Nationwide Trends in Hospitalizations for Atrial Fibrillation and Flutter in the United States before and during the Outbreak of the COVID-19 Pandemic
by
, ,
Sarah Daoudi
1,†, 
Kevin John
1,†,
Fadi Chalhoub
2,
Jennifer Chee
1,
Margaret Infeld
1,
Gabby Elbaz-Greener
3,
Munther Homoud
1,
Jeremy N. Ruskin
4,
E. Kevin Heist
4,
Christopher Madias
1,
James Udelson
1 and
Guy Rozen
1,4,*
1
Cardiac Arrhythmia Center, Tufts Medical Center, Tufts University School of Medicine, Boston, MA 02111, USA
2
Baystate Medical Center, Springfield, MA 01199, USA
3
Department of Cardiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem 9190400, Israel
4
Cardiac Arrhythmia Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2024, 13(16), 4883; https://doi.org/10.3390/jcm13164883
Submission received: 31 May 2024
/
Revised: 12 August 2024
/
Accepted: 15 August 2024
/
Published: 19 August 2024
(This article belongs to the Special Issue Atrial Fibrillation: Advances in Diagnosis and Contemporary Management Strategies)
Abstract
:Background/Objectives: Atrial fibrillation (AF) and flutter (AFL) are the most common cardiac arrhythmias worldwide. Cardiovascular complications are a common manifestation of acute and post-acute COVID-19 infection. We aimed to analyze the nationwide trends in clinical characteristics and outcomes of patients hospitalized for AF/AFL before and during the COVID-19 outbreak in the U.S. Methods: This study is a retrospective analysis of patients, aged 18 and older, hospitalized for AF/AFL in the U.S. between 2016 and 2020. We drew data from the National Inpatient Sample (NIS) database. Baseline sociodemographic and clinical data, as well as outcomes including stroke, acute coronary syndrome (ACS), and mortality, were analyzed. Multivariable analysis was performed to identify independent associations between the different clinical and demographic characteristics and the composite endpoint of Mortality/ACS/Stroke. Results: An estimated total of 2,163,699 hospitalizations for AF/AFL were identified. The hospitalization volume between 2016 and 2019 was stable, averaging 465,176 a year, followed by a significant drop to 302,995 in 2020. Patients’ median age was 72 years (IQR 62–80), 50.9% were male, and 81.5% were white. The composite endpoint steadily increased from 6.5% in 2016 to 11.8% in 2020 (Ptrend < 0.001). In a multivariable regression analysis, age > 75 (OR: 1.35; 95% CI 1.304–1.399, p < 0.001), ischemic heart disease (OR: 1.466; 95% CI: 1.451–1.481; p < 0.001), and chronic kidney disease (OR: 1.635; 95% CI: 1.616–1.653; p < 0.001) were associated with the composite endpoint. COVID-19 was associated with the composite endpoint outcome in the year 2020 (OR: 1.147; 95% CI: 1.037–1.265; p = 0.007). Conclusions: Hospitalization for AF/AFL dropped significantly during the first year of the COVID-19 pandemic outbreak, possibly due to patients’ avoidance of hospital visits. The composite endpoint of Mortality/ACS/Stroke uptrended significantly during the study period. COVID-19 was shown to be independently associated with the adverse composite outcome Mortality/ACS/Stroke.
1. Introduction
Atrial fibrillation (AF) and flutter (AFL) are the most common cardiac arrythmias globally and the most common heart rhythm abnormalities leading to hospitalization [1]. AF/AFL are a major burden on public health, with an estimated worldwide prevalence of 37.574 million cases (0.51% of the worldwide population) [2]. In addition to its significant toll on many patients’ quality of life, AF/AFL have been associated with adverse outcomes such as stroke and heart failure, consequently affecting patients’ mortality [3]. Additionally, as a result of the significant uptrend in the incidence and prevalence of AF/AFL, the cost associated with AF/AFL management is rising, driven primarily by an increase in ED visits and hospitalizations [1].
The COVID-19 pandemic outbreak at the end of 2019 overwhelmed the global healthcare system. In the months and years that followed, it has been shown that cardiovascular complications are a common manifestation of acute and post-acute COVID-19 infection due to the virus’s tendency to target the cardiovascular system [4,5]. Among other cardiovascular morbidity, cardiac arrhythmias—especially AF/AFL—have been shown to be associated with higher morbidity and mortality among patients who are hospitalized with COVID-19 infection [4,5]. These initial observations were limited by the studies’ scope and small numbers.
The purpose of this study was to examine the “real-world”, nationwide trends in AF/AFL-related hospitalizations in the United States before and during the COVID-19 era, specifically comparing trends in baseline characteristics and in-hospital outcomes between the two periods.
2. Materials and Methods
2.1. Data Sources
The data were drawn from the Nationwide Inpatient Sample (NIS), a database developed for the Healthcare Cost and Utilization Project (HCUP), sponsored by the Agency for Healthcare Research and Quality (AHRQ) [6]. The NIS is the largest collection of all-payer data on inpatient hospitalizations in the United States. The dataset represents an approximate 20% stratified sample of all inpatient discharges from U.S. hospitals. The database provides de-identified information for each hospital stay. This information includes patient-level and hospital-level factors: patients’ demographic characteristics; primary and secondary diagnoses and procedural diagnoses; AHRQ comorbidities; length of stay (LOS); hospital region; hospital teaching status; hospital bed size; and cost of hospitalization. Accurate national estimates are calculated based on this sample, using validated patient-level and hospital-level sampling weights that are provided by the NIS. We obtained data from January 2016 to December 2020 [6].
2.2. Study Patients and Variables
The NIS database reports diagnoses and procedures through the International Classification of Diseases-10th Revision-Clinical Modification and Procedure Coding system (ICD-10-CM/PCS). For each index hospitalization, the database provides a principal discharge diagnosis and a maximum of 39 additional diagnoses, in addition to a maximum of 25 procedures [6]. We identified patients who were 18 years of age or older who were hospitalized with a primary diagnosis of AF or AFL in the U.S. between 2016 and 2020 using the ICD-10-CM codes I48.xx (see Appendix A Table A1). Patients with missing age, sex, or mortality status (~0.07% of AF/AFL patients identified in the NIS database) were excluded from the dataset.
Utilizing the ICD-10-CM diagnosis codes (see Appendix A Table A1), we collected and analyzed baseline characteristics of patients with a primary diagnosis of AF/AFL, including common comorbidities (hypertension, acute and chronic heart failure, chronic ischemic heart disease, diabetes, chronic kidney disease, chronic respiratory disease, history of cerebrovascular disease, and COVID-19) to compare trends between the pre-COVID-19 era (2016–2019) and the COVID-19 era (2020). Patient demographic data including sex, age, race, and income percentile, as well as hospital-level data including hospital region, hospital teaching status, and hospital bed size, were also collected. For the purpose of calculating the Deyo–Charlson comorbidity index (Deyo-CCI), an additional list of comorbidities was identified from the database by using ICD-10-CM codes (detailed information on Deyo-CCI is provided in Appendix A Table A2). Deyo-CCI is a modification of the Charlson comorbidity index. It contains 17 comorbid conditions with differential weights, with a total score ranging from 0 to 33. Higher Deyo-CCI scores indicate a greater burden of comorbid diseases and are associated with mortality 1 year after admission [7,8]. The index has been used extensively in studies from administrative databases, with proven validity in predicting short- and long-term outcomes [8,9].
