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Abstract
Rationale: The impact of coronavirus disease (COVID-19) on patients with interstitial lung disease (ILD) has not been established.
Objectives: To assess outcomes in patients with ILD hospitalized for COVID-19 versus those without ILD in a contemporaneous age-, sex-, and comorbidity-matched population.
Methods: An international multicenter audit of patients with a prior diagnosis of ILD admitted to the hospital with COVID-19 between March 1 and May 1, 2020, was undertaken and compared with patients without ILD, obtained from the ISARIC4C (International Severe Acute Respiratory and Emerging Infection Consortium Coronavirus Clinical Characterisation Consortium) cohort, admitted with COVID-19 over the same period. The primary outcome was survival. Secondary analysis distinguished idiopathic pulmonary fibrosis from non–idiopathic pulmonary fibrosis ILD and used lung function to determine the greatest risks of death.
Measurements and Main Results: Data from 349 patients with ILD across Europe were included, of whom 161 were admitted to the hospital with laboratory or clinical evidence of COVID-19 and eligible for propensity score matching. Overall mortality was 49% (79/161) in patients with ILD with COVID-19. After matching, patients with ILD with COVID-19 had significantly poorer survival (hazard ratio [HR], 1.60; confidence interval, 1.17–2.18; P = 0.003) than age-, sex-, and comorbidity-matched controls without ILD. Patients with an FVC of <80% had an increased risk of death versus patients with FVC ≥80% (HR, 1.72; 1.05–2.83). Furthermore, obese patients with ILD had an elevated risk of death (HR, 2.27; 1.39−3.71).
Conclusions: Patients with ILD are at increased risk of death from COVID-19, particularly those with poor lung function and obesity. Stringent precautions should be taken to avoid COVID-19 in patients with ILD.
The outcome of patients with interstitial lung disease (ILD) admitted to the hospital with coronavirus disease (COVID-19) infection and the risk factors associated with severe COVID-19 are not known.
The study demonstrates that patients with ILD are at higher risk of death after COVID-19 than matched patients without ILD, and the risk factors for death include male sex, an FVC <80% predicted, and obesity.
Interstitial lung diseases (ILDs) represent a group of fibroinflammatory diseases affecting the alveolar interstitium of the lung. ILDs are characterized by alveolar damage and interstitial thickening and, if left untreated, lead to remorseless progression of breathlessness, cough, and, ultimately, death from respiratory failure. The prevalence of ILDs in Europe is just under 1 per 1,000 people, with an annual incidence of approximately 20 per 100,000 people (1). The commonest ILD is sarcoidosis, but one of the most severe ILDs is idiopathic pulmonary fibrosis (IPF), a high incidence of which is found in the UK (12 per 100,000) (2, 3). IPF tends to affect older people, with a mean age at diagnosis of 72 years. Men are more often affected than women. Furthermore, IPF is associated with diabetes mellitus type 2, hypertension, and ischemic heart disease (4, 5). People suffering from other ILDs (non-IPF ILD) tend to be younger, with a higher proportion of female sufferers, and often receive immunosuppressive therapy.
All ILDs, most notably IPF, are characterized by acute exacerbations, which have a particularly high mortality rate ranging from 35% to 70% (6). The precise cause of acute exacerbations is unknown, but they have been associated with thoracic surgical procedures and viral infections (6). Furthermore, an acute exacerbation is associated with the development of acute respiratory distress syndrome (ARDS), which carries a high mortality and morbidity. However, there is no consensus on treatment of ARDS in this group of patients, given that mechanical ventilation is the cornerstone of supportive therapy in patients without ILD (7).
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to coronavirus disease (COVID-19), characterized by a severe viral pneumonia and ARDS in approximately 20% of patients admitted to the hospital (8). Patients most at risk of severe COVID-19 include elderly males with comorbidities including diabetes mellitus type 2, hypertension, and ischemic heart disease (9, 10), which are shared by patients with ILD, most notably IPF (5). To understand the risk to patients with ILD hospitalized with COVID-19 and therefore the potential benefits of self-isolation during the pandemic, we undertook an international multicenter analysis of patients admitted to the hospital between March 1 and May 1, 2020, with COVID-19. We compared outcomes of patients with ILD with age-, sex-, and comorbidity-matched controls admitted with COVID-19 but without ILD, from a prospective UK cohort, the ISARIC4C CCP-UK (International Severe Acute Respiratory and Emerging Infection Consortium Coronavirus Clinical Characterisation Protocol UK).
Some of the results of these studies have been previously reported in the form of a preprint (https://doi.org/10.1101/2020.07.15.20152967).
Physicians admitting patients with ILD throughout Europe were contacted through research networks, e-mail contacts, and social media and asked to identify all patients with a preexisting diagnosis of ILD admitted to the hospital between March 1 and May 1, 2020, during the first peak of the COVID-19 pandemic. Participating centers included tertiary ILD centers from Denmark, Germany, Italy, the Republic of Ireland, Spain, and the UK and secondary care hospitals from the Republic of Ireland and the UK to obtain as representative a European ILD population as possible. Deidentified, unlinked data from individuals were included in the audit. Each contributing site was asked to identify individuals admitted to their local hospital. As SARS-CoV-2 was an emerging virus at the start of the pandemic and PCR testing has a high false-negative rate, we defined the diagnosis of COVID-19 based on a SARS-CoV-2–positive PCR swab and/or clinicoradiological diagnosis. Audit data were unified with the ISARIC4C CCP-UK database, with separate categorization of individuals who reported an existing chronic pulmonary disease. The ISARIC4C CCP-UK database is a prospective cohort study enrolling patients across the UK admitted to the hospital with COVID-19. Patients admitted were identified by local investigators and followed up prospectively by clinical research staff. Detailed data on comorbidities, treatments received, and outcomes were captured. The protocol is available at https://isaric.net/ccp/.
