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. 2021 Mar 18;16(3):e0247316. doi: 10.1371/journal.pone.0247316

Interstitial lung disease in a veterans affairs regional network; a retrospective cohort study

Armando Bedoya 1, Roy A Pleasants 2, Joel C Boggan 1,3, Danielle Seaman 4, Anne Reihman 1, Lauren Howard 5, Robert Kundich 3, Karen Welty-Wolf 1,3, Robert M Tighe 1,3,*
Editor: Mehrdad Arjomandi6
PMCID: PMC7971476  PMID: 33735247

Abstract

Background

The epidemiology of Interstitial Lung Diseases (ILD) in the Veterans Health Administration (VHA) is presently unknown.

Research question

Describe the incidence/prevalence, clinical characteristics, and outcomes of ILD patients within the Veteran’s Administration Mid-Atlantic Health Care Network (VISN6).

Study design and methods

A multi-center retrospective cohort study was performed of veterans receiving hospital or outpatient ILD care from January 1, 2008 to December 31st, 2015 in six VISN6 facilities. Patients were identified by at least one visit encounter with a 515, 516, or other ILD ICD-9 code. Demographic and clinical characteristics were summarized using median, 25th and 75th percentile for continuous variables and count/percentage for categorical variables. Characteristics and incidence/prevalence rates were summarized, and stratified by ILD ICD-9 code. Kaplan Meier curves were generated to define overall survival.

Results

3293 subjects met the inclusion criteria. 879 subjects (26%) had no evidence of ILD following manual medical record review. Overall estimated prevalence in verified ILD subjects was 256 per 100,000 people with a mean incidence across the years of 70 per 100,000 person-years (0.07%). The prevalence and mean incidence when focusing on people with an ILD diagnostic code who had a HRCT scan or a bronchoscopic or surgical lung biopsy was 237 per 100,000 people (0.237%) and 63 per 100,000 person-years respectively (0.063%). The median survival was 76.9 months for 515 codes, 103.4 months for 516 codes, and 83.6 months for 516.31.

Interpretation

This retrospective cohort study defines high ILD incidence/prevalence within the VA. Therefore, ILD is an important VA health concern.

Introduction

Interstitial lung diseases (ILDs) are a rare group of heterogeneous respiratory disorders characterized by progressive infiltration of the interstitium by immune cells and matrix producing fibroblasts, ultimately leading to development of fibrosis. While idiopathic pulmonary fibrosis (IPF) is associated with the most substantial morbidity and mortality among ILDs, the epidemiology of others that are environmentally associated or secondary to other systemic diseases is less frequently studied. The morbidity and mortality of ILD is dependent on the specific ILD subtype. World Health Organization data of males diagnosed with IPF in the European Union (EU) defined the median mortality at 3.75 per 100,000 people in the EU from 2001–2013 [1]. Additionally, the individual economic impact of ILD is substantial, as analysis of USA Medicare claims showed the total direct cost for patients with IPF was $26,000/person-year between 2001–2008, and the incremental cost over control subjects was $12,124 [2]. Despite the clear impact of ILD on human health [3] and the ongoing efforts to define clinical characteristics, there remain considerable deficits in our understanding of the incidence and prevalence of ILD across various groups.

The Veterans Health Administration (VHA) is one such group where there is a gap in knowledge about epidemiology of ILD. The VHA is the largest integrated health system in the USA and has led epidemiology efforts in other disease processes such as diabetes [4], coronary disease [5], and hypertension [6]. The VA healthcare database, as of 2017, contains more than 9 million subjects [7] and is enriched with older males with smoking histories, which are known risk factors for increased ILD incidence. Based on this, it has been hypothesized that ILD is more prevalent in veterans’ populations. However, to our knowledge there have been no large epidemiological studies on ILD in veterans.

The primary objective of this study is to describe the incidence/prevalence, clinical characteristics, and outcomes of patients with ILD who received care within the Veteran’s Administration Mid-Atlantic Health Care Network.

