Survival Follow-up and Mortality Prediction Model for Elderly Severe COVID-19 Patients following the complete relaxation of pandemic restrictions in China

Survival Follow-up and Mortality Prediction Model for Elderly Severe COVID-19 Patients following the complete relaxation of pandemic restrictions in China | 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 Survival Follow-up and Mortality Prediction Model for Elderly Severe COVID-19 Patients following the complete relaxation of pandemic restrictions in China Tao Jin, Lu Mingfeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6898119/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The global outbreak of the novel coronavirus has been a significant public health crisis in recent years. Elderly patients with severe COVID − 19 are a special group with extremely high morbidity and mortality rates. Early identification of important factors influencing prognosis and implementation of intervention measures can significantly improve the prognosis of elderly patients with severe COVID − 19.A retrospective cohort study was conducted on elderly patients (≥ 60 years) with severe COVID-19 admitted to the emergency room and Emergency Intensive Care Unit (EICU) of North Jiangsu People's Hospital affiliated with Yangzhou University between December 2022 and January 2023. Patients were randomly divided into training and validation sets (8:2 ratio). Variable selection was performed using LASSO and multifactorial stepwise backward Cox regression. Cox regression was then utilized to construct survival probability plots at 1-month and 1-year. Model performance was assessed using calibration curves, time-dependent ROC curves, and decision curve analysis (DCA). The study included 199 elderly patients with severe COVID-19. The mean survival time in the mortality group was 80.092 days, with a median survival time of 22.000 days. The analysis identified gender (male), lung consolidation, age, lymphocyte ratio, lactate levels, and endotracheal intubation as factors associated with poorer survival prognosis (HR > 1), while gender (female), hospitalization, and oxygenation index were associated with better prognosis (HR < 1). A prediction model based on these factors demonstrated good predictive performance, with a C-index of 0.842 (training set) and 0.809 (validation set). The AUC values for the model were 0.920 (training set) and 0.930 (validation set) for 1-month mortality prediction, and 0.941 (training set) and 0.947 (validation set) for 1-year mortality prediction, indicating strong diagnostic accuracy. Calibration curves showed good model fit. DCA indicated the model's clinical utility. Clinical interventions targeting elderly severe COVID-19 patients, optimizing pulmonary infection control, enhancing oxygenation and circulation, and improving immune function (lymphocyte counts) may significantly improve outcomes in critically ill patients with severe COVID-19. Elderly Severe COVID-19 patients Survival prognosis Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction The global outbreak of the novel coronavirus has emerged as a significant public health crisis in recent years. As of April 21, 2024, the World Health Organization (WHO) reported over 775 million confirmed cases of coronavirus disease 2019 (COVID-19) worldwide, resulting in 7.04 million deaths [1] . Research indicates that advanced age is a primary risk factor for mortality associated with novel coronavirus infection. Meta-analyses have revealed that individuals over 60 years old face a mortality rate as high as 20% [2 , 3 , 4] . Elderly patients with COVID-19 often present with multiple comorbidities such as cancer, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, and diabetes mellitus [5 , 6 , 7 , 8 , 9] . The elderly population, particularly those over 80 years old, exhibit a significantly elevated morbidity and mortality rate, reaching up to 46.9% [10 , 11] . Early identification of severe cases among the elderly, understanding risk factors for poor outcomes, and prompt intervention are crucial in reducing morbidity and mortality rates associated with COVID-19. Despite limited research on prognostic factors for severe and critical cases in elderly patients, existing studies have primarily focused on epidemic control measures predating the full reopening in 2022 [12 , 13] . Some elderly patients remain uncured at discharge, with unresolved pulmonary lesions persisting for 1-2 years, leading to persistent respiratory dysfunction or even long-term mortality. This study examines the survival follow-up analysis and Nomogram prognosis prediction model for elderly patients with severe COVID-19 following the comprehensive relaxation of China's COVID-19 policies. The Nomogram prediction model offers a more thorough assessment of patients' prognosis risks, enabling timely interventions and proactive treatments for those with a poor prognosis, ultimately maximizing patient benefits. Information and methodology 1. Sample Size Calculation and Study Flowchart Based on the 10EPV predictive model principle, the anticipated number of predictor variables ranged from 8 to 10, with an expected positive sample size of 80-100 cases and a total sample size estimate of 160-200 cases. The study's overall workflow was illustrated in Figure 1 . 2. Clinical data collection A total of 199 cases (119 deceased and 80 surviving) of severe and critical COVID-19 patients aged 60 years or older, who were admitted to the emergency room and EICU of Northern Jiangsu People's Hospital Affiliated to Yangzhou University between December 2022 and January 2023, were included in this study. Inclusion criteria encompassed patients meeting the diagnostic guidelines outlined in the Diagnostic and Treatment Program for Novel Coronavirus Infections (Trial Implementation of the Tenth Edition), exhibiting clinical manifestations consistent with new coronavirus infections, and presenting with one or more pieces of pathogenetic and serologic evidence of the novel coronavirus. Excluded from the study were individuals in the terminal stage of chronic illnesses, those lost to follow-up, and those with incomplete clinical records. Retrospective survey methods were employed to collect observational data. Clinical information of elderly patients with severe novel coronavirus pulmonary infections in the emergency room and during hospitalization was retrieved from the Hospital Information System (HIS). The data encompassed demographic details and medical history (including age, gender, hypertension, coronary heart disease, coronary artery disease, chronic conditions, and other comorbidities), clinical information (such as hypertension, coronary heart disease, chronic obstructive pulmonary disease, stroke, diabetes mellitus, tumor, and atrial fibrillation), screening test results (worst values post-admission), and treatment specifics (including total leukocyte count, neutrophil ratio, lymphocyte ratio, urea nitrogen, blood creatinine, lactic acid, oxygen saturation, oxygenation index, body temperature, respiratory rate, pH, blood sodium, blood potassium, blood chloride, D-dimer, troponin I, lung lesions (CT diagnosis), impaired consciousness (clinical diagnosis), endotracheal intubation, and hospitalization status). 3. Follow-up visits The survival of patients who contracted new crown infections was monitored through clinic visits or Telephone Follow-up. A total of 199 cases were included in the analysis. Survival time was computed from the onset of the first infection symptom, with the endpoint defined as the date of the last follow-up or the occurrence of the patient's demise, which was recorded as of August 21, 2024. Deaths unrelated to new crown infections (e.g., car accidents, trauma) were excluded from the analysis. 