Data on the presence of chronic lung disease and asthma and prescription data were collected for the comparator group. Based on these variables, patients with no previous respiratory disease were selected to most accurately estimate the change in hazard for patients with ILD. Significant comorbidities of diabetes (type 1 or type 2), hypertension, chronic heart disease, and malignancy, as well as immunomodulatory therapies, were recorded in both ILD audit data and the ISARIC database.
The primary outcome was in-hospital mortality, and other recorded outcomes were whether the patient was discharged or remained hospitalized at the date of censoring (May 1, 2020). Secondary outcomes included whether patients were ventilated or received continuous positive airway pressure, other noninvasive ventilation, or high-flow oxygen and were described as “enhanced respiratory support.” Length of stay was recorded at date of death, discharge, or date of censoring. All outcomes were recorded on a standardized case report form and entered without any identifiers directly onto a secure REDcap database.
All data were entered by the local clinical care team in anonymized fashion without linkage to any patient identifiers in line with national and local audit guidance. In the UK, Health Research Authority guidance was followed (https://www.hra.nhs.uk/covid-19-research/guidance-using-patient-data/), and ethical approval was not required. Similar regulations applied in Denmark, Italy, and the Republic of Ireland. ISARIC4C CCP-UK received ethical approval from the South Central - Oxford C Research Ethics Committee in England (Ref: 13/SC/0149), and by the Scotland A Research Ethics Committee (Ref: 20/SS/0028). Data from Spain were collected under the approval for observational studies (Ref: UIC-IBU-2020-03 and PR217/20). Data from Germany were collected under approval from ethics committee of the Medical faculty of the University of Heidelberg (Ref: S-186/2020).
Summary statistics are presented as frequencies and percentages for categorical data and mean (SD) for normally distributed continuous data. Where continuous data were not normally distributed, the median (interquartile range [IQR]) was used. Differences in categorical data were compared using the chi-square test or Fisher’s exact test when expected counts were below 5 in any group. For continuous normally distributed two-group data, we compared differences using Welch’s t test or Mann-Whitney U if data were not normally distributed. For multiple group comparisons of continuous data, we used Kruskal-Wallis tests. Data from the ILD data set were matched in a 1:2 ratio with patients who did not have ILD from the ISARIC4C CCP-UK data set using a nearest neighbor propensity score matching algorithm. Patients in ISARIC4C CCP-UK with ILD were excluded to avoid double counting. We matched on confounders known to affect outcomes from COVID-19 disease including age, sex, diabetes, chronic cardiac disease, cancer, hypertension, and renal disease. Unmatched and matched populations were compared using standardized mean differences, plots, and summary statistics. For survival analyses, time was taken as admission to death (in-hospital survival) using length of stay where date was not available. Discharge from hospital was considered an absorbing state (once discharged, patients were considered no longer at risk of death). Discharged patients were not censored and included in the risk set until the end of follow-up; thus, discharge did not compete with death. We generated subclasses based on the propensity score distance and included these as terms within Cox proportional hazards models, where estimates are presented as hazard ratios (HRs), alongside the corresponding 95% confidence interval (CI) (11). Data were analyzed using R version 3.6.3 (R Foundation for Statistical Computing) with tidyverse, matchit, and finalfit packages.
Between March 1 and May 1, 2020, 349 patients with ILD were admitted to 37 hospitals throughout the UK and Europe (see Table E1 in the online supplement). A total of 185 patients with ILD had a diagnosis of COVID-19, and 161 were suitable for propensity score matching (Figure 1). The majority of these 161 patients (114) had a positive SARS-CoV-2 PCR test, and only 47 were diagnosed clinically. Of these, 110 (68%) were male and the mean age was 73.2 (11.5) years. The most common ILD was IPF, with 68 (42%) patients admitted to the hospital (Table 1). During the same period, 164 patients with ILD were admitted with an alternative non-COVID diagnosis, of whom 69 (42.1%) had IPF. Overall, the in-hospital mortality for patients with ILD and COVID-19 was 49% (79/161) compared with 17% (28/164) for patients with ILD admitted for other reasons.
Figure 1. Audit flow diagram showing patients recruited and flow to matching. COVID-19 = coronavirus disease; ILD = interstitial lung disease; ISARIC4C = International Severe Acute Respiratory and Emerging Infection Consortium Coronavirus Clinical Characterisation Consortium.
