Copd Readmissions in the U.S.: Mortality, Length of Stay, and Healthcare Utilization Analysis from the National Readmission Database

Copd Readmissions in the U.S.: Mortality, Length of Stay, and Healthcare Utilization Analysis from the National Readmission Database | 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 Copd Readmissions in the U.S.: Mortality, Length of Stay, and Healthcare Utilization Analysis from the National Readmission Database Anandu Mathew Anto, Rayan Alataa, Balakrishnan Arivalagan, Sai Harsha Yendluri, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8876829/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity, mortality, and healthcare utilization in the United States with acute exacerbations being associated with high rates of early readmission, reflecting disease severity, comorbidity burden, and gaps in post-discharge care. This study aimed to evaluate the national burden of COPD readmissions in the United States using the 2022 National Readmission Database (NRD), with a focus on readmission rates, in-hospital mortality, healthcare utilization, and independent predictors of readmission, including socioeconomic and hospital-level factors. Methods We conducted a retrospective cohort study using the 2022 National Readmission Database, the largest all-payer inpatient readmission dataset in the United States. Adult patients aged ≥ 18 years hospitalized with a primary diagnosis of COPD were identified using ICD-10-CM codes. Primary outcomes included 30-day readmission rates, while secondary outcomes included in-hospital mortality during index and readmission hospitalizations, length of stay, hospital charges and costs, and predictors of readmission. Survey-weighted univariable and multivariable regression models were used to identify independent predictors of readmission. Results 222,347 index COPD hospitalizations were identified nationwide in 2022, of which 219,232 patients were discharged alive and included in the analysis. The mean age was 68.5 years, and 57.5% of patients were female. The overall 30-day all-cause readmission rate was 17.9%, corresponding to approximately 46,490 readmissions nationwide. In-hospital mortality during index admissions was 1.36%, compared with a substantially higher mortality rate of 4.18% during readmissions. In multivariable analyses, higher Charlson Comorbidity Index was the strongest predictor of readmission. Female sex and residence in higher-income neighborhoods were independently associated with lower odds of readmission, while admission to medium or large hospitals was associated with modestly higher readmission risk. Conclusion Almost one in five patients hospitalized for COPD experienced a 30-day readmission in 2022, with readmissions associated with significantly higher in-hospital mortality and substantial healthcare utilization. These findings highlight the need to target transitional care and interventions that address social determinants of health to reduce COPD readmissions and improve outcomes at the national level. Chronic obstructive pulmonary disease Readmission In-hospital mortality Healthcare utilization National Readmission Database Figures Figure 1 Figure 2 Introduction Chronic obstructive pulmonary disease is a common respiratory disease characterized by persistent respiratory symptoms and persistent airway and/or airway abnormalities resulting in major sequelae including chronic respiratory failure, lung cancer, and cardiovascular disease[ 1 , 2 ]. Multiple studies have worked on the prevalence of COPD and have found the prevalence to be in the order of millions, approximately 300–400 million individuals globally in 2018–2019. A study evaluating possible COPD prevalence by 2050 identified that there is an expected increase of 23%, reaching a global number of 592 million patients, likely due to a growing and ageing population that continues to get exposed to cigarette smoking, genetics, indoor air pollution, outdoor air pollution, occupational hazards and infections. COPD, being a chronic disease of significant prevalence, has a huge expected economic impact. In US, the cost attributable to COPD are expected to increase over the next 20 years approximately 40 billion dollars per year or 800.9 billion dollars projected cost[ 3 , 4 ]. A projection study reported that the direct cost related to COPD are expected to be15.43 trillion and exacerbations to be 15.6 billion in 2050, compared to 2025, a growth in the order of more than 500%[ 5 ] . In view of the significant burden of COPD and readmissions, we decided to evaluate the burden of readmissions in the United States in 2022 and to see if we could identify any social factors contributing to the readmissions, in hospital mortality, and the economic perspective of the hospital stays, Methodology We conducted a retrospective cohort study using the National Readmission Database (NRD) 2022, which is part of the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality [ 6 ]. The study included adults aged 18 and older who were hospitalized with a primary diagnosis of chronic obstructive pulmonary disease (COPD), identified by ICD-10-CM code J44. We excluded patients admitted for trauma, elective procedures, or those transferred between facilities. The first admission in 2022 with a primary diagnosis of COPD was considered the index admission. All non-traumatic admissions occurring within 30 days of discharge from the index admission were considered readmissions[ 7 ]. The NRD dataset extends from January 1st to December 31st of the same year. However, since we are evaluating 30-day readmissions, we had to exclude patients with an index admission in December, as readmissions are not captured in the database, which ends on December 31st. The database does not track the patient across the years. Patients who died in the index admission were excluded for readmission analysis. We collected demographic and clinical information from the NRD, including age, sex, and the income quartile for each patient's zip code. Insurance was grouped as Medicare, Medicaid, private, or self-pay. We classified residential areas by population size, from large urban centers with over 1 million residents to small metropolitan, micropolitan, and rural areas. Hospital features included bed size (small, medium, or large) and whether the hospital was a teaching facility. We assessed comorbidities using Deyo’s version of the Charlson Comorbidity Index.