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COVID-19 is known to be associated with a myriad of cardiovascular (CV) complications during acute illness, but the rates of readmissions for CV complications after COVID-19 infection are less well established.
METHODS
The U.S Nationwide Readmission Database was utilized to identify COVID-19 admissions from April 1st to November 30th, 2020 using ICD-10-CM administrative claims.
RESULTS
A total of 521,351 admissions for COVID-19 were identified. The all-cause 30-day readmission rate was 11.6% (n=60,262). The incidence of CV readmissions was 5.1% (n=26,725), accounting for 44.3% of all-cause 30-day readmissions. Both CV and non-CV readmissions occurred at a median of 7 days. Patients readmitted with CV causes had a higher comorbidity burden with Charlson comorbidity median score of 6. The most common CV cause of readmission was acute heart failure (HF) (8.5%) followed by acute myocardial infarction (MI) (5.2%). Venous thromboembolism and stroke during 30-day readmission occurred at a rate of 4.6% and 3.6%, respectively. Stress cardiomyopathy and acute myocarditis were less frequent with an incidence of 0.1% and 0.2%, respectively. CV readmissions were associated with higher mortality compared with non-CV readmissions (16.5% vs. 7.5%, p<0.01). Each 30-day CV readmission was associated with greater cost of care than each non-CV readmission ($13,803 vs. $10,310, p=<0.01).
CONCLUSIONS
Among survivors of index COVID-19 admission, 44.7% of all 30-day readmissions were attributed to CV causes. Acute HF remains the most common cause of readmission after COVID-19, followed closely by acute MI. CV causes of readmissions remain a significant source of mortality, morbidity, and resource utilization.
Coronavirus disease 2019 (COVID-19) is known to be associated with a myriad of acute cardiovascular (CV) complications including acute myocardial infarction (MI), heart failure (HF), venous thromboembolism (VTE), stress cardiomyopathy, myocarditis, and cardiac arrhythmias.
Writing C, Gluckman TJ, Bhave NM, et al. 2022 ACC Expert Consensus Decision Pathway on Cardiovascular Sequelae of COVID-19 in Adults: Myocarditis and Other Myocardial Involvement, Post-Acute Sequelae of SARS-CoV-2 Infection, and Return to Play: A Report of the American College of Cardiology Solution Set Oversight Committee. J Am Coll Cardiol. 2022;79:1717-1756.
Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: A retrospective cohort study from the TriNetX US collaborative networks.
Nguyen NT, Chinn J, Nahmias J, et al. Outcomes and Mortality Among Adults Hospitalized With COVID-19 at US Medical Centers. JAMA Netw Open. 2021;4:e210417-e210417.
however, data are scarce specifically on CV outcomes of COVID-19 beyond the index hospitalization. Based on prior literature, it is known that readmissions attributed to CV causes remain a source of significant mortality, morbidity, and resource utilization.
Yeo I, Cheung JW, Feldman DN, et al. Assessment of Hospital Readmission Rates, Risk Factors, and Causes After Cardiac Arrest: Analysis of the US Nationwide Readmissions Database. JAMA Netw Open. 2019;2:e1912208-e1912208.
In the United States (U.S.), the Hospital Readmissions Reduction Program (HRRP) evaluates hospital performance by excess readmission ratio which comprises of MI and HF.
Khera R, Dharmarajan K, Wang Y, et al. Association of the Hospital Readmissions Reduction Program With Mortality During and After Hospitalization for Acute Myocardial Infarction, Heart Failure, and Pneumonia. JAMA Netw Open. 2018;1:e182777-e182777.
Yet there has been no large-scale study that has evaluated readmission rates due to CV causes following discharge from index COVID-19 admission in the United States. Furthermore, current literature pertaining to all-cause readmission following COVID-19 has been limited to a few hospital systems or organizations and has not been reported in a diverse national database.
Hence, we aimed to evaluate outcomes and predictors of 30-day CV readmissions in a U.S. national cohort, among survivors of COVID-19 index hospitalization using the Nationwide Readmission Database (NRD) during the pandemic year 2020.
