Association of α1-Blocker Receipt With 30-Day Mortality and Risk of Intensive Care Unit Admission Among Adults Hospitalized With Influenza or Pneumonia in Denmark | Critical Care Medicine | JAMA Network Open | JAMA Network
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Figure 1.  Patient Flow Diagram
Patient Flow Diagram

α1-blocker indicates α1–adrenergic receptor blocking agent.

Figure 2.  Cumulative Risk of 30-Day Mortality and Intensive Care Unit (ICU) Admission
Cumulative Risk of 30-Day Mortality and Intensive Care Unit (ICU) Admission

α1-Blocker indicates α1–adrenergic receptor blocking agent and RR, risk ratio.

Figure 3.  Forest Plot of Risk of 30-Day Mortality and Intensive Care Unit (ICU) Admission
Forest Plot of Risk of 30-Day Mortality and Intensive Care Unit (ICU) Admission
Table 1.  Characteristics of Patients Currently Receiving α1-Blockers vs Patients Not Receiving α1-Blockersa
Characteristics of Patients Currently Receiving α1-Blockers vs Patients Not Receiving α1-Blockersa
Table 2.  Risk of Different Outcomes After Propensity Score Weighting Among Patients Currently Receiving α1-Blockers vs Patients Not Receiving α1-Blockersa
Risk of Different Outcomes After Propensity Score Weighting Among Patients Currently Receiving α1-Blockers vs Patients Not Receiving α1-Blockersa
1.
Zhou  P, Yang  X-L, Wang  X-G,  et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin.   Nature. 2020;579(7798):270-273. doi:10.1038/s41586-020-2012-7 PubMedGoogle ScholarCrossref
2.
Huang  C, Wang  Y, Li  X,  et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.   Lancet. 2020;395(10223):497-506. doi:10.1016/S0140-6736(20)30183-5 PubMedGoogle ScholarCrossref
3.
Xu  Z, Shi  L, Wang  Y,  et al.  Pathological findings of COVID-19 associated with acute respiratory distress syndrome.   Lancet Respir Med. 2020;8(4):420-422. doi:10.1016/S2213-2600(20)30076-X PubMedGoogle ScholarCrossref
4.
Cao  C, Yu  M, Chai  Y.  Pathological alteration and therapeutic implications of sepsis-induced immune cell apoptosis.   Cell Death Dis. 2019;10(10):782. doi:10.1038/s41419-019-2015-1 PubMedGoogle ScholarCrossref
5.
Hotchkiss  RS, Karl  IE.  The pathophysiology and treatment of sepsis.   N Engl J Med. 2003;348(2):138-150. doi:10.1056/NEJMra021333 PubMedGoogle ScholarCrossref
6.
Staedtke  V, Bai  R-Y, Kim  K,  et al.  Disruption of a self-amplifying catecholamine loop reduces cytokine release syndrome.   Nature. 2018;564(7735):273-277. doi:10.1038/s41586-018-0774-y PubMedGoogle ScholarCrossref
7.
Vogelstein  JT, Powell  M, Koenecke  A,  et al. Alpha-1 adrenergic receptor antagonists for preventing acute respiratory distress syndrome and death from cytokine storm syndrome. arXiv. Preprint posted online April 21, 2020. Updated September 9, 2020. Accessed August 1, 2020. https://arxiv.org/abs/2004.10117
8.
Konig  MF, Powell  M, Staedtke  V,  et al.  Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists.   J Clin Invest. 2020;130(7):3345-3347. doi:10.1172/JCI139642 PubMedGoogle ScholarCrossref
9.
Clerkin  KJ, Fried  JA, Raikhelkar  J,  et al.  COVID-19 and cardiovascular disease.   Circulation. 2020;141(20):1648-1655. doi:10.1161/CIRCULATIONAHA.120.046941 PubMedGoogle ScholarCrossref
10.
Vandenbroucke  JP, von Elm  E, Altman  DG,  et al; STROBE Initiative.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.   Epidemiology. 2007;18(6):805-835. doi:10.1097/EDE.0b013e3181577511 PubMedGoogle ScholarCrossref
11.
Schmidt  M, Schmidt  SAJ, Adelborg  K,  et al.  The Danish health care system and epidemiological research: from health care contacts to database records.   Clin Epidemiol. 2019;11:563-591. doi:10.2147/CLEP.S179083 PubMedGoogle ScholarCrossref
12.
Schmidt  M, Pedersen  L, Sørensen  HT.  The Danish Civil Registration System as a tool in epidemiology.   Eur J Epidemiol. 2014;29(8):541-549. doi:10.1007/s10654-014-9930-3 PubMedGoogle ScholarCrossref
13.
Schmidt  M, Schmidt  SAJ, Sandegaard  JL, Ehrenstein  V, Pedersen  L, Sørensen  HT.  The Danish National Patient Registry: a review of content, data quality, and research potential.   Clin Epidemiol. 2015;7:449-490. doi:10.2147/CLEP.S91125 PubMedGoogle ScholarCrossref
14.
Christiansen  CF, Møller  MH, Nielsen  H, Christensen  S.  The Danish Intensive Care Database.   Clin Epidemiol. 2016;8:525-530. doi:10.2147/CLEP.S99476 PubMedGoogle ScholarCrossref
15.
Pottegård  A, Schmidt  SAJ, Wallach-Kildemoes  H, Sørensen  HT, Hallas  J, Schmidt  M.  Data Resource Profile: The Danish National Prescription Registry.   Int J Epidemiol. 2017;46(3):798-798f.PubMedGoogle Scholar
16.
Lederer  DJ, Bell  SC, Branson  RD,  et al.  Control of confounding and reporting of results in causal inference studies. guidance for authors from editors of respiratory, sleep, and critical care journals.   Ann Am Thorac Soc. 2019;16(1):22-28. doi:10.1513/AnnalsATS.201808-564PS PubMedGoogle ScholarCrossref
17.
Desai  RJ, Franklin  JM.  Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners.   BMJ. 2019;367:l5657. doi:10.1136/bmj.l5657 PubMedGoogle ScholarCrossref
18.
Chen  G, Wu  D, Guo  W,  et al.  Clinical and immunological features of severe and moderate coronavirus disease 2019.   J Clin Invest. 2020;130(5):2620-2629. doi:10.1172/JCI137244 PubMedGoogle ScholarCrossref
19.
Mehta  P, McAuley  DF, Brown  M, Sanchez  E, Tattersall  RS, Manson  JJ; HLH Across Speciality Collaboration, UK.  COVID-19: consider cytokine storm syndromes and immunosuppression.   Lancet. 2020;395(10229):1033-1034. doi:10.1016/S0140-6736(20)30628-0 PubMedGoogle ScholarCrossref
20.
Channappanavar  R, Perlman  S.  Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology.   Semin Immunopathol. 2017;39(5):529-539. doi:10.1007/s00281-017-0629-x PubMedGoogle ScholarCrossref
21.
Thomsen  RW, Riis  A, Nørgaard  M,  et al.  Rising incidence and persistently high mortality of hospitalized pneumonia: a 10-year population-based study in Denmark.   J Intern Med. 2006;259(4):410-417. doi:10.1111/j.1365-2796.2006.01629.x PubMedGoogle ScholarCrossref
22.
Lund  LC, Reilev  M, Hallas  J,  et al.  Association of nonsteroidal anti-inflammatory drug use and adverse outcomes among patients hospitalized with influenza.   JAMA Netw Open. 2020;3(7):e2013880. doi:10.1001/jamanetworkopen.2020.13880 PubMedGoogle Scholar
23.
Zhang  ZX, Yong  Y, Tan  WC, Shen  L, Ng  HS, Fong  KY.  Prognostic factors for mortality due to pneumonia among adults from different age groups in Singapore and mortality predictions based on PSI and CURB-65.   Singapore Med J. 2018;59(4):190-198. doi:10.11622/smedj.2017079 PubMedGoogle ScholarCrossref
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    Original Investigation
    Infectious Diseases
    February 10, 2021

