Pre-existing cardiovascular diseases (CVDs) have been proposed to identify patients at higher risk of adverse COVID-19 outcomes, but existing evidence is conflicting. Thus, it is unclear whether pre-existing CVDs are independently important predictors for severe COVID-19.
In a nationwide Danish cohort of hospital-screened COVID-19 patients aged >=40, we investigated if pre-existing CVDs predict the 30-day risk of (1) composite outcome of severe COVID-19 and (2) all-cause mortality. We estimated 30-day risks using a Cox regression model including age, sex, each CVD comorbidity, COPD-asthma, diabetes, and chronic kidney disease. To illustrate CVD comorbidities' importance, we evaluated the predicted risks of death and severe infection, for each sex, along ages 40 -85. 4,090 COVID-19 hospital-screened patients were observed as of August 26, 2020; 22.1% had ≥ 1 CVD, 23.7% had severe infection within 30 days and 12.6% died. Predicted risks of both outcomes at age 75 among men with single CVD comorbidities did not differ in clinically meaningful amounts compared to men with no comorbidities risks for the composite outcome of severe infection; women with heart failure (28.2%; 95% CI 21.1%-37.0%) or atrial fibrillation (30.0%; 95% CI: 24.2%-36.9%) showed modest increases compared to women with no comorbidities (24.0%; 95% CI: 21.4%-26.9%).
The results showing only modest effects of CVDs on increased risks of poor outcomes are important in allowing public health authorities and clinicians to provide more tailored guidance to cardiovascular patients, who have heretofore been grouped together as high-risk due to their disease status.
Based upon preliminary data, several countries have considered people with cardiovascular diseases (CVDs) to be a high-risk group for severe outcomes following infection with SARS-CoV-2, the causative agent of coronavirus disease 2019 . 1 Many health authorities have thus recommended stricter social distancing and/or isolation for CVD patients as compared to the baseline population. 2 While these measures may reduce the risk of COVID-19 infection, they likely have negative consequences ; for example, they potentially contribute to poor mental and physical health during isolation, 3 and/or possibly a reduced likelihood of receiving treatment for non-COVID-19 related issues. 4, 5 Additionally, in-hospital decisions, such as triaging for intensive care unit (ICU) admission, are informed by the perceived likelihood of surviving. Therefore, it is imperative that clinicians and health policy makers have an accurate understanding of the interplay between CVD, COVID-19 infection, and outcomes. However, the evidence concerning the link between individual CVDs and risks of severe COVID-19 outcomes is mixed.
Several studies suggest that pre-existing CVD comorbidities are associated with an increased risk of mortality following COVID-19 infection. [6] [7] [8] [9] [10] [11] [12] However, it remains unclear how COVID-19 prognosis differs between different types of CVDs. One study using a New York cohort shows that heart failure, but not coronary artery disease is associated with increased risk, 12 while a study with a smaller Chinese cohort shows those with coronary heart disease were less likely to recover. 10 Additionally a smaller case-series analysis shows those with underlying CVD who do not develop myocardial injury during admission are not at increased risk. 8 Moreover, one cohort study finds no independent association between CVDs and COVID-19 mortality. 13 Identifying groups most likely to have severe outcomes of COVID-19 is critical in allowing the healthcare system to prioritize allocation of prevention and treatment resources where it is most needed. 14, 15 This will allow more tailored guidance that can minimize the burden of the epidemic without increasing the risk of poor outcomes due to COVID-19 infection.
Additionally, it will be useful for improving epidemic models and projections of epidemic burden that are important for allocating appropriate resources. Lastly, clinicians can use the data to better inform their treatment and advice to patients.
Denmark has had 4,090 cases among people ≥40 years of age and 516 deaths within 30 days of COVID-19 diagnosis as of August 26, 2020 as registered in the National Patient Registry that includes hospitalised cases as well as hospital outpatient screenings. In this study, we 
Data for this study were sourced from several national data registries in Denmark. All residents of Denmark are assigned a unique numeric identifier that allows easy linking between the various registries. Hospital contact information, including date and diagnosis came from the Danish National Patient Registry; diagnoses were classified according to the Danish version of the International Classification of Diseases, 10th revision (ICD-10). PCR data came from the Danish Microbiology Database. Prescription drug information came from the Danish National Prescription Registry and contains data on the dispensing date, strength and quantity of all prescription drugs purchased nationally. Age, sex, and vital status (whether a person is a current resident, dead, or has emigrated, along with the date of the event) came from The Danish Civil Registration System. The completeness and quality of the Danish registries have been described and validated previously. 16, 17 The present study was approved by the data responsible institution Capital Region, approval number P-2019-191.
Personal data from Danish registries is pseudonymized before being delivered for research purposes. No further ethical approval is required for registry studies in Denmark.