METHODS:
Study Data
The NRD is sponsored by the Agency for Healthcare Research and Quality (AHRQ) and developed through the Federal-State Industry partnership.13, 14 The database was developed for the Healthcare Cost and Utilization Project (HCUP) and houses data on approximately 35 million annual weighted discharges. The discharge data available from 28 states represent 59.7% of the U.S. population and 58.7% of inpatient hospitalizations. NRD is an all-payer database that captures all admissions and readmissions in the U.S., facilitating the analysis of causes for readmissions as well as resources utilization in terms of cost of care. Each patient is assigned a unique identifier code (NRD_VistLink) for tracing readmissions within a calendar year. The NRD days to event variable capture readmissions within a calendar year but not across different years.
Study Design and Data Selection
For the study, International Classification of Diseases (ICD-10-CM) administrative claims were used to identify patients with COVID-19 (ICD-10-CM code U07.1) from April 1st, 2020 to November 30, 2020. The NRD contains data on total hospital charges, which is the amount billed by the hospital. However, charges differ from the actual cost, including the total expense of hospital services, counting utilities, wages, and supplies. To calculate the cost, HCUP provides Cost-to-Charge Ratio files that provide hospital-specific ratios or weighted average ratios to supplement the original NRD file.
Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality. Rockville, MD. https://www.ahrq.gov/data/hcup/index.html [Accessed April 20, 2023]
The cost information was obtained from accounting reports of the participating hospitals collected by the Centers for Medicare and Medicaid, with the imputation of missing values when necessary. We determined the adjusted cost of care by multiplying the elements of the total charge provided by the NRD by the Cost to Charge ratios. A detailed flow chart of study methods is shown in Figure 1.
Index admissions were defined as patients admitted with COVID-19 who were discharged alive from the hospital. Index admissions were identified per calendar year from April to November 2020, and December admissions were excluded. Since NRD cannot track cases across different years, we had to exclude December COVID-19 admissions to allow for the analysis of 30-day readmission data. Readmission was defined as emergent non-elective or elective readmissions within 30 days of discharge. In patients who had multiple 30-day hospitalizations, only the first hospitalization was included in the analysis. Readmission mortality was defined as any death occurring in the hospital within 30 days of discharge from index hospitalization (excluding deaths occurring outside the hospital). Median household annual income was categorized into four quartiles: 0-25th quartile ($1 to $55,999), 26-50th quartile ($56,000 to $70,999), 51-75th quartile ($71,000 to $93,999) and 76-100th quartile ($94,000+). Based on prior literature review and HRRP definitions
of the common cardiac complications, CV readmission was defined as a composite of acute HF (including acute myocarditis and stress cardiomyopathy), acute MI, acute stroke, VTE, and cardiac arrhythmia. All variables were defined by the ICD-10 codes used, and a complete list of all codes is given in Supplemental Table S1.
Study Outcomes
The primary outcome was 30-day readmission rates for CV causes after index hospital discharge with COVID-19. Secondary outcomes included predictors of 30-day CV readmissions, and comparing the outcomes of CV versus non-CV readmissions in terms of in-hospital mortality, discharge disposition, in-hospital complications (acute kidney injury [AKI], AKI requiring dialysis, percutaneous endoscopic gastrostomy [PEG tube], tracheostomy), and resource utilization (length of stay [LOS] and cost of hospitalization). We used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines to report the study findings. The STROBE checklist can be found in Supplemental Table S2.
Statistical Analysis
All analysis were performed using non-weighted data. Categorical variables were presented as frequencies and percentages, and continuous variables were reported as medians with an interquartile range (IQR). The Shapiro-Wilk test was used to assess the normality of continuous data. Baseline characteristics were compared using Pearson’s Chi-square and Fisher's exact tests for categorical variables and Mann-Whitney U test and Kruskal Wallis for continuous variables.
