Covid-19 and comedications in atrial fibrillation – a case-control study in Stockholm

doi:10.21203/rs.3.rs-1947925/v1

Objectives

To test the main hypothesis that anticoagulation reduces risk of hospitalization, intensive care unit (ICU) admission and death in COVID-19.

Design

Nested case-control study among patients with atrial fibrillation (AF) in Stockholm. Cases were matched to five controls with same sex, born within +/- 1 years, and without COVID-19.

Setting

Source population was individuals in Stockholm with AF 1997-2020. Swedish regional and national registers are used. National registers cover hospitals and outpatient clinics, local registers cover primary care. Records were linked through the personal identity number assigned to each Swedish resident.

Participants

Cases were individuals with diagnosis of, ICU admission for, or death with COVID-19. The source population consisted of 179,381 individuals from which 7,548 cases were identified together with 37,145 controls. The number of cases (controls) for hospitalization, ICU admission and death were 5,916 (29,035), 160 (750) and 1,472 (7,360). The proportion of women was 40% for hospitalization and death, but 20% and 30% for admission to ICU in wave one and two, respectively.

Main outcome measures

Primary outcome was mortality, secondary outcome was hospitalization, tertiary outcome was ICU admission, all with COVID-19.

Results

Odds ratios (95% confidence interval) for antithrombotics were 0.79 (0.66 to 0.95) for the first wave and 0.80 (0.64 to 1.01) for the second wave.

Conclusions

Use of anticoagulation among patients with COVID-19 and arrythmias is associated with lower risk of hospitalization and death. If further COVID-variants emerge, or other infections with prothrombotic properties, this emphasize need for physicians to ensure compliance among vulnerable patients.

Anticoagulants

Communicable diseases

COVID-19

Epidemiology

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes Coronavirus disease 2019 (COVID-19). Since 31 December 2019 until today (March 2022) over 420 million cases and close to 6 million deaths have been reported [1] and it continues to be a global health threat. The Stockholm Region in Sweden was hit hard by the pandemic with a surge in cases at the end of March and beginning of April of 2020.

The infection principally causes respiratory symptoms, ranging from intermittent coughing, via dyspnea, to life threatening acute respiratory distress syndrome [2]. Additionally, coagulopathy is a common and dangerous complication, in particular among severe cases of COVID-19 in SARS-CoV-2-infected patients [3, 4]. In critically ill COVID-19 patients, the incidence of thrombotic complications, including pulmonary embolism, is reported to be over 30% [3, 5]. This insight led to changes in how hospitalized COVID-19 patients are treated with regards to thromboprophylaxis. A recent systemic review of international guidance revealed that 8/10 documents recommended thromboprophylaxis for all patients, and that intermediate or therapeutic doses of low molecular weight heparin (LMWH) should be given to patients with elevated risk of venous thromboembolism (VTE) [6]. Notably, these recommendations exist despite limited evidence. In studies comparing different treatment strategies, results vary. One randomized controlled, adaptive, open label clinical trial of 465 hospitalized adults compared therapeutic with prophylactic dose heparin (LMWH or unfractionated heparin), there was no difference in their primary composite outcome, but with slightly lower odds of death at 30 days [7]. In a propensity score-matched cohort study on COVID-19 patients in medical wards, intermediate-dose prophylactic anticoagulation (AC) compared with standard-dose prophylactic AC did not detect difference in in-hospital mortality [8]. Moreover, a Danish study from 2020 found only modest effects of preexisting cardiovascular disease on increased risks of poor COVID-19 outcomes [9]. Also, an Italian study from 2020 concluded that AF was a frequent among hospitalized patients with COVID-19. In their study, AF was associated with an increased risk of severe ARDS, but it did not affect the risk of death. The type of pre-existing AF did not impact the outcome [10]. Finally, in a US study, Kuno et al found that in-hospital mortality was not significantly different in patients with anticoagulation before admission compared to those without [11].

Naturally, speculation as to if pre-medication with oral anticoagulants (OAC) is beneficial for patients later infected with SARS-CoV-2 has been prevalent. Results from investigations vary, the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) registry found oral anticoagulants to be associated with lower risk of death and other outcomes [12]. In contrast, a large Swedish registry study did not find ongoing OAC use to reduce risk of severe COVID-19 [13]. One problem with most previous studies of anticoagulant therapy is that they are hampered by confounding; patients with anticoagulation therapy are in general sicker than patients without the same medication which will affect outcomes especially death and/or severity of disease. This investigation addresses the issue of confounding by restricting the study base to patients with atrial fibrillation using a very high-resolution dataset from Stockholm. Moreover, ICU admission in Stockholm during the first wave was prone to triage/patient selection due to the massive surge of patients. This issue is addressed by reporting results by wave.

In a nested case-control study of patients with atrial fibrillation (AF), we tested the main hypothesis that oral anticoagulation as a class would confer a reduced risk of hospitalization, ICU admission and death in individuals infected with SARS-CoV-2. We also tested secondary hypotheses of reduced Covid-19 risks for other drug classes and comorbidities. All hypotheses were tested pooled and by wave for the first two Covid-19 waves in Sweden.

Study design and data sources

A case-control study design was chosen since it allows for investigation of several risk factors and is suitable for establishing associations within new fields [14]. This nested case-control study uses various Swedish regional and national health registers as data sources covering data until March 2021. Specifically, we utilize:

The Stockholm regional healthcare data warehouse (VAL-database). It contains information from both hospitals and outpatient clinics on preexisting conditions according to the International Classification of Diseases (ICD), as well as data from the Swedish Prescribed Drug Register on collected prescriptions coded by anatomical therapeutic classification (ATC) codes [15].

The Swedish National Patient register (NPR), was used in addition to the VAL-database. It too has information from hospitals and outpatient clinics, with ICD codes regarding preexisting conditions.

The Swedish Cause of Death Registry (SCR), allowed us to record COVID-19 related deaths [16].

The Swedish intensive care register (SIR) was used to track ICU admissions.

SmiNet [17] - a national electronic surveillance system for reporting of communicable diseases - has information on infection with COVID-19, since February 1, 2020, it is mandatory for all Swedish laboratories to report findings of COVID-19 to SmiNet.

Registries and records were linked through the unique personal identity number assigned to each Swedish resident at birth or immigration [18].

Study population

The source population consisted of all individuals with a recorded diagnosis of atrial fibrillation (ICD10: I48, including paroxysmal atrial fibrillation, persistent atrial fibrillation, chronic atrial fibrillation, typical atrial flutter, atypical atrial flutter, and unspecified atrial fibrillation/flutter) between January 1^st 1997 and December 31^st 2020 residing in the Stockholm region.

Exposure, cases and controls

All individuals recorded with a diagnosis of COVID-19 (ICD10: U071, U072, U109) or a COVID-19 related procedure (NCSP: ZV100) together with either actions concerning notifiable communicable diseases (NCSP: AV097, DV091, GD001), admission to inpatient care (NCSP: XS100) or oxygen treatment (NCSP: DG009, DG015, DG028, DV028, QD014) in NPR or the VAL-database, recorded as treated in intensive care for COVID-19 in SIR or recorded as dead with COVID-19 (U071, U072, U109) as underlying or contributory cause in SCR were selected as cases.

For COVID-19 hospitalization the first date retrieved from NPR or the VAL-database were used as index date. For COVID-19 in intensive care the first date recorded in SIR was identified as index date. For COVID-19 deaths the date of death was the index date. Only cases with a history of AF were included, i.e. only cases who had an AF-diagnosis recorded before their index date.