    Association of α1-Blocker Receipt With 30-Day Mortality and Risk of Intensive Care Unit Admission Among Adults Hospitalized With Influenza or Pneumonia in Denmark

    Author Affiliations
    • 1Department of Clinical Epidemiology, Aarhus University Hospital, Aarhus, Denmark
    • 2Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, Maryland
    • 3Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
    • 4Ludwig Center, Lustgarten Laboratory, Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center, Baltimore, Maryland
    • 5Johns Hopkins University School of Medicine, Baltimore, Maryland
    • 6Center for Population Health and Sciences, Stanford University, Stanford, California
    • 7Stanford Graduate School of Business, Stanford University, Stanford, California
    JAMA Netw Open. 2021;4(2):e2037053. doi:10.1001/jamanetworkopen.2020.37053
    Key Points

    Question  Is the receipt of α1–adrenergic receptor blocking agents (α1-blockers) associated with protective benefits against adverse outcomes, such as mortality and intensive care unit admission, among adult patients with severe respiratory tract infections?

    Findings  In this cohort study of 528 467 Danish adults hospitalized with influenza or pneumonia, current receipt of α1-blockers was associated with a 14% reduction in the relative risk of 30-day mortality.

    Meaning  This study’s findings suggest that the receipt of α1-blockers may have a clinically relevant association with protective benefits against adverse outcomes among patients with severe respiratory tract infections.

    Abstract

    Importance  Alpha 1–adrenergic receptor blocking agents (α1-blockers) have been reported to have protective benefits against hyperinflammation and cytokine storm syndrome, conditions that are associated with mortality in patients with coronavirus disease 2019 and other severe respiratory tract infections. However, studies of the association of α1-blockers with outcomes among human participants with respiratory tract infections are scarce.

    Objective  To examine the association between the receipt of α1-blockers and outcomes among adult patients hospitalized with influenza or pneumonia.

    Design, Setting, and Participants  This population-based cohort study used data from Danish national registries to identify individuals 40 years and older who were hospitalized with influenza or pneumonia between January 1, 2005, and November 30, 2018, with follow-up through December 31, 2018. In the main analyses, patients currently receiving α1-blockers were compared with those not receiving α1-blockers (defined as patients with no prescription for an α1-blocker filled within 365 days before the index date) and those currently receiving 5α-reductase inhibitors. Propensity scores were used to address confounding factors and to compute weighted risks, absolute risk differences, and risk ratios. Data were analyzed from April 21 to December 21, 2020.