A multivariable logistic regression model was developed to compute independent predictors of 30-day CV readmission by using the enter regression method. The index hospitalization characteristics of patients readmitted with CV causes were compared with those who were not readmitted (Supplemental Table S3). Variables included in the model included demographic variables (age and sex), socioeconomic variables (median household income and insurance status), comorbidities, complications during index admission, and medications (antiplatelet or anticoagulants). A complete list of variables included in the regression model is shown in Supplemental Table S3. A logistic regression model was also constructed to compare outcomes of CV vs non-CV readmissions. Variables included in the model included demographic variables (age and sex), socioeconomic variables (median household income and insurance status), comorbidities, complications during index admission and medications (antiplatelet or anticoagulants). A complete list of variables included in the regression model is shown in Table 1. R’s survival package
Therneau TM. A package for survival analysis in R. 20202020. R package version 3.5-5, https://CRAN.R-project.org/package=survival [Accessed April 20, 2023]
was used to compute cumulative incidence of readmission and survivals using log-rank test. Complete data were available for all variables except for primary expected payer (n=1,022, 0.1%) and median household income (n=5,963,1.1%). As the overall missing values were minimal, we used listwise deletion and did not include missing values in the logistic regression analysis. All missing values are reported in the baseline characteristics Table 1 and Supplemental Table S3.
Table 1Baseline characteristics of patients with index COVID-19 infection who were readmitted at 30-days with cardiovascular vs non-cardiovascular causes.
Non-CV readmission (n=33,537)
CV readmission (n=26,725)
P value
Demographics [n (%) or as noted]
Age [years, median (IQR)]
65 (52-76)
73 (53-82)
<0.01
Age categories
<=64
16,529 (49.3)
7,706 (28.8)
<0.01
65-74
7,801 (23.3)
6,806 (25.5)
<0.01
75-84
6,084 (18.1)
7,503 (28.1)
<0.01
>=85
3,123 (9.3)
4,710 (17.6)
<0.01
Female Sex
16,424 (49.0)
11,444 (42.8)
<0.01
Socioeconomic characteristics [n (%)]
Primary payer
19,155 (57.2)
19,338 (72.5)
<0.01
Medicare
6,174 (18.4)
2,697 (10.1)
<0.01
Medicaid
6,205 (18.5)
3,525 (13.2)
<0.01
Private insurance
793 (2.4)
374 (1.4)
<0.01
Self-pay
86 (0.3)
43 (0.2)
<0.01
No charge
1,055 (3.2)
708 (2.7)
<0.01
Others
19,155 (57.2)
19,338 (72.5)
<0.01
Missing
69 (0.2)
40 (0.1)
Median household income [n (%)]
0-25% percentile
12,275 (37.0)
9,020 (34.1)
<0.01
26-50% percentile
9,158 (27.6)
7,477 (28.3)
<0.01
51-75th percentile
6,914 (20.9)
5,759 (21.8)
<0.01
76 to 100th percentile
4,802 (14.5)
4,191 (15.8)
<0.01
Missing
388 (1.2)
278 (1.0)
Comorbidities [n (%) or as noted]
Charlson comorbidity index (median [IQR])
5 (3-6)
6 (5-8)
<0.01
Charlson comorbidity index Score >6
7,618 (22.7)
12,032 (45.0)
<0.01
Anemias
1,783 (5.3)
1,683 (6.3)
<0.01
Hypertension
2,2412 (66.8)
21,481 (80.4)
<0.01
Pre-existing heart failure
4,618 (13.8)
11,571 (43.3)
<0.01
Coronary artery Disease
5,642 (16.8)
8,625 (32.3)
<0.01
Cerebrovascular Disease
1,681 (5.0)
3,100 (11.6)
<0.01
Diabetes
1,4800 (44.1)
12,995 (48.6)
<0.01
Chronic obstructive pulmonary disease
8,187 (24.4)
7,960 (29.8)
<0.01
Pulmonary hypertension
776 (2.3)
4,929 (18.4)
<0.01
Valvular disease
573 (1.7)
1,392 (5.2)
<0.01
Peripheral vascular disease
1,350 (4.0)
1,855 (6.9)
<0.01
Obesity
6,782 (20.2)
5,794 (21.7)
<0.01
Liver disease
2,367 (7.1)
1,670 (6.2)
<0.01
Chronic kidney disease
8,573 (25.6)
9,856 (36.9)
<0.01
End-stage kidney disease
2,928 (8.7)
2,673 (10.0)
<0.01
Prior MI
1,464 (4.4)
2,145 (8.0)
<0.01
Prior PCI
1,367 (4.1)
1,819 (6.8)
<0.01
Prior CABG
1 000 (3.0)
1,661 (6.2)
<0.01
Preexisting pacemaker
<11**
1,855 (6.9)
<0.01
Complications during the index admission [n (%)]
Pacemaker implanted during index hospitalization
<11**
65 (0.2)
<0.01
Atrial fibrillation
983 (2.9)
10,982 (41.1)
<0.01
Cardiac arrest
418 (1.2)
972 (3.6)
<0.01
Stress cardiomyopathy
17 (0.1)
37 (0.1)
<0.01
Acute myocarditis
35 (0.1)
69 (0.3)
<0.01
Acute kidney injury
9013 (26.9)
9,334 (34.9)
<0.01
Respiratory failure
12,916 (38.5)
12,489 (46.7)
<0.01
Cardiogenic shock
46 (0.1)
201 (0.8)
<0.01
Vasopressor use
398 (1.2)
451 (1.7)
<0.01
Septic shock
7,317 (21.8)
6,214 (23.3)
<0.01
Mechanical ventilation
2,783 (8.3)
3,009 (11.3)
<0.01
Venous thromboembolism
603 (1.8)
1,089 (4.1)
<0.01
Mechanical circulatory support during the index admission*
For all analyses, a two-tailed p-value of 0.05 was considered statistically significant. Analyses were performed using SPSS version 27 and R software for statistical computing version 4.3.