[More] [Minimize]| Total (n) | No ILD | ILD | P Value | |
|---|---|---|---|---|
| Total | 483 | 322 (66.7) | 161 (33.3) | — |
| Age, yr, mean (SD) | 483 | 72.6 (13.4) | 73.2 (11.5) | 0.639* |
| Age categories | 483 | |||
| <50 yr | 10 (3.1) | 5 (3.1) | 1.000 | |
| 50–69 yr | 112 (34.8) | 56 (34.8) | — | |
| 70–79 yr | 96 (29.8) | 48 (29.8) | — | |
| ≥80 yr | 104 (32.3) | 52 (32.3) | — | |
| Sex at birth, M | 483 | 220 (68.3) | 110 (68.3) | 1.000 |
| ILD | ||||
| Chronic hypersensitivity pneumonitis | 0 (0.0) | 14 (8.7) | — | |
| Connective tissue disease–related ILD | 0 (0.0) | 13 (8.1) | — | |
| Idiopathic pulmonary fibrosis | 0 (0.0) | 68 (42.2) | — | |
| Other | 0 (0.0) | 47 (29.2) | — | |
| Rheumatoid-related ILD | 0 (0.0) | 10 (6.2) | — | |
| Sarcoidosis | 0 (0.0) | 9 (5.6) | — | |
| Chronic cardiac disease, yes | 483 | 92 (28.6) | 46 (28.6) | 1.000 |
| Diabetes, yes | 483 | 98 (30.4) | 49 (30.4) | 1.000 |
| Malignant neoplasm, yes | 483 | 56 (17.4) | 28 (17.4) | 1.000 |
| Obesity | 419 | |||
| Yes | 23 (7.1) | 45 (28.0) | <0.001 | |
| Missing | 32 (9.9) | 32 (19.9) | — | |
| Hypertension, yes | 483 | 148 (46.0) | 74 (46.0) | 1.000 |
| Chronic kidney disease, yes | 483 | 16 (5.0) | 8 (5.0) | 1.000 |
| Pirfenidone, yes | 483 | 0 (0.0) | 7 (4.3) | — |
| Nintedanib, yes | 483 | 0 (0.0) | 13 (8.1) | — |
| Corticosteroids, yes | 483 | 28 (8.7) | 45 (28.0) | <0.001 |
After propensity score matching, 161 patients with ILD were compared with 322 patients admitted to the hospital between March 1 and May 1, 2020, but without ILD or other chronic lung disease (Table 1). There was significantly higher mortality in patients with ILD than in those without ILD (49% [79/161] vs. 35% [114/322]; P = 0.013), and ILD was associated with an increased risk of death in a matched adjusted analysis (HR, 1.60; 95% CI, 1.17–2.18; P = 0.003). There was significantly higher mortality in patients with ILD than in those without ILD (Figure 2), which was greatest in men and increased with age, which persisted for men after adjusting for age and comorbidity (adjusted HR, 1.98; 95% CI, 1.14–3.43; P = 0.015). Survival was significantly poorer in patients with IPF (HR, 1.74; 95% CI, 1.16–2.60; P = 0.007), and poorer survival was also seen in non-IPF ILD (HR, 1.50; 95% CI, 1.02–2.21; P = 0.040; Figure 3A) than in matched patients without ILD. Of patients with non-IPF ILD, those with chronic hypersensitivity pneumonitis and rheumatoid ILD had the highest mortality (50% [7/14] and 40% [4/10], respectively), whereas those with sarcoidosis and connective tissue disease (excluding rheumatoid)–related ILD had the lowest observed mortality (33% [3/9] and 23% [3/13], respectively). Overall, median length of stay in those who were still alive was not substantially different between patients without ILD (9 [IQR, 11] d) and those with non-IPF ILD (10 [IQR, 8] d) and IPF (10 [IQR, 8] d) (P = 0.725) (Table 2).
Figure 2. (A) In-hospital mortality with 95% confidence intervals for female patients with interstitial lung disease (ILD) hospitalized with coronavirus disease (COVID-19) stratified by age compared with those without ILD. (B) In-hospital mortality with 95% confidence intervals for male patients with ILD hospitalized with COVID-19 stratified by age compared with those without ILD.
[More] [Minimize]
Figure 3. (A) Effect of interstitial lung disease (ILD) on mortality after coronavirus disease (COVID-19) (propensity matched), (B) effect of obesity and ILD (propensity matched), and (C) effect of FVC on outcome in patients with ILD only. CI = confidence interval; HR = hazard ratio; IPF = idiopathic pulmonary fibrosis.
[More] [Minimize]| Total (n) | No ILD | ILD | P Value | |
|---|---|---|---|---|
| Died | 483 | 114 (35.4) | 79 (49.1) | 0.013 |
| Respiratory support | 483 | |||
| Unenhanced respiratory support* | 254 (78.9) | 136 (84.5) | 0.288 | |
| Enhanced respiratory support | 39 (12.1) | 20 (12.4) | — | |
| Ventilation | 29 (9.0) | 6 (3.7) | — | |
| Length of stay for those alive, median (IQR) | 290 | 9.0 (11.0) | 10.0 (8.0) | 0.725† |
Obesity has been previously described to increase the risk of death from COVID-19. After obesity was included in the propensity score matching (129/161 patients with ILD with data available, matched to 260 patients without ILD), being obese and having ILD was significantly associated with an increased hazard of death from COVID-19 (adjusted HR, 2.27; 95% CI, 1.39–3.71; P = 0.001; Figure 3B), which was greater than the effect of obesity in patients without ILD.