4. Statistical methods Certain parameters, such as blood sodium levels, did not exhibit a simple linear relationship with survival time. For instance, in the blood sodium group, the differences in survival rates between the low-sodium group (n=119, 59.8% incidence) and the normal group (n=70, 35.2% incidence) were significant (P=.0375), as were the differences between the high-sodium group (n=10, 5% incidence) and the normal group (P=.0232). The differences in survival among the low-sodium, high-sodium, and normal groups were all statistically significant. To optimize the obtained parameters, a method was employed where the absolute difference between the value and the mean value was calculated. The optimized variables were denoted by adding "X" after the variable name (e.g., "optimized lymphocyte ratio" as "LX"). These optimized data were used for survival analysis to better reflect clinical reality. Predictor variables were selected using LASSO regression, with Lambda.1se determining the optimal solution. Variables with non-zero coefficients were included in multifactorial stepwise backward Cox regression analysis using the "glmnet" package. Columnar plots were constructed, and time-dependent ROC curves were generated to assess prognostic accuracy, with AUC calculated at 1 month and 1 year. The C index was used to evaluate model prediction accuracy. Calibration curves were employed to assess the consistency of survival probabilities predicted by the column-line graphs through self-guided resampling. Decision curve analysis (DCA) evaluated the net benefit and clinical utility of the constructed models. Statistical analyses were conducted using R (version 4.4.2) with packages including "rms," "ggplot2," "pROC," "timeROC," and "dca." A significance level of P≤0.05 was considered statistically significant in all analyses. Results 1. Survival data analysis A total of 199 patients were included in this study, comprising a training set (n = 159) and a validation set (n = 40), with 119 deaths and 80 survivors as of August 21, 2024. The maximum follow-up duration was 626 days. The mean survival time was 299.553 days (95%CI: 259.693-339.413), while the median survival time was 160.000 days (95%CI: 52.000-416.000). The one-month, two-month, and one-year survival rates were 62.825% (95%CI: 56.128%-69.472%), 55.836% (95%CI: 48.901%-62.699%), and 44.724% (95%CI: 38.801%-51.599%), respectively. The overall survival curve is depicted in Fig. 2 A.Analysis of the data from the 119 patients in the deceased group revealed a significantly skewed distribution, as illustrated in Fig. 2 B. The quartiles for this group were 11, 22, 62, and 461 days, corresponding to 25%, 50%, 75%, and 95% of the data, respectively. The mean survival time for all patients in the deceased group was 80.092 days (95%CI: 56.000-104.185), with a median survival time of 22.000 days (95%CI: 17.000–27.000). Fifty percent of deaths occurred within 22 days, 75% within 62 days, and 95% within 461 days. 2. Survival analysis of basic information In this study, 199 elderly patients with severe new coronary infections were analyzed. The majority were males (69.352%). The older the age group, the higher the proportion of severe cases: 24.623% for 60–69 years, 32.162% for 70–79 years, and 43.225% for 80 years and above. These patients presented with various comorbidities, including hypertension (61.312%), diabetes mellitus (34.173%), history of stroke (26.632%), coronary heart disease (13.572%), tumor (16.584%), and chronic obstructive pulmonary disease (10.556%). Survival curves depicted in Fig. 3 illustrate the impact of gender and age on survival across all time points. 3. LASSO + multifactor stepwise backward COX regression screening of risk variables A total of 33 variables were assessed in the cases, including demographic information (age, sex), medical history (hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, chronic obstructive pulmonary disease, stroke, neoplasm, chronic kidney disease), laboratory values (leukocyte count, neutrophil percentage, lymphocyte percentage, urea nitrogen, blood creatinine, blood sodium, blood potassium, blood chloride, D-dimer, procalcitonin, troponin I, lactate), clinical parameters (oxygen saturation, oxygenation index, body temperature, respiratory rate, mean arterial pressure, heart rate, pH), imaging findings (solid lung lesions on CT scan), interventions (endotracheal intubation), neurological status (impaired consciousness), and hospitalization status. The cases were randomly divided into a training set (n = 159) and a validation set (n = 40) in an 8:2 ratio. Statistical analysis of baseline characteristics revealed no significant differences between the groups (P > .05), Specific results are shown in Appendix 1. LASSO regression identified an optimal model at Lambda.1se = .1416008, with the following variables and regression coefficients: LUNG .743949479, AGE .017200253, FEMALE − .014520366, NX .011729544, LX .006233896, BUNX .006946385, LAC .033917083, OI − .005749998, EI .304848752, HOSP − .511221638. The LASSO graph is presented in Fig. 4 .The variables selected by LASSO were further analyzed using multifactor stepwise backward COX regression, resulting in the identification of eight variables: "LUNG, AGE, FEMALE, LX, LAC, OI, EI, and HOSP." Detailed analysis findings are presented in Table 1 . A forest plot in Fig. 5 illustrates the hazard ratio (HR) values for each variable. Solid lung lesions, male gender, older age, higher lymphocyte ratio, elevated lactate levels, and endotracheal intubation were positively associated with increased risk of death (HR > 1), while female gender, hospitalization, and higher oxygenation index were negatively associated with the risk of death (HR < 1). 4. The construction of a Nomogram predictive model The LASSO + COX regression identified eight variables ("LUNG, AGE, FEMALE, LX, LAC, OI, EI, HOSP") for inclusion in a predictive model with January and one year as the prediction time points. This model, depicted in Fig. 6 , was used to interpret the column line graph as follows: For instance, considering the case of ID = 9, a vertical line was drawn at the respective value of each variable to determine the score on the "Points" axis. The cumulative scores across all test indices were then calculated to derive the total points, which in turn were used to estimate the patient's probability of mortality at 1 month and 1 year. 5. Assessment of the effectiveness of the Nomogram predictive model The model demonstrated strong predictive performance, with a C-index of 0.842 (95% CI: 0.807–0.876) for the training set and 0.809 (95% CI: 0.742–0.876) for the validation set. Furthermore, the model exhibited high accuracy in predicting the likelihood of death at 1 month, as evidenced by the training set AUC of 0.920 (95% CI: 0.880–0.959) and the validation set AUC of 0.930 (95% CI: 0.855–1.004). Similarly, for predicting the probability of death at 1 year, the model achieved an AUC of 0.941 (95% CI: 0.907–0.975) in the training set and 0.947 (95% CI: 0.877–1.017) in the validation set, indicating robust diagnostic performance. These findings are illustrated in Fig. 7 . The calibration curves for predicting the probability of death at both 1 month and 1 year exhibit a high degree of overlap between the training and validation cohorts, closely resembling the ideal curves. This alignment indicates that the model's calibration is optimal, as illustrated in Fig. 8 and Fig. 9 . The DCA curves for predicting the probability of death at 1 month and 1 year (Fig. 10and Fig. 11 ) demonstrate that both the training and validation groups exhibit the model positioned predominantly above the ALL and None lines across a broad spectrum. This positioning suggests that the model holds practical utility, indicating fair predictive value for implementation. Discussion Current research indicates that the novel coronavirus infection poses significant challenges to the human body beyond a typical acute viral illness [ 14 ] . The virus can severely compromise the human immune system, leading to persistent damage to the heart, lungs, and nervous system even after the acute phase has resolved. Some individuals may experience enduring symptoms such as palpitations, shortness of breath, dry cough, and dyspnea as sequelae of the infection [ 15 ] . Our study delved into the long-term survival outcomes of patients affected by the novel coronavirus, revealing that survival rates are influenced by factors such as gender, age, and underlying health conditions. Through comprehensive analysis of follow-up data, we gained valuable insights into the overall survival prognosis for elderly patients with severe COVID-19. Among the 199 elderly patients studied, males were predominant, with a higher