Developing a DRG framework for COPD patients: application of the E-CHAID algorithm

Developing a DRG framework for COPD patients: application of the E-CHAID algorithm | 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 Developing a DRG framework for COPD patients: application of the E-CHAID algorithm Minglu Mo, Jifang Yang, Yunqiang Fan, Dejian Hou, Ganggang Su, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9159919/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The chronic obstructive pulmonary disease(COPD) is the third leading cause of death worldwide. It is expected to impose a global direct medical cost of up to USD 24.35 trillion, creates a heavy economic burden for patients, families, and society. This study aimed to develop the diagnosis-related groups (DRG) scheme for patients with chronic obstructive pulmonary disease (COPD) that better reflects clinical practice. It also aimed to provide evidence for setting inpatient cost standards and promoting DRG-based medical insurance payment reform. Methods A total of 8,054 COPD patients admitted to a tertiary hospital in Sichuan Province of China from 2021 to 2025 were included. Medical record front-page data were collected retrospectively. A non-parametric rank-sum test and multiple linear regression were used to identify factors associated with hospitalization costs. An E-CHAID decision tree was then used to construct a DRG case-mix model for COPD patients. Payment standards and upper limits for cost control were calculated for each DRG group. Results The results showed that age, sex, admission status, length of stay, discharge status, rescue treatment, use of antibiotics, use of invasive mechanical ventilation, use of noninvasive ventilation, and surgical treatment were associated with hospitalization costs in COPD patients. Five variables were selected as classification nodes: length of stay, antibiotic use, surgical treatment, rescue treatment, and invasive mechanical ventilation. The E-CHAID decision tree divided patients into eight DRG groups. The reduction in between-group variance was 0.55. The coefficient of variation of hospitalization costs ranged from 0.21 to 0.39 across groups, indicating reasonable grouping. Conclusion The DRG scheme developed in this study reflects the level of medical resource use among COPD patients. It may provide evidence for optimizing DRG grouping and setting standard payment levels in medical insurance payment reform. chronic obstructive pulmonary disease diagnosis-related groups E-CHAID algorithm decision tree Figures Figure 1 Background Chronic obstructive pulmonary disease (COPD) is a disease characterized by persistent respiratory symptoms and airflow limitation caused by abnormalities of the airways and/or alveoli [ 1 ] . COPD is the third leading cause of death worldwide. The number of COPD cases is projected to increase by 23.3% by 2050. By 2060, annual global deaths from COPD may exceed 5.4 million. The disease is expected to impose a global direct medical cost of up to USD 24.35 trillion [ 2 – 4 ] . In China, the prevalence of COPD among people aged 40 years and older has reached 13.7%. Nearly 100 million individuals are affected. This creates a heavy economic burden for patients, families, and society [ 5 ] . To control the rapid growth of medical expenditure, the National Healthcare Security Administration of China released the China Healthcare Security Diagnosis-Related Groups (CHS-DRG) scheme in 2019 [ 6 ] . Pilot reforms of DRG-based medical insurance payment were then gradually implemented in several cities. DRGs are a modern case-mix classification system. Patients are grouped according to major diagnosis, treatment methods, and disease severity. Each group is assigned a payment standard and a disease weight. This system can help control medical costs, evaluate healthcare quality, optimize resource allocation, and reduce the length of hospital stay [ 7 – 10 ] . Therefore, more refined DRG grouping is particularly important. The E-CHAID model is a decision tree algorithm based on chi-square automatic interaction detection. It can identify key influencing factors and create hierarchical subgroups automatically. Compared with the traditional CHAID method, E-CHAID has stronger segmentation and stratification ability. It is also more stable and detailed than the classical CART algorithm [ 11 ][ 12 ] . In this study, univariate analysis and multiple linear regression were used to identify factors associated with hospitalization costs in COPD patients. An E-CHAID decision tree was then applied to construct DRG groups and estimate reference standards for hospitalization costs. The aim was to explore a more reasonable case-mix classification scheme and provide evidence for better control of hospitalization costs for COPD patients in the local healthcare system. Methods Data Source and Inclusion/Exclusion Criteria Patients discharged from a tertiary Grade A hospital in Sichuan Province between January 1, 2021 and December 31, 2025 were screened. Eligible cases had a principal discharge diagnosis of COPD (ICD-10 code category J44). Data were extracted from the medical record front page and entered into a study database. The exclusion criteria were as follows: (1) missing or incomplete information; (2) length of stay 60 days; (3) hospitalization costs below the 2.5th percentile (P2.5) or above the 97.5th percentile (P97.5). A total of 8,636 cases were initially identified. Among them, 19 cases were excluded due to incomplete information, 137 cases due to a length of stay 60 days, and 426 cases due to hospitalization costs below P2.5 or above P97.5. Finally, 8,054 valid cases were included in the analysis, with an inclusion rate of 93.26%. Statistical Analysis Data were analyzed using SPSS 26.0. Categorical variables were expressed as percentages. Hospitalization costs were tested for normality; as they were not normally distributed, costs were reported as medians (M) with interquartile ranges (P25, P75). Nonparametric rank-sum tests (Kruskal-Wallis or Wilcoxon test) were used for univariate analysis of factors affecting hospitalization costs in COPD patients. Since hospitalization costs are influenced by the Consumer Price Index (CPI), costs were adjusted using the healthcare-related CPI for Sichuan Province from the China Statistical Yearbook 2021–2024 [ 13 ] . Variables with P < 0.05 in univariate analysis were included in multiple linear regression. Hospitalization costs showed right-skewed distribution and were log-transformed using the natural logarithm for multivariate analysis. Factors with P < 0.05 were considered significant. Variance inflation factor (VIF) < 5 indicated no multicollinearity among independent variables, suggesting good model fit. The significant factors identified were used as classification nodes in an E-CHAID decision tree to construct the DRG grouping model. The maximum tree depth was set to three layers, with at least 100 cases in the parent node and 50 cases in each child node. The significance level for node splitting was set at α = 0.05, and cross-validation was performed for appropriate pruning of the model [ 14 ] . Evaluation Indicators The rationality of DRG grouping was assessed based on between-group heterogeneity and within-group homogeneity. Reduction in variance (RIV) was used to evaluate between-group heterogeneity; a larger RIV indicates stronger heterogeneity and better grouping. Within-group homogeneity was assessed using the coefficient of variation (CV = standard deviation / mean); a smaller CV indicates lower within-group variation and better grouping. In this study, RIV > 0.4 and CV < 1.0 were considered indicative of satisfactory grouping. The median hospitalization cost of each DRG group was defined as the standard cost. The cost upper limit was calculated as P75 + 1.5 × IQR; cases exceeding this limit were considered outliers. The weight of each DRG group was calculated as the average hospitalization cost of the group divided by the overall average cost. A higher weight indicates greater medical resource consumption [ 15 ] . Results Basic Characteristics A total of 8,054 COPD patients were included in this study, of whom 5,782 (71.79%) were male and 2,272 (28.21%) were female. The mean age was 74.99 years, with a median age of 76 years (P25–P75: 70–81 years). The majority of patients (61.53%) were aged 66–84 years. The average length of stay was 9 days, with a median of 8 days (P25–P75: 7–11 days). The mean hospitalization cost per patient was 6,051.45 ± 2,525.94 CNY, with a median of 5,529.00 CNY (P25–P75: 4,343.75–7,123.25 CNY). Detailed demographic and clinical characteristics are shown in Table 1 . Univariate Analysis of Factors Affecting Hospitalization Costs Based on consultation with respiratory medicine experts and relevant literature, potential factors influencing hospitalization costs were included as independent variables. These factors were: age, sex, type of medical insurance, admission type, length of stay, discharge status, rescue treatment, antibiotic use, use of invasive mechanical ventilation, use of noninvasive ventilation, surgical treatment, presence of complications/comorbidities, and traditional Chinese medicine treatment. Univariate analysis showed that all