Methods

This was a multi-center retrospective cohort study including patients who received inpatient or outpatient ILD care from January 1, 2008 to December 31st, 2015 at six VHA medical facilities and associated clinics in North Carolina (Asheville, Durham, Fayetteville, Salisbury) and Virginia (Richmond, Salem). Subjects were identified by any single visit encounter coded with either a 515, 516, or other ILD ICD-9 code (135, 501, 508.1, or 518.89) (S1 Table). The selection of these codes was based on a review of the available ILD ICD-9 diagnostic codes. During the study period on 10/01/2015 there was a transition to ICD-10 coding. ICD-10 codes were not used to capture patients during this study period. There were no exclusion criteria. This study was reviewed and approved by the Durham VA Institutional Review Board (IRB #01882/001). The study was performed under a waiver of consent and a waiver of HIPPA as approved by the IRB. Under the approved protocol, the study team had access to patient identifiers during the performance of the electronic record review. During data extraction performed for the analysis, the data was anonymized and no individual patient identifiers are reported in this study.

Data abstraction used data from the VA Corporate Data Warehouse (CDW) and other electronic medical record (EMR) sources, including VistaWeb. CDW data included demographics, site of visits, ICD-9 diagnostic codes to include pre-selected comorbidities, and dates of procedures (pulmonary function tests, bronchoscopies, radiographs, and surgical procedures). EMR data included smoking history, ILD diagnostic information including thoracic computed tomographic (CT) scans, bronchoscopies, and surgical biopsies, pulmonary function tests, and drug therapies. To supplement CDW data, an ILD specialist, pulmonary specialty pharmacist, and a pulmonary fellow performed EMR abstractions. The ICD-9 diagnosis was confirmed based on a thorough chart review. The data from this chart review included physician notes, and CT scan reports. This was reviewed by the study team to confirm or refute the diagnosis. A random sample of charts (10% of total) were re-reviewed by a separate member of the research team to confirm chart abstraction quality. In instances where patients were coded with both 515 and 516 codes, the study team used the physician notes, exam, laboratory values and CT scan report to clarify which of the ICD-9 codes were accurate and this was then used to define their ICD-9 group. Subject data was collected through January 2019 from the time of first ILD diagnosis until their last visit to the facility, death, lung transplantation, or lost to follow-up. Initial diagnosis date was reviewed and updated if it was earlier than captured in the time range.

The pattern of baseline PFTs using spirometry and DLCO measures were categorized as: 1) restrictive: forced vital capacity (FVC) < 80%; forced expiratory volume in 1 second (FEV1)/FVC > 70%); 2) obstructive: FEV1/FVC ≤ 70%; 3) Isolated DLCO Impartment: FVC > 80%, FEV1/FVC > 70%, diffusing capacity for carbon monoxide (DLCO) ≤ 80%); and 4) normal: FVC > 80%, FEV1/FVC > 70%, DLCO > 80%.

Demographic and clinical characteristics were summarized using median, 25th and 75th percentile for continuous variables and count and percentage for categorical variables. Broad and narrow case definitions were defined. Broad included any individual with an ILD diagnostic code, while narrow required an ILD diagnostic code with evidence of a HRCT scan or a bronchoscopic or surgical lung biopsy. Characteristics were summarized, stratified by ICD-9 code of ILD diagnosis and presence or absence of computed tomography of the chest, surgical biopsy, or transbronchial biopsy and differences were compared using Kruskal-Wallis tests for continuous variables and chi-squared tests for categorical variables. Incidence per 100,000 patients was calculated as the number of subjects diagnosed with an ILD in each year divided by the number of unique patients who visited one of the VA centers in that calendar year. Incidence rates were averaged across years. Date of diagnosis was based on the date of physician note or radiologic study identifying and ILD. Though subjects were initially identified by and ICD-9 billing code from 2008–2015, chart review information was used to define the date of diagnosis and therefore could have been prior to 2008. Prevalence per 100,000 patients was calculated as the number of patients diagnosed with an ILD before December 31st, 2015 and still alive at this date, divided by the number of unique patients who visited one of the VA centers in 2013–2015 and were alive as of December 31st, 2015 (as an estimate of the number of VA patients in the system at these centers). Kaplan-Meier curves were generated based on ICD-9 diagnosis codes. The curves were based on any recorded mortality in the study population through January 2019 when the database collection stopped. A log-rank test was used to test the differences in survival among groups. Statistical analysis was performed using SAS v9.4 (SAS Institute, Cary, NC). A p-value <0.05 was used as the threshold for statistical significance.