2.3. Study Outcomes
We also utilized the ICD-10 codes (see Appendix A Table A1) to identify in-hospital complications and outcomes of the study population, including stroke, acute coronary syndrome (ACS), and in-hospital mortality, to compare trends between the pre-COVID-19 and COVID-19 eras. Multivariable analysis was performed to identify predictors of a composite endpoint of Mortality/ACS/Stroke.
2.4. Statistical Analysis
Based on recommendations from AHRQ for analysis of survey data, variable DISCWT was used for sampling weights to account for sampling methods used in the construction of the NIS dataset [10]. Analyses of temporal trends for AF hospitalizations were conducted with the Cochrane–Armitage modification of the Chi-square test and Cuzick’s non-parametric test for trend for categorical and continuous variables, respectively [11,12,13]. Multivariable logistic regression analysis was utilized to identify clinical characteristics that were independently associated with the outcomes. In all comparisons, a p-value of <0.01 was considered significant. Statistical analysis was conducted using SPSS software version 25.0 (IBM Corp., Armonk, NY, USA) and R version 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria). All tests were two-tailed.
3. Results
3.1. Trends in AF/AFL Hospitalizations
Out of the 34,955,252 U.S. hospitalizations documented in the NIS database (20% sample of all U.S. hospitalizations) between 2016 and 2020, a total of 432,740 hospitalizations with a primary discharge diagnosis of AF or AFL were included in the analysis based on the inclusion criteria. After weighting, these represent an estimated total of 2,163,699 hospitalizations for AF/AFL in the U.S. during the study period. From 2016 to 2019, the AF/AFL hospitalization volume stayed stable, with an average of 465,176 hospitalizations per year between 2016 and 2019, followed by a significant 34.9% drop in hospitalizations to 302,995 in the first year of the COVID-19 pandemic outbreak (p < 0.001) (Figure 1).
The demographic and clinical characteristics of the study population are presented in Table 1. The patients’ median age was 72 years (IQR 62–80), 50.9% were male, and 81.5% were white. A total of 39% of the patients were 75 years or greater in age, and 68.5% were insured by Medicare. The most common comorbidities were hypertension (79.4%), chronic ischemic heart disease (32.6%), and chronic respiratory disease (24.9%). COVID-19 infection was diagnosed in 1.2% of the study patients hospitalized in 2020.
When compared with the pre-COVID-19 era (2016–2019), patients who were admitted with AF/AFL in 2020 were statistically more likely to be black and to be male, with more chronic renal disease (p < 0.001) (Table 1). Hospitals in the South had the most admissions (40.9%), followed by the Midwest (24.1%), the Northeast (19.7%), and the West (15.3%). In addition, there was a significant increase in patients’ individual comorbidities, reflected by a higher average Deyo-CCI score in 2020 in comparison to the pre-COVID-19 years (2016–2019), p < 0.001 (Table 1).
As documented in Table 2, there was a minimal change in the rate of in-hospital mortality from 0.8% during pre-COVID-19 to 1% in the COVID-19 era (p = 0.007). The rate of stroke did not change significantly during the study period (p = 0.3816). However, a 60% increase in the rate of ACS was documented in 2020 in comparison to the pre-COVID-19 years, p < 0.001 (Table 2). This resulted in a 51% increase in the composite endpoint in the COVID-19 year from an average 7.8% pre-COVID-19 to 11.8% in 2020 (p < 0.001) (Table 2).
The composite endpoint of Mortality/ACS/Stroke steadily increased from 2016 through 2019, with a spike in 2020, driven by the increase in ACS incidence (Figure 2).
In addition, as shown in Figure 3, there was a steady, approximately linear decrease in the mean length of hospital stay for patients hospitalized with AF from 2016 to 2020. Nevertheless, the mean hospitalization cost for these patients followed an opposite trend, increasing steadily from 2016 to 2020.
3.2. Trends in Adverse Endpoint Outcomes
Even in the years before the COVID-19 pandemic, the data showed an increased incidence of the composite endpoint outcome Mortality/ACS/Stroke. A univariable analysis reveals that all the following comorbidities were correlated with increased rates of the composite endpoint outcome in the years 2016–2020: hypertension (OR: 1.358; 95% CI: 1.341–1.376; p < 0.001), acute heart failure (OR: 1.636; 95% CI: 1.621–1.657; p < 0.001), chronic heart failure (OR: 1.189; 95% CI: 1.176–1.203; p < 0.001), ischemic heart disease (OR: 1.800; 95% CI: 1.782–1.817; p < 0.001), diabetes mellitus (OR: 1.051; 95% CI: 1.038–1.063; p < 0.001), chronic kidney disease (OR: 2.426; 95% CI: 2.401–2.451; p < 0.001), chronic respiratory disease (OR: 1.103; 95% CI: 1.091–1.115; p < 0.001), and a history of cerebrovascular disease (OR: 1.161; 95% CI: 1.144–1.178; p < 0.001).
3.3. Inpatient Catheter Ablation Procedure and Outcomes
An additional analysis of AF/AFL hospitalizations revealed that approximately 0.8% of patients (n = 17,910) who were hospitalized for AF/AFL from 2016 to 2020 also underwent a catheter ablation procedure to treat their AF/AFL during the same index hospitalization. Of these patients, 65.3% were male, 81.5% were white, and the median age was 67 years (IQR 59–74). The most common comorbidities were also hypertension (74.5%), chronic ischemic heart disease (33.5%), and chronic respiratory disease (23.2%). COVID-19 infection was not diagnosed in any of these patients. When compared to individuals who did not undergo a catheter ablation procedure, these patients were statistically more likely to be under the age of 75 and less likely to have hypertension (p < 0.001), acute heart failure (p < 0.001), or a history of cerebrovascular disease (p < 0.001). These patients were also more likely to have been admitted to a large, urban hospital (p < 0.001).
Regarding outcomes, individuals who underwent a catheter ablation procedure during their AF/AFL hospital admission had a lower likelihood of developing the adverse endpoint outcome Mortality/ACS/Stroke (p < 0.001). These individuals also had a longer average length of stay (p < 0.001).
3.4. Multivariable Regression Analysis for Predictors of Endpoint Outcomes
In a multivariable regression analysis for the years 2016 to 2020, female gender (OR: 1.085; 95% CI: 1.073–1.096; p < 0.001), non-white race (OR: 1.086; 95% CI: 1.072–1.100; p < 0.001), acute heart failure (OR: 1.056; 95% CI: 1.043–1.069; p < 0.001), ischemic heart disease (OR: 1.467; 95% CI: 1.451–1.481; p < 0.001), and chronic kidney disease (OR: 1.635; 95% CI: 1.617–1.654; p < 0.001) were shown to be independently associated with the composite endpoint outcome of Mortality/ACS/Stroke (Figure 4). In addition, “catheter ablation” (OR: 0.608; 95% CI: 0.566–0.653; p < 0.001) was found to be negatively associated with the composite endpoint outcome of Mortality/ACS/Stroke (Figure 4).