[ 8 ]. The primary outcome was in-hospital 30-day readmission rates. The secondary outcomes were(a) in-hospital mortality rate for index admissions, (b) readmission in-hospital mortality rate; (c) resource use associated with readmission: length of hospital stay, total hospitalization costs, and charges; and (d) independent risk factors for readmission[ 9 ]. In-hospital deaths were identified using the NRD’s DIED variable. The NRD provides length of stay and total charges directly; however, because charges represent billed rather than actual costs, actual costs were calculated using hospital-specific cost-to-charge ratios from the Centers for Medicare and Medicaid Services, as provided by HCUP[ 10 ]. Statistical analyses were conducted using STATA, version 19 (StataCorp, College Station, TX). To identify predictors of 30-day readmission, we first performed univariable survey-weighted logistic regression analyses. Variables with a p-value < 0.20 in univariable analysis were included in a multivariable survey-weighted logistic regression model to identify independent predictors of readmission while adjusting for potential confounders. We used survey-weighted percentages for categorical variables and weighted means for continuous variables. All statistical tests were two-sided, with a significance level of p < 0.05. No missingness was observed in the variables used for regression. The institutional review board was not required as the research project was exempt from approval because it is a retrospective review of already collected de-identified data[ 11 ]. Results Using the 2022 Nationwide Readmissions Database, an estimated 222,347 index hospitalizations for chronic obstructive pulmonary disease (COPD) were identified. Of these, 219,232 index admissions were discharged alive and included in the readmission analysis. Patient and hospital characteristics are summarized in Table 1 . The mean age of patients was 68.5 years, and 57.5% were female. A large proportion of patients resided in lower-income areas, with 38.1% living in the lowest neighborhood income quartile, followed by 28.7%, 20.5%, and 12.7% in progressively higher income quartiles. Table 1 Patient and hospital characteristics of index hospitalizations for chronic obstructive pulmonary disease Characteristic Value Mean age, years 68.5 Female sex, % 57.5 Neighborhood income quartile, % Lowest quartile 38.1 Second quartile 28.7 Third quartile 20.5 Highest quartile 12.7 Hospital location/teaching status, % Urban teaching 62.8 Urban non-teaching 17.3 Rural 20.0 Hospital bed size, % Small 26.6 Medium 28.0 Large 45.4 Most index admissions occurred in urban teaching hospitals (62.8%), followed by rural hospitals (20.0%) and urban non-teaching hospitals (17.3%). Nearly half of patients were treated at large hospitals (45.4%), with the remainder evenly split between medium (28.0%) and small hospitals (26.6%). Among patients discharged alive after the index hospitalization, the 30-day all-cause readmission rate was 17.9%. This corresponded to an estimated 46,490 readmissions nationwide. The in-hospital mortality rate during index admissions was 1.36%, corresponding to an estimated 3,031 deaths. In contrast, in-hospital mortality during readmissions was substantially higher at 4.18%, representing approximately 1,943 deaths during the readmission hospitalization. The mean length of stay for index admissions was 4.27 days. Across all readmissions, the total cumulative length of stay was 278,652 hospital days. Mean total hospital charges for index admissions were $ 44,267. Readmissions accounted for $ 3.15 billion in total hospital charges nationwide. In univariable analyses, female sex was associated with lower odds of readmission. Increasing Charlson comorbidity index was strongly associated with higher odds of readmission. Compared with patients from the lowest income quartile, those from higher income quartiles generally had lower odds of readmission. Admissions to medium and large hospitals were associated with higher odds of readmission compared with small hospitals. Urban teaching hospitals had higher odds of readmission compared with the reference hospital category. In the multivariable survey-weighted logistic regression model (Table 2 ), female sex remained independently associated with lower odds of readmission. A higher Charlson Comorbidity Index was the strongest predictor of readmission. Residence in higher-income zip codes remained protective, while treatment at medium- or large-sized hospitals remained associated with modestly increased odds of readmission (Fig. 2 ). Table 2 Multivariable predictors of 30-day all-cause readmission after index hospitalization for chronic obstructive pulmonary disease Variable Adjusted OR 95% CI P value Female sex 0.87 0.84–0.89 < 0.001 Charlson comorbidity index (per point) 1.13 1.12–1.14 < 0.001 Income quartile 2 vs 1 0.91 0.87–0.95 < 0.001 Income quartile 3 vs 1 0.96 0.91–1.00 0.07 Income quartile 4 vs 1 0.93 0.88–0.98 0.01 Medium hospital vs small 1.07 1.02–1.13 0.007 Large hospital vs small 1.10 1.05–1.16 < 0.001 Discussion In a nationwide analysis of over 222,000 index hospitalizations for Chronic Obstructive Pulmonary Disease (COPD) in the United States, we identified a significant burden of 30-day all-cause readmissions, mortality, and resource utilization. Our study found a 30-day readmission rate of 17.9%, a finding that is consistent with the range of 6% to 24% reported in a recent systematic review [ 12 ]. This emphasizes the persistent challenge of post-discharge care for COPD patients and highlights the need for effective transitional care strategies to prevent early rehospitalization. A particular finding of our study is the nearly threefold increase in inpatient mortality during readmission (4.18%) compared to the index hospitalization (1.36%). While our index mortality rate aligns with previous reports [ 13 ], the substantially higher risk of death upon readmission signals that these subsequent hospitalizations represent episodes of critical clinical deterioration. This finding emphasizes that readmissions are not merely a measure of healthcare utilization but are sentinel events associated with a significantly elevated risk of mortality, warranting aggressive management and proactive post-discharge monitoring. The most potent predictor of readmission in our multivariable model was the burden of comorbid disease, as quantified by the Charlson Comorbidity Index. Each one-point increase in the index was associated with a 13% increase in the odds of readmission. This finding is supported by the literature, which consistently identifies multimorbidity as a primary driver of adverse outcomes in COPD[ 14 , 15 ]. Comorbid conditions such as cardiovascular disease, diabetes, and renal disease complicate the management of COPD, increase symptom burden, and predispose patients to clinical instability following discharge. Our results reinforce the concept that effective COPD management cannot occur in isolation but requires an integrated, holistic approach