Dear Editors,
We read with interest the study by Lee et al.1 The authors conducted a propensity score (PS)-matched analysis of a national South Korean cohort evaluating the association between PPI use and SARS-CoV-2 susceptibility (primary outcome) and COVID-19 clinical severity (secondary outcome). Between January and May 2020, 4,785 patients tested positive for SARS-CoV-2 (3.6% positivity); 267 current PPI-users and 148 former PPI-users were 1:1 PS-matched to non-users for the secondary outcomes. The authors reported current PPI use vs non-use was associated with a statistically significant increased risk of the composite endpoints: 1) oxygen therapy, intensive care unit (ICU) admission, mechanical ventilation use or death (composite odds ratio 1.63, 95% confidence interval 1.03–2.53); and 2) ICU admission, mechanical ventilation, or death (composite OR 1.79, 95% CI 1.30–3.10).
We assembled a national retrospective cohort of United States (US) veterans who tested positive for SARS-CoV-2 (index date). Current outpatient PPI use up to and including the index date (primary exposure) was compared to non-use, defined as no PPI prescription fill in the 365 days pre-index date (Supplemental Figure 1). The primary composite outcome was mechanical ventilation use or death within 60 days; the secondary composite outcome also included hospital or ICU admission. In contrast to PS-matching, PS-weighting allowed inclusion of all patients. Weighted logistic regression models evaluated severe COVID-19 outcomes between current PPI users vs non-users.
Our analytic cohort included 97,674 veterans with SARS-CoV-2 testing, of whom 14,958 (15.3%) tested positive (6,262 [41.9%] current PPI-users, 8,696 [58.1%] non-users). In the unweighted cohort, current PPI-users were older, more often current or former smokers, and had more comorbidities than non-users. After weighting, all covariates were well-balanced (Table 1, Supplemental Figure 2). In the unweighted cohort, we observed higher odds of the primary (9.3% vs 7.5%; OR 1.27, 95% CI 1.13–1.43) and secondary (25.8% vs 21.4%; OR 1.27, 95% CI 1.18–1.37) composite outcomes among PPI users vs non-users (Figure 1, Supplemental Table 1). After PS-weighting, PPI use vs non-use did not affect the primary (8.2% vs 8.0%; OR 1.03, 95% CI 0.91–1.16) or secondary (23.4% vs 22.9%;OR 1.03, 95% CI 0.95–1.12) composite outcomes. There were no significant interactions between age and PPI use on composite or individual outcomes.
Table 1.
Characteristics of veterans with positive SARS-CoV-2 testing, stratified by current PPI user vs. PPI non-user
| UNWEIGHTED COHORT of veterans with positive SARS-CoV-2 test | WEIGHTED COHORT of veterans with positive SARS-CoV-2 test | ||||
|---|---|---|---|---|---|
|
| |||||
| COVARIATES | PPI non-users (N=8,696) |
Current PPI users (N=6,262) |
PPI non-users (N=8,696) | Current PPI users (N=6,262) |
SMD* |
|
|
|||||
| VHA Facility ^ | ^ | ^ | ^ | ^ | 0.070 |
| Age, mean years (SD) | 60.46 (15.77) | 64.37 (13.42) | 61.94 (15.14) | 62.15 (14.52) | 0.014 |
| Male sex, n (%) | 7,382 (84.9) | 5,578 (89.1) | 7,529 (86.6) | 5,441 (86.9) | 0.009 |
| Race/Ethnicity, n (%) | 0.033 | ||||
| Non-Hispanic White | 4,437 (51.0) | 3,885 (62.0) | 4,757 (54.7) | 3,527 (56.3) | |
| Non-Hispanic Black | 2,243 (25.8) | 1,315 (21.0) | 2,125 (24.4) | 1,477 (23.6) | |
| Non-Hispanic other or unknown | 794 (9.1) | 515 (8.2) | 764 (8.8) | 540 (8.6) | |