Data availability statement
NRD data are publicly available. The specific data supporting this study’s findings are available from the corresponding author upon request.
RESULTS:
Baseline characteristics of the study population
A total of 521,351 index hospitalizations for COVID-19 were included in the analysis for the pandemic year 2020. Of the included cases, 461,089 did not get readmitted (Figure 1). The all-cause readmission rate within 30 days of discharge was 11.6% (n=60,262). The incidence of CV readmissions was 5.1% (n=26,725), accounting for 44.3% of all-cause 30-day readmissions. Non-CV readmissions were 6.4% (33,537), accounting for 55.7% of all-cause readmissions. Out of the patients who survived the index admission, the mortality rate during the episode of readmission at 30 days was 1.3%. Among CV readmissions, the in-hospital mortality rate was 0.8% out of the total index hospitalization survivors.
Table 1 shows the baseline characteristics of those readmitted at 30-days for CV (n=26,725) vs non-CV (n=33,537) causes. Supplemental Table S3 shows the baseline characteristics of those readmitted at 30-days for CV causes (n=26,725) compared to those who were not readmitted within 30 days after index hospitalization (n=461,089). Patients with CV readmission had a higher median age of 73 years compared with the age of patients with non-CV readmissions of 65 years (p<0.01) (Table 1). Both CV and non-CV readmissions occurred at a median of 7 days (IQR 3-15). A temporal trend of 30-day CV readmission rates during the study period is shown in Supplemental Figure S1.
Predictors of 30-day CV readmissions
The hospitalization characteristics of the 26,725 30-day readmissions for CV causes were compared with the 461,089 index hospitalizations who did not get readmitted. Independent variables associated with 30-day CV readmission after index hospitalization discharge with COVID-19 included: Charlson Comorbidity sore >6 (Odds Ratio (OR) 1.19, 95% Confidence Interval (CI) [1.14-1.24]), hypertension (OR 1.08, 95% CI [1.04-1.11]), pre-existing HF (OR 2.04, 95% CI [1.97-2.10]), coronary artery disease (OR 1.18, 95% CI [1.14-1.23]), chronic obstructive pulmonary disease (COPD) (OR 1.17, 95% CI [1.14-1.21]), pulmonary hypertension (OR 3.41, 95% CI [3.28-3.54]), atrial fibrillation (OR 1.98, 95% CI [1.91-2.04]), VTE on index admission (OR 1.25, 95% CI [1.16-1.34]), chronic kidney disease (CKD) (OR 1.09, 95% CI [1.05-1.13]), and end-stage kidney disease (OR 1.99, 95% CI [1.88-2.10]). In contrast, female sex (OR 0.75, 95% CI [0.73-0.77]), private insurance (OR 0.70, 95% CI [0.67-0.73]), and high median household income (OR 0.87, 95% CI [0.84-0.91]) were associated with lower odds of readmission within a month after discharge from index COVID-19 hospitalization. A complete list of variables and their association with 30-day CV readmissions is shown in Figure 2.