Controls were matched to cases by sex and age. Up to five controls [19] per case were sampled with replacement from the source population with the same sex as the case, born within +/- 1 years of the case, alive, with a history of AF and without recorded signs of COVID-19 at the index date of the case. Each outcome, hospitalization, ICU admission or death, were matched separately.

Outcome

The primary outcome was mortality with confirmed COVID-19 infection. The secondary outcome was hospitalization, and the tertiary outcome was intensive care unit (ICU) admission with confirmed COVID-19 infection.

Exposure

Exposure was defined via ATC-codes from filled prescriptions. The included exposures were the drug classes (ATC-code): drugs used in diabetes (A10), antithrombotic agents (B01), cardiac therapy (C01), antihypertensives (C02), diuretics (C03), beta blocking agents (C07), calcium channel blockers (C08), agents acting on the renin-angiotensin system (C09), lipid modifying agents (C10), sex hormones and modulators of the genital system (G03), corticosteroids for systemic use (H02), and endocrine therapy (L02). For a complete list of drugs included in each class, see Supplementary table 1. An individual was assumed to be exposed to a drug class if they had supply of a drug within that class at the index date. Supply was assessed from number of filled defined daily doses (DDDs) plus a grace period of 30 days for the prescription filled closest before the index date. The DDD is defined by WHO as the assumed average maintenance dose per day for a drug used for its main indication in adults [20].

Statistical analysis

Each outcome (mortality, hospitalization and ICU admission) was modelled separately by two COVID‑19 waves and pooled using conditional logistic regression adjusted for all exposures and identified confounders. The COVID-19 waves were defined as first wave between January 1^st 2020 and August 31^st 2020, second wave between September 1^st 2020 and March 31^st 2021. It was important to analyze the two pandemic waves separately due to the massive surge during the first one. Confounders included: ischemic heart disease, heart failure/cardiomyopathy, valve disorder, ischemic stroke/TIA/systemic thromboembolism, hemorrhagic/unspecified stroke, other vascular disease, arrhythmia (other than AF/flutter), lung disease, renal disease, liver disease, venous thromboembolism, and malignancies [13]. See Supplementary table 2 for ICD-10 and the Nordic Medico-Statistical Committee Classification of Surgical Procedures (NCSP) codes used for defining the covariates. All analyses were performed using SAS software version 9.4 (SAS Institute Inc., Cary, NC, USA).

Patient and Public Involvement

No patients were actively involved or asked for advice in the current study.

The source population consisted of 179 381 individuals from which 7 548 cases with COVID-19 overall were identified together with 37 145 matched controls. The number of included cases (controls) for hospital admission, ICU admission and death were 5916 (29 035), 160 (750) and 1472 (7360), respectively. Table 1 shows baseline data.

Table 1: Baseline characteristics, number (proportion) of patients by COVID-19 wave and pooled for cases and controls for each outcome (hospitalization, ICU admission and death)