To determine whether patients with more severe ILD had a greater risk of mortality with COVID-19, the last available lung function results before hospital admission were analyzed. For all patients with ILD, the mean FVC in patients surviving COVID-19 was 82.2% predicted compared with 76.8% predicted in patients who died (P = 0.121). Similarly, the mean DlCO was 56.4% predicted in survivors compared with 49.6% in patients dying with COVID-19 (P = 0.072; Table 3). When the FVC was dichotomized using the 80% predicted threshold between mild and moderate ILD, in line with UK prescribing policy for antifibrotic medication, survival was significantly poorer in patients with moderate or severe ILD (HR, 1.72; 95% CI, 1.05–2.83) than in patients with mild disease (Figure 3C).
| Label | Total (n) | Alive | Died | P Value |
|---|---|---|---|---|
| FVC, mean (SD) | 147 | 2.7 (1.0) | 2.6 (0.9) | 0.372* |
| FVC, percentage of predicted, mean (SD) | 151 | 82.2 (23.0) | 76.8 (21.0) | 0.135* |
| FVC, percentage of predicted | 151 | |||
| <80% | 35 (42.7) | 42 (60.9) | 0.039 | |
| >80% | 47 (57.3) | 27 (39.1) | — | |
| FVC, percentage of predicted | 151 | |||
| <50% | 7 (6.9) | 4 (4.8) | 0.070 | |
| 50–80% | 28 (27.7) | 38 (45.8) | — | |
| 80–100% | 33 (32.7) | 17 (20.5) | — | |
| >100% | 14 (13.9) | 10 (12.0) | — | |
| DlCO, mean (SD) | 81 | 5.2 (3.4) | 4.2 (1.9) | 0.093* |
| DlCO, percentage of predicted, mean (SD) | 122 | 56.4 (19.4) | 49.6 (21.6) | 0.072* |
Most patients with ILD (84%; 135/161) did not receive enhanced respiratory support, which was similar to matched patients without ILD (79%; 254/322; Table 2). Significantly more patients who survived did not receive enhanced respiratory support (93%; 76/82) compared with those who died (75%; 59/79; P = 0.015). Of the 26 patients receiving enhanced respiratory support, 77% (20) died, including 83% (5/6) of matched patients with ILD receiving ventilation (Table 4).
| Total (n) | Alive | Died | P Value | |
|---|---|---|---|---|
| Respiratory support | 161 | |||
| Unenhanced respiratory support* | 76 (92.7) | 59 (74.7) | 0.015 | |
| Enhanced respiratory support | 5 (6.0) | 15 (23) | 0.005 | |
| Ventilation | 1 (1.2) | 5 (3.8) | 0.190 |
In patients with ILDs, 106 were taking no immunosuppressants or antifibrotics, of whom 50% died (53/106; Table 5). Almost one-third of patients with IPF (20/68) were receiving antifibrotic therapy at the time of admission. Seven patients were receiving pirfenidone and 13 were receiving nintedanib. Of those receiving antifibrotic therapy, 50% (10/20) died. Where immunosuppressants were reported, corticosteroids were most frequently prescribed (45/55) followed by mycophenolate (17/55); overall, 47% (26/55) of patients taking antifibrotics or immunosuppressants died. Significantly more patients with ILD received oral corticosteroids in the hospital than patients without ILD (27.8% vs. 9.9%; P < 0.001), but there was no difference in outcome between those receiving corticosteroids (48.9% mortality [22/55]) and those who did not (48.7% mortality [57/117]).
| Alive | Died | |
|---|---|---|
| Received immunosuppressants/antifibrotics, yes | 29 (34.9) | 26 (32.9) |
| Methotrexate/azathioprine, yes | 3 (3.6) | 2 (1.3) |
| Nintedanib, yes | 9 (10.8) | 4 (5.1) |
| Pirfenidone, yes* | 1 (1.2) | 6 (7.6) |
| Mycophenolate, yes | 10 (12.0) | 7 (8.9) |
| Corticosteroid, yes | 23 (27.7) | 22 (27.8) |
Determining the risk of poor outcome in patients with preexisting conditions who acquire SARS-CoV-2 infection is crucial to determine what mitigation measures are required in the community. Our study shows that patients with existing interstitial lung disease were at very high risk of death when hospitalized with COVID-19, especially if they have reduced prior lung function or obesity. Most died without being offered mechanical ventilation; in those who were ventilated, mortality was very high.
Our observations were in keeping with other studies that have identified increasing age, comorbidities (diabetes mellitus, ischemic heart disease, and other non-ILD chronic pulmonary diseases), and male sex as predictors of poor outcome in COVID-19 (9, 12). Unfortunately, the risk for patients with ILD has not yet been quantified because patients with uncommon diseases often do not have their specific diagnostic information recorded in large observational studies or are often not accurately reported in administrative healthcare data sets. Despite the unknown risk of COVID-19 for patients with ILD, many patients chose to self-isolate, and therefore, there appeared to be apparent “protection” from COVID-19 observed to many ILD physicians around the world. To accurately understand the risk of COVID-19 and inform decision- and risk-matrices going forward, the European ILD community undertook an audit of hospitalized patients with ILD between the March 1 and May 1, 2020, to coincide with the first peak of COVID-19 within Europe. These data represent the largest assessment of the impact of SARS-CoV-2 infection on patients with ILD to date. These data demonstrate that patients with ILD are at increased risk of death after hospitalization for COVID-19, particularly elderly males with poor lung function and obesity. It is notable that the risk of in-hospital mortality from COVID-19 for patients with ILD is particularly associated with male sex and obesity. These risk factors are associated both with development of IPF and with mortality from COVID-19 in the general population, which may suggest synergy between shared mechanisms of pathogenesis (13).
