Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients anticoagulated at discharge

Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients anticoagulated at discharge | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients anticoagulated at discharge Isuru Induruwa, Elizabeth Cattermole, Christopher Paisey, Colver Ken Howe Ne, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2458970/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Sep, 2023 Read the published version in Clinical Medicine → Version 1 posted You are reading this latest preprint version Abstract Background Infection is a well-known contributor to developing cardiac arrythmias such as atrial fibrillation (AF), which contributes to over 25% of all ischaemic stroke. We wanted to quantify the incidence of first-diagnosed (new) AF (nAF) during hospitalisation with COVID-19 compared to a lower respiratory tract infection (LRTI), as well as compare anticoagulation rates at discharge, reasons for non-prescription of anticoagulation and determine factors associated with developing nAF with COVID-19. Methods We analysed a comprehensive hospital coding database on patients hospitalised due to COVID-19+/-AF or LRTI +/-AF, between 1/3/2020 and 31/12/2020 at a large tertiary hospital in the UK. Incidence of nAF during COVID-19 or LRTI, and the proportions of nAF patients discharged on anticoagulation and reasons for non-prescription from both cohorts were quantified. Results 2243 patients were hospitalised with LRTI and 488 with COVID-19. nAF was diagnosed in significantly more COVID-19 patients compared to LRTI (7.0% vs 3.6%, P =0.003). However, significantly less COVID-19 patients were discharged on anticoagulation compared to LRTI (19.2% vs 55.9%, P =0.003) despite similar CHA 2 DS 2 -VASc scores, and lower ORBIT scores. 14/26 LRTI +nAF patients had documented contraindication not to be anticoagulated, whereas only 1/12 patients with COVID-19 +nAF did. Patients who developed nAF during hospitalisation with COVID-19 were older ( P <0.001), had pre-existing congestive cardiac failure ( P =0.004), ischaemic heart disease (IHD) or peripheral vascular disease (PVD) ( P <0.001), and a higher CHA 2 DS 2 -VASc score ( P =0.02). Older age (Odds ratio (OR) 1.03, P =0.007) and IHD/PVD (OR 2.87, P =0.01) increased the odds of developing nAF with COVID-19. Conclusion Higher incidence of nAF and lower anticoagulation rates in COVID-19 patients were observed, compared to LRTI. A larger proportion of COVID-19 +nAF patients did not have a clear documented reason for non-prescription of anticoagulation in their notes. Whilst we await further research and clear guidelines, a pragmatic approach would be to holistically consider anticoagulation in all patients with COVID-19+nAF and a high ischaemic stroke risk. COVID-19 Atrial Fibrillation Anticoagulation Ischaemic Stroke Figures Figure 1 Background The global pandemic caused by COVID-19 has identified new challenges to healthcare systems and treating the medical complications that follow COVID-19 will remain an ongoing challenge facing healthcare providers in the future. The association between respiratory infections and cardiovascular sequalae are well established; in particular cardiac complications such as sudden cardiac death, myocardial infarction, cardiac arrhythmias and cardiac failure, are diagnosed in patients with lower respiratory tract infections 1 . Although many patients with such respiratory infections, including COVID-19, experience mild symptoms that can be managed in the community, some require hospital admission for treatment of the disease. Atrial fibrillation (AF) is a common cardiac arrythmia, responsible for at least a fifth of all ischaemic stroke in the United Kingdom 2 , yet an individual’s stroke risk can be mitigated by about 65% with anticoagulation treatment 3 . A first diagnosis of AF (nAF), discovered during admission to hospital with an infection is common, with large studies associating a 6-fold higher risk of developing AF in patients with severe sepsis 4 . In particular, studies have shown that patients who develop nAF during pneumonia, have an increased risk of thromboembolism or death at 3-years 5 . The prevalence of AF in patients hospitalised with COVID-19 infection is reported to be between 8–11% 6,7 . A recent meta-analysis demonstrated that AF and COVID-19 is significantly associated with poor outcomes 8 , particularly as it was more common in elderly patients 7 . Although only a few studies have investigated nAF in patients hospitalised with COVID-19, they consistently report a higher incidence of nAF during COVID-19 infection compared to other cohorts 9 – 11 . We performed a database analysis to compare the incidence of nAF in patients admitted with a lower respiratory tract infection (LRTI) or COVID-19, compare anticoagulation rates at discharge, as well as determine factors associated with developing nAF in patients admitted with COVID-19. Materials And Methods This single-centre analysis of prospectively collected coding data included adult patients ( \(\ge\) 18 years) admitted between 1st March 2020 and 31st December 2020 to a large University hospital in the United Kingdom. Data was extracted from electronic patient records where diseases are coded using International Classification of Diseases (ICD-10) and attributed to the patient during their hospital admission. Further information on the codes were used for the preliminary extraction can be found in supplementary table 1. After excluding duplicate or incorrectly coded entries, patients admitted with LRTI or COVID-19 +/- AF were included in the final dataset for analysis. If there were multiple admissions to hospital during the study period, only details of the first admission were included. For all LRTI and COVID-19 populations, basic data such as age, sex, length of hospital stay and whether they have been coded with AF was collected. Additionally, for COVID-19 patients with AF, whether known or nAF, demographic variables including age, sex and past medical history [hypertension, congestive cardiac failure (CCF), diabetes mellitus, hypercholesterolemia, ischemic heart disease (IHD) and/or peripheral vascular disease (PVD), previous stroke] were also extracted from ICD-10 codes and used in CHA 2 DS 2 -VASc and ORBIT score calculations. Admission haemoglobin, white cell count, neutrophil count, and C-reactive protein (CRP) were collected, as well as the admission National Early Warning Score 2 (NEWS), numbers of inpatient death, myocardial infarction, ischaemic or haemorrhagic stroke at six months and death at one year. Hospital records were then manually accessed to determine patients’ anticoagulation status on admission, the proportion started on anticoagulation during their inpatient stay, proportion discharged on anticoagulation as well as the reasons for non-prescription of anticoagulation in both LRTI and COVID-19 patients with nAF. For all nAF patients within the LRTI and COVID-19 cohorts, as well as the all known AF patients within the COVID-19 cohort, electronic records, including GP or medical records, telemetry reports and 12-lead electrocardiograms were checked to confirm the diagnosis of AF during that admission. This project was registered with Cambridge University Hospitals Quality Surveillance Team. Formal confirmation was received that ethical approval from an Institutional Review Board was not required. Statistical analysis The cut-off for statistical significance was set at P < 0.05. Continuous data was tested for normality using Shapiro-Wilk testing. Parametric data is reported as mean ± standard deviation (SD) and non-parametric data as median and interquartile range (Q 1 -Q 3 ). Cohort comparisons between parametric data was carried out using Students t-test, or the Mann-Whitney U test for non-parametric data. Comparison of proportions between categorical data was carried out using the Chi-squared test. Common population predictors of developing AF were used in a backward stepwise logistic regression model in the COVID-19 population, excluding those with known AF, to determine which variables are associated with developing nAF in COVID-19. The independent variables modelled were age, pre-existing hypertension, CCF, diabetes, stroke, IHD/PVD as well as the CHA 2 DS 2 -VASc score, admission CRP and NEWS score and sex. At each stage in the regression model, the least significant variable was eliminated until only variables with P < 0.10 remain. Unstandardised coefficient (B) and significance ( P ) are reported for each of the significantly associated variables. These variables were then used in a binary logistic regression model to report strength of association in developing nAF in COVID-19 as an odds ratio. Data was analysed using Prism v.9.4.1 (GraphPad, San Diego, CA) and SPSS v.29 (IBM Corp., Armonk, NY). Results The initial search returned 5301 entries of which 2570 entries were excluded as detailed in the methods section. Out of the remaining 2731 patients, 2243 patients (82.1%) had been coded as a LRTI and 488 (17.9%) as COVID-19. We then determined which of these developed nAF via manual patient record review. 