Abstract
Purpose
To explore the association of cardiovascular disease (CVD) count and combinations with prolonged length of stay (LOS) and 1-year acute exacerbation of chronic obstructive pulmonary disease (AECOPD)-related readmission among patients hospitalized for AECOPD.
Patients and Methods
This single-centre retrospective study included 3684 admissions from 2281 patients with AECOPD between 2015 and 2024, without adjustment for baseline COPD severity. The assessed CVDs were hypertension (HTN), ischemic heart disease (IHD), arrhythmia, heart failure (HF), and peripheral arterial disease (PAD). The primary outcome was prolonged LOS, defined as a hospital stay of ≥14 days. Generalized estimating equation logistic regression models and Cox models with patient-level clustering were used to evaluate prolonged LOS and 1-year AECOPD-related readmission, respectively. Subgroup and sensitivity analyses assessed the robustness of the findings for prolonged LOS.
Results
At least one CVD was present in 71.7% of admissions. After full adjustment, 2 CVDs (odds ratio [OR]: 1.60; 95% confidence interval [CI]: 1.18–2.16) and ≥3 CVDs (OR: 1.95; 95% CI: 1.30–2.93) were associated with prolonged LOS (P for trend <0.001). The adjusted hazard ratios for 1-year AECOPD-related readmission were 1.38, 1.42, and 1.68 for 1, 2, and ≥3 CVDs, respectively (P for trend = 0.006). HTN + IHD + PAD showed the strongest association with prolonged LOS (OR: 2.53; 95% CI: 1.25–5.12). HTN + Arrhythmia was associated with both prolonged LOS (OR: 1.83; 95% CI: 1.15–2.92) and readmission (hazard ratio: 1.59; 95% CI: 1.04–2.45). Associations of CVD count with prolonged LOS remained generally consistent in subgroup and sensitivity analyses.
Conclusion
Increasing CVD count was associated with progressively higher risks of prolonged LOS and 1-year AECOPD-related readmission, with the association with prolonged LOS becoming evident from two coexisting CVDs. Associations with these outcomes varied across CVD combinations, suggesting that both CVD count and combination may help identify patients at increased risk of adverse hospital and post-discharge outcomes.
Keywords: chronic obstructive pulmonary disease, acute exacerbation, cardiovascular comorbidity, length of stay, readmission
Introduction
Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is defined as worsening of dyspnea and/or cough and sputum occurring over less than 14 days.1 It represents a critical event in the progression of chronic obstructive pulmonary disease (COPD) and a period of heightened clinical risk.2 Length of stay (LOS) and AECOPD-related readmission are important clinical outcomes in patients hospitalized for AECOPD.3,4 Prolonged LOS may reflect slower recovery and greater treatment needs during the index hospitalization, whereas AECOPD-related readmission reflects recurrent severe exacerbations that contribute to accelerated lung function decline and poorer prognosis.5,6 Both outcomes also impose substantial healthcare costs and resource utilization.7,8 Therefore, identifying factors associated with prolonged LOS and readmission is important for improving the management of patients hospitalized for AECOPD.
Adverse outcomes following hospitalization for AECOPD are influenced by multiple factors.1 COPD severity, including impaired lung function as reflected by lower FEV1% predicted, as well as a history of previous exacerbations or hospitalizations, have been associated with poorer outcomes after AECOPD.5 Other factors, including smoking status, peripheral blood eosinophil levels, medication use, and overall comorbidity burden, may also contribute to the risk of adverse outcomes.9–11 Among these factors, comorbidities have received particular attention because patients with COPD frequently have multiple coexisting chronic conditions that may complicate both the acute course and post-discharge management.12
Cardiovascular disease (CVD) is among the most common and clinically important categories of comorbidity in patients with COPD.2,12–14 Among patients hospitalized for AECOPD, previous studies have linked CVD, particularly ischemic heart disease (IHD) and heart failure (HF), to higher in-hospital mortality, more frequent intensive care unit admission, longer LOS, and increased readmission risk.15–17 A large cohort study of 98,127 patients hospitalized for AECOPD further demonstrated that increasing numbers of cardiovascular comorbidities were associated with progressively higher readmission risks.18 In addition, the risks of severe cardiovascular events and death increase substantially after a COPD exacerbation and may remain elevated beyond one year.19,20 These findings highlight the importance of cardiovascular assessment and management in patients with AECOPD, including appropriate treatment of coexisting CVD.21–23 Nevertheless, evidence remains limited on how CVD count relates to prolonged LOS and whether different CVD combinations show distinct associations with prolonged LOS and 1-year AECOPD-related readmission.
Therefore, this retrospective study evaluated the associations of CVD count and combinations with prolonged LOS and 1-year AECOPD-related readmission among patients hospitalized for AECOPD. We sought to better characterize cardiovascular risk patterns associated with adverse in-hospital and post-discharge outcomes and to provide evidence for more targeted clinical risk assessment and management.
Material and Methods
Study Population
This single-centre retrospective study comprised patients hospitalized for AECOPD at Beijing Friendship Hospital, Capital Medical University, between January 2015 and December 2024. The inclusion criteria were as follows: (1) a primary admission diagnosis of AECOPD (ICD-10 code J44.101) and (2) age ≥40 years. Admissions were excluded if relevant laboratory test results were unavailable or if the tests were performed more than 48 hours after admission. Ultimately, 3684 admissions from 2281 patients were included in the analysis.