| Hispanic | 1,222 (14.1) | 547 (8.7) | 1,049 (12.1) | 717 (11.5) | |
| Days from Jan 1, 2020 to index date, mean (SD)# | 282 (82.8) | 289 (78.7) | 285 (81.5) | 285 (81.2) | 0.005 |
| Smoking Status, n (%) | 0.067 | ||||
| Current Smoker | 1,030 (11.8) | 754 (12.0) | 1,047 (12.0) | 762 (12.2) | |
| Former Smoker | 3,441 (39.6) | 3,085 (49.3) | 3,773 (43.4) | 2,780 (44.4) | |
| Never Smoker | 3,430 (39.4) | 2,180 (34.8) | 3,260 (37.5) | 2,376 (37.9) | |
| Unknown | 795 (9.1) | 243 (3.9) | 616 (7.1) | 344 (5.5) | |
| Comorbidities, n (%) | |||||
| Asthma | 629 (7.2) | 685 (10.9) | 747 (8.6) | 571 (9.1) | 0.018 |
| Coronary Artery Disease | 1,645 (18.9) | 1,911 (30.5) | 2,034 (23.4) | 1,500 (24.0) | 0.013 |
| Cancer | 1,770 (20.4) | 1,750 (27.9) | 2,020 (23.2) | 1,508 (24.1) | 0.020 |
| Cardiomyopathy | 255 (2.9) | 279 (4.5) | 309 (3.5) | 228 (3.6) | 0.005 |
| Charlson Comorbidity Index, mean (SD) | 1.82 (2.22) | 2.55 (2.54) | 2.12 (2.38) | 2.17 (2.40) | 0.022 |
| Congestive Heart Failure | 621 (7.1) | 746 (11.9) | 791 (9.1) | 582 (9.3) | 0.007 |
| Chronic Lung Disease | 2,652 (30.5) | 2,757 (44.0) | 3,129 (36.0) | 2,315 (37.0) | 0.020 |
| Chronic Neuromuscular Disease | 394 (4.5) | 323 (5.2) | 420 (4.8) | 308 (4.9) | 0.004 |
| Chronic Kidney Disease | 1,161 (13.4) | 1,219 (19.5) | 1,390 (16.0) | 1,026 (16.4) | 0.011 |
| Chronic Kidney Failure | 153 (1.8) | 151 (2.4) | 180 (2.1) | 130 (2.1) | 0.001 |
| Chronic Obstructive Pulmonary Disease | 1,316 (15.1) | 1,646 (26.3) | 1,694 (19.5) | 1,270 (20.3) | 0.020 |
| Cerebrovascular Disease | 2,800 (32.2) | 2,966 (47.4) | 3,320 (38.2) | 2,453 (39.2) | 0.021 |
| Diabetes | 3,070 (35.3) | 2,815 (45.0) | 3,415 (39.3) | 2,469 (39.4) | 0.003 |
| Drug Dependency | 379 (4.4) | 345 (5.5) | 416 (4.8) | 311 (5.0) | 0.008 |
| Emphysema | 126 (1.4) | 170 (2.7) | 171 (2.0) | 126 (2.0) | 0.003 |
| Heart Disease | 2,093 (24.1) | 2,281 (36.4) | 2,533 (29.1) | 1,846 (29.5) | 0.008 |
| Heart Failure (non-congestive) | 790 (9.1) | 898 (14.3) | 988 (11.4) | 709 (11.3) | 0.001 |
| H. pylori positive | 1,841 (21.2) | 1,138 (18.2) | 1,746 (20.1) | 1,232 (19.7) | 0.022 |
| HIV | 120 (1.4) | 51 (0.8) | 105 (1.2) | 78 (1.2) | 0.003 |
| Hypertension | 5,075 (58.4) | 4,620 (73.8) | 5,616 (64.6) | 4,149 (66.3) | 0.035 |
| Lower Respiratory Infection | 1,010 (11.6) | 855 (13.7) | 1,076 (12.4) | 796 (12.7) | 0.010 |
| Obstructive Sleep Apnea | 2,884 (33.2) | 2,773 (44.3) | 3,254 (37.4) | 2,454 (39.2) | 0.037 |
| Medications, n (%) | |||||
| ACE Inhibitors | 2,233 (25.7) | 2,152 (34.4) | 2,549 (29.3) | 1,887 (30.1) | 0.018 |
| ARBs | 1,170 (13.5) | 1,239 (19.8) | 1,378 (15.8) | 1,036 (16.6) | 0.019 |
| H2RAs | 626 (7.2) | 423 (6.8) | 638 (7.3) | 459 (7.3) | 0.001 |
| NSAIDs | 5,359 (61.6) | 4,745 (75.8) | 5,812 (66.8) | 4,358 (69.6) | 0.059 |
| Statins | 4,176 (48.0) | 4,249 (67.9) | 4,832 (55.6) | 3,656 (58.4) | 0.057 |
Abbreviations: Angiotensin converting enzyme inhibitors (ACE inhibitors); Angiotensin II receptor blockers (ARBs); Helicobacter pylori (H. pylori); Histamine-2 receptor antagonists (H2RAs); Non-steroidal anti-inflammatory agents (NSAIDs); Proton pump inhibitor (PPI); standard deviation (SD); standardized mean difference (SMD); Veterans Health Administration (VHA)
Only SMDs for the weighted cohort are provided in this table. Please refer to Supplemental Figure 1 for the SMD plots for both the unweighted and weighted cohorts.