The primary population was defined as all people ≥40 years of age diagnosed with COVID- (Table S1) .
Subsets of the primary and secondary populations were made based on diagnosis or test date.
The first two subsets were defined from the primary population: a subset including only those with a COVID-19 diagnosis date < 1 May, 2020, a subset including only those with a diagnosis date >= 1 May, 2020. The second set of subsets were defined from the secondary population: a subset including only those with a SARS-CoV-2 positive test date < 1 May, 2020, a subset including only those with a SARS-CoV-2 positive test date >= 1 May, 2020.
These two time periods roughly correspond to the lockdown period (< 1st May), during which time COVID-19 testing was not widely used, especially for patients not considered high-risk, and the post-lockdown period (>= 1st May), during which time testing became more frequent nationwide. 18 A sensitivity analysis was included on a subset of the primary population restricted to all COVID-19 patients admitted to the hospital, defined as a continuous hospital stay of ≥ 12 hours. The outcomes and the exposures were the same as the main analysis. Figure 1 shows the definitions of each population.
The exposures of interest were pre-existing ischemic heart disease, heart failure, and atrial fibrillation. Exposures to control for included, pulmonary disease (chronic obstructive pulmonary disease or asthma), pre-existing diabetes (type I or II), and chronic kidney disease.
Exposures were defined using a combination of ICD-10 codes and history of prescription medication usage (table S2) . Cardiovascular comorbidities were included if diagnosis occurred between January 1, 2000, and the day before COVID-19 diagnosis. Patients diagnosed with acute coronary syndrome in the six months preceding COVID-19 diagnosis were excluded, as they likely represent a qualitatively different patient group due to the recent, acute nature of their condition. COPD-asthma was defined as the receipt of drugs for obstructive airway diseases at least twice within any 180-period after the age of 40 or 
Categorical and continuous variables were presented as n (%) and mean (standard deviation), respectively. The time origin for all patients was the date of COVID-19 diagnosis. Patients were followed until August 26, 2020 or death, whichever came first. Cox regression was used to predict the risk of the outcomes within 30 days. Separate models were fitted for the composite outcome of severe infection and the death-only outcome. The fitted models were used to predict the absolute risk of each outcome for each exposure of interest (ischemic heart disease, heart failure, and atrial fibrillation), while holding all other exposures negative, across ages 40 -85 for both sexes.
The Cox model included patient age at COVID-19 diagnosis/PCR test date, and interaction between sex and all exposures (ischemic heart disease, heart failure, atrial fibrillation, COPDasthma and diabetes). Patients' age was included as a continuous variable with non-linear effects by way of restricted cubic splines. 19 Data management and statistical analysis was performed in R version 3.6.1. 20
A total of 4,090 hospital-screened people aged >=40 were diagnosed with COVID-19 and included in the primary population. A total of 904(22.1%) had at least one CVD comorbidity, 541(13.2%) had ischemic heart disease, 271 (6.6%) had heart failure, and 479(11.7%) had atrial fibrillation ( Table 1 ). The three most frequent combinations of comorbidities among those with at least one CVD were ischemic heart disease only (n = 173, 4.2%), atrial fibrillation only (n = 150, 3.7%), and ischemic heart disease combined with diabetes(n = 45, 1.1%) ( Tables S8, S9, and S10 respectively and the distributions of the outcomes of interests are shown in tables S11, S12, and S13.
To illustrate the importance of the CVD comorbidities as risk factors, Figure 2 for 75 year males were as follows: ischemic heart disease and heart failure, 36.5% (95% CI:
28.4% -46.0%); ischemic heart disease and atrial fibrillation, 32.9% (95% CI: 26.6% -40.3%); heart failure and atrial fibrillation 42.2% (95% CI: 33.9% -51.7%); ischemic heart disease, heart failure and atrial fibrillation, 35.5% (95% CI: 28.0% -44.2%).
The predicted risk of the composite outcome of severe infection for 75-year-old females with no comorbidities is 24.0% (95% CI: 21.4 -26.9%). The corresponding risk for 75-year-old females with single CVD comorbidities were as follows: ischemic heart disease, 22.6% (95% CI: 17.3% -29.1%); heart failure, 28.2% (95% CI: 21.1% -37.0%); and atrial fibrillation, 30 .0% (95% CI: 24.2% -37.0%). For multiple CVD comorbidities, the corresponding risks for 75 year females were as follows: ischemic heart disease and heart failure, 26.6% (95% CI:
18.9% -36.6%); ischemic heart disease and atrial fibrillation, 28.3% (95% CI: 21.5% -36.7%); heart failure and atrial fibrillation 35.0% (95% CI: 26.6% -45.1%); ischemic heart disease, heart failure and atrial fibrillation, 33.1% (95% CI: 24.9% -43.2%).