proportion of severe cases observed in older age groups and among those with multiple comorbidities. Examination of mortality data revealed a mean survival time of 80.092 days (95% CI: 56.000–104.185) and a median survival time of 22 days (95% CI: 17.000–27.000), with 50% of deaths occurring within 22 days, 75% within 62 days, and 95% within 461 days. These findings underscore the long-lasting impact of novel coronavirus infection on patient survival beyond the acute phase. This study conducted a comprehensive analysis of patient diagnosis and treatment data from the emergency department and hospitalization. The diagnostic and treatment information was scrutinized and confirmed through various methodologies. The research revealed that factors such as lymphocyte ratio, age, gender, blood lactate levels, and oxygenation index significantly influence the prognosis of individuals with severe neocoronaryngitis [ 16 , 17 , 18 , 19 ] . Furthermore, our study confirmed that these factors not only impact short-term prognosis but also long-term survival outcomes. Specifically, our model focused on prognostic time points at 1 month and 1 year. Kaplan-Meier survival curves were plotted to illustrate the influence of these variables on overall survival at different time intervals. Notably, certain variables, like blood sodium levels, demonstrated a non-linear relationship with survival time. To address this, we optimized the data by calculating the absolute difference between the values and the mean. Subsequent statistical analyses were conducted on the optimized dataset to better align with clinical practice. Nomograms have demonstrated their validity and reliability across various medical disciplines [ 20 ] . This allows for a more straightforward interpretation of survival prediction values, enhancing the practical utility of the model. In the prognostic model developed in this study, easily obtainable variables were utilized, enabling swift estimation of the probability of death at specific time points (1 month, 1 year). Noteworthy risk factors influencing survival included lung solid lesions, male gender, advanced age, lymphocyte ratio, lactate levels, and tracheal intubation, while female gender, hospitalization, and oxygenation index were identified as positive factors. Vigilant monitoring and prompt intervention, particularly by emergency healthcare providers, are crucial. Mitigating the risk of mortality in patients involves targeted interventions such as aggressive anti-infection measures for lung solidification, optimized oxygen therapy, correction of circulatory disturbances, enhancement of immune function, and prompt hospitalization. Evaluation of the model's performance through metrics like the C-index, ROC curve, calibration curve, and decision curve analysis (DCA) revealed favorable predictive accuracy. The model's efficacy, applicability, and reliability have been rigorously validated, underscoring its significant practical utility. The study has several limitations. Firstly, its retrospective nature may result in missing information on key study factors in the cases. Secondly, the samples were collected from the Northern Jiangsu People's Hospital Affiliated to Yangzhou University, potentially leading to selection bias as patients with varying economic statuses may seek care at different healthcare facilities, including primary hospitals, or may not have access to higher-level medical centers. Additionally, due to the limited number of cases, the model utilized medical records from a single hospital, resulting in constraints on incorporating more parameters for enhanced model stability. Consequently, only a basic K-M survival analysis was conducted for specific inflammatory indexes, and comprehensive evaluation of long-term lung CT and lung function in patients with severe neocoronary deficiency was lacking. Moreover, the absence of data from multiple external and independent validation sources underscores the necessity for additional data. Insufficient data also hindered the assessment of long-term lung CT and lung function in critically ill neocoronary patients, emphasizing the need for more external validation data from diverse sources. Lastly, it is important to highlight that the predictive model aims to complement clinicians' clinical judgment by aiding in the implementation of preventive measures based on the likelihood of disease onset and progression. A robust prediction model holds significant clinical utility [ 21 ] . Declarations Ethics approval and consent to participate: The study received approval from the Ethics Committee of Northern Jiangsu People's Hospital Affiliated to Yangzhou University and was registered in the China Clinical Trial Registry under the ethical number: 2025ky028. Informed consent to participate was obtained from all of the participants in the study. The study adhered to the Declaration of Helsinki. Clinical Trial: Not applicable. Consent for publication: All authors (Tao Jin and Lu Mingfeng) have reviewed the manuscript and approved this submission. Availability of data and material: Data is provided within the manuscript or supplementary information files，Doctor TaoJin can be contacted (email-address: [email protected] )to obtain access to the raw data analysed in your study. Competing interests: All authors declare no conflicts. Funding: Key R & D Project (Social Development) of Yangzhou City in 2024 (YZ2024095) Authors' contributions: Tao Jin: article writing, statistical analysis, graphing, and data organization; Lu Mingfeng: experimental design, research supervision, paper review, and financial support. Acknowledgements: We would like to thank our supervisor and colleagues in the department for their guidance and support during this research and thesis collaboration. References World Health Organization. WHO COVID-19 dashboard [EB/OL]. (2024-04-21). https://data.who.int/dashboards/covid19/cases? n = c Smati S, Tramunt B, Wargny M, et al. COVID-19 and Diabetes Outcomes: Rationale for and Updates from the CORONADO Study[J]. Curr Diab Rep. 2022;22(2):53–63. NEUMANN⁃PODCZASKA A, AL⁃SAAD S R,KARBOWSKI L M, et al. COVID 19 clinical picture in the elderly population:a qualitative systematic review[J]. Aging Dis. 2020;11(4):988–1008. WANG, L,HE W B,YU X, M, et al. Coronavirus diseases 2019 in elderly patients: Characteristics and prognostic factors based on 4 weeks follow⁃up[J]. Infect. 2020;80(6):639–45. Djorwé S, Bousfiha A, ,Nzoyikorera N et al. Impact and prevalence of comorbidities and complications on the severity of COVID-19 in association with age, gender, obesity, and pre-existing smoking: A meta-analysis.[J].BioMedicine,2024,14(1):20–38. Huang C, Wang Y, Li X, et al. Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China[J]. Lancet. 2020;395(10223):497–506. Wang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China[J]. JAMA. 2020;323(11):1061–9. Hill MA, Mantzoros C, Sowers JR, Commentary. COVID-19 in patients with diabetes[J]. Metabolism. 2020;107:154217. Jayakrishnan B, Nair P. COVID-19, Obstructive Airway Disease and Eosinophils: A complex interplay[J]. Sultan Qaboos Univ Med J. 2022;22(2):163–6. Ramos-Rincon JM, Buonaiuto V, Ricci M et al. Clinical characteristics and risk factors for mortality in very old patients hospitalized with COVID-19 in Spain[J]. J Gerontol Biol Sci Med Sci 2021,76(3): e28–37. L L, E. R ER et al. HP05: Diagnostic and prognostic assessment in respiratory and hemodynamic changes related to prone position in COVID-19 patients[J].Clinical Neurophysiology,2022,135e2-e2. Chao RRQY, ,Di R et al. The Clinical Features and Prognostic Assessment of SARS-CoV-2 Infection-Induced Sepsis Among COVID-19 Patients in Shenzhen, China.[J].Frontiers in medicine,2020,7570853 – 570853. Zhang D, Zhang C, Li X, et al. Thin-section computed tomography findings and longitudinal variations of the residual pulmonary sequelae after discharge in patients with COVID-19: a short-term follow-up study. Eur Radiol. 2021;31(9):7172–83. C M P,A I C, B R M et al. SARS-CoV-2 productively infects primary human immune system cells in vitro and in COVID-19 patients.[J].Journal of molecular cell biology,2022,14(4). Kartik K, Ratnaprashanthika R, M S C et al. Chest CT features and functional correlates of COVID-19 at 3 months and 12 months follow-up.