			Hospitalized		ICU		Death
Diagnosis or treatment	Value	Wave^a	Controls	Cases	Controls	Cases	Controls	Cases
Overall		1	18396 (100%)	3754 (100%)	601 (100%)	130 (100%)	4435 (100%)	887 (100%)
		2	10639 (100%)	2162 (100%)	149 (100%)	30 (100%)	2925 (100%)	585 (100%)
		Pooled	29035 (100%)	5916 (100%)	750 (100%)	160 (100%)	7360 (100%)	29035 (100%)
Sex	Female	1	7834 (42.6%)	1604 (42.7%)	122 (20.3%)	28 (21.5%)	1870 (42.2%)	374 (42.2%)
	Male	1	10562 (57.4%)	2150 (57.3%)	479 (79.7%)	102 (78.5%)	2565 (57.8%)	513 (57.8%)
	Female	2	4595 (43.2%)	938 (43.4%)	44 (29.5%)	9 (30.0%)	1250 (42.7%)	250 (42.7%)
	Male	2	6044 (56.8%)	1224 (56.6%)	105 (70.5%)	21 (70.0%)	1675 (57.3%)	335 (57.3%)
	Female	Pooled	12429 (42.8%)	2542 (43.0%)	166 (22.1%)	37 (23.1%)	3120 (42.4%)	624 (42.4%)
	Male	Pooled	16606 (57.2%)	3374 (57.0%)	584 (77.9%)	123 (76.9%)	4240 (57.6%)	848 (57.6%)
Age	0-9	1	40 (0.2%)	8 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	10-19	1	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	20-29	1	18 (0.1%)	6 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	30-39	1	52 (0.3%)	9 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	40-49	1	245 (1.3%)	49 (1.3%)	27 (4.5%)	6 (4.6%)	0 (0.0%)	0 (0.0%)
	50-59	1	784 (4.3%)	164 (4.4%)	71 (11.8%)	17 (13.1%)	35 (0.8%)	7 (0.8%)
	60-69	1	1946 (10.6%)	396 (10.5%)	207 (34.4%)	47 (36.2%)	160 (3.6%)	33 (3.7%)
	70-79	1	5301 (28.8%)	1069 (28.5%)	224 (37.3%)	44 (33.8%)	730 (16.5%)	143 (16.1%)
	80-89	1	6826 (37.1%)	1399 (37.3%)	61 (10.1%)	13 (10.0%)	1936 (43.7%)	382 (43.1%)
	90+	1	3184 (17.3%)	654 (17.4%)	11 (1.8%)	3 (2.3%)	1574 (35.5%)	322 (36.3%)
Age	0-9	2	25 (0.2%)	5 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	10-19	2	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	20-29	2	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	30-39	2	28 (0.3%)	6 (0.3%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	40-49	2	139 (1.3%)	30 (1.4%)	9 (6.0%)	2 (6.7%)	0 (0.0%)	0 (0.0%)
	50-59	2	492 (4.6%)	98 (4.5%)	13 (8.7%)	2 (6.7%)	11 (0.4%)	2 (0.3%)
	60-69	2	1022 (9.6%)	204 (9.4%)	47 (31.5%)	10 (33.3%)	77 (2.6%)	16 (2.7%)
	70-79	2	3150 (29.6%)	636 (29.4%)	32 (21.5%)	7 (23.3%)	560 (19.1%)	109 (18.6%)
	80-89	2	4076 (38.3%)	841 (38.9%)	43 (28.9%)	8 (26.7%)	1299 (44.4%)	268 (45.8%)
	90+	2	1707 (16.0%)	342 (15.8%)	5 (3.4%)	1 (3.3%)	978 (33.4%)	190 (32.5%)
Age	0-9	Pooled	65 (0.2%)	13 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	10-19	Pooled	18 (0.1%)	6 (0.1%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	20-29	Pooled	80 (0.3%)	15 (0.3%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
	30-39	Pooled	384 (1.3%)	79 (1.3%)	36 (4.8%)	8 (5.0%)	0 (0.0%)	0 (0.0%)
	40-49	Pooled	1276 (4.4%)	262 (4.4%)	84 (11.2%)	19 (11.9%)	46 (0.6%)	9 (0.6%)
	50-59	Pooled	2968 (10.2%)	600 (10.1%)	254 (33.9%)	57 (35.6%)	237 (3.2%)	49 (3.3%)
	60-69	Pooled	8451 (29.1%)	1705 (28.8%)	256 (34.1%)	51 (31.9%)	1290 (17.5%)	252 (17.1%)
	70-79	Pooled	10902 (37.5%)	2240 (37.9%)	104 (13.9%)	21 (13.1%)	3235 (44.0%)	650 (44.2%)
	80-89	Pooled	4891 (16.8%)	996 (16.8%)	16 (2.1%)	4 (2.5%)	2552 (34.7%)	512 (34.8%)
	90+	Pooled	65 (0.2%)	13 (0.2%)	0 (0.0%)	0 (0.0%)	0 (0.0%)	0 (0.0%)
Arrhythmia, other than AF		1	4528 (25%)	901 (24%)	154 (26%)	17 (13%)	1080 (24%)	221 (25%)
		2	2591 (24%)	527 (24%)	34 (23%)	6 (20%)	750 (26%)	146 (25%)
		Pooled	7119 (25%)	1428 (24%)	188 (25%)	23 (14%)	1830 (25%)	367 (25%)
Bleeding Stroke		1	695 (4%)	204 (5%)	9 (1%)	2 (2%)	205 (5%)	81 (9%)
		2	424 (4%)	127 (6%)	3 (2%)	0 (0%)	119 (4%)	46 (8%)
		Pooled	1119 (4%)	331 (6%)	12 (2%)	2 (1%)	324 (4%)	127 (9%)
Cancer		1	1997 (11%)	569 (15%)	49 (8%)	17 (13%)	551 (12%)	141 (16%)
		2	1175 (11%)	317 (15%)	17 (11%)	2 (7%)	352 (12%)	107 (18%)
		Pooled	3172 (11%)	886 (15%)	66 (9%)	19 (12%)	903 (12%)	248 (17%)
Heart Failure		1	6513 (35%)	1861 (50%)	169 (28%)	33 (25%)	1886 (43%)	528 (60%)
		2	3649 (34%)	1038 (48%)	41 (28%)	8 (27%)	1182 (40%)	366 (63%)
		Pooled	10162 (35%)	2899 (49%)	210 (28%)	41 (26%)	3068 (42%)	894 (61%)
Ischemic heart disease		1	4860 (26%)	1282 (34%)	115 (19%)	27 (21%)	1354 (31%)	327 (37%)
		2	2800 (26%)	737 (34%)	29 (19%)	9 (30%)	874 (30%)	242 (41%)