    Exposures  Current receipt of α1-blockers compared with nonreceipt of α1-blockers and with current receipt of 5α-reductase inhibitors.

    Main Outcomes and Measures  Death within 30 days of hospital admission and risk of intensive care unit (ICU) admission.

    Results  A total of 528 467 adult patients (median age, 75.0 years; interquartile range, 64.4-83.6 years; 273 005 men [51.7%]) were hospitalized with influenza or pneumonia in Denmark between 2005 and 2018. Of those, 21 772 patients (4.1%) were currently receiving α1-blockers compared with a population of 22 117 patients not receiving α1-blockers who were weighted to the propensity score distribution of those receiving α1-blockers. In the propensity score–weighted analyses, patients receiving α1-blockers had lower 30-day mortality (15.9%) compared with patients not receiving α1-blockers (18.5%), with a corresponding risk difference of −2.7% (95% CI, −3.2% to −2.2%) and a risk ratio (RR) of 0.85 (95% CI, 0.83-0.88). The risk of ICU admission was 7.3% among patients receiving α1-blockers and 7.7% among those not receiving α1-blockers (risk difference, −0.4% [95% CI, −0.8% to 0%]; RR, 0.95 [95% CI, 0.90-1.00]). A comparison between 18 280 male patients currently receiving α1-blockers and 18 228 propensity score–weighted male patients currently receiving 5α-reductase inhibitors indicated that those receiving α1-blockers had lower 30-day mortality (risk difference, −2.0% [95% CI, −3.4% to −0.6%]; RR, 0.89 [95% CI, 0.82-0.96]) and a similar risk of ICU admission (risk difference, −0.3% [95% CI, −1.4% to 0.7%]; RR, 0.96 [95% CI, 0.83-1.10]).

    Conclusions and Relevance  This cohort study’s findings suggest that the receipt of α1-blockers is associated with protective benefits among adult patients hospitalized with influenza or pneumonia.

    Introduction

    Acute respiratory syndrome coronaviruses are associated with severe viral pneumonia and death.1 In the ongoing coronavirus disease 2019 (COVID-19) pandemic, mortality appears to be associated with acute respiratory distress syndrome and a dysregulated immune response with hyperinflammation and cytokine storm syndrome,2,3 factors that have also been observed in patients with other severe respiratory tract infections and sepsis.4,5 In mouse models, α1–adrenergic receptor blocking agents (α1-blockers), which are mainly used to treat benign prostatic hyperplasia (BPH) and hypertension, have recently been reported to protect against hyperinflammation and cytokine storm syndrome after exposure to various inflammatory stimuli.6-8

    Given the safety profile and low cost of treatment with α1-blockers, any benefits associated with protection against adverse outcomes among patients hospitalized with COVID-19 or other severe respiratory tract infections would have substantial clinical and public health importance.9 Studies of the association of α1-blockers with outcomes among human study participants with respiratory tract infections are scarce or nonexistent.8 To address this gap, we conducted a large population-based study using data from Danish national registries to investigate the association of the receipt of α1-blockers with intensive care unit (ICU) admission and 30-day mortality among patients hospitalized with influenza or pneumonia.

    Methods
    Study Design and Setting

    The population for this nationwide cohort study included all patients 40 years and older who were hospitalized with influenza or pneumonia in Denmark between January 1, 2005, and November 30, 2018, with follow-up through December 31, 2018 (eFigure 1 in the Supplement). Data collection and processing were reported to the Danish Data Protection Agency through Aarhus University. Ethics review board approval and informed consent are not required for registry-based observational studies in Denmark. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cohort studies.10

    Denmark has a tax-supported health care system that provides health care services, including acute care and hospital care for influenza and pneumonia, to all residents.11 All Danish residents receive a personal identity number at birth or immigration that allows individual-level linkage across the extensive Danish registry system, which includes national-level information on residence; prescriptions; vital status (dead or alive); and primary, specialty, and hospital-based care.12

    We assessed hospitalizations (including direct inpatient hospital admissions and emergency department visits leading to either inpatient hospital admission or discharge to home) among patients 40 years and older (because receipt of α1-blockers is rare among individuals younger than 40 years) who had either a primary or secondary diagnosis of influenza or pneumonia recorded in the Danish National Patient Registry (Figure 1).13 This registry includes data on primary and secondary diagnoses; procedure codes; and dates of hospital contacts; admissions, and discharges. Hospitalizations that were preceded by an influenza or pneumonia diagnosis within the previous 3 months were excluded to avoid the inclusion of readmissions. We predefined subgroups based on an influenza diagnosis or a diagnosis of pneumonia that specified a bacterial or nonspecific pathogen. Specific diagnostic codes used in the study are listed in eTable 1 in the Supplement.