Figure 2Independent predictors of 30-day readmissions for CV causes, Abbreviations: IQR: Interquartile range; MI: Myocardial Infarction; PCI: Percutaneous coronary intervention; CABG: Coronary artery bypass graft surgery; OR: Odds Ratio, *Mechanical circulatory support devices included intra-aortic balloon pump, Impella®, percutaneous ventricular assist device, and extracorporeal membrane oxygenation (ECMO)., Adjusted Odds Ratio is based on multivariable logistic regression model adjusted for age, sex socioeconomic characteristics, median household income, comorbidities, complications during index admissions and current (long-term) use of antiplatelets and anticoagulants
Cumulative incidence of 30-day readmission and 30-day readmission mortality by demographic variables and socioeconomic characteristics
The cumulative incidence of 30-day CV readmission was highest among male sex, ages >84 years, lower quartile of income, and patients with Medicare as their primary insurance (Figure 3). Similarly, the cumulative survival rates concerning CV deaths were lowest for male sex, ages >84 years, lowest quartile of income, and Medicare insurance (p log-rank <0.01) (Figure 4).
Figure 3Cumulative incidence of 30-day readmissions for cardiovascular causes, Panel A: The cumulative incidence curve shows increased cumulative incidence of readmissions among ages >85 compared to other age groups (p log-rank=<0.01), Panel B: The cumulative incidence curve shows increased cumulative incidence of readmissions among male patients compared to females (p log-rank=<0.01), Panel C: The cumulative incidence curve shows increased cumulative incidence of readmissions among patients with Medicare as their primary insurance compared to other types of insurance coverage (p log-rank=<0.01), Panel D: The cumulative incidence curve shows increased cumulative incidence of readmissions among patients in the lowest quartile of income vs other income quartiles (p log-rank=<0.01)
Figure 4Cumulative survival rates for 30-day mortality during readmission for cardiovascular causes, Panel A: Kaplan-Meier curve shows reduced survival rates among ages >85, compared to other age groups (p log-rank=<0.01), Panel B: Kaplan-Meier curve shows reduced survival rates among male patients compared to females (p log-rank=<0.01), Panel C: Kaplan-Meier curve shows reduced survival rates among patients in the lower quartile of income compared to other income groups (p log-rank=<0.01), Panel D: Kaplan-Meier curve shows reduced survival rates among patients with Medicare as their primary insurance compared to other insurance groups (p log-rank=<0.01)
Cardiovascular and non-cardiovascular causes of 30-day readmissions
The most common CV cause of readmission was acute HF (8.5%), followed by acute MI (5.2%). VTE and stroke during the episode of 30-day readmission occurred at a rate of 4.6% and 3.6%, respectively. Stress cardiomyopathy and acute myocarditis were less frequent causes of CV readmissions with an incidence of 0.1% and 0.2%, respectively. The incidence of cardiac arrhythmias was 31.6% among all readmissions. The most common non-CV cause of readmission was recurrent COVID-19 which occurred at a rate of 64.6%. Acute respiratory failure was seen in 40.7% of cases and bacterial pneumonia in 14% of cases (Figure 5).
Figure 5Causes of readmissions, Abbreviations: ARDS: Acute respiratory distress syndrome; COVID-19: Coronavirus disease-2019, Bar chart shows causes of readmissions among survivors of index COVID-19 hospitalization, Red denotes primary cardiovascular causes, Blue denotes primary non-cardiovascular cause of readmission
Figure 6Adjusted odds for in-hospital complications for CV versus non-CV readmissions Abbreviations: SNF: skilled nursing facility; AKI: acute kidney injury; LOS: length of stay; PEG: percutaneous endoscopic gastrostomy, Adjusted Odds Ratio is based on multivariable logistic regression model adjusted for age, sex, socioeconomic characteristics, median household income, comorbidities, complications during index admissions and current (long-term) use of antiplatelets and anticoagulants
Outcomes of CV readmissions compared with non-CV readmissions
A total of 26,725 CV readmissions were compared with non-CV readmissions (n=33,537). CV readmissions were associated with higher mortality when compared with readmissions for non-CV causes (16.5% vs. 7.5%, p<0.01). Similarly, more patients with HF readmissions were discharged to skilled nursing facilities or rehab than non-CV readmission patients (32.7% vs. 26.6%, p<0.01). Furthermore, rates of AKI, AKI requiring dialysis, need for tracheostomy and placement of PEG tubes were higher for CV readmissions when compared with readmissions for non-CV causes (Table 2).