factors except type of medical insurance (P = 0.29) and presence of complications/comorbidities (P = 0.200) were significantly associated with hospitalization costs in COPD patients (all P < 0.001). Detailed results are provided in Table 1 . Table 1 Univariate Analysis of Hospitalization Costs in COPD Patients by Different Characteristics Variables n(%) Total Hospitalization Costs (CNY) Z / H P Value P25 M P75 Age (years) ≤ 70 2223(27.60) 4204.00 5292.00 6698.00 37.933 <0.001 71–84 4726(58.68) 4420.75 5623.00 7161.50 ≥ 85 1105(13.72) 4282.50 5636.00 7558.00 Sex Male 5782(71.79) 4384.50 5625.00 7259.50 −5.638 <0.001 Female 2272(28.21) 4253.00 5301.00 6754.50 Type of medical insurance Urban Employee Insurance 656(8.15) 4475.75 5803.00 7420.75 7.101 0.29 Urban Resident Insurance 7368(91.48) 4335.00 5510.00 7087.00 Other 30(0.37) 4804.50 5375.00 7124.75 Admission Type General 1624(20.16) 4295.00 5392.00 6906.25 209.947 <0.001 Critical 179(2.22) 6957.00 9455.00 12852.00 Emergency 6251(77.62) 4329.00 5518.00 7064.00 Length of Stay (days) ≤ 8 4098(50.88) 3707.75 4502.00 5374.25 −57.180 8 3956(49.12) 5757.25 6905.00 8474.75 Discharge Status Effective (Cured, Improved) 7901(98.10) 4337.00 5511.00 7075.50 −6.969 <0.001 Ineffective (Not Cured, Death, Other) 153(1.90) 5090.00 7188.00 10605.00 Rescue Treatment Yes 442(5.49) 6389.00 8868.00 12382.75 −20.326 <0.001 No 7612(94.51) 4297.25 5445.50 6909.50 Antibiotic Use Not Used 3558(44.18) 3949.75 4941.50 6168.25 −26.000 <0.001 Used 4496(55.82) 4820.25 6074.00 7811.00 Invasive Mechanical Ventilation Not Used 7995(99.27) 4338.00 5513.00 7076.00 −10.532 <0.001 Used 59(0.73) 9203.00 11435.00 15640.00 Noninvasive Ventilation Not Used 7881(97.85) 4326.50 5488.00 7038.50 −13.674 <0.001 Used 173(2.15) 6876.50 9851.00 13058.50 Surgical Treatment No Surgery 7371(91.52) 4272.00 5403.00 6829.00 −22.331 <0.001 Surgery Performed 683(8.48) 5875.00 7821.00 10939.00 Comorbidities /Complications None 3334(41.40) 4328.50 5494.50 6973.50 3.218 0.200 General Comorbidity/Complication 2786(34.59) 4365.00 5552.00 7165.75 Major Comorbidity/Complication 1934(24.01) 4336.50 5554.50 7275.00 Traditional Chinese Medicine Treatment Treatment cost < 60% 2560(31.79) 4631.00 6206.50 8310.00 −16.724 <0.001 Treatment cost ≥ 60% 5494(68.21) 4263.75 5287.50 6620.50 Multiple Linear Regression Analysis of Factors Affecting Hospitalization Costs Variables with statistically significant differences in the univariate analysis were included as independent variables in the multiple linear regression model. Binary variables remained unchanged, while unordered categorical variables were converted into dummy variables using the first level of each factor as the reference group. The results showed that age, sex, admission type, length of stay, discharge status, rescue treatment, antibiotic use, use of invasive mechanical ventilation, use of noninvasive ventilation, and surgical treatment were all significant factors influencing hospitalization costs in COPD patients (P < 0.05). The adjusted R² of the model was 0.506, indicating that these ten variables explained 50.6% of the variance in hospitalization costs. Variance inflation factors (VIF) were all below 2, suggesting no multicollinearity among variables and good model fit. Detailed results are presented in Table 2 . Table 2 Multiple Linear Regression Analysis of Factors Affecting Hospitalization Costs in COPD Patients Variables Unstandardized Coefficient Standardized Coefficient (β) T Value P Value 95% CI for β Collinearity Statistics B S.E. Tolerance VIF Constant 7.920 0.033 241.659 <0.001 7.856 7.984 Age 70–84 years 0.053 0.007 0.069 7.699 <0.001 0.040 0.067 0.766 1.305 ≥ 85 years 0.066 0.010 0.060 6.688 <0.001 0.047 0.086 0.761 1.314 Sex -0.019 0.007 -0.023 -2.886 0.004 -0.032 -0.006 0.991 1.009 Admission Type Emergency 0.024 0.007 0.026 3.194 0.001 0.009 0.039 0.918 1.090 Critical 0.169 0.024 0.065 7.040 <0.001 0.122 0.216 0.712 1.404 Length of Stay 0.430 0.006 0.564 70.567 <0.001 0.418 0.442 0. 961 1.041 Discharge Status -0.054 0.025 -0.020 -2.199 0.028 -0.103 -0.006 0.780 1.282 Rescue Treatment 0.229 0.017 0.137 13.315 <0.001 0.195 0.262 0.583 1.716 Antibiotic Use 0.125 0.006 0.163 19.712 <0.001 0.113 0.137 0.899 1.113 Invasive Mechanical Ventilation 0.335 0.040 0.075 8.477 <0.001 0.257 0.412 0.784 1.275 Noninvasive Ventilation 0.105 0.024 0.040 4.433 <0.001 0.059 0.152 0.750 1.334 Surgical Treatment 0.219 0.013 0.161 17.454 <0.001 0.195 0.244 0.725 1.379 Traditional Chinese Medicine Treatment -0.010 0.007 -0.012 -1.378 0.168 -0.023 0.004 0.850 1.177 DRG Case-Mix Based on the E-CHAID Decision Tree Model Hospitalization cost was set as the dependent variable. Based on the absolute values of the β coefficients from the multiple linear regression, five significant factors were selected as independent variables in the decision tree model: length of stay, antibiotic use, surgical treatment, rescue treatment, and use of invasive mechanical ventilation. The E-CHAID model ultimately divided patients into eight DRG groups. The first-level classification node was length of stay. The second-level node was rescue treatment. The third-level nodes were surgical treatment and antibiotic use. The model achieved a between-group RIV of 0.55, indicating moderate between-group heterogeneity and good grouping performance. The coefficient of variation (CV) within each DRG group ranged from 0.21 to 0.39, all below 1, suggesting low within-group variation and reasonable grouping. Details of the DRG groups are shown in Fig. 1 and Table 3 . Table 3 Results of DRG grouping for COPD Patients Group Group Description n % Hospitalization Cost (CNY) CV Mean SD DRG1 Length of stay > 8 days, received rescue treatment, underwent surgery 87 1.08 12868.678 2744.870 0.21 DRG2 Length of stay > 8 days, received rescue treatment, no surgery 140 1.74 10152.529 3134.502 0.31 DRG3 Length of stay > 8 days, no rescue treatment, underwent surgery 305 3.79 9205.036 3078.107 0.33 DRG4 Length of stay > 8 days, no rescue treatment, no surgery 3424 42.51 7057.279 2160.649 0.31 DRG5 Length of stay ≤ 8 days, received rescue treatment, underwent surgery 105 1.30 9294.524 3589.736 0.39 DRG6 Length of stay ≤ 8 days, received rescue treatment, no surgery 110 1.37 5841.327 2240.472 0.38 DRG7 Length of stay ≤ 8 days, no rescue treatment, used antibiotics 1818 22.57 4820.245 1283.377 0.27 DRG8 Length of stay ≤ 8 days, no rescue treatment, no antibiotics 2065 25.64 4282.876 1080.149 0.25 Estimation of Hospitalization Costs by DRG Groups Among all DRG groups, only 191 patients (2.37%) had hospitalization costs exceeding the cost upper limit. The highest proportions of outliers were observed in DRG5 and DRG4, accounting for 4.76% and 4.18%, respectively. No cost outliers were found in DRG1, DRG2, or DRG3, indicating that the overall grouping was reasonable. The highest disease weight was observed in DRG1 (2.13), indicating that patients with “length of stay > 8 days, who received rescue treatment and surgery” consumed more medical resources and incurred higher hospitalization costs. The lowest disease weight was found in DRG8 (0.71), representing patients with “length of stay ≤ 8 days, no rescue treatment, and no antibiotic therapy,” who consumed fewer resources and had lower hospitalization costs. Detailed information on DRG costs and weights is presented in Table 4 . Table 4 Standard Hospitalization Costs and Disease Weights of DRG Groups in COPD Patients Group Standard Cost (CNY) Hospitalization Cost P25 (CNY) Hospitalization Cost P75 (CNY) Interquartile Range (IQR, CNY) Cost Upper Limit (CNY) Number of Outliers Outlier Proportion (%) Disease Weight DRG1 13173.00 10763.00 15019.00 4256.00 21403.00 0 0 2.13 DRG2 9327.00 7934.25 12495.75 4561.50 19338.00 0 0 1.68 DRG3 8685.00 6911.00 11034.00 4123.00 17218.50 0 0 1.52 DRG4 6651.00 5633.25 7967.75 2334.50 11469.50 143 4.18 1.17 DRG5 8756.00 6642.50 10846.50 4204.00 17152.50 5 4.76 1.54 DRG6 5370.50 4500.00 6959.50 2459.50 10648.75 2 1.82 0.97 DRG7 4758.50 3892.00 5572.25 1680.25 8092.63 24 1.32 0.80 DRG8 4210.00 3474.50 4973.50 1499.00 7222.00 17 0.82 0.71 Discussion In this study, significant factors influencing hospitalization costs in COPD patients were identified through univariate analysis and multiple linear regression, providing a more rational basis for selecting classification nodes. Interestingly, the presence of complications or comorbidities was not found to significantly affect hospitalization costs, which differs from some previous studies [ 16 – 19 ] . This discrepancy may be because complications/comorbidities in this study were defined according to the Major Comorbidity or Complication (MCC) and Comorbidity or Complication (CC) lists from the China National Healthcare Security Administration DRG Technical Guidance (“DRG Payment by Disease Group 2.0”), rather than a purely clinical perspective. This also suggests that using a uniform complications/comorbidities catalog across all diagnoses may not fully reflect clinical reality. Using the E-CHAID decision tree model, eight DRG groups were established. Model evaluation indicated good within-group homogeneity and between-group heterogeneity, demonstrating satisfactory grouping performance. The tree structure showed that length of stay was the