[J].Clinical medicine (London, England),2023,23(5):467–477. Jin Q, Ma W, ,Zhang W et al. Clinical and hematological characteristics of children infected with the omicron variant of SARS-CoV-2: role of the combination of the neutrophil: lymphocyte ratio and eosinophil count in distinguishing severe COVID-19[J].Frontiers in Pediatrics,2024,121305639-1305639. Mansouri HG, Darjiyani F, ,Robati KF et al. Exploring factors influencing COVID-19 severity: a matched case-control study.[J].European review for medical and pharmacological sciences,2024,28(21):4553–60. Nimra F, Ahmed SK, ,Muhammad RR, U F. Correlation between oxygen saturation of patient and severity index of Covid 19 pneumonia on CT.[J].JPMA. J Pak Med Assoc. 2023;73(1):60–3. Izabela K, Paweł C, Patryk M et al. Factors influencing death in COVID-19 patients treated in the ICU: a single-centre, cross-sectional study.[J].Anaesthesiology intensive therapy,2022,54(2). Xiaoxue W, Jingliang L, ,Zixuan S et al. From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021) [J].Frontiers in Public Health,2022,10997713-997713. Marco B, Nicola F, ,Alberto B. Nomograms in urologic oncology, advantages and disadvantages.[J].Current opinion in urology,2019,29(1):42–51. Additional Declarations No competing interests reported. Supplementary Files test.xlsx train.xlsx LASSODATA.xls telephonefollowup.xlsx TelephoneFollowupQuestionnaire.docx Appendix1.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviews received at journal 04 Aug, 2025 Reviewers agreed at journal 01 Aug, 2025 Reviewers agreed at journal 15 Jul, 2025 Reviewers invited by journal 15 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Editor invited by journal 20 Jun, 2025 Submission checks completed at journal 19 Jun, 2025 First submitted to journal 19 Jun, 2025 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-6898119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":485924453,"identity":"63385115-59a2-4a48-8195-2950b52a79da","order_by":0,"name":"Tao Jin","email":"","orcid":"","institution":"People's Hospital Of Yangzhong City Affiliated to the Medical College of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Jin","suffix":""},{"id":485924454,"identity":"54fedb6e-a3b8-4c9c-8700-dade75a1fb5e","order_by":1,"name":"Lu Mingfeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIie3QPwrCMBiH4RQhLh/i+BWl9QJCoOBU6lUsQl0dO6aI6XWcnBMCdal0LbjYG/QEYuvmlIyCeeffQ/4Q4nL9ZBNO8hyBToui6+2Ix0ldx8sZ6FOE1uQusjjAg5iDDWCNOl8k1UD9ThAkSbDmJiKVaCUMZJGK55Hso400EVWIR48fUjIkMr0aifaGU9h4MSUQrEg1kl0GFD1L4tcjkTFQSIdPZhZvmTW6atULt2F567o+TwIjWX0PmGE+FnKLkcvlcv15b1L/SAj1HEpuAAAAAElFTkSuQmCC","orcid":"","institution":"Northern Jiangsu People's Hospital Affiliated to Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Lu","middleName":"","lastName":"Mingfeng","suffix":""}],"badges":[],"createdAt":"2025-06-15 11:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6898119/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6898119/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87272450,"identity":"fb1e2afe-0c18-4ef2-900e-3b63a09956cd","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic diagram of the study flow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/f8644b566d9cf06ccbaa76e8.png"},{"id":87270400,"identity":"db6089e9-175b-4d85-a2a7-68acb5eb8f4d","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.Survival curve of the death group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.Survival curves for the overall group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/62ad3581b2ad9df3338c6fb2.png"},{"id":87270407,"identity":"008fdef8-7427-4208-9009-838e9f5e95c0","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115281,"visible":true,"origin":"","legend":"\u003cp\u003eillustrate the impact of gender and age on survival across all time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.Gender Survival\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.CurveAge Survival Curve\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/7f8d8207c056e435a6a4158a.png"},{"id":87270403,"identity":"b314610b-c6b1-475b-b6af-a71d3aefc582","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97842,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003edisplays LASSO-Cox regression plots. Panel A \u0026nbsp;\u0026nbsp;shows LASSO coefficient contours, while panel B illustrates biased likelihood \u0026nbsp;\u0026nbsp;deviations. Each colored curve depicts the variation of a feature's LASSO \u0026nbsp;\u0026nbsp;coefficient contour with the log(λ) series.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/35dd684f15b306d3782ededc.png"},{"id":87270413,"identity":"de7cfbc9-74a9-488a-a445-d0e9a5b83e6f","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":132202,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest plot of LASSO+COX regression screening variables\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/150db8b6d08b9c0fcac8e2b6.png"},{"id":87272458,"identity":"824fe29f-76ff-4e04-ba23-84c844962c45","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":131725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram of 1 month and 1 year death probability of severe new crown patients over 60 years old.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/cdae74d0992b1e19e40ec2a8.png"},{"id":87270427,"identity":"ee558da2-0ec1-4222-a408-43d0fbe78354","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":71133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.Projected 1-month probability of death\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.Projected 1-year probability of death\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/54616172cef49fed52868f21.png"},{"id":87272453,"identity":"bf4529ab-4a45-47cd-98f2-09bc809018c9","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":79217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. 1-month training group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.1-year training group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/b44cc3393d552097527553f5.png"},{"id":87273031,"identity":"5873731b-b399-4ea8-8d95-1330b834d144","added_by":"auto","created_at":"2025-07-22 08:35:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":80921,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.1-month validation group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.1-year validation group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/73ef35804283cb3b593094c5.png"},{"id":87272460,"identity":"e5454306-a95f-443d-9257-698d44456ee7","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":67797,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. 1-month training group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. 1-year training group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/d468fe2e6b30371e9a9e54a9.png"},{"id":87272463,"identity":"72efeb0d-9835-4212-b3d5-b9a8d8e0f8f6","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":67942,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.1-month validation group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.1-year validation group\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/381366bda515a8990aaebbfd.png"},{"id":87468605,"identity":"3c700a0b-5afe-4ead-9ca3-069ec56fe005","added_by":"auto","created_at":"2025-07-24 08:25:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1975236,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/c9eac48b-4d6b-4f5f-b47f-f6db29520163.pdf"},{"id":87270402,"identity":"dedf838a-96f5-4e10-b07b-5798a1584b2b","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16220,"visible":true,"origin":"","legend":"","description":"","filename":"test.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/704b432583d5837adc558d19.xlsx"},{"id":87273026,"identity":"f0ea1189-c68b-42a7-ba94-9eeddd11b447","added_by":"auto","created_at":"2025-07-22 08:35:16","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":37203,"visible":true,"origin":"","legend":"","description":"","filename":"train.