		Pooled	7660 (26%)	2019 (34%)	144 (19%)	36 (23%)	2228 (30%)	569 (39%)
Ischemic Stroke		1	2602 (14%)	745 (20%)	46 (8%)	9 (7%)	735 (17%)	225 (25%)
		2	1472 (14%)	439 (20%)	20 (13%)	2 (7%)	508 (17%)	172 (29%)
		Pooled	4074 (14%)	1184 (20%)	66 (9%)	11 (7%)	1243 (17%)	397 (27%)
Liver disease		1	417 (2%)	174 (5%)	19 (3%)	6 (5%)	62 (1%)	20 (2%)
		2	226 (2%)	91 (4%)	6 (4%)	3 (10%)	45 (2%)	26 (4%)
		Pooled	643 (2%)	265 (4%)	25 (3%)	9 (6%)	107 (1%)	46 (3%)
Lung disease		1	3835 (21%)	1222 (33%)	110 (18%)	32 (25%)	915 (21%)	261 (29%)
		2	2180 (20%)	670 (31%)	29 (19%)	5 (17%)	627 (21%)	206 (35%)
		Pooled	6015 (21%)	1892 (32%)	139 (19%)	37 (23%)	1542 (21%)	467 (32%)
Other Vascular disease		1	1883 (10%)	607 (16%)	56 (9%)	7 (5%)	502 (11%)	145 (16%)
		2	1052 (10%)	307 (14%)	12 (8%)	3 (10%)	316 (11%)	106 (18%)
		Pooled	2935 (10%)	914 (15%)	68 (9%)	10 (6%)	818 (11%)	251 (17%)
Renal disease		1	2533 (14%)	1031 (27%)	63 (10%)	34 (26%)	808 (18%)	370 (42%)
		2	1473 (14%)	561 (26%)	18 (12%)	5 (17%)	478 (16%)	241 (41%)
		Pooled	4006 (14%)	1592 (27%)	81 (11%)	39 (24%)	1286 (17%)	611 (42%)
Valve disorder		1	2331 (13%)	628 (17%)	56 (9%)	9 (7%)	642 (14%)	151 (17%)
		2	1391 (13%)	336 (16%)	13 (9%)	3 (10%)	382 (13%)	96 (16%)
		Pooled	3722 (13%)	964 (16%)	69 (9%)	12 (8%)	1024 (14%)	247 (17%)
Venous Thromboembolism		1	1666 (9%)	485 (13%)	43 (7%)	21 (16%)	443 (10%)	144 (16%)
		2	947 (9%)	268 (12%)	11 (7%)	3 (10%)	284 (10%)	89 (15%)
		Pooled	2613 (9%)	753 (13%)	54 (7%)	24 (15%)	727 (10%)	233 (16%)
Insulins and Analogues		1	2263 (12%)	687 (18%)	79 (13%)	27 (21%)	492 (11%)	139 (16%)
		2	1262 (12%)	422 (20%)	18 (12%)	6 (20%)	293 (10%)	98 (17%)
		Pooled	3525 (12%)	1109 (19%)	97 (13%)	33 (21%)	785 (11%)	237 (16%)
Antithrombotic Agents		1	13653 (74%)	2615 (70%)	407 (68%)	58 (45%)	3386 (76%)	633 (71%)
		2	7855 (74%)	1570 (73%)	94 (63%)	21 (70%)	2090 (71%)	377 (64%)
		Pooled	21508 (74%)	4185 (71%)	501 (67%)	79 (49%)	5476 (74%)	1010 (69%)
Cardiac Therapy		1	3002 (16%)	694 (18%)	88 (15%)	9 (7%)	725 (16%)	175 (20%)
		2	1740 (16%)	414 (19%)	27 (18%)	6 (20%)	488 (17%)	93 (16%)
		Pooled	4742 (16%)	1108 (19%)	115 (15%)	15 (9%)	1213 (16%)	268 (18%)
Antihypertensives		1	201 (1%)	52 (1%)	8 (1%)	1 (1%)	54 (1%)	10 (1%)
		2	121 (1%)	37 (2%)	2 (1%)	1 (3%)	17 (1%)	8 (1%)
		Pooled	322 (1%)	89 (2%)	10 (1%)	2 (1%)	71 (1%)	18 (1%)
Diuretics		1	5851 (32%)	1658 (44%)	121 (20%)	24 (18%)	1761 (40%)	469 (53%)
		2	3344 (31%)	947 (44%)	31 (21%)	6 (20%)	1001 (34%)	315 (54%)
		Pooled	9195 (32%)	2605 (44%)	152 (20%)	30 (19%)	2762 (38%)	784 (53%)
Beta Blocking Agents		1	10196 (55%)	2161 (58%)	339 (56%)	49 (38%)	2533 (57%)	540 (61%)
		2	5726 (54%)	1262 (58%)	74 (50%)	15 (50%)	1470 (50%)	297 (51%)
		Pooled	15922 (55%)	3423 (58%)	413 (55%)	64 (40%)	4003 (54%)	837 (57%)
Calcium Channel Blockers		1	4351 (24%)	894 (24%)	121 (20%)	37 (28%)	1016 (23%)	183 (21%)
		2	2492 (23%)	549 (25%)	28 (19%)	10 (33%)	659 (23%)	99 (17%)
		Pooled	6843 (24%)	1443 (24%)	149 (20%)	47 (29%)	1675 (23%)	282 (19%)
Agents Acting on the Renin-Angiotensin System		1	9519 (52%)	1883 (50%)	302 (50%)	55 (42%)	2221 (50%)	396 (45%)
		2	5426 (51%)	1138 (53%)	75 (50%)	16 (53%)	1414 (48%)	248 (42%)
		Pooled	14945 (51%)	3021 (51%)	377 (50%)	71 (44%)	3635 (49%)	644 (44%)
Lipid Modifying Agents		1	6541 (36%)	1430 (38%)	220 (37%)	38 (29%)	1466 (33%)	275 (31%)
		2	3850 (36%)	880 (41%)	64 (43%)	10 (33%)	1008 (34%)	172 (29%)
		Pooled	10391 (36%)	2310 (39%)	284 (38%)	48 (30%)	2474 (34%)	447 (30%)
Sex Hormones and Modulators of the Genital System		1	691 (4%)	138 (4%)	18 (3%)	2 (2%)	146 (3%)	20 (2%)
		2	414 (4%)	77 (4%)	7 (5%)	2 (7%)	93 (3%)	3 (1%)
		Pooled	1105 (4%)	215 (4%)	25 (3%)	4 (3%)	239 (3%)	23 (2%)
Corticosteroids for Systemic Use		1	913 (5%)	438 (12%)	25 (4%)	11 (8%)	252 (6%)	93 (10%)
		2	501 (5%)	216 (10%)	10 (7%)	2 (7%)	159 (5%)	79 (14%)
		Pooled	1414 (5%)	654 (11%)	35 (5%)	13 (8%)	411 (6%)	172 (12%)
Endocrine Therapy		1	561 (3%)	157 (4%)	16 (3%)	1 (1%)	176 (4%)	34 (4%)
		2	348 (3%)	72 (3%)	3 (2%)	0 (0%)	123 (4%)	25 (4%)
		Pooled	909 (3%)	229 (4%)	19 (3%)	1 (1%)	299 (4%)	59 (4%)