    Outcomes and Exposures

    Primary study outcomes were 30-day mortality and 30-day ICU admission during the index hospitalization associated with an influenza or pneumonia diagnosis. Secondary outcomes included the receipt of organ-supportive treatment (mechanical ventilation, noninvasive ventilation, and treatment with inotropic and/or vasopressor medications) during ICU admission. Dialysis-treated acute kidney injury was defined as treatment with acute renal replacement therapy among patients with no history of previous dialysis for the treatment of chronic kidney disease. Outcomes were ascertained using population registry1 data for all-cause death and patient registry2 data for diagnoses and procedures associated with all other outcomes.12-14

    Data on all filled prescriptions were obtained from the Danish National Prescription Registry.15 This registry contains data on all prescription drugs obtained by Danish residents at any community pharmacy in Denmark since 1995. The main exposure of interest was current receipt of α1-blockers, which are primarily used for 2 indications: BPH and hypertension (eTable 1 in the Supplement). We defined current receipt of α1-blockers as a prescription filled within 90 days before a hospitalization for influenza or pneumonia. This definition was consistent with the 3-month supply of α1-blockers that is dispensed most often in Danish pharmacies.

    In our main analyses, patients currently receiving α1-blockers were compared with those not receiving α1-blockers (defined as patients with no prescription for an α1-blocker filled within 365 days before the index date). In a secondary analysis, patients who formerly received α1-blockers (defined as patients with a prescription for an α1-blocker that was filled 91-365 days before the index date) were compared with those who were not receiving α1-blockers to address potential confounding by treatment indication. In an additional analysis to account for confounding by treatment indication, patients currently receiving α1-blockers were compared with those currently receiving a different type of medication, 5α-reductase inhibitors (finasteride and dutasteride), for the treatment of BPH. This analysis was restricted to male patients who did not receive the 2 types of drugs (α1-blockers and 5α-reductase inhibitors) as combination therapy for BPH.

    Potential Confounders

    We considered a range of potential confounders in our study.16 We obtained patient age and sex from the population registry,12 and we collected information from the patient registry on the presence of a range of comorbidities that required inpatient or outpatient hospital contact within 10 years before the index hospitalization (Table 1; eTable 1 in the Supplement).13

    Prescriptions for relevant concurrent medications that were filled within 90 days before hospital admission were also ascertained; these medications included angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, calcium channel blockers, thiazides, β-blockers, other antihypertensive medications, statins, aspirin, loop diuretics, antibiotics, antiviral agents, glucocorticoids, other immunosuppressants, nonsteroidal anti-inflammatory drugs, opioids, vitamin K antagonists, proton pump inhibitors, antidepressants, hypnotics or sedatives, and antipsychotics.15 Because lifestyle and social factors are associated with health, we included information on obesity, alcohol use, smoking, marital status, and urban vs rural residence.

    Statistical Analysis

    We applied propensity score balancing of potential confounders across treatment groups.17 Continuous covariates were included as a cubic spline with 7 knots. We used propensity score weighting to generate a population of relevant comparison groups (eg, patients not receiving α1-blockers or 5α-reductase inhibitors) that resembled the number and covariate distribution of patients receiving α1-blockers. The exposed patients were assigned a weight of 1, and the unexposed patients were assigned a weight equivalent to their estimated propensity score divided by the difference between 1 and their estimated propensity score (ie, the weight was the individual’s estimated odds of being exposed). If successful, this weighting method produces a comparison population with size and covariate distribution resembling that of the exposed population.17 Covariate balance was assessed using standardized differences and was deemed acceptable.

    Follow-up started on the date of the first hospital admission associated with an influenza or pneumonia diagnosis and continued until a specific outcome of interest, emigration, or the completion of 30 days, whichever occurred first. The 30-day risks (both unadjusted and weighted by propensity score) of death and ICU admission were computed and plotted. Risk differences were calculated for all outcomes by subtracting propensity score–weighted risks. Risk ratios (RRs) were estimated as the ratios of propensity score–weighted risk estimates. All estimates were accompanied by 95% CIs that were obtained using bootstrapping with 200 bootstrap samples.

    Several subgroup analyses were conducted that were stratified by (1) patient age, (2) restriction to patients with a diagnosis of influenza or pneumonia that was listed first in the hospital discharge summary, (3) restriction to patients with BPH or hypertension as underlying conditions, and (4) the 3 most frequently prescribed α1-blocker medications (doxazosin, alfuzosin, and tamsulosin). We also performed an analysis of 30-day risk of death associated with the receipt of α1-blockers that was restricted to patients transferred to the ICU during their hospitalization, with follow-up beginning on the date of ICU admission. In additional analyses, current receipt and nonreceipt of α1-blockers were compared among male patients only.

    Statistical analyses were performed using SAS software, version 9.4 (SAS Institute). Data were analyzed from April 21 to December 21, 2020.

    Results

    The final study cohort included 528 467 Danish residents 40 years and older who were hospitalized with influenza or pneumonia (median age, 75.0 years; interquartile range [IQR], 64.4-83.6 years; 273 005 men [51.7%]) (Figure 2; eTable 2 in the Supplement). Of those, 21 772 patients (4.1%) were currently receiving α1-blockers, 9119 patients (1.7%) had formerly received α1-blockers, and 497 576 patients (95.8%) had not received α1-blockers. A total of 41 276 hospitalizations included admission to the ICU; in most cases, transfer to the ICU occurred early after the initial hospital admission (median, 1 day; 25th-75th percentile, 0-5 days). In total, 77 197 patients (14.6%) died within 30 days.

