Abstract
Takotsubo syndrome (TTS) is a transitory stress-related cardiomyopathy characterized by acute but reversible left ventricular failure. The disease most commonly affects postmenopausal women following a traumatic experience, often presenting as an acute myocardial infarction (MI), and its prevalence is increasing globally. Cardiovascular events such as Acute Coronary Syndrome (ACS) and stroke have well-defined seasonal variations and are most common in the winter [[8], [9], [10]]. However, there is insufficient data on the impact of such climatic variations on the etiopathogenesis and outcomes of TTS-related hospitalization in the United States.
We used data from the Nationwide Inpatient Sample (NIS) database, a nationally representative survey of hospitalizations conducted by the Healthcare Cost and Utilization Project in collaboration with participating states [1], to identify seasonal fluctuation based on meteorological classification of the northern hemisphere's Spring, Summer, Fall, and Winter. NIS is the largest all-payer inpatient dataset in the United States and includes a 20% sample of US community hospitals that approximates 20% of all US community hospitals. In the past, the NIS database has been used to conduct health quality and outcome-focused studies on TTS hospitalization [2]. Each hospitalization is identified and maintained as a distinct entry in the NIS with one primary discharge diagnosis and ≤14 secondary diagnoses. All TTS-related admissions among adults in the United States in 2019 were identified using the prior validated International Classification of Diseases (10th Edition) Clinical Modification (ICD-10-CM) diagnosis code I51.81 (Takotsubo syndrome) as the admission diagnosis. Categorical and continuous data were compared using Pearson's Chi-square test and Mann Whitney U test, respectively. Multivariable regression models were subsequently applied to assess the odds of outcomes of TTS-related admissions among adults in the US adjusting for baseline patient and hospital level characteristics and relevant preexisting comorbidities using complex survey sample models. Results were reported in adjusted odds ratio and 95% confidence interval for odds of outcomes. A two-tailed p-value below 0.05 was considered a threshold for statistical significance. IBM SPSS Statistics version 25.0 (IBM Corp., Armonk, NY, USA) was used to perform all analyses.
The TTS cohort (n = 41,830) in 2019 was primarily caucasian (80.6%), female (82.1%), and had a median age of ≥65 years (61.9%), similar to the findings in Ahuja et al. [3]. Fall admissions were the greatest (25.9%), followed by summer (25%), spring (24.6%), and winter (24.5%), which was similar to what was reported by Aryal et al. [4] and Deshmukh et al. [2]. Despite a similar median length of stay (4 days; p < 0.001), winter hospitalization expenditures (USD 56763) were the highest and fall the lowest (USD 51649). Winter admissions showed higher all-cause mortality (7.3% vs.6.7%) and dysrhythmias (29.8% vs.28.5%), including Atrial fibrillation (AF) (20.7% vs. 19.7%) [5]. Spring hospitalizations had a greater rate of cardiac arrest (4.8% vs.4.1%) and Acute Venous Thromboembolism (VTE) (4.7% vs.3.5%) than total TTS-related admissions [Table 1]. With confounders controlled, there was a higher risk of dysrhythmias in winter (OR:1.20; 95%CI:1.03–1.39), spring (OR:1.15; 95% CI:1.00–1.33), and fall (OR:1.18; 95% CI:1.03–1.36) compared to summer (p = 0.063). When compared to summer, there was a greater incidence of AF in winter (OR:1.22; 95% CI:1.02–1.45) [4], spring (OR:1.20; 95% CI:1.02–1.42), and fall (OR:1.28; 95% CI:1.08–1.51). Spring admissions had a greater risk of VTE than summer admissions [(OR:1.54; 95% CI:1.09–2.16) vs. summer; p = 0.067] which is what was reported by Zhao H et al. [6]. Other outcomes, such as all-cause mortality and cardiogenic shock, were not associated after controlling for confounding variables.
Table 1.
