Author response: Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection (2021)

Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract In severe viral pneumonia, including Coronavirus disease 2019 (COVID-19), the viral replication phase is often followed by hyperinflammation, which can lead to acute respiratory distress syndrome, multi-organ failure, and death. We previously demonstrated that alpha-1 adrenergic receptor (⍺1-AR) antagonists can prevent hyperinflammation and death in mice. Here, we conducted retrospective analyses in two cohorts of patients with acute respiratory distress (ARD, n = 18,547) and three cohorts with pneumonia (n = 400,907). Federated across two ARD cohorts, we find that patients exposed to ⍺1-AR antagonists, as compared to unexposed patients, had a 34% relative risk reduction for mechanical ventilation and death (OR = 0.70, p = 0.021). We replicated these methods on three pneumonia cohorts, all with similar effects on both outcomes. All results were robust to sensitivity analyses. These results highlight the urgent need for prospective trials testing whether prophylactic use of ⍺1-AR antagonists ameliorates lower respiratory tract infection-associated hyperinflammation and death, as observed in COVID-19. Introduction Each year, approximately 300 million people develop pneumonia (GBD 2016 Lower Respiratory Infections Collaborators, 2018), which usually results in appropriate, self-limiting immune responses and clearance of the bacterial or viral pathogen. In some cases (Figure 1), the pathogen overwhelms host defenses, causing massive lung damage and compromising other organs. In other patients, however, pathological immune activation ('hyperinflammation') occurs in the lungs and systemically (Hay et al., 2017), resulting in immune-mediated end-organ damage that can compromise gas exchange. Dysregulated immune responses may lead to acute respiratory distress syndrome (ARDS), need for mechanical ventilation, and failure of other organ systems, contributing to the global pneumonia death toll of 3 million per year (The top 10 causes of death, 2021). The clinical picture is similar in Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2; hyperinflammation compromises organ function in the lungs and systemically, causing high morbidity and mortality (Zhou et al., 2020; Ruan et al., 2020; Qin et al., 2020; Huang et al., 2020). Figure 1 Download asset Open asset Model of clinical progression of respiratory dysfunction from local infection to hyperinflammation. The timing and relation of hyperinflammation to specific organ manifestations of severe acute respiratory distress syndrome (ARDS) are areas of uncertainty and investigation. Disease-modifying strategies for COVID-19 include targeting the virus and treating (or ideally preventing) secondary hyperinflammation with immunomodulatory or immunosuppressive drugs. Here, we propose an approach using the latter strategy of hyperinflammation prevention (Flierl et al., 2007; Shaked et al., 2015). Immune cells communicate with each other by secreting peptides called cytokines and chemokines, which initially amplify the response and later restore homeostasis after the threat has receded. However, in hyperinflammation, production of cytokines is dysregulated, forming a 'cytokine storm' that can damage healthy tissue and overwhelm the host. COVID-19-associated hyperinflammation is characterized by profound elevation of many pro-inflammatory cytokines (Ruan et al., 2020; Huang et al., 2020; Mehta et al., 2020; McGonagle et al., 2020; Pedersen and Ho, 2020; Chen et al., 2020). Several immunosuppressive treatment approaches are being studied or used in clinical practice to ameliorate hyperinflammation-associated morbidity in patients who have already developed severe complications of COVID-19, including blocking specific cytokine signaling axes (e.g., IL-6, IL-1, or TNF-alpha) and broader immunosuppressive approaches (e.g. dexamethasone or baricitinib) (Investigators et al., 2021; RECOVERY Collaborative Group, 2021; Huet et al., 2020; Rosas et al., 2021; Salama et al., 2021; Stone et al., 2020). Dexamethasone is one of few drugs that have shown a mortality benefit in hospitalized patients with COVID-19 who require oxygen or mechanical ventilation, but glucocorticoids may not be beneficial - and may even be harmful - when given earlier in the disease course (RECOVERY Collaborative Group, 2021; Gianfrancesco et al., 2020). Conflicting data on tocilizumab and other inhibitors of IL-6 signaling in patients with severe COVID-19 suggests that immunosuppressive strategies may be of limited benefit once end-organ damage has developed (Investigators et al., 2021; Rosas et al., 2021; Salama et al., 2021; Stone et al., 2020; Lescure et al., 2021; Della-Torre et al., 2020), and highlight the importance of identifying drugs that can prevent dysregulated immune responses and immune-mediated damage. Inhibition of catecholamine signaling has emerged as a promising approach to prevent hyperinflammation and related mortality. The catecholamine pathway, beyond its role in neurotransmission and endocrine signaling, is involved in immunomodulation of innate and adaptive immune cells. In mice, catecholamine release coincides with hyperinflammation and enhances inflammatory injury by augmenting cytokine production via a self-amplifying process that requires alpha-1 adrenergic receptor (⍺1-AR) signaling (Staedtke et al., 2018). Catecholamine synthesis inhibition reduces cytokine responses and