Alpha-1 adrenergic receptor antagonists to prevent hyperinflammation and death from lower respiratory tract infection
- Institute for Computational & Mathematical Engineering, Stanford University, United States
- Department of Biomedical Engineering, Institute for Computational Medicine, The Johns Hopkins University, United States
- Management Science & Engineering, Stanford University, United States
- Department of Statistics, Stanford University, United States
- The Johns Hopkins University School of Medicine, United States
- Department of Neurosurgery and Neurology, The Johns Hopkins University School of Medicine, United States
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Sweden, Sweden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health at Johns Hopkins University, United States
- Ludwig Center, Lustgarten Laboratory, and the Howard Hughes Medical Institute at The Johns Hopkins Kimmel Cancer Center, United States
- Division of Rheumatology, Department of Medicine, The Johns Hopkins University School of Medicine, United States
- Janelia Research Campus, Howard Hughes Medical Institute, United States
- Stanford Graduate School of Business, Stanford University, United States
Figures
Figure 1
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.
Figure 2
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…
Figure 3 with 5 supplements
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 …
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 …
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), …
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), …
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), …
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), …
Figure 4 with 1 supplement
We plot the proportion of exposed and unexposed patients having any inpatient admissions a certain number of months prior to the first ARD or pneumonia admission date, and present corresponding confidence intervals.
Both exposed and unexposed groups had similar trends of declining health leading up to the target admission date, where health decline is defined as having more frequent inpatient visits.
Figure 4—figure supplement 1
We plot the average residuals of inpatient visits after controlling for age effects (as well as age squared and age cubed) for exposed and unexposed patients having inpatient admissions a certain number of months prior to the first ARD or pneumonia admission date, and present corresponding confidence intervals.
Both exposed and unexposed groups had similar trends of declining health leading up to the target admission date, where health decline is defined as having more frequent inpatient visits after …
Additional files
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Source code 1
R Markdown code to run retrospective analysis and generate figures.
- https://cdn.elifesciences.org/articles/61700/elife-61700-supp1-v2.zip
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Source code 2
R script to generate cohorts and perform propensity matching.
- https://cdn.elifesciences.org/articles/61700/elife-61700-supp2-v2.zip
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Transparent reporting form
- https://cdn.elifesciences.org/articles/61700/elife-61700-transrepform1-v2.docx
