Abstract
Background
Cardiovascular and cerebrovascular comorbidities are prevalent in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD), but their impact on in-hospital outcomes and the risk of readmission remains unclear. This study aimed to describe the proportions of these comorbidities and assess their influence on patient outcomes.
Methods
Hospital admission records from 2013 to 2020 with a primary discharge diagnosis of AECOPD were retrieved from Beijing Public Health Information Centre database. Comorbidities were identified through discharge diagnoses, while in-hospital outcomes and subsequent readmissions were tracked. Logistic regression model, generalised linear model and subdistributional hazard model were used to evaluate the associations between comorbidities and adverse outcomes.
Results
Among 98 127 patients, cardiovascular comorbidities were present in 78.3% of cases and cerebrovascular comorbidities were present in 30.3% of cases. Patients with cardiovascular comorbidities or cerebrovascular comorbidities or both had prolonged length of stay (ORs: 1.29, 95% CI: 1.23 to 1.35; 1.20, 95% CI: 1.10 to 1.32; 1.52, 95% CI: 1.44 to 1.60) and higher in-hospital mortality (ORs: 1.39, 95% CI: 1.19 to 1.62; 1.34, 95% CI: 1.04 to 1.75; 1.25, 95% CI: 1.06 to 1.48) compared with those without these conditions. Patients with cardiovascular comorbidities and those with both cardiovascular and cerebrovascular comorbidities were at increased risk of readmission (HRs: 1.14, 95% CI: 1.10 to 1.19; 1.19, 95% CI: 1.14 to 1.25), whereas cerebrovascular comorbidities alone were not. The impact of individual comorbidity varied, with heart failure, ischaemic heart disease, arrhythmia, hypertension, ischaemic stroke and cerebrovascular sequelae showing positive associations with adverse outcomes, but the opposite was observed for peripheral arterial disease, arterial stenosis and other cerebrovascular diseases.
Conclusion
Most cardiovascular comorbidities and major cerebrovascular comorbidities are significant predictors of length of stay, in-hospital mortality and readmission in AECOPD patients. These findings highlight the need for targeted management strategies to improve outcomes in this high-risk population. Further research is needed to explore the mechanisms underlying these associations.
Keywords: COPD Exacerbations, Clinical Epidemiology
WHAT IS ALREADY KNOWN ON THIS TOPIC
Patients with chronic obstructive pulmonary disease (COPD) are at increased risk of subsequent cardiovascular and cerebrovascular events, but how cardiovascular and cerebrovascular comorbidities affect patient short-term and long-term prognoses in COPD patients remains unclear.
WHAT THIS STUDY ADDS
Most cardiovascular comorbidities and major cerebrovascular comorbidities are predictive factors for prolonged hospital stay, increased in-hospital mortality and readmission in acute exacerbation of COPD patients. The associations between individual cardiovascular/cerebrovascular comorbidity and patient outcomes are complex and vary by disease.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY
Targeted management strategies are needed to improve outcomes in COPD patients comorbid with heart failure, ischaemic heart disease, arrhythmia, hypertension, ischaemic stroke and cerebrovascular sequelae.
Background
Population ageing has contributed to a rise in multimorbidity, particularly chronic diseases, placing a significant burden on global healthcare systems.1 2 Among the leading contributors to this burden are cardiovascular disease, malignant neoplasms and chronic respiratory disease, including chronic obstructive pulmonary disease (COPD).1 The increasing prevalence of multimorbidity chronic diseases has become a global challenge, complicating disease management and exacerbating healthcare demands.3
COPD, characterised by chronic respiratory symptoms due to abnormalities of the airways and/or alveoli that cause persistent, progressive, airflow obstruction, is now the third leading cause of death worldwide. Many individuals endure COPD for years, facing premature mortality either from the disease itself or from its associated comorbidities.4 Most COPD patients have at least one additional, clinically relevant chronic disease, complicating disease management.5
Cardiovascular and cerebrovascular comorbidities are of great increasing interest because they are prevalent, share similar risk factors and inflammatory mechanisms with COPD and may affect patient prognoses.4,8 Current research evidence suggests that patients with COPD are at increased risk of subsequent cardiovascular and cerebrovascular events,8,11 but how these comorbidities affect patient short-term and long-term prognoses remains unclear and sometimes controversial.12,15 This may be explained by differences in study design, such as varying disease definitions, small sample sizes and limited follow-up periods. For example, a study of 400 patients hospitalised with acute exacerbation of COPD (AECOPD) had longer length of stay (LOS), higher intensive care unit (ICU) admission rate and in-hospital mortality (IHM) when comorbid ischaemic heart disease (IHD).16 Meanwhile, Cui et al found that comorbid coronary artery disease does not affect 30-day readmission and death in 3906 patients with AECOPD.12 Therefore, large-scale studies with comprehensive follow-up data are essential to clarify the impact of cardiovascular and cerebrovascular comorbidities on the prognosis of patients with COPD, particularly those experiencing acute exacerbations.
In response to gaps in the literature, this study aimed to describe the proportions of cardiovascular and cerebrovascular comorbidities in AECOPD patients hospitalised in Beijing and to assess how specific comorbidities influence both in-hospital outcomes and the risk of AECOPD readmission. By using a large, city-wide hospital discharge dataset, this study offers valuable insights that could inform more effective, targeted management strategies for this high-risk population.