dramatically increases survival after inflammatory stimuli. The ⍺1-AR antagonist prazosin (at clinically prescribed dosages)—but not beta-adrenergic receptor (β-AR) antagonists—offers similar protection, providing in vivo evidence that this drug class can prevent cytokine storm (Staedtke et al., 2018). These preclinical findings provide a rationale for clinical studies that assess whether ⍺1-AR antagonists can prevent hyperinflammation and its sequelae associated with severe infection. To date, no controlled trials have studied whether ⍺1-AR antagonism improves clinical outcomes in patients with lower respiratory tract infection (pneumonia, acute respiratory distress syndrome, or COVID-19). Replicated retrospective cohort studies offer the highest level of evidence prior to prospective studies (Burns et al., 2011). We thus conducted a series of five retrospective cohort studies, spanning different populations, age groups, demographics, and countries. Our primary research question is whether ⍺1-AR antagonists (e.g., through its known modulation of hyperinflammation) can mitigate disease and prevent mortality. We operationalized this research question by testing the statistical hypothesis that patients exposed to ⍺1-AR antagonists, as compared to unexposed patients, have a reduced risk of adverse outcomes in lower respiratory tract infection. We considered two outcomes (mechanical ventilation, and mechanical ventilation followed by death) and three exposures (any ⍺1-AR antagonist, tamsulosin specifically, and doxazosin specifically). Tamsulosin is the most commonly used ⍺1-AR antagonist in the United States and demonstrates a 'uroselective' binding pattern (predominantly inhibits ⍺1A- and ⍺1D-AR subtypes). In contrast, doxazosin is a non-selective ⍺1-AR antagonist that demonstrates clinically significant inhibition of all three known ⍺1-AR subtypes, including antagonism on the ⍺1B-AR. This antagonism of ⍺1B-AR expressed in the peripheral vasculature is thought to mediate the antihypertensive effects of doxazosin and related drugs. Importantly, all three ⍺1-AR subtypes have been implicated in catecholamine signaling on immune cells, and signaling redundancy suggests a theoretical benefit for pan-⍺1-AR antagonists (e.g., doxazosin or prazosin) in preventing catecholamine signaling and hyperinflammation. Relatively few patients in our samples are prescribed doxazosin, so we are only able to study this drug in our much larger pneumonia cohorts (since there is insufficient statistical power to analyze the drug in ARD cohorts). Results The statistical analysis plan described in Materials and methods was fixed across all cohorts (to the extent possible) to limit researcher degrees of freedom and to emulate a prospective trial (Dickerman et al., 2019). We computed the probabilities of outcomes, relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), and p-values (p) using an unadjusted model as well as adjusted and matched modeling approaches that account for demographic and health-related confounders. We focus our discussion of reported results on the adjusted model comparing patients exposed to ⍺1-AR antagonists to unexposed patients. The adjusted model is an inverse propensity-weighted regression on a reduced sample satisfying propensity overlap; see Materials and methods for details. Participants We studied two cohorts of patients who were diagnostically coded with acute respiratory distress (ARD, a surrogate precursor state to ARDS) from two de-identified databases: the IBM MarketScan Research Database (which we refer to as MarketScan) and Optum's Clinformatics Data Mart Database (OptumInsight, Eden Prairie, MN), a commercial and Medicare Advantage claims database (which we refer to as Optum). We further studied three cohorts of patients with pneumonia from the MarketScan, Optum, and Swedish National Patient Register (Patientregistret, 2021) databases. ICD codes were used to identify the first instance of inpatient admission for each patient in each cohort (see Materials and methods for details). Our main analysis employed federated analyses on the ARD cohort using pooled MarketScan and Optum results. We limit the study to older men because of the widespread use in the United States of ⍺1-AR antagonists as a treatment for benign prostatic hyperplasia (BPH), a diagnosis clinically unrelated to the respiratory system or immune disorders. Focusing on men over the age of 45 facilitated examining a patient population in which a large portion of the exposed group faced similar risks of poor outcomes from respiratory conditions as the unexposed group, thus mitigating confounding by indication. We allowed a maximum age of 85 years to reflect the ongoing clinical trials investigating these interventions (Prazosin to Prevent COVID-19 (PREVENT-COVID Trial); https://clinicaltrials.gov/ct2/show/NCT04365257) . Figure 2 shows the CONSORT flow diagram for selecting patients from the four claims datasets (with slight modifications in the Swedish National Patient Register analysis to reflect different practices in that population); see Materials and methods for details. Focusing this study exclusively on older men limits the study's internal validity to older men, and it would take additional assumptions to justify external validity claims including excluded demographic groups (e.g., women and younger patients). We nevertheless note that there is an important literature on demographic fairness with regard to clinical studies (Holdcroft, 2007; McMurray, 1991). Figure 