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/a0dfb56f5a41d80522392f72.xlsx"},{"id":87272455,"identity":"a4c1090d-f90c-4687-a607-f809cae9a6f8","added_by":"auto","created_at":"2025-07-22 08:27:16","extension":"xls","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":92672,"visible":true,"origin":"","legend":"","description":"","filename":"LASSODATA.xls","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/ae7b25e7e589ac8b7ddf8a64.xls"},{"id":87273992,"identity":"fbba53cb-c234-4974-9a4a-102c97d2bc63","added_by":"auto","created_at":"2025-07-22 08:43:16","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":23854,"visible":true,"origin":"","legend":"","description":"","filename":"telephonefollowup.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/e19c85ae59afe04d1996c041.xlsx"},{"id":87273030,"identity":"251410e8-9e86-4340-ba93-747bf1cc98bf","added_by":"auto","created_at":"2025-07-22 08:35:16","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17142,"visible":true,"origin":"","legend":"","description":"","filename":"TelephoneFollowupQuestionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/b77822101385610c23ae257e.docx"},{"id":87270424,"identity":"3058d2b0-a3ce-4992-9379-c75351397199","added_by":"auto","created_at":"2025-07-22 08:19:16","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":26864,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6898119/v1/6ee4cb730a051199642d1a47.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Survival Follow-up and Mortality Prediction Model for Elderly Severe COVID-19 Patients following the complete relaxation of pandemic restrictions in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe global outbreak of the novel coronavirus has emerged as a significant public health crisis in recent years. As of April 21, 2024, the World Health Organization (WHO) reported over 775 million confirmed cases of coronavirus disease 2019 (COVID-19) worldwide, resulting in 7.04 million deaths\u003csup\u003e[1]\u003c/sup\u003e\u003csup\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/sup\u003e Research indicates that advanced age is a primary risk factor for mortality associated with novel coronavirus infection. Meta-analyses have revealed that individuals over 60 years old face a mortality rate as high as 20%\u003csup\u003e[2\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e3\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e4]\u003c/sup\u003e. Elderly patients with COVID-19 often present with multiple comorbidities such as cancer, chronic kidney disease, chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), stroke, and diabetes mellitus\u003csup\u003e[5\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e6\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e7\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e8\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e9]\u003c/sup\u003e. The elderly population, particularly those over 80 years old, exhibit a significantly elevated morbidity and mortality rate, reaching up to 46.9%\u003csup\u003e[10\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e11]\u003c/sup\u003e. Early identification of severe cases among the elderly, understanding risk factors for poor outcomes, and prompt intervention are crucial in reducing morbidity and mortality rates associated with COVID-19. Despite limited research on prognostic factors for severe and critical cases in elderly patients, existing studies have primarily focused on epidemic control measures predating the full reopening in 2022\u003csup\u003e[12\u003c/sup\u003e\u003csup\u003e,\u003c/sup\u003e\u003csup\u003e13]\u003c/sup\u003e. Some elderly patients remain uncured at discharge, with unresolved pulmonary lesions persisting for 1-2 years, leading to persistent respiratory dysfunction or even long-term mortality. This study examines the survival follow-up analysis and Nomogram prognosis prediction model for elderly patients with severe COVID-19 following the comprehensive relaxation of China\u0026apos;s COVID-19 policies. The Nomogram prediction model offers a more thorough assessment of patients\u0026apos; prognosis risks, enabling timely interventions and proactive treatments for those with a poor prognosis, ultimately maximizing patient benefits.\u003c/p\u003e"},{"header":"Information and methodology","content":"\u003cp\u003e1. Sample Size Calculation and Study Flowchart\u003c/p\u003e\n\u003cp\u003eBased on the 10EPV predictive model principle, the anticipated number of predictor variables ranged from 8 to 10, with an expected positive sample size of 80-100 cases and a total sample size estimate of 160-200 cases. The study\u0026apos;s overall workflow was illustrated in\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e2. Clinical data collection\u003c/p\u003e\n\u003cp\u003eA total of 199 cases (119 deceased and 80 surviving) of severe and critical COVID-19 patients aged 60 years or older, who were admitted to the emergency room and EICU of Northern Jiangsu People\u0026apos;s Hospital Affiliated to Yangzhou University between December 2022 and January 2023, were included in this study. Inclusion criteria encompassed patients meeting the diagnostic guidelines outlined in the Diagnostic and Treatment Program for Novel Coronavirus Infections (Trial Implementation of the Tenth Edition), exhibiting clinical manifestations consistent with new coronavirus infections, and presenting with one or more pieces of pathogenetic and serologic evidence of the novel coronavirus. Excluded from the study were individuals in the terminal stage of chronic illnesses, those lost to follow-up, and those with incomplete clinical records. Retrospective survey methods were employed to collect observational data. Clinical information of elderly patients with severe novel coronavirus pulmonary infections in the emergency room and during hospitalization was retrieved from the Hospital Information System (HIS). The data encompassed demographic details and medical history (including age, gender, hypertension, coronary heart disease, coronary artery disease, chronic conditions, and other comorbidities), clinical information (such as hypertension, coronary heart disease, chronic obstructive pulmonary disease, stroke, diabetes mellitus, tumor, and atrial fibrillation), screening test results (worst values post-admission), and treatment specifics (including total leukocyte count, neutrophil ratio, lymphocyte ratio, urea nitrogen, blood creatinine, lactic acid, oxygen saturation, oxygenation index, body temperature, respiratory rate, pH, blood sodium, blood potassium, blood chloride, D-dimer, troponin I, lung lesions (CT diagnosis), impaired consciousness (clinical diagnosis), endotracheal intubation, and hospitalization status).\u003c/p\u003e\n\u003cp\u003e3. Follow-up visits\u003c/p\u003e\n\u003cp\u003eThe survival of patients who contracted new crown infections was monitored through clinic visits or Telephone Follow-up. A total of 199 cases were included in the analysis. Survival time was computed from the onset of the first infection symptom, with the endpoint defined as the date of the last follow-up or the occurrence of the patient\u0026apos;s demise, which was recorded as of August 21, 2024. Deaths unrelated to new crown infections (e.g., car accidents, trauma) were excluded from the analysis.\u003c/p\u003e\n\u003cp\u003e4. Statistical methods\u003c/p\u003e\n\u003cp\u003eCertain parameters, such as blood sodium levels, did not exhibit a simple linear relationship with survival time. For instance, in the blood sodium group, the differences in survival rates between the low-sodium group (n=119, 59.8% incidence) and the normal group (n=70, 35.2% incidence) were significant (P=.0375), as were the differences between the high-sodium group (n=10, 5% incidence) and the normal group (P=.0232). The differences in survival among the low-sodium, high-sodium, and normal groups were all statistically significant. To optimize the obtained parameters, a method was employed where the absolute difference between the value and the mean value was calculated. The optimized variables were denoted by adding \u0026quot;X\u0026quot; after the variable name (e.g., \u0026quot;optimized lymphocyte ratio\u0026quot; as \u0026quot;LX\u0026quot;). These optimized data were used for survival analysis to better reflect clinical reality. Predictor variables were selected using LASSO regression, with Lambda.1se determining the optimal solution. Variables with non-zero coefficients were included in multifactorial stepwise backward Cox regression analysis using the \u0026quot;glmnet\u0026quot; package. Columnar plots were constructed, and time-dependent ROC curves were generated to assess prognostic accuracy, with AUC calculated at 1 month and 1 year. The C index was used to evaluate model prediction accuracy. Calibration curves were employed to assess the consistency of survival probabilities predicted by the column-line graphs through self-guided resampling. Decision curve analysis (DCA) evaluated the net benefit and clinical utility of the constructed models. Statistical analyses were conducted using R (version 4.4.2) with packages including \u0026quot;rms,\u0026quot; \u0026quot;ggplot2,\u0026quot; \u0026quot;pROC,\u0026quot; \u0026quot;timeROC,\u0026quot; and \u0026quot;dca.