All of the 127 VHA facilities were included as covariates in this analysis; however, the proportion of patients at each station for each group is not listed here due to space considerations. The SMD between PPI users and non-users in the unweighted cohort was 0.36, with balance achieved after weighting (SMD 0.07).
This variable represents the days from January 1, 2020 to the index date of SARS-CoV-2 testing to account for temporal differences
Figure 1. Forest plot of primary and secondary COVID-19 outcomes within 60 days of the index date, weighted and unweighted cohorts.
In the unweighted cohort, current outpatient PPI use compared to PPI non-use was associated with increased odds of severe COVID-19 outcomes, defined based on composite (primary: death or mechanical ventilation; secondary: death, mechanical ventilation, intensive care unit admission, or hospitalization) and individual component outcomes. Each of these associations were statistically nonsignificant after more fully accounting for covariates in the propensity-weighted cohort, including date of SARS-CoV-2 testing and VHA facility location. Of note, there was no significant interaction between age group and PPI use on these outcomes.
Disparate results are reported in studies analyzing COVID-19-related outcomes among PPI users vs non-users2–6 due to varied PPI exposure definitions; COVID-19 severity outcomes; covariate assessment and adjustment; study design and populations; contemporaneous treatments; and healthcare infrastructure. In our unweighted analysis, we also observed an association between PPI use and severe COVID-19 outcomes (separately and as composites) which was not demonstrated in the PS-weighted cohort, suggesting that the associations in previous studies might reflect incomplete covariate adjustment.7 Indeed, the low E-values (all <2.0) for the weak associations between PPI exposure and COVID-19 severity outcomes (albeit variably defined), which have been demonstrated in previous studies, supports incomplete covariate adjustment and residual confounding (see Supplemental Material).8 Similar to the Lee et al. study, prior studies also include data from the first months of the COVID-19 pandemic, when management and available treatments were rapidly evolving. Lee et al.’s outcome definition also included oxygen therapy. Oxygen administration may not correlate with COVID-19 severity and may be considered routine protocol, especially early in the pandemic. Similarly, ICU admission may be influenced by health system factors, such as bed availability. Our study was designed to avoid immortal time, lag time, and protopathic biases, which have been present in some PPI studies (see Supplemental Material).9 We further accounted for the pandemic timeframe and clinical management evolution by considering COVID-19 prevalence and geography.
In conclusion, with respect to COVID-19, our robust PS-weighted analysis provides patients and providers with further evidence for PPI safety.
Supplementary Material
Acknowledgments
Funding Information: American Gastroenterological Association 2019 Research Scholar Award (SCS); US Dept of Veterans Affairs ICX002027A01 (SCS), Million Veteran Program Core MVP000 (KC); National Institute of Health K23HL143161A01 (ST)
Footnotes
Disclosures/conflicts of interest: The authors report no conflicts of interest that are relevant to this article. Dr. Shah is an ad hoc consultant for Phathom Pharmaceuticals.
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