Methods
Design and dataset
This study is a city-wide electronic medical records-based study of patients hospitalised with AECOPD in Beijing, with a retrospective cohort study design. The data were retrieved from a large-scale hospital discharge database maintained by the Beijing Public Health Information Centre (BPHIC). In Beijing, all secondary-level and tertiary-level hospitals are required to submit the first page of a standardised medical record to the discharge database. This first page of standardised medical record includes patients’ demographic characteristics, date of admission, discharge outcome (alive or dead), LOS, total hospitalisation costs, primary and other discharge diagnoses. The diagnosis is coded using the International Classification of Diseases, 10th Revision (ICD-10) by certified professional medical coders at each hospital. Quality control is automatically performed at the time of submission to guarantee the completeness, consistency and accuracy of data. This study was approved by the institutional review board (IRB) of Beijing Chaoyang Hospital (2018-ke-303). The data were deidentified by the BPHIC, and it is impossible to identify individual information. Given the anonymous and mandatory nature of the data, informed consent was waived by IRB.
Study population, cardiovascular and cerebrovascular comorbidities and outcomes
Patients aged ≥40 years hospitalised with a primary discharge diagnosis of AECOPD (ICD-10 codes J44.0–J44.9) from 1 January 2013 to 31 December 2019 were included in this analysis. If a patient had more than one hospitalisation during that period, only the first one was included and counted as the index admission. The comorbidities were identified by ICD-10 codes in discharge diagnoses. Charlson Comorbidity Index (CCI) was calculated based on 19 categories of diseases.17 The cardiovascular comorbidities, including heart failure (I50.1-I50.9), IHD (I20.0-I25.9), arrhythmia (I47.0-I48.x), hypertension (I10.x-I15.9), peripheral arterial disease (PAD, I70.2, I70.8, I73, I74.2-I74.9) and cerebrovascular comorbidities, including ischaemic stroke (I63.0-I63-9), non-traumatic intracranial haemorrhage (I60.0-I60.9, I61.0-I61.9, I62.0-I62.9), not specified stroke (I64.x), occlusion and stenosis of precerebral or cerebral arteries (I65.0-I65.9, I66.0-I66.9), cerebrovascular sequelae (I69.0-I69.8), other cerebrovascular disease (I67.0-I67.9) were chosen as exposure in our study. In-hospital outcomes, including IHM and LOS >14 days, were identified in patient’s index admission. Patients who died or were discharged against medical advice or were planned to readmit in 30 days during the index hospitalisation were excluded from follow-up analyses. The readmission for acute exacerbation was defined as another hospitalisation with a primary diagnosis of AECOPD (ICD-10 codes of J44.0–J44.9) that happened after the index hospitalisation, which was identified in our hospital discharge database. Each patient was followed up for 1 year after discharge. The 30-day, 90-day, 180-day and 1-year readmission for AECOPD, number ofacute exacerbation (AE) readmissions within 1 year and annual AE readmission rate were collected as follow-up outcomes.
Statistical analysis
Descriptive statistics were presented as means and SDs or medians and IQRs for continuous variables with or without normal distributions and as frequencies and percentages for categorical variables. Characteristics between groups were compared using χ2 test or Fisher’s exact test for nominal categorical data, the Mantel-Haenszel χ2 test for ordered categorical data, and the Wilcoxon rank sum test or Kruskal-Wallis test for continuous variables without normal distribution, respectively.
The logistic regression model was used to estimate the OR between comorbidities and patients’ binary outcomes (LOS>14 days, IHM, 30-day, 90-day, 180-day and 1-year readmission for AECOPD post discharge). The generalised linear model was used to estimate the rate ratio (RR) between comorbidities and annual AE readmission rate (with negative binomial distribution and log link function). The Fine and Gray subdistributional hazard model was used to estimate the HR for comorbidities and hazard of readmission for AECOPD post discharge with competing risk for death.
All analyses were performed using SAS V.9.4 (SAS Institute, Cary, North Carolina, USA). To account for multiple testing across the associations between comorbidities and outcomes, the Benjamini-Hochberg procedure was applied to adjust p values, with the false discovery rate (FDR) threshold set at q<0.05.
Results
Study population and the distribution of cardiovascular and cerebrovascular comorbidities
Overall, data of 98 127 patients hospitalised with AECOPD were available for analyses. The mean age of included patients was 75.0 (±10.2) years old and most of them were male (62.4%). Of these patients, 76 792 (78.3%) had at least one cardiovascular comorbidity, 29 780 (30.3%) had at least one cerebrovascular comorbidity (online supplemental figure S1A). The proportions of those with hypertension, IHD, heart failure, arrhythmia and PAD were 57.0%, 45.2%, 37.8%, 12.4% and 11.7%, respectively (online supplemental figure S1B). And the proportions of those with ischaemic stroke, other stroke, occlusion and stenosis of precerebral or cerebral arteries, cerebrovascular sequelae and other cerebrovascular disease were 11.1%, 0.2%, 3.9%,14.3% and 5.4%, respectively (online supplemental figure S1C).
Patients with cardiovascular and/or cerebrovascular comorbidities were older, had higher CCI, were more frequently admitted to secondary hospitals, had longer LOS and higher total costs (table 1). The baseline characteristics of patients with heart failure, IHD, arrhythmia, hypertension, PAD and those without cardiovascular comorbidities, as well as those with ischaemic stroke, other types of stroke, occlusion and stenosis of precerebral or cerebral arteries, cerebrovascular sequelae, other cerebrovascular disease and those without cerebrovascular comorbidities, are shown in online supplemental tables S1 and S2.