Results

We identified 3293 subjects in our cohort who met our inclusion criteria. S1 Fig illustrates the breakdown of the subjects into “No ILD, 515 ICD 9, 516 ICD 9, or Other ILD” following EMR review. Baseline characteristics for the total population (including the “No ILD” category) are noted in S2 Table. The median age was 69 years-old with a wide age distribution. The majority of individuals are male (96%), white (79%) and current or former smokers (75%). The median BMI was 27.8, with 39% of the population considered overweight and 35% obese. Co-morbid diseases were frequently observed in the cohort. Airway disease was the most frequent reported with chronic obstructive lung disease (COPD; 41%) and asthma (8%) documented in 49% overall. Consistent with this, 29% of the cohort had spirometric obstruction. Interestingly, 65% of total cohort did not have pulmonary function testing recorded in the EMR. Gastroesophageal reflux disease was also a frequent comorbidity, recorded in 40% of subjects. Lung cancer and mixed connective tissue disease were noted at 8% and 1%, respectively.

Review of the available clinical information in the EMR determined that 879 subjects (26%) had no radiologic or clinical evidence of ILD. Given that this represented a large portion of the cohort, Table 1 segregates characteristics by individual ICD-9 code from those who had no evidence of ILD. The vast majority of subjects had an ICD-9 515 code (47%). Clinical characteristics between the ICD-9 groups were similar, with the exception of COPD, where 49% of 515 coded subjects having this co-morbidity versus only 39% in the 516 grouping. This was reflected in the 515 group spirometry, as they had an increased percentage of individuals with obstruction when compared to the 516 group. Alternatively, the 516 group predominantly exhibited a restrictive spirometric pattern. Subjects with no documented ILD on chart review had similar characteristics to those with ILD except for lower rates of smoking (29% never smokers) and COPD (30%). As the 516 ICD code included several sub-groupings, we stratified by individual 516 ICD subset (S3 Table). Within the 516 group, the majority (81.1%) were coded as 516.31 (Idiopathic Pulmonary Fibrosis), or 516.8 (Other specified alveolar and parietoalveolar pneumonopathies). Interestingly, minimal differences existed in the clinical characteristics or co-morbidities between these 516.31 or 516.8 groupings.

Table 1. Characteristics of patient population stratified by ICD-9 code.

515 (N = 1552) 516 (N = 742) No ILD (N = 879) Other ILD (N = 120)
Age at diagnosis
 Median (Q1, Q3) 69 (62, 78) 69 (62, 79) N/A 68 (63, 76)
Gender
 Female 65 (4%) 23 (3%) 46 (5%) 7 (6%)
 Male 1487 (96%) 719 (97%) 833 (95%) 113 (94%)
Race
 Black 253 (16%) 110 (15%) 142 (16%) 20 (17%)
 White 1212 (78%) 592 (80%) 703 (80%) 97 (81%)
 Hispanic 8 (1%) 7 (1%) 8 (1%) 0 (0%)
 Other 15 (1%) 8 (1%) 3 (0%) 1 (1%)
 Not Reported 64 (4%) 25 (3%) 23 (3%) 2 (2%)
BMI
 Median (Q1, Q3) 27.7 (24.7, 31.7) 27.5 (24.7, 30.7) 28.3 (25.1, 32.6) 28.8 (25.1, 32.3)
BMI–categorized
 <25 kg/m2 425 (27%) 207 (28%) 215 (25%) 28 (24%)
 25–29.9 kg/m2 585 (38%) 316 (43%) 328 (37%) 39 (33%)
 ≥30 kg/m2 540 (35%) 219 (30%) 334 (38%) 51 (43%)
Smoker
 Current 321 (21%) 125 (17%) 169 (19%) 18 (15%)
 Ever 910 (59%) 468 (63%) 399 (45%) 76 (63%)
 Never 258 (17%) 124 (17%) 259 (29%) 21 (18%)
 Not Reported 63 (4%) 25 (3%) 52 (6%) 5 (4%)
COPD 755 (49%) 291 (39%) 263 (30%) 54 (45%)
Asthma 117 (8%) 49 (7%) 82 (9%) 6 (5%)
Lung cancer 110 (7%) 58 (8%) 59 (7%) 23 (19%)
Gastroesophageal reflux disease 620 (40%) 283 (38%) 363 (41%) 40 (33%)
Coronary artery disease 515 (33%) 298 (40%) 250 (28%) 43 (36%)
Coronary heart failure 282 (18%) 137 (18%) 108 (12%) 19 (16%)
Stroke 172 (11%) 84 (11%) 68 (8%) 11 (9%)
Diabetes mellitus 575 (37%) 282 (38%) 306 (35%) 35 (29%)
Obesity 292 (19%) 103 (14%) 205 (23%) 28 (23%)
PFT group
 Not Reported 829 391 879 56
 1. Restrictive 347 (48%) 211 (60%) 0 (0%) 35 (55%)
 2. Obstructive 245 (34%) 71 (20%) 0 (0%) 15 (23%)
 3. Isolated DLCO Impairment 108 (15%) 63 (18%) 0 (0%) 12 (19%)
 4. Normal 23 (3%) 6 (2%) 0 (0%) 2 (3%)
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Table 2 reports the differences in clinical care of subjects stratified by ICD code. Overall, the cohort had relatively high rates of high-resolution CT scans performed across both 515 and 516 ICD-9 codes (>79%). Subjects with 515 and 516 codes had similar, but low, rates of bronchoscopy. Across all groups, there were low rates of transbronchial or surgical lung biopsies performed. There was moderate usage of oxygen therapy, which increased from initial diagnosis to the last recorded visit. There was a moderate use of corticosteroids and low rates of IPF therapy use across the cohort.