that addresses the patient’s full spectrum of chronic conditions. Our analysis also sheds light on the significant impact of social determinants of health on COPD outcomes. We found a clear socioeconomic gradient, with patients in the lowest-income quartiles at significantly higher risk of readmission. This is in strong agreement with previous population-based studies demonstrating that low socioeconomic status, material deprivation, and other markers of social marginalization are independent risk factors for readmission and death in COPD patients [ 16 ]. These disparities may reflect a complex interplay of factors, including limited access to medications, poor health literacy, residence in areas with higher levels of environmental pollution, and barriers to outpatient follow-up care. Addressing these socioeconomic inequities is a critical, albeit challenging, component of reducing the national burden of COPD readmissions. Interestingly, our study identified female sex as an independent protective factor against 30-day readmission. This observation aligns with several other large-scale studies that have noted gender-based differences in COPD outcomes, with women often having lower mortality and readmission rates compared to men, despite a rising prevalence of the disease in women [ 17 , 18 ]. The underlying reasons for this disparity are not fully understood but may involve biological, behavioral, and healthcare-seeking differences between sexes. Furthermore, we observed that admission to medium or large hospitals was associated with slightly higher odds of readmission compared to small hospitals. This may be a reflection of case-mix, with larger, often urban teaching centers treating a more complex and severely ill patient population who are inherently at higher risk for readmission. Our study has several limitations, of which the key is that it is retrospective data, which is obtained based on the coding of clinical data, with high potential for misclassification. The database also lacks clinical data and hence it limits our analysis. Thus, we were unable to adjust for the clinical factors that might have contributed to the readmission. In addition, the NRD database does not link patients over the years, and hence it is not possible to follow a patient over different year. We focused on 30-day readmissions, and hence we had to exclude the admission of COPD in the month of December, as we will not have the data for readmissions in December because of the very fact. Conclusion COPD readmissions account for a huge mortality and morbidity risk for the patient and for health care system with huge expenses which we could cut down by significant investment in policy and social support for our community to prevent readmissions. Abbreviations • AHRQ Agency for Healthcare Research and Quality • CCR Cost–to–Charge Ratio • CI Confidence Interval • CMS Centers for Medicare and Medicaid Services • COPD Chronic Obstructive Pulmonary Disease • HCUP Healthcare Cost and Utilization Project • ICD 10–CM–International Classification of Diseases, Tenth Revision, Clinical Modification • LOS Length of Stay • NRD National Readmission Database • OR Odds Ratio • SES Socioeconomic Status • SID State Inpatient Databases • US United States Declarations Ethics approval and consent to participate - The National Readmission Database (NRD) is a publicly available, all-payer inpatient database containing de-identified patient information. Because the dataset does not include direct patient identifiers, this study was exempt from institutional review board oversight and informed consent requirements under U.S. federal regulations (45 CFR 46). All analyses were conducted in compliance with the HCUP Data Use Agreement and relevant data security guidelines. Consent for publication- Not Applicable Availability of data and materials - The data that support the findings of this study are available from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality (AHRQ), but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from HCUP upon reasonable request and completion of the required Data Use Agreement (DUA) . Competing interests -The authors declare that they have no competing interests Funding - None Authors' contributions - AMA conceived the study idea, designed the study, performed data analysis, interpreted the results, and was a major contributor to writing the manuscript. RA contributed to data interpretation and co-wrote the manuscript. MK provided overall guidance and supervision for the study and critically revised the manuscript for important intellectual content. BA and SHY contributed to data interpretation and manuscript revision. All authors read and approved the final manuscript. Acknowledgements - Parvati Pillai References R R-R, JB S. Chronic obstructive pulmonary disease with lung cancer and/or cardiovascular disease - PubMed. Proceedings of the American Thoracic Society. 12/01/2008, 5. 10.1513/pats.200807-075TH Boers E, Barrett M, Su JG, et al. Global Burden of COPD Through 2050. JAMA Netw Open. 2023;12/01:6. 10.1001/jamanetworkopen.2023.46598 . AJ G, SM R, CK F, TH S: The clinical and economic burden of chronic obstructive pulmonary disease in the USA - PubMed. ClinicoEconomics and outcomes research: CEOR. 06/17/2013,5. 10.2147/CEOR.S34321. Z Z SL. Projecting Long-term Health and Economic Burden of COPD in the United States - PubMed. Chest. 2021 Apr;159. 10.1016/j.chest.2020.09.255 . E B, A A, M B, et al.: Forecasting the Global Economic and Health Burden of COPD From 2025 Through 2050 - PubMed. Chest 2025 Oct, 168. 10.1016/j.chest.2025.03.029 Popp R, Savaliya BP, Raikot SR et al. Impact of Surgical Refusal on Overall Survival in Patients With Melanoma: A Comprehensive Analysis. Anticancer Res. 2025/03, 45. 10.21873/anticanres.17493 Vaqueriza Cubillo D, Beltran Herrera C, Dominguez Munoa M, et al. A multidisciplinary program based on early follow-up in a day hospital after heart failure hospitalizations. Reduction of 30-day readmissions in a population analysis over a 6-year period. Eur Heart J. 2022;10/03:43. 10.1093/eurheartj/ehac544.1032 . Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;06/01. 10.1016/0895–4356(92)90133-8 . Mathew D, Kosuru B, Agarwal S, Shrestha U, Sherif A. Impact of sleep apnoea on 30 day hospital readmission rate and cost in heart failure with reduced ejection fraction. ESC Heart Fail. 2023 Jun;9:10. 10.1002/ehf2.14430 . L H, E I, X L, J H, R H: Collection of economic data using UB-04s: Is it worth the effort? Evidence from two clinical trials - PubMed. PloS one. 11/17/2022, 17. 10.1371/journal.pone.0277685 Salim M, El-amir Z, Kichloo A, Wani F, Edigin E, Shaka H. Outcomes and Predictors of 30-Day Readmissions for Hyperthyroidism: A Nationwide Study. Endocrinol Metabolism. 