Interestingly, in a multivariable analysis, hypertension (OR: 0.944; 95% CI: 0.931–0.957; p < 0.001), chronic heart failure (OR: 0.785; 95% CI: 0.775–0.795; p < 0.001), diabetes mellitus (OR: 0.801; 95% CI: 0.791–0.811; p < 0.001), chronic respiratory disease (OR: 0.752; 95% CI: 0.743–0.760; p < 0.001), and a history of cerebrovascular disease (OR: 0.936; 95% CI: 0.922–0.951; p < 0.001) were found to be associated with a lower risk of the composite endpoint outcome of Mortality/ACS/Stroke (Figure 4).
An analysis of 2020 hospitalizations also showed COVID-19 infection to be independently associated with the composite endpoint outcome of Mortality/ACS/Stroke (OR: 1.147; 95% CI: 1.037–1.265; p = 0.007).
4. Discussion
This study reports trends in hospitalizations for atrial fibrillation and flutter in the United States over a 4-year period from 2016 to 2020. The main findings of our analysis are as follows: (1) the AF/AFL hospitalization volume was steady during the pre-COVID-19 years from 2016 to 2019, followed by sharp decline in 2020, corresponding with the onset of the COVID-19 pandemic; (2) the patient population admitted for AF/AFL in the COVID-19 era included more male and black patients, with a higher prevalence of chronic renal failure; (3) there was a significant spike in the composite endpoint of Mortality/ACS/Stroke during the COVID-19 outbreak, as well as slightly higher in-hospital mortality in 2020 compared to the pre-COVID-19 era; and (4) COVID-19 was independently associated with a significantly higher rate of the adverse composite endpoint outcome in the year 2020.
The data in this study show a slight increase in hospital admissions for AF/AFL from 2016 to 2019, although not reaching statistical significance in this analysis. The existing literature shows an increasing trend in AF hospitalizations over the years [1,14,15,16,17,18]. However, we were unable to find studies looking into the trends for recent years (2016–2020), and it is possible that the uptrend in AF admissions was attenuated during these years, as shown in our analysis. This is to our knowledge the first nationwide analysis of trends in hospitalized AF/AFL population characteristics during the outbreak of the COVID-19 pandemic, showing a sharp decline in hospitalizations and increase in comorbidities of hospitalized AF/AFL patients in the COVID-19 era. These findings align with the CDC report published in June 2020, estimating that 41% of U.S. adults delayed or avoided medical care due to COVID-19 concerns [19]. A literature review reveals that other cardiac conditions were undertreated during the COVID-19 pandemic. Mafham et al. reported that hospital admissions for acute coronary syndrome in England declined between February and March 2020 [20]. Similarly, a Beijing inpatient database study found that admissions for STEMI, Non-STEMI, and unstable angina all declined significantly between January and June 2020 [21].
In our study, men made up the majority of AF/AFL hospitalizations compared to women (50.9% vs. 49.1%), which is consistent with the existing literature. Prior studies report that the age-standardized prevalence of AF/AFL has been consistently higher in men than in women [22,23]. A majority of hospital admissions (40.9%) for AF/AFL in our study occurred in southern states of the U.S. (Table 1). The southern states are well known for an increased prevalence of known risk factors for AF/AFL like obesity, diabetes mellitus, hypertension, and heart failure, as well as incidence of stroke, compared with other regions [24,25].
Individuals who underwent a catheter ablation procedure during their hospitalization showed lower rates of the adverse composite endpoint outcome in comparison to patients who did not undergo catheter ablation. The multivariable analysis also revealed catheter ablation to be negatively associated with the adverse composite endpoint outcome for patients hospitalized with AF/AFL. These findings are in alignment with the growing evidence suggesting that rhythm control therapies are favorable rate control therapies in patients with AF [26]. Given that the percentage of patients who underwent an ablation procedure in the study was very limited (<1%), further studies, with longer follow-up, are needed to assess the effect of inpatient ablation procedures on patients admitted to hospitals with AF/AFL.
Interestingly, some comorbidities like hypertension, chronic heart failure, diabetes mellitus, chronic respiratory disease, and a history of cerebrovascular disease were shown to be protective of the adverse composite outcome in a multivariable analysis. The nature of the NIS database limits our ability to dive deeper into analyzing the nature of this protective effect. One possible explanation, for instance, is that some of these comorbidities are associated with obesity—which has a well-documented protective effect in patients with various cardiovascular conditions [27,28]. It is also likely that patients in our study who had pre-existing comorbid conditions prior to their AF hospitalizations captured in this study were more likely to have been followed closely by their primary care physician and more likely to have been treated with anticoagulation medications and/or beta blockers prior to hospitalization. It is well known that AF is often asymptomatic and can go undetected in about 30% of patients [29,30]. This can result in a greater chance of adverse outcomes for these AF patients following hospitalization. Thus, we postulate that the protective effects of hypertension, chronic heart failure, diabetes mellitus, chronic respiratory disease, and a history of cerebrovascular disease are a result of early diagnosis and better follow-up for patients with a greater number of comorbid conditions to AF.
Our nationwide analysis also showed a statistically significant change in the rate of in-hospital mortality in 2020 compared to the pre-COVID-19 era. A study by Coromilas et al. showed that 70% of patients who were hospitalized with COVID-19 who developed a cardiac arrhythmia developed atrial arrhythmias, and that the presence of arrhythmia was associated with significant morbidity and mortality [31]. In alignment with these findings, our study’s multivariable analysis for the year 2020 also showed COVID-19 to worsen the composite adverse outcome endpoint in patients admitted for AF/AFL. Similar findings were reported by Alharbi et al. showing that COVID-19 infection is worsening the outcomes of patients who are admitted for heart failure in the U.S. [32].
The mean length of hospital stay trended down over the study duration, while the total hospitalization cost trended up (Figure 3). The increase in the cost of hospital stay per day over the years, as well as the increase in cardiovascular comorbidities and associated treatments, may provide explanations for the increase in the hospitalization costs (Table 1). Medicare was the primary source of payment for patients, further reflecting an increase in hospitalizations in elderly patients. To reduce this economic burden, certain interventions, such as the implementation of “AF pathway” protocols for the emergency departments (EDs), utilizing ED observation units versus hospital admission, and using low-molecular-weight heparin and direct oral anticoagulants versus unfractionated heparin, may be employed [33,34]. These methods could limit the number of hospitalizations and decrease the length of stay and have previously been described as potential means to reduce the cost associated with the treatment of AF/AFL [33,34].
Several limitations of this study need to be acknowledged. First, the NIS database is a retrospective administrative database that contains discharge-level records and, as such, is susceptible to coding errors. Secondly, the lack of patient identifiers in the NIS database prevented us from using other outcome variables and mortality measures, such as at-30-days mortality. We could only capture events that occurred in the same index of hospitalization. The NIS database also does not include detailed information about patients’ clinical characteristics, including the duration of AF, medication, blood tests, etc. Therefore, we cannot rule out residual confounding within the associations we observed.