Table 2. Clinical care characteristics stratified by ICD-9 code.

515 (N = 1552) 516 (N = 742) No ILD (N = 879) Other ILD (N = 120) p-value
Had a high resolution CT scan 1347 (87%) 589 (79%) 637 (73%) 110 (92%) <0.0011
Bronchoscopy 119 (8%) 73 (10%) 28 (3%) 24 (20%) <0.0011
Biopsy type <0.0011
 Not Reported 6 2 1 0
 Surgery 82 (5%) 94 (13%) 38 (4%) 9 (8%)
 TBBX 76 (5%) 47 (6%) 23 (3%) 12 (10%)
Oxygen therapy at diagnosis 252 (16%) 165 (22%) 36 (4%) 18 (15%) <0.0011
Oxygen therapy at last visit 629 (41%) 436 (59%) 173 (20%) 47 (39%) <0.0011
PPI drug 942 (61%) 454 (61%) 510 (58%) 79 (66%) 0.2851
Corticosteroid drug 758 (49%) 334 (45%) 268 (30%) 51 (43%) <0.0011
Antifibrotic drug 18 (1%) 46 (6%) 0 (0%) 0 (0%) <0.0012
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1Chi-Square

2Fisher Exact

ICD- International Classification of Diseases; ILD–Interstitial Lung Disease; TBBX–Transbronchial Biopsy; PPI–Proton Pump Inhibitor

We then defined the incidence and prevalence of ILD in VISN6. Given our broad entry criteria for chart review, we applied broad and narrow case definitions to this analysis. A broad case definition was defined as any individual with an ILD diagnostic code. The narrow definition was defined as any individual with an ILD diagnostic code who had a HRCT scan or a bronchoscopic or surgical lung biopsy. The overall estimated prevalence in the verified ILD subjects (including 515 and 516) was 256 per 100,000 people (0.256%) with a mean incidence across the years of 70 per 100,000 person-years (0.07%). Prevalence and incidence rates stratified by VISN6 locations and year are in Table 3. Using the individual ICD-9 groupings, 515 had a prevalence of 158 (0.158%) and a mean incidence of 49 (0.049%), while the 516 group were 98 (0.098%) and 22 (0.022%), respectively. To determine the impact of the diagnosis on mortality, we used clinical data from this cohort over the course of their medical care within the VA. Fig 1 illustrates a Kaplan Meier curve comparing subjects with a 515, 516 (excluding 516.31), or 516.31. The median survival was 76.9 months for 515 codes, 103.4 months for 516 codes (excluding 516.31), and 83.6 months for 516.31. Notably, 515 and 516.31 had a lower survival rate than 516 subjects (excluding 516.31) (log-rank, p<0.001).

Table 3.

A. Prevalence and incidence of ILD at six VA health care centers among patients with ICD-9 515 or 516. B. Prevalence and incidence of ILD at six VA health care centers among patients with ICD-9 515 or 516 and HRCT, surgical lung biopsy, or transbronchial lung biopsy.