first-level node, rescue treatment was the second-level node, and surgical treatment and antibiotic use were third-level nodes. This suggests that hospitalization duration, rescue interventions, invasive procedures, and anti-infection treatment are key factors differentiating cost heterogeneity among COPD patients. The resulting grouping scheme closely aligns with clinical practice and effectively reflects variations in hospitalization costs. Health insurance authorities could consider these variables when optimizing DRG grouping for COPD patients. Length of stay was the most influential factor affecting COPD hospitalization costs, as reflected by its position as the first-level node. Longer stays generally indicate more severe illness, leading to increased consumption of medications, diagnostic services, and bed occupancy, which in turn drive up hospitalization costs [ 20 ] . Therefore, while maintaining medical quality and patient safety, healthcare institutions could implement measures such as improving bed turnover efficiency, streamlining diagnostic and treatment processes, and enhancing service efficiency to reasonably reduce length of stay and achieve more effective cost control [ 21 ][ 22 ] . Rescue treatment, surgical intervention, and antibiotic use were identified as the second- and third-level nodes affecting hospitalization costs. Rescue treatment primarily targets critically ill patients with unstable vital signs. These patients often require intensive monitoring, life-support equipment, and multidisciplinary care, all of which significantly increase medical resource consumption and drive up hospitalization costs. Surgical interventions involve anesthesia, medical consumables, and specialized procedures, making them a major contributor to higher hospitalization costs [ 21 ] . Antibiotics are a core therapy for acute exacerbations of COPD. The choice of drug, treatment duration, and combination regimens directly determine medication costs and have a substantial impact on total hospitalization expenses. Healthcare institutions should therefore standardize surgical indications, enhance perioperative management, and implement precise antibiotic stewardship. Measures such as standardized clinical pathways, optimized critical care protocols, graded surgical management, and tiered antibiotic use can help control unnecessary resource consumption, achieve scientific cost management for COPD patients, and improve the efficiency of medical resource allocation [ 23 – 26 ] . In this study, only 2.37% of cases exceeded the cost upper limit, indicating that the DRG grouping scheme based on the decision tree is reasonable and feasible. The highest proportions of cost outliers were observed in DRG5 (length of stay ≤ 8 days with rescue and surgery) and DRG4 (length of stay > 8 days without rescue or surgery), at 4.76% and 4.18%, respectively. For these groups, healthcare institutions should prioritize monitoring, investigate potential over-treatment, inappropriate resource use, or adverse events during hospitalization, and continue optimizing clinical workflows. Strengthening supervision of medical practices can help strictly control unnecessary cost increases and reduce the economic burden on patients [ 26 ] . Limitations This study has some limitations. Data were collected from a single institution, which limits the representativeness of the sample, and not all factors affecting hospitalization costs were included. Future research should expand the sample to multiple centers across the city, incorporate additional relevant factors, and refine the grouping model to improve reliability. Such efforts will provide stronger support for COPD management and DRG-based payment reform. Conclusion This study identified the key factors influencing hospitalization costs in COPD patients and constructed a DRG-based payment grouping system using the E-CHAID algorithm. Unlike traditional DRG grouping, which primarily relies on diagnosis and surgical procedures, this study innovatively incorporated clinical characteristics and treatment-related indicators. The resulting grouping scheme better aligns with clinical practice and more accurately reflects variations in hospitalization costs. By establishing cost standards for each DRG group, analyzing cases exceeding cost limits, and calculating disease weights, this model provides hospitals with precise guidance for cost control, targeted management of high-resource groups, and optimization of medical resource allocation. It also offers a reliable reference for health insurance authorities to set standard payment levels for each DRG, regulate medical expenditures, and curb unreasonable cost growth. Abbreviations DRG the diagnosis-related groups COPD chronic obstructive pulmonary disease CHS-DRG the China Healthcare Security Diagnosis-Related Groups E- CHAID Exhaustive Chi-squared Automatic Interaction Detector CPI the Consumer Price Index VIF variance inflation factor RIV reduction in variance CV coefficient of variation MCC Major Comorbidity or Complication CC Comorbidity or Complication Declarations Ethics approval and consent to participate This study complied with the ethical principles of the Declaration of Helsinki. The analysis was based on retrospective and anonymized data, and no intervention or risk was involved for the patients. The study protocol was approved by the Ethics Committee of Shehong Hospital of Traditional Chinese Medicine, and the requirement for informed consent was waived. Consent for publication Not applicable. Competing interests None Funding This study was funded by the Suining Municipal Health Science and Technology Program(24RKX10). Author Contribution Y.J.F. and F.Y.Q., developed the concept; H.D.J., S.G.G. and M.M.L., analyzed and determined the independent variables ultimately included in the study; H.L.H., H.Z.K., T.F.Q.and J.Z.Q., performed the data collection and analysis ; M.M.L. wrote the manuscript; H.Z.Y. was responsible for research ethics. All authors reviewed the final manuscript. Acknowledgements None Data Availability The datasets for this study can be obtained from the corresponding author upon any reasonable request. References Global Initiative for Chronic Obstructive Lung Disease - GOLD. Global strategy for the diagnosis, management and prevention ofchronic obstructive pulmonary disease 2026report [EB/OL]. https://goldcopd.org/2026-gold-report-and-pocket-guide/ (2025-11-11)[2026-01-18]. Boers E et al. 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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-9159919","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":628552494,"identity":"1c190146-7cae-4af1-824f-23f53f6c14d3","order_by":0,"name":"Minglu Mo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYLCCBww2PPzszQcOfPhBrJYEhjQ5yZ5jiQdn9hCv5bCxwQ0f48McbESo1m3vTnyQ2Mac2HCD58NhBh4GeX6xA/i1mJ05u9kgsY0tsXF274bDBRYMhjNnJxDQciN3m0RiG09is8zZDYdn8DAkGNwmTgsI5Tw4zMNGvBYDYx6JHAYitYD8knAuQU6C55gBMJAliPDL8d6NDz6U/eexP978+MOHHzby/NIEtKADCdKUj4JRMApGwSjADgDUO0vScyH03QAAAABJRU5ErkJggg==","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":true,"prefix":"","firstName":"Minglu","middleName":"","lastName":"Mo","suffix":""},{"id":628552495,"identity":"70a77a6b-af94-4c09-a40c-6932709180ca","order_by":1,"name":"Jifang Yang","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Jifang","middleName":"","lastName":"Yang","suffix":""},{"id":628552496,"identity":"d371683e-767a-4174-b141-20494d5abf9f","order_by":2,"name":"Yunqiang Fan","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Yunqiang","middleName":"","lastName":"Fan","suffix":""},{"id":628552497,"identity":"b378ea8f-1d6b-42bb-8448-90314aa3130f","order_by":3,"name":"Dejian Hou","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Dejian","middleName":"","lastName":"Hou","suffix":""},{"id":628552498,"identity":"d3dff889-cba6-4f66-9725-8355ac44bbe9","order_by":4,"name":"Ganggang Su","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Ganggang","middleName":"","lastName":"Su","suffix":""},{"id":628552499,"identity":"a50b3475-4f9c-4cc5-82d5-8037f5b67c8c","order_by":5,"name":"Zhongying Huang","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Zhongying","middleName":"","lastName":"Huang","suffix":""},{"id":628552501,"identity":"6b777474-d5c4-4e79-a2da-2c2d0faaa27c","order_by":6,"name":"Zhiqiang Jiang","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiang","middleName":"","lastName":"Jiang","suffix":""},{"id":628552502,"identity":"3065a319-0c9f-4af2-976c-3132a8b1f6e6","order_by":7,"name":"Fuqiang Tan","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Fuqiang","middleName":"","lastName":"Tan","suffix":""},{"id":628552508,"identity":"39082f61-7415-4725-839f-145fc8a49d0c","order_by":8,"name":"Lihong He","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"He","suffix":""},{"id":628552515,"identity":"0fe1dd00-b352-4f90-9d70-b1e62516c3f1","order_by":9,"name":"Zekai Hao","email":"","orcid":"","institution":"Shehong Traditional Chinese Medicine Hospital，Shehong","correspondingAuthor":false,"prefix":"","firstName":"Zekai","middleName":"","lastName":"Hao","suffix":""}],"badges":[],"createdAt":"2026-03-18 13:24:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9159919/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9159919/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107832596,"identity":"617e04dc-65f6-4ea8-894a-b798c4fb8333","added_by":"auto","created_at":"2026-04-26 15:34:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14114,"visible":true,"origin":"","legend":"\u003cp\u003eDecision Tree Model of Factors Influencing Hospitalization Costs in COPD\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9159919/v1/4d1ca44654dfeff2c13ad334.png"},{"id":107869665,"identity":"9cbcfe2c-1690-4455-8e40-32ebef91f40d","added_by":"auto","created_at":"2026-04-27 07:37:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":497676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9159919/v1/c23706ce-071d-4605-bbbc-51be7ecf1a11.