The predicted risk of death for 75-year-old males with no comorbidities is 22.3% (95% CI:
19.4% -25.5%). The corresponding risk for 75-year-old males with single CVD comorbidities were as follows: ischemic heart disease, 15.5% (95% CI: 12.1% -19.8%); heart failure, 27.6% (20.5% -36. 5%); and atrial fibrillation, 23.7% (19.0% -29.3%). For multiple CVD comorbidities, the corresponding risks for 75 year males were as follows:
ischemic heart disease and heart failure, 19.4% (95% CI: 13.9% -26.7%); ischemic heart disease and atrial fibrillation, 16.5% (95% CI: 12.3% -22.1%); heart failure and atrial fibrillation 29.3% (95% CI: 22.2% -38.0%); ischemic heart disease, heart failure and atrial fibrillation, 20.7% (95% CI: 15.2% -27.7%).
The predicted risk of death for 75-year-old females with no comorbidities is 12.8% (95% CI:
10.8% -15.2%). The corresponding risk for 75-year-old females with single CVD comorbidities were as follows: ischemic heart disease, 10.1% (95% CI: 7.0% -14.2%); heart failure, 19.1% (13.4% -26.7%); and atrial fibrillation, 18.1% (13.8% -23.4%). For multiple CVD comorbidities, the corresponding risks for 75 year males were as follows: ischemic heart disease and heart failure, 15.1% (95% CI: 9.9% -22.6%); ischemic heart disease and atrial fibrillation, 14.3% (95% CI: 9.9% -20.3%); heart failure and atrial fibrillation 26.5% (95% CI: 19.1% -35.9%); ischemic heart disease, heart failure and atrial fibrillation, 21.1% (95% CI: 14.8% -29.5%).
The predicted risks of the composite outcome of severe infection across all combinations of included risk factors for a 75-year-old male and female can be found in Figure S11 and S12 respectively, and the corresponding predicted risks of death for a 75 year old male and female can be found in Figure S13 and S14 .
A sensitivity analysis performed using only hospital admitted patients from the primary population (n = 2,380) showed similar results of predicted risks of death or severe infection ( Figures S15 -S16 ).
We examined if pre-existing ischemic heart disease, heart failure, or atrial fibrillation were predictors of worse infection among hospital-screened COVID-19 patients. The major novel findings in this study include establishing that, among men with single CVD comorbidities, neither ischemic heart disease, heart failure, nor atrial fibrillation predicted a higher risk of severe COVID-19 outcomes as compared to baseline risk of someone with no comorbidities, while among women, heart failure and atrial fibrillation each modestly increased risk.
However, when restricting the analysis to the period during which COVID-19 testing was widely available (the post-lockdown period), the increased risk among women with heart failure or atrial fibrillation disappeared. The same trends appeared in the analyses of both the primary (hospital diagnosed) and the secondary (PCR positive) populations. Additionally, while women who have both heart failure and atrial fibrillation had a substantially higher predicted risk of death or severe infection when including the entire primary population, when restricting the data to the post-lockdown this increased risk also disappeared.
Our study adds important nuances to previous findings that pre-existing CVDs are risk factors for poor COVID-19 outcomes. [8] [9] [10] [11] [12] While others have reported on the increased risk due to heart failure, 12 previous investigations of atrial fibrillation included cohorts with too few patients with the comorbidity to meaningfully assess the importance on COVID-19 outcomes. 10 Additionally we showed for the first time that sex may play a role in determining the effect that pre-existing CVDs have on the risk of poor COVID-19 outcomes.
While we found that men in general had a higher risk of severe infection and death, preexisting CVD seemed to predict a higher risk of poor COVID-19 outcomes only in women.
However, the role of sex changed when restricting the analysis to the post-lockdown population, where heart failure showed a protective effect on risk of poor COVID-19
outcomes. The reason for this sex discrepancy is unknown, though female sex has been linked to lower ACE-2 receptor expression, which has been proposed to play a role in COVID-19 and cardiac disease. 21 Our findings are speculative, however, and further studies are needed to explore whether the sex differences arise from biological mechanisms or from the way in which our study population was selected.