2 Download asset Open asset CONSORT flow diagram for four claims datasets where M represents MarketScan and O represents Optum; ARD represents acute respiratory distress. Note that patients are considered exposed to ⍺1-AR antagonists if they have a medication possession ratio ≥50 % in the prior year, and are considered unexposed if they have not taken any amount of ⍺1-AR antagonists in the prior year. ARD inpatient visits are not considered between 2017–2018 as ARD ICD-9 codes were being phased out while ICD-10 codes for ARD were not yet commonly used. Within a single dataset (MarketScan or Optum), there exists some patient overlap for the two cohort diagnoses (pneumonia and ARD): 5,100 patients in MarketScan and 2,778 in Optum. This diagram only presents four of the five cohorts studied; the fifth cohort (the Swedish National Patient Register) uses a different set of inclusion/exclusion criteria (see section Sweden National Patient Register for criteria description, and Figure 3—figure supplement 1 for information on dataset characteristics). Cohort-specific results We conducted the same statistical analysis in each of the five cohorts. In each cohort, we measured incidence and odds ratios (OR) for patients exposed to any ⍺1-AR antagonist, or tamsulosin specifically, as compared to unexposed patients, for each outcome. In the two largest pneumonia cohorts, we additionally consider doxazosin exposure. Our main analysis, described in the following section, involves pooling the results from individual cohorts. For the sake of completion, Figure 3—figure supplements 1–5 show results for each of the five individual cohorts. We generally found a positive RRR given any combination of exposure and outcome, and found that the adjusted model yielded ORs consistently less than 1. In all cohorts, the OR point estimates were robust to model changes, including other doubly robust approaches (Funk et al., 2011) such as causal forests (Wager and Athey, 2018), each of which yielded similar results to those presented here. Federated results We employed federated causal methods to enable combining patient-level results while maintaining patient privacy and data usage agreement constraints; see Materials and methods for details. We pooled results across MarketScan and Optum for each of the ARD and pneumonia cohorts (the Swedish dataset used a different outcome because ventilation was not reliably coded, so we did not pool these results). For ARD patients (n = 18,547) exposed to any ⍺1-AR antagonist, as compared to unexposed ARD patients, we found for ventilation and death: RRR = 34%, OR = 0.70, 95% CI (0.49–0.99), p = 0.021; for pneumonia patients (n = 338,674) we found: RRR = 8%, OR = 0.86, 95% CI (0.82–0.91), p < 0.001 (Figure 3). The treatment effect was similar across the outcome of ventilation only, exposure to tamsulosin only, and other analysis methods (unadjusted and matched), with ORs consistently less than 1. Doxazosin, a non-selective ⍺1-AR antagonist hypothesized to have a greater efficacy than other ⍺1-AR antagonists, demonstrated a twofold stronger effect than tamsulosin, which blocks fewer ⍺1-adrenoreceptors. Figure 3 with 5 supplements see all Download asset Open asset Cohorts across datasets (MarketScan and Optum) associated with the same disease (ARD in top row, pneumonia in bottom row) were pooled using federated causal learning techniques described in Materials and methods. In each quadrant, we show: (left) plotted odds ratios (OR) with confidence intervals (CI), and (right) values for relative risk reductions (RRR), OR, CI, p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models, including any ⍺1-AR antagonists or specifically tamsulosin or doxazosin. We only study exposure to doxazosin in the pneumonia cohorts since there is insufficient statistical power to analyze the drug in ARD cohorts. Results are shown for outcomes of mechanical ventilation (left column) and mechanical ventilation leading to death (right column). In general, ⍺1-AR antagonists were associated with reducing risk of adverse events across exposures, outcomes, and modeling approaches. Each federated analysis yielded an OR point estimate below 1. Discussion The results of this retrospective clinical study extend preclinical findings to support the hypothesis that ⍺1-AR antagonists may reduce morbidity and mortality in patients at risk of hyperinflammation (Konig et al., 2020). A challenge resolved by our retrospective analysis is that patients exposed to ⍺1-AR antagonists may differ from unexposed patients in ways that might also affect their outcomes from respiratory diseases. Individuals are often prescribed ⍺1-AR antagonists for chronic diseases, and we consider only patients who used the medications for at least 6 months in the year prior (i.e., for a medical possession ratio of at least 50%) to the index hospital admission for pneumonia or ARDS where we measure patient outcomes. This makes it less likely that the unmeasured severity of respiratory illness at time of admission for our patient population differs substantially between exposed and unexposed groups; however, we cannot rule out such differences. We refer to confounders as factors that vary between patients exposed and unexposed to ⍺1-AR antagonists and also relate to patient outcomes. One important confounder is age, which in turn is related to other health conditions. For this reason, our research design (outlined in more detail in the Materials and methods section) includes several approaches to