Figure 3. 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.
Figure 3—figure supplement 1. Patients from the Swedish National Patient Register with pneumonia.
(i) Distributions of sample proportion estimates for comorbidities identified from healthcare encounters in the year prior to a patient’s first pneumonia inpatient admission: cardiovascular disease (CVD), chronic obstructive pulmonary disorder (COPD), diabetes mellitus (DM), and hypertension (HTN). Data distributions for additional covariates within the area of propensity score overlap: age and year. (ii) Covariate balance plots before (cyan) and after (red) matching. (iii) Assessing the outcome of progression to death: (left) number and proportion of patients taking indicated medications who experience the outcome, (right) relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models. Here, the exposed group includes any patients who have filled at least one prescription for an ⍺1-AR antagonist in the prior year; the unexposed group includes any patients who have never filled a prescription for an ⍺1-AR antagonist in the year prior to hospitalization.
Figure 3—figure supplement 2. Patients from MarketScan Research Database with acute respiratory distress.
(i) Distributions of sample proportion estimates for comorbidities identified from healthcare encounters in the year prior to a patient’s first ARD inpatient admission: diabetes mellitus (DM), hypertension (HTN), heart failure (HF), ischemic heart disease (IHD), acute myocardial infarction (AMI), chronic obstructive pulmonary disorder (COPD), and cancer (CAN). Data distributions for additional covariates within the area of propensity score overlap: age, total weeks with inpatient admissions in the prior year (STAYS12), total outpatient visits in the prior year (VISIT12), total prior days as an inpatient in the prior year (DAYS12), total weeks with prior inpatient stays in the previous two months (STAYS2), and fiscal year (YEAR). (ii) Covariate balance plots before (cyan) and after (red) inverse propensity weighting. (iii) For the outcome of progressing to ventilation and death: (left) number and proportion of patients taking indicated medications who experienced the outcome, (right) relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models, including any ⍺1-AR antagonists and specifically tamsulosin. Likewise for the secondary outcome of requiring ventilation. In general, ⍺1-AR antagonists are associated with reducing risk of adverse events across treatments, outcomes, and modeling approaches. The raw outcome count and corresponding RRR for tamsulosin in the ventilation and death outcome are redacted per MarketScan policy for displaying small counts.
Figure 3—figure supplement 3. Patients from Optum with acute respiratory distress.
(i) Distributions of sample proportion estimates for comorbidities identified from healthcare encounters in the year prior to a patient’s first ARD inpatient admission: diabetes mellitus (DM), hypertension (HTN), heart failure (HF), ischemic heart disease (IHD), acute myocardial infarction (AMI), chronic obstructive pulmonary disorder (COPD), and cancer (CAN). Data distributions for additional covariates within the area of propensity score overlap: age, total weeks with inpatient admissions in the prior year (STAYS12), total outpatient visits in the prior year (VISIT12), total prior days as an inpatient in the prior year (DAYS12), total weeks with prior inpatient stays in the previous two months (STAYS2), and fiscal year (YEAR). (ii) Covariate balance plots before (cyan) and after (red) inverse propensity weighting. (iii) For the outcome of progressing to ventilation and death: (left) number and proportion of patients taking indicated medications who experienced the outcome, (right) relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models, including any ⍺1-AR antagonists and specifically tamsulosin. Likewise for the secondary outcome of requiring ventilation. In general, ⍺1-AR antagonists are associated with reducing risk of adverse events across treatments, outcomes, and modeling approaches.
Figure 3—figure supplement 4. Patients from MarketScan Research Database with pneumonia.
(i) Distributions of sample proportion estimates for comorbidities identified from healthcare encounters in the year prior to a patient’s first ARD inpatient admission: diabetes mellitus (DM), hypertension (HTN), heart failure (HF), ischemic heart disease (IHD), acute myocardial infarction (AMI), chronic obstructive pulmonary disorder (COPD), and cancer (CAN). Data distributions for additional covariates within the area of propensity score overlap: age, total weeks with inpatient admissions in the prior year (STAYS12), total outpatient visits in the prior year (VISIT12), total prior days as an inpatient in the prior year (DAYS12), total weeks with prior inpatient stays in the previous two months (STAYS2), and fiscal year (YEAR). (ii) Covariate balance plots before (cyan) and after (red) inverse propensity weighting. (iii) For the outcome of progressing to ventilation and death: (left) number and proportion of patients taking indicated medications who experienced the outcome, (right) relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models, including any ⍺1-AR antagonists and specifically tamsulosin. Likewise for the secondary outcome of requiring ventilation. In general, ⍺1-AR antagonists are associated with reducing risk of adverse events across treatments, outcomes, and modeling approaches.
Figure 3—figure supplement 5. Patients from Optum with pneumonia.
(i) Distributions of sample proportion estimates for comorbidities identified from healthcare encounters in the year prior to a patient’s first ARD inpatient admission: diabetes mellitus (DM), hypertension (HTN), heart failure (HF), ischemic heart disease (IHD), acute myocardial infarction (AMI), chronic obstructive pulmonary disorder (COPD), and cancer (CAN). Data distributions for additional covariates within the area of propensity score overlap: age, total weeks with inpatient admissions in the prior year (STAYS12), total outpatient visits in the prior year (VISIT12), total prior days as an inpatient in the prior year (DAYS12), total weeks with prior inpatient stays in the previous two months (STAYS2), and fiscal year (YEAR). (ii) Covariate balance plots before (cyan) and after (red) inverse propensity weighting. (iii) For the outcome of progressing to ventilation and death: (left) number and proportion of patients taking indicated medications who experienced the outcome, (right) relative risk reductions (RRR), odds ratios (OR), confidence intervals (CI), p-values (p), and sample sizes (n) for unadjusted, adjusted, and matched models, including any ⍺1-AR antagonists and specifically tamsulosin. Likewise for the secondary outcome of requiring ventilation. In general, ⍺1-AR antagonists are associated with reducing risk of adverse events across treatments, outcomes, and modeling approaches.