The role of viral infection promoting acute exacerbations of ILD has been investigated for a number of years without a definitive answer, which probably reflects the range of viruses studied, the number of patients needed to explore such a hypothesis, and more broadly the challenges studying acute exacerbations of ILD (14–17). These data confirm that patients with ILD are at increased risk of mortality from COVID-19 compared with a matched population without ILD, and the risk is greatest for IPF consistent with a respiratory virus inducing acute exacerbations of ILD.
Factors associated with poor prognosis in acute exacerbations of ILD include pulmonary fibrosis and poor lung function before admission and fibrotic ILDs (18–21). The data presented here support these prior studies demonstrating that patients with a recorded FVC <80% predicted before admission had an increased mortality compared with those with an FVC >80%. Similarly, patients with fibrotic ILDs such as chronic hypersensitivity pneumonitis had mortality rates comparable with IPF and higher than those with sarcoid or connective tissue disease–associated ILD. Interestingly, obese patients with ILD had a substantially increased risk of mortality. This may reflect use of steroids in severe disease, although we do not favor this hypothesis as there was no apparent increased risk of death associated with corticosteroid use. Obesity may be due to limited activity in patients with ILD, a condition that leads to progressive reduction in exercise tolerance as it becomes more severe (22). Alternatively, and perhaps most intriguingly, it may lend support to the hypothesis that severe ILD is part of the “metabolic syndrome” (23).
This analysis also assessed respiratory support offered to patients hospitalized with ILD. In line with current practice and guidelines, most patients did not receive any ventilatory support (7, 24). Most patients who survived did not require enhanced respiratory support such as high-flow oxygen, continuous positive airway pressure, or ventilation, and of those receiving enhanced respiratory support, the majority died. These data continue to support the use of supportive care in preference to either noninvasive or invasive mechanical ventilation except in clearly defined cases such as predominantly inflammatory ILD or bridging to transplantation.
This analysis has a number of strengths. It is the largest international, multicenter study to assess the outcome of patients with interstitial lung disease hospitalized with the respiratory infection SARS-CoV-2. Merging of the ILD Audit data set with the ISARIC4C CCP-UK data set enabled accurate, contemporary propensity score matching for a number of potential confounding factors to assess the risk from COVID-19 disease. This enabled an assessment of the risk to patients with ILD infected with SARS-CoV-2, facilitating evidence-based mitigation strategies, such as self-isolation, for this vulnerable group of patients.
This study also has some potential weaknesses. Because of the retrospective nature of the data collection, recall bias might have led to overselection of severe cases of COVID-19 in patients with ILD; however, this is mitigated by the large number of centers participating in the audit. Similarly, because only hospitalized patients could be included, it is possible that a large number of patients with ILD and COVID-19 were omitted, and therefore, the risk of COVID-19 could be overstated. However, given the demographic associated with ILD, we think this is unlikely. Also, the comparator group only included patients from the UK and some younger patients, which could have led to residual confounding by geographical determinants of disease severity and treatment; however, because 86% of the patients with ILD were recruited from the UK, we think this is unlikely to be a major source of confounding. Although patients with ILD were less likely to receive invasive mechanical ventilation, this is more likely due to their severe respiratory disease; however, there was insufficient detail in the audit data collection to match for COVID-19 severity based on admission severity scores or provide more granularity relating to the nature of respiratory support received. However, we did not observe any patterns across the groups that were likely to have an effect. Similarly, the propensity score matching will have helped to address any imbalances that are observed within the matching variables.
The effect of obesity was assessed in the matched population, suggesting an increased risk in patients without ILD; however, in contrast with prior reports (9, 25), no effect of obesity was observed in the control population. This reflects the limitations associated with recording obesity using inpatient data where obesity is not objectively measured as well as the relatively small numbers of patients used in the matching and the large amount of missing data relating to weight. Therefore, the data relating to obesity must be interpreted accordingly, but we believe as a modifiable risk factor it is important to highlight the risk of obesity in patients with ILD who might develop COVID-19. Finally, it was not possible to evaluate specific treatment effects, such as the use of antifibrotics, immunosuppressants, or antiviral therapies. The audit was undertaken in the early part of the pandemic when hydroxychloroquine and remdesivir were only available in the context of clinical trials. The UK Medicine Health Regulatory Agency did not approve remdesivir for use in the UK until May 26, 2020, and hydroxychloroquine has been shown to have no effect on outcome in any circumstance of COVID-19 infection (26, 27); therefore, it is highly unlikely that the results are confounded by antiviral therapy. Only a proportion of patients received antifibrotics during their admission. Furthermore, owing to the unknown effect of antifibrotics and concerns regarding immunosuppression on the clinical course of COVID-19, there is likely to be considerable confounding owing to indication. There are both potential benefits (28, 29) and harms associated with background therapy for ILD (30). With specific reference to corticosteroids, our analysis did not show any effect on mortality in patients with ILD. Although the RECOVERY (Randomised Evaluation of COVID-19 Therapy) study demonstrated an overall beneficial effect of dexamethasone in patients with COVID-19, there was considerable heterogeneity in the response (27), and it is not clear to what extent steroid choice, steroid dosing, steroid duration, or pre–COVID-19 steroid therapy impact on outcome. However, it is reassuring from a safety perspective that there was no obvious signal suggesting harms associated with background therapy for ILD in patients with COVID-19, although further study to evaluate the consequence of antifibrotic and immunomodulatory therapy in ILD are needed.