\u0026quot; A significance level of P\u0026le;0.05 was considered statistically significant in all analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e1. Survival data analysis\u003c/p\u003e\n\u003cp\u003eA total of 199 patients were included in this study, comprising a training set (n\u0026thinsp;=\u0026thinsp;159) and a validation set (n\u0026thinsp;=\u0026thinsp;40), with 119 deaths and 80 survivors as of August 21, 2024. The maximum follow-up duration was 626 days. The mean survival time was 299.553 days (95%CI: 259.693-339.413), while the median survival time was 160.000 days (95%CI: 52.000-416.000). The one-month, two-month, and one-year survival rates were 62.825% (95%CI: 56.128%-69.472%), 55.836% (95%CI: 48.901%-62.699%), and 44.724% (95%CI: 38.801%-51.599%), respectively. The overall survival curve is depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA.Analysis of the data from the 119 patients in the deceased group revealed a significantly skewed distribution, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB. The quartiles for this group were 11, 22, 62, and 461 days, corresponding to 25%, 50%, 75%, and 95% of the data, respectively. The mean survival time for all patients in the deceased group was 80.092 days (95%CI: 56.000-104.185), with a median survival time of 22.000 days (95%CI: 17.000\u0026ndash;27.000). Fifty percent of deaths occurred within 22 days, 75% within 62 days, and 95% within 461 days.\u003c/p\u003e\n\u003cp\u003e2. Survival analysis of basic information\u003c/p\u003e\n\u003cp\u003eIn this study, 199 elderly patients with severe new coronary infections were analyzed. The majority were males (69.352%). The older the age group, the higher the proportion of severe cases: 24.623% for 60\u0026ndash;69 years, 32.162% for 70\u0026ndash;79 years, and 43.225% for 80 years and above. These patients presented with various comorbidities, including hypertension (61.312%), diabetes mellitus (34.173%), history of stroke (26.632%), coronary heart disease (13.572%), tumor (16.584%), and chronic obstructive pulmonary disease (10.556%). Survival curves depicted in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrate the impact of gender and age on survival across all time points.\u003c/p\u003e\n\u003cp\u003e3. LASSO\u0026thinsp;+\u0026thinsp;multifactor stepwise backward COX regression screening of risk variables\u003c/p\u003e\n\u003cp\u003eA total of 33 variables were assessed in the cases, including demographic information (age, sex), medical history (hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, chronic obstructive pulmonary disease, stroke, neoplasm, chronic kidney disease), laboratory values (leukocyte count, neutrophil percentage, lymphocyte percentage, urea nitrogen, blood creatinine, blood sodium, blood potassium, blood chloride, D-dimer, procalcitonin, troponin I, lactate), clinical parameters (oxygen saturation, oxygenation index, body temperature, respiratory rate, mean arterial pressure, heart rate, pH), imaging findings (solid lung lesions on CT scan), interventions (endotracheal intubation), neurological status (impaired consciousness), and hospitalization status. The cases were randomly divided into a training set (n\u0026thinsp;=\u0026thinsp;159) and a validation set (n\u0026thinsp;=\u0026thinsp;40) in an 8:2 ratio. Statistical analysis of baseline characteristics revealed no significant differences between the groups (P\u0026thinsp;\u0026gt;\u0026thinsp;.05), Specific results are shown in \u003cstrong\u003eAppendix 1.\u003c/strong\u003e LASSO regression identified an optimal model at Lambda.1se\u0026thinsp;=\u0026thinsp;.1416008, with the following variables and regression coefficients: LUNG .743949479, AGE .017200253, FEMALE \u0026minus;\u0026thinsp;.014520366, NX .011729544, LX .006233896, BUNX .006946385, LAC .033917083, OI \u0026minus;\u0026thinsp;.005749998, EI .304848752, HOSP \u0026minus;\u0026thinsp;.511221638. The LASSO graph is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e.The variables selected by LASSO were further analyzed using multifactor stepwise backward COX regression, resulting in the identification of eight variables: \u0026quot;LUNG, AGE, FEMALE, LX, LAC, OI, EI, and HOSP.\u0026quot; Detailed analysis findings are presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. A forest plot in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the hazard ratio (HR) values for each variable. Solid lung lesions, male gender, older age, higher lymphocyte ratio, elevated lactate levels, and endotracheal intubation were positively associated with increased risk of death (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), while female gender, hospitalization, and higher oxygenation index were negatively associated with the risk of death (HR\u0026thinsp;\u0026lt;\u0026thinsp;1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"424\" height=\"491\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e4. The construction of a Nomogram predictive model\u003c/p\u003e\n\u003cp\u003eThe LASSO\u0026thinsp;+\u0026thinsp;COX regression identified eight variables (\u0026quot;LUNG, AGE, FEMALE, LX, LAC, OI, EI, HOSP\u0026quot;) for inclusion in a predictive model with January and one year as the prediction time points. This model, depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, was used to interpret the column line graph as follows: For instance, considering the case of ID\u0026thinsp;=\u0026thinsp;9, a vertical line was drawn at the respective value of each variable to determine the score on the \u0026quot;Points\u0026quot; axis. The cumulative scores across all test indices were then calculated to derive the total points, which in turn were used to estimate the patient\u0026apos;s probability of mortality at 1 month and 1 year.\u003c/p\u003e\n\u003cp\u003e5. Assessment of the effectiveness of the Nomogram predictive model\u003c/p\u003e\n\u003cp\u003eThe model demonstrated strong predictive performance, with a C-index of 0.842 (95% CI: 0.807\u0026ndash;0.876) for the training set and 0.809 (95% CI: 0.742\u0026ndash;0.876) for the validation set. Furthermore, the model exhibited high accuracy in predicting the likelihood of death at 1 month, as evidenced by the training set AUC of 0.920 (95% CI: 0.880\u0026ndash;0.959) and the validation set AUC of 0.930 (95% CI: 0.855\u0026ndash;1.004). Similarly, for predicting the probability of death at 1 year, the model achieved an AUC of 0.941 (95% CI: 0.907\u0026ndash;0.975) in the training set and 0.947 (95% CI: 0.877\u0026ndash;1.017) in the validation set, indicating robust diagnostic performance. These findings are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe calibration curves for predicting the probability of death at both 1 month and 1 year exhibit a high degree of overlap between the training and validation cohorts, closely resembling the ideal curves. This alignment indicates that the model\u0026apos;s calibration is optimal, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe DCA curves for predicting the probability of death at 1 month and 1 year (Fig. 10and Fig. \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) demonstrate that both the training and validation groups exhibit the model positioned predominantly above the ALL and None lines across a broad spectrum. This positioning suggests that the model holds practical utility, indicating fair predictive value for implementation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCurrent research indicates that the novel coronavirus infection poses significant challenges to the human