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (Approval No. 2025-P2-065-01). Given the retrospective nature of the study, the requirement for informed consent was waived. All patient data were anonymized before analysis.
Variables and Measurements
Data Collection
All admission data were obtained from a continuously updated real-world clinical data platform that automatically extracts information from the electronic health record system of Beijing Friendship Hospital, Capital Medical University. For each eligible admission, we extracted hospitalization timing information, demographic characteristics, medical history, vital signs, laboratory tests of blood (complete blood count and blood chemistry, etc), and inflammatory indices (neutrophil-to-lymphocyte ratio [NLR], monocyte-to-lymphocyte ratio [MLR], eosinophil-to-lymphocyte ratio [ELR], basophil-to-lymphocyte ratio [BLR], platelet-to-lymphocyte ratio [PLR], C-reactive protein [CRP]). For patients with multiple admissions, all eligible admission records were retained and linked using a unique patient identifier.
CVD comorbidities were identified from the medical history documented in the electronic health record for each admission. Based on the Chinese expert consensus,24 five CVD comorbidities were included: hypertension (HTN), IHD, arrhythmia, HF, and peripheral arterial disease (PAD). For each admission, the CVD count was calculated as the total number of these five comorbidities and ranged from 0 to 5. Admissions were categorized into four groups: no CVD, 1 CVD, 2 CVDs, and ≥3 CVDs. Because CVD count was assessed separately for each admission, the same patient could be classified into different groups across admissions.
Outcome Definitions
The primary outcome was prolonged LOS. LOS was calculated as the number of days between the admission date and discharge date for each admission. Based on previous studies, prolonged LOS was defined as a hospital stay of ≥14 days.15,25 To assess the robustness of the findings, a sensitivity analysis was performed in which prolonged LOS was redefined as an LOS above the 75th percentile of the LOS distribution in the study cohort, consistent with the approach used in previous studies.26,27 LOS was also analyzed as a continuous outcome to reduce the potential information loss associated with dichotomization.
AECOPD-related readmission was assessed as a secondary outcome using a 1-year observation window. For each eligible admission, we identified whether a subsequent AECOPD-related admission was recorded within 1 year after discharge. If multiple admissions occurred during this period, only the earliest subsequent admission was considered the readmission event. Time to readmission was calculated as the number of days from discharge to the admission date of this earliest subsequent admission.
Statistical Analysis
All statistical analyses were performed using Python version 3.12.6. Categorical variables were summarized as n(%) and compared using the chi-square test or Fisher’s exact test, as appropriate. Normally distributed continuous variables were expressed as mean ± standard deviation and compared using one-way analysis of variance. Non-normally distributed continuous variables were expressed as median (interquartile range) and compared using the Kruskal–Wallis H-test. For descriptive comparisons, P values were additionally adjusted using the Benjamini-Hochberg false discovery rate (FDR) method.
To examine whether the associations differed across specific CVD combinations, combination-specific analyses were performed. To reduce the instability of estimates arising from sparse categories, these analyses were restricted to CVD combinations represented by at least 50 admissions.28,29
To alleviate the potential impact of within-patient correlation arising from multiple admissions, generalized estimating equation (GEE) logistic regression models were used to assess the association between CVD comorbidities and prolonged LOS.30 The unique patient identifier was specified as the clustering variable. CVD count and CVD combinations were both modeled as categorical variables, with the no-CVD group serving as the reference. The results were reported as odds ratios (ORs) with 95% confidence intervals (CIs).
Cox proportional hazards regression (Cox-PH) models were used to assess the association between CVD comorbidities and the risk of readmission. Similarly, patient-level cluster-robust sandwich variance estimators were applied in Cox-PH models to account for within-patient correlation.31,32 The discharge date of each eligible admission was defined as time zero. In the analyses of both CVD count and specific CVD combinations, the no-CVD group served as the reference category. The results were reported as hazard ratios (HRs) with 95% CIs.
For both the GEE and Cox-PH analyses, the CVD-count groups of 0, 1, 2, and ≥3 CVDs were assigned values of 0, 1, 2, and 3, respectively. The resulting ordinal variable was entered into the models to test for a linear trend. Three models were fitted for each analysis. Model 1 was unadjusted. Model 2 adjusted for demographic characteristics, lifestyle factors, and diabetes. Model 3 was the fully adjusted model and additionally included vital signs and hematologic and coagulation indicators selected based on previous evidence of their associations with clinical outcomes.3,9,33,34
Using the fully adjusted model, subgroup analyses of prolonged LOS, defined as an LOS of ≥14 days, were conducted according to age, sex, smoking status, drinking status, and diabetes status. Interactions were assessed by including the corresponding interaction terms. For the sensitivity analyses, all three adjustment models were refitted using the alternative outcome definitions described above. The percentile-based binary outcome was analyzed using GEE logistic regression. LOS was log-transformed and analyzed as a continuous outcome using GEE Gaussian models.35 The regression coefficients were exponentiated and expressed as percentage differences in LOS.
All statistical tests were two-sided, and P < 0.05 was considered statistically significant.