A.
Prevalence (per 100,000 people) Incidence (per 100,000 person-years)
as of 12/31/15 2008 2009 2010 2011 2012 2013 2014 2015 Average
Durham 181 25 41 48 48 77 58 94 80 59
Fayetteville 106 18 32 39 38 46 38 45 29 36
Asheville 393 79 121 114 97 98 43 123 91 96
Richmond 240 40 101 73 81 81 56 135 70 80
Salem 395 59 96 64 77 88 65 98 80 78
Salisbury 138 20 147 99 111 117 78 158 108 105
Average 256 39 80 66 69 80 55 102 73 70
B.
Prevalence (per 100,000 people) Incidence (per 100,000 person-years)
as of 12/31/15 2008 2009 2010 2011 2012 2013 2014 2015 Average
Durham 167 19 40 39 45 76 56 83 70 53
Fayetteville 97 18 30 32 31 41 37 37 29 32
Asheville 379 85 97 96 91 92 51 123 89 91
Richmond 254 33 99 73 71 86 54 126 64 76
Salem 365 57 90 59 59 69 62 97 74 71
Salisbury 106 13 103 79 77 90 64 147 93 83
Average 237 35 69 57 57 72 52 95 67 63
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Incidence per 100,000 subjects was calculated as the number of subjects diagnosed with an ILD in each year divided by the number of unique subjects who visited one of the included VA centers in that calendar year. Incidence rates were then averaged across years. Prevalence per 100,000 subjects was calculated as the number of subjects diagnosed with an ILD before December 31st, 2015 and still alive at this date, divided by the number of unique subjects who visited one of the included VA centers in 2013–2015 and were alive as of December 31st, 2015 (as an estimate of the number of VA subjects in the system at these centers).

ILD–Interstitial Lung Disease

Fig 1. Kaplan Meier survival based on ICD-9 code.

Fig 1

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Information obtained from electronic medical record review was used to define survival for all-cause mortality.

Discussion

The present study identifies and describes a cohort of ILD subjects within the Veteran’s Health Administration in the Mid-Atlantic Region. We embarked on this study to address if the veteran population is enriched for ILD. Our hypothesis was based on that fact that the veteran population exhibits risk factors (male sex, advanced age and active or prior smoking history) known to associate with increased ILD prevalence. In this study, we confirmed that ILDs are enriched in our veteran cohort, including IPF. Additionally, we noted a number of individuals (26% of cohort) coded with an ILD diagnostic code, but in whom there was no observed ILD following chart review.

Our study identified an estimated prevalence of 256 per 100,000 people and an average incidence across the study period of 70 per 100,000 person-years for ILD subjects using ICD-9 codes and EMR manual review. We analyzed this data using broad and narrow case definitions as some veterans do not receive all of their care in the VA, and to address concerns about the accuracy of the diagnosis. Even with this narrow case definition, the ILD incidence and prevalence was significantly higher than other literature on ILD epidemiology [8]. Previous ILD registries in the USA have estimated prevalence rates of 14.3–63 per 100,000 people and incidence rates of 7.4–17.3 per 100,000 person-years [9–11]. European ILD epidemiological studies have also estimated rates much lower than this study with incidence rates ranging from 0.76–34.34 per 100,000 person-years [12–17].

Our findings confirm the prevailing hypothesis that ILD is enriched within the VA. Though not proven in the present study this is likely due to increased risk factors for ILD in veterans. Whereas the prevalence of ever-smoking is less than 50% among the general population in the US, we found smoking rates to be more than 70%, consistent with enriched smoking rates in veterans [18]. Another potential explanation for the higher observed values is that our dataset evaluates a more generalized population than a specific registry. This is supported by recent studies using general population cohorts that demonstrated higher incident and prevalence rates than prior registry studies [19]. A limitation to our analysis is that the denominator we used to calculate incidence and prevalence was based on the number of patients who visited one of the included VA centers in a given time frame and did not include patients who are in the VA system but did not visit during that time period. Therefore, it is possible that we underestimated the total number of patients and therefore overestimated the incidence and prevalence rates. Our generous entry criteria (any individual with any ILD ICD-9 billing code during the defined study period) likely also identified more ILD subjects. Given that each of these underwent a manual EMR review, we are confident that this is not just a misclassification of individuals without ILD. Future studies will need to validate our observations in other VA regional networks and/or VA national databases.

We observed a significant population of subjects who had an ILD-associated billing code but no evidence of ILD following manual chart abstraction. This could suggest an inherent misunderstanding of the clinical criteria of ILD or coding based on an impression prior to obtaining imaging or other diagnostic studies. The extent of the miscoding was likely higher in our cohort due to our broad search criteria. This was purposely designed to be more inclusive in an attempt to capture as many individuals with ILD as possible. Despite this, our data are consistent with studies documenting inaccuracies in the use of billing codes for clinical research [20–22]. This issue was noted in recent a survey where more than half of the respondents with ILD had at least one clinical misdiagnosis [23]. Overall, our data highlights that caution should be used when defining ILD epidemiology solely based on cohorts identified by ICD codes without careful validation of their accuracy.