a) the 1st wave covers 2020-01-01 - 2020-08-31, the 2nd wave covers 2020-09-01 - 2020-12-31, pooled means the two waves together

Figure 1 and table 2 details the odds ratios (ORs) and confidence intervals (95% CI) pooled and for the first and second wave of COVID-19 in Stockholm, with respect to exposure of pre-medication. Several drugs are associated to lower odds of hospital admission and death. Antithrombotic drugs stand out, as they were associated with low odds of hospitalization and death both during the first and second wave. The finding that lipid lowering agents have low ORs for death has been shown previously [21].

Table 2: Odds ratios with 95% confidence intervals for exposures for each outcome, hospitalization, ICU admission and death, by COVID-19 wave and pooled

		Hospitalized		ICU		Death
Exposure	Wave*	Crude	Adjusted^a	Crude	Adjusted^a	Crude	Adjusted^a
Insulins and analogues	1	1.63 (1.48-1.79)	1.41 (1.27-1.56)	1.86 (1.13-3.06)	2.39 (1.25-4.58)	1.49 (1.22-1.83)	1.34 (1.07-1.68)
	2	1.84 (1.62-2.08)	1.54 (1.35-1.77)	1.82 (0.64-5.16)	1.64 (0.42-6.49)	1.83 (1.42-2.35)	1.58 (1.17-2.13)
	Pooled	1.44 (1.36-1.54)	1.32 (1.23-1.40)	1.70 (1.58-1.83)	1.46 (1.34-1.58)	1.86 (1.18-2.91)	2.19 (1.26-3.80)
Antithrombotic agents	1	0.79 (0.73-0.85)	0.73 (0.67-0.80)	0.33 (0.21-0.51)	0.35 (0.20-0.62)	0.77 (0.65-0.90)	0.79 (0.66-0.95)
	2	0.94 (0.84-1.04)	0.83 (0.74-0.93)	1.44 (0.58-3.57)	1.47 (0.52-4.16)	0.70 (0.57-0.85)	0.80 (0.64-1.01)
	Pooled	0.90 (0.86-0.95)	0.86 (0.82-0.91)	0.84 (0.79-0.89)	0.76 (0.71-0.82)	0.44 (0.30-0.65)	0.51 (0.32-0.81)
Cardiac therapy	1	1.17 (1.06-1.28)	0.99 (0.90-1.10)	0.45 (0.22-0.91)	0.69 (0.30-1.58)	1.27 (1.05-1.53)	1.07 (0.86-1.32)
	2	1.22 (1.08-1.37)	1.00 (0.88-1.14)	1.13 (0.42-3.08)	1.10 (0.35-3.50)	0.94 (0.74-1.20)	0.80 (0.60-1.06)
	Pooled	1.15 (1.08-1.22)	1.02 (0.96-1.08)	1.18 (1.10-1.27)	0.99 (0.92-1.08)	0.58 (0.33-1.03)	0.81 (0.43-1.53)
Antihypertensives	1	1.29 (0.95-1.75)	0.90 (0.65-1.25)	0.53 (0.06-4.44)	0.11 (0.01-1.16)	0.92 (0.47-1.83)	0.77 (0.37-1.59)
	2	1.54 (1.06-2.23)	1.16 (0.78-1.73)	2.50 (0.23-27.57)	2.04 (0.12-35.48)	2.35 (1.02-5.45)	1.90 (0.72-5.00)
	Pooled	1.20 (0.99-1.46)	0.96 (0.79-1.18)	1.38 (1.09-1.75)	0.99 (0.77-1.28)	0.90 (0.19-4.26)	0.37 (0.06-2.10)
Diuretics	1	1.77 (1.65-1.91)	1.26 (1.15-1.37)	0.89 (0.54-1.48)	0.69 (0.35-1.38)	1.74 (1.50-2.01)	1.18 (0.99-1.40)
	2	1.78 (1.61-1.96)	1.26 (1.12-1.41)	0.95 (0.35-2.56)	1.50 (0.45-5.00)	2.43 (2.01-2.94)	1.78 (1.41-2.25)
	Pooled	1.56 (1.49-1.64)	1.26 (1.20-1.33)	1.78 (1.67–1.88)	1.26 (1.17-1.35)	0.90 (0.58-1.42)	0.75 (0.43-1.31)
Beta blocking agents	1	1.10 (1.02-1.18)	0.96 (0.89-1.04)	0.47 (0.31-0.70)	0.59 (0.36-0.98)	1.17 (1.01-1.36)	1.05 (0.89-1.24)
	2	1.21 (1.10-1.34)	1.01 (0.91-1.12)	1.01 (0.45-2.25)	0.83 (0.28-2.40)	1.02 (0.85-1.23)	0.86 (0.70-1.07)
	Pooled	1.08 (1.03-1.13)	0.98 (0.94-1.03)	1.14 (1.08-1.21)	0.98 (0.92-1.04)	0.54 (0.38-0.77)	0.65 (0.43-1.00)
Calcium channel blockers	1	1.01 (0.93-1.09)	1.05 (0.96-1.15)	1.69 (1.07-2.64)	2.30 (1.26-4.20)	0.87 (0.73-1.04)	0.94 (0.78-1.15)
	2	1.12 (1.00-1.24)	1.13 (1.00-1.26)	2.22 (0.92-5.35)	2.34 (0.77-7.10)	0.70 (0.56-0.89)	0.74 (0.57-0.96)
	Pooled	0.98 (0.93-1.04)	1.01 (0.95-1.06)	1.05 (0.98-1.12)	1.08 (1.00-1.16)	1.78 (1.19-2.66)	2.19 (1.34-3.58)
Agents acting on the renin-angiotensin system	1	0.94 (0.87-1.01)	0.88 (0.82-0.96)	0.74 (0.50-1.09)	0.98 (0.58-1.66)	0.80 (0.69-0.93)	0.79 (0.68-0.93)
	2	1.08 (0.98-1.18)	0.94 (0.85-1.04)	1.14 (0.50-2.58)	0.83 (0.27-2.56)	0.78 (0.65-0.94)	0.82 (0.66-1.01)
	Pooled	0.92 (0.88-0.96)	0.85 (0.81-0.89)	0.99 (0.93-1.05)	0.90 (0.85-0.96)	0.80 (0.56-1.14)	0.92 (0.59-1.45)
Lipid modifying agents	1	1.12 (1.04-1.21)	0.94 (0.87-1.03)	0.74 (0.48-1.14)	0.65 (0.36-1.17)	0.90 (0.77-1.06)	0.76 (0.63-0.92)
	2	1.23 (1.12-1.36)	0.99 (0.88-1.11)	0.63 (0.26-1.54)	0.42 (0.12-1.42)	0.79 (0.65-0.96)	0.61 (0.48-0.78)
	Pooled	1.06 (1.02-1.12)	0.92 (0.87-0.97)	1.16 (1.09-1.23)	0.96 (0.90-1.03)	0.72 (0.49-1.06)	0.57 (0.34-0.96)
Sex hormones and modulators of the genital system	1	0.97 (0.81-1.18)	0.99 (0.81-1.20)	0.48 (0.11-2.15)	0.44 (0.09-2.14)	0.67 (0.42-1.08)	0.69 (0.42-1.15)
	2	0.89 (0.69-1.16)	0.90 (0.69-1.17)	1.47 (0.24-8.85)	1.64 (0.21-13.04)	0.15 (0.05-0.47)	0.18 (0.05-0.58)
	Pooled	0.91 (0.81-1.03)	0.94 (0.84-1.06)	0.95 (0.81-1.10)	0.96 (0.82-1.12)	0.71 (0.23-2.15)	0.70 (0.22-2.24)
Corticosteroids for systemic use	1	2.55 (2.26-2.87)	1.93 (1.69-2.19)	2.01 (0.97-4.17)	2.27 (0.94-5.44)	1.96 (1.52-2.52)	1.49 (1.14-1.96)
	2	2.29 (1.94-2.72)	1.84 (1.54-2.20)	0.98 (0.20-4.71)	0.48 (0.05-4.29)	2.75 (2.06-3.67)	1.95 (1.40-2.72)
	Pooled	1.98 (1.82-2.15)	1.65 (1.51-1.80)	2.46 (2.23-2.71)	1.89 (1.70-2.10)	1.73 (0.90-3.35)	1.49 (0.70-3.17)
Endocrine therapy	1	1.40 (1.16-1.68)	1.23 (1.00-1.50)	0.24 (0.03-1.86)	0.25 (0.03-2.20)	0.96 (0.66-1.41)	0.83 (0.54-1.26)
	2	1.03 (0.80-1.34)	0.84 (0.64-1.12)	N.E.	N.E.	1.02 (0.66-1.58)	0.81 (0.49-1.34)
	Pooled	1.09 (0.96-1.23)	0.95 (0.83-1.08)	1.26 (1.08-1.46)	1.07 (0.91-1.26)	0.21 (0.03-1.59)	0.25 (0.03-2.09)