Results
Baseline Characteristics
Table 1 summarized the baseline characteristics of 3684 admissions. Of these, 1044 (28.3%) had no CVD, whereas 2640 (71.7%) had at least one CVD. Specifically, 1389 admissions (37.7%) had 1 CVD, 873 (23.7%) had 2 CVDs, and 378 (10.3%) had ≥3 CVDs. The overall median age was 74.0 years, and 72.2% of admissions involved male patients. The prevalence of diabetes increased from 10.0% in the no-CVD group to 41.3% in the ≥3-CVDs group. Compared with the no-CVD group, the ≥3-CVDs group had higher median RDW-CV (13.4% vs 12.7%), NLR (4.6 vs 3.1), and D-dimer (0.7 vs 0.5 µg/mL). In contrast, median eosinophil percentage (1.3% vs 2.0%), platelet count (197.0 vs 218.0 ×109/L), and hemoglobin (128.0 vs 137.0 g/L) were lower. All these laboratory indicators were statistically significant after FDR adjustment (all adjusted P<0.001).
Table 1.
Comparison of Baseline Characteristics Across Four CVD-Count Groups
| Variable | Total (n = 3684) | No CVD (n = 1044) | 1 CVD (n = 1389) | 2 CVDs (n = 873) | ≥3 CVDs (n = 378) | P Value+ |
|---|---|---|---|---|---|---|
| Demographics | ||||||
| Age, years | 74.0 (66.0, 82.0) | 70.0 (63.0, 77.0) | 74.0 (66.0, 82.0) | 78.0 (70.0, 83.0) | 80.0 (73.0, 85.0) | <0.001*** |
| Sex, n (%) | <0.001*** | |||||
| Male | 2660 (72.2) | 804 (77.0) | 993 (71.5) | 603 (69.1) | 260 (68.8) | |
| Female | 1024 (27.8) | 240 (23.0) | 396 (28.5) | 270 (30.9) | 118 (31.2) | |
| Smoking status, n (%) | <0.001*** | |||||
| Never | 1128 (30.6) | 296 (28.4) | 417 (30.0) | 283 (32.4) | 132 (34.9) | |
| Former | 1460 (39.6) | 367 (35.2) | 564 (40.6) | 358 (41.0) | 171 (45.2) | |
| Current | 1096 (29.8) | 381 (36.5) | 408 (29.4) | 232 (26.6) | 75 (19.8) | |
| Drinking status, n (%) | 0.067 | |||||
| Never | 2252 (61.1) | 630 (60.3) | 860 (61.9) | 535 (61.3) | 227 (60.1) | |
| Former | 429 (11.6) | 117 (11.2) | 143 (10.3) | 108 (12.4) | 61 (16.1) | |
| Current | 1003 (27.2) | 297 (28.4) | 386 (27.8) | 230 (26.3) | 90 (23.8) | |
| Diabetes, n (%) | <0.001*** | |||||
| Yes | 796 (21.6) | 104 (10.0) | 272 (19.6) | 264 (30.2) | 156 (41.3) | |
| No | 2888 (78.4) | 940 (90.0) | 1117 (80.4) | 609 (69.8) | 222 (58.7) | |
| Vital signs | ||||||
| Heart rate, beats/minute | 78.0 (70.0, 89.0) | 79.0 (72.0, 89.0) | 79.0 (71.0, 90.0) | 76.0 (67.0, 86.0) | 79.0 (70.0, 90.0) | <0.001*** |
| Respiratory rate, breaths/min | 19.0 (18.0, 20.0) | 19.0 (18.0, 20.0) | 19.0 (18.0, 20.0) | 19.0 (18.0, 20.0) | 20.0 (18.0, 20.0) | <0.001*** |
| SBP, mmHg | 130.0 (120.0, 140.0) | 125.0 (116.2, 135.0) | 130.0 (120.0, 140.0) | 130.0 (120.0, 141.0) | 130.0 (120.0, 140.0) | <0.001*** |
| DBP, mmHg | 75.0 (69.0, 80.0) | 76.0 (70.0, 80.0) | 76.0 (70.0, 81.0) | 72.0 (66.0, 80.0) | 70.0 (64.0, 80.0) | <0.001*** |
| Laboratory indicators | ||||||
| WBC, ×109/L | 6.9 (5.5, 8.7) | 6.5 (5.3, 8.2) | 6.9 (5.5, 8.7) | 7.0 (5.7, 8.9) | 7.1 (5.7, 9.7) | <0.001*** |
| RBC, ×1012/L | 4.3 (4.0, 4.8) | 4.4 (4.0, 4.8) | 4.4 (4.0, 4.8) | 4.3 (3.9, 4.7) | 4.2 (3.8, 4.6) | <0.001*** |
| RDW-CV, % | 12.9 (12.3, 13.6) | 12.7 (12.2, 13.4) | 12.8 (12.3, 13.4) | 13.0 (12.4, 13.8) | 13.4 (12.7, 14.1) | <0.001*** |
| Lymphocyte percent, % | 19.7 (13.2, 26.1) | 22.0 (15.2, 28.6) | 19.4 (12.9, 25.7) | 18.7 (12.6, 24.6) | 16.4 (11.0, 22.3) | <0.001*** |
| Neutrophil percent, % | 70.9 (63.6, 78.8) | 68.2 (60.5, 76.3) | 71.1 (63.8, 79.0) | 71.5 (64.8, 79.6) | 74.1 (67.5, 81.1) | <0.001*** |
| Monocyte percent, % | 6.1 (4.5, 7.5) | 6.0 (4.5, 7.5) | 6.2 (4.5, 7.5) | 6.1 (4.6, 7.6) | 6.0 (4.8, 7.6) | 0.558 |