5. Conclusions
The AF/AFL hospitalization volume in the U.S. remained stable between 2016 and 2019, followed by a significant decline in 2020, the year of the COVID-19 pandemic’s outbreak, possibly due to patients’ attempts to avoid hospital visits. The composite endpoint of Mortality/ACS/Stroke, as well as hospitalization costs, uptrended significantly during the study period, in parallel to the uptrend in patients’ comorbidities. COVID-19 was found to be independently associated with the adverse composite endpoint outcome in the year 2020.
Author Contributions
Conceptualization, F.C., J.N.R. and G.R.; Formal analysis, S.D. and K.J.; Methodology, F.C., J.N.R. and G.R.; Resources, C.M. and J.U.; Supervision, G.R.; Writing—original draft, S.D. and K.J.; Writing—review and editing, F.C., J.C., M.I., G.E.-G., M.H., E.K.H., C.M., J.U. and G.R. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original data presented in this study are openly available in the HCUP Nationwide Inpatient Sample (NIS) database at https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2020.jsp (accessed on 23 December 2022).
Conflicts of Interest
The authors declare no conflicts of interest.
Appendix A. ICD-10 CM Codes Used in the Research
Table A1.
ICD-10 CM diagnosis and procedure codes.
| Condition | ICD-10 CM Codes |
|---|---|
| AF/AFL | I48.0, I48.11, I48.19, I48.20, I48.21, I48.3, I48.4. I48.91, I48.92 |
| Hypertension | I10, I11.0, I11.9, I12.0, I12.9, I13.0, I13.10, I13.11, I13.2 I15.0, I15.1, I15.2, I15.8, I15.9, I16.0, I16.1, I16.9 |
| Chronic Heart Failure | I50.1, I50.20, I50.22, I50.30, I50.32, I50.40, I50.42, I50.810, I50.812, I50.814, I50.82, I50.83, I50.84, I50.89, I50.9 |
| Acute Heart Failure | I50.21, I50.23, I50.31, I50.33, I50.41, I50.43, I50.811, I50.813 |
| Chronic Ischemic Heart Disease | I25.1xx, I25.4x, I25.5, I25.7xx, I25.8xx, I25.9 |
| Diabetes Mellitus | E08.42, E08.649, E08.65, E08.9, E09.22, E09.649, E09.65, E09.8, E09.9, E10.0x, E10.1x, E10.6x, E10.8x, E10.9x, E11.0x, E11.1x, E11.6x, E11.8x, E11.9x, E13.0x, E13.1x, E13.6x, E13.8x, E13.9x |
| Chronic Kidney Disease | N17.0, N17.1, N17.2, N17.8, N17.9, N18.1, N18.2, N18.3, N18.30, N18.31, N18.32, N18.4, N18.5, N18.6, N18.9, N19 |
| Chronic Respiratory Disease | J40, J41.0, J41.1, J41.8, J42, J43.0, J43.1, J43.2, J43.8, J43.9, J44.0, J44.1, J44.9, J45.20, J45.21, J45.30, J45.40, J45.41, J45.42, J45.50, J45.51, J45.901, J45.902, J45.909, J45.990, J47.0, J47.1, J47.9 |
| History of Cerebrovascular Disease | I69.0x, I69.1x, I69.2x, I69.3x, I69.4x, I69.6x, I69.7x, I69.8x, I69.9x, Z86.73 |
| COVID-19 | U07.1, J12.82 |
| Acute Coronary Syndrome | I20.0, I20.1, I21.01, I21.02, I21.09, I21.11, I21.19 I21.21, I21.29, I21.3, I21.4, I21.9, I21.A1, I21.A9, I21.B, I22.0, I22.1, I22.2, I22.8, I22.9, I23.0, I24.0, I24.8, I24.9 |
| Stroke | I63.0x, I63.1x, I63.2x, I63.3x, I63.40, I63.41x, I63.42x, I63.43x, I63.44x, I63.49, I63.5x, I63.8x, I63.9, I69.05x |
| Catheter ablation | 025S0ZZ, 025S3ZZ, 025S4ZZ, 025T0ZZ, 025T3ZZ, 025T4ZZ, 02560ZZ, 02563ZZ, 02564ZZ, 02570ZK, 02570ZZ, 02573ZK, 02573ZZ, 02574ZK, 02574ZZ |
Table A2.
ICD-10 CM codes for Deyo-CCI score [27].
Table A2.
ICD-10 CM codes for Deyo-CCI score [27].
| Condition | ICD-10 CM Codes for Deyo-CCI Score | Score |
|---|---|---|
| Myocardial infarction | I21.x, I22.x, I25.2 | 1 |
| Congestive heart failure | I09.9, I11.0, I13.0, I13.2, I25.5, I42.0, I42.5–I42.9, I43.x, I50.x, P29.0 | 1 |
| Peripheral vascular disease | I70.x, I71.x, I73.1, I73.8, I73.9, I77.1, I79.0, I79.2, K55.1, K55.8, K55.9, Z95.8, Z95.9 | 1 |
| Cerebrovascular disease | G45.x, G46.x, H34.0, I60.x–I69.x | 1 |
| Dementia | F00.x–F03.x, F05.1, G30.x, G31.1 | 1 |
| Chronic pulmonary disease | I27.8, I27.9, J40.x–J47.x, J60.x–J67.x, J68.4, J70.1, J70.3 | 1 |
| Rheumatologic disease | M05.x, M06.x, M31.5, M32.x–M34.x, M35.1, M35.3, M36.0 | 1 |
| Peptic ulcer disease | K25.x–K28.x | 1 |
| Mild liver disease | B18.x, K70.0–K70.3, K70.9, K71.3–K71.5, K71.7, K73.x, K74.x, K76.0, K76.2–K76.4, K76.8, K76.9, Z94.4 | 1 |
| Diabetes | E10.0, E10.l, E10.6, E10.8, E10.9, E11.0, E11.1, E11.6, E11.8, E11.9, E12.0, E12.1, E12.6, E12.8, E12.9, E13.0, E13.1, E13.6, E13.8, E13.9, E14.0, E14.1, E14.6, E14.8, E14.9 | 1 |
| Diabetes with chronic complications | E10.2–E10.5, E10.7, E11.2–E11.5, E11.7, E12.2–E12.5, E12.7, E13.2–E13.5, E13.7, E14.2–E14.5, E14.7 | 2 |
| Hemiplegia or paraplegia | G04.1, G11.4, G80.1, G80.2, G81.x, G82.x, G83.0–G83.4, G83.9 | 2 |
| Renal disease | I12.0, I13.1, N03.2–N03.7, N05.2–N05.7, N18.x, N19.x, N25.0, Z49.0–Z49.2, Z94.0, Z99.2 | 2 |