These data demonstrate that patients with ILD, particularly those with fibrotic ILD, are at higher risk of mortality from COVID-19 than patients without ILD. Furthermore, the risk is heightened in elderly males and those with obesity or poor lung function, and we would recommend dietary advice in overweight patients. We also propose that patients with ILD, particularly severe fibrotic ILD, continue to be regarded as high risk of mortality from COVID-19 and follow national self-isolation guidelines for vulnerable individuals and be prioritized for SARS-CoV-2 vaccination at such time as it becomes available. Finally, we believe these data demonstrate the importance of international collaboration to collect data to understand the consequences of emerging threats to patients with ILD.
The authors thank the 2,648 frontline National Health Service clinical and research staff and volunteer medical students, who collected these data in challenging circumstances, and the participants and their families for their individual contributions in these difficult times. They thank Jeremy J. Farrar, Nahoko Shindo, Devika Dixit, Nipunie Rajapakse, Lyndsey Castle, Martha Buckley, Debbie Malden, Katherine Newell, Kwame O’Neill, Emmanuelle Denis, Claire Petersen, Scott Mullaney, Sue MacFarlane, Nicole Maziere, Julien Martinez, Oslem Dincarslan, and Annette Lake. They also thank Mustafa Al-Zubaidy, Hayoung Lee, Oliver Awad, Verity Loake, Anex Wilson, Lise-Ann Grundy, Mark Major, and Amanda Bell, who collected data. This work uses data provided by patients and collected by the National Health Service as part of their care and support #DataSavesLives.
ISARIC4C Investigators
Consortium Lead Investigator: J. Kenneth Baillie, Chief Investigator: Malcolm G. Semple, Co–Lead Investigator: Peter J. M. Openshaw, and ISARIC Clinical Coordinator: Gail Carson.
Coinvestigators: Beatrice Alex, Benjamin Bach, Wendy S. Barclay, Debby Bogaert, Meera Chand, Graham S. Cooke, Annemarie B. Docherty, Jake Dunning, Ana da Silva Filipe, Tom Fletcher, Christopher A. Green, Ewen M. Harrison, Julian A. Hiscox, Antonia Ying Wai Ho, Peter W. Horby, Samreen Ijaz, Saye Khoo, Paul Klenerman, Andrew Law, Wei Shen Lim, Alexander J. Mentzer, Laura Merson, Alison M. Meynert, Mahdad Noursadeghi, Shona C. Moore, Massimo Palmarini, William A. Paxton, Georgios Pollakis, Nicholas Price, Andrew Rambaut, David L. Robertson, Clark D. Russell, Vanessa Sancho-Shimizu, Janet T. Scott, Thushan de Silva, Louise Sigfrid, Tom Solomon, Shiranee Sriskandan, David Stuart, Charlotte Summers, Richard S. Tedder, Emma C. Thomson, A. A. Roger Thompson, Ryan S. Thwaites, Lance C. W. Turtle, and Maria Zambon.
Project Managers: Hayley Hardwick, Chloe Donohue, Ruth Lyons, Fiona Griffiths, and Wilna Oosthuyzen.
Data Analysts: Lisa Norman, Riinu Pius, Tom M. Drake, Cameron J. Fairfield, Stephen Knight, Kenneth A. Mclean, Derek Murphy, and Catherine A. Shaw.
Data and Information System Manager: Jo Dalton, Michelle Girvan, Egle Saviciute, Stephanie Roberts, Janet Harrison, Laura Marsh, Marie Connor, Sophie Halpin, Clare Jackson, and Carrol Gamble.
Data integration and presentation: Gary Leeming, Andrew Law, Murray Wham, Sara Clohisey, Ross Hendry, and James Scott-Brown.
Material Management: William Greenhalf, Victoria Shaw, and Sarah McDonald.
Patient Engagement: Seán Keating.
Outbreak Laboratory Staff and Volunteers: Katie A. Ahmed, Jane A. Armstrong, Milton Ashworth, Innocent G. Asiimwe, Siddharth Bakshi, Samantha L. Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Benjamin W. A. Catterall, Jordan J. Clark, Emily A. Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis W. S. Fisher, Terry Foster, Isabel Garcia-Dorival, Willliam Greenhalf, Philip Gunning, Catherine Hartley, Antonia Ho, Rebecca L. Jensen, Christopher B. Jones, Trevor R. Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, L. Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A. Livoti, Maria Mancini, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S. Miah, Joanna Middleton, Joyce Mitchell, Shona C. Moore, Ellen G. Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, Will Reynolds, P. Matthew Ridley, Debby Sales, Victoria E. Shaw, Rebecca K. Shears, Benjamin Small, Krishanthi S. Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby van Tonder, Eve Wilcock, and J. Eunice Zhang.