a) The model was adjusted for Arrhythmia (other than AF), Bleeding Stroke, Cancer, Heart Failure, Ischemic heart disease, Ischemic Stroke, Liver disease, Lung disease, Other Vascular disease, Renal disease, Valve disorder, Venous Thromboembolism, Insulins and Analogues, Antithrombotic Agents, Cardiac Therapy, Antihypertensives, Diuretics, Beta Blocking Agents, Calcium Channel Blockers, Agents Acting on the Renin-Angiotensin System, Lipid Modifying Agents, Sex Hormones and Modulators of the Genital System, Corticosteroids for Systemic Use, Endocrine Therapy and Year of birth

Figure 2 and table 3 displays data on comorbid covariates. Notably, arrythmia (other than AF) was associated with decreased odds ratios of hospitalization and ICU admission during the first wave; it was close to significance regarding mortality. In contrast, most other comorbid conditions were – as expected, and as previously reported [22] – related to increased risks of all three outcomes.

Table 3: Odds ratios with 95% confidence intervals for comorbidities for each outcome, hospitalization, ICU admission and death, by COVID-19 wave and pooled

		Hospitalized		ICU		Death
Exposure	Wave	Crude	Adjusted^a	Crude	Adjusted^a	Crude	Adjusted^a
Arrhythmia, other than AF	1	0.97 (0.89-1.05)	0.89 (0.81-0.97)	0.44 (0.25-0.76)	0.38 (0.20-0.72)	1.03 (0.87-1.22)	0.94 (0.78-1.12)
	2	1.01 (0.90-1.12)	0.94 (0.84-1.05)	0.85 (0.32-2.26)	0.77 (0.24-2.45)	0.96 (0.78-1.18)	0.78 (0.62-0.99)
	Pooled	0.98 (0.92-1.05)	0.91 (0.85-0.97)	0.50 (0.31-0.81)	0.44 (0.26-0.74)	1.00 (0.88-1.14)	0.88 (0.76-1.01)
Bleeding Stroke	1	1.48 (1.26-1.74)	1.19 (1.00-1.41)	1.05 (0.23-4.89)	1.97 (0.34-11.53)	2.09 (1.59-2.73)	1.74 (1.29-2.35)
	2	1.51 (1.23-1.85)	1.25 (1.01-1.56)	N.E.	N.E.	1.99 (1.40-2.82)	1.49 (0.99-2.24)
	Pooled	1.49 (1.32-1.69)	1.21 (1.06-1.39)	0.80 (0.18-3.57)	1.16 (0.23-5.84)	2.05 (1.65-2.53)	1.63 (1.28-2.07)
Cancer	1	1.49 (1.34-1.65)	1.30 (1.16-1.45)	1.58 (0.86-2.90)	1.67 (0.82-3.38)	1.34 (1.09-1.64)	1.32 (1.05-1.66)
	2	1.40 (1.22-1.60)	1.32 (1.13-1.53)	0.56 (0.12-2.56)	0.39 (0.06-2.52)	1.65 (1.30-2.10)	1.53 (1.15-2.03)
	Pooled	1.45 (1.34-1.58)	1.31 (1.19-1.43)	1.32 (0.75-2.30)	1.39 (0.73-2.62)	1.46 (1.25-1.70)	1.40 (1.17-1.67)
Heart Failure	1	1.87 (1.74-2.02)	1.33 (1.22-1.46)	0.89 (0.57-1.38)	0.69 (0.37-1.26)	2.02 (1.74-2.34)	1.41 (1.17-1.69)
	2	1.84 (1.67-2.02)	1.33 (1.18-1.49)	0.96 (0.39-2.35)	1.02 (0.33-3.16)	2.48 (2.06-2.98)	1.41 (1.12-1.78)
	Pooled	1.86 (1.75-1.97)	1.33 (1.24-1.43)	0.90 (0.61-1.34)	0.75 (0.44-1.27)	2.19 (1.95-2.46)	1.42 (1.23-1.63)
Ischemic heart disease	1	1.48 (1.37-1.59)	1.21 (1.10-1.32)	1.16 (0.71-1.89)	1.85 (0.97-3.56)	1.34 (1.15-1.55)	1.07 (0.90-1.28)
	2	1.46 (1.32-1.62)	1.18 (1.05-1.33)	1.83 (0.73-4.60)	2.54 (0.78-8.29)	1.65 (1.38-1.98)	1.44 (1.15-1.80)
	Pooled	1.47 (1.38-1.56)	1.20 (1.11-1.28)	1.28 (0.83-1.97)	2.08 (1.21-3.56)	1.46 (1.30-1.64)	1.20 (1.05-1.38)
Ischemic Stroke	1	1.52 (1.39-1.67)	1.38 (1.25-1.53)	0.96 (0.45-2.03)	0.99 (0.39-2.47)	1.71 (1.44-2.03)	1.63 (1.34-1.98)
	2	1.59 (1.41-1.79)	1.42 (1.24-1.61)	0.45 (0.10-2.07)	0.33 (0.05-2.05)	2.03 (1.65-2.50)	2.03 (1.59-2.58)
	Pooled	1.55 (1.44-1.66)	1.39 (1.29-1.51)	0.80 (0.41-1.57)	0.88 (0.40-1.90)	1.84 (1.61-2.09)	1.76 (1.52-2.05)
Liver disease	1	2.10 (1.75-2.52)	1.45 (1.20-1.76)	1.52 (0.60-3.86)	0.76 (0.25-2.31)	1.64 (0.98-2.73)	1.18 (0.68-2.05)
	2	2.03 (1.58-2.60)	1.45 (1.11-1.88)	2.66 (0.62-11.31)	3.67 (0.59-22.86)	2.94 (1.80-4.79)	2.33 (1.32-4.13)
	Pooled	2.07 (1.79-2.40)	1.45 (1.24-1.69)	1.77 (0.81-3.86)	1.05 (0.43-2.56)	2.19 (1.54-3.11)	1.60 (1.09-2.35)
Lung disease	1	1.86 (1.72-2.01)	1.49 (1.37-1.62)	1.52 (0.96-2.40)	1.29 (0.74-2.22)	1.61 (1.37-1.89)	1.26 (1.05-1.50)
	2	1.75 (1.58-1.94)	1.45 (1.30-1.62)	0.82 (0.29-2.37)	0.89 (0.25-3.14)	2.01 (1.66-2.45)	1.58 (1.27-1.97)
	Pooled	1.82 (1.71-1.93)	1.48 (1.38-1.58)	1.37 (0.90-2.07)	1.24 (0.77-1.99)	1.76 (1.56-2.00)	1.37 (1.20-1.57)
Other Vascular disease	1	1.70 (1.54-1.88)	1.32 (1.18-1.47)	0.53 (0.23-1.22)	0.48 (0.19-1.25)	1.55 (1.26-1.90)	1.18 (0.94-1.47)
	2	1.53 (1.33-1.75)	1.19 (1.03-1.38)	1.27 (0.34-4.68)	1.95 (0.39-9.67)	1.84 (1.45-2.35)	1.27 (0.96-1.69)
	Pooled	1.64 (1.51-1.77)	1.27 (1.17-1.38)	0.65 (0.32-1.31)	0.62 (0.29-1.35)	1.66 (1.42-1.94)	1.22 (1.03-1.45)
Real disease	1	2.46 (2.26-2.68)	1.69 (1.53-1.85)	3.38 (2.05-5.57)	3.29 (1.73-6.27)	3.27 (2.80-3.83)	2.49 (2.09-2.96)
	2	2.26 (2.02-2.54)	1.57 (1.38-1.77)	1.50 (0.49-4.61)	1.81 (0.41-7.86)	3.62 (2.98-4.40)	2.38 (1.89-2.99)
	Pooled	2.39 (2.23-2.56)	1.64 (1.52-1.77)	2.93 (1.86-4.61)	2.63 (1.50-4.59)	3.41 (3.01-3.85)	2.43 (2.12-2.79)
Valve disorder	1	1.40 (1.27-1.54)	1.13 (1.02-1.25)	0.75 (0.36-1.56)	1.22 (0.49-3.03)	1.21 (1.00-1.47)	1.01 (0.82-1.24)
	2	1.23 (1.08-1.40)	1.01 (0.88-1.16)	1.16 (0.32-4.23)	0.81 (0.16-4.13)	1.30 (1.02-1.66)	0.90 (0.68-1.19)
	Pooled	1.34 (1.24-1.44)	1.08 (1.00-1.18)	0.83 (0.44-1.56)	1.20 (0.57-2.51)	1.24 (1.07-1.45)	0.97 (0.82-1.15)
Venous Thromboembolism	1	1.48 (1.33-1.66)	1.25 (1.11-1.40)	2.59 (1.46-4.59)	2.73 (1.36-5.50)	1.74 (1.42-2.13)	1.40 (1.13-1.74)
	2	1.45 (1.25-1.67)	1.22 (1.05-1.42)	1.43 (0.36-5.62)	1.68 (0.35-8.14)	1.70 (1.31-2.21)	1.28 (0.96-1.73)
	Pooled	1.47 (1.35-1.61)	1.24 (1.13-1.36)	2.35 (1.39-3.98)	2.46 (1.34-4.50)	1.72 (1.47-2.02)	1.35 (1.14-1.61)
Year of birth	1	0.98 (0.94-1.03)	0.99 (0.94-1.05)	0.92 (0.71-1.19)	0.86 (0.63-1.18)	0.86 (0.78-0.95)	0.89 (0.80-0.99)
	2	0.98 (0.92-1.04)	0.99 (0.93-1.05)	1.08 (0.65-1.80)	1.22 (0.66-2.26)	0.89 (0.79-1.01)	0.84 (0.73-0.97)
	Pooled	0.98 (0.94-1.02)	0.99 (0.95-1.03)	0.95 (0.75-1.20)	0.93 (0.71-1.21)	0.88 (0.81-0.94)	0.87 (0.80-0.95)

a) The model was adjusted for Arrhythmia (other than AF), Bleeding Stroke, Cancer, Heart Failure, Ischemic heart disease, Ischemic Stroke, Liver disease, Lung disease, Other Vascular disease, Renal disease, Valve disorder, Venous Thromboembolism, Insulins and Analogues, Antithrombotic Agents, Cardiac Therapy, Antihypertensives, Diuretics, Beta Blocking Agents, Calcium Channel Blockers, Agents Acting on the Renin-Angiotensin System, Lipid Modifying Agents, Sex Hormones and Modulators of the Genital System, Corticosteroids for Systemic Use, Endocrine Therapy and Year of birth

In this nested case-control study from the Stockholm Region the use of antithrombotic drugs was associated with lower risk of hospitalization, ICU admission and death during infection with SARS-CoV-2 in patients with arrythmias.

The evidence regarding medication with antithrombotic drugs before exposure- and admission for COVID-19 is plagued by an inability to properly adjust for confounding. Additionally, the field has a relative lack of large-scale investigations and high-resolution data. This triad of issues contributes to the confusion; findings with regards to pre-COVID-antithrombotic medication range from associations with higher mortality, via no discernable effects, to improved outcomes including lower mortality. Sub analysis of the HOPE COVID-19 registry, using a cohort of 1002 patients and 110 of which were on oral anticoagulation, showed a higher mortality risk compared to propensity score matched patients [23]. In a single-center, retrospective observational study, conducted in the emergency department of an Italian teaching hospital, 1407 patients over 65 years, with- (9.6%) or without (90.4%) OACs were evaluated [24]. The authors report that crude hospital mortality rate was higher for medicated patients, but not so after multivariable adjustments. Similarly, an observational study from Poland demonstrated that pre-COVID-19-anticoagulation had no impact on middle-term mortality [25]. A nationwide pharmacoepidemiologic study from Sweden [13] assessed impact of antithrombotic medication on hospitalization and a composite outcome of ICU admission and death from February to May 2020. In a publication from 2020, Harenberg et al notes that the number of patients with NVAF and severe COVID-19 symptoms is low, which they suggest might be explained by a beneficial effect of prehospital oral anticoagulation and in-hospital heparinization [26].