| Any malignancy including leukemia and lymphoma | C00.x–C26.x, C30.x–C34.x, C37.x–C41.x, C43.x, C45.x–C58.x, C60.x–C76.x, C81.x–C85.x, C88.x, C90.x–C97.x | 2 |
| Moderate or severe liver disease | I85.0, I85.9, I86.4, I98.2, K70.4, K71.1, K72.1, K72.9, K76.5, K76.6, K76.7 | 3 |
| Metastatic solid tumor | C77.x–C80.x | 6 |
| Acquired immunodeficiency syndrome (AIDS) | B20.x–B22.x, B24.x | 6 |
Appendix B. Trends in Baseline Characteristics of Patients Hospitalized with Atrial Fibrillation in the U.S. between 2016 and 2020
| Total | 2016 | 2017 | 2018 | 2019 | 2020 | P (Trend) | ||
| N | Unweighted | 432,740 | 92,760 | 93,400 | 94,367 | 91,614 | 60,599 | 0.2301 |
| Weighted | 2,163,699 | 463,800 | 467,000 | 471,835 | 458,070 | 302,995 | ||
| Age (%) | 18–44 | 4 | 4.2 | 4 | 3.9 | 3.9 | 4.4 | <0.001 |
| 45–59 | 17.1 | 17.8 | 17.4 | 17 | 16.3 | 16.9 | ||
| 60–74 | 40 | 39 | 39.9 | 40.4 | 40.3 | 40.5 | ||
| 75 or older | 38.9 | 39 | 38.8 | 38.8 | 39.5 | 38.2 | ||
| Gender (%) | Male | 50.9 | 50.4 | 50.4 | 50.9 | 51.4 | 51.5 | <0.001 |
| Female | 49.1 | 49.6 | 49.6 | 49.1 | 48.6 | 48.5 | ||
| Race (%) | White | 81.5 | 81.8 | 81.6 | 81.2 | 81.8 | 81 | 0.024 |
| Black | 8.6 | 8.5 | 8.4 | 8.4 | 8.6 | 9.2 | ||
| Hispanic | 5.9 | 5.8 | 6 | 6.3 | 5.7 | 5.7 | ||
| Asian/Pacific Islander | 1.5 | 1.5 | 1.5 | 1.6 | 1.5 | 1.5 | ||
| Native American | 0.4 | 0.3 | 0.4 | 0.4 | 0.4 | 0.4 | ||
| Others | 2.1 | 2.1 | 2 | 2.1 | 2.1 | 2.1 | ||
| Comorbidities (%) | Hypertension | 79.4 | 77.5 | 79 | 80 | 80.5 | 80.3 | <0.001 |
| Chronic Heart Failure | 21.2 | 19.6 | 20.5 | 21.8 | 22.6 | 21.9 | <0.001 | |
| Ischemic Heart Disease | 32.6 | 32.3 | 32 | 32.7 | 33.1 | 33 | <0.001 | |
| Diabetes Mellitus | 21.1 | 24 | 20.8 | 20.3 | 20.1 | 20.3 | <0.001 | |
| Chronic Kidney Disease | 20.1 | 18.9 | 19.1 | 19.7 | 20.7 | 23.5 | <0.001 | |
| Chronic Respiratory Disease | 24.9 | 24.8 | 25.1 | 24.9 | 25.1 | 24.4 | 0.237 | |
| H/O Cerebrovascular Disease | 11.5 | 11 | 11.3 | 11.6 | 11.9 | 11.7 | <0.001 | |
| COVID-19 | 0.2 | 0 | 0 | 0 | 0 | 1.2 | <0.001 | |
| Disposition of Patients (%) | Routine | 74.6 | 74.1 | 74.2 | 74.9 | 75 | 74.6 | 0.21 |
| Transfer to Short term Hosp | 2.1 | 2.2 | 2.1 | 2 | 2.1 | 2 | ||
| Transfer other | 10.1 | 10.7 | 10.6 | 10.2 | 9.9 | 8.7 | ||
| Home Healthcare | 12 | 11.9 | 11.8 | 11.8 | 11.9 | 13.1 | ||
| Against Medical Advice | 1.2 | 1.1 | 1.2 | 1.1 | 1.1 | 1.5 | ||
| Deyo-CCI, % | 0 | 22.6 | 24.2 | 23.4 | 22.2 | 21.6 | 21.4 | <0.001 |
| 1 | 25.6 | 26 | 26.1 | 25.6 | 25.2 | 24.9 | ||
| 2 or higher | 51.8 | 49.8 | 50.5 | 52.2 | 53.2 | 53.7 | ||
| Status of hospital (%) | Rural | 10.6 | 10.9 | 10.8 | 10.5 | 10.2 | 10.7 | <0.001 |
| Urban non-teaching | 23.1 | 28.5 | 24.1 | 22.2 | 19.6 | 19.8 | ||
| Urban teaching | 66.3 | 60.6 | 65.1 | 67.3 | 70.1 | 69.5 | ||
| Hospital Bed Size (%) | Small | 21.1 | 19.1 | 20.3 | 21.3 | 22.2 | 23.6 | <0.001 |
| Medium | 30.1 | 29.7 | 30.8 | 30 | 30.2 | 29.4 | ||
| Large | 48.8 | 51.1 | 48.9 | 48.7 | 47.6 | 46.9 | ||
| Primary Expected Payer (%) | Medicare | 68.5 | 68 | 68.7 | 68.9 | 68.9 | 67.3 | 0.00548 |
| Medicaid | 6.3 | 6.2 | 6.3 | 6.2 | 6.2 | 6.9 | ||
| Private | 20.5 | 21.2 | 20.5 | 20.4 | 19.9 | 20.5 | ||
| Self-pay | 2.5 | 2.3 | 2.3 | 2.5 | 2.6 | 2.7 | ||
| No change | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | ||
| Other | 2.1 | 2.1 | 1.9 | 1.9 | 2.1 | 2.4 | ||
| Region of hospital (%) | Northeast | 19.7 | 20.5 | 20.1 | 19.3 | 19.3 | 19 | <0.001 |
| Midwest | 24.1 | 24.2 | 24.3 | 24.3 | 24.1 | 23.2 | ||
| South | 40.9 | 40.3 | 40.4 | 40.9 | 41.1 | 42.2 | ||
| West | 15.3 | 15 | 15.3 | 15.4 | 15.5 | 15.5 | ||
Appendix C. Trends in Outcomes of Patients Hospitalized with AF/AFL in the U.S. between 2016 and 2020
| Total | 2016 | 2017 | 2018 | 2019 | 2020 | p-Value | |
| Overall mortality (%) * | 0.9 | 0.9 | 0.9 | 0.8 | 0.8 | 1 | 0.644 |
| Acute Heart Failure (%) | 18.6 | 18.2 | 18.1 | 19.2 | 18.9 | 18.9 | <0.001 |
| Acute stroke (%) | 0.6 | 0.6 | 0.6 | 0.5 | 0.5 | 0.6 | 0.175 |
| Acute Coronary Syndrome (%) ^ | 7.2 | 5.3 | 6.2 | 7.1 | 8.1 | 10.7 | <0.001 |
| Mortality/ACS/Stroke (%) | 8.4 | 6.5 | 7.4 | 8.2 | 9.2 | 11.8 | <0.001 |
| Mean length of hospital stay, days | 3.34 | 3.41 | 3.37 | 3.34 | 3.3 | 3.22 | <0.001 |
| Mean hospitalization cost, USD | 44,908 | 38,841 | 41,620 | 46,067 | 48,913 | 51,347 | <0.001 |
| * Overall mortality refers to in-hospital mortality only. ^ Acute coronary syndrome refers to acute myocardial infarction, as well as other acute ischemic heart disease. | |||||||