284 patients (12.6%) in the LRTI cohort were coded with AF, of which 73 were new diagnoses. In the COVID-19 cohort, 77 patients (15.8%) were coded with AF, of which 31 were nAF (Fig. 1 ). [Insert Fig. 1 .] Incidence of nAF in COVID-19 compared to LRTI In general, patients with COVID-19 were significantly younger than patients with LRTI, (median age (Q 1 -Q 3 ) COVID-19; 64 (48–79), LRTI 77 (64–86), P < 0.001 (Supp Table 2 )). Patients with COVID-19 and nAF were also significantly younger than patients with LRTI and nAF (median age (Q 1 -Q 3 ) COVID-19 + nAF; 75 (64–84), LRTI + nAF 83 (74–90), P = 0.02) and had significantly lower white cell ( P = 0.007) and neutrophil counts at admission ( P = 0.03) but similar haemoglobin, CRP and NEWS scores. The proportion of vascular risk factors, CHA 2 DS 2 -VASc scores and events since discharge; ischaemic stroke, myocardial infarction, or death at 1 year, were also not significantly different (Table 1 ). Table 1 Incidence of first-diagnosed AF in COVID-19 and LRTI patients. Demographic details, admission blood parameters, disease scores, comorbidities, and events since discharge of the LRTI + nAF and COVID + nAF cohorts. Individuals were classified as having a risk factor if there was a documented history of it, were on treatment for it, or were diagnosed with it during the admission. Differences between cohorts were calculated using students t-test for continuous parametric data or Mann-Whitney U-testing for non-parametric data and Chi-squared test for categorical data and are reported as a P value. SD = standard deviation, (Q 1 -Q 3 ) = interquartile range. *= Excluding those who already have a diagnosis of AF. LRTI (+ nAF) COVID-19 (+ nAF) P N (%) 73 (3.6) 31 (7.0) 0.003* Age (Q 1 -Q 3 ) 83 (74–90) 75 (64–84) 0.02 Female (%) 30 (41.1) 10 (32.3) 0.51 Length of stay (Q 1 -Q 3 ) 10 (4–21) 15 (8–28) 0.16 Admission Values Hemoglobin (g\L) ± SD 126.1 ± 22.8 124.7 ± 33.5 0.81 White Cell Count (X) (Q 1 -Q 3 ) 9.8 (8–14) 6.5 (5–10) 0.007 Neutrophil Count (X) (Q 1 -Q 3 ) 8.2 (6–12) 5.4 (4–10) 0.03 C-Reactive Protein (Q 1 -Q 3 ) 78.0 (19–135) 61.2 (20–125) 0.81 NEWS Score 4 (2–7) 3 (1–7) 0.37 Risk factors for thrombotic disease , n (%) Hypertension 46 (63.0) 18 (58.1) 0.80 Congestive Cardiac Failure 18 (24.7) 7 (22.6) 0.82 Diabetes 14 (19.2) 7 (22.6) 0.90 IHD/PVD 17 (23.3) 11 (35.5) 0.30 Previous stroke † 7 (1.0) 2 (6.5) 0.90 Median CHA 2 DS 2 -VASc Score (Q 1 -Q 3 ) 3 (2.5-4) 3 (1–5) 0.32 Median ORBIT Score (Q 1 -Q 3 ) 2 (1–4) 1 (0–2) 0.02 Events since discharge, n (%) Deaths during admission 17 (23.3) 8 (25.8) 0.98 Outpatient cardiac monitoring 1 (1.3) 1 (3.2) - Myocardial infarction at 6 months 0 1 (3.2) - Ischaemic stroke at 6 months 0 0 - Haemorrhagic stroke at 6 months 1 (1.4) 0 - Deaths at 1 year § 34 (46.6) 13 (41.9) 0.83 nAF = first-diagnosed AF, NEWS = National Early Warning Score 2, IHD = ischaemic heart disease, PVD = peripheral vascular disease †= ischaemic or haemorrhagic stroke, §= includes those who died during admission. CHA 2 DS 2 -VASc = Ischaemic stroke risk in AF scoring system which assigns 1 point where there is a history of congestive cardiac failure (C), hypertension (H), diabetes mellitus (D), vascular disease (V), age ≥ 65 years (A) and female sex (Sc), and 2 points if age ≥ 75 years (A2) or there is a history of prior stroke/transient ischaemic attack (S2). ORBIT score to assess risk of bleeding in AF (older age [75 + years], reduced haemoglobin/haematocrit/history of anaemia, bleeding history, reduced kidney function (GFR < 60), and treatment with antiplatelets). Table 2 Proportions of first-diagnosed AF patients newly started or discharged on anticoagulation, and reasons provided in notes for non-prescription . *Excluding those who died as inpatients and those already on anticoagulation, nAF = first-diagnosed AF. LRTI (+ nAF) COVID-19 (+ nAF) P N* 59 23 Started anticoagulation as inpatient, n (%) 23 (53.5) 7 (26.9) 0.057 Discharged on anticoagulation, n (%) 33 (55.9) 5 (19.2) 0.003 Patients not discharged on anti-coagulation N 26 12 Age (Q 1 -Q 3 ) 79 (73–92) 74 (64–81) 0.07 Median CHA 2 DS 2 -VASc (Q 1 -Q 3 ) 3 (2–4) 3 (1–4) 0.95 Median ORBIT score (Q 1 -Q 3 ) 2 (0–3) 1 (0–2) 0.03 Reason for non-prescription of anticoagulation No reason provided in notes 10 8 Limited life expectancy 9 0 Intracerebral haemorrhage 3 0 Paroxysmal AF 1 1 End-stage renal failure 1 1 CHA 2 DS 2 -VASc = 0 1 0 Patient preference 1 0 Incorrect CHA 2 DS 2 -VASc calculation 0 1 Decision left to GP 0 1 However, despite COVID-19 patients being younger, the incidence of nAF was significantly higher in patients admitted with COVID-19, compared to LRTI (COVID-19; 7.0%, LRTI; 3.6%, P = 0.003) (Table 1 ). Proportions of patients discharged on anticoagulation in COVID-19 and LRTI and reasons for non-prescription A smaller proportion of patients with COVID-19 who developed nAF were started on anticoagulation during their inpatient stay compared to LRTI and nAF (COVID-19 + nAF; 26.9%, LRTI + nAF; 53.5%, P = 0.057), excluding those who were already on anticoagulation for alternative diagnoses, and inpatient deaths. Despite ages and CHA 2 DS 2 -VASc scores being similar, significantly less patients with nAF in the COVID-19 cohort compared to LRTI (COVID-19 + nAF; 19.2%, LRTI + nAF; 55.9%, P = 0.003) were discharged on anticoagulation (Table 2 ). Patients with COVID-19 + nAF also had a lower risk of bleeding as per ORBIT scores 12 , compared to the LRTI + nAF patients. Individual analysis of medical notes in patients not prescribed anticoagulation at discharge revealed that 14/26 (53.8%) LRTI with nAF patients had a clear contraindication not to be anticoagulated (9 limited life expectancy, 3 recent or current intracerebral haemorrhage, 1 CHA 2 DS 2 -VASc = 0, 1 end-stage renal failure), whereas this was only the case in 1/12 (8.3%) patients with COVID-19 and nAF (1 end-stage renal failure). In 10/26 (38.4%) LRTI + nAF and 8/12 (66.7%) COVID-19 + nAF, a reason for non-prescription of anticoagulation was not documented in the notes (Table 2 ). Of those that were discharged on anticoagulation, apixaban was the choice in 69.2% of LRTI and 80% of COVID-19 patients (Supp Table 3 ). Table 3 Factors associated with developing first-diagnosed AF during hospitalisation with COVID-19. The demographic details, admission NEWS score and comorbidities of the COVID-19 + No AF and COVID + nAF cohorts. Individuals were classified as having a risk factor if there was a documented history of it, were on treatment for it, or were diagnosed with it during the admission. Differences between cohorts were calculated using Mann-Whitney U-testing for non-parametric data and Chi-squared test for categorical data and are reported as a P value. nAF = first-diagnosed AF, SD = standard deviation, (Q 1 -Q 3 ) = interquartile range. I HD = ischaemic heart disease, PVD = peripheral vascular disease *= ischaemic or haemorrhagic stroke COVID-19 (No AF) COVID-19 (+ nAF) P n 411 31 Age (Q 1 -Q 3 ) 62 (46–79) 75 (64–84) < 0.001 Female (%) 205 (49.9) 10 (32.3) 0.06 Admission Values (Q 1 -Q 3 ) NEWS Score 3 (1–7) 3 (1–7) 0.29 Length of stay (days) 5 (1–12) 15 (8–28) < 0.001 Hemoglobin (g\L) ± SD 129.5 ± 20.4 129.1 ± 24.8 0.82 C-Reactive Protein (Q 1 -Q 3 ) 42 (13–104) 61 (20–125) 0.16 Risk factors for thrombotic disease , n (%) Hypertension 173 (42.2) 18 (58.1) 0.08 Congestive Cardiac Failure 31 (7.5) 7 (22.6) 0.004 Diabetes 82 (20.0) 7 (22.6) 0.72 IHD/PVD 44 (10.7) 11 (35.5) < 0.001 Previous stroke* 10 (2.4) 2 (6.5) 0.18 CHA 2 DS 2 -VASc 2 (1–3) 3 (1–5) 0.02 Variables and outcomes associated with nAF in COVID-19 Excluding those with already known AF, there were 411 (84.2%) patients admitted with COVID-19 who did not develop AF, and 31 patients (7.0%) that developed nAF. Patients that developed nAF during hospitalisation with COVID-19 were significantly older (median age (Q 1 - Q 3 ) COVID-19 + nAF; 75 (64–84), COVID-19 No AF; 62 (46–79), P < 0.001). As per Table 3 . there was a significantly higher proportion of pre-existing CCF ( P = 0.004), IHD or PVD ( P < 0.001) within the COVID-19 and nAF cohort and also a higher CHA 2 DS 2 -VASc score (median (Q 1 -Q 3 ) COVID-19 + nAF; 3 (1–5), COVID-19 No AF; 2 (1–3), P = 0.02). There was no significant difference between the admission NEWS score, haemoglobin or CRP, but patients hospitalised with COVID-19 who developed nAF had a significantly longer length of stay in hospital (LOS) compared to COVID-19 patients without AF (median LOS (Q 1 - Q 3 ) COVID-19 + nAF; 15 (8–28), COVID-19 No AF; 5 (1–12), P < 0.001). Predictors of nAF in patients with COVID-19 Backward stepwise logistic regression revealed significant associations between age ( P = 0.007) and having a history of IHD/PVD ( P = 0.01) with developing nAF during hospitalisation with COVID-19. Sex demonstrated no significant association ( P = 0.09). When age, IHD/PVD and sex were modelled in binary logistic regression with nAF as the dependent variable, the odds of nAF were significantly higher if patients were older (OR 1.03 (1.01–1.06), P = 0.007) or had a previous diagnosis of IHD/PVD (OR 2.87 (1.23–6.66), P = 