Table 1. The baseline characteristics of patients with and without cardiovascular and/or cerebrovascular comorbidities.
| Total | Without cardiovascular or cerebrovascular comorbidities | With only cardiovascular comorbidities | With only cerebrovascular comorbidities | With both cardiovascular and cerebrovascular comorbidities | P value | |
|---|---|---|---|---|---|---|
| Number | 98 127 | 18 139 | 50 208 | 3196 | 26 584 | |
| Age | 74.96 (10.20) | 69.11 (11.04) | 75.24 (9.75) | 75.52 (9.85) | 78.38 (8.62) | <0.001 |
| Male | 61 245 (62.41) | 13 073 (72.07) | 29 418 (58.59) | 2337 (73.12) | 16 417 (61.76) | <0.001 |
| Charlson Comorbidity Index | ||||||
| 1 | 25 191 (25.67) | 11 275 (62.16) | 11 032 (21.97) | 601 (18.80) | 2283 (8.59) | <0.001* |
| 2 | 29 781 (30.35) | 4726 (26.05) | 18 254 (36.36) | 1331 (41.65) | 5470 (20.58) | |
| >=3 | 43 155 (43.98) | 2138 (11.79) | 20 922 (41.67) | 1264 (39.55) | 18 831 (70.84) | |
| Institute level | ||||||
| Secondary hospitals | 29 288 (29.85) | 4957 (27.33) | 14 100 (28.08) | 1150 (35.98) | 9081 (34.16) | <0.001 |
| Tertiary hospitals | 68 839 (70.15) | 13 182 (72.67) | 36 108 (71.92) | 2046 (64.02) | 17 503 (65.84) | |
| Admission year | ||||||
| 2013 | 24 791 (25.26) | 4268 (23.53) | 13 040 (25.97) | 830 (25.97) | 6653 (25.03) | 0.002* |
| 2014 | 20 726 (21.12) | 3742 (20.63) | 10 818 (21.55) | 708 (22.15) | 5458 (20.53) | |
| 2015 | 8109 (8.26) | 1785 (9.84) | 4026 (8.02) | 266 (8.32) | 2032 (7.46) | |
| 2016 | 11 950 (12.18) | 2245 (12.38) | 6147 (12.24) | 366 (11.45) | 3192 (12.01) | |
| 2017 | 10 935 (11.140 | 2069 (11.41) | 5464 (10.88) | 353 (11.05) | 3049 (11.47) | |
| 2018 | 10 636 (10.84) | 2015 (11.11) | 5310 (10.58) | 330 (10.33) | 2981 (11.21) | |
| 2019 | 10 980 (11.19) | 2015 (11.11) | 5403 (10.76) | 343 (10.73) | 3219 (12.11) | |
| Length of stay, day | 11 (8–14) | 10 (7–13) | 11 (8–14) | 11 (8–14) | 12 (8–16) | <0.001 |
| Total costs, ¥10000 | 1.41 (0.97–2.08) | 1.14 (0.81–1.63) | 1.41 (0.98–2.07) | 1.35 (0.94–1.96) | 1.62 (1.12–2.39) | <0.001 |
P value for trend.
Associations between cardiovascular and cerebrovascular comorbidities and outcomes
As shown in table 2, compared with patients without cardiovascular or cerebrovascular comorbidities, those with only cardiovascular comorbidities, only cerebrovascular comorbidities or both had longer LOS, with adjusted ORs of 1.29 (95% CI: 1.23 to 1.35), 1.20 (95% CI: 1.10 to 1.32) and 1.52 (95% CI: 1.44 to 1.60), as well as higher IHM, with adjusted ORs of 1.39 (95% CI: 1.19 to 1.62), 1.34 (95% CI: 1.04 to 1.75) and 1.25 (95% CI: 1.06 to 1.48), respectively.
Table 2. The differences in the outcomes of patients with and without cardiovascular and/or cerebrovascular comorbidities.
| Without cardiovascular nor cerebrovascular comorbidities | With only cardiovascular comorbidities | With only cerebrovascular comorbidities | With both cardiovascular and cerebrovascular comorbidities | P * | |
|---|---|---|---|---|---|
| In-hospital outcomes | |||||
| Number | 18 139 | 50 208 | 3196 | 26 584 | |
| Length of stay | |||||
| >14 days, n (%) | 3131 (17.26) | 12 279 (24.46) | 757 (23.69) | 8062 (30.33) | |
| Adjusted OR (95% CI) † | Ref | 1.29 (1.23 to 1.35) | 1.20 (1.10 to 1.32) | 1.52 (1.44 to 1.60) | <0.001 |
| In-hospital mortality | |||||
| n (%) | 192 (1.06) | 1348 (2.68) | 87 (2.72) | 952 (3.58) | |
| Adjusted OR (95% CI)† | Ref | 1.39 (1.19 to 1.62) | 1.34 (1.04 to 1.75) | 1.25 (1.06 to 1.48) | <0.001 |
| Follow-up outcomes | |||||
| Number | 15 426 | 41 465 | 2679 | 21 920 | |
| 30–day readmission for AECOPD | |||||
| n (%) | 852 (5.52) | 2527 (6.09) | 154 (5.75) | 1454 (6.63) | |
| Adjusted OR (95% CI) ‡ | Ref | 1.10 (1.02 to 1.20) | 0.95 (0.79 to 1.32) | 1.15 (1.06to 1.26) | 0.009 |
| 90–day readmission for AECOPD | |||||
| n (%) | 1482 (9.61) | 4689 (11.31) | 271 (10.12) | 2675 (12.20) | |
| Adjusted OR (95% CI) ‡ | Ref | 1.17 (1.10 to 1.24) | 0.95 (0.83 to 1.10) | 1.22 (1.13 to 1.31) | <0.001 |
| 180–day readmission for AECOPD | |||||
| n (%) | 2146 (13.91) | 6913 (16.67) | 403 (15.04) | 3951 (18.02) | |
| Adjusted OR (95% CI) ‡ | Ref | 1.18 (1.12 to 1.25) | 0.97 (0.87 to 1.09) | 1.24 (1.16 to 1.31) | <0.001 |
| 1 year readmission for AECOPD | |||||
| n (%) | 3158 (20.47) | 10 069 (24.28) | 603 (22.51) | 5702 (26.01) | |