    Patient Characteristics

    The median age was higher among patients receiving α1-blockers (79.7 years; IQR, 72.8-85.4 years) compared with patients not receiving α1-blockers (74.6 years; IQR, 63.8-83.5 years). The cohort receiving α1-blockers comprised a substantially larger proportion of male patients (20 984 men [96.4%]) than the cohort not receiving α1-blockers (243 314 men [48.9%]). Higher prevalence of previous hospital-diagnosed BPH (5960 patients [27.4%] vs 26 240 patients [5.3%]), hypertension (9477 patients [43.5%] vs 156 419 patients [31.4%]), and other comorbidities (eg, atrial fibrillation, 5689 patients [26.1%] vs 87 846 patients [17.7%]) were observed among those receiving α1-blockers compared with those not receiving α1-blockers, respectively (Table 1). Cotreatment with most cardiovascular medications, including other antihypertensive drugs, was also more frequent among patients receiving α1-blockers (eg, 2704 patients [12.4%] receiving α1-blockers were also receiving 5α-reductase inhibitors compared with 4382 patients [0.9%] not receiving α1-blockers).

    After propensity score weighting of patients receiving α1-blockers (eFigure 2 in the Supplement), treatment groups were well balanced on all measured covariates, with absolute standardized differences for all covariates decreasing from between 0 and 1.78 before propensity score balancing to less than 0.10 after propensity score balancing (Table 1). The final cohorts included in the propensity score–weighted outcome analysis consisted of 21 772 patients currently receiving α1-blockers and 22 117 patients not receiving α1-blockers (weighted to the propensity score distribution of the patients currently receiving α1-blockers).

    Patient Outcomes

    In the unadjusted analyses before propensity score weighting, patients receiving α-1 blockers had higher 30-day mortality (15.9%) than those not receiving α-1 blockers (14.5%) (eTable 3 in the Supplement), which was likely associated with the older age and greater comorbidity burden of this cohort compared with the cohort not receiving α-1 blockers. After covariate balancing by propensity score weighting, among all patients with influenza or pneumonia, 30-day mortality was 15.9% for patients receiving α1-blockers and 18.5% for patients not receiving α1-blockers, with a corresponding risk difference of −2.7% (95% CI, −3.2% to −2.2%) and an RR of 0.85 (95% CI, 0.83-0.88) (Table 2). The risk of ICU admission was 7.3% in patients receiving α1-blockers and 7.7% in those not receiving α1-blockers, which corresponded to a risk difference of −0.4% (95% CI, −0.8% to 0%) and an RR of 0.95 (95% CI, 0.90-1.00). The RRs among patients receiving α1-blockers were almost identical when the analysis was restricted to the risk of ICU admission within 7 days vs 30 days (6.4% vs 6.9%, respectively; risk difference, −0.4% [95% CI, −0.7% to 0%]; RR, 0.94 [95% CI, 0.89-1.00]) (Figure 3). The RR for 30-day ICU admission among those receiving α1-blockers was 0.95 (95% CI, 0.90-1.00), with RRs lower than 1.00 for mechanical ventilation (0.92; 95% CI, 0.86-0.99) and inotropic treatment (0.95; 95% CI, 0.89-1.02) during ICU admission. The RRs for noninvasive ventilation and dialysis-treated acute kidney injury during ICU admission were 1.00 (95% CI, 0.92-1.08) and 1.11 (95% CI, 0.96-1.29), respectively (Table 2).

    In the outcome analysis of the subgroup of 7636 patients diagnosed with influenza (which included 327 patients receiving α1-blockers and 336 propensity score–weighted patients not receiving α1-blockers), 30-day mortality was 7.3% among those receiving α1-blockers and 7.6% among those not receiving α1-blockers (risk difference, −0.2% [95% CI, −3.7% to 3.2%]; RR, 0.97 [95% CI, 0.61-1.55]) (Table 2). The risk of ICU admission was slightly higher among patients with influenza who were receiving vs not receiving α1-blockers (RR, 1.34; 95% CI, 0.88-2.02), whereas the risk of dialysis-treated acute kidney injury was lower (RR, 0.50; 95% CI, 0.19-1.36); however, these estimates were imprecise owing to the limited number of outcomes available for analysis. Outcomes for those receiving vs not receiving α1-blockers were only similar when restricting the analysis to male patients (eTable 4 in the Supplement).

    The characteristics of patients currently receiving α1-blockers and those currently receiving 5α-reductase inhibitors were well balanced after propensity score weighting (eTable 5 in the Supplement). In a comparison between 18 280 men receiving α1-blockers and 18 228 propensity score–weighted men receiving 5α-reductase inhibitors, those receiving α1-blockers had lower 30-day mortality (16.0% vs 18.0%, respectively) (Figure 3; eTable 6 in the Supplement). The corresponding risk difference was −2.0% (95% CI, −3.4 to −0.6), and the RR was 0.89 (95% CI, 0.82-0.96). The risk of ICU admission was similar in the 2 groups (7.4% for those receiving α1-blockers and 7.8% for those receiving 5α-reductase inhibitors; risk difference, −0.3% [95% CI, −1.4% to 0.7%]; RR, 0.96 [95% CI, 0.83-1.10], respectively) (eTable 6 in the Supplement; Figure 3). Other outcomes and infection subgroups could not be examined because of sample size constraints.