2021;12/1:36. 10.3803/EnM.2021.1190 . H R, H Z, J W, H Z, W H, J L: Readmission rate for acute exacerbation of chronic obstructive pulmonary disease: A systematic review and meta-analysis - PubMed. Respiratory medicine. 2023 Jan, 206. 10.1016/j.rmed.2022.107090 X J, WC HXRS. M, H P: Trends in Readmission Rates, Hospital Charges, and Mortality for Patients With Chronic Obstructive Pulmonary Disease (COPD) in Florida From 2009 to 2014 - PubMed. Clinical therapeutics. 2018 Apr, 40. 10.1016/j.clinthera.2018.03.006 Buhr RG, Jackson NJ, Kominski GF, Dubinett SM, Ong MK, Mangione CM. Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices. BMC Health Serv Res. 2019 Oct;15:19. 10.1186/s12913-019-4549-4 . OW RC, JHB S. I, : Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) - A Systematic Review - PubMed. International journal of chronic obstructive pulmonary disease. 11/18/2023, 18. 10.2147/COPD.S418295 AS G. D T, S A, : Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: A population-based cohort study - PubMed. PloS one. 05/21/2019, 14. 10.1371/journal.pone.0216741 undefined: Sex differences in asthma and COPD hospital admission, readmission and mortality. BMJ Open Respiratory Research. 2025-03-17, 12. 10.1136/bmjresp-2024-002808 dT JP, C CC, A A-J HJA. BR C: Gender and COPD in patients attending a pulmonary clinic - PubMed. Chest 2005 Oct, 128. 10.1378/chest.128.4.2012 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 04 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 26 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Editor invited by journal 03 Mar, 2026 Submission checks completed at journal 27 Feb, 2026 First submitted to journal 27 Feb, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8876829","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613714127,"identity":"3636a06b-c618-4589-961a-6ddbe11442ba","order_by":0,"name":"Anandu Mathew Anto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYNCCigOkqWdsYDhDshbGNlK08LefPf7g47w7+fzSPYYffu6xYeCXPn4BrxaJM3mJjTO3PbOcOedYsmTPszQGyb6cAvzWHMgxbObddtjA4EbyMQaeA4cZDM7wJODVIX/+DVDLHJCWxDbGPwf+E9ZicANkSwPEFmaeAweAWtgP4NVieOON4cwZx54ZSM5IS5aWOZDMI9nDg98rcudzDD58qLljwC+RY/jxzQE7OX4e9gf49aADoBU8BqRpAQJSbRkFo2AUjILhDgCRMUq4NcIjAAAAAABJRU5ErkJggg==","orcid":"","institution":"BronxCare Health System Bronx","correspondingAuthor":true,"prefix":"","firstName":"Anandu","middleName":"Mathew","lastName":"Anto","suffix":""},{"id":613714128,"identity":"6f0656e7-582d-45d8-9e05-087e1ce410e1","order_by":1,"name":"Rayan Alataa","email":"","orcid":"","institution":"BronxCare Health System Bronx","correspondingAuthor":false,"prefix":"","firstName":"Rayan","middleName":"","lastName":"Alataa","suffix":""},{"id":613714129,"identity":"2887660a-ac14-4860-bbfd-f55fa5ff42da","order_by":2,"name":"Balakrishnan Arivalagan","email":"","orcid":"","institution":"BronxCare Health System Bronx","correspondingAuthor":false,"prefix":"","firstName":"Balakrishnan","middleName":"","lastName":"Arivalagan","suffix":""},{"id":613714130,"identity":"9177da9f-9cf2-4f88-bb6c-cbe0114826f0","order_by":3,"name":"Sai Harsha Yendluri","email":"","orcid":"","institution":"BronxCare Health System Bronx","correspondingAuthor":false,"prefix":"","firstName":"Sai","middleName":"Harsha","lastName":"Yendluri","suffix":""},{"id":613714131,"identity":"2df2074b-bf1e-4f2d-8291-e806bc2e34c0","order_by":4,"name":"Misbahuddhin Khaja","email":"","orcid":"","institution":"BronxCare Health System Bronx","correspondingAuthor":false,"prefix":"","firstName":"Misbahuddhin","middleName":"","lastName":"Khaja","suffix":""}],"badges":[],"createdAt":"2026-02-14 04:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8876829/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8876829/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105720444,"identity":"36673851-3e4f-499c-abfd-fa69bbdb4d16","added_by":"auto","created_at":"2026-03-30 09:30:31","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52170,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of cohort selection from the 2022 Nationwide Readmissions Database\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8876829/v1/6e9be8edc1b3ec019872cc8f.jpg"},{"id":105729279,"identity":"d1ca7f3c-36d2-44f6-8b3a-2ad762bc8894","added_by":"auto","created_at":"2026-03-30 11:14:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78733,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of multivariable predictors of 30-day all-cause readmission after COPD hospitalization\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8876829/v1/aa9f6acdc1dc890b4cc0b94f.jpg"},{"id":105730891,"identity":"89e7cd30-3125-4046-b9fc-e67650778dc4","added_by":"auto","created_at":"2026-03-30 11:26:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":615007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8876829/v1/b8ee5051-9d7f-4a7b-b448-3e9b247c3907.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eCopd Readmissions in the U.S.: Mortality, Length of Stay, and Healthcare Utilization Analysis from the National Readmission Database\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease is a common respiratory disease characterized by persistent respiratory symptoms and persistent airway and/or airway abnormalities resulting in major sequelae including chronic respiratory failure, lung cancer, and cardiovascular disease[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Multiple studies have worked on the prevalence of COPD and have found the prevalence to be in the order of millions, approximately 300\u0026ndash;400\u0026nbsp;million individuals globally in 2018\u0026ndash;2019. A study evaluating possible COPD prevalence by 2050 identified that there is an expected increase of 23%, reaching a global number of 592\u0026nbsp;million patients, likely due to a growing and ageing population that continues to get exposed to cigarette smoking, genetics, indoor air pollution, outdoor air pollution, occupational hazards and infections. COPD, being a chronic disease of significant prevalence, has a huge expected economic impact. In US, the cost attributable to COPD are expected to increase over the next 20 years approximately 40\u0026nbsp;billion dollars per year or 800.9\u0026nbsp;billion dollars projected cost[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A projection study reported that the direct cost related to COPD are expected to be15.43 trillion and exacerbations to be 15.6\u0026nbsp;billion in 2050, compared to 2025, a growth in the order of more than 500%[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eIn view of the significant burden of COPD and readmissions, we decided to evaluate the burden of readmissions in the United States in 2022 and to see if we could