| Eosinophil percent, % | 1.7 (0.8, 3.3) | 2.0 (0.9, 3.8) | 1.8 (0.8, 3.1) | 1.7 (0.8, 3.3) | 1.3 (0.4, 2.4) | <0.001*** |
| Basophil percent, % | 0.3 (0.2, 0.5) | 0.4 (0.2, 0.6) | 0.3 (0.2, 0.5) | 0.3 (0.1, 0.5) | 0.2 (0.1, 0.4) | <0.001*** |
| PLT, ×109/L | 213.0 (172.0, 262.0) | 218.0 (176.0, 265.2) | 219.0 (172.0, 269.0) | 210.0 (172.0, 251.0) | 197.0 (157.0, 237.8) | <0.001*** |
| Hemoglobin, g/L | 134.0 (122.0, 145.0) | 137.0 (125.8, 147.0) | 134.0 (123.0, 146.0) | 131.0 (119.0, 142.0) | 128.0 (114.0, 140.0) | <0.001*** |
| NLR | 3.6 (2.4, 5.9) | 3.1 (2.1, 4.9) | 3.6 (2.5, 6.0) | 3.8 (2.7, 6.2) | 4.6 (3.0, 7.2) | <0.001*** |
| MLR | 0.3 (0.2, 0.5) | 0.3 (0.2, 0.4) | 0.3 (0.2, 0.5) | 0.3 (0.2, 0.5) | 0.4 (0.2, 0.6) | <0.001*** |
| PLR | 162.9 (120.4, 232.4) | 158.2 (116.3, 211.8) | 166.7 (121.1, 242.3) | 163.0 (123.8, 235.8) | 166.8 (122.4, 240.8) | <0.001*** |
| ELR | 0.1 (0.0, 0.2) | 0.1 (0.1, 0.2) | 0.1 (0.1, 0.2) | 0.1 (0.0, 0.2) | 0.1 (0.0, 0.1) | <0.001*** |
| BLR | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.0 (0.0, 0.0) | 0.002** |
| CRP, mg/L | 6.0 (2.3, 22.0) | 6.0 (2.2, 22.4) | 6.1 (2.3, 22.6) | 5.9 (2.1, 20.0) | 7.8 (3.0, 21.0) | 0.460 |
| Fibrinogen, g/L | 3.4 (2.8, 4.4) | 3.4 (2.7, 4.3) | 3.4 (2.8, 4.5) | 3.4 (2.8, 4.3) | 3.6 (3.0, 4.5) | 0.069 |
| D-dimer, µg/mL | 0.6 (0.3, 0.9) | 0.5 (0.3, 0.8) | 0.6 (0.3, 0.9) | 0.6 (0.3, 1.0) | 0.7 (0.4, 1.2) | <0.001*** |
Notes: **P<0.01; ***P<0.001; +: P values were adjusted using the Benjamini-Hochberg FDR method.
Abbreviations: CVD, cardiovascular disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell count; RBC, red blood cell count; RDW-CV, red blood cell distribution width-coefficient of variation; PLT, platelet count; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; ELR, eosinophil-to-lymphocyte ratio; BLR, basophil-to-lymphocyte ratio; CRP, C-reactive protein; FDR, false discovery rate.
Figure 1A shows the prevalence of the five CVD comorbidities. HTN was the most prevalent and was recorded in 2213 admissions (60.07%), followed by IHD in 1081 admissions (29.34%). In this cohort, 28 mutually exclusive CVD combinations were identified. Figure 1B presents the 10 most frequent combinations. HTN alone was the most common combination (n = 1072; 29.10%), followed by HTN + IHD (n = 507; 13.76%).
Figure 1.
Distribution of CVD comorbidities among admissions for AECOPD. (A) Prevalence of the five CVD comorbidities. (B) The 10 most frequent mutually exclusive CVD combinations.
Abbreviations: CVD, cardiovascular disease; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; HTN, hypertension; IHD, ischemic heart disease; PAD, peripheral arterial disease; HF, heart failure.
Seven CVD combinations were therefore included in the subsequent combination-specific outcome analyses: HTN (n = 1072), IHD (n = 161), Arrhythmia (n = 124), HTN + IHD (n = 507), HTN + Arrhythmia (n = 207), HTN + IHD + Arrhythmia (n = 155), and HTN + IHD + PAD (n = 63). The no-CVD group was included as the reference category. The complete distribution of all CVD combinations and their corresponding descriptive clinical outcomes is provided in Table S1.
Clinical Outcomes
Table 2 presented the clinical outcomes across CVD-count groups. The 75th percentile of LOS in this cohort was 13 days. Median LOS increased from 9.0 days in the no-CVD group to 12.0 days in the ≥3-CVDs group. The proportions of admissions with LOS ≥14 days increased from 14.7% to 39.9%, and those with LOS ≥13 days increased from 20.6% to 49.7%. 1-year readmission for AECOPD increased from 22.2% to 32.8% (all FDR-adjusted P<0.001).
Table 2.