Table 2Hospital outcomes and resource utilization associated with 30-day readmissions for cardiovascular vs non-cardiovascular causes
Resource utilization for CV readmissions compared to non-CV readmissions
On average, each 30-day CV readmission was associated with a greater increase in the cost of care than non-CV readmission ($13,803 vs $10,310, p<0.01). Similarly, length of stay (LOS) on readmission was significantly higher for CV readmissions at 6 days (IQR 3-11) compared with readmissions for non-CV causes at 5 days (IQR 3-8) (Table 2).
DISCUSSION:
Our U.S. nationwide analysis of COVID-19 index hospitalizations and 30-day readmissions during the pandemic year 2020 revealed the following principal findings: 1) 30-day CV readmission rate following index COVID-19 hospitalization discharge with COVID-19 was 5.1%, accounting for 44.3% of all-causes 30-day readmissions; 2) The most frequent CV complication following COVID-19 hospital discharge was acute HF followed by acute MI; 3) Thrombotic complications such as VTE and cerebrovascular complications such as stroke occurred at a rate of 4.6% and 2.6%, respectively; 5) As opposed to non-CV readmissions, CV readmissions were associated with a 2-fold increased risk of death during the episode of the 30-day readmission; 6) CV readmissions posed a significant burden to the health care system as they were associated with increased resource utilization in terms of the need for dialysis, PEG tube placement, tracheostomies, increased LOS, and the cost of hospitalizations.
CV readmission rates following discharge after COVID-19 hospitalization
Prior studies have shown that adverse events such as hospital readmissions and mortality among survivors of COVID-19 occur within 30-days of discharge.
However, the prior literature had reported variable all-cause readmission rates after COVID-19 hospitalization, which ranged between 5% to 20%, with large variability in the study design as well as the study population.
For example, Donnelly and colleagues reported data from a U.S. cohort from the Veterans Affairs (VA) health system comprising 2,179 index COVID-19 cases of whom 19.9% were readmitted within 60 days of discharge.
Furthermore, there is a scarcity of data on what burden of readmissions following COVID-19 were due to CV causes. Using a national U.S. dataset, our study now demonstrated an all-cause readmission rate of 11% within 30 days of a COVID-19 hospitalization. Our study has a noteworthy advantage over previous reports, as we used a U.S. population that includes all individuals without geographical, demographic, or healthcare system restrictions. This feature makes our analysis important, as our findings are widely generalizable and representative of the U.S. population. Additionally, we found that approximately half of all readmissions were due to CV causes, which has not been previously reported in a national dataset from the United States.
CV complications during 30-day readmission
Previous reports from the VA healthcare system in the U.S. reported that the most common cause of readmissions in addition to COVID-19, sepsis, and pneumonia, was HF.
It has been established from the literature that individuals are still at elevated risk of developing CV complications after their discharge even if they have completely recovered from their COVID-19 symptoms.
A prior large Swedish study of 86,742 patients showed a significant association of COVID-19 with an approximately threefold higher risk of developing MI in the first week (OR 2.9, 95% CI 1.51-5.55) and 2.5-fold risk in the second week (OR 2.53, 95% CI 1.29-4.94) after COVID-19 hospitalization.
Furthermore, an association of VTE with COVID-19 has been well established with some studies suggesting that the risk for VTE may remain high even 1-year after recovery from COVID-19.
On the same note, acute myocarditis and stress cardiomyopathy have also been shown to have an association with COVID-19 with prior literature restricted to case reports and case series.
Jabri A, Kalra A, Kumar A, et al. Incidence of Stress Cardiomyopathy During the Coronavirus Disease 2019 Pandemic. JAMA Netw Open. 2020;3:e2014780-e2014780.
Our study constitutes a significant addition to the existing literature by leveraging an all-payer national database to report rates of CV complications beyond the index admission within 30 days of discharge. We report that HF is a frequent cause of readmissions, followed closely by MI. Similarly, thrombotic complications such as VTE as well as stroke is also seen in about 2-4% of the patients. We also showed that the median time to readmission was 7 days which suggests that this is a critical period for instituting preventive interventions against CV readmissions and complications.