identify any social factors contributing to the readmissions, in hospital mortality, and the economic perspective of the hospital stays,\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eWe conducted a retrospective cohort study using the National Readmission Database (NRD) 2022, which is part of the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The study included adults aged 18 and older who were hospitalized with a primary diagnosis of chronic obstructive pulmonary disease (COPD), identified by ICD-10-CM code J44. We excluded patients admitted for trauma, elective procedures, or those transferred between facilities. The first admission in 2022 with a primary diagnosis of COPD was considered the index admission. All non-traumatic admissions occurring within 30 days of discharge from the index admission were considered readmissions[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The NRD dataset extends from January 1st to December 31st of the same year. However, since we are evaluating 30-day readmissions, we had to exclude patients with an index admission in December, as readmissions are not captured in the database, which ends on December 31st. The database does not track the patient across the years. Patients who died in the index admission were excluded for readmission analysis.\u003c/p\u003e \u003cp\u003eWe collected demographic and clinical information from the NRD, including age, sex, and the income quartile for each patient's zip code. Insurance was grouped as Medicare, Medicaid, private, or self-pay. We classified residential areas by population size, from large urban centers with over 1\u0026nbsp;million residents to small metropolitan, micropolitan, and rural areas. Hospital features included bed size (small, medium, or large) and whether the hospital was a teaching facility. We assessed comorbidities using Deyo\u0026rsquo;s version of the Charlson Comorbidity Index.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary outcome was in-hospital 30-day readmission rates. The secondary outcomes were(a) in-hospital mortality rate for index admissions, (b) readmission in-hospital mortality rate; (c) resource use associated with readmission: length of hospital stay, total hospitalization costs, and charges; and (d) independent risk factors for readmission[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn-hospital deaths were identified using the NRD\u0026rsquo;s DIED variable. The NRD provides length of stay and total charges directly; however, because charges represent billed rather than actual costs, actual costs were calculated using hospital-specific cost-to-charge ratios from the Centers for Medicare and Medicaid Services, as provided by HCUP[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using STATA, version 19 (StataCorp, College Station, TX). To identify predictors of 30-day readmission, we first performed univariable survey-weighted logistic regression analyses. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.20 in univariable analysis were included in a multivariable survey-weighted logistic regression model to identify independent predictors of readmission while adjusting for potential confounders. We used survey-weighted percentages for categorical variables and weighted means for continuous variables. All statistical tests were two-sided, with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. No missingness was observed in the variables used for regression.\u003c/p\u003e \u003cp\u003eThe institutional review board was not required as the research project was exempt from approval because it is a retrospective review of already collected de-identified data[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eUsing the 2022 Nationwide Readmissions Database, an estimated 222,347 index hospitalizations for chronic obstructive pulmonary disease (COPD) were identified. Of these, 219,232 index admissions were discharged alive and included in the readmission analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatient and hospital characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of patients was 68.5 years, and 57.5% were female. A large proportion of patients resided in lower-income areas, with 38.1% living in the lowest neighborhood income quartile, followed by 28.7%, 20.5%, and 12.7% in progressively higher income quartiles.\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\u003ePatient and hospital characteristics of index hospitalizations for chronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeighborhood income quartile, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLowest quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecond quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThird quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighest quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital location/teaching status, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban non-teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHospital bed size, %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMost index admissions occurred in urban teaching hospitals (62.8%), followed by rural hospitals (20.0%) and urban non-teaching hospitals (17.3%). Nearly half of patients were treated at large hospitals (45.4%), with the remainder evenly split between medium (28.0%) and small hospitals (26.6%).\u003c/p\u003e \u003cp\u003eAmong patients discharged alive after the index hospitalization, the 30-day all-cause readmission rate was 17.9%. This corresponded to an estimated 46,490 readmissions nationwide. The in-hospital mortality rate during index admissions was 1.36%, corresponding to an estimated 3,031 deaths. In contrast, in-hospital mortality during readmissions was substantially higher at 4.18%, representing approximately 1,943 deaths during the readmission hospitalization.\u003c/p\u003e \u003cp\u003eThe mean length of stay for index admissions was 4.27 days. Across all readmissions, the total cumulative length of stay was 278,652 hospital days. Mean total hospital charges for index admissions were \u003cspan\u003e$\u003c/span\u003e44,267. Readmissions accounted for \u003cspan\u003e$\u003c/span\u003e3.15\u0026nbsp;billion in total hospital charges nationwide.