0.01) (Table 4 ). Table 4 Strength of association between age, IHD/PVD and female sex and developing first-diagnosed AF during hospitalisation with COVID-19. Binary logistic regression analysis of age, IHD/PVD and female sex to determine strength of association between these variables and developing first-diagnosed AF during COVID-19, reported as an odds ratio. B = unstandardised coefficient, CI = confidence interval. IHD = ischaemic heart disease, PVD = peripheral vascular disease B Odds Ratio 95% CI P Age (years) 0.03 1.03 1.01–1.06 0.007 IHD/PVD 1.05 2.87 1.23–6.66 0.01 Female -0.69 0.50 0.23–1.12 0.09 Discussion AF and COVID-19 are both implicated as thrombogenic disorders due to their pro-inflammatory nature, causing local and systemic activation of thrombo-inflammatory pathways. The inflammatory response in COVID-19 is a mix of pro-inflammatory cytokine release, compliment pathway activation and vascular endothelial dysfunction creating a thrombogenic surface 13 . This can lead to venous and arterial thromboses; explaining the efficacy of anticoagulant agents in preventing and treating COVID-19 associated thrombosis 14 . As a result, cases of ischaemic stroke related to COVID-19 have been seen worldwide, even in the absence of AF 15 . Our results demonstrate an incidence of 7% nAF in patients hospitalised with COVID-19, a significantly higher value than in those admitted with a LRTI (3.6%, P = 0.003). What is striking is that patients who developed nAF with COVID-19 were significantly younger compared to the LRTI cohort but had a similar proportion of vascular risk factors and CHA 2 DS 2 -VASc scores. Furthermore, significantly less patients with nAF and COVID-19 were discharged on anticoagulation compared to patients with LRTI (19.2% vs 55.9%, P = 0.003), despite similar ages, CHA 2 DS 2 -VASc scores and lower ORBIT scores between the two cohorts, conveying a similar future risk of ischaemic stroke and a reduced risk of possible haemorrhage associated with anticoagulation. Further analysis of medical notes of those not discharged on anticoagulation revealed that 14/26 LRTI patients had a clear contraindication not to be anticoagulated, whereas this was only the case in 1/12 patients with COVID-19 and nAF. It is difficult to ascertain whether anticoagulation was considered in these cases as a reason for non-prescription was not documented but in general, no characteristic within COVID-19 patients seemed predictive of non-prescription. Whether this illustrates physician reluctance to anticoagulate patients in this cohort, requires assessment in larger, focussed studies. Although the link between COVID-19, nAF and the risk of thrombotic disease including ischaemic stroke is not fully understood, studies that have looked at outcomes in patients who develop nAF during sepsis, consistently demonstrate an increased risk of AF recurrence, as well as ischaemic stroke, in this population 16 . For example, a large population study of 274,000 patients hospitalised with pneumonia determined that a third of patients who develop nAF during pneumonia, have at least one other hospital admission with AF within 3 years and have a higher risk of ischaemic stroke compared to those that did not develop AF 5 . This is believed to be because of a pre-existing substrate for AF, often due to the burden of existing vascular risk-factors, which is unmasked by infection, leading to developing AF. Studies do support this in hospitalised patients with COVID-19 who develop nAF also, reporting a higher incidence of thromboembolic events 17 , and ischaemic stroke 11 . This suggests that in patients identified as being high risk of ischemic stroke in the setting of nAF and COVID-19, stroke preventative measures, including offering anticoagulation should be considered. In our study, further outpatient cardiac monitoring was organised in only 1 out of the 31 patients in the COVID-19 group, therefore, whether AF identified during COVID-19 recurs in our population cannot be ascertained. Within patients who were hospitalised with COVID-19, those who developed nAF were significantly older ( P < 0.001), with a higher proportion of pre-existing congestive cardiac failure ( P = 0.004), ischaemic heart disease or peripheral arterial disease ( P < 0.001) and a higher CHA 2 DS 2 -VASc score ( P = 0.02) compared to those who did not develop nAF, consistent with previously published data 18 . This strengthens the need to consider stroke prevention in these patients as the risk factors identified here are commonly also shared with ischaemic stroke caused by AF, outside of infection. Our data also reveal that patients who developed nAF with COVID-19 had a significantly longer hospitalisation compared to COVID-19 patients without AF ( P < 0.001), despite COVID-19 patients’ LOS being significantly less than LRTI overall (Supp Table 2 , P < 0.001). Finally, in our population, patients who are older and those with a history of ischaemic heart disease or peripheral arterial disease have increased odds of developing nAF during a COVID-19 infection. Although our dataset is representative of the wider population and has results from an appropriate control group, its reliance on accurate clinical coding of disease in electronic records may introduce information bias due to missing, misclassified or incorrectly entered data. Although this was reduced through manual review of the data as described in the methods section. The follow-up data: readmissions, myocardial infarction, and stroke within 6 months, could be incomplete as data on patients that are subsequently treated out of area, treated at another local hospital, or managed in primary care would not be captured in our hospital records. The chance of missing these data was minimised as much as possible by checking electronic GP records that are linked to our system, however, it is difficult to ascertain whether the events since discharge for patients with nAF in both cohorts are representative. Conclusion As COVID-19 will continue to remain endemic throughout the world, the true impact of nAF in the setting of COVID-19 may become apparent over the coming years. Our results demonstrate poorer anticoagulation rates in COVID-19 patients with nAF, compared to LRTI, despite similar ischaemic stroke risk between the cohorts, which could be because research quantifying the recurrence rate of AF secondary to COVID-19 and its implication on thrombotic diseases like stroke are lacking. However, until such evidence is available, clinicians should consider offering anticoagulation to patients who develop nAF with COVID-19 who have high ischaemic stroke risk, facilitating discussions between patients and specialists allowing informed and collaborative decision making in secondary care. Declarations Ethical approval This project was registered with Cambridge University Hospitals Quality Surveillance Team for service improvement in AF detection and anticoagulation rates. Formal confirmation from Cambridge University Hospitals was received that ethical approval from an Institutional Review Board was not required. Availability of data and materials All data is available in the manuscript or supplemental section. A reasonable request for any additional data can be sent to the corresponding author. Consent for publication All authors have read and approved this manuscript and consent for its publication. Participant consent is not required. Competing Interests KK has received travel grants from Bayer, Boehringer Ingelheim, Daiichi-Sankyo and Pfizer. The other authors declare no conflicts of interest. Funding This work was supported by an NIHR Academic Clinical Lectureship (RG85316) to Dr Induruwa as well as support from the British Heart Foundation Cambridge Centre of Research Excellence and the Addenbrookes Charitable Trust. The funders had no role in the design, data collection, data analysis, data interpretation or writing of the manuscript. Authors’ Contributions The study was designed by all EC, II and KK. Data was collected and analysed by EC, CP and CN. II analysed the data and wrote the manuscript. KK critically read the manuscript. Acknowledgments We would like to thank the Cambridge University Hospitals Clinical Coding department for their assistance with this study. References Corrales-Medina VF, Musher DM, Wells GA, et al. Cardiac complications in patients with community-acquired pneumonia incidence, timing, risk factors, and association with short-term mortality. Circulation 2012; 125: 773–781. Royal College of Physicians Sentinel Stroke National Audit Programme (SSNAP). National clinical audit annual results portfolio April 2020 - March – 2021, https://www.strokeaudit.org/results/Clinical-audit/National-Results.aspx (accessed 6 October 2022). Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007; 146: 857–67. Walkey AJ, Wiener RS, Ghobrial JM, et al. 