References
- Patel, N.J.; Deshmukh, A.; Pant, S.; Singh, V.; Patel, N.; Arora, S.; Shah, N.; Chothani, A.; Savani, G.T.; Mehta, K.; et al. Contemporary Trends of Hospitalization for Atrial Fibrillation in the United States, 2000 Through 2010. Circulation 2014, 129, 2371–2379. [Google Scholar] [CrossRef] [PubMed]
- Lippi, G.; Sanchis-Gomar, F.; Cervellin, G. Global epidemiology of atrial fibrillation: An increasing epidemic and public health challenge. Int. J. Stroke 2021, 16, 217–221. [Google Scholar] [CrossRef] [PubMed]
- Escudero-Martínez, I.; Morales-Caba, L.; Segura, T. Atrial fibrillation and stroke: A review and new insights. Trends Cardiovasc. Med. 2023, 33, 23–29. [Google Scholar] [CrossRef] [PubMed]
- Terzic, C.M.; Medina-Inojosa, B.J. Cardiovascular Complications of Coronavirus Disease-2019. Phys. Med. Rehabil. Clin. N. Am. 2023, 34, 551–561. [Google Scholar] [CrossRef] [PubMed]
- Farshidfar, F.; Koleini, N.; Ardehali, H. Cardiovascular complications of COVID-19. JCI Insight 2021, 6, e148980. [Google Scholar] [CrossRef] [PubMed]
- Introduction to the HCUP National Inpatient Sample (NIS). Available online: https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2020.jsp (accessed on 30 December 2023).
- Deyo, R.A.; Cherkin, D.C.; Ciol, M.A. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J. Clin. Epidemiol. 1992, 45, 613–619. [Google Scholar] [CrossRef] [PubMed]
- Glasheen, W.P.; Cordier, T.; Gumpina, R.; Haugh, G.; Davis, J.; Renda, A. Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation. Am. Health Drug Benefits 2019, 12, 188–197. [Google Scholar] [PubMed]
- Radovanovic, D.; Seifert, B.; Urban, P.; Eberli, F.R.; Rickli, H.; Bertel, O.; Puhan, M.A.; Erne, P.; AMIS Plus Investigators. Validity of Charlson Comorbidity Index in patients hospitalised with acute coronary syndrome. Insights from the nationwide AMIS Plus registry 2002–2012. Heart 2014, 100, 288–294. [Google Scholar] [CrossRef] [PubMed]
- Houchens, R.; Ross, D.; Elixhauser, A.; Jiang, J. Nationwide Inpatient Sample (NIS) Redesign Final Report. Available online: http://www.hcupus.ahrq.gov/db/nation/nis/nisrelatedreports.jsp. (accessed on 30 December 2023).
- Cochran, W.G. Some Methods for Strengthening the Common χ2 Tests. Biometrics 1954, 10, 417–451. [Google Scholar] [CrossRef]
- Armitage, P. Tests for Linear Trends in Proportions and Frequencies. Biometrics 1955, 11, 375–386. [Google Scholar] [CrossRef]
- Cuzick, J. A wilcoxon-type test for trend. Stat. Med. 1985, 4, 87–90. [Google Scholar] [CrossRef] [PubMed]
- Miyasaka, Y.; Barnes, M.E.; Gersh, B.J.; Cha, S.S.; Bailey, K.R.; Abhayaratna, W.P.; Seward, J.B.; Tsang, T.S.M. Secular Trends in Incidence of Atrial Fibrillation in Olmsted County, Minnesota, 1980 to 2000, and Implications on the Projections for Future Prevalence. Circulation 2006, 114, 119–125. [Google Scholar] [CrossRef]
- Wong, C.X.; Brooks, A.G.; Leong, D.P.; Roberts-Thomson, K.C.; Sanders, P. The Increasing Burden of Atrial Fibrillation Compared with Heart Failure and Myocardial Infarction: A 15-Year Study of All Hospitalizations in Australia. Arch. Intern. Med. 2012, 172, 739–741. [Google Scholar] [CrossRef] [PubMed]
- Kim, D.; Yang, P.-S.; Jang, E.; Yu, H.T.; Kim, T.-H.; Uhm, J.-S.; Kim, J.-Y.; Pak, H.-N.; Lee, M.-H.; Joung, B.; et al. 10-year nationwide trends of the incidence, prevalence, and adverse outcomes of non-valvular atrial fibrillation nationwide health insurance data covering the entire Korean population. Am. Heart J. 2018, 202, 20–26. [Google Scholar] [CrossRef] [PubMed]
- Lee, H.; Kim, T.-H.; Baek, Y.-S.; Uhm, J.-S.; Pak, H.-N.; Lee, M.-H.; Joung, B. The Trends of Atrial Fibrillation-Related Hospital Visit and Cost, Treatment Pattern and Mortality in Korea: 10-Year Nationwide Sample Cohort Data. Korean Circ. J. 2017, 47, 56–64. [Google Scholar] [CrossRef] [PubMed]
- Nisar, M.U.; Munir, M.B.; Sharbaugh, M.S.; Thoma, F.W.; Althouse, A.D.; Saba, S. Trends in atrial fibrillation hospitalizations in the United States: A report using data from the National Hospital Discharge Survey. Indian Pacing Electrophysiol. J. 2018, 18, 6–12. [Google Scholar] [CrossRef] [PubMed]
- Czeisler, M.É.; Marynak, K.; Clarke, K.E.; Zainab, S.; Iju, S.; Thierry, J.M.; Ali, N.; McMillan, H.; Wiley, J.F.; Weaver, M.D.; et al. Delay or Avoidance of Medical Care Because of COVID-19–Related Concerns—United States, June 2020. 2020. Available online: https://www.cdc.gov/mmwr/volumes/69/wr/mm6936a4.htm?s_cid=mm6936a4_w (accessed on 16 April 2024).