    The subgroup analyses (stratified by age, patients with a first-listed diagnosis of influenza or pneumonia, and patients with BPH or hypertension) were generally consistent with our main findings (Figure 3). An analysis of α1-blockers by type indicated that tamsulosin, alfuzosin, and doxazosin were all associated with reductions in the risk of mortality (for tamsulosin, RR, 0.84 [95% CI, 0.80-0.88]; for alfuzosin, RR, 0.87 [95% CI, 0.82-0.92]; and for doxazosin, RR, 0.92 [95% CI, 0.98-1.00]) and the risk of ICU admission (for tamsulosin, RR, 0.92 [95% CI, 0.86-0.99]; for alfuzosin, RR, 0.94 [95% CI, 0.85-1.04; and for doxazosin, RR, 1.00 [95% CI, 0.90-1.12]) (eTable 7 in the Supplement).

    Discussion

    In this large nationwide population-based cohort study of 528 467 Danish patients 40 years or older who were hospitalized with influenza or pneumonia, receipt of α1-blockers was associated with a decreased risk of death, compared with nonreceipt of α1-blockers and receipt of 5α-reductase inhibitors.

    This study provides novel information about the association of α1-blockers with protective benefits against adverse outcomes among patients with severe respiratory tract infections. These results are consistent with and extend the findings of a preliminary epidemiological analysis of this association conducted in the US.7 That study found that among men aged 45 to 85 years (108 956 men from the MarketScan database and 252 708 men from the Optum database) who were hospitalized with pneumonia, the propensity score–matched odds ratio for in-hospital ventilation or death among those receiving vs not receiving α1-blockers was 0.91 (95% CI, 0.87-0.96).7 This finding is consistent with the RRs in the current study of 0.85 (95% CI, 0.83-0.88) for 30-day mortality and 0.92 (95% CI, 0.86-0.99) for mechanical ventilation associated with receipt vs nonreceipt of α1-blockers among patients with influenza or pneumonia.

    Of interest, the US analysis also included 13 125 men from the Market Scan database and 6534 men from the Optum database who had a diagnosis code for acute respiratory distress, which is a potential precursor of acute respiratory distress syndrome. In this group, the association between α1-blocker receipt and protective benefits was clearer, with an adjusted odds ratio for in-hospital ventilation or death of 0.67 (95% CI, 0.46-0.96).7

    In studies of mouse models, it has recently been reported that macrophages secrete and respond to catecholamines through adrenergic receptors when exposed to inflammatory stimuli such as bacteria. Catecholamines orchestrate cytokine production and severity of inflammation injury,6 and catecholamine synthesis inhibition reduces cytokine responses. When mice injected with bacterial lipopolysaccharide were pretreated with pharmacologic catecholamine blockade through metyrosine therapy, they were protected from the fatal complications of cytokine release syndrome.6

    Emerging data from human studies suggest that a subset of patients with COVID-19 develops cytokine storm syndrome that is associated with increased production of proinflammatory cytokines (including interleukin 6, interleukin 2R, interleukin 8, tumor necrosis factor α, and granulocyte colony-stimulating factor)8,18,19 similar to the excessive cytokine production by lung-infiltrating pneumocytes and monocytes or macrophages observed in patients with severe acute respiratory coronavirus and Middle East respiratory syndrome coronavirus infections.20 Alveolar inflammation culminates in acute respiratory distress syndrome, which necessitates mechanical ventilation and is a main factor associated with COVID-19 mortality. Preventing hyperinflammation in an early phase seems important to avoid this progression,4,5 and the catecholamine pathway is a potential target for preventing hyperinflammation in patients with COVID-19. Randomized clinical trials will be needed to further test the hypothesis raised by animal experiments and epidemiological studies indicating that α1-blockers may be associated with decreases in the risks of cytokine storm syndrome and death among patients with COVID-19.

    Limitations

    This study has several limitations. One limitation was the study’s reliance on diagnostic coding of influenza and pneumonia. Although some patients with these infections may not receive a diagnosis, we believe that restriction to physician-coded influenza and pneumonia discharge diagnoses ensured inclusion of only those patients with clinically relevant infections. The positive predictive value of pneumonia diagnoses in the Danish patient registry is 90%.21,22 Overall 30-day mortality after pneumonia diagnosis in this study’s population-based cohort was similar to that reported in other parts of the world.23 Any selection bias in this study should be minimal because follow-up was almost complete, and different associations between included and nonincluded patients would not be expected. Given the chronic receipt of the drugs included in the study, any misclassification from sporadic use that was not captured before hospitalization should be minor and not associated with the outcome of interest.

    This study lacked data on continuous in-hospital use of α1-blockers, and possible drug discontinuation during acute illness may have produced an underestimation of any association. Deaths are accurately recorded in the Danish population registry and updated daily.12 Intensive care unit admissions and treatments are also accurately recorded, as the Danish patient registry is used for financial reimbursement to hospitals and for mandatory reporting to quality-of-care databases.14 Because conditions treated with α1-blockers(eg, BPH and hypertension) and severe influenza and pneumonia infections may both lead to acute hospitalization, conditioning the analyses on hospitalized patients may, in theory, introduce collider bias. However, in cases of influenza and pneumonia in which death occurs shortly after diagnosis, the infection is likely to be a main factor associated with hospitalization and death rather than an inconsequential variable. Moreover, this study found robust estimates among patients with a primary diagnosis of influenza or pneumonia and among patients with severe infections who were admitted to the ICU. Potential confounding by drug treatment indication was handled by using an active comparator in 1 analysis, using propensity score weighting that included a large number of potential confounders, and restricting analyses to subgroups according to treatment indication. Nevertheless, it is possible that unmeasured confounding factors impacted the study’s risk estimates. Healthy-user bias is an unlikely explanation for the findings, given that the comorbidities and lifestyle factors captured did not indicate healthier lifestyles among patients receiving α1-blockers. Moreover, patients who formerly received α1-blockers did not experience improved outcomes. Although the study included more than 500 000 patients, the precision of risk estimates was limited in some subgroups. Because more than 95% of patients receiving α1-blockers were men and more than 90% of people in Denmark are White, it remains uncertain whether these results will also apply to women and non-White individuals.