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Talasaz AH, Sadeghipour P, Kakavand H, et al. Recent Randomized Trials of Antithrombotic Therapy for Patients With COVID-19: JACC State-of-the-Art Review. Journal of the American College of Cardiology 2021; 77: 1903–1921. Perry RJ, Smith CJ, Roffe C, et al. Characteristics and outcomes of COVID-19 associated stroke: A UK multicentre case-control study. J Neurol Neurosurg Psychiatry 2021; 92: 242–248. Induruwa I, Hennebry E, Hennebry J, et al. Sepsis-driven atrial fibrillation and ischaemic stroke. Is there enough evidence to recommend anticoagulation? Eur J Intern Med 2022; 98: 32–36. Sanz AP, Tahoces LS, Pérez RO, et al. New-onset atrial fibrillation during COVID-19 infection predicts poor prognosis. Cardiol J 2021; 28: 34–40. Kelesoglu S, Yilmaz Y, Ozkan E, et al. New onset atrial fibrilation and risk faktors in COVID-19. J Electrocardiol 2021; 65: 76–81. Additional Declarations No competing interests reported. Supplementary Files IncidenceofCOVIDAFsuppInduruwaetal.docx Cite Share Download PDF Status: Published Journal Publication published 01 Sep, 2023 Read the published version in Clinical Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2458970","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":166437118,"identity":"fda67b52-4121-4cfd-b90a-66b8bf21b8cb","order_by":0,"name":"Isuru Induruwa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYBACNmbmww8SKuD8BGK0sKUZPDhDihYGBh4DyYdtpGjhY2cwMEicZyev28D88ANjWxoxDmNIeJC4Ldlw2wE2YwnGthyitBwwSNx2gHHbAQYzBsa2CmK0MDZIJM45YL/tAPs3YrUwM0gkNhwAWsQDsoUoh7GxGSQcS07edpinWCLhHBHel+8///nhjxo7223H2zd++FCWTFgLAjAzEBmRo2AUjIJRMAoIAwDG4DPlJqzVEQAAAABJRU5ErkJggg==","orcid":"","institution":"Cambridge University Hospitals NHS Foundation Trust","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Isuru","middleName":"","lastName":"Induruwa","suffix":""},{"id":166437119,"identity":"913e5565-b366-46a1-b5d0-ae7c88bc1718","order_by":1,"name":"Elizabeth Cattermole","email":"","orcid":"","institution":"Cambridge University Hospitals NHS Foundation Trust","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Cattermole","suffix":""},{"id":166437120,"identity":"27d29df5-90e0-47a4-b9cc-375e3f500956","order_by":2,"name":"Christopher Paisey","email":"","orcid":"","institution":"Cambridge University Hospitals NHS Foundation Trust","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Paisey","suffix":""},{"id":166437121,"identity":"e3d6f4f8-2345-4539-b00d-da83fb80eae7","order_by":3,"name":"Colver Ken Howe Ne","email":"","orcid":"","institution":"Cambridge University Hospitals NHS Foundation Trust","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Colver","middleName":"Ken Howe","lastName":"Ne","suffix":""},{"id":166437122,"identity":"b2f22242-ca8c-43cd-94ee-22e7e5592b37","order_by":4,"name":"Kayvan Khadjooi","email":"","orcid":"","institution":"Cambridge University Hospitals NHS Foundation Trust","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kayvan","middleName":"","lastName":"Khadjooi","suffix":""}],"badges":[],"createdAt":"2023-01-09 12:59:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2458970/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2458970/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.7861/clinmed.2023-0188","type":"published","date":"2023-09-01T08:22:28+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":31502998,"identity":"fc23fcac-6091-492e-a060-c18db9efd614","added_by":"auto","created_at":"2023-01-12 21:13:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":66127,"visible":true,"origin":"","legend":"\u003cp\u003eA flow diagram showing the number of cases included or excluded at each stage and the final numbers analysed within each cohort.\u003c/p\u003e","description":"","filename":"IncidenceofCOVIDAFFig1Induruwaetal.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2458970/v1/0547e20733b771559961ce1a.jpg"},{"id":54481996,"identity":"9b64c983-b4eb-4302-ab65-086ec09934d3","added_by":"auto","created_at":"2024-04-11 08:22:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":524990,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2458970/v1/1d0d5de9-e591-477c-af8e-65c03cda2ea8.pdf"},{"id":31502999,"identity":"b070fb75-3fd8-45cf-9dc7-5dd1f8068179","added_by":"auto","created_at":"2023-01-12 21:13:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23771,"visible":true,"origin":"","legend":"","description":"","filename":"IncidenceofCOVIDAFsuppInduruwaetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-2458970/v1/f096cabd794089934743d9f2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients anticoagulated at discharge","fulltext":[{"header":"Background","content":"\u003cp\u003eThe global pandemic caused by COVID-19 has identified new challenges to healthcare systems and treating the medical complications that follow COVID-19 will remain an ongoing challenge facing healthcare providers in the future. The association between respiratory infections and cardiovascular sequalae are well established; in particular cardiac complications such as sudden cardiac death, myocardial infarction, cardiac arrhythmias and cardiac failure, are diagnosed in patients with lower respiratory tract infections\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Although many patients with such respiratory infections, including COVID-19, experience mild symptoms that can be managed in the community, some require hospital admission for treatment of the disease.\u003c/p\u003e \u003cp\u003eAtrial fibrillation (AF) is a common cardiac arrythmia, responsible for at least a fifth of all ischaemic stroke in the United Kingdom\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, yet an individual\u0026rsquo;s stroke risk can be mitigated by about 65% with anticoagulation treatment\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A first diagnosis of AF (nAF), discovered during admission to hospital with an infection is common, with large studies associating a 6-fold higher risk of developing AF in patients with severe sepsis\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In particular, studies have shown that patients who develop nAF during pneumonia, have an increased risk of thromboembolism or death at 3-years\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prevalence of AF in patients hospitalised with COVID-19 infection is reported to be between 8\u0026ndash;11%\u003csup\u003e6,7\u003c/sup\u003e. A recent meta-analysis demonstrated that AF and COVID-19 is significantly associated with poor outcomes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, particularly as it was more common in elderly patients\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Although only a few studies have investigated nAF in patients hospitalised with COVID-19, they consistently report a higher incidence of nAF during COVID-19 infection compared to other cohorts\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe performed a database analysis to compare the incidence of nAF in patients admitted with a lower respiratory tract infection (LRTI) or COVID-19, compare anticoagulation rates at discharge, as well as determine factors associated with developing nAF in patients admitted with COVID-19.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003eThis single-centre analysis of prospectively collected coding data included adult patients (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e18 years) admitted between 1st March 2020 and 31st December 2020 to a large University hospital in the United Kingdom. Data was extracted from electronic patient records where diseases are coded using International Classification of Diseases (ICD-10) and attributed to the patient during their hospital admission. Further information on the codes were used for the preliminary extraction can be found in supplementary table 1. After excluding duplicate or incorrectly coded entries, patients admitted with LRTI or COVID-19 +/- AF were included in the final dataset for analysis. If there were multiple admissions to hospital during the study period, only details of the first admission were included.\u003c/p\u003e \u003cp\u003eFor all LRTI and COVID-19 populations, basic data such as age, sex, length of hospital stay and whether they have been coded with AF was collected.\u003c/p\u003e \u003cp\u003eAdditionally, for COVID-19 patients with AF, whether known or nAF, demographic variables including age, sex and past medical history [hypertension, congestive cardiac failure (CCF), diabetes mellitus, hypercholesterolemia, ischemic heart disease (IHD) and/or peripheral vascular disease (PVD), previous stroke] were also extracted from ICD-10 codes and used in CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc and ORBIT score calculations. Admission haemoglobin, white cell count, neutrophil count, and C-reactive protein (CRP) were collected, as well as the admission National Early Warning Score 2 (NEWS), numbers of inpatient death, myocardial infarction, ischaemic or haemorrhagic stroke at six months and death at one year.