- Mafham, M.M.; Spata, E.; Goldacre, R.; Gair, D.; Curnow, P.; Bray, M.; Hollings, S.; Roebuck, C.; Gale, C.P.; Mamas, M.A.; et al. COVID-19 pandemic and admission rates for and management of acute coronary syndromes in England. Lancet 2020, 396, 381–389. [Google Scholar] [CrossRef] [PubMed]
- He, L.; Lu, F.; Du, X.; Long, D.; Sang, C.; Tang, R.; Dong, J.; Guo, M.; Ma, C. Impact of COVID-19 Pandemic on Hospital Admissions of Acute Coronary Syndrome: A Beijing Inpatient Database Study. Lancet Reg. Health–West. Pac. 2022, 19, 100335. [Google Scholar] [CrossRef]
- Stewart, S.; Hart, C.L.; Hole, D.J.; McMurray, J.J.V. Population prevalence, incidence, and predictors of atrial fibrillation in the Renfrew/Paisley study. Heart 2001, 86, 516–521. [Google Scholar] [CrossRef]
- Khairallah, F.; Ezzedine, R.; Ganz, L.I.; London, B.; Saba, S. Epidemiology and determinants of outcome of admissions for atrial fibrillation in the United States from 1996 to 2001. Am. J. Cardiol. 2004, 94, 500–504. [Google Scholar] [CrossRef]
- Mujib, M.; Zhang, Y.; Feller, M.A.; Ahmed, A. Evidence of a “Heart Failure Belt” in the Southeastern United States. Am. J. Cardiol. 2011, 107, 935–937. [Google Scholar] [CrossRef] [PubMed]
- Gillum, R.; Ingram, D. Relation between Residence in the Southeast Region of the United States and Stroke Incidence The NHANES I Epidemiologic Followup Study. Am. J. Epidemiol. 1996, 144, 665–673. [Google Scholar] [CrossRef]
- Kirchhof, P.; Camm, A.J.; Goette, A.; Brandes, A.; Eckardt, L.; Elvan, A.; Fetsch, T.; Gelder, I.C.v.; Haase, D.; Haegeli, L.M.; et al. Early Rhythm-Control Therapy in Patients with Atrial Fibrillation. N. Engl. J. Med. 2020, 383, 1305–1316. [Google Scholar] [CrossRef]
- Rozen, G.; Elbaz-Greener, G.; Marai, I.; Heist, E.K.; Ruskin, J.N.; Carasso, S.; Birati, E.Y.; Amir, O. The relationship between the body mass index and in-hospital mortality in patients admitted for sudden cardiac death in the United States. Clin. Cardiol. 2021, 44, 1673–1682. [Google Scholar] [CrossRef] [PubMed]
- Rozen, G.; Elbaz-Greener, G.; Margolis, G.; Marai, I.; Heist, E.K.; Ruskin, J.N.; Carasso, S.; Roguin, A.; Birati, E.Y.; Amir, O. The Obesity Paradox in Real-World Nation-Wide Cohort of Patients Admitted for a Stroke in the U.S. J. Clin. Med. 2022, 11, 1678. [Google Scholar] [CrossRef] [PubMed]
- Reiffel, J.A.; Verma, A.; Kowey, P.R.; Halperin, J.L.; Gersh, B.J.; Wachter, R.; Pouliot, E.; Ziegler, P.D.; REVEAL AF Investigators. Incidence of Previously Undiagnosed Atrial Fibrillation Using Insertable Cardiac Monitors in a High-Risk Population: The REVEAL AF Study. JAMA Cardiol. 2017, 2, 1120–1127. [Google Scholar] [CrossRef] [PubMed]
- Rienstra, M.; Lubitz, S.A.; Mahida, S.; Magnani, J.W.; Fontes, J.D.; Sinner, M.F.; Van Gelder, I.C.; Ellinor, P.T.; Benjamin, E.J. Symptoms and Functional Status of Patients With Atrial Fibrillation. Circulation 2012, 125, 2933–2943. [Google Scholar] [CrossRef] [PubMed]
- Coromilas, E.J.; Kochav, S.; Goldenthal, I.; Biviano, A.; Garan, H.; Goldbarg, S.; Kim, J.-H.; Yeo, I.; Tracy, C.; Ayanian, S.; et al. Worldwide Survey of COVID-19—Associated Arrhythmias. Circ. Arrhythmia Electrophysiol. 2021, 14, e009458. [Google Scholar] [CrossRef] [PubMed]
- Alharbi, A.; Alfatlawi, H.; Mohamed, A.; Mhanna, M.; Mahmoud, M.; Elsheik, R.; Moukarbel, G.; Assaly, R. Outcomes of Heart Failure Related Hospitalizations During the COVID-19 Pandemic. Cureus 2023, 15, e36935. [Google Scholar] [CrossRef]
- Decker, W.W.; Smars, P.A.; Vaidyanathan, L.; Goyal, D.G.; Boie, E.T.; Stead, L.G.; Packer, D.L.; Meloy, T.D.; Boggust, A.J.; Haro, L.H.; et al. A Prospective, Randomized Trial of an Emergency Department Observation Unit for Acute Onset Atrial Fibrillation. Ann. Emerg. Med. 2008, 52, 322–328. [Google Scholar] [CrossRef]
- Kim, M.H.; Decena, B.F.; Bruckman, D.; Eagle, K.A. Use patterns of low-molecular weight heparin and the impact on length of stay in patients hospitalized for atrial fibrillation. Am. Heart J. 2003, 145, 665–669. [Google Scholar] [CrossRef] [PubMed]
Figure 1.
AF/AFL Hospitalizations from 2016 to 2020. From 2016 to 2019, AF/AFL hospitalization volume was stable, followed by sudden drop in hospitalizations in 2020 (COVID-19 year).
Figure 1.
AF/AFL Hospitalizations from 2016 to 2020. From 2016 to 2019, AF/AFL hospitalization volume was stable, followed by sudden drop in hospitalizations in 2020 (COVID-19 year).
Figure 2.
Prevalence of the composite endpoint Mortality/ACS/Stroke from 2016 to 2020. The incidence of acute coronary syndrome, as well as the composite adverse endpoint outcome Mortality/ACS/Stroke, increased steadily from 2016 to 2019, with a noticeable increase in 2020.
Figure 2.
Prevalence of the composite endpoint Mortality/ACS/Stroke from 2016 to 2020. The incidence of acute coronary syndrome, as well as the composite adverse endpoint outcome Mortality/ACS/Stroke, increased steadily from 2016 to 2019, with a noticeable increase in 2020.
Figure 3.
Hospitalization costs vs. length of hospital stay from 2016 to 2020. The mean length of a hospital stay steadily decreased from 2016 to 2020, while the hospitalization cost steadily increased from 2016 to 2020.
Figure 3.
Hospitalization costs vs. length of hospital stay from 2016 to 2020. The mean length of a hospital stay steadily decreased from 2016 to 2020, while the hospitalization cost steadily increased from 2016 to 2020.
Figure 4.
Forest Plot predictors of the composite adverse outcome (Mortality/ACS/Stroke). Non-white race, female gender, age > 60, acute heart failure, ischemic heart disease, and chronic kidney disease were independently associated with the composite endpoint outcome. Hypertension, chronic heart failure, diabetes mellitus, chronic respiratory disease, and a history of cerebrovascular disease were associated with a lower risk of the composite endpoint outcome.
Figure 4.
Forest Plot predictors of the composite adverse outcome (Mortality/ACS/Stroke). Non-white race, female gender, age > 60, acute heart failure, ischemic heart disease, and chronic kidney disease were independently associated with the composite endpoint outcome. Hypertension, chronic heart failure, diabetes mellitus, chronic respiratory disease, and a history of cerebrovascular disease were associated with a lower risk of the composite endpoint outcome.