Although we found that several CVD comorbidities are not linked with worse COVID-19 outcomes, especially among men, our data supported previous findings indicating a higher overall comorbidity burden coincides in a higher risk of worse outcomes. 22 It is thus possible that CVD comorbidities not associated with clinically significant increases in risk of poor COVID-19 outcomes in our study still present a real increase in risk; the effect size, however, may be too small to be illuminated by our sample. Additionally, patients with multiple preexisting CVD comorbidities, including comorbidities not investigated in our study, likely represent a higher risk of poor outcomes. This would be consistent with previous work showing that pre-existing CVD comorbidities impact both the clinical course and outcomes of CVD patients. 23 Our results support the utility of more granular COVID-19 recommendations for CVD patients. Given the potential longevity of the pandemic, it is imperative for the mental and physical well-being of CVD patients that restrictions on social and physical activity are as minimal as necessary. Tailoring recommendations for CVD patients not at higher risk from COVID-19 infections would be an important dimension for improving quality of life and reducing collateral damage for these patients. However, although our findings suggest that ischemic heart disease, heart failure, or atrial fibrillation are not important predictors of severe outcomes of COVID-19, especially among men, many patients with these highly prevalent cardiovascular comorbidities will be at increased risk of severe COVID-19 due to age alone.
A major strength of the present study was our access to complete medical histories of all hospital-screened COVID-19 patients. This allowed us to investigate the importance of preexisting CVD comorbidities on the absolute risk of severe COVID-19 outcomes in a nationwide cohort of hospital-screened COVID-19 patients in a public healthcare system controlling for age, sex, and other comorbidities with multivariate analysis. Additionally, we expect to receive monthly data updates from the Danish health authorities.
There were, however, some data limitations to our study. The study population during the lockdown period was likely a highly selected population, as testing rates were low, testpositivity rates high. Additionally, the primary population only included those who have had contact with the Danish hospital system (both in-and out-patient contacts). This suggests our estimates of absolute risk were likely biased upwards during the lockdown period and thus likely represent more severe cases as compared to the entirety of the true SARS-CoV-2 positive population. Moreover, if COVID-19 patients who had no CVD history had less severe infections, and were less likely to be in contact with the hospital system and enter our primary population, our estimates of the effect of CVD diseases would be biased towards the null. Conversely, there is evidence of a reduction in healthcare seeking behaviour during the pandemic among those with a history of CVD, or those experiencing CVD related symptoms. [24] [25] [26] [27] [28] [29] If those with pre-existing CVD comorbidities were more likely to avoid contact with the hospital system than their CVD-free peers, unless their condition deteriorated to the point requiring intervention, then our estimates would be biased in the opposite direction, away from the null. Indeed, the analysis using only the post-lockdown data from both the primary (hospital contacts) and secondary populations (PCR positive cases from Region Zealand and Capital Region of Denmark) suggests data from during the lockdown period was biased away from the null. Taken together, this suggests our conclusions of minimal increases in absolute risk due to pre-existing CVD comorbidities were robust.
Our dataset was likely missing ICU admission data for those patients who were not yet discharged from hospital as of August 26, 2020, and 2.4% of the primary study population
had not yet reached an endpoint, either 30-day event free, severe infection, or death, by the date of our data capture. Another data limitation arose from lack of data concerning potential confounding variables, such as body mass index (BMI). Previous work has demonstrated that individuals with obesity had higher odds of poor COVID-19 outcomes. 30 The interactions between CVD comorbidities, obesity, and COVID-19 outcomes deserves further
investigation. Yet despite these limitations, our analysis allowed us to bracket the potential effect size of the increased absolute risk of severe outcomes predicted by pre-existing CVD status.
Finally, the generalizability of our findings may present another limitation. The epidemiology of the COVID-19 outbreak varies across countries, as different countries employed different management and control approaches, which may affect the apparent impact of CVD comorbidities. The Danish authorities enacted strict lockdowns earlier in the epidemic as compared to some countries where the epidemic was seeded earlier in the calendar year. The resulting epidemic peak in Denmark, and the corresponding peak burden on the health care system, was lower than many countries and regions. 18 In some regions where healthcare systems were more stressed, and/or the ICU capacity exceeded during the epidemic peak, patients with pre-existing comorbidities may have received substandard care These patients groups in these regions may have thus been at higher risk of poor outcomes, independent of any causal relationship between CVD comorbidities and SARS-CoV-2 infection. 32, 33 In contrast, Danish ICU capacity was not exceeded during the study period. 34 Additionally, Denmark has an advanced healthcare system with universal healthcare for all residents and a high quality of cardiac care. 31 Thus, patients in the present study with stable CVD may have represented a relatively well-treated population. Our findings may not be generalizable to countries with poorer cardiac care or instances where healthcare systems are overburdened by the COVID-19 epidemic.
Pre-existing ischemic heart disease, heart failure, or atrial fibrillation in a population of hospital-screened COVID-19 patients were not important predictors for risk of severe infection among men, while heart failure and atrial fibrillation were both potentially a predictor of modest increased risk of poor outcomes among women, but only early in the epidemic when COVID-19 testing rates were low. Our findings show the need for more bespoke public health and clinical recommendations concerning cardiovascular patients.
The data underlying this article cannot be shared publicly due to the Privacy of individuals that participated in the study. Post-lockdown (after May 1) n = 1,447 