Comparison of Clinical Outcomes Across Four CVD-Count Groups
| Variable | Total (n=3684) | No CVD (n=1044) | 1 CVD (n=1389) | 2 CVDs (n = 873) | ≥3 CVDs (n = 378) | P Value+ |
|---|---|---|---|---|---|---|
| LOS ≥ 14 days, n (%) | 782 (21.2) | 153 (14.7) | 244 (17.6) | 234 (26.8) | 151 (39.9) | <0.001*** |
| LOS, days | 10.0 (7.0, 13.0) | 9.0 (7.0, 12.0) | 10.0 (7.0, 12.0) | 10.0 (8.0, 14.0) | 12.0 (8.0, 16.8) | <0.001*** |
| LOS ≥ 13 days, n (%) | 1044 (28.3) | 215 (20.6) | 339 (24.4) | 302 (34.6) | 188 (49.7) | <0.001*** |
| 1-year readmission, n (%) | 935 (27.6) | 212 (22.2) | 367 (28.8) | 242 (29.8) | 114 (32.8) | <0.001*** |
Notes: ***P<0.001; +: P values were adjusted using the Benjamini-Hochberg FDR method.
Abbreviations: CVD, cardiovascular disease; LOS, length of stay; FDR, false discovery rate.
GEE Analyses
GEE logistic regression was used to assess the association between CVD comorbidities and prolonged LOS. Given the between-group differences shown in Table 1, covariates were introduced sequentially. Model 2 adjusted for age, sex, diabetes, smoking status, and drinking status. Model 3 was fully adjusted and additionally included heart rate, respiratory rate, NLR, hemoglobin, RDW-CV, eosinophil percentage, platelet count, and D-dimer.
As shown in Table 3, all three CVD-count groups had higher odds of prolonged LOS than the no-CVD group in the unadjusted analysis. The associations were attenuated after full adjustment. The 1-CVD group was not significantly associated with prolonged LOS (OR: 1.10; 95% CI: 0.83–1.45; P = 0.515). The association remained significant in the 2-CVDs group (OR: 1.60; 95% CI: 1.18–2.16; P = 0.002). A significant association was also observed in the ≥3-CVDs group (OR: 1.95; 95% CI: 1.30–2.93; P = 0.001). A significant positive trend across increasing CVD counts was observed in all three models (all P for trend <0.001).
Table 3.
Association Between CVD Count and LOS ≥14 Days
| CVD Count | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| OR (95% CI) | P Value | OR (95% CI) | P Value | OR (95% CI) | P Value | |
| 0 CVD | Reference | Reference | Reference | |||
| 1 CVD | 1.34 (1.04–1.72) | 0.026* | 1.06 (0.81–1.38) | 0.691 | 1.10 (0.83–1.45) | 0.515 |
| 2 CVDs | 2.19 (1.67–2.86) | <0.001*** | 1.47 (1.10–1.95) | 0.009** | 1.60 (1.18–2.16) | 0.002** |
| ≥3 CVD | 3.68 (2.59–5.21) | <0.001*** | 2.00 (1.37–2.91) | <0.001*** | 1.95 (1.30–2.93) | 0.001** |
| P for trend | <0.001*** | <0.001*** | <0.001*** | |||
Notes: *P<0.05; **P<0.01; ***P<0.001.
Abbreviations: CVD, cardiovascular disease; LOS, length of stay; OR, odds ratio; CI, confidence interval.
Beyond the CVD-count analysis, the associations between CVD combinations and LOS ≥14 days were examined. Table S2 presents the results for the seven combinations included in the analysis. After full adjustment, HTN + IHD was associated with higher odds of LOS ≥14 days (OR: 1.70; 95% CI: 1.22–2.37; P = 0.002). Similar associations were observed for HTN + Arrhythmia (OR: 1.83; 95% CI: 1.15–2.92; P = 0.011) and HTN + IHD + Arrhythmia (OR: 1.85; 95% CI: 1.16–2.94; P = 0.010). The strongest association was observed for HTN + IHD + PAD (OR: 2.53; 95% CI: 1.25–5.12; P = 0.010). In contrast, none of the 1-CVD groups was significantly associated with LOS ≥14 days.
Cox-PH Analyses
Cox-PH analyses were used to assess the association between CVD comorbidities and 1-year AECOPD-related readmission. After excluding 295 admissions with an observation window of less than 1 year, 3389 admissions remained for the Cox-PH analyses.
Table 4 reports the associations between CVD count and 1-year readmission. In the unadjusted analysis, all three CVD-count groups had significantly higher hazards of readmission than the no-CVD group. These associations remained significant after full adjustment. The HR was 1.38 for the 1-CVD group (95% CI: 1.06–1.80; P = 0.018). The HR was 1.42 for the 2-CVDs group (95% CI: 1.07–1.89; P = 0.017). The ≥3-CVDs group had the highest hazard of readmission (HR: 1.68; 95% CI: 1.14–2.49; P = 0.009). A significant positive trend remained across increasing CVD counts (P for trend = 0.006).
Table 4.
Association Between CVD Count and 1-Year AECOPD-Related Readmission
| CVD Count | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| HR (95% CI) | P Value | HR (95% CI) | P Value | HR (95% CI) | P Value | |
| 0 CVD | Reference | Reference | Reference | |||
| 1 CVD | 1.37 (1.06–1.76) | 0.015* | 1.38 (1.07–1.79) | 0.014* | 1.38 (1.06–1.80) | 0.018* |
| 2 CVDs | 1.42 (1.09–1.85) | 0.009** | 1.43 (1.08–1.89) | 0.013* | 1.42 (1.07–1.89) | 0.017* |
| ≥3 CVD | 1.61 (1.17–2.21) | 0.004** | 1.62 (1.13–2.35) | 0.010* | 1.68 (1.14–2.49) | 0.009** |
| P for trend | 0.001** | 0.006** | 0.006** | |||
Notes: *P<0.05; **P<0.01.