Local Principal Investigators: Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Ana Alegria, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Nicola Best, Pieter Bothma, David Brealey, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, David Chadwick, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B. Hawcutt, Maria Hobrok, Luke Hodgson, Anita Holme, Anil Hormis, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Mariyam Mirfenderesky, Kavya Mohandas, Quen Mok, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Megan Nagel, Thapas Nagarajan, Mark Nelson, Igor Otahal, Mark Pais, Selva Panchatsharam, Hassan Paraiso, Brij Patel, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Jagtur Singh Pooni, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Brendan Ryan, Taranprit Saluja, Aarti Shah, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jeremy Sizer, Manu Shankar-Hari, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Andrew Ustianowski, Nick Vallotton, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Sarah Wilson, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G. Wooton, Andrew Workman, Bryan Yates, and Peter Young.
| 1. | Duchemann B, Annesi-Maesano I, Jacobe de Naurois C, Sanyal S, Brillet P-Y, Brauner M, et al. Prevalence and incidence of interstitial lung diseases in a multi-ethnic county of Greater Paris. Eur Respir J 2017;50:1602419. |
| 2. | Idiopathic pulmonary fibrosis statistics: Respiratory Health of the Nation project; 2016 [accessed 2020 Jan 10]. Available from: https://www.blf.org.uk/support-for-you/idiopathic-pulmonary-fibrosis-ipf/statistics. |
| 3. | Olson AL, Gifford AH, Inase N, Fernández Pérez ER, Suda T. The epidemiology of idiopathic pulmonary fibrosis and interstitial lung diseases at risk of a progressive-fibrosing phenotype. Eur Respir Rev 2018;27:180077. |
| 4. | Kreuter M, Ehlers-Tenenbaum S, Palmowski K, Bruhwyler J, Oltmanns U, Muley T, et al. Impact of comorbidities on mortality in patients with idiopathic pulmonary fibrosis. PLoS One 2016;11:e0151425. |
| 5. | Hyldgaard C, Hilberg O, Bendstrup E. How does comorbidity influence survival in idiopathic pulmonary fibrosis? Respir Med 2014;108:647–653. |
| 6. | Collard HR, Ryerson CJ, Corte TJ, Jenkins G, Kondoh Y, Lederer DJ, et al. Acute exacerbation of idiopathic pulmonary fibrosis: an International Working Group report. Am J Respir Crit Care Med 2016;194:265–275. |
| 7. | Kreuter M, Polke M, Walsh SLF, Krisam J, Collard HR, Chaudhuri N, et al. Acute exacerbation of idiopathic pulmonary fibrosis: international survey and call for harmonisation. Eur Respir J 2020;55:1901760. |
| 8. | Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al.; Latin American Network of Coronavirus Disease 2019-COVID-19 Research (LANCOVID-19).Clinical, laboratory and imaging features of COVID-19: a systematic review and meta-analysis. Travel Med Infect Dis 2020;34:101623. |
| 9. | Docherty AB, Harrison EM, Green CA, Hardwick HE, Pius R, Norman L, et al.; ISARIC4C investigators. Features of 20 133 UK patients in hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: prospective observational cohort study. BMJ 2020;369:m1985. |
| 10. | Zhou F, Yu T, Du R, Fan G, Liu Y, Liu Z, et al. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet 2020;395:1054–1062. |
| 11. | Ho DE, Imai K, King G, Stuart EA. Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. Polit Anal 2007;15:199–236. |
| 12. | Banerjee A, Pasea L, Harris S, Gonzalez-Izquierdo A, Torralbo A, Shallcross L, et al. Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study. Lancet 2020;395:1715–1725. |
| 13. | . Jenkins G. Demystifying pulmonary fibrosis. Am J Physiol Lung Cell Mol Physiol 2020;319:L554–L559. |
| 14. | Ushiki A, Yamazaki Y, Hama M, Yasuo M, Hanaoka M, Kubo K. Viral infections in patients with an acute exacerbation of idiopathic interstitial pneumonia. Respir Investig 2014;52:65–70. |
| 15. | Saraya T, Kimura H, Kurai D, Tamura M, Ogawa Y, Mikura S, et al. Clinical significance of respiratory virus detection in patients with acute exacerbation of interstitial lung diseases. Respir Med 2018;136:88–92. |
| 16. | . Weng D, Chen X-Q, Qiu H, Zhang Y, Li Q-H, Zhao M-M, et al. The role of infection in acute exacerbation of idiopathic pulmonary fibrosis. Mediators Inflamm 2019;2019:5160694. |
| 17. | Sheng G, Chen P, Wei Y, Yue H, Chu J, Zhao J, et al. Viral infection increases the risk of idiopathic pulmonary fibrosis: a meta-analysis. Chest 2020;157:1175–1187. |
| 18. | Qiu M, Chen Y, Ye Q. Risk factors for acute exacerbation of idiopathic pulmonary fibrosis: a systematic review and meta-analysis. Clin Respir J 2018;12:1084–1092. |
| 19. | Enomoto N, Naoi H, Aono Y, Katsumata M, Horiike Y, Yasui H, et al. Acute exacerbation of unclassifiable idiopathic interstitial pneumonia: comparison with idiopathic pulmonary fibrosis. Ther Adv Respir Dis 2020;14:1753466620935774. |