In total, 360 patients were hospitalized for COVID-19 and 160 patients presented the composite outcome, but this did not differ significantly from the comparator group. This composite outcome is problematic, since ICU admission in Stockholm during the first wave was prone to triage/patient selection due to the massive surge of patients; described in detail in a recent publication [22]. In contrast, mentioned in the introduction, the multinational Lean European Open Survey on SARS-CoV-2 infected patients (LEOSS) [12] analyzed 1433 patients, with 334 (23.3%) using OAC [12]. After adjustments, pre-existing OAC was associated with lower risk of death (OR 0.64), as non-recovery (OR 0.66) and a combined endpoint of death or thrombotic event (OR 0.71). In a single-center study from Spain of 1612 subjects, lower ICU admission rates was found for patients on anticoagulation therapy [27]. A nationwide cohort study of 6637 hospitalized patients in Germany evaluated impact of oral anticoagulation (n = 1578) on clinical outcomes of COVID-19 [28]. The primary endpoint was a composite of all-cause mortality or need for invasive or non-invasive ventilation or extracorporeal membrane oxygenation. Even after propensity scoring, direct oral anticoagulants or vitamin-K antagonists, but not antiplatelet therapy, were significantly associated with improved clinical outcomes. In Italy, the multicenter GeroCovid observational study allowed for a closer evaluation of exclusively atrial fibrillation patients, with and without anticoagulation therapy [29]. Among these 171 patients, use of both vitamin K-antagonists and direct OACs, was associated with lower mortality. The Italian study, albeit much smaller, is similar to our study: using only patients with a diagnosis of atrial fibrillation.

Arrythmias, including atrial fibrillation, are coupled with other more severe cardiac diseases and frequently with significant non-cardiac comorbidities [30]. This may explain why some studies on anticoagulation therapy fail to fully adjust- or account for this confounding. Most researchers and clinicians do agree that AF patients should – like all patients with cardiac co-morbidities - be considered vulnerable, with increased risk of fatal outcome, prompting high-level clinical monitoring and treatment [31]. In line with this, a New York based cohort was evaluated using propensity scoring of patients with and without AF; demonstrating a hospital mortality of 54 vs 37% [32]. Systemic inflammation of epicardial fat is prevalent among AF patients. Speculation as to if this inflammatory state is amplified by COVID-19, leading to worse outcomes exist [33]. Therefore, it is noteworthy that we present Stockholm data indicating that arrythmias differs to most other comorbid conditions with regards to risk of death. Naturally, the prevalence of possibly protective co-medication may play a role. COVID-19 causes widespread thromboembolic complications [34], both in small and large vessels. Reducing these complications, in particular pulmonary embolism, may be one explanation to the favorable outcome seen in the present study.

This study has strengths and weaknesses. We have access to high resolution data on medications, co-morbid conditions, COVID-infections, hospital and ICU admission and date of death. This allowed us to create a large dataset using only AF-patients. Specifically, this was done to address issues of confounding by indication. The detailed data was paramount for the matching procedure as described in the methods. Moreover, it made it possible to adjust for confounding factors to an extent that is unusual. Weaknesses include an inability to know why some patients with atrial fibrillation were unmedicated. Reasons are likely spanning from a spectrum of non-compliant/non-health seeking behavior, often associated with elevated risks of adverse outcomes, to the polar opposite: low risk patients where their doctors’ choice was not to medicate, due to only intermittent AF. Moreover, one can never be sure that the prescribed and collected medication actually is used by every single subject. Another weakness has been mentioned; using ICU admission as an outcome during the first wave in Sweden is problematic. Data on ICU admission is of high quality, but the March and April 2020 surge of patients means that this outcome was distorted by patient selection issues: elderly patients with high illness severity, i.e., more comorbidities or co-medication, were often not admitted [22]. This is demonstrated in the tables, where especially co-medication was seen as “protective” during the first wave, but not during the second. Finally, residual confounding cannot be ruled out.

We conclude that the use of anticoagulation therapy among patients with COVID-19 and arrythmias is associated with lower risk of hospitalization and death. This data should significantly alter how medication and risk-reward is evaluated in these patients. If further COVID-variants emerge, or in the event of other infectious diseases with prothrombotic properties, this further emphasize the need for physicians to ensure compliance among these vulnerable patient groups, such as those with arrythmias.

Contributors:

ML: Methodology, Software, Validation, Formal analysis, Data Curation, Writing - Review & Editing, Visualization

MB: Conceptualization, Methodology, Writing - Original Draft, Writing - Review & Editing

AE: Conceptualization, Methodology, Writing - Review & Editing, Supervision

Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Competing interests: ML is an employee of the Centre for Pharmacoepidemiology, Karolinska Institutet, which receives grants from several entities (pharmaceutical companies, regulatory authorities, and contract research organizations) for performance of drug safety and drug utilization studies. MB has received research grants from Baxter Medical and has had speaker assignments from Fresenius Medical, Astute Medical and Baxter Medical, all outside the current work. AE has received grants from Bayer outside the current work.

Ethical approval: The study was granted ethical permission by the Swedish Ethical Review Authority (registration number 2020-03631 [amendment] to 2013/1850–31/1 [original]).

Data sharing: Data cannot be shared due to Swedish legislation concerning secrecy.

Transparency statement: The lead author Max Bell affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.

Dissemination to participants and related patient and public communities: to be provided if accepted.

Consent to participate: Informed consent is not required for register-based research i Sweden. The ethical approval and the data holders’ secrecy assessments cover this aspect, see Ludvigsson JF, Håberg SE, Knudsen GP, Lafolie P, Zoega H, Sarkkola C, von Kraemer S, Weiderpass E, Nørgaard M. Ethical aspects of registry-based research in the Nordic countries. Clin Epidemiol. 2015 Nov 23;7:491-508. doi: 10.2147/CLEP.S90589. PMID: 26648756; PMCID: PMC4664438.

