| Literature DB >> 35721837 |
Haijuan Yao1, Hongyu Li1, Zhuang Ma1, Yanyan Wu1, Yufu Tang1, Hao Meng1, Hao Yu1, Chengfei Peng1, Yue Teng1, Quanyu Zhang1, Tianyi Zhu1, Haitao Zhao1, Guiyang Chu1, Zhenhua Tong1, Lu Liu2, Hui Lu2, Xingshun Qi3.
Abstract
Background: Coronavirus disease 2019 (COVID-19) has triggered a global public health crisis. Proton pump inhibitors (PPIs) are one of the most commonly prescribed drugs. However, the effect of PPIs on the clinical outcomes of COVID-19 patients remains unclear.Entities:
Keywords: coronavirus disease 2019; proton pump inhibitors; severe acute respiratory syndrome coronavirus 2
Year: 2022 PMID: 35721837 PMCID: PMC9201367 DOI: 10.1177/17562848221104365
Source DB: PubMed Journal: Therap Adv Gastroenterol ISSN: 1756-283X Impact factor: 4.802
Differences between PPIs and non-PPIs groups.
| Variables | Overall | PPIs group | Non-PPIs group | ||||
|---|---|---|---|---|---|---|---|
| No. pts | Median (range) mean ± SD or frequency (percentage) | No. pts | Median (range) mean ± SD or frequency (percentage) | No. pts | Median (range) mean ± SD or frequency (percentage) | ||
| Age (years) | 3024 | 60.00 (11.00–100.00) | 694 | 64.00 (17.00–93.00) | 2330 | 58.00 (11.00–100.00) |
|
| Male (%) | 3024 | 1538 (50.86%) | 694 | 299 (43.08%) | 2330 | 1239 (53.18%) |
|
| Severity of COVID-19 | |||||||
| Severe/critical (%) | 3024 | 837 (27.68%) | 694 | 289 (41.64%) | 2330 | 548 (23.52%) |
|
| Clinical symptoms | |||||||
| Fever (%) | 3024 | 2216 (73.28%) | 694 | 515 (74.21%) | 2330 | 1701 (73.00%) | 0.529 |
| Cough (%) | 3024 | 2104 (69.58%) | 694 | 517 (74.50%) | 2330 | 1587 (68.10%) |
|
| Shortness of breath (%) | 3024 | 1000 (33.07%) | 694 | 285 (41.07%) | 2330 | 715 (30.69%) |
|
| Fatigue (%) | 3024 | 1666 (55.09%) | 694 | 404 (58.21%) | 2330 | 1262 (54.16%) | 0.060 |
| Abdominal distention (%) | 3024 | 177 (5.90%) | 694 | 114 (16.40%) | 2330 | 63 (2.70%) |
|
| Nausea/vomiting (%) | 3024 | 132 (4.40%) | 694 | 60 (8.60%) | 2330 | 73 (3.10%) |
|
| Heartburn/regurgitation (%) | 3024 | 69 (2.30%) | 694 | 61 (8.80%) | 2330 | 8 (0.30%) |
|
| Abdominal pain (%) | 3024 | 64 (2.12%) | 694 | 44 (6.30%) | 2330 | 20 (0.90%) |
|
| Hematochezia/melena (%) | 3024 | 55 (1.80%) | 694 | 31 (4.50%) | 2330 | 24 (1.00%) |
|
| Comorbidities | |||||||
| Hypertension (%) | 3024 | 532 (17.60%) | 694 | 118 (17.00%) | 2330 | 414 (17.80%) | 0.642 |
| Diabetes (%) | 3024 | 434 (14.40%) | 694 | 126 (18.20%) | 2330 | 308 (13.20%) |
|
| Cardiovascular diseases (%) | 3024 | 268 (8.90%) | 694 | 97 (14.00%) | 2330 | 171 (7.30%) |
|
| Malignancy (%) | 3024 | 77 (2.50%) | 694 | 20 (2.90%) | 2330 | 57 (2.40%) | 0.523 |
| Chronic kidney diseases (%) | 3024 | 50 (1.70%) | 694 | 18 (2.60%) | 2330 | 32 (1.40%) |
|
| Laboratory tests | |||||||
| TBIL (µmol/L) | 2225 | 9.70 (2.00–124.00) | 472 | 9.70 (3.00–124.00) | 1753 | 9.70 (2.00–112.00) | 0.982 |
| INR | 1924 | 1.07 (0.00–3.59) | 404 | 1.07 (0.00–3.59) | 1520 | 1.07 (0.60–1.97) | 0.479 |
| Albumin (g/L) | 2224 | 38.50 (16.00–59.00) | 472 | 37.20 (16.00–47.00) | 1752 | 38.80 (22.00–59.00) |
|
| CRP (mg/L) | 2226 | 1.94 (0.00–257.77) | 480 | 3.71 (0.00–257.77) | 1746 | 1.69 (0.00–234.17) |
|
| Interleukin 6 (pg/mL) | 1004 | 1.59 (0.00–3392.00) | 201 | 2.44 (0.00–3392.00) | 803 | 0.00 (0.00–360.30) |
|
| WBC (109/L) | 2387 | 5.70 (0.00–49.30) | 525 | 5.90 (2.30–34.10) | 1862 | 5.70 (0.00–49.30) |
|
| Other medications | |||||||
| Antivirals (%) | 3024 | 1472 (48.7%) | 694 | 401 (57.80%) | 2330 | 1071 (46.0%) |
|
| Antibiotics (%) | 3024 | 991 (32.80%) | 694 | 339 (48.80%) | 2330 | 652 (28.0%) |
|
| Corticosteroids (%) | 3024 | 443 (13.60%) | 694 | 235 (33.90%) | 2330 | 208 (8.90%) |
|
| Traditional Chinese medicines (%) | 3024 | 2377 (78.60%) | 694 | 521 (75.10%) | 2330 | 1856 (79.70%) |
|
COVID-19, coronavirus disease 2019; CRP, C-reactive protein; INR, international normalized ratio; PPIs, proton pump inhibitors; Pts, patients; SD, standard deviation; TBIL, total bilirubin; WBC, white blood cell.
Figure 1.Forest plots showing the major results of univariate and multivariate analyses regarding the association between PPIs use and the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.
Figure 2.Forest plots showing the major results of univariate and multivariate analyses regarding the association between routes of PPIs and the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.
Figure 3.Forest plots showing the major results of univariate and multivariate analyses regarding the association between types of PPIs and the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.
Figure 4.Forest plots showing the major results of univariate and multivariate analyses regarding the association between dosage of PPIs and the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.
Figure 5.Forest plots showing the major results of univariate and multivariate analyses regarding the association between duration of PPIs and the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.
Figure 6.Potential mechanisms regarding the impact of PPIs on the outcomes of COVID-19 patients.
COVID-19, coronavirus disease 2019; PPIs, proton pump inhibitors.