Abbreviations: CVD, cardiovascular disease; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; HR, hazard ratio; CI, confidence interval.
The combination-specific results are provided in Table S3. Among the 1-CVD group, HTN alone was significantly associated with a higher hazard of 1-year readmission (HR: 1.42; 95% CI: 1.06–1.91; P = 0.019). Among the multi-CVD combinations, HTN + Arrhythmia was associated with a higher hazard of readmission (HR: 1.59; 95% CI: 1.04–2.45; P = 0.034).
Subgroup and Sensitivity Analyses
Subgroup analyses were conducted to assess whether the association between CVD count and prolonged LOS varied across clinically relevant patient characteristics. Figure 2 summarizes the results stratified by age, sex, smoking status, drinking status, and diabetes status. The association was generally consistent across these subgroups. No significant effect modification was identified, with all P values for interaction >0.05.
Figure 2.
Subgroup analyses of the association between CVD count and LOS ≥ 14 days.
Abbreviations: CVD, cardiovascular disease; LOS, length of stay; OR, odds ratio; CI, confidence interval.
Two sensitivity analyses were conducted to assess the robustness of the association between CVD count and LOS. Table 5 reports the results from all three adjustment models. When prolonged LOS was redefined as LOS ≥13 days, the fully adjusted results were consistent with the primary analysis. The 1-CVD group was not significantly associated with prolonged LOS (OR: 1.08; 95% CI: 0.85–1.37; P = 0.554). In contrast, the odds of prolonged LOS were higher in the 2-CVDs group (OR: 1.51; 95% CI: 1.15–1.99; P = 0.003) and the ≥3-CVDs group (OR: 1.99; 95% CI: 1.41–2.82; P<0.001). When log-transformed LOS was analyzed as a continuous outcome, the fully adjusted percentage difference was 2.75% for the 1-CVD group (95% CI: −0.98% to 6.63%; P = 0.150). LOS was 7.47% higher in the 2-CVDs group (95% CI: 2.51%-12.67%; P = 0.003) and 17.04% higher in the ≥3-CVDs group (95% CI: 7.28%-27.68%; P<0.001). In all three models, increasing CVD count was associated with progressively higher odds of LOS ≥13 days and greater percentage increases in LOS (all P for trend <0.001).
Table 5.
Sensitivity Analyses of the Association Between CVD Count and LOS
| CVD Count | Model 1 | Model 2 | Model 3 | |||
|---|---|---|---|---|---|---|
| Effect Estimate (95% CI)+ | P value | Effect Estimate (95% CI) | P value | Effect Estimate (95% CI) | P Value | |
| 0 CVD | Reference | Reference | Reference | |||
| LOS ≥ 13 days | ||||||
| 1 CVD | 1.30 (1.05–1.62) | 0.016* | 1.05 (0.83–1.31) | 0.702 | 1.08 (0.85–1.37) | 0.554 |
| 2 CVDs | 2.07 (1.64–2.62) | <0.001*** | 1.43 (1.11–1.84) | 0.006** | 1.51 (1.15–1.99) | 0.003** |
| ≥3 CVDs | 3.67 (2.72–4.94) | <0.001*** | 2.08 (1.51–2.86) | <0.001*** | 1.99 (1.41–2.82) | <0.001*** |
| P for trend | <0.001*** | <0.001*** | <0.001*** | |||
| LOS | ||||||
| 1 CVD | 8.47 (4.54–12.55) | <0.001*** | 2.62 (−1.09–6.46) | 0.169 | 2.75 (−0.98–6.63) | 0.150 |
| 2 CVDs | 18.15 (12.67–23.89) | <0.001*** | 7.17 (2.34–12.21) | 0.003** | 7.47 (2.51–12.67) | 0.003** |
| ≥3 CVDs | 37.52 (25.77–50.36) | <0.001*** | 18.62 (9.09–28.98) | <0.001*** | 17.04 (7.28–27.68) | <0.001*** |
| P for trend | <0.001*** | <0.001*** | <0.001*** | |||
Notes: *P<0.05; **P<0.01; ***P<0.001. +: For LOS ≥13 days, effect estimates are presented as ORs. For log-transformed LOS, estimates were exponentiated and expressed as percentage differences in LOS using [exp(β) - 1] × 100%.
Abbreviations: CVD, cardiovascular disease; LOS, length of stay; CI, confidence interval; OR, odds ratio.
Discussion
This real-world retrospective study explored the associations of CVD count and combinations with prolonged LOS and 1-year AECOPD-related readmission among patients hospitalized for AECOPD. Overall, 71.7% of admissions involved at least one CVD, with HTN being the most prevalent. The risks of prolonged LOS and 1-year AECOPD-related readmission generally increased with increasing CVD count. Among the CVD combinations, HTN + IHD + PAD showed the strongest association with prolonged LOS (OR: 2.53; 95% CI: 1.25–5.12). HTN + Arrhythmia was associated with both prolonged LOS (OR: 1.83; 95% CI: 1.15–2.92) and 1-year AECOPD-related readmission (HR: 1.59; 95% CI: 1.04–2.45). These findings suggest that, in addition to CVD count, CVD combinations may provide complementary information for identifying the burden of inpatient management and post-discharge readmission risk among patients hospitalized for AECOPD.