\u003c/p\u003e \u003cp\u003eHospital records were then manually accessed to determine patients\u0026rsquo; anticoagulation status on admission, the proportion started on anticoagulation during their inpatient stay, proportion discharged on anticoagulation as well as the reasons for non-prescription of anticoagulation in both LRTI and COVID-19 patients with nAF. For all nAF patients within the LRTI and COVID-19 cohorts, as well as the all known AF patients within the COVID-19 cohort, electronic records, including GP or medical records, telemetry reports and 12-lead electrocardiograms were checked to confirm the diagnosis of AF during that admission. This project was registered with Cambridge University Hospitals Quality Surveillance Team. Formal confirmation was received that ethical approval from an Institutional Review Board was not required.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe cut-off for statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Continuous data was tested for normality using Shapiro-Wilk testing. Parametric data is reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and non-parametric data as median and interquartile range (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e). Cohort comparisons between parametric data was carried out using Students t-test, or the Mann-Whitney U test for non-parametric data. Comparison of proportions between categorical data was carried out using the Chi-squared test.\u003c/p\u003e \u003cp\u003eCommon population predictors of developing AF were used in a backward stepwise logistic regression model in the COVID-19 population, excluding those with known AF, to determine which variables are associated with developing nAF in COVID-19. The independent variables modelled were age, pre-existing hypertension, CCF, diabetes, stroke, IHD/PVD as well as the CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score, admission CRP and NEWS score and sex. At each stage in the regression model, the least significant variable was eliminated until only variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 remain. Unstandardised coefficient (B) and significance (\u003cem\u003eP\u003c/em\u003e) are reported for each of the significantly associated variables. These variables were then used in a binary logistic regression model to report strength of association in developing nAF in COVID-19 as an odds ratio. Data was analysed using Prism v.9.4.1 (GraphPad, San Diego, CA) and SPSS v.29 (IBM Corp., Armonk, NY).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe initial search returned 5301 entries of which 2570 entries were excluded as detailed in the methods section. Out of the remaining 2731 patients, 2243 patients (82.1%) had been coded as a LRTI and 488 (17.9%) as COVID-19. We then determined which of these developed nAF via manual patient record review. 284 patients (12.6%) in the LRTI cohort were coded with AF, of which 73 were new diagnoses. In the COVID-19 cohort, 77 patients (15.8%) were coded with AF, of which 31 were nAF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). [Insert Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.]\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eIncidence of nAF in COVID-19 compared to LRTI\u003c/h2\u003e \u003cp\u003eIn general, patients with COVID-19 were significantly younger than patients with LRTI, (median age (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e) COVID-19; 64 (48\u0026ndash;79), LRTI 77 (64\u0026ndash;86), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (Supp Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)). Patients with COVID-19 and nAF were also significantly younger than patients with LRTI and nAF (median age (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e) COVID-19\u0026thinsp;+\u0026thinsp;nAF; 75 (64\u0026ndash;84), LRTI\u0026thinsp;+\u0026thinsp;nAF 83 (74\u0026ndash;90), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and had significantly lower white cell (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and neutrophil counts at admission (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) but similar haemoglobin, CRP and NEWS scores. The proportion of vascular risk factors, CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores and events since discharge; ischaemic stroke, myocardial infarction, or death at 1 year, were also not significantly different (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eIncidence of first-diagnosed AF in COVID-19 and LRTI patients.\u003c/b\u003e Demographic details, admission blood parameters, disease scores, comorbidities, and events since discharge of the LRTI\u0026thinsp;+\u0026thinsp;nAF and COVID\u0026thinsp;+\u0026thinsp;nAF cohorts. Individuals were classified as having a risk factor if there was a documented history of it, were on treatment for it, or were diagnosed with it during the admission. Differences between cohorts were calculated using students t-test for continuous parametric data or Mann-Whitney U-testing for non-parametric data and Chi-squared test for categorical data and are reported as a \u003cem\u003eP\u003c/em\u003e value. SD\u0026thinsp;=\u0026thinsp;standard deviation, (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;interquartile range. *= Excluding those who already have a diagnosis of AF.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRTI (+\u0026thinsp;nAF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 (+\u0026thinsp;nAF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83 (74\u0026ndash;90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (64\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (4\u0026ndash;21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (8\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAdmission Values\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g\\L) \u0026plusmn; SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.1 \u0026plusmn; 22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124.7 \u0026plusmn; 33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Cell Count (X) (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.8 (8\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5 (5\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophil Count (X) (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2 (6\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4 (4\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78.0 (19\u0026ndash;135)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.2 (20\u0026ndash;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (2\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRisk factors for thrombotic disease\u003c/span\u003e, \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003en (%)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (63.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Cardiac Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (24.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD/PVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stroke\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc Score (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.5-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian ORBIT Score (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eEvents since discharge, n (%)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths during admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (23.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutpatient cardiac monitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyocardial infarction at 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIschaemic stroke at 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaemorrhagic stroke at 6 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeaths at 1 year\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003enAF\u0026thinsp;=\u0026thinsp;first-diagnosed AF, NEWS\u0026thinsp;=\u0026thinsp;National Early Warning Score 2, IHD\u0026thinsp;=\u0026thinsp;ischaemic heart disease, PVD\u0026thinsp;=\u0026thinsp;peripheral vascular disease \u0026dagger;= ischaemic or haemorrhagic stroke, \u0026sect;= includes those who died during admission.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc\u0026thinsp;=\u0026thinsp;Ischaemic stroke risk in AF scoring system which assigns 1 point where there is a history of congestive cardiac failure (C), hypertension (H), diabetes mellitus (D), vascular disease (V), age\u0026thinsp;\u0026ge;\u0026thinsp;65 years (A) and female sex (Sc), and 2 points if age\u0026thinsp;\u0026ge;\u0026thinsp;75 years (A2) or there is a history of prior stroke/transient ischaemic attack (S2).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eORBIT score to assess risk of bleeding in AF (older age [75\u0026thinsp;+\u0026thinsp;years], reduced haemoglobin/haematocrit/history of anaemia, bleeding history, reduced kidney function (GFR\u0026thinsp;\u0026lt;\u0026thinsp;60), and treatment with antiplatelets).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eProportions of first-diagnosed AF patients newly started or discharged on anticoagulation, and reasons provided in notes for non-prescription\u003c/b\u003e. *Excluding those who died as inpatients and those already on anticoagulation, nAF\u0026thinsp;=\u0026thinsp;first-diagnosed AF.