TTS hospitalizations and outcomes by seasons, 2019.
| Variable | WINTER |
SPRING |
SUMMER |
FALL |
Total TTS |
P-value | |
|---|---|---|---|---|---|---|---|
| n = 10,255 | n = 10,275 | n = 10,450 | n = 10,850 | n = 41,830 | |||
| Age (years) at admission | Median [IQR] | 69 [59–77] | 69 [59–77] | 68 [59–77] | 69 [59–78] | 68 [59–77] | 0.724 |
| 18–44 | 6.4% | 6.6% | 7.4% | 6.8% | 6.8% | 0.020 | |
| 45–64 | 32.4% | 30.9% | 31.0% | 31.2% | 31.4% | ||
| > = 65 | 61.2% | 62.5% | 61.7% | 62.0% | 61.9% | ||
| Sex | Male | 17.6% | 18.6% | 17.2% | 17.9% | 17.9% | 0.059 |
| Female | 82.4% | 81.4% | 82.8% | 82.1% | 82.1% | ||
| Race | White | 80.9% | 81.2% | 81.2% | 79.2% | 80.6% | <0.001 |
| Black | 7.9% | 8.9% | 7.8% | 7.5% | 8.0% | ||
| Hispanic | 5.8% | 5.0% | 6.7% | 7.8% | 6.4% | ||
| Asian or Pacific Islander | 1.8% | 2.6% | 1.7% | 1.9% | 2.0% | ||
| Native American | 0.6% | 0.5% | 0.6% | 0.7% | 0.6% | ||
| Others | 2.9% | 1.7% | 2.0% | 2.8% | 2.4% | ||
| Median household income national quartile for patient ZIP Code | 0-25th | 25.6% | 25.0% | 24.5% | 25.9% | 25.3% | 0.001 |
| 76-100th | 22.8% | 23.5% | 21.8% | 21.4% | 22.4% | ||
| Primary expected payer | Medicare | 63.6% | 63.6% | 63.8% | 63.4% | 63.6% | <0.001 |
| Medicaid | 9.6% | 10.7% | 10.0% | 10.4% | 10.2% | ||
| Others | 2.3% | 2.1% | 2.1% | 1.8% | 2.1% | ||
| Non-elective admission | 93.2% | 92.7% | 93.5% | 93.0% | 93.1% | 0.159 | |
| Location/teaching status of hospital |
Rural | 6.4% | 6.2% | 6.6% | 5.9% | 6.3% | 0.004 |
| Urban non-teaching | 15.2% | 15.4% | 15.2% | 16.9% | 15.7% | ||
|
Urban teaching |
78.4% |
78.4% |
78.2% |
77.2% |
78.1% |
||
| COMORBIDITIES | |||||||
| Hypertension, complicated | 31.8% | 32.7% | 32.2% | 28.3% | 31.2% | <0.001 | |
| Hypertension, uncomplicated | 31.5% | 30.0% | 31.0% | 35.2% | 32.0% | <0.001 | |
| Diabetes, chronic complications | 13.9% | 13.3% | 12.8% | 13.8% | 13.5% | 0.070 | |
| Diabetes without chronic complications | 9.2% | 9.2% | 9.2% | 9.5% | 9.3% | 0.770 | |
| Hyperlipidemia | 45.9% | 47.1% | 46.5% | 47.0% | 46.6% | 0.291 | |
| Obesity | 11.7% | 11.5% | 11.9% | 11.6% | 11.7% | 0.824 | |
| Smoking | 17.6% | 18.6% | 17.4% | 18.4% | 18.0% | 0.071 | |
| Peripheral vascular disease | 9.0% | 11.2% | 9.7% | 10.0% | 10.0% | <0.001 | |
| Prior MI | 11.1% | 11.2% | 10.6% | 12.3% | 11.3% | 0.002 | |
| Prior PCI | 0.4% | 0.3% | 0.5% | 0.3% | 0.4% | 0.030 | |
| Prior CABG | 4.7% | 4.9% | 3.7% | 5.3% | 4.6% | <0.001 | |
| Prior TIA/Stroke | 6.8% | 7.1% | 6.0% | 7.7% | 6.9% | <0.001 | |
| Prior VTE | 6.1% | 5.7% | 5.7% | 5.9% | 5.9% | 0.498 | |
| Drug abuse | 5.0% | 5.9% | 5.1% | 5.9% | 5.5% | 0.001 | |
| Depression | 19.1% | 19.3% | 19.2% | 18.8% | 19.1% | 0.734 | |