| 20. | Taya T, Chiba H, Yamada G, Takahashi M, Ikeda K, Mori Y, et al. Risk factors for acute exacerbation of idiopathic interstitial pneumonia in patients undergoing lung cancer treatment. Jpn J Clin Oncol 2019;49:1126–1133. |
| 21. | Enomoto N, Oyama Y, Enomoto Y, Mikamo M, Karayama M, Hozumi H, et al. Prognostic evaluation of serum ferritin in acute exacerbation of idiopathic pulmonary fibrosis. Clin Respir J 2018;12:2378–2389. |
| 22. | Bahmer T, Kirsten AM, Waschki B, Rabe KF, Magnussen H, Kirsten D, et al. Prognosis and longitudinal changes of physical activity in idiopathic pulmonary fibrosis. BMC Pulm Med 2017;17:104. |
| 23. | . Papaioannou O, Karampitsakos T, Barbayianni I, Chrysikos S, Xylourgidis N, Tzilas V, et al. Metabolic disorders in chronic lung diseases. Front Med (Lausanne) 2018;4:246. |
| 24. | Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, et al.; ATS/ERS/JRS/ALAT Committee on Idiopathic Pulmonary Fibrosis. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 2011;183:788–824. |
| 25. | Cummings MJ, Baldwin MR, Abrams D, Jacobson SD, Meyer BJ, Balough EM, et al. Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study. Lancet 2020;395:1763–1770. |
| 26. | Boulware DR, Pullen MF, Bangdiwala AS, Pastick KA, Lofgren SM, Okafor EC, et al. A randomized trial of hydroxychloroquine as postexposure prophylaxis for Covid-19. N Engl J Med 2020;383:517–525. |
| 27. | . Cavalcanti AB, Zampieri FG, Rosa RG, Azevedo LCP, Veiga VC, Avezum A, et al.; Coalition Covid-19 Brazil I Investigators. Hydroxychloroquine with or without Azithromycin in mild-to-moderate Covid-19. N Engl J Med [online ahead of print] 23 Jul 2020; DOI: 10.1056/NEJMoa2019014. |
| 28. | . George PM, Wells AU, Jenkins RG. Pulmonary fibrosis and COVID-19: the potential role for antifibrotic therapy. Lancet Respir Med 2020;8:807–815 |
| 29. | . Horby P, Lim WS, Emberson JR, Mafham M, Bell JL, Linsell L, et al.; RECOVERY Collaborative Group. Effect of dexamethasone in hospitalized patients with COVID-19: preliminary report. N Engl J Med [online ahead of print] 17 Jul 2020; DOI: 10.1056/NEJMoa2021436. |
| 30. | Clausen ES, Zaffiri L. Infection prophylaxis and management of viral infection. Ann Transl Med 2020;8:415. |
R.G.J. is supported by a National Institute for Health Research (NIHR) Research Professor Award (RP-2017-08-ST2-014). L.-P.H. is supported by the NIHR Oxford Biomedical Research Centre. This work is supported by grants from the NIHR (award CO-CIN-01) and the Medical Research Council (grant MC_PC_19059) and by the NIHR Health Protection Research Unit in Emerging and Zoonotic Infections at University of Liverpool in partnership with Public Health England, in collaboration with the Liverpool School of Tropical Medicine and the University of Oxford (NIHR award 200907), Wellcome Trust and Department for International Development (215091/Z/18/Z), and the Bill and Melinda Gates Foundation (OPP1209135), and the Liverpool Experimental Cancer Medicine Centre, which provided infrastructure support for this research (grant reference: C18616/A25153). The views expressed are those of the authors and not necessarily those of the Department of Health and Social Care, Department of International Development, National Institute of Health Research, Medical Research Council, Wellcome Trust, or Public Health England.
Author Contributions: R.G.J. designed and conceived the study and drafted the manuscript. T.M.D. analyzed the data and drafted the manuscript. A.B.D., E.M.H., J.K.Q., and I.S. developed the analysis plan and analyzed the data and revised the manuscript. G.H. and L.H. collated and curated all data and revised the manuscript. H.A., S.A., S. Babu, C.M.B., S. Barratt, E.B., S. Bianchi, D.C.V., N.C., F.C., R.C., W.C., A.C., L.E.C., D.D., C.A.F., I.A.F., P.M.G., M.A.G., K.G., S.H., S.P.H., E.H., M.H., L.-P.H., R.K.H., J.H., K.H., M.J., M. Kokosi, M. Kreuter, L.S.M., S.M., G.M., M.M.-M., P.L.M., A.O’B., K.O’R., A.P., H.P., V.P., J.C.P., E.R., P.R.-O., A.-M.R., G.S., L.G.S., G.M.S., H.S., S.S., D.T., M.T., T.W., K.W., A.U.W., A.W., M.W., F.W., J.K.B., P.J.M.O., and M.G.S. helped design the study, contributed data, and revised the manuscript for important intellectual content. S.J. helped design the study and revised the manuscript for important intellectual content. All authors have provided final approval of the version submitted for publication and agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.
A complete list of the ISARIC4C Investigators may be found before the beginning of the References.
This article has an online supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Originally Published in Press as DOI: 10.1164/rccm.202007-2794OC on October 2, 2020
Author disclosures are available with the text of this article at www.atsjournals.org.