body beyond a typical acute viral illness\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The virus can severely compromise the human immune system, leading to persistent damage to the heart, lungs, and nervous system even after the acute phase has resolved. Some individuals may experience enduring symptoms such as palpitations, shortness of breath, dry cough, and dyspnea as sequelae of the infection\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Our study delved into the long-term survival outcomes of patients affected by the novel coronavirus, revealing that survival rates are influenced by factors such as gender, age, and underlying health conditions. Through comprehensive analysis of follow-up data, we gained valuable insights into the overall survival prognosis for elderly patients with severe COVID-19. Among the 199 elderly patients studied, males were predominant, with a higher proportion of severe cases observed in older age groups and among those with multiple comorbidities. Examination of mortality data revealed a mean survival time of 80.092 days (95% CI: 56.000\u0026ndash;104.185) and a median survival time of 22 days (95% CI: 17.000\u0026ndash;27.000), with 50% of deaths occurring within 22 days, 75% within 62 days, and 95% within 461 days. These findings underscore the long-lasting impact of novel coronavirus infection on patient survival beyond the acute phase.\u003c/p\u003e\n\u003cp\u003eThis study conducted a comprehensive analysis of patient diagnosis and treatment data from the emergency department and hospitalization. The diagnostic and treatment information was scrutinized and confirmed through various methodologies. The research revealed that factors such as lymphocyte ratio, age, gender, blood lactate levels, and oxygenation index significantly influence the prognosis of individuals with severe neocoronaryngitis\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Furthermore, our study confirmed that these factors not only impact short-term prognosis but also long-term survival outcomes. Specifically, our model focused on prognostic time points at 1 month and 1 year. Kaplan-Meier survival curves were plotted to illustrate the influence of these variables on overall survival at different time intervals. Notably, certain variables, like blood sodium levels, demonstrated a non-linear relationship with survival time. To address this, we optimized the data by calculating the absolute difference between the values and the mean. Subsequent statistical analyses were conducted on the optimized dataset to better align with clinical practice.\u003c/p\u003e\n\u003cp\u003eNomograms have demonstrated their validity and reliability across various medical disciplines\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. This allows for a more straightforward interpretation of survival prediction values, enhancing the practical utility of the model. In the prognostic model developed in this study, easily obtainable variables were utilized, enabling swift estimation of the probability of death at specific time points (1 month, 1 year). Noteworthy risk factors influencing survival included lung solid lesions, male gender, advanced age, lymphocyte ratio, lactate levels, and tracheal intubation, while female gender, hospitalization, and oxygenation index were identified as positive factors. Vigilant monitoring and prompt intervention, particularly by emergency healthcare providers, are crucial. Mitigating the risk of mortality in patients involves targeted interventions such as aggressive anti-infection measures for lung solidification, optimized oxygen therapy, correction of circulatory disturbances, enhancement of immune function, and prompt hospitalization. Evaluation of the model\u0026apos;s performance through metrics like the C-index, ROC curve, calibration curve, and decision curve analysis (DCA) revealed favorable predictive accuracy. The model\u0026apos;s efficacy, applicability, and reliability have been rigorously validated, underscoring its significant practical utility.\u003c/p\u003e\n\u003cp\u003eThe study has several limitations. Firstly, its retrospective nature may result in missing information on key study factors in the cases. Secondly, the samples were collected from the Northern Jiangsu People\u0026apos;s Hospital Affiliated to Yangzhou University, potentially leading to selection bias as patients with varying economic statuses may seek care at different healthcare facilities, including primary hospitals, or may not have access to higher-level medical centers. Additionally, due to the limited number of cases, the model utilized medical records from a single hospital, resulting in constraints on incorporating more parameters for enhanced model stability. Consequently, only a basic K-M survival analysis was conducted for specific inflammatory indexes, and comprehensive evaluation of long-term lung CT and lung function in patients with severe neocoronary deficiency was lacking. Moreover, the absence of data from multiple external and independent validation sources underscores the necessity for additional data. Insufficient data also hindered the assessment of long-term lung CT and lung function in critically ill neocoronary patients, emphasizing the need for more external validation data from diverse sources. Lastly, it is important to highlight that the predictive model aims to complement clinicians\u0026apos; clinical judgment by aiding in the implementation of preventive measures based on the likelihood of disease onset and progression. A robust prediction model holds significant clinical utility\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe study received approval from the Ethics Committee of Northern Jiangsu People\u0026apos;s Hospital Affiliated to Yangzhou University\u0026nbsp;and was registered in the China Clinical Trial Registry under the ethical number: 2025ky028. Informed consent to participate was obtained from all of the participants in the study. The study adhered to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors (Tao Jin and Lu Mingfeng) have reviewed the manuscript and approved this submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eData is provided within the manuscript or supplementary information files，Doctor TaoJin can be contacted (email-address:[email protected])to obtain access to the raw data analysed in your study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eAll authors declare no conflicts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eKey R \u0026amp; D Project (Social Development) of Yangzhou City in 2024 (YZ2024095)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u0026nbsp;\u003c/strong\u003eTao Jin: article writing, statistical analysis, graphing, and data organization; Lu Mingfeng: experimental design, research supervision, paper review, and financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eWe would like to thank our supervisor and colleagues in the department for their guidance and support during this research and thesis collaboration.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. WHO COVID-19 dashboard [EB/OL]. (2024-04-21). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.who.int/dashboards/covid19/cases? n\u0026thinsp;=\u0026thinsp;c\u003c/span\u003e\u003cspan address=\"https://data.who.int/dashboards/covid19/cases? n\u0026thinsp;=\u0026thinsp;c\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmati S, Tramunt B, Wargny M, et al. COVID-19 and Diabetes Outcomes: Rationale for and Updates from the CORONADO Study[J]. Curr Diab Rep. 2022;22(2):53\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNEUMANN⁃PODCZASKA A, AL⁃SAAD S R,KARBOWSKI L M, et al. COVID 19 clinical picture in the elderly population:a qualitative systematic review[J]. Aging Dis. 2020;11(4):988\u0026ndash;1008.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWANG, L,HE W B,YU X, M, et al. Coronavirus diseases 2019 in elderly patients: Characteristics and prognostic factors based on 4 weeks follow⁃up[J]. Infect. 2020;80(6):639\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDjorw\u0026eacute; S, Bousfiha A, ,Nzoyikorera N et al. Impact and prevalence of comorbidities and complications on the severity of COVID-19 in association with age, gender, obesity, and pre-existing smoking: A meta-analysis.