Interestingly, we observed a difference in survival between the 515 vs. 516 groups. Generally, IPF is considered the ILD with the highest mortality risk. As IPF falls under the 516 group (coded as 516.31), we expected that 516 mortality would be worse than those coded as 515. To address this, we also performed the analysis of the mortality of 516.31 separate from 516. The reason we observed worse mortality in the 515 group in our cohort is not clear. It is possible that IPF cases were misclassified as a 515 code. Similar to other epidemiological studies using ICD codes, we believe that the 515 code is sometimes used as the generic code for ILD, of which IPF is one of the most common diagnoses [11, 19, 24]. The Veteran population, based on demographics, has a pretest probability of IPF and thus even general 515 diagnostic codes may be capturing IPF. We observed a similar issue with use of the 516 codes, where the code for “IPF” (516.31) and the code for “other specified alveolar and parietoalveolar pneumonopathies” (516.8) exhibited very similar clinical characteristics, suggesting that the 516.8 code is likely a group that would be clinically diagnosed as IPF. Future studies will review specific clinical and radiographic criteria to evaluate miscoding between the 515 and 516 codes to understand the difference in mortality rates.

The comorbidities across the 515, 516, and other ILDs were similar except for higher rates of COPD in 515 codes. This is also reflected in the pulmonary function testing which showed a greater frequency of obstructed patterns in the 515 vs the other groups. Pulmonary function testing also noted a number of individuals with “isolated DLCO impairment” physiologic patterns. The high rates of obstructive and isolated DLCO impairment patterns could suggest a higher rate of co-existing emphysema, also known as combined pulmonary fibrosis and emphysema [25]. This could be explained by the higher rates of smoking generally seen in veteran cohorts [18]. We also noted higher rates of coronary disease in 516 ICD 9 codes, which have been associated with progressive fibrotic diseases [26, 27].

We also defined the frequency of diagnostic testing and therapeutic interventions for ILD within our cohort to determine how often veterans received appropriate interventions. The American Thoracic Society, European Respiratory Society, and British Thoracic Society all provide best practice and guidelines on the diagnosis, treatment, and monitoring of subjects with ILD [28, 29]. High resolution computed tomography (HRCT) is critical for the initial diagnostic approach. Pulmonary function testing at diagnosis complements HRCT by providing a representation of severity. Furthermore, serial pulmonary function tests have prognostic value in ILD [30–33]. Despite relatively high performance of HRCT in this cohort, a significant proportion of the VA cohort did not receive PFTs, nor were these performed at later points as a measure to follow disease progression. A possible explanation is that many veterans receive healthcare principally through community-based providers and are only infrequently followed at the VA. Therefore, some of these studies may have been performed outside of the VA and were not captured in our database. Alternatively, it is possible that clinical care for ILD subjects in VA sites does not prioritize PFTs as an important clinical measure or that these patients have limited access to pulmonary providers with ILD expertise. Additionally, we noted low use of approved IPF therapies. A potential explanation is that the drugs were approved during the study period. Additionally, there was then a period of required central VA authorization of use and implementation at the individual VA sites in VISN6. Alternatively, this could reflect an unmet need in the VA, particularly at VISN6 sites which are not affiliated with academic medical centers where there is access to ILD specialists. In future work, we plan to explore differences by facility and available site resources. ‬

Similar to other retrospective studies done on large datasets there are limitations to our analysis. First, our population only included United States military veterans from the Mid-Atlantic region, therefore this may not be generalizable to other VA sites. The veteran population is predominantly male, elderly, more-likely to be white, non-Hispanic, and economically dis-advantaged compared to the general population [34, 35]. Additionally, there may be a selection bias, as our cohort includes veterans who have opted to receive their medical care within the Veterans Health Administration. VHA users tend to be older, less economically advantaged, report more chronic medical conditions, and have higher rates of combat exposure than non-VHA users [36, 37]. We did not acquire data from outside the VHA health system for veterans that receive care through the community (i.e. non-VA community care). Prior studies of the veteran’s population has shown that two-thirds of veterans have access to non-VHA care through other government programs and that one-half of these veterans will receive care outside the VHA [38]. Veterans can also be sent to community providers through various VHA