\u003c/p\u003e \u003cp\u003eIn univariable analyses, female sex was associated with lower odds of readmission. Increasing Charlson comorbidity index was strongly associated with higher odds of readmission. Compared with patients from the lowest income quartile, those from higher income quartiles generally had lower odds of readmission. Admissions to medium and large hospitals were associated with higher odds of readmission compared with small hospitals. Urban teaching hospitals had higher odds of readmission compared with the reference hospital category. In the multivariable survey-weighted logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), female sex remained independently associated with lower odds of readmission. A higher Charlson Comorbidity Index was the strongest predictor of readmission. Residence in higher-income zip codes remained protective, while treatment at medium- or large-sized hospitals remained associated with modestly increased odds of readmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" 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\u003eMultivariable predictors of 30-day all-cause readmission after index hospitalization for chronic obstructive pulmonary disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted OR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale sex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u0026ndash;0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson comorbidity index (per point)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u0026ndash;1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome quartile 2 vs 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome quartile 3 vs 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome quartile 4 vs 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026ndash;0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium hospital vs small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge hospital vs small\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.05\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn a nationwide analysis of over 222,000 index hospitalizations for Chronic Obstructive Pulmonary Disease (COPD) in the United States, we identified a significant burden of 30-day all-cause readmissions, mortality, and resource utilization. Our study found a 30-day readmission rate of 17.9%, a finding that is consistent with the range of 6% to 24% reported in a recent systematic review [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This emphasizes the persistent challenge of post-discharge care for COPD patients and highlights the need for effective transitional care strategies to prevent early rehospitalization.\u003c/p\u003e \u003cp\u003eA particular finding of our study is the nearly threefold increase in inpatient mortality during readmission (4.18%) compared to the index hospitalization (1.36%). While our index mortality rate aligns with previous reports [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], the substantially higher risk of death upon readmission signals that these subsequent hospitalizations represent episodes of critical clinical deterioration. This finding emphasizes that readmissions are not merely a measure of healthcare utilization but are sentinel events associated with a significantly elevated risk of mortality, warranting aggressive management and proactive post-discharge monitoring.\u003c/p\u003e \u003cp\u003eThe most potent predictor of readmission in our multivariable model was the burden of comorbid disease, as quantified by the Charlson Comorbidity Index. Each one-point increase in the index was associated with a 13% increase in the odds of readmission. This finding is supported by the literature, which consistently identifies multimorbidity as a primary driver of adverse outcomes in COPD[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Comorbid conditions such as cardiovascular disease, diabetes, and renal disease complicate the management of COPD, increase symptom burden, and predispose patients to clinical instability following discharge. Our results reinforce the concept that effective COPD management cannot occur in isolation but requires an integrated, holistic approach that addresses the patient\u0026rsquo;s full spectrum of chronic conditions.\u003c/p\u003e \u003cp\u003eOur analysis also sheds light on the significant impact of social determinants of health on COPD outcomes. We found a clear socioeconomic gradient, with patients in the lowest-income quartiles at significantly higher risk of readmission. This is in strong agreement with previous population-based studies demonstrating that low socioeconomic status, material deprivation, and other markers of social marginalization are independent risk factors for readmission and death in COPD patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These disparities may reflect a complex interplay of factors, including limited access to medications, poor health literacy, residence in areas with higher levels of environmental pollution, and barriers to outpatient follow-up care. Addressing these socioeconomic inequities is a critical, albeit challenging, component of reducing the national burden of COPD readmissions.\u003c/p\u003e \u003cp\u003eInterestingly, our study identified female sex as an independent protective factor against 30-day readmission. This observation aligns with several other large-scale studies that have noted gender-based differences in COPD outcomes, with women often having lower mortality and readmission rates compared to men, despite a rising prevalence of the disease in women [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The underlying reasons for this disparity are not fully understood but may involve biological, behavioral, and healthcare-seeking differences between sexes. Furthermore, we observed that admission to medium or large hospitals was associated with slightly higher odds of readmission compared to small hospitals. This may be a reflection of case-mix, with larger, often urban teaching centers treating a more complex and severely ill patient population who are inherently at higher risk for readmission.\u003c/p\u003e \u003cp\u003eOur study has several limitations, of which the key is that it is retrospective data, which is obtained based on the coding of clinical data, with high potential for misclassification. The database also lacks clinical data and hence it limits our analysis. Thus, we were unable to adjust for the clinical factors that might have contributed to the readmission. In addition, the NRD database does not link patients over the years, and hence it is not possible to follow a patient over different year. We focused on 30-day readmissions, and hence we had to exclude the admission of COPD in the month of December, as we will not have the data for readmissions in December because of the very fact.