Death during an episode of readmission for CV causes
Prior literature suggests that the post-discharge period following COVID-19 admission in high-risk individuals is a vulnerable time associated with increased odds of mortality.
The reason for the variability in reporting of post-discharge mortality is due to variation in study designs, sample size, and population of interest. In our study, the reported 30-day readmission mortality rate was 1.3%, which is similar to prior reported estimates. Moreover, we also reported that CV causes of readmissions contribute considerably to the mortality burden, thereby, highlighting an area of focus for future interventions. Furthermore, our reported mortality rates may still be underestimated since our study sample does not capture deaths that occur outside the hospital or in the emergency room.
Demographic and socioeconomic factors associated with post-COVID CV readmissions
Demographic factors such as age and sex have been shown to have a significant association with adverse COVID-19 outcomes. For instance, increasing age, as well as male sex, are markers of poor outcomes.
Our study extends these findings beyond the index admission to 30-day readmission. More importantly, socioeconomic disparities were uncovered by the pandemic in the U.S. with the most vulnerable population groups at risk for the greatest complications.
Magesh S, John D, Li WT, et al. Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021;4:e2134147-e2134147.
Our study enhances the existing literature by revealing that post-COVID-19 CV readmissions and mortality rates were highest among individuals in the lowest socioeconomic groups. This highlights the significance of our current analysis, as it incorporates unique features specific to the U.S. population, including annual household incomes and insurance status, with a particular focus on Medicare and Medicaid beneficiaries. Prior to our study, such associations between these factors and COVID-19-related CV readmissions had not been well described, particularly at the national level. These findings warrant an urgent public health intervention with a focus on underserved groups. Though racial disparities are well documented we were not able to take those into account since NRD does not report data on race/ethnicity.
Health care cost utilization
We also report U.S. national cost estimates for the burden of COVID-19 related to CV causes which have not been explored before. The pandemic paralyzed the healthcare systems in many countries including the United States.
Now that the peak of the pandemic is over and we enter into post-pandemic era where COVID-19 appears to have taken on an endemic pattern, the financial burden of CV complications through COVID-19 continues to linger on.
Our study provides national estimates of the cost to the health care system during the episode of readmission. Furthermore, events beyond readmission, such as the increased rates of the tracheostomies, PEG as well as the need for skilled nursing facility adds to the cumulative financial burden.
Limitations
Our study is constrained by the following limitations. First, NRD cannot capture deaths that occur outside of the hospital. Hence our study estimates should be interpreted as 30-day readmission mortality and not as 30-day mortality. Second, granular data such as home medications with their doses, inpatient treatment regimens, and laboratory data such as blood chemistry, radiological findings, and echocardiographic findings are lacking; Third, NRD is an administrative claims-based database that uses ICD-10-CM codes for diagnosis and identifying procedures. Although procedural codes are less prone to error, coding errors and variability cannot be excluded entirely. It also means that the study outcomes were not centrally adjudicated but rather defined by the ICD-10 codes used. However, HCUP does have internal validation and quality control measures to minimize coding errors and accurate reporting of data. Fourth, though we performed multivariable logistic regression analysis to report adjusted estimates, residual confounding cannot be completely ruled out. Fifth, collider bias is well documented in recent observational studies evaluating COVID-19.
Though we did not restrict our study sample to any particular group and NRD provides a random sample of the U.S. population, the introduction of bias through adjusting for variables in the logistic regression model may still exist. Sixth, our study captured outcomes during the first year of the pandemic in 2020, when vaccines were not available. Hence, we could not study outcomes stratified by vaccination status. The NRD data for the years 2021 and 2022 are currently not available to the public. Hence, it would be appropriate to conduct a new analysis of the readmission rates and reasons following hospitalization for COVID-19 when the latest data become available in the post-vaccine era. In this regard, our study can serve as a benchmark for future research, allowing for a comparison of readmission rates for COVID-19-related CV causes across the U.S. healthcare system at the national level for both vaccinated and non-vaccinated individuals. This comparison will provide valuable insights into changes in the trends of readmission rates for vaccinated individuals compared to data collected during the peak of the pandemic in the current study. Vaccination against the influenza virus has already been demonstrated to reduce CV risk,
and one would anticipate that COVID-19 vaccination would have similar impact on reducing CV outcomes. The seventh point to consider is that even though there has been a significant reduction in COVID-19 hospitalizations recently, our study was conducted during the peak of the pandemic and hence may not reflect current COVID-19 readmission rates. However, it is important to note that since COVID-19 virus remains present in the community, it continues to increase the risk of CV complications in susceptible patients similar to the influenza virus.
