Heart failure (HF) and acute exacerbation of chronic obstructive pulmonary disease (AECOPD) are considered significant causes of morbidity and mortality worldwide. Concurrent presentation of HF with AECOPD can pose a diagnostic challenge due to an overlap in symptomatology. We queried the National Inpatient Sample (NIS) database to assess outcomes of HF hospitalizations with a secondary diagnosis of AECOPD. We performed a retrospective analysis of discharge data from the Healthcare Cost Utilization Project NIS between January 1, 2004, and December 31, 2014, with a primary diagnosis of HF with and without a secondary diagnosis of AECOPD. Data was abstracted from the NIS using International Classification of Disease 9 codes. Primary outcomes included mortality, length of stay, and inflation-adjusted cost of stay. During 2004-2014, a total of (n = 10,392,628) HF hospitalizations were identified without a secondary diagnosis of AECOPD while (n = 989,713) HF hospitalizations were identified with a secondary diagnosis of AECOPD. We identified higher mortality (3.25% vs 3.56%, p <0.001), length of stay (5.2 vs 6.1 days, p <0.001) and inflation-adjusted cost of stay (12,562 vs 13,072 USD, p <0.001) in HF hospitalizations with AECOPD when compared to HF without AECOPD from 2004 to 2014. We presented AECOPD as an independent predictor of mortality in patients admitted for HF. In conclusion, further interdisciplinary collaboration between pulmonologists and cardiologists is needed for the identification and stratification of patients who present with concurrent HF and COPD for better outcomes.
Congestive heart failure and chronic obstructive pulmonary disease (COPD) are associated with significant morbidity and mortality. Between 2013 and 2016, Heart Failure (HF) affected approximately 6.2 million Americans, and prevalence is expected to increase by 46% by the year 2030. Data from the 2010 update from the Global Burden of Disease study approximated that 328 million people suffer from COPD worldwide. The clinical signs and symptoms of these disorders comprise significant overlap, making appropriate diagnosis and treatment challenging for clinicians when these 2 disease states coexist. . Dyspnea at rest and on exertion is common to both HF and COPD. , In addition, the resultant physiologic changes of COPD may lead to the development of several symptoms also seen in HF, such as right HF causing lower extremity edema and hepatic displacement secondary to lung hyperinflation mimicking hepatomegaly. The purpose of this study is to evaluate outcomes in patients hospitalized with a primary diagnosis of HF and a secondary diagnosis of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) using data from the National Inpatient Sample (NIS) database.
Methods
A retrospective analysis of the NIS database from January 1, 2004, to December 31, 2014, was performed. The NIS is a publicly accessible database of all payers, approximating a 20% stratified sample of discharges from US community hospitals participating in the Healthcare Cost and Utilization Project. The data includes clinical and non-clinical elements such as primary and secondary diagnoses, source of payment, patient demographics, measurement of co-morbidity, and length of stay. The NIS database was searched to identify all hospitalizations with patients who were 18 years or older from January 1, 2004, through December 31, 2014, with a primary discharge diagnosis of HF using ICD-9-CM diagnostic codes; 402.01,402.11,402.91, 404.01,404.03,404.11,404.13,404.91,404.93, and 428.x. Additionally, patients with AECOPD were identified using ICD-9-CM diagnostic code 491.21. A detailed overview of HCUP NIS is available on https://www.hcup-us.ahrq.gov/nisoverview.jsp .
We used descriptive statistics to summarize the continuous and categorical variables. The mean and standard error were used for continuous variables and percentages were used to express categorical variables. For between-group comparison, we utilized the Rao-Scott Chi-square test for categorical variables (e.g., gender and risk factors), and weighted simple linear regression for continuous variables (e.g., age). Weighted logistic regression was performed to estimate unadjusted and adjusted Odds ratios and 95% confidence intervals to determine the impact of AECOPD on various clinical outcomes in the patients with a primary diagnosis of HF. The logistic regression models were adjusted for baseline and hospital characteristics in Table 1 . Race and median income were not included in these models due to a high percentage of missing values. Trends for continuous variables (including length of stay [LOS] and inflation-adjusted cost) were examined using linear regression. Binary logistic regression was used for the categorical variable of mortality, with year as the sole predictor. LOS and inflation-adjusted cost were log-transformed to achieve normality of the data. For each record, an estimate of comorbid conditions was calculated using the Charlson Comorbidity Index which is a scoring system for predicting mortality by weighting comorbid conditions.
| Variables | HF without AECOPD n = 10,392,628 | HF with AECOPD n = 898,713 | p Value |
|---|---|---|---|
| Age (mean [S.E]) years | 72.5 (0.06) | 73.4 (0.05) | <0.001 |
| Women | 50.67% | 49.39% | <0.001 |
| White | 67.09% | 76.49% | <0.001 |
| Black | 20.27% | 14.1% | <0.001 |
| Others | 12.64% | 9.4% | <0.001 |
| Pulmonary circulation disorders | 0.23% | 0.23% | 0.82 |
| Diabetes uncomplicated | 33.01% | 34.49% | <0.001 |
| Diabetes with complications | 10.1% | 8.58% | <0.001 |
| Hypertension | 64.04% | 62.75% | <0.001 |
| Obesity * | 13.73% | 17.32% | <0.001 |
| Peripheral vascular disease | 10.64% | 13.35% | <0.001 |
| Renal failure | 34.7% | 32.35% | <0.001 |
| Liver disease | 2.47% | 2.06% | <0.001 |
| Neurological disorders | 6.26% | 6.28% | 0.72 |
| Hypothyroidism | 14.73% | 13.93% | <0.001 |
| Coagulopathy | 4.29% | 4.42% | 0.01 |
| Valvular disease | 0.35% | 0.25% | <0.001 |
| Solid tumor without metastasis | 1.6% | 2.23% | <0.001 |
| Metastatic cancer | 0.94% | 0.93% | 0.54 |
| Tobacco abuse | 18.87% | 35.74% | <0.001 |
| Alcohol abuse | 2.56% | 3.2% | <0.001 |
| Drug abuse | 2.36% | 2% | <0.001 |
| Previous myocardial infarction | 12.51% | 11.6% | <0.001 |
| Deficiency anemia | 26.6% | 26.55% | 0.66 |
| Hospital Location | <0.001 | ||
| Rural | 15.13% | 19.03% | |
| Urban non-teaching | 42.16% | 47.2% | |
| Urban teaching | 42.71% | 33.78% | |
| Bed Size of the hospital | <0.001 | ||
| Small | 14.61% | 15.42% | |
| Medium | 25.54% | 27.09% | |
| Large | 59.85% | 57.49% | |
| Region | <0.001 | ||
| Northeast | 20.08% | 18.46% | |
| Midwest | 23.59% | 23.36% | |
| South | 40.83% | 44.52% | |
| West | 15.51% | 13.67% | |
| Median income | <0.001 | ||
| 0-25th | 33.2% | 35.5% | |
| 26-50th | 26.43% | 28.05% | |
| 50-75th | 22.26% | 21.22% | |
| 75-100th | 18.11% | 15.23% | |
| Discharge disposition of surviving patients | <0.001 | ||
| Routine/Self-care | 54.24% | 46.6% | |
| Short-term hospital | 3.16% | 2.98% | |
| Another type of facility | 19% | 23.28% | |
| Home healthcare | 19.25% | 22.38% | |
| Charlson comorbidity index | <0.001 | ||
| 0 | 0.07% | 0% | |
| 1 | 20.75% | 0.07% | |
| ≥2 | 79.18% | 99.93% |
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