendous challenge confronting us.\u003c/p\u003e \u003cp\u003eOur findings align with growing evidence that biomarkers such as KL-6 reflect lung epithelial injury and systemic inflammation, which are central to COVID-19 pathogenesis. The integration of KL-6 with routine clinical parameters provides a preliminary reference for risk stratification, particularly in resource-limited settings. Our model may thus not only aid in acute-phase risk stratification but also inspire longitudinal studies linking acute biomarker profiles to long-term outcomes.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it is a single-center retrospective analysis without a pre-registered protocol, and no external or formal internal validation (e.g., bootstrapping, cross-validation) was performed, so the model is preliminary and requires further prospective multi-center validation. Second, the sample size is modest, and no power calculation was performed a priori. Third, the composite endpoint of \u0026ldquo;poor prognosis,\u0026rdquo; while clinically relevant, may encompass heterogeneous outcomes. Fourth, our cohort consisted exclusively of hospitalized Chinese patients infected with the Omicron variant, which may limit generalizability to other populations or SARS-CoV-2 variants. Nevertheless, our model exhibited favorable discriminative performance in this single-center cohort and provides preliminary evidence for the value of integrating KL-6 with routine parameters. Prospective multi-center validation with a larger sample size is mandatory before clinical application.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConsent to participate and publish:\u003c/strong\u003e Written informed consent was waived by the ethics committee due to the retrospective nature of the study. Patient data were anonymized and maintained with confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study was supported by several Natural Science Foundation of Hunan Province (2023JJ40949, 2023JJ40971, 2023JJ30965, 2023JJ40962).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eEthical approval was waived by the ethics committee of Xiangya Hospital of Central South University in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData and/or Code availability:\u0026nbsp;\u003c/strong\u003eThe datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. Our statistical analyses were conducted by R software for Windows (Version 3.6.1, https://www.r-project.org/) and the Deepwise and Beckman Coulter DxAI platform (https://dxonline.deepwise.com).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions:\u0026nbsp;\u003c/strong\u003eKH wrote the main manuscript draft, KH and YL designed this manuscript, YL analyzed the original data, KW and QH prepared figures, LL and YZ prepared Tables. BY amended the first draft and approved the final version. All authors reviewed the manuscript. All authors agree to participate in and publish this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eNo.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. WHO Coronavirus (COVID-19) Dashboard. 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrożdżal S, et al. 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JAMA. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1001/jama.2023.16498\u003c/span\u003e\u003cspan address=\"10.1001/jama.2023.16498\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"COVID-19, KL-6, BUN, CRP, Age, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8528169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8528169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe coronavirus disease 2019 (COVID-19) pneumonia pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused millions of deaths worldwide, and is still threatening our life, making profound impact on the global economy and society in every field. Exploring risk factors for poor prognosis in COVID-19 patients would help optimize clinical management and improve health outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical characteristics and laboratory data of 144 COVID-19 patients (confirmed by SARS-CoV-2 nucleic acid or antigen testing) admitted to Xiangya Hospital of Central South University between December 2022 and February 2023 (including 103 with favorable prognosis and 41 with poor prognosis) were collected in this retrospective study. Factors such as age, serum levels of KL-6, BUN, Scr, IL-6, and CRP were analyzed using R software and the Deepwise and Beckman Coulter DxAI platform. Poor prognosis was defined as a composite endpoint of in-hospital death, ICU admission, or clinical deterioration (escalation of respiratory support) during hospitalization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical results showed that age, serum KL-6, BUN, Scr, IL-6 and CRP levels in poor prognosis COVID-19 patients were obviously higher than that in the favorable prognosis group. Spearman correlation analysis demonstrated that serum levels of CRP (r=0.48), IL-6 (r=0.40), cTn (r=0.37), Scr (r=0.37), BUN (r=0.58), KL-6 (r=0.35) and age (r=0.20) were positively correlated with the outcome of the COVID-19 patients. Age, serum KL-6, CRP and BUN levels were identified as risk factors for poor prognosis of COVID-19. Meanwhile, KL-6 combined with BUN, CRP and age revealed good discriminative performance (AUC=0.947, sensitivity=0.842, specificity=0.898) in diagnosis of poor prognosis of COVID-19 through ROC analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur retrospective study identified age, serum KL-6, CRP, and BUN levels as reliable risk factors and preliminary prognostic indicators for poor prognosis (a composite endpoint of in-hospital death, ICU admission, or respiratory support escalation) in COVID-19.\u003c/p\u003e","manuscriptTitle":"A Preliminary Prognostic Model for Predicting Poor Prognosis in COVID-19 Integrating Lung Epithelial Injury (KL-6) with Routine Care Markers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 15:48:37","doi":"10.21203/rs.3.rs-8528169/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-02-02T14:07:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"113265140909355222629401804116792973626","date":"2026-01-31T12:53:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-29T12:29:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-08T11:12:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T10:56:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T10:54:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-01-06T07:28:57+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":"d385a122-f243-4582-b752-43515fa6eab1","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-03T15:48:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 15:48:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8528169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8528169","identity":"rs-8528169","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}