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eCOPD readmissions account for a huge mortality and morbidity risk for the patient and for health care system with huge expenses which we could cut down by significant investment in policy and social support for our community to prevent readmissions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; AHRQ\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAgency for Healthcare Research and Quality\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCost\u0026ndash;to\u0026ndash;Charge Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; CMS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCenters for Medicare and Medicaid Services\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; COPD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChronic Obstructive Pulmonary Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HCUP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthcare Cost and Utilization Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; ICD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e10\u0026ndash;CM\u0026ndash;International Classification of Diseases, Tenth Revision, Clinical Modification\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LOS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLength of Stay\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; NRD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Readmission Database\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; OR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocioeconomic Status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eState Inpatient Databases\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; US\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e-\u0026nbsp;The National Readmission Database (NRD) is a publicly available, all-payer inpatient database containing de-identified patient information. Because the dataset does not include direct patient identifiers, this study was exempt from institutional review board oversight and informed consent requirements under U.S. federal regulations (45 CFR 46). All analyses were conducted in compliance with the HCUP Data Use Agreement and relevant data security guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication-\u003c/strong\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e- The data that support the findings of this study are available from the Healthcare Cost and Utilization Project (HCUP), Agency for Healthcare Research and Quality (AHRQ), but restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available. Data are, however, available from HCUP upon reasonable request and completion of the required Data Use Agreement (DUA) .\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e -The authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e- None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e- AMA conceived the study idea, designed the study, performed data analysis, interpreted the results, and was a major contributor to writing the manuscript. RA contributed to data interpretation and co-wrote the manuscript. MK provided overall guidance and supervision for the study and critically revised the manuscript for important intellectual content. BA and SHY contributed to data interpretation and manuscript revision. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e- Parvati Pillai\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eR R-R, JB S. Chronic obstructive pulmonary disease with lung cancer and/or cardiovascular disease - PubMed. Proceedings of the American Thoracic Society. 12/01/2008, 5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1513/pats.200807-075TH\u003c/span\u003e\u003cspan address=\"10.1513/pats.200807-075TH\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers E, Barrett M, Su JG, et al. Global Burden of COPD Through 2050. JAMA Netw Open. 2023;12/01:6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2023.46598\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.46598\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAJ G, SM R, CK F, TH S: The clinical and economic burden of chronic obstructive pulmonary disease in the USA - PubMed. ClinicoEconomics and outcomes research: CEOR. 06/17/2013,5. 10.2147/CEOR.S34321.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ Z SL. Projecting Long-term Health and Economic Burden of COPD in the United States - PubMed. Chest. 2021 Apr;159. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2020.09.255\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2020.09.255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE B, A A, M B, et al.: Forecasting the Global Economic and Health Burden of COPD From 2025 Through 2050 - PubMed. Chest 2025 Oct, 168. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chest.2025.03.029\u003c/span\u003e\u003cspan address=\"10.1016/j.chest.2025.03.029\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePopp R, Savaliya BP, Raikot SR et al. Impact of Surgical Refusal on Overall Survival in Patients With Melanoma: A Comprehensive Analysis. Anticancer Res. 2025/03, 45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21873/anticanres.17493\u003c/span\u003e\u003cspan address=\"10.21873/anticanres.17493\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVaqueriza Cubillo D, Beltran Herrera C, Dominguez Munoa M, et al. A multidisciplinary program based on early follow-up in a day hospital after heart failure hospitalizations. Reduction of 30-day readmissions in a population analysis over a 6-year period. Eur Heart J. 2022;10/03:43. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehac544.1032\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehac544.1032\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;06/01. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0895\u0026ndash;4356(92)90133-8\u003c/span\u003e\u003cspan address=\"10.1016/0895\u0026ndash;4356(92)90133-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMathew D, Kosuru B, Agarwal S, Shrestha U, Sherif A. Impact of sleep apnoea on 30 day hospital readmission rate and cost in heart failure with reduced ejection fraction. ESC Heart Fail. 2023 Jun;9:10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ehf2.14430\u003c/span\u003e\u003cspan address=\"10.1002/ehf2.14430\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL H, E I, X L, J H, R H: Collection of economic data using UB-04s: Is it worth the effort? Evidence from two clinical trials - PubMed. PloS one. 11/17/2022, 17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0277685\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0277685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalim M, El-amir Z, Kichloo A, Wani F, Edigin E, Shaka H. Outcomes and Predictors of 30-Day Readmissions for Hyperthyroidism: A Nationwide Study. Endocrinol Metabolism. 2021;12/1:36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3803/EnM.2021.1190\u003c/span\u003e\u003cspan address=\"10.3803/EnM.2021.1190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH R, H Z, J W, H Z, W H, J L: Readmission rate for acute exacerbation of chronic obstructive pulmonary disease: A systematic review and meta-analysis - PubMed. Respiratory medicine. 