| Adjusted OR (95% CI) ‡ | Ref | 1.10 (1.02 to 1.20) | 0.95 (0.79 to 1.32) | 1.15 (1.06 to 1.26) | <0.001 |
| Adjusted HR (95% CI)§ | Ref | 1.14 (1.10 to 1.19) | 1.05 (0.96 to 1.14) | 1.19 (1.14 to 1.25) | <0.001 |
| Number of AE readmissions with 1 year | |||||
| 0 | 12 268 (79.53) | 31 396 (75.72) | 2076 (77.49) | 16 218 (73.99) | |
| 1 | 1936 (12.55) | 6049 (14.59) | 374 (13.96) | 3439 (15.69) | |
| >=2 | 1222 (7.92) | 4020 (9.69) | 229 (8.55) | 2263 (10.32) | |
| AE readmission rate, n/year | |||||
| Rate (95% CI) | 0.47 (0.45 to 0.49) | 0.50 (0.49 to 0.52) | 0.45 (0.41 to 0.50) | 0.54 (0.52 to 0.55) | |
| Adjusted RR (95% CI)¶ | Ref | 1.11 (1.06 to 1.17) | 0.93 (0.84 to 1.04) | 1.10 (1.11 to 1.23) | <0.001 |
Type III p values for the group effect were adjusted for multiple testing across all prespecified outcomes using the Benjamini-Hochberg false discovery rate procedure.
Adjusted for sex, continuous age, Charlson Comorbidity Index, institute level and admission year through logistic regression model.
Adjusted for sex, continuous age, institute level and admission year through logistic regression model.
Adjusted for sex, continuous age, institute level and admission year with competing risk for death through Fine and Gray subdistributional hazard model.
Adjusted for sex, continuous age, institute level and admission year through generalised linear model with negative binomial distribution and log link function.
AE, acute exacerbation; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; RR, rate ratio.
Compared with patients without cardiovascular nor cerebrovascular comorbidity, those with only cardiovascular comorbidities had higher 30-day (OR=1.10, 95% CI: 1.02 to 1.20), 90-day (OR=1.17, 95% CI: 1.10 to 1.24), 180-day (OR=1.18, 95% CI: 1.12 to 1.25), 1-year readmission for AECOPD (OR=1.10, 95% CI: 1.02 to 1.20), higher AE readmission hazard (HR=1.14, 95% CI: 1.10 to 1.19) and higher annual AE readmission rate (RR=1.11, 95% CI: 1.06 to 1.17) post discharge. Similar results were observed in those with both cardiovascular and cerebrovascular comorbidities. However, no significant difference was observed in those with only cerebrovascular comorbidities (table 2).
Associations between each cardiovascular comorbidity and outcomes
As shown in table 3, compared with patients without cardiovascular comorbidities, those with heart failure, IHD, arrhythmia or hypertension had longer LOS, higher IHM during hospitalisation and higher 30-day, 90-day, 180-day and 1-year readmission for AECOPD, higher AE readmission hazard and higher annual AE readmission rate post discharge. However, those with PAD showed longer LOS but lower IHM during hospitalisation, lower 30-day and 90-day readmission for AECOPD and lower annual AE readmission rate post discharge.
Table 3. The differences in the outcomes of patients with each cardiovascular comorbidity and those without cardiovascular comorbidities.
| Without cardiovascular comorbidities* | Heart failure | P value† | IHD | P value† | Arrhythmia | P value† | PAD | P value† | Hypertension | P value† | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| In-hospital outcomes | |||||||||||
| Number | 21 335 | 37 090 | 44 326 | 12 184 | 11 455 | 55 929 | |||||
| Length of stay, day | |||||||||||
| >14 days, n (%) | 3888 (18.22) | 11 417 (30.78) | 12 734 (28.73) | 3926 (32.22) | 2889 (25.22) | 14 979 (26.78) | |||||
| Adjusted OR (95% CI)‡ | Ref | 1.75 (1.67 to 1.83) | <0.001 | 1.56 (1.49 to 1.63) | <0.001 | 1.80 (1.70 to 1.91) | <0.001 | 1.34 (1.26 to 1.42) | <0.001 | 1.42 (1.36 to 1.48) | <0.001 |
| In-hospital mortality | |||||||||||
| n (%) | 279 (1.31) | 1668 (4.50) | 1524 (3.44) | 742 (6.09) | 184 (1.61) | 1460 (2.61) | |||||
| Adjusted OR (95% CI)‡ | Ref | 2.16 (1.89 to 2.46) | <0.001 | 1.58 (1.39 to 1.81) | <0.001 | 2.68 (2.31 to 3.10) | <0.001 | 0.76 (0.62 to 0.93) | 0.010 | 1.28 (1.12 to 1.46) | <0.001 |
| Follow-up outcomes | |||||||||||
| Number | 18 105 | 29 833 | 36 541 | 9496 | 9638 | 46 539 | |||||
| 30–day readmission for AECOPD | |||||||||||
| n (%) | 1006 (5.56) | 2222 (7.45) | 2521 (6.90) | 646 (6.80) | 441 (4.58) | 2878 (6.18) | |||||
| Adjusted OR (95% CI)‡ | Ref | 1.39 (1.28 to 1.51) | <0.001 | 1.25 (1.15 to 1.35) | <0.001 | 1.15 (1.03 to 1.28) | 0.023 | 0.76 (0.67 to 0.86) | <0.001 | 1.13 (1.04 to 1.22) | 0.004 |