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing a DRG framework for COPD patients: application of the E-CHAID algorithm","fulltext":[{"header":"Background","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a disease characterized by persistent respiratory symptoms and airflow limitation caused by abnormalities of the airways and/or alveoli\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. COPD is the third leading cause of death worldwide. The number of COPD cases is projected to increase by 23.3% by 2050. By 2060, annual global deaths from COPD may exceed 5.4\u0026nbsp;million. The disease is expected to impose a global direct medical cost of up to USD 24.35 trillion\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. In China, the prevalence of COPD among people aged 40 years and older has reached 13.7%. Nearly 100\u0026nbsp;million individuals are affected. This creates a heavy economic burden for patients, families, and society\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. To control the rapid growth of medical expenditure, the National Healthcare Security Administration of China released the China Healthcare Security Diagnosis-Related Groups (CHS-DRG) scheme in 2019\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Pilot reforms of DRG-based medical insurance payment were then gradually implemented in several cities. DRGs are a modern case-mix classification system. Patients are grouped according to major diagnosis, treatment methods, and disease severity. Each group is assigned a payment standard and a disease weight. This system can help control medical costs, evaluate healthcare quality, optimize resource allocation, and reduce the length of hospital stay\u003csup\u003e[\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Therefore, more refined DRG grouping is particularly important. The E-CHAID model is a decision tree algorithm based on chi-square automatic interaction detection. It can identify key influencing factors and create hierarchical subgroups automatically. Compared with the traditional CHAID method, E-CHAID has stronger segmentation and stratification ability. It is also more stable and detailed than the classical CART algorithm\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In this study, univariate analysis and multiple linear regression were used to identify factors associated with hospitalization costs in COPD patients. An E-CHAID decision tree was then applied to construct DRG groups and estimate reference standards for hospitalization costs. The aim was to explore a more reasonable case-mix classification scheme and provide evidence for better control of hospitalization costs for COPD patients in the local healthcare system.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Inclusion/Exclusion Criteria\u003c/h2\u003e \u003cp\u003ePatients discharged from a tertiary Grade A hospital in Sichuan Province between January 1, 2021 and December 31, 2025 were screened. Eligible cases had a principal discharge diagnosis of COPD (ICD-10 code category J44). Data were extracted from the medical record front page and entered into a study database. The exclusion criteria were as follows: (1) missing or incomplete information; (2) length of stay\u0026thinsp;\u0026lt;\u0026thinsp;2 days or \u0026gt;\u0026thinsp;60 days; (3) hospitalization costs below the 2.5th percentile (P2.5) or above the 97.5th percentile (P97.5). A total of 8,636 cases were initially identified. Among them, 19 cases were excluded due to incomplete information, 137 cases due to a length of stay\u0026thinsp;\u0026lt;\u0026thinsp;2 days or \u0026gt;\u0026thinsp;60 days, and 426 cases due to hospitalization costs below P2.5 or above P97.5. Finally, 8,054 valid cases were included in the analysis, with an inclusion rate of 93.26%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS 26.0. Categorical variables were expressed as percentages. Hospitalization costs were tested for normality; as they were not normally distributed, costs were reported as medians (M) with interquartile ranges (P25, P75). Nonparametric rank-sum tests (Kruskal-Wallis or Wilcoxon test) were used for univariate analysis of factors affecting hospitalization costs in COPD patients. Since hospitalization costs are influenced by the Consumer Price Index (CPI), costs were adjusted using the healthcare-related CPI for Sichuan Province from the \u003cem\u003eChina Statistical Yearbook\u003c/em\u003e 2021\u0026ndash;2024\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were included in multiple linear regression. Hospitalization costs showed right-skewed distribution and were log-transformed using the natural logarithm for multivariate analysis. Factors with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered significant. Variance inflation factor (VIF)\u0026thinsp;\u0026lt;\u0026thinsp;5 indicated no multicollinearity among independent variables, suggesting good model fit. The significant factors identified were used as classification nodes in an E-CHAID decision tree to construct the DRG grouping model. The maximum tree depth was set to three layers, with at least 100 cases in the parent node and 50 cases in each child node. The significance level for node splitting was set at α\u0026thinsp;=\u0026thinsp;0.05, and cross-validation was performed for appropriate pruning of the model\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation Indicators\u003c/h3\u003e\n\u003cp\u003eThe rationality of DRG grouping was assessed based on between-group heterogeneity and within-group homogeneity. Reduction in variance (RIV) was used to evaluate between-group heterogeneity; a larger RIV indicates stronger heterogeneity and better grouping. Within-group homogeneity was assessed using the coefficient of variation (CV\u0026thinsp;=\u0026thinsp;standard deviation / mean); a smaller CV indicates lower within-group variation and better grouping. In this study, RIV\u0026thinsp;\u0026gt;\u0026thinsp;0.4 and CV\u0026thinsp;\u0026lt;\u0026thinsp;1.0 were considered indicative of satisfactory grouping. The median hospitalization cost of each DRG group was defined as the standard cost. The cost upper limit was calculated as P75\u0026thinsp;+\u0026thinsp;1.5 \u0026times; IQR; cases exceeding this limit were considered outliers. The weight of each DRG group was calculated as the average hospitalization cost of the group divided by the overall average cost. A higher weight indicates greater medical resource consumption\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eBasic Characteristics\u003c/h2\u003e \u003cp\u003eA total of 8,054 COPD patients were included in this study, of whom 5,782 (71.79%) were male and 2,272 (28.21%) were female. The mean age was 74.99 years, with a median age of 76 years (P25\u0026ndash;P75: 70\u0026ndash;81 years). The majority of patients (61.53%) were aged 66\u0026ndash;84 years. The average length of stay was 9 days, with a median of 8 days (P25\u0026ndash;P75: 7\u0026ndash;11 days). The mean hospitalization cost per patient was 6,051.45\u0026thinsp;\u0026plusmn;\u0026thinsp;2,525.94 CNY, with a median of 5,529.00 CNY (P25\u0026ndash;P75: 4,343.75\u0026ndash;7,123.25 CNY). Detailed demographic and clinical characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate Analysis of Factors Affecting Hospitalization Costs\u003c/h2\u003e \u003cp\u003eBased on consultation with respiratory medicine experts and relevant literature, potential factors influencing hospitalization costs were included as independent variables. These factors were: age, sex, type of medical insurance, admission type, length of stay, discharge status, rescue treatment, antibiotic use, use of invasive mechanical ventilation, use of noninvasive ventilation, surgical treatment, presence of complications/comorbidities, and traditional Chinese medicine treatment. Univariate analysis showed that all factors except type of medical insurance (P\u0026thinsp;=\u0026thinsp;0.29) and presence of complications/comorbidities (P\u0026thinsp;=\u0026thinsp;0.200) were significantly associated with hospitalization costs in COPD patients (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Detailed results are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Analysis of Hospitalization Costs in COPD Patients by Different Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eTotal Hospitalization Costs (CNY)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eZ / H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP25\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP75\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2223(27.