[J].BioMedicine,2024,14(1):20\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang C, Wang Y, Li X, et al. Clinical Features of Patients Infected with 2019 Novel Coronavirus in Wuhan, China[J]. Lancet. 2020;395(10223):497\u0026ndash;506.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang D, Hu B, Hu C, et al. Clinical Characteristics of 138 Hospitalized Patients with 2019 Novel Coronavirus-infected Pneumonia in Wuhan, China[J]. JAMA. 2020;323(11):1061\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHill MA, Mantzoros C, Sowers JR, Commentary. COVID-19 in patients with diabetes[J]. Metabolism. 2020;107:154217.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJayakrishnan B, Nair P. COVID-19, Obstructive Airway Disease and Eosinophils: A complex interplay[J]. Sultan Qaboos Univ Med J. 2022;22(2):163\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRamos-Rincon JM, Buonaiuto V, Ricci M et al. Clinical characteristics and risk factors for mortality in very old patients hospitalized with COVID-19 in Spain[J]. J Gerontol Biol Sci Med Sci 2021,76(3): e28\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL L, E. R ER et al. HP05: Diagnostic and prognostic assessment in respiratory and hemodynamic changes related to prone position in COVID-19 patients[J].Clinical Neurophysiology,2022,135e2-e2.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChao RRQY, ,Di R et al. The Clinical Features and Prognostic Assessment of SARS-CoV-2 Infection-Induced Sepsis Among COVID-19 Patients in Shenzhen, China.[J].Frontiers in medicine,2020,7570853\u0026thinsp;\u0026ndash;\u0026thinsp;570853.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang D, Zhang C, Li X, et al. Thin-section computed tomography findings and longitudinal variations of the residual pulmonary sequelae after discharge in patients with COVID-19: a short-term follow-up study. Eur Radiol. 2021;31(9):7172\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eC M P,A I C, B R M et al. SARS-CoV-2 productively infects primary human immune system cells in vitro and in COVID-19 patients.[J].Journal of molecular cell biology,2022,14(4).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKartik K, Ratnaprashanthika R, M S C et al. Chest CT features and functional correlates of COVID-19 at 3 months and 12 months follow-up.[J].Clinical medicine (London, England),2023,23(5):467\u0026ndash;477.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJin Q, Ma W, ,Zhang W et al. Clinical and hematological characteristics of children infected with the omicron variant of SARS-CoV-2: role of the combination of the neutrophil: lymphocyte ratio and eosinophil count in distinguishing severe COVID-19[J].Frontiers in Pediatrics,2024,121305639-1305639.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMansouri HG, Darjiyani F, ,Robati KF et al. Exploring factors influencing COVID-19 severity: a matched case-control study.[J].European review for medical and pharmacological sciences,2024,28(21):4553\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNimra F, Ahmed SK, ,Muhammad RR, U F. Correlation between oxygen saturation of patient and severity index of Covid 19 pneumonia on CT.[J].JPMA. J Pak Med Assoc. 2023;73(1):60\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIzabela K, Paweł C, Patryk M et al. Factors influencing death in COVID-19 patients treated in the ICU: a single-centre, cross-sectional study.[J].Anaesthesiology intensive therapy,2022,54(2).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXiaoxue W, Jingliang L, ,Zixuan S et al. From past to future: Bibliometric analysis of global research productivity on nomogram (2000\u0026ndash;2021) [J].Frontiers in Public Health,2022,10997713-997713.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarco B, Nicola F, ,Alberto B. Nomograms in urologic oncology, advantages and disadvantages.[J].Current opinion in urology,2019,29(1):42\u0026ndash;51.\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-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Elderly, Severe COVID-19 patients, Survival prognosis, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-6898119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6898119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global outbreak of the novel coronavirus has been a significant public health crisis in recent years. Elderly patients with severe COVID \u0026minus;\u0026thinsp;19 are a special group with extremely high morbidity and mortality rates. Early identification of important factors influencing prognosis and implementation of intervention measures can significantly improve the prognosis of elderly patients with severe COVID \u0026minus;\u0026thinsp;19.A retrospective cohort study was conducted on elderly patients (\u0026ge;\u0026thinsp;60 years) with severe COVID-19 admitted to the emergency room and Emergency Intensive Care Unit (EICU) of North Jiangsu People's Hospital affiliated with Yangzhou University between December 2022 and January 2023. Patients were randomly divided into training and validation sets (8:2 ratio). Variable selection was performed using LASSO and multifactorial stepwise backward Cox regression. Cox regression was then utilized to construct survival probability plots at 1-month and 1-year. Model performance was assessed using calibration curves, time-dependent ROC curves, and decision curve analysis (DCA). The study included 199 elderly patients with severe COVID-19. The mean survival time in the mortality group was 80.092 days, with a median survival time of 22.000 days. The analysis identified gender (male), lung consolidation, age, lymphocyte ratio, lactate levels, and endotracheal intubation as factors associated with poorer survival prognosis (HR\u0026thinsp;\u0026gt;\u0026thinsp;1), while gender (female), hospitalization, and oxygenation index were associated with better prognosis (HR\u0026thinsp;\u0026lt;\u0026thinsp;1). A prediction model based on these factors demonstrated good predictive performance, with a C-index of 0.842 (training set) and 0.809 (validation set). The AUC values for the model were 0.920 (training set) and 0.930 (validation set) for 1-month mortality prediction, and 0.941 (training set) and 0.947 (validation set) for 1-year mortality prediction, indicating strong diagnostic accuracy. Calibration curves showed good model fit. DCA indicated the model's clinical utility. Clinical interventions targeting elderly severe COVID-19 patients, optimizing pulmonary infection control, enhancing oxygenation and circulation, and improving immune function (lymphocyte counts) may significantly improve outcomes in critically ill patients with severe COVID-19.\u003c/p\u003e","manuscriptTitle":"Survival Follow-up and Mortality Prediction Model for Elderly Severe COVID-19 Patients following the complete relaxation of pandemic restrictions in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-22 08:19:11","doi":"10.21203/rs.3.rs-6898119/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"230182623453999439203194931341794377695","date":"2026-05-18T13:51:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-04T13:07:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278064030065486238024046429018598500134","date":"2025-08-01T15:42:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"207235520277761372790357650535394675687","date":"2025-07-15T17:57:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-15T04:13:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T11:07:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-20T05:06:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-19T13:45:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-06-19T13:22:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2bda2d7-11d1-4b3d-956f-f06b44bd0e92","owner":[],"postedDate":"July 22nd, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"230182623453999439203194931341794377695","date":"2026-05-18T13:51:38+00:00","index":94,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-22T08:19:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-22 08:19:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6898119","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6898119","identity":"rs-6898119","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}