| 90-day readmission for AECOPD | |||||||||||
| n (%) | 1753 (9.68) | 4129 (13.84) | 4645 (12.71) | 1225 (12.90) | 859 (8.91) | 5257 (11.30) | |||||
| Adjusted OR (95% CI)‡ | Ref | 1.49 (1.39 to 1.59) | <0.001 | 1.30 (1.22 to 1.39) | <0.001 | 1.28 (1.18 to 1.40) | <0.001 | 0.87 (0.79 to 0.96) | 0.007 | 1.16 (1.09 to 1.23) | <0.001 |
| 180-day readmission for AECOPD | |||||||||||
| n (%) | 2549 (14.08) | 5969 (20.01) | 6784 (18.57) | 1788 (18.83) | 1371 (14.22) | 7776 (16.71) | |||||
| Adjusted OR (95% CI)‡ | Ref | 1.48 (1.40 to 1.57) | <0.001 | 1.30 (1.24 to 1.38) | <0.001 | 1.30 (1.21 to 1.40) | <0.001 | 0.98 (0.90 to 1.05) | 0.548 | 1.17 (1.11 to 1.23) | <0.001 |
| 1-year readmission for AECOPD | |||||||||||
| n (%) | 3761 (20.77) | 8508 (28.52) | 9764 (26.72) | 2489 (26.21) | 2118 (21.98) | 11 302 (24.29) | |||||
| Adjusted OR (95% CI)‡ | Ref | 1.45 (1.38 to 1.52) | <0.001 | 1.28 (1.23 to 1.35) | <0.001 | 1.22 (1.15 to 1.31) | <0.001 | 1.02 (0.95 to 1.09) | 0.608 | 1.15 (1.10 to 1.20) | <0.001 |
| Adjusted HR (95% CI)§ | Ref | 1.32 (1.27 to 1.38) | <0.001 | 1.20 (1.16 to 1.25) | <0.001 | 1.17 (1.10 to 1.24) | <0.001 | 1.05 (0.98 to 1.11) | 0.177 | 1.12 (1.07 to 1.16) | <0.001 |
| Number of AE readmission with 1 year | |||||||||||
| 0 | 14 344 (79.23) | 21 325 (71.48) | 26 777 (73.28) | 7007 (73.79) | 7520 (78.02) | 35 237 (75.71) | |||||
| 1 | 2310 (12.76) | 4990 (16.73) | 5814 (15.91) | 1538 (16.20) | 1332 (13.82) | 6810 (14.63) | |||||
| >=2 | 1451 (8.01) | 3518 (11.79) | 3950 (10.81) | 951 (10.01) | 786 (8.16) | 4492 (9.65) | |||||
| AE readmission rate, n/year | |||||||||||
| Rate (95% CI) | 0.46 (0.45 to 0.47) | 0.60 (0.59 to 0.61) | 0.56 (0.55 to 0.56) | 0.48 (0.47 to 0.49) | 0.40 (0.39 to 0.41) | 0.51 (0.51 to 0.52) | |||||
| Adjusted RR (95% CI)¶ | Ref | 1.39 (1.33 to 1.46) | <0.001 | 1.25 (1.19 to 1.31) | <0.001 | 1.11 (1.03 to 1.19) | 0.007 | 0.91 (0.84 to 0.98) | 0.019 | 1.16 (1.11 to 1.21) | <0.001 |
Including heart failure, ischaemic heart disease, arrhythmia, peripheral arterial disease, hypertension.
P value for the group effect was adjusted for multiple testing across all pre-specified outcomes using the Benjamini-Hochberg false discovery rate procedure.
Adjusted for sex, continuous age, cerebrovascular comorbidity, institute level and admission year through logistic regression model.
Adjusted for sex, continuous age, cerebrovascular comorbidity, institute level and admission year with competing risk for death through Fine and Gray subdistributional hazard model.
Adjusted for sex, continuous age, cerebrovascular comorbidity, institute level and admission year through generalised linear model with negative binomial distribution and log link function.
AE, acute exacerbation; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; IHD, ischaemic heart disease; PAD, peripheral arterial disease; RR, rate ratio.
The dose-response analyses showed that patients with more cardiovascular comorbidities (including heart failure, IHD, arrhythmia, hypertension) had higher risks of readmission for AECOPD at 30-day, 90-day, 180-day and 1-year post discharge (figure 1).
Figure 1. Dose-response relationship of the number of influential cardiovascular comorbidities and the risks of readmission for AECOPD post discharge. AECOPD, acute exacerbation of chronic obstructive pulmonary disease. Influential cardiovascular disease comorbidities include heart failure, ischaemic heart disease, arrhythmia and hypertension. The ORs were estimated adjusted for sex, continuous age, cerebrovascular comorbidity, institute level and admission year through logistic regression model. P value for trend was adjusted for multiple testing across all prespecified outcomes using the Benjamini-Hochberg false discovery rate procedure.
Associations between each cerebrovascular comorbidity and outcomes
As shown in table 4, compared with patients without cerebrovascular comorbidities, those with ischaemic stroke, other stroke or cerebrovascular sequelae had longer LOS and higher IHM during hospitalisation. Those with occlusion and stenosis of precerebral or cerebral arteries and those with other cerebrovascular disease had longer LOS but lower IHM.