In this cohort, 71.7% of admissions involved at least one CVD, with HTN and IHD being the most prevalent cardiovascular conditions, consistent with previous research.13,15 Increasing CVD count was accompanied by older age, a higher prevalence of diabetes, higher NLR, RDW-CV, and D-dimer levels, and lower hemoglobin levels, platelet counts, and eosinophil percentages. These between-group differences suggest that patients with AECOPD and multiple CVDs may have a more complex comorbidity burden and lower physiological reserve. Previous studies have identified shared risk factors and interconnected pathophysiological processes between COPD and CVD, including systemic inflammation, oxidative stress, endothelial dysfunction, hypoxemia, and autonomic dysfunction, which may become more prominent during acute exacerbations.17,36 The decrease in eosinophil percentage with increasing CVD count should be interpreted cautiously. Blood eosinophils primarily reflect an eosinophilic inflammatory phenotype in COPD and may help predict the likelihood of benefit from corticosteroid treatment, rather than serving as a general measure of inflammatory severity.25,33 Although a previous study of stable COPD reported higher blood eosinophil levels among patients with concomitant CVD, blood eosinophil levels may vary dynamically during AECOPD, which may partly account for differences observed across disease stages.37,38 Our study used a single eosinophil percentage measured within 48 hours after admission, which may have been influenced by acute infection or physiological stress, corticosteroid exposure, and the concurrent increase in neutrophil percentage. Therefore, this finding reflects differences in admission leukocyte profiles across CVD-count groups and should not be interpreted as demonstrating an independent relationship between lower eosinophil levels and a greater number of pre-existing CVDs.
For the primary outcome, a graded association was observed between CVD count and prolonged LOS. After full adjustment, admissions with 2 and ≥3 CVDs had 60% and 95% higher odds of prolonged LOS, respectively, compared with admissions without CVD, with a significant linear trend. The sensitivity analyses further supported these findings. When prolonged LOS was redefined as LOS ≥13 days, 2 and ≥3 CVDs remained associated with 51% and 99% higher odds, respectively. When LOS was analyzed as a continuous outcome, LOS was 7.47% and 17.04% longer in the 2-CVDs and ≥3-CVDs groups, respectively. No statistically significant interactions were identified across subgroups defined by age, sex, smoking status, drinking status, or diabetes, suggesting that the overall direction of the association was generally consistent across these patient characteristics. The previous DiMECO study considered prolonged hospitalization a clinical marker of difficult-to-manage COPD exacerbations and showed that greater acute clinical complexity was related to longer hospitalization.39 The absence of a statistically significant association in the 1-CVD group after full adjustment does not necessarily indicate that a single CVD has no influence on hospitalization. Its unadjusted association was substantially attenuated after controlling for age, diabetes, and other clinical indicators, suggesting that part of the between-group difference may have been explained by age and overall clinical status. In addition, the 1-CVD group consisted predominantly of patients with isolated HTN, which may partly explain why the association with prolonged LOS was attenuated after adjustment.
The coexistence of multiple CVDs may further complicate the differentiation and simultaneous management of respiratory deterioration and cardiac dysfunction, thereby increasing the complexity of inpatient care.40 Hypoxemia, hypercapnia, infection, and systemic inflammation during AECOPD may also precipitate or aggravate blood-pressure instability, arrhythmia, myocardial ischemia, or HF, and patients with a greater pre-existing CVD burden may be more susceptible to cardiovascular decompensation.41 Even without a clearly documented acute cardiovascular event, patients with multiple CVDs may require closer cardiovascular monitoring, additional investigations, and medication adjustments, as well as stabilization of both respiratory and cardiovascular conditions before discharge. The accumulation of these management and discharge requirements may help explain why the independent association with prolonged LOS became evident mainly from two coexisting CVDs.
The combination-specific analysis further indicated that CVD patterns containing the same number of conditions may not necessarily have identical associations with prolonged LOS. Among the two-CVD combinations, HTN + IHD and HTN + arrhythmia were associated with prolonged LOS, with fully adjusted ORs of 1.70 and 1.83, respectively. Among the three-CVD combinations, the corresponding ORs were 1.85 for HTN + IHD + Arrhythmia and 2.53 for HTN + IHD + PAD. For HTN + Arrhythmia, hypoxemia, hypercapnia, inflammation, autonomic dysfunction, and exposure to some bronchodilators may increase susceptibility to heart-rate and rhythm instability.42 β2-agonists may affect heart rate, cardiac rhythm, and serum potassium in susceptible patients, whereas patients with arrhythmia, IHD, or HF may require β-blockers for established cardiovascular indications. Current evidence indicates that β-blockers should not be withheld solely because of COPD when a clear cardiovascular indication is present, with cardioselective β1-blockers generally preferred.43 Balancing adequate bronchodilation with the maintenance of heart-rate and rhythm stability may require closer monitoring and more frequent medication adjustments during hospitalization. HTN + IHD + PAD yielded the largest OR point estimate. PAD is not only a manifestation of lower-extremity arterial disease but also a marker of systemic atherosclerotic burden and impaired functional status; its coexistence with IHD may therefore reflect more extensive vascular disease and lower functional reserve.44
The association between CVD count and 1-year AECOPD-related readmission emerged at a lower CVD count than that observed for prolonged LOS. After full adjustment, even one CVD was associated with a higher risk of readmission, and the risk increased further among patients with two and ≥3 CVDs (P for trend = 0.006). In contrast, a significant association with prolonged LOS was observed only among patients with at least two CVDs. This difference suggests that the presence of any CVD may help identify patients at increased risk after discharge, whereas multiple coexisting CVDs may be more closely related to the complexity of clinical management during hospitalization. Therefore, CVD count may have different implications for in-hospital and post-discharge outcomes among patients hospitalized for AECOPD. Feng et al similarly reported progressively higher risks of AECOPD-related readmission with increasing numbers of CVD comorbidities, consistent with the trend observed in this study.18 Systematic-review evidence and the EXACOS-CV studies have shown that the risk of severe cardiovascular events increases markedly after COPD exacerbations and may remain elevated for several months, one year, or longer.19,20,45–47 This sustained period of cardiovascular vulnerability may reduce patients’ ability to tolerate subsequent respiratory stress and provide a possible explanation for the association between CVD count and readmission.