adjusting for observed demographic and health-related confounders. Given that older men were generally using ⍺1-AR antagonists for reasons unrelated to ARDS, it was feasible to balance the exposed and unexposed groups on a large set of prognostically important covariates, reducing concerns that differences in outcomes might be due to confounding. Nonetheless, there is an urgent need for randomized prospective trials to further test this hypothesis in patients with COVID-19 and other lower respiratory tract infections. In such trials, early administration of ⍺1-AR antagonists prior to development of severe symptoms is preferred because the goal is to prevent, rather than treat, hyperinflammation-related damage. While this study could only examine medications received outside the inpatient environment due to dataset limitations, future work should attempt to address whether continued use of ⍺1-AR antagonists by hospitalized patients is important for preventing severe symptoms. Additionally, future work should consider whether the patients' respiratory illness is community- or hospital-acquired (Whittle et al., 1997). ⍺1-AR antagonists with various receptor subtype characteristics have been used to treat millions of patients with BPH, hypertension, and other disorders. This history supports their safety profile (Yasukawa et al., 2001), although caution is warranted in using any medication for the first time in a new disease such as COVID-19. Given the poorly understood relationship between COVID-19 severity and hypertension (Konig et al., 2020), it is important to note that nonselective (⍺1A=⍺1B=⍺1D) ⍺1-ARs (e.g., prazosin and doxazosin) are often primarily used to reduce blood pressure, whereas subtype-selective (⍺1A=⍺1D>⍺1B) ⍺1-ARs (e.g. tamsulosin) have fewer hemodynamic effects. However, given the known redundancy of catecholamine signaling (Grisanti et al., 2011), we expect ⍺1-AR antagonists that inhibit all three ⍺1-AR subtypes (e.g. prazosin and doxazosin) to be more efficacious in the prevention of hyperinflammation. ⍺1-AR antagonists are inexpensive and administered orally, enabling widespread use if prospective trials support their efficacy and safety. Beyond COVID-19 and other lower respiratory tract infections, ⍺1-AR antagonists may also reduce hyperinflammation and their sequelae in adoptive cell therapy ('cytokine release syndrome') and autoimmune rheumatic disease. Materials and methods Study definitions Request a detailed protocol Patients were considered exposed if they were users of ⍺1-AR antagonists (doxazosin, alfuzosin, prazosin, silodosin, terazosin, or tamsulosin). ⍺1-AR antagonist usage is defined by having a medication possession ratio ≥50 % (i.e., a minimum prescribed supply covering six of the last 12 months) for a qualifying drug in the year prior to the first inpatient admission date for ARD or pneumonia. In additional analyses, we used the same definition to consider exposed patients who were taking tamsulosin or doxazosin. Patients are considered unexposed if they have never been prescribed the relevant drug class or drug within the year prior to the admission date. We exclude patients having a medical possession ratio greater than 0% and less than 50% of the relevant drug class or drug (equivalently, we exclude patients who do use ⍺1-AR antagonists, but for less than 6 months of the prior year). While all patients from the MarketScan, Optum, and Swedish National Patient Register (Patientregistret, 2021) databases were restricted to a 45–85 year age range, the MarketScan database only includes patients up to age 65 due to Medicare exclusion; we adjust for age in our analyses. The data used in this study do not include ⍺1-AR antagonist usage during the ARD or pneumonia admission. The first instances of ARD or pneumonia inpatient admissions for each patient were identified using ICD codes. Because the MarketScan database only includes United States patient data through fiscal year 2016, we only used ICD-9 codes to identify diagnoses from this database. The Optum database includes United States patient data from fiscal year through for use of both ICD-9 and ICD-10 codes to identify The Swedish data only ICD-10 codes. To identify we used the ICD-9 and the ICD-10 of that the latter was only from the Optum ARD cohort only patients identified by their first inpatient admission for an ARD diagnosis from to or from to To identify pneumonia, we used the for Research and pneumonia for both ICD-9 and ICD-10 codes. We considered the following age, fiscal year, with inpatient admissions in the prior year, visits in the prior year, as an inpatient in the prior year, with inpatient admissions in the prior two and identified from in the prior hypertension, acute failure, chronic and All were defined to ICD by the Data 2020). The confounders relevant to prior inpatient and visits were all confounders were and to The outcomes of in this study mechanical ventilation and death. ventilation was as an outcome for patients a to one of the following ICD-9 codes or ICD-10 codes death outcomes, we note a in death databases. the MarketScan we identified death outcomes by in claims the Optum death data were so we identified death outcomes by whether a of death was within 1 of the relevant ARD or pneumonia inpatient For both our analyses studied the outcome of death defined with mechanical ventilation during inpatient The Swedish National Patient were in a similar to the MarketScan database. 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