    Conclusions

    In this study, patients receiving α1-blockers who were hospitalized with influenza or pneumonia had lower mortality after confounding factors were controlled for compared with those not receiving α1-blockers and those receiving 5α-reductase inhibitors. Thus, these data support the hypothesis that α1-blockers may have a clinically relevant association with outcomes among patients with acute respiratory tract infections. These findings will need to be reproduced among patients with confirmed COVID-19 infection. Randomized clinical trials may enable more definitive conclusions to be reached regarding the association between α1-blockers and ICU admission and mortality among patients with COVID-19 and other respiratory tract infections. Such clinical trials may evaluate any benefits associated with initiating treatment with α1-blockers early in the course of severe respiratory infection rather than the more chronic treatment examined in this study, and clinical trials may also consider possible adverse effects associated with α-1 blocker therapy.

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    Article Information

    Accepted for Publication: December 21, 2020.

    Published: February 10, 2021. doi:10.1001/jamanetworkopen.2020.37053

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2021 Thomsen RW et al. JAMA Network Open.

    Corresponding Author: Reimar W. Thomsen, Department of Clinical Epidemiology, Aarhus University Hospital, Olof Palmes Allé 43-45, DK-8200 Aarhus N, Denmark (rwt@clin.au.dk).

    Author Contributions: Drs Thomsen and Heide-Jørgensen had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Thomsen, Christiansen, J. Vogelstein, Bettegowda, Tamang, Athey, Sorensen.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Thomsen, J. Vogelstein.

    Critical revision of the manuscript for important intellectual content: Christiansen, Heide-Jørgensen, J. Vogelstein, B. Vogelstein, Bettegowda, Tamang, Athey, Sorensen.

    Statistical analysis: Heide-Jørgensen, J. Vogelstein, B. Vogelstein, Athey, Sorensen.

    Obtained funding: J. Vogelstein, Sorensen.

    Administrative, technical, or material support: J. Vogelstein, Bettegowda, Sorensen.

    Supervision: Christiansen, J. Vogelstein, Athey, Sorensen.

    Conflict of Interest Disclosures: Dr B. Vogelstein reported being a founder of Personal Genome Diagnostics and Thrive Earlier Detection; having equity in CAGE Pharma, Catalio Capital Management, NeoPhore, Personal Genome Diagnostics, and Thrive Earlier Detection; and serving as a consultant for CAGE Pharma, Catalio Capital Management, Eisai, NeoPhore, and Sysmex outside the submitted work. Dr Bettegowda reported serving as a consultant for Bionaut Labs and Depuy Synthes outside the submitted work. Dr Athey reported receiving grants from the Mercatus Center at George Mason University and Microsoft Research during the conduct of the study and serving as an advisor for Prealize Health outside the submitted work. No other disclosures were reported.

    Funding/Support: The study was funded by Aarhus University.

    Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    References
    1.
    Zhou  P, Yang  X-L, Wang  X-G,  et al.  A pneumonia outbreak associated with a new coronavirus of probable bat origin.   Nature. 2020;579(7798):270-273. doi:10.1038/s41586-020-2012-7 PubMedGoogle ScholarCrossref
    2.
    Huang  C, Wang  Y, Li  X,  et al.  Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China.   Lancet. 2020;395(10223):497-506. doi:10.1016/S0140-6736(20)30183-5 PubMedGoogle ScholarCrossref
    3.
    Xu  Z, Shi  L, Wang  Y,  et al.  Pathological findings of COVID-19 associated with acute respiratory distress syndrome.   Lancet Respir Med. 2020;8(4):420-422. doi:10.1016/S2213-2600(20)30076-X PubMedGoogle ScholarCrossref
    4.
    Cao  C, Yu  M, Chai  Y.  Pathological alteration and therapeutic implications of sepsis-induced immune cell apoptosis.   Cell Death Dis. 2019;10(10):782. doi:10.1038/s41419-019-2015-1 PubMedGoogle ScholarCrossref
    5.
    Hotchkiss  RS, Karl  IE.  The pathophysiology and treatment of sepsis.   N Engl J Med. 2003;348(2):138-150. doi:10.1056/NEJMra021333 PubMedGoogle ScholarCrossref
    6.
    Staedtke  V, Bai  R-Y, Kim  K,  et al.  Disruption of a self-amplifying catecholamine loop reduces cytokine release syndrome.   Nature. 2018;564(7735):273-277. doi:10.1038/s41586-018-0774-y PubMedGoogle ScholarCrossref
    7.
    Vogelstein  JT, Powell  M, Koenecke  A,  et al. Alpha-1 adrenergic receptor antagonists for preventing acute respiratory distress syndrome and death from cytokine storm syndrome. arXiv. Preprint posted online April 21, 2020. Updated September 9, 2020. Accessed August 1, 2020. https://arxiv.org/abs/2004.10117
    8.
    Konig  MF, Powell  M, Staedtke  V,  et al.  Preventing cytokine storm syndrome in COVID-19 using α-1 adrenergic receptor antagonists.   J Clin Invest. 2020;130(7):3345-3347. doi:10.1172/JCI139642 PubMedGoogle ScholarCrossref
    9.
    Clerkin  KJ, Fried  JA, Raikhelkar  J,  et al.  COVID-19 and cardiovascular disease.   Circulation. 2020;141(20):1648-1655. doi:10.1161/CIRCULATIONAHA.120.046941 PubMedGoogle ScholarCrossref
    10.
    Vandenbroucke  JP, von Elm  E, Altman  DG,  et al; STROBE Initiative.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.   Epidemiology. 2007;18(6):805-835. doi:10.1097/EDE.0b013e3181577511 PubMedGoogle ScholarCrossref
    11.
    Schmidt  M, Schmidt  SAJ, Adelborg  K,  et al.  The Danish health care system and epidemiological research: from health care contacts to database records.   Clin Epidemiol. 2019;11:563-591. doi:10.2147/CLEP.S179083 PubMedGoogle ScholarCrossref
    12.
    Schmidt  M, Pedersen  L, Sørensen  HT.  The Danish Civil Registration System as a tool in epidemiology.   Eur J Epidemiol. 2014;29(8):541-549. doi:10.1007/s10654-014-9930-3 PubMedGoogle ScholarCrossref
    13.
    Schmidt  M, Schmidt  SAJ, Sandegaard  JL, Ehrenstein  V, Pedersen  L, Sørensen  HT.  The Danish National Patient Registry: a review of content, data quality, and research potential.   Clin Epidemiol. 2015;7:449-490. doi:10.2147/CLEP.S91125 PubMedGoogle ScholarCrossref
    14.
    Christiansen  CF, Møller  MH, Nielsen  H, Christensen  S.  The Danish Intensive Care Database.   Clin Epidemiol. 2016;8:525-530. doi:10.2147/CLEP.S99476 PubMedGoogle ScholarCrossref
    15.
    Pottegård  A, Schmidt  SAJ, Wallach-Kildemoes  H, Sørensen  HT, Hallas  J, Schmidt  M.  Data Resource Profile: The Danish National Prescription Registry.   Int J Epidemiol. 2017;46(3):798-798f.PubMedGoogle Scholar
    16.
    Lederer  DJ, Bell  SC, Branson  RD,  et al.  Control of confounding and reporting of results in causal inference studies. guidance for authors from editors of respiratory, sleep, and critical care journals.   Ann Am Thorac Soc. 2019;16(1):22-28. doi:10.1513/AnnalsATS.201808-564PS PubMedGoogle ScholarCrossref
    17.
    Desai  RJ, Franklin  JM.  Alternative approaches for confounding adjustment in observational studies using weighting based on the propensity score: a primer for practitioners.   BMJ. 2019;367:l5657. doi:10.1136/bmj.l5657 PubMedGoogle ScholarCrossref
    18.
    Chen  G, Wu  D, Guo  W,  et al.  Clinical and immunological features of severe and moderate coronavirus disease 2019.   J Clin Invest. 2020;130(5):2620-2629. doi:10.1172/JCI137244 PubMedGoogle ScholarCrossref
    19.
    Mehta  P, McAuley  DF, Brown  M, Sanchez  E, Tattersall  RS, Manson  JJ; HLH Across Speciality Collaboration, UK.  COVID-19: consider cytokine storm syndromes and immunosuppression.   Lancet. 2020;395(10229):1033-1034. doi:10.1016/S0140-6736(20)30628-0 PubMedGoogle ScholarCrossref
    20.
    Channappanavar  R, Perlman  S.  Pathogenic human coronavirus infections: causes and consequences of cytokine storm and immunopathology.   Semin Immunopathol. 2017;39(5):529-539. doi:10.1007/s00281-017-0629-x PubMedGoogle ScholarCrossref
    21.
    Thomsen  RW, Riis  A, Nørgaard  M,  et al.  Rising incidence and persistently high mortality of hospitalized pneumonia: a 10-year population-based study in Denmark.   J Intern Med. 2006;259(4):410-417. doi:10.1111/j.1365-2796.2006.01629.x PubMedGoogle ScholarCrossref
    22.
    Lund  LC, Reilev  M, Hallas  J,  et al.  Association of nonsteroidal anti-inflammatory drug use and adverse outcomes among patients hospitalized with influenza.   JAMA Netw Open. 2020;3(7):e2013880. doi:10.1001/jamanetworkopen.2020.13880 PubMedGoogle Scholar
    23.
    Zhang  ZX, Yong  Y, Tan  WC, Shen  L, Ng  HS, Fong  KY.  Prognostic factors for mortality due to pneumonia among adults from different age groups in Singapore and mortality predictions based on PSI and CURB-65.   Singapore Med J. 2018;59(4):190-198. doi:10.11622/smedj.2017079 PubMedGoogle ScholarCrossref
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