Different CVD combinations were not uniformly associated with 1-year AECOPD-related readmission. Among the 1-CVD group, HTN alone was associated with a 42% higher hazard of readmission. HTN may reflect accumulated vascular risk and hypertension-mediated cardiac, renal, or vascular damage.48 Among the 2-CVDs group, HTN + Arrhythmia was associated with a 59% higher hazard of 1-year readmission. Jiang et al similarly found that atrial fibrillation independently predicted 1-year readmission among patients hospitalized for AECOPD, providing related evidence for the finding concerning HTN + Arrhythmia.15 COPD-related autonomic dysfunction, reduced heart-rate variability, and susceptibility to rhythm instability may be involved; however, this evidence is primarily derived from studies of atrial fibrillation or physiological mechanisms.42,49 HTN + Arrhythmia may therefore represent a clinically relevant CVD combination, but this finding requires validation in studies with detailed information on arrhythmia type and severity.
Several limitations should be acknowledged. First, this was a single-centre retrospective study, and AECOPD-related readmissions to other hospitals could not be captured, which may limit generalizability and underestimate readmission risk. Second, the retrospective nature of this study and the use of routinely collected electronic health record data limited the availability of more detailed and comprehensive clinical information. COPD diagnoses were based predominantly on diagnostic codes without systematic spirometric confirmation, and detailed information on baseline COPD was not consistently available. In addition, assessment of AECOPD severity according to the Rome proposal was limited by missing arterial blood gas data and by some measurements being obtained during oxygen therapy. Third, CVD diagnoses and severity may have been incompletely captured, and estimates for less frequent CVD combinations may have been imprecise. Future multicenter prospective studies incorporating more comprehensive respiratory and cardiovascular assessments are needed to validate these findings.
Conclusion
This study demonstrated that CVD comorbidities were significantly associated with prolonged LOS and an increased risk of 1-year AECOPD-related readmission. These findings suggest that CVD profiles may serve as useful indicators of inpatient complexity and adverse prognosis. Further multicenter prospective studies are needed to validate these findings and clarify their potential clinical implications.
Funding Statement
This work was supported by Beijing Natural Science Foundation (Grant No. L259060) and IMICAMS Youth Talent Development Fund (Grant No. 2024YT01).
Abbreviations
AECOPD, acute exacerbation of chronic obstructive pulmonary disease; COPD, chronic obstructive pulmonary disease; LOS, length of stay; CVD, cardiovascular disease; IHD, ischemic heart disease; HF, heart failure; NLR, neutrophil-to-lymphocyte ratio; MLR, monocyte-to-lymphocyte ratio; ELR, eosinophil-to-lymphocyte ratio; BLR, basophil-to-lymphocyte ratio; PLR, platelet-to-lymphocyte ratio; CRP, C-reactive protein; HTN, hypertension; PAD, peripheral arterial disease; FDR, false discovery rate; GEE, generalized estimating equations; OR, odds ratio; CI, confidence interval; Cox-PH, Cox proportional hazards; HR, hazard ratio; RDW-CV, red blood cell distribution width–coefficient of variation; SBP, systolic blood pressure; DBP, diastolic blood pressure; WBC, white blood cell count; RBC, red blood cell count; PLT, platelet count.
Data Sharing Statement
The datasets analyzed in this study are not publicly available but are available from the corresponding author upon reasonable request.
Ethics Approval and Informed Consent
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Beijing Friendship Hospital, Capital Medical University (Approval No. 2025-P2-065-01). Given the retrospective nature of the study, the requirement for informed consent was waived. All patient data were anonymized before analysis.
Author Contributions
Conceptualization: Yuxiao Fu, Jie Hao, Ganggang Yu; Methodology: Yuxiao Fu, Jie Hao, Yin Zeng, Xuwen Wang; Software: Yuxiao Fu; Validation: Yuxiao Fu, Ganggang Yu; Formal analysis: Yuxiao Fu, Yin Zeng; Investigation: Yuxiao Fu, Lin Chen, Wen Qi; Data curation: Yuxiao Fu, Lin Chen; Visualization: Yuxiao Fu; Writing - original draft: Yuxiao Fu; Writing - review & editing: Jie Hao, Jiao Li, Xuwen Wang, Ganggang Yu; Funding acquisition: Ganggang Yu, Jie Hao; Resources: Xuwen Wang, Ganggang Yu, Jie Hao; Supervision: Wen Qi, Xuwen Wang, Jie Hao; Project administration: Jie Hao, Xuwen Wang, Ganggang Yu. All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets analyzed in this study are not publicly available but are available from the corresponding author upon reasonable request.