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4204.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5292.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6698.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e37.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71\u0026ndash;84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4726(58.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4420.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5623.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7161.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1105(13.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4282.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5636.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7558.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5782(71.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4384.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5625.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7259.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;5.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2272(28.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4253.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5301.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6754.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eType of medical insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Employee Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e656(8.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4475.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5803.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7420.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e7.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban Resident Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7368(91.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4335.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5510.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7087.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(0.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4804.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5375.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7124.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAdmission Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1624(20.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4295.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5392.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6906.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e209.947\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e179(2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6957.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9455.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12852.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6251(77.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4329.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5518.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7064.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLength of Stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4098(50.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3707.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4502.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5374.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;57.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3956(49.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5757.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6905.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8474.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDischarge Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffective (Cured, Improved)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7901(98.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4337.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5511.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7075.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;6.969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIneffective (Not Cured, Death, Other)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153(1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5090.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7188.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10605.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRescue Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442(5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6389.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8868.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12382.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;20.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7612(94.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4297.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5445.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6909.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAntibiotic Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3558(44.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3949.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4941.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6168.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;26.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4496(55.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4820.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6074.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7811.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInvasive Mechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7995(99.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4338.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5513.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7076.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;10.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59(0.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9203.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11435.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15640.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNoninvasive Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7881(97.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4326.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5488.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7038.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;13.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e173(2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6876.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9851.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13058.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSurgical Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo Surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7371(91.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4272.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5403.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6829.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;22.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSurgery Performed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e683(8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5875.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7821.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10939.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eComorbidities /Complications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3334(41.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4328.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5494.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6973.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Comorbidity/Complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2786(34.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4365.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5552.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7165.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMajor Comorbidity/Complication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1934(24.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4336.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5554.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7275.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraditional Chinese Medicine Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment cost\u0026thinsp;\u0026lt;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2560(31.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4631.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6206.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8310.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026minus;16.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTreatment cost\u0026thinsp;\u0026ge;\u0026thinsp;60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5494(68.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4263.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5287.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6620.