| COPD | 32.7% | 32.8% | 30.7% | 28.8% | 31.2% | <0.001 | |
| Cancer | 8.2% | 7.5% | 8.3% | 9.3% | 8.4% | <0.001 | |
| Prior Radiation | 2.5% | 2.1% | 1.9% | 2.5% | 2.2% | 0.002 | |
| Chemotherapy |
0.3% |
0.3% |
0.6% |
0.5% |
0.4% |
0.019 |
|
| OUTCOMES | |||||||
| All-cause Mortality | 7.3% | 7.1% | 6.3% | 6.1% | 6.7% | 0.001 | |
| Dysrhythmia | 29.8% | 29.1% | 26.1% | 29.0% | 28.5% | <0.001 | |
| AF | 20.7% | 20.3% | 17.3% | 20.6% | 19.7% | <0.001 | |
| Cardiogenic Shock | 7.1% | 7.1% | 7.4% | 7.2% | 7.2% | 0.808 | |
| Cardiac arrest | 3.9% | 4.8% | 3.9% | 4.1% | 4.1% | 0.003 | |
| Acute VTE |
4.1% |
4.7% |
3.2% |
3.5% |
3.9% |
<0.001 |
|
| Multivariate odds [aOR (95% CI)] of outcomes in TTS-related Hospitalizations by Season 2019 | |||||||
| All-cause Mortality | 1.16 (0.90–1.50) | 1.14 (0.87–1.48) | Referent | 0.97 (0.74–1.27) | 0.426 | ||
| Dysrhythmia | 1.20 (1.03–1.39) | 1.15 (1.00–1.33) | Referent | 1.18 (1.03–1.36) | 0.063 | ||
| AF | 1.22 (1.02–1.45) | 1.20 (1.02–1.42) | Referent | 1.28 (1.08–1.51) | 0.028 | ||
| Cardiogenic Shock | 0.93 (0.73–1.19) | 0.92 (0.71–1.19) | Referent | 0.97 (0.77–1.24) | 0.906 | ||
| Acute VTE | 1.15 (0.81–1.65) | 1.54 (1.09–2.16) | Referent | 1.11 (0.79–1.58) | 0.067 | ||
A p < 0.05 indicates statistical significance. Multivariable regression analyses were adjusted for baseline characteristics, hospital-level characteristics, and relevant cardiac-extra cardiac comorbidities.
MI = myocardial infarction, PCI = percutaneous coronary intervention, CABG = coronary artery bypass grafting, TIA = transient ischemic attack, VTE = venous thromboembolic events, COPD = chronic obstructive pulmonary disease, AF = atrial fibrillation, aOR = adjusted odds ratio, CI = confidence interval.
In conclusion, this nationwide retrospective cohort study found that hospitalizations throughout the winter increased the risk of dysrhythmia and atrial fibrillation (AF). At the same time, admissions during the spring showed a higher risk of venous thromboembolism (VTE). Seasonal variations in hospitalization and mortality have clinical and economic consequences. During vulnerable times, emergency care and other hospital resources should be available. Susceptible patients should be aware of the increased risk throughout the winter, and the increased risk may assist health practitioners to boost causative prevention measures, treatment, and educational methods [7]. Enhanced vigilance and improved access to emergency services and other hospital resources during the vulnerable period can improve in-hospital outcomes and ultimately reduce hospitalization costs.
Funding
None.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
None
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