Table 1.
Baseline characteristics of patients hospitalized with atrial fibrillation or flutter before (2016–2019) and during (2020) the COVID-19 era.
Table 1.
Baseline characteristics of patients hospitalized with atrial fibrillation or flutter before (2016–2019) and during (2020) the COVID-19 era.
| Total | 2016–2019 (Mean) | 2020 | ||
|---|---|---|---|---|
| N | Unweighted | 432,740 | 93,035 | 60,599 |
| Weighted | 2,163,699 | 465,176 | 302,995 | |
| Age (%) | 18–44 | 4 | 4 | 4.4 |
| 45–59 | 17.1 | 17.1 | 16.9 | |
| 60–74 | 40 | 39.9 | 40.5 | |
| 75 or older | 38.9 | 39 | 38.2 | |
| Gender (%) | Male | 50.9 | 50.8 | 51.5 |
| Female | 49.1 | 49.2 | 48.5 | |
| Race (%) | White | 81.5 | 81.6 | 81 |
| Black | 8.6 | 8.5 | 9.2 | |
| Hispanic | 5.9 | 6 | 5.7 | |
| Asian/Pacific Islander | 1.5 | 1.5 | 1.5 | |
| Native American | 0.4 | 0.4 | 0.4 | |
| Others | 2.1 | 2.1 | 2.1 | |
| Comorbidities (%) | Hypertension | 79.4 | 79.2 | 80.3 |
| Chronic Heart Failure | 21.2 | 21.1 | 21.9 | |
| Ischemic Heart Disease | 32.6 | 32.5 | 33 | |
| Diabetes Mellitus | 21.1 | 21.3 | 20.3 | |
| Chronic Kidney Disease | 20.1 | 19.6 | 23.5 | |
| Chronic Respiratory Disease | 24.9 | 25 | 24.4 | |
| H/O Cerebrovascular Disease | 11.5 | 11.5 | 11.7 | |
| COVID-19 | 0.2 | 0 | 1.2 | |
| Deyo-CCI, % | 0 | 22.6 | 22.8 | 21.4 |
| 1 | 25.6 | 25.7 | 24.9 | |
| 2 or higher | 51.8 | 51.4 | 53.7 | |
| Disposition of Patients (%) | Routine | 74.6 | 74.6 | 74.6 |
| Transfer to Short- term Hosp | 2.1 | 2.1 | 2 | |
| Transfer other | 10.1 | 10.4 | 8.7 | |
| Home Healthcare | 12 | 11.8 | 13.1 | |
| Against Medical Advice | 1.2 | 1.1 | 1.5 | |
| Status of hospital (%) | Rural | 10.6 | 10.6 | 10.7 |
| Urban non-teaching | 23.1 | 23.6 | 19.8 | |
| Urban teaching | 66.3 | 65.8 | 69.5 | |
| Hospital Bed Size (%) | Small | 21.1 | 20.7 | 23.6 |
| Medium | 30.1 | 30.2 | 29.4 | |
| Large | 48.8 | 49.1 | 46.9 | |
| Primary Expected Payer (%) | Medicare | 68.5 | 68.6 | 67.3 |
| Medicaid | 6.3 | 6.2 | 6.9 | |
| Private | 20.5 | 20.5 | 20.5 | |
| Self-pay | 2.5 | 2.4 | 2.7 | |
| No change | 0.2 | 0.2 | 0.2 | |
| Other | 2.1 | 2 | 2.4 | |
| Region of hospital (%) | Northeast | 19.7 | 19.8 | 19 |
| Midwest | 24.1 | 24.2 | 23.2 | |
| South | 40.9 | 40.7 | 42.2 | |
| West | 15.3 | 15.3 | 15.5 |
Table 2.
Outcomes of patients hospitalized with AF/AFL in the U.S. before (2016–2019) and during (2020) the COVID-19 era.
Table 2.
Outcomes of patients hospitalized with AF/AFL in the U.S. before (2016–2019) and during (2020) the COVID-19 era.
| Total | 2016–2019 | 2020 | Percent Change | p-Value | |
|---|---|---|---|---|---|
| Overall mortality (%) * | 0.9 | 0.8 | 1 | 25% | 0.007 |
| Acute Heart Failure | 18.6 | 18.6 | 18.9 | 1.61% | 0.0347 |
| Acute stroke (%) | 0.6 | 0.6 | 0.6 | 0% | 0.3816 |
| Acute Coronary Syndrome (%) ^ | 7.2 | 6.7 | 10.7 | 60% | <0.001 |
| Mortality/ACS/Stroke (%) | 8.4 | 7.8 | 11.8 | 51% | <0.001 |
| Mean length of hospital stay, days | 3.34 | 3.35 | 3.22 | −3.88% | <0.001 |
| Mean hospitalization cost, USD | 44,908 | 43,860 | 51,347 | 17% | <0.001 |
* Overall mortality refers to in-hospital mortality only. ^ Acute coronary syndrome refers to acute myocardial infarction, as well as other acute ischemic heart disease.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Daoudi, S.; John, K.; Chalhoub, F.; Chee, J.; Infeld, M.; Elbaz-Greener, G.; Homoud, M.; Ruskin, J.N.; Heist, E.K.; Madias, C.; et al. Nationwide Trends in Hospitalizations for Atrial Fibrillation and Flutter in the United States before and during the Outbreak of the COVID-19 Pandemic. J. Clin. Med. 2024, 13, 4883. https://doi.org/10.3390/jcm13164883
AMA Style
Daoudi S, John K, Chalhoub F, Chee J, Infeld M, Elbaz-Greener G, Homoud M, Ruskin JN, Heist EK, Madias C, et al. Nationwide Trends in Hospitalizations for Atrial Fibrillation and Flutter in the United States before and during the Outbreak of the COVID-19 Pandemic. Journal of Clinical Medicine. 2024; 13(16):4883. https://doi.org/10.3390/jcm13164883
Chicago/Turabian StyleDaoudi, Sarah, Kevin John, Fadi Chalhoub, Jennifer Chee, Margaret Infeld, Gabby Elbaz-Greener, Munther Homoud, Jeremy N. Ruskin, E. Kevin Heist, Christopher Madias, and et al. 2024. "Nationwide Trends in Hospitalizations for Atrial Fibrillation and Flutter in the United States before and during the Outbreak of the COVID-19 Pandemic" Journal of Clinical Medicine 13, no. 16: 4883. https://doi.org/10.3390/jcm13164883
APA StyleDaoudi, S., John, K., Chalhoub, F., Chee, J., Infeld, M., Elbaz-Greener, G., Homoud, M., Ruskin, J. N., Heist, E. K., Madias, C., Udelson, J., & Rozen, G. (2024). Nationwide Trends in Hospitalizations for Atrial Fibrillation and Flutter in the United States before and during the Outbreak of the COVID-19 Pandemic. Journal of Clinical Medicine, 13(16), 4883. https://doi.org/10.3390/jcm13164883
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