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultiple Linear Regression Analysis of Factors Affecting Hospitalization Costs\u003c/h3\u003e\n\u003cp\u003eVariables with statistically significant differences in the univariate analysis were included as independent variables in the multiple linear regression model. Binary variables remained unchanged, while unordered categorical variables were converted into dummy variables using the first level of each factor as the reference group. The results showed that age, sex, admission type, length of stay, discharge status, rescue treatment, antibiotic use, use of invasive mechanical ventilation, use of noninvasive ventilation, and surgical treatment were all significant factors influencing hospitalization costs in COPD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The adjusted R\u0026sup2; of the model was 0.506, indicating that these ten variables explained 50.6% of the variance in hospitalization costs. Variance inflation factors (VIF) were all below 2, suggesting no multicollinearity among variables and good model fit. Detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression Analysis of Factors Affecting Hospitalization Costs in COPD Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStandardized Coefficient (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eT Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c8\" namest=\"c7\" rowspan=\"2\"\u003e \u003cp\u003e95% CI for β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eCollinearity Statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e241.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u0026ndash;84 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;85 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e \u003cp\u003eAdmission Type\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmergency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCritical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of Stay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0. 961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarge Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRescue Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.716\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.899\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInvasive Mechanical Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNoninvasive Ventilation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgical Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraditional Chinese Medicine Treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.177\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDRG Case-Mix Based on the E-CHAID Decision Tree Model\u003c/h3\u003e\n\u003cp\u003eHospitalization cost was set as the dependent variable. Based on the absolute values of the β coefficients from the multiple linear regression, five significant factors were selected as independent variables in the decision tree model: length of stay, antibiotic use, surgical treatment, rescue treatment, and use of invasive mechanical ventilation. The E-CHAID model ultimately divided patients into eight DRG groups. The first-level classification node was length of stay. The second-level node was rescue treatment. The third-level nodes were surgical treatment and antibiotic use. The model achieved a between-group RIV of 0.55, indicating moderate between-group heterogeneity and good grouping performance. The coefficient of variation (CV) within each DRG group ranged from 0.21 to 0.39, all below 1, suggesting low within-group variation and reasonable grouping. Details of the DRG groups are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of DRG grouping for COPD Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroup Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eHospitalization Cost (CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days, received rescue treatment, underwent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12868.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2744.870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days, received rescue treatment, no surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10152.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3134.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days, no rescue treatment, underwent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9205.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3078.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days, no rescue treatment, no surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7057.279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2160.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days, received rescue treatment, underwent surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9294.524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3589.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days, received rescue treatment, no surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5841.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2240.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days, no rescue treatment, used antibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4820.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1283.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLength of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days, no rescue treatment, no antibiotics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4282.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1080.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of Hospitalization Costs by DRG Groups\u003c/h2\u003e \u003cp\u003eAmong all DRG groups, only 191 patients (2.37%) had hospitalization costs exceeding the cost upper limit. The highest proportions of outliers were observed in DRG5 and DRG4, accounting for 4.76% and 4.18%, respectively. No cost outliers were found in DRG1, DRG2, or DRG3, indicating that the overall grouping was reasonable. The highest disease weight was observed in DRG1 (2.13), indicating that patients with \u0026ldquo;length of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days, who received rescue treatment and surgery\u0026rdquo; consumed more medical resources and incurred higher hospitalization costs. The lowest disease weight was found in DRG8 (0.71), representing patients with \u0026ldquo;length of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days, no rescue treatment, and no antibiotic therapy,\u0026rdquo; who consumed fewer resources and had lower hospitalization costs. Detailed information on DRG costs and weights is presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStandard Hospitalization Costs and Disease Weights of DRG Groups in COPD Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandard Cost (CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHospitalization Cost P25 (CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHospitalization Cost P75 (CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInterquartile Range (IQR, CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCost Upper Limit (CNY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of Outliers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOutlier Proportion (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDisease Weight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13173.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10763.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15019.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4256.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21403.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9327.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7934.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12495.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4561.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19338.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8685.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6911.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11034.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4123.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17218.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6651.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5633.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7967.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2334.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11469.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8756.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6642.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10846.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4204.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17152.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5370.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4500.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6959.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2459.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10648.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4758.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3892.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5572.