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLRTI (+\u0026thinsp;nAF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 (+\u0026thinsp;nAF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStarted anticoagulation as inpatient, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDischarged on anticoagulation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatients not discharged on anti-coagulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79 (73\u0026ndash;92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (64\u0026ndash;81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian ORBIT score (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (0\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0\u0026ndash;2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReason for non-prescription of anticoagulation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo reason provided in notes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"8\" rowspan=\"9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited life expectancy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntracerebral haemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParoxysmal AF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnd-stage renal failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc\u0026thinsp;=\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncorrect CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc calculation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecision left to GP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, despite COVID-19 patients being younger, the incidence of nAF was significantly higher in patients admitted with COVID-19, compared to LRTI (COVID-19; 7.0%, LRTI; 3.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eProportions of patients discharged on anticoagulation in COVID-19 and LRTI and reasons for non-prescription\u003c/h2\u003e \u003cp\u003eA smaller proportion of patients with COVID-19 who developed nAF were started on anticoagulation during their inpatient stay compared to LRTI and nAF (COVID-19\u0026thinsp;+\u0026thinsp;nAF; 26.9%, LRTI\u0026thinsp;+\u0026thinsp;nAF; 53.5%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.057), excluding those who were already on anticoagulation for alternative diagnoses, and inpatient deaths. Despite ages and CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores being similar, significantly less patients with nAF in the COVID-19 cohort compared to LRTI (COVID-19\u0026thinsp;+\u0026thinsp;nAF; 19.2%, LRTI\u0026thinsp;+\u0026thinsp;nAF; 55.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) were discharged on anticoagulation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Patients with COVID-19\u0026thinsp;+\u0026thinsp;nAF also had a lower risk of bleeding as per ORBIT scores\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, compared to the LRTI\u0026thinsp;+\u0026thinsp;nAF patients.\u003c/p\u003e \u003cp\u003eIndividual analysis of medical notes in patients not prescribed anticoagulation at discharge revealed that 14/26 (53.8%) LRTI with nAF patients had a clear contraindication not to be anticoagulated (9 limited life expectancy, 3 recent or current intracerebral haemorrhage, 1 CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc\u0026thinsp;=\u0026thinsp;0, 1 end-stage renal failure), whereas this was only the case in 1/12 (8.3%) patients with COVID-19 and nAF (1 end-stage renal failure). In 10/26 (38.4%) LRTI\u0026thinsp;+\u0026thinsp;nAF and 8/12 (66.7%) COVID-19\u0026thinsp;+\u0026thinsp;nAF, a reason for non-prescription of anticoagulation was not documented in the notes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Of those that were discharged on anticoagulation, apixaban was the choice in 69.2% of LRTI and 80% of COVID-19 patients (Supp Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eFactors associated with developing first-diagnosed AF during hospitalisation with COVID-19.\u003c/b\u003e The demographic details, admission NEWS score and comorbidities of the COVID-19\u0026thinsp;+\u0026thinsp;No AF and COVID\u0026thinsp;+\u0026thinsp;nAF cohorts. Individuals were classified as having a risk factor if there was a documented history of it, were on treatment for it, or were diagnosed with it during the admission. Differences between cohorts were calculated using Mann-Whitney U-testing for non-parametric data and Chi-squared test for categorical data and are reported as a \u003cem\u003eP\u003c/em\u003e value. nAF\u0026thinsp;=\u0026thinsp;first-diagnosed AF, SD\u0026thinsp;=\u0026thinsp;standard deviation, (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;interquartile range. \u003cb\u003eI\u003c/b\u003eHD\u0026thinsp;=\u0026thinsp;ischaemic heart disease, PVD\u0026thinsp;=\u0026thinsp;peripheral vascular disease *= ischaemic or haemorrhagic stroke\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOVID-19 (No AF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOVID-19 (+\u0026thinsp;nAF)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (46\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75 (64\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e205 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eAdmission Values (Q\u003c/span\u003e\u003csub\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e-Q\u003c/span\u003e\u003csub\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e3\u003c/span\u003e\u003c/sub\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNEWS Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLength of stay (days)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (1\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (8\u0026ndash;28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g\\L) \u0026plusmn; SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129.5 \u0026plusmn; 20.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.1 \u0026plusmn; 24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-Reactive Protein (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (13\u0026ndash;104)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61 (20\u0026ndash;125)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003eRisk factors for thrombotic disease\u003c/span\u003e, \u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003en (%)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e173 (42.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongestive Cardiac Failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e82 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD/PVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious stroke*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (6.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (1\u0026ndash;5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eVariables and outcomes associated with nAF in COVID-19\u003c/h2\u003e \u003cp\u003eExcluding those with already known AF, there were 411 (84.2%) patients admitted with COVID-19 who did not develop AF, and 31 patients (7.0%) that developed nAF. Patients that developed nAF during hospitalisation with COVID-19 were significantly older (median age (Q\u003csub\u003e1\u003c/sub\u003e- Q\u003csub\u003e3\u003c/sub\u003e) COVID-19\u0026thinsp;+\u0026thinsp;nAF; 75 (64\u0026ndash;84), COVID-19 No AF; 62 (46\u0026ndash;79), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As per Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. there was a significantly higher proportion of pre-existing CCF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), IHD or PVD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) within the COVID-19 and nAF cohort and also a higher CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score (median (Q\u003csub\u003e1\u003c/sub\u003e-Q\u003csub\u003e3\u003c/sub\u003e) COVID-19\u0026thinsp;+\u0026thinsp;nAF; 3 (1\u0026ndash;5), COVID-19 No AF; 2 (1\u0026ndash;3), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). There was no significant difference between the admission NEWS score, haemoglobin or CRP, but patients hospitalised with COVID-19 who developed nAF had a significantly longer length of stay in hospital (LOS) compared to COVID-19 patients without AF (median LOS (Q\u003csub\u003e1\u003c/sub\u003e- Q\u003csub\u003e3\u003c/sub\u003e) COVID-19\u0026thinsp;+\u0026thinsp;nAF; 15 (8\u0026ndash;28), COVID-19 No AF; 5 (1\u0026ndash;12), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of nAF in patients with COVID-19\u003c/h2\u003e \u003cp\u003eBackward stepwise logistic regression revealed significant associations between age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and having a history of IHD/PVD (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) with developing nAF during hospitalisation with COVID-19. Sex demonstrated no significant association (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). When age, IHD/PVD and sex were modelled in binary logistic regression with nAF as the dependent variable, the odds of nAF were significantly higher if patients were older (OR 1.03 (1.01\u0026ndash;1.06), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) or had a previous diagnosis of IHD/PVD (OR 2.87 (1.23\u0026ndash;6.66), \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eStrength of association between age, IHD/PVD and female sex and developing first-diagnosed AF during hospitalisation with COVID-19.