2023 Jan, 206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rmed.2022.107090\u003c/span\u003e\u003cspan address=\"10.1016/j.rmed.2022.107090\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX J, WC HXRS. M, H P: Trends in Readmission Rates, Hospital Charges, and Mortality for Patients With Chronic Obstructive Pulmonary Disease (COPD) in Florida From 2009 to 2014 - PubMed. Clinical therapeutics. 2018 Apr, 40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinthera.2018.03.006\u003c/span\u003e\u003cspan address=\"10.1016/j.clinthera.2018.03.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuhr RG, Jackson NJ, Kominski GF, Dubinett SM, Ong MK, Mangione CM. Comorbidity and thirty-day hospital readmission odds in chronic obstructive pulmonary disease: a comparison of the Charlson and Elixhauser comorbidity indices. BMC Health Serv Res. 2019 Oct;15:19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-019-4549-4\u003c/span\u003e\u003cspan address=\"10.1186/s12913-019-4549-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOW RC, JHB S. I, : Predictors of Readmission, for Patients with Chronic Obstructive Pulmonary Disease (COPD) - A Systematic Review - PubMed. International journal of chronic obstructive pulmonary disease. 11/18/2023, 18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/COPD.S418295\u003c/span\u003e\u003cspan address=\"10.2147/COPD.S418295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAS G. D T, S A, : Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: A population-based cohort study - PubMed. PloS one. 05/21/2019, 14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0216741\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0216741\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eundefined: Sex differences in asthma and COPD hospital admission, readmission and mortality. BMJ Open Respiratory Research. 2025-03-17, 12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjresp-2024-002808\u003c/span\u003e\u003cspan address=\"10.1136/bmjresp-2024-002808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edT JP, C CC, A A-J HJA. BR C: Gender and COPD in patients attending a pulmonary clinic - PubMed. Chest 2005 Oct, 128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1378/chest.128.4.2012\u003c/span\u003e\u003cspan address=\"10.1378/chest.128.4.2012\" 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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chronic obstructive pulmonary disease, Readmission, In-hospital mortality, Healthcare utilization, National Readmission Database","lastPublishedDoi":"10.21203/rs.3.rs-8876829/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8876829/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic obstructive pulmonary disease (COPD) is a leading cause of morbidity, mortality, and healthcare utilization in the United States with acute exacerbations being associated with high rates of early readmission, reflecting disease severity, comorbidity burden, and gaps in post-discharge care. This study aimed to evaluate the national burden of COPD readmissions in the United States using the 2022 National Readmission Database (NRD), with a focus on readmission rates, in-hospital mortality, healthcare utilization, and independent predictors of readmission, including socioeconomic and hospital-level factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using the 2022 National Readmission Database, the largest all-payer inpatient readmission dataset in the United States. Adult patients aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years hospitalized with a primary diagnosis of COPD were identified using ICD-10-CM codes. Primary outcomes included 30-day readmission rates, while secondary outcomes included in-hospital mortality during index and readmission hospitalizations, length of stay, hospital charges and costs, and predictors of readmission. Survey-weighted univariable and multivariable regression models were used to identify independent predictors of readmission.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e222,347 index COPD hospitalizations were identified nationwide in 2022, of which 219,232 patients were discharged alive and included in the analysis. The mean age was 68.5 years, and 57.5% of patients were female. The overall 30-day all-cause readmission rate was 17.9%, corresponding to approximately 46,490 readmissions nationwide. In-hospital mortality during index admissions was 1.36%, compared with a substantially higher mortality rate of 4.18% during readmissions. In multivariable analyses, higher Charlson Comorbidity Index was the strongest predictor of readmission. Female sex and residence in higher-income neighborhoods were independently associated with lower odds of readmission, while admission to medium or large hospitals was associated with modestly higher readmission risk.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlmost one in five patients hospitalized for COPD experienced a 30-day readmission in 2022, with readmissions associated with significantly higher in-hospital mortality and substantial healthcare utilization. These findings highlight the need to target transitional care and interventions that address social determinants of health to reduce COPD readmissions and improve outcomes at the national level.\u003c/p\u003e","manuscriptTitle":"Copd Readmissions in the U.S.: Mortality, Length of Stay, and Healthcare Utilization Analysis from the National Readmission Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-30 09:30:26","doi":"10.21203/rs.3.rs-8876829/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"163094585730561571614213302716939494979","date":"2026-04-04T20:44:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-04T15:59:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144951453098303806862584577953866595846","date":"2026-04-02T20:25:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-26T09:10:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T09:20:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T06:35:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T19:20:59+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-02-27T12:19:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c0b3cbb-cfac-4a30-8ab4-c0adc09e12a1","owner":[],"postedDate":"March 30th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T09:30:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-30 09:30:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8876829","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8876829","identity":"rs-8876829","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}