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1680.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8092.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDRG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4210.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3474.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4973.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1499.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7222.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, significant factors influencing hospitalization costs in COPD patients were identified through univariate analysis and multiple linear regression, providing a more rational basis for selecting classification nodes. Interestingly, the presence of complications or comorbidities was not found to significantly affect hospitalization costs, which differs from some previous studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. This discrepancy may be because complications/comorbidities in this study were defined according to the \u003cem\u003eMajor Comorbidity or Complication (MCC)\u003c/em\u003e and \u003cem\u003eComorbidity or Complication (CC)\u003c/em\u003e lists from the China National Healthcare Security Administration DRG Technical Guidance (\u0026ldquo;DRG Payment by Disease Group 2.0\u0026rdquo;), rather than a purely clinical perspective. This also suggests that using a uniform complications/comorbidities catalog across all diagnoses may not fully reflect clinical reality. Using the E-CHAID decision tree model, eight DRG groups were established. Model evaluation indicated good within-group homogeneity and between-group heterogeneity, demonstrating satisfactory grouping performance. The tree structure showed that length of stay was the first-level node, rescue treatment was the second-level node, and surgical treatment and antibiotic use were third-level nodes. This suggests that hospitalization duration, rescue interventions, invasive procedures, and anti-infection treatment are key factors differentiating cost heterogeneity among COPD patients. The resulting grouping scheme closely aligns with clinical practice and effectively reflects variations in hospitalization costs. Health insurance authorities could consider these variables when optimizing DRG grouping for COPD patients.\u003c/p\u003e \u003cp\u003eLength of stay was the most influential factor affecting COPD hospitalization costs, as reflected by its position as the first-level node. Longer stays generally indicate more severe illness, leading to increased consumption of medications, diagnostic services, and bed occupancy, which in turn drive up hospitalization costs\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Therefore, while maintaining medical quality and patient safety, healthcare institutions could implement measures such as improving bed turnover efficiency, streamlining diagnostic and treatment processes, and enhancing service efficiency to reasonably reduce length of stay and achieve more effective cost control\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e][\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRescue treatment, surgical intervention, and antibiotic use were identified as the second- and third-level nodes affecting hospitalization costs. Rescue treatment primarily targets critically ill patients with unstable vital signs. These patients often require intensive monitoring, life-support equipment, and multidisciplinary care, all of which significantly increase medical resource consumption and drive up hospitalization costs. Surgical interventions involve anesthesia, medical consumables, and specialized procedures, making them a major contributor to higher hospitalization costs\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Antibiotics are a core therapy for acute exacerbations of COPD. The choice of drug, treatment duration, and combination regimens directly determine medication costs and have a substantial impact on total hospitalization expenses. Healthcare institutions should therefore standardize surgical indications, enhance perioperative management, and implement precise antibiotic stewardship. Measures such as standardized clinical pathways, optimized critical care protocols, graded surgical management, and tiered antibiotic use can help control unnecessary resource consumption, achieve scientific cost management for COPD patients, and improve the efficiency of medical resource allocation\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, only 2.37% of cases exceeded the cost upper limit, indicating that the DRG grouping scheme based on the decision tree is reasonable and feasible. The highest proportions of cost outliers were observed in DRG5 (length of stay\u0026thinsp;\u0026le;\u0026thinsp;8 days with rescue and surgery) and DRG4 (length of stay\u0026thinsp;\u0026gt;\u0026thinsp;8 days without rescue or surgery), at 4.76% and 4.18%, respectively. For these groups, healthcare institutions should prioritize monitoring, investigate potential over-treatment, inappropriate resource use, or adverse events during hospitalization, and continue optimizing clinical workflows. Strengthening supervision of medical practices can help strictly control unnecessary cost increases and reduce the economic burden on patients\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations. Data were collected from a single institution, which limits the representativeness of the sample, and not all factors affecting hospitalization costs were included. Future research should expand the sample to multiple centers across the city, incorporate additional relevant factors, and refine the grouping model to improve reliability. Such efforts will provide stronger support for COPD management and DRG-based payment reform.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identified the key factors influencing hospitalization costs in COPD patients and constructed a DRG-based payment grouping system using the E-CHAID algorithm. Unlike traditional DRG grouping, which primarily relies on diagnosis and surgical procedures, this study innovatively incorporated clinical characteristics and treatment-related indicators. The resulting grouping scheme better aligns with clinical practice and more accurately reflects variations in hospitalization costs. By establishing cost standards for each DRG group, analyzing cases exceeding cost limits, and calculating disease weights, this model provides hospitals with precise guidance for cost control, targeted management of high-resource groups, and optimization of medical resource allocation. It also offers a reliable reference for health insurance authorities to set standard payment levels for each DRG, regulate medical expenditures, and curb unreasonable cost growth.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDRG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe diagnosis-related groups\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCOPD\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\"\u003eCHS-DRG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe China Healthcare Security Diagnosis-Related Groups\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE- CHAID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExhaustive Chi-squared Automatic Interaction Detector\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCPI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ethe Consumer Price Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVIF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003evariance inflation factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRIV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereduction in variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecoefficient of variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Comorbidity or Complication\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eComorbidity or Complication\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 \u003cp\u003e This study complied with the ethical principles of the Declaration of Helsinki. The analysis was based on retrospective and anonymized data, and no intervention or risk was involved for the patients. The study protocol was approved by the Ethics Committee of Shehong Hospital of Traditional Chinese Medicine, and the requirement for informed consent was waived.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by the Suining Municipal Health Science and Technology Program(24RKX10).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.J.F. and F.Y.Q., developed the concept; H.D.J., S.G.G. and M.M.L., analyzed and determined the independent variables ultimately included in the study; H.L.H., H.Z.K., T.F.Q.and J.Z.Q., performed the data collection and analysis ; M.M.L. wrote the manuscript; H.Z.Y. was responsible for research ethics. All authors reviewed the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eNone\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets for this study can be obtained from the corresponding author upon any reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Chronic Obstructive Lung Disease - GOLD. Global strategy for the diagnosis, management and prevention ofchronic obstructive pulmonary disease 2026report [EB/OL]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://goldcopd.org/2026-gold-report-and-pocket-guide/\u003c/span\u003e\u003cspan address=\"https://goldcopd.org/2026-gold-report-and-pocket-guide/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025-11-11)[2026-01-18].