Therefore, our study's data are valuable in providing ongoing insight into the prevalence, rates, and epidemiology of CV complications following the initial hospitalization for COVID-19 infection. Finally, like any observational, retrospective study, association does not imply causation, and conclusions are hypothesis-generating and should be interpreted cautiously.
CONCLUSIONS:
In our national cohort study of U.S. population, among survivors of index COVID-19 hospitalization, 44.3% of all 30-day readmissions were attributed to CV causes. Acute HF remains the most common cause of COVID-19 readmission followed closely by acute MI. CV causes of readmissions remain a significant source of mortality, morbidity, and resource utilization, when compared with non-CV causes of readmissions. Furthermore, the rates of CV readmissions were the highest for vulnerable population groups belonging to lower quartiles of income as well as individuals with high comorbidity burden. Moreover, the first week after discharge remains the most vulnerable time for CV readmissions.
These findings warrant public health intervention as well as reforms at a level of hospital systems to implement strategies for the prevention of CV readmissions among patients discharged following COVID-19 hospitalization, with a special focus on vulnerable population groups in the first week following discharge. Prevention of CV readmissions will have broad implications in lowering the financial burden on an already strained health system. The current findings in this study may help policymakers in decision-making and future planning. Further studies are needed to evaluate the impact of COVID-19 vaccination on CV readmissions, mortality, and morbidity after COVID-19 infection.
CLINICAL PERSPECTIVE
1)
What is new?
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We evaluated national U.S. hospitalization data during the pandemic year 2020 and found that among survivors of index COVID-19 hospitalization, 44.3% of all 30-day readmissions were attributed to cardiovascular (CV) causes.
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Acute heart failure remains the most common cause of CV readmission with acute myocardial infarction as the second most frequent cause.
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CV causes of readmissions remain a significant source of mortality, morbidity, and resource utilization.
2)
What are the clinical implications?
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Clinicians need to be vigilant about CV complications among individuals discharged from the hospital following COVID-19 hospitalization.
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Our study stresses the importance of early discharge follow-up (within 1 week) with a special focus on vulnerable population groups to prevent CV readmissions and associated morbidity
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Further studies are needed to evaluate the impact of COVID-19 vaccination on CV readmissions, mortality, and morbidity after COVID-19 infection.
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Long-term cardiovascular outcomes in COVID-19 survivors among non-vaccinated population: A retrospective cohort study from the TriNetX US collaborative networks.
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Khera R, Dharmarajan K, Wang Y, et al. Association of the Hospital Readmissions Reduction Program With Mortality During and After Hospitalization for Acute Myocardial Infarction, Heart Failure, and Pneumonia. JAMA Netw Open. 2018;1:e182777-e182777.
Healthcare Cost and Utilization Project (HCUP). Agency for Healthcare Research and Quality. Rockville, MD. https://www.ahrq.gov/data/hcup/index.html [Accessed April 20, 2023]
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Jabri A, Kalra A, Kumar A, et al. Incidence of Stress Cardiomyopathy During the Coronavirus Disease 2019 Pandemic. JAMA Netw Open. 2020;3:e2014780-e2014780.
Magesh S, John D, Li WT, et al. Disparities in COVID-19 Outcomes by Race, Ethnicity, and Socioeconomic Status: A Systematic Review and Meta-analysis. JAMA Netw Open. 2021;4:e2134147-e2134147.
Disclosures: Unrelated to this work, Dr. Michos reports consulting for Amgen, AstraZeneca, Amarin, Bayer, Boehringer Ingelheim, Edwards Life science, Esperion, Medtronic, Novartis, Novo Nordisk, and Pfizer. The remaining authors have no conflicts of interest to disclose.
Funds: Dr. Michos is supported by the Amato Fund for Women's Cardiovascular Health research at Johns Hopkins University. The remaining authors have no funding resources to disclose.
Given the deidentified nature of the database, Institutional Review Board approval and informed consent were not required for this study.
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