\u003c/b\u003e Binary logistic regression analysis of age, IHD/PVD and female sex to determine strength of association between these variables and developing first-diagnosed AF during COVID-19, reported as an odds ratio. B\u0026thinsp;=\u0026thinsp;unstandardised coefficient, CI\u0026thinsp;=\u0026thinsp;confidence interval. IHD\u0026thinsp;=\u0026thinsp;ischaemic heart disease, PVD\u0026thinsp;=\u0026thinsp;peripheral vascular disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u0026ndash;1.06\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIHD/PVD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23\u0026ndash;6.66\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.23\u0026ndash;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAF and COVID-19 are both implicated as thrombogenic disorders due to their pro-inflammatory nature, causing local and systemic activation of thrombo-inflammatory pathways. The inflammatory response in COVID-19 is a mix of pro-inflammatory cytokine release, compliment pathway activation and vascular endothelial dysfunction creating a thrombogenic surface\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This can lead to venous and arterial thromboses; explaining the efficacy of anticoagulant agents in preventing and treating COVID-19 associated thrombosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. As a result, cases of ischaemic stroke related to COVID-19 have been seen worldwide, even in the absence of AF\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur results demonstrate an incidence of 7% nAF in patients hospitalised with COVID-19, a significantly higher value than in those admitted with a LRTI (3.6%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). What is striking is that patients who developed nAF with COVID-19 were significantly younger compared to the LRTI cohort but had a similar proportion of vascular risk factors and CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores. Furthermore, significantly less patients with nAF and COVID-19 were discharged on anticoagulation compared to patients with LRTI (19.2% vs 55.9%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), despite similar ages, CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores and lower ORBIT scores between the two cohorts, conveying a similar future risk of ischaemic stroke and a reduced risk of possible haemorrhage associated with anticoagulation.\u003c/p\u003e \u003cp\u003eFurther analysis of medical notes of those not discharged on anticoagulation revealed that 14/26 LRTI patients had a clear contraindication not to be anticoagulated, whereas this was only the case in 1/12 patients with COVID-19 and nAF. It is difficult to ascertain whether anticoagulation was considered in these cases as a reason for non-prescription was not documented but in general, no characteristic within COVID-19 patients seemed predictive of non-prescription. Whether this illustrates physician reluctance to anticoagulate patients in this cohort, requires assessment in larger, focussed studies.\u003c/p\u003e \u003cp\u003eAlthough the link between COVID-19, nAF and the risk of thrombotic disease including ischaemic stroke is not fully understood, studies that have looked at outcomes in patients who develop nAF during sepsis, consistently demonstrate an increased risk of AF recurrence, as well as ischaemic stroke, in this population\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. For example, a large population study of 274,000 patients hospitalised with pneumonia determined that a third of patients who develop nAF during pneumonia, have at least one other hospital admission with AF within 3 years and have a higher risk of ischaemic stroke compared to those that did not develop AF\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis is believed to be because of a pre-existing substrate for AF, often due to the burden of existing vascular risk-factors, which is unmasked by infection, leading to developing AF. Studies do support this in hospitalised patients with COVID-19 who develop nAF also, reporting a higher incidence of thromboembolic events\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, and ischaemic stroke\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. This suggests that in patients identified as being high risk of ischemic stroke in the setting of nAF and COVID-19, stroke preventative measures, including offering anticoagulation should be considered. In our study, further outpatient cardiac monitoring was organised in only 1 out of the 31 patients in the COVID-19 group, therefore, whether AF identified during COVID-19 recurs in our population cannot be ascertained.\u003c/p\u003e \u003cp\u003eWithin patients who were hospitalised with COVID-19, those who developed nAF were significantly older (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a higher proportion of pre-existing congestive cardiac failure (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), ischaemic heart disease or peripheral arterial disease (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a higher CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) compared to those who did not develop nAF, consistent with previously published data\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This strengthens the need to consider stroke prevention in these patients as the risk factors identified here are commonly also shared with ischaemic stroke caused by AF, outside of infection. Our data also reveal that patients who developed nAF with COVID-19 had a significantly longer hospitalisation compared to COVID-19 patients without AF (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), despite COVID-19 patients\u0026rsquo; LOS being significantly less than LRTI overall (Supp Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, in our population, patients who are older and those with a history of ischaemic heart disease or peripheral arterial disease have increased odds of developing nAF during a COVID-19 infection.\u003c/p\u003e \u003cp\u003eAlthough our dataset is representative of the wider population and has results from an appropriate control group, its reliance on accurate clinical coding of disease in electronic records may introduce information bias due to missing, misclassified or incorrectly entered data. Although this was reduced through manual review of the data as described in the methods section. The follow-up data: readmissions, myocardial infarction, and stroke within 6 months, could be incomplete as data on patients that are subsequently treated out of area, treated at another local hospital, or managed in primary care would not be captured in our hospital records. The chance of missing these data was minimised as much as possible by checking electronic GP records that are linked to our system, however, it is difficult to ascertain whether the events since discharge for patients with nAF in both cohorts are representative.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAs COVID-19 will continue to remain endemic throughout the world, the true impact of nAF in the setting of COVID-19 may become apparent over the coming years. Our results demonstrate poorer anticoagulation rates in COVID-19 patients with nAF, compared to LRTI, despite similar ischaemic stroke risk between the cohorts, which could be because research quantifying the recurrence rate of AF secondary to COVID-19 and its implication on thrombotic diseases like stroke are lacking. However, until such evidence is available, clinicians should consider offering anticoagulation to patients who develop nAF with COVID-19 who have high ischaemic stroke risk, facilitating discussions between patients and specialists allowing informed and collaborative decision making in secondary care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was registered with Cambridge University Hospitals Quality Surveillance Team for service improvement in AF detection and anticoagulation rates. Formal confirmation from Cambridge University Hospitals was received that ethical approval from an Institutional Review Board was not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data is available in the manuscript or supplemental section. A reasonable request for any additional data can be sent to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved this manuscript and consent for its publication. Participant consent is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKK has received travel grants from Bayer, Boehringer Ingelheim, Daiichi-Sankyo and Pfizer. The other authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by an NIHR Academic Clinical Lectureship (RG85316) to Dr Induruwa as well as support from the British Heart Foundation Cambridge Centre of Research Excellence and the Addenbrookes Charitable Trust. The funders had no role in the design, data collection, data analysis, data interpretation or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was designed by all EC, II and KK. Data was collected and analysed by EC, CP and CN. II analysed the data and wrote the manuscript. KK critically read the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the Cambridge University Hospitals Clinical Coding department for their assistance with this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCorrales-Medina VF, Musher DM, Wells GA, et al. Cardiac complications in patients with community-acquired pneumonia incidence, timing, risk factors, and association with short-term mortality. Circulation 2012; 125: 773\u0026ndash;781.