Table 4. The differences in the outcomes of patients with each cerebrovascular comorbidity and those without cerebrovascular comorbidities.
| Without cerebrovascular comorbidities * | Ischaemic stroke | P value† | Other stroke | P value† | Occlusion and stenosis | P value† | Other cerebrovascular disease | P value† | Cerebrovascular sequelae | P value† | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| In-hospital outcomes | |||||||||||
| Number | 68 347 | 10 889 | 233 | 3782 | 5347 | 13 996 | |||||
| Length of stay, day | |||||||||||
| >14 days, n (%) | 15 410 (22.55) | 3552 (32.62) | 94 (40.34) | 902 (23.85) | 1451 (27.14) | 4186 (29.91) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.45 (1.39 to 1.52) | <0.001 | 2.05 (1.57 to 2.67) | <0.001 | 1.10 (1.02 to 1.20) | 0.019 | 1.18 (1.11 to 1.26) | <0.001 | 1.30 (1.25 to 1.36) | <0.001 |
| In-hospital mortality | |||||||||||
| n (%) | 1540 (2.25) | 426 (3.91) | 21 (9.01) | 48 (1.27) | 115 (2.15) | 544 (3.89) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.20 (1.07 to 1.34) | 0.003 | 3.30 (2.08 to 5.24) | <0.001 | 0.48 (0.36 to 0.65) | <0.001 | 0.75 (0.62 to 0.92) | 0.007 | 1.23 (1.11 to 1.36) | <0.001 |
| Follow-up outcomes | |||||||||||
| Number | 56 891 | 9193 | 172 | 3165 | 4467 | 11 353 | |||||
| 30–day readmission for AECOPD | |||||||||||
| n (%) | 3379 (5.94) | 631 (6.86) | 9 (5.23) | 128 (4.04) | 218 (4.88) | 830 (7.31) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.08 (0.99 to 1.18) | 0.119 | 0.81 (0.41 to 1.59) | 0.564 | 0.69 (0.57 to 0.83) | <0.001 | 0.78 (0.68 to 0.90) | <0.001 | 1.17 (1.08 to 1.27) | <0.001 |
| 90–day readmission for AECOPD | |||||||||||
| n (%) | 6171 (10.85) | 1137 (12.37) | 18 (10.47) | 261 (8.25) | 442 (9.89) | 1471 (12.96) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.05 (0.98 to 1.13) | 0.172 | 0.88 (0.54 to 1.43) | 0.608 | 0.77 (0.68 to 0.88) | <0.001 | 0.85 (0.77 to 0.95) | 0.004 | 1.13 (1.06 to 1.21) | <0.001 |
| 180–day readmission for AECOPD | |||||||||||
| n (%) | 9059 (15.92) | 1675 (18.22) | 28 (16.28) | 410 (12.95) | 685 (15.33) | 2115 (18.63) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.05 (0.99 to 1.11) | 0.145 | 0.93 (0.62 to 1.40) | 0.723 | 0.83 (0.74 to 0.92) | <0.001 | 0.89 (0.82 to 0.97) | 0.012 | 1.10 (1.05 to 1.17) | <0.001 |
| 1 year readmission for AECOPD | |||||||||||
| n (%) | 13 227 (23.25) | 2429 (26.42) | 33 (19.19) | 655 (20.70) | 1068 (23.91) | 8377 (26.21) | |||||
| Adjusted OR (95% CI) ‡ | Ref | 1.05 (1.00 to 1.11) | 0.079 | 0.70 (0.48 to 1.03) | 0.090 | 0.90 (0.82 to 0.99) | 0.034 | 0.96 (0.89 to 1.03) | 0.299 | 1.06 (1.01 to 1.11) | 0.025 |
| Adjusted HR (95% CI)§ | Ref | 1.06 (1.01 to 1.12) | 0.026 | 0.70 (0.43 to 1.12) | 0.155 | 0.98 (0.89 to 1.07) | 0.594 | 1.02 (0.96 to 1.09) | 0.489 | 1.05 (1.00 to 1.10) | 0.075 |
| Number of AE readmission within 1 year | |||||||||||
| 0 | 43 664 (76.75) | 6764 (73.58) | 139 (80.81) | 2510 (79.30) | 3399 (76.09) | 8377 (73.79) | |||||
| 1 | 7985 (14.04) | 1462 (15.90) | 21 (12.21) | 448 (14.15) | 654 (14.64) | 1769 (15.58) | |||||
| >=2 | 5242 (9.21) | 967 (10.52) | 12 (6.98) | 207 (6.54) | 414 (9.27) | 1207 (10.63) | |||||
| AE readmission rate, n/year | |||||||||||
| Rate (95% CI) | 0.49 (0.48 to 0.50) | 0.53 (0.50 to 0.55) | 0.36 (0.24 to 0.54) | 0.35 (0.32 to 0.38) | 0.46 (0.42 to 0.49) | 0.58 (0.55 to 0.61) | |||||
| Adjusted RR (95% CI)¶ | Ref | 1.04 (0.99 to 1.10) | 0.173 | 0.71 (0.48 to 1.06) | 0.110 | 0.79 (0.72 to 0.87) | <0.001 | 0.92 (0.85 to 1.00) | 0.056 | 1.12 (1.06 to 1.18) | <0.001 |
Including ischaemic stroke, other stroke, occlusion and stenosis, other cerebrovascular disease, cerebrovascular sequelae.
P value for the group effect was adjusted for multiple testing across all prespecified outcomes using the Benjamini–Hochberg false discovery rate procedure.