European Center for Disease Prevention and Control; COVID-19 situation update worldwide, as of 8 September 2020 2020 [updated Daily. Available from: https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases.
Marini JJ, Gattinoni L. Management of COVID-19 Respiratory Distress. JAMA. 2020;323(22):2329–30. doi:10.1001/jama.2020.6825 [published Online First: 2020/04/25].
Jalde FC, Beckman MO, Svensson AM, et al. Widespread Parenchymal Abnormalities and Pulmonary Embolism on Contrast-Enhanced CT Predict Disease Severity and Mortality in Hospitalized COVID-19 Patients. Front Med (Lausanne). 2021;8:666723. doi:10.3389/fmed.2021.666723 [published Online First: 2021/07/17].
Tang N, Li D, Wang X, et al. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost. 2020;18(4):844–47. doi:10.1111/jth.14768 [published Online First: 2020/02/20].
Klok FA, Kruip M, van der Meer NJM, et al. Incidence of thrombotic complications in critically ill ICU patients with COVID-19. Thromb Res. 2020;191:145–47. doi:10.1016/j.thromres.2020.04.013 [published Online First: 2020/04/16].
Kyriakoulis KG, Kollias A, Kyriakoulis IG, et al. Thromboprophylaxis in Patients with COVID-19: Systematic Review of National and International Clinical Guidance Reports. Curr Vasc Pharmacol. 2021. doi:10.2174/1570161119666210824160332 [published Online First: 2021/08/26].
Sholzberg M, Tang GH, Rahhal H, et al. Effectiveness of therapeutic heparin versus prophylactic heparin on death, mechanical ventilation, or intensive care unit admission in moderately ill patients with covid-19 admitted to hospital: RAPID randomised clinical trial. BMJ. 2021;375:n2400. doi:10.1136/bmj.n2400 [published Online First: 2021/10/16].
Smadja DM, Bonnet G, Gendron N, et al. Intermediate- vs. Standard-Dose Prophylactic Anticoagulation in Patients With COVID-19 Admitted in Medical Ward: A Propensity Score-Matched Cohort Study. Front Med (Lausanne). 2021;8:747527. doi:10.3389/fmed.2021.747527 [published Online First: 2021/11/02].
Phelps M, Christensen DM, Gerds T, et al. Cardiovascular comorbidities as predictors for severe COVID-19 infection or death. Eur Heart J Qual Care Clin Outcomes. 2021;7(2):172–80. doi:10.1093/ehjqcco/qcaa081 [published Online First: 2020/10/28].
Russo V, Silverio A, Scudiero F, et al. Clinical Outcome of Hospitalized COVID-19 Patients with History of Atrial Fibrillation. Med (Kaunas) 2022;58(3) doi:10.3390/medicina58030399 [published Online First: 2022/03/27].
Kuno T, Takahashi M, So M, et al. The association of anticoagulation before admission and survival of patients with COVID-19. J Cardiol. 2022;79(4):489–93. doi:10.1016/j.jjcc.2021.12.007 [published Online First: 2022/01/04].
Rieder M, Gauchel N, Kaier K, et al. Pre-medication with oral anticoagulants is associated with better outcomes in a large multinational COVID-19 cohort with cardiovascular comorbidities. Clin Res Cardiol. 2021. doi:10.1007/s00392-021-01939-3 [published Online First: 2021/09/22].
Flam B, Wintzell V, Ludvigsson JF, et al. Direct oral anticoagulant use and risk of severe COVID-19. J Intern Med. 2021;289(3):411–19. doi:10.1111/joim.13205 [published Online First: 2020/12/02].
Taur SR. Observational designs for real-world evidence studies. Perspect Clin Res. 2022;13(1):12–6. doi:10.4103/picr.picr_217_21 [published Online First: 2022/02/25].
Wettermark B, Hammar N, Fored CM, et al. The new Swedish Prescribed Drug Register–opportunities for pharmacoepidemiological research and experience from the first six months. Pharmacoepidemiol Drug Saf. 2007;16(7):726–35. doi:10.1002/pds.1294 [published Online First: 2006/08/10].
Brooke HL, Talbäck M, Hörnblad J, et al. The Swedish cause of death register. Eur J Epidemiol. 2017;32(9):765–73. doi:10.1007/s10654-017-0316-1 [published Online First: 20171005].
Rolfhamre P, Jansson A, Arneborn M, et al. SmiNet-2: Description of an internet-based surveillance system for communicable diseases in Sweden. Euro Surveill. 2006;11(5):103–7. [published Online First: 2006/06/08].
Ludvigsson JF, Otterblad-Olausson P, Pettersson BU, et al. The Swedish personal identity number: possibilities and pitfalls in healthcare and medical research. Eur J Epidemiol. 2009;24(11):659–67. doi:10.1007/s10654-009-9350-y [published Online First: 2009/06/09].
Gail M, Williams R, Byar DP, et al. How many controls? J Chronic Dis. 1976;29(11):723–31. doi:10.1016/0021-9681(76)90073-4 [published Online First: 1976/11/01].
WHO. Defined Daily Dose (DDD). 2022. https://www.who.int/tools/atc-ddd-toolkit/about-ddd.
Bergqvist R, Ahlqvist VH, Lundberg M, et al. HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: A registry-based cohort study. PLoS Med. 2021;18(10):e1003820. doi:10.1371/journal.pmed.1003820 [published Online First: 2021/10/15].
Hergens MP, Bell M, Haglund P, et al. Risk factors for COVID-19-related death, hospitalization and intensive care: a population-wide study of all inhabitants in Stockholm. Eur J Epidemiol. 2022. doi:10.1007/s10654-021-00840-7 [published Online First: 2022/01/28].
Rivera-Caravaca JM, Nunez-Gil IJ, Vivas D, et al. Clinical profile and prognosis in patients on oral anticoagulation before admission for COVID-19. Eur J Clin Invest. 2021;51(1):e13436. doi:10.1111/eci.13436 [published Online First: 2020/10/21].
Covino M, De Matteis G, Della Polla D, et al. Does chronic oral anticoagulation reduce in-hospital mortality among COVID-19 older patients? Aging Clin Exp Res. 2021;33(8):2335–43. doi:10.1007/s40520-021-01924-w [published Online First: 2021/07/04].
Protasiewicz M, Reszka K, Kosowski W, et al. Anticoagulation Prior to COVID-19 Infection Has No Impact on 6 Months Mortality: A Propensity Score-Matched Cohort Study. J Clin Med 2022;11(2) doi:10.3390/jcm11020352 [published Online First: 2022/01/22].
Harenberg J, Bauersachs R, Ageno W. Does Chronic Treatment with Oral Anticoagulants Ameliorate the Clinical Course of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection in Coronavirus Disease 2019 (COVID-19)? Semin Thromb Hemost. 2021;47(4):338–40. doi:10.1055/s-0040-1715091 [published Online First: 2020/09/04].
Corrochano M, Acosta-Isaac R, Mojal S, et al. Impact of pre-admission antithrombotic therapy on disease severity and mortality in patients hospitalized for COVID-19. J Thromb Thrombolysis. 2022;53(1):96–102. doi:10.1007/s11239-021-02507-2 [published Online First: 2021/06/18].
Frohlich GM, Jeschke E, Eichler U, et al. Impact of oral anticoagulation on clinical outcomes of COVID-19: a nationwide cohort study of hospitalized patients in Germany. Clin Res Cardiol. 2021;110(7):1041–50. doi:10.1007/s00392-020-01783-x [published Online First: 2021/01/09].
Fumagalli S, Trevisan C, Del Signore S, et al. COVID-19 and Atrial Fibrillation in Older Patients: Does Oral Anticoagulant Therapy Provide a Survival Benefit?-An Insight from the GeroCovid Registry. Thromb Haemost. 2022;122(1):105–12. doi:10.1055/a-1503-3875 [published Online First: 2021/05/08].
LaMori JC, Mody SH, Gross HJ, et al. Burden of comorbidities among patients with atrial fibrillation. Ther Adv Cardiovasc Dis. 2013;7(2):53–62. doi:10.1177/1753944712464101 [published Online First: 2012/10/24].
Inciardi RM, Adamo M, Lupi L, et al. Atrial fibrillation in the COVID-19 era: simple bystander or marker of increased risk? Eur Heart J. 2020;41(32):3094. doi:10.1093/eurheartj/ehaa576 [published Online First: 2020/07/24].
Mountantonakis SE, Saleh M, Fishbein J, et al. Atrial fibrillation is an independent predictor for in-hospital mortality in patients admitted with SARS-CoV-2 infection. Heart Rhythm. 2021;18(4):501–07. doi:10.1016/j.hrthm.2021.01.018 [published Online First: 2021/01/26].
Bisbal F, Baranchuk A, Braunwald E, et al. Atrial Failure as a Clinical Entity: JACC Review Topic of the Week. J Am Coll Cardiol. 2020;75(2):222–32. doi: 10.1016/j.jacc.2019.11.013 [published Online First: 2020/01/18].
Perico L, Benigni A, Casiraghi F, et al. Immunity, endothelial injury and complement-induced coagulopathy in COVID-19. Nat Rev Nephrol. 2021;17(1):46–64. doi:10.1038/s41581-020-00357-4 [published Online First: 2020/10/21].

Covid-19 and comedications in atrial fibrillation – a case-control study in Stockholm

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Methods

Study design and data sources

Study population

Exposure, cases and controls

Outcome

Exposure

Statistical analysis

Patient and Public Involvement

Results

Discussion

Declarations

References

Supplementary Files

Status:

Journal Publication

Version 1