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoyal College of Physicians Sentinel Stroke National Audit Programme (SSNAP). National clinical audit annual results portfolio April 2020 - March \u0026ndash; 2021, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.strokeaudit.org/results/Clinical-audit/National-Results.aspx\u003c/span\u003e\u003cspan address=\"https://www.strokeaudit.org/results/Clinical-audit/National-Results.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 6 October 2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007; 146: 857\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalkey AJ, Wiener RS, Ghobrial JM, et al. Incident Stroke and Mortality Associated With New-Onset Atrial Fibrillation in Patients Hospitalized With Severe Sepsis. JAMA 2011; 306: 2248\u0026ndash;2255.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS\u0026oslash;gaard M, Skj\u0026oslash;th F, Nielsen PB, et al. Thromboembolic Risk in Patients with Pneumonia and New-Onset Atrial Fibrillation Not Receiving Anticoagulation Therapy. JAMA Netw Open 2022; 5: E2213945.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePellicori P, Doolub G, Wong CM, et al. COVID-19 and its cardiovascular effects: a systematic review of prevalence studies. \u003cem\u003eCochrane Database of Systematic Reviews\u003c/em\u003e; 2021. Epub ahead of print 11 March 2021. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/14651858.CD013879\u003c/span\u003e\u003cspan address=\"10.1002/14651858.CD013879\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z, Shao W, Zhang J, et al. Prevalence of Atrial Fibrillation and Associated Mortality Among Hospitalized Patients With COVID-19: A Systematic Review and Meta-Analysis. Front Cardiovasc Med 2021; 8: 1314.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Liang X, Xu J, et al. Meta-Analysis of Atrial Fibrillation in Patients With COVID-19. American Journal of Cardiology 2021; 144: 152\u0026ndash;156.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusikantow DR, Turagam MK, Sartori S, et al. Atrial Fibrillation in Patients Hospitalized with COVID-19: Incidence, Predictors, Outcomes and Comparison to Influenza. \u003cem\u003eJACC Clin Electrophysiol\u003c/em\u003e. Epub ahead of print February 2021. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jacep.2021.02.009\u003c/span\u003e\u003cspan address=\"10.1016/j.jacep.2021.02.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWollborn J, Karamnov S, Fields KG, et al. COVID-19 increases the risk for the onset of atrial fibrillation in hospitalized patients. \u003cem\u003eSci Rep\u003c/em\u003e; 12. Epub ahead of print 1 December 2022. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-16113-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-16113-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosenblatt AG, Ayers CR, Rao A, et al. New-Onset Atrial Fibrillation in Patients Hospitalized With COVID-19: Results From the American Heart Association COVID-19 Cardiovascular Registry. Circ Arrhythm Electrophysiol 2022; 15: e010666.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Brien EC, Simon DN, Thomas LE, et al. The ORBIT bleeding score: a simple bedside score to assess bleeding risk in atrial fibrillation. Eur Heart J 2015; 36: ehv476.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoo J, Spittle DA, Newnham M. COVID-19, immunothrombosis and venous thromboembolism: Biological mechanisms. Thorax 2021; 76: 412\u0026ndash;420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTalasaz AH, Sadeghipour P, Kakavand H, et al. Recent Randomized Trials of Antithrombotic Therapy for Patients With COVID-19: JACC State-of-the-Art Review. Journal of the American College of Cardiology 2021; 77: 1903\u0026ndash;1921.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerry RJ, Smith CJ, Roffe C, et al. Characteristics and outcomes of COVID-19 associated stroke: A UK multicentre case-control study. J Neurol Neurosurg Psychiatry 2021; 92: 242\u0026ndash;248.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInduruwa I, Hennebry E, Hennebry J, et al. Sepsis-driven atrial fibrillation and ischaemic stroke. Is there enough evidence to recommend anticoagulation? Eur J Intern Med 2022; 98: 32\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanz AP, Tahoces LS, P\u0026eacute;rez RO, et al. New-onset atrial fibrillation during COVID-19 infection predicts poor prognosis. Cardiol J 2021; 28: 34\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelesoglu S, Yilmaz Y, Ozkan E, et al. New onset atrial fibrilation and risk faktors in COVID-19. J Electrocardiol 2021; 65: 76\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Atrial Fibrillation, Anticoagulation, Ischaemic Stroke","lastPublishedDoi":"10.21203/rs.3.rs-2458970/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2458970/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInfection is a well-known contributor to developing cardiac arrythmias such as atrial fibrillation (AF), which contributes to over 25% of all ischaemic stroke. We wanted to quantify the incidence of first-diagnosed (new) AF (nAF) during hospitalisation with COVID-19 compared to a lower respiratory tract infection (LRTI), as well as compare anticoagulation rates at discharge, reasons for non-prescription of anticoagulation and determine factors associated with developing nAF with COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed a comprehensive hospital coding database on patients hospitalised due to COVID-19+/-AF or LRTI +/-AF, between 1/3/2020 and 31/12/2020 at a large tertiary hospital in the UK. Incidence of nAF during COVID-19 or LRTI, and the proportions of nAF patients discharged on anticoagulation and reasons for non-prescription from both cohorts were quantified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e2243 patients were hospitalised with LRTI and 488 with COVID-19. nAF was diagnosed in significantly more COVID-19 patients compared to LRTI (7.0% vs 3.6%, \u003cem\u003eP\u003c/em\u003e=0.003). However, significantly less COVID-19 patients were discharged on anticoagulation compared to LRTI (19.2% vs 55.9%, \u003cem\u003eP\u003c/em\u003e=0.003) despite similar CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc scores, and lower ORBIT scores. 14/26 LRTI +nAF patients had documented contraindication not to be anticoagulated, whereas only 1/12 patients with COVID-19 +nAF did. Patients who developed nAF during hospitalisation with COVID-19 were older (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), had pre-existing congestive cardiac failure (\u003cem\u003eP\u003c/em\u003e=0.004), ischaemic heart disease (IHD) or peripheral vascular disease (PVD) (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001), and a higher CHA\u003csub\u003e2\u003c/sub\u003eDS\u003csub\u003e2\u003c/sub\u003e-VASc score (\u003cem\u003eP\u003c/em\u003e=0.02). Older age (Odds ratio (OR) 1.03, \u003cem\u003eP\u003c/em\u003e=0.007) and IHD/PVD (OR 2.87, \u003cem\u003eP\u003c/em\u003e=0.01) increased the odds of developing nAF with COVID-19.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher incidence of nAF and lower anticoagulation rates in COVID-19 patients were observed, compared to LRTI. A larger proportion of COVID-19 +nAF patients did not have a clear documented reason for non-prescription of anticoagulation in their notes. Whilst we await further research and clear guidelines, a pragmatic approach would be to holistically consider anticoagulation in all patients with COVID-19+nAF and a high ischaemic stroke risk.\u003c/p\u003e","manuscriptTitle":"Higher incidence of new atrial fibrillation in hospitalised COVID-19 patients compared to lower respiratory tract infection, however, less patients anticoagulated at discharge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-01-12 21:13:37","doi":"10.21203/rs.3.rs-2458970/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"61de1620-ba2a-4fef-8414-600022aa4e73","owner":[],"postedDate":"January 12th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-11T08:22:28+00:00","versionOfRecord":{"articleIdentity":"rs-2458970","link":"https://doi.org/10.7861/clinmed.2023-0188","journal":{"identity":"clinical-medicine","isVorOnly":true,"title":"Clinical Medicine"},"publishedOn":"2023-09-01 08:22:28","publishedOnDateReadable":"September 1st, 2023"},"versionCreatedAt":"2023-01-12 21:13:37","video":"","vorDoi":"10.7861/clinmed.2023-0188","vorDoiUrl":"https://doi.org/10.7861/clinmed.2023-0188","workflowStages":[]},"version":"v1","identity":"rs-2458970","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2458970","identity":"rs-2458970","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}