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers E et al. Global burden of chronic obstructive pulmonary disease through 2050. JAMA Netw Open 6, e2346598.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, Chen Q, Sun H et al. Economic evaluations of screening and case-finding for Chronic Obstructive Pulmonary Disease (COPD): a systematic review. NPJ Prim Care Respir Med 36, 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers E et al. Forecasting the global economic and health burden of COPD from 2025 through2050[J]. Chesl.168, 880\u0026ndash;889.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang C et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 391, 1706\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Healthcare Security Administration of China. Notice on the issuance of the three-year action plan for DRG/DIP payment reform [EB/OL]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/zhengce/zhengceku/2021-11/28/content_5653858.htm#:~:text=DRG/DI\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/zhengce/zhengceku/2021-11/28/content_5653858.htm#:~:text=DRG/DI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. (2021-11-19)[2025-06-20].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu XF, Hou YL. Impact of DRG payment reform on revenue and expenditure structure of public hospitals in China: evidence from difference-in-differences analysis. Front Public Health 13, 1725444.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu L, Fang S\u0026amp;, Zhang QL. Trend analysis of medical expenses under DRG payment reform in China. Sci Rep 15, 36961.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang W, Zhong LJ, Tao SC, Chen M. e. How does DRG reform affect length of stay and hospitalization costs in traditional Chinese medicine hospitals?\u0026mdash;an empirical analysis using interrupted time-series model from two cities in western China. Front Public Health.14, 1762327.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang X, Zhu J, Zhang J. Impacts of DRG point-based payment system on healthcare resource utilization and provider behavior: a pilot quasi-experimental study in China. Front Public Health 13, 1678259.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou Z, Deng D. Diagnosis-related groups study of uterine leiomyoma patients based on E-CHAID. Sci Rep. 15, 6460.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong J, Wang Y, Huang ST, Chiu HC. Study of Hospitalization Costs in Patients with Cerebral Ischemia Based on E-CHAID Algorithm. J Healthc Eng. 2022, 3978577.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Bureau of Statistics of China. Consumer Price Indices by Category and Region [EB/OL]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.stats.gov.cn/sj/ndsj/2025/indexch.htm\u003c/span\u003e\u003cspan address=\"https://www.stats.gov.cn/sj/ndsj/2025/indexch.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025-09-29) [2025-12-10].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu SW, Pan Q, Chen T. Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model[J]. World J Clin Cases. 8, 2484\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo AJ et al. Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm. Int J Ophthalmol. 11, 308\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh JA. Utilization due to chronic obstructive pulmonary disease and its predictors: a study using the U.S. National Emergency Department Sample (NEDS). Respir Res 17, 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen YH et al. Economic analysis in admitted patients with acute exacerbation of chronic obstructive pulmonary disease. Chin Med J (Engl). 121, 587\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen H et al. Effects of complications associated with chronic obstructive pulmonary disease on lung function and hospitalization expenses: A retrospective study. Med (Baltim). 104, e42274.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDombai B et al. Comparison of healthcare costs of patients with COPD on maintenance inhaled therapies between 2011 and 2019 in Hungary using a nationwide database. PLoS ONE 20, e0320949.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParekh TM et al. Implications of DRG Classification in a Bundled Payment Initiative for COPD. Am J Accountable Care 5, 12\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu SW, Pan Q, Chen T. Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model. World J Clin Cases. 8, 2484\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu XL, Li NN. Optimization of diagnosis-related groups for patients with acute appendicitis using a machine learning model. Front Public Health 13, 1581441.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim N, Teng W, Akande. O.,Rhodes,D.\u0026amp;Rochester,C. Impact of an Inpatient COPD Care Pathway on Hospital Care Process and Outcome Metrics. Chronic Obstr Pulm Dis.12, 304\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M et al. Factors contributing to hospitalization costs for patients with COPD in China: a retrospective analysis of medical record data. Int J Chron Obstruct Pulmon Dis 13, 3349\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang C et al. Characteristics, Management and In-Hospital Clinical Outcomes Among Inpatients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease in China: Results from the Phase I Data of ACURE Study. Int J Chron Obstruct Pulmon Dis 16, 451\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Y et al. Optimization of Diagnosis-Related Groups for 14,246 Patients with Uterine Leiomyoma in a Single Center in Western China Using a Machine Learning Model. Risk Manag Healthc Policy 17, 473\u0026ndash;85.\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, diagnosis-related groups, E-CHAID algorithm, decision tree","lastPublishedDoi":"10.21203/rs.3.rs-9159919/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9159919/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe chronic obstructive pulmonary disease(COPD) is the third leading cause of death worldwide. It is expected to impose a global direct medical cost of up to USD 24.35 trillion, creates a heavy economic burden for patients, families, and society. This study aimed to develop the diagnosis-related groups (DRG) scheme for patients with chronic obstructive pulmonary disease (COPD) that better reflects clinical practice. It also aimed to provide evidence for setting inpatient cost standards and promoting DRG-based medical insurance payment reform.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 8,054 COPD patients admitted to a tertiary hospital in Sichuan Province of China from 2021 to 2025 were included. Medical record front-page data were collected retrospectively. A non-parametric rank-sum test and multiple linear regression were used to identify factors associated with hospitalization costs. An E-CHAID decision tree was then used to construct a DRG case-mix model for COPD patients. Payment standards and upper limits for cost control were calculated for each DRG group.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results showed that age, sex, admission status, length of stay, discharge status, rescue treatment, use of antibiotics, use of invasive mechanical ventilation, use of noninvasive ventilation, and surgical treatment were associated with hospitalization costs in COPD patients. Five variables were selected as classification nodes: length of stay, antibiotic use, surgical treatment, rescue treatment, and invasive mechanical ventilation. The E-CHAID decision tree divided patients into eight DRG groups. The reduction in between-group variance was 0.55. The coefficient of variation of hospitalization costs ranged from 0.21 to 0.39 across groups, indicating reasonable grouping.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe DRG scheme developed in this study reflects the level of medical resource use among COPD patients. It may provide evidence for optimizing DRG grouping and setting standard payment levels in medical insurance payment reform.\u003c/p\u003e","manuscriptTitle":"Developing a DRG framework for COPD patients: application of the E-CHAID algorithm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:34:48","doi":"10.21203/rs.3.rs-9159919/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-16T10:19:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T18:55:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T02:50:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T02:50:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-18T13:15:54+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":"39338bfe-6665-4974-a0c9-0ac4e5b8f05a","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:34:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:34:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9159919","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9159919","identity":"rs-9159919","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}