Adjusted for sex, continuous age, cardiovascular comorbidity, institute level and admission year through logistic regression model.
Adjusted for sex, continuous age, cardiovascular comorbidity, institute level and admission year with competing risk for death through Fine and Gray subdistributional hazard model.
Adjusted for sex, continuous age, cardiovascular comorbidity, institute level and admission year through generalised linear model with negative binomial distribution and log link function.
AE, acute exacerbation; AECOPD, acute exacerbation of chronic obstructive pulmonary disease; RR, rate ratio.
Compared with patients without cerebrovascular comorbidities, those with cerebrovascular sequelae had higher 30-day, 90-day, 180-day and 1-year readmission for AECOPD and higher AE readmission rate post discharge. Those with ischaemic stroke had higher AE readmission hazard post discharge. However, those with occlusion and stenosis of precerebral or cerebral arteries showed lower 30-day, 90-day, 180-day and 1-year readmission for AECOPD and lower annual AE readmission rate post discharge. Similar results were observed in those with other cerebrovascular disease (table 4).
Discussion
In this study, we systematically described the proportions of cardiovascular and cerebrovascular comorbidities among patients hospitalised with AECOPD and evaluated their impact on in-hospital outcomes and subsequent risk of AECOPD readmission. Our findings demonstrate that these comorbidities are highly prevalent and significantly associated with worse clinical outcomes. To our knowledge, this is the first study to comprehensively assess these associations using a large, city-wide dataset, providing valuable insights into the management of AECOPD patients with complex comorbid conditions.
Consistent with previous studies,12 14 18 the present study found that the majority of patients with AECOPD had comorbid cardiovascular or cerebrovascular diseases, with a higher proportion of cardiovascular diseases than cerebrovascular diseases. Hypertension, IHD and heart failure were the most common (>30%), while cerebrovascular sequelae, arrhythmias, PAD and ischaemic stroke were present in over 10% of cases. These higher proportions of cardiovascular and cerebrovascular comorbidities among COPD patients than those in the general population are a well-documented clinical phenomenon,19,21 likely due to shared risk factors (eg,smoking) and pathophysiological processes (eg, increased oxidative stress).22,24 The proportions observed in the present study were higher than those reported in community-based studies in China,25 probably because this study focused on hospitalised patients, who tend to be older (mean age: 75 years vs 67 years) and have a greater burden of comorbidities.
While cardiovascular and cerebrovascular comorbidities have been recognised as potential predictors of hospitalisation risk in COPD patients,26 their specific impact on outcomes during acute exacerbations remains incompletely characterised in large patient populations. We found AECOPD patients with cardiovascular comorbidities had worse in-hospital outcomes and an increased risk of subsequent AECOPD readmission. However, the impact of specific cardiovascular diseases on prognosis was not uniform. Patients with hypertension, IHD, heart failure and arrhythmias demonstrated worse in-hospital outcomes and a higher risk of AECOPD readmission. A retrospective US single-centre study involving 507 patients hospitalised with AECOPD similarly reported worse short-term outcomes—longer LOS, higher ICU admission rates and increased IHM among those with IHD.16 In contrast, data from the ACURE registry study of 3906 AECOPD inpatients revealed that only male patients with coronary artery disease had longer LOS, while cardiovascular comorbidities overall did not significantly affect 30-day readmission or mortality (HR=1.39, 95% CI:0.90 to 2.14).27 This discrepancy may stem from differences in the sample size and follow-up duration between studies. Our findings have also been reported in studies focusing on stable COPD patients,13 suggesting the impact of cardiovascular comorbidities on COPD prognosis is likely generalisable across different clinical settings. The potential mechanisms underlying this relationship include hypoxia, systemic inflammation, endothelial dysfunction, elevated platelet reactivity, arterial stiffness and right ventricular remodelling, which interact to worsen the prognosis of patients with both COPD and cardiovascular disease.23 Additionally, our study identified a dose-response relationship between the number of cardiovascular comorbidities and the risk of AECOPD readmission, indicating a cumulative effect that strengthens the validity of these findings.
Meanwhile, we revealed that AECOPD patients with PAD exhibited a reduced risk of AECOPD readmission. This could potentially be attributed to the comprehensive management of PAD, such as moderate physical activity to enhance circulation, the use of vasodilator therapies and lifestyle modifications.28 These interventions may exert a beneficial effect on COPD management, thereby lowering the risk of AE readmission.28,30 In contrast, a study based on German primary care data identified an elevated risk of exacerbations in PAD patients (identifying exacerbation based on patient’s oral corticosteroid prescription).27 Given the limited evidence, further cohort studies are needed to investigate whether PAD comorbidity represents a distinctive prognostic factor in COPD patients and to elucidate the underlying mechanisms involved.
In this study, cerebrovascular diseases were associated with increased LOS and IHM, but had limited impact on the risk of AECOPD readmission. Notably, different cerebrovascular comorbidities exhibited varying effects on patient outcomes. Patients with stroke and cerebrovascular sequelae experienced prolonged LOS and elevated IHM, consistent with findings from the RePoSI Registry study.12 This may be due to the higher risk of pulmonary and extrapulmonary complications, including dysphagia, gastro-oesophageal reflux disease, aspiration and pneumonia.24 The elevated risk of AECOPD readmission in patients with ischaemic stroke and cerebrovascular sequelae may be associated with heightened oxidative stress and reduced antioxidant enzyme activity.31 32 It is worth noting that, despite having longer LOS, patients with occlusion and stenosis of precerebral or cerebral arteries, as well as those with other cerebrovascular diseases, demonstrated lower IHM and reduced risk of AECOPD readmission. These findings warrant further investigation to confirm the associations and explore the underlying mechanisms.
The present study has several significant advantages. First, by using a multidimensional outcome assessment—including in-hospital outcomes and readmission rates at various time points post discharge—we were able to capture the dynamic changes in prognostic risk comprehensively. These detailed findings not only aid clinicians in accurately identifying high-risk patients and optimising both in-hospital and postdischarge management, but also serve as a robust reference for designing future interventional studies, particularly in terms of sample size estimation and outcome selection. Second, the standard use of ICD-10 coding to define cardiovascular and cerebrovascular comorbidities facilitates a detailed exploration of prognostic differences across various types of these conditions in AECOPD patients. Furthermore, by analysing the associations between the subtypes and the number of cardiovascular and cerebrovascular comorbidities with multidimensional outcomes, this study contributes valuable evidence for developing individualised treatment and disease management strategies in clinical practice.
However, there are several limitations to this study. First, our study was conducted using data from the first page of standardised medical record, which lacked access to key variables such as exacerbation history, smoking status, lung function and treatment details. The absence of these factors restricts our ability to fully assess their impact on patient outcomes. Second, while our study includes data from 150 hospitals across Beijing, covering both secondary and tertiary care institutions, spanning from 2013 to 2020, the findings may still have limitations when applied to regions with different healthcare systems or disease patterns or to the post-pandemic era. The diversity of our data supports generalisability within China, but future studies involving international cohorts or regions with varying healthcare structures and those with post-pandemic data are needed to further strengthen and test these findings. Third, due to the lack of out-of-hospital mortality data, our analysis was limited to in-hospital deaths, which may underestimate overall mortality and introduce bias in long-term outcome interpretation. Despite this, the study provides valuable insights into in-hospital outcomes. Fourth, distinguishing an admission as heart failure or AECOPD can be challenging when competing diagnoses are present, as these conditions often share similar symptoms. There may be a risk of misclassification bias, though we used primary discharge diagnosis rather than admitting diagnosis to minimise this risk. Fifth, comorbidities were identified from discharge diagnoses because the database lacks present-on-admission flags. Although this approach may misclassify a small number of acute in-hospital events, the cardiovascular and cerebrovascular conditions studied are predominantly chronic; any residual misclassification would likely attenuate, rather than inflate, the observed associations. Finally, as an observational study, residual confounding remains a possibility despite adjustments. Future research, like prospective cohort studies with longer follow-up and more comprehensive data, is needed to better explore the causal relationships between comorbidities and COPD outcomes.
Notably, COPD is among the leading contributors to potentially preventable hospital bed-days worldwide.33 Our findings reveal that cardiovascular comorbidities substantially prolong LOS and increase readmission risk. Consequently, effective interventions for prevention and control of cardiopulmonary risk in COPD patients are crucial for reducing this system-level burden.34 35 Given the complexity of these conditions, a multidisciplinary team (MDT) approach involving pulmonologists, cardiologists, neurologists and nutritionists is crucial for optimising care during hospitalisation and post discharge.36 37 Additionally, integrating mHealth technologies, including wearable devices for real-time monitoring, could enhance remote monitoring and enable early detection of cardiopulmonary events, potentially reducing readmissions.38 39 Despite the recognition in current COPD guidelines, such as the Global Initiative for Chronic Obstructive Lung Disease 2024 report,4 detailed guidance on personalised care and technology integration remains limited. Further research is needed to establish evidence-based MDT care models and to validate remote monitoring tools to improve long-term outcomes in this high-risk population. Our findings also suggest that specific cardiovascular and cerebrovascular comorbidities like arrhythmias and hypertension provide additional prognostic value beyond the CCI for AECOPD outcomes, which has implications for risk adjustment in hospital and health service performance measures and comparative effectiveness studies.
Conclusions
This study emphasises that most cardiovascular comorbidities and major cerebrovascular comorbidities, particularly heart failure, IHD, arrhythmias, ischaemic stroke and cerebrovascular sequelae are predictors of prolonged LOS, increased IHM and readmission risk in AECOPD patients. These findings emphasise the need for tailored management strategies, including MDT approaches and mHealth technologies, to optimise care during hospitalisation and post discharge. Further research is required to confirm whether specific cardiovascular and cerebrovascular comorbidities serve as unique prognostic indicators in COPD and to explore the mechanisms underlying these associations.
Supplementary material
Acknowledgements
We thank the technical team and the cooperating team in the Beijing Public Health Information Centre.
Footnotes
Funding: This study was funded by Beijing Municipal Science & Technology Commission (Z201100005520028), Beijing Key Specialists in Major Epidemic Prevention and Control, Reform and Development Program of Beijing Institute of Respiratory Medicine (Ggyfz202424), Clinical Research Incubating Program of Beijing Chao-Yang Hospital (CYFH202210).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Not applicable.
Ethics approval: This study involves human participants and this study was approved by the Institutional Review Board (IRB) of Beijing Chaoyang Hospital (2018-ke-303). The data were de-identified by the BPHIC, and it is impossible to identify individual information. Given the anonymous and mandatory nature of the data, informed consent was waived by IRB.
Data availability free text: The authors are not allowed to distribute this data or cannot deposit the data file used in this study in any openly accessible data repository. The datasets analysed in this study are available for limited use from the corresponding author upon reasonable request.
Patient and public involvement: Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.
Data availability statement
Data are available upon reasonable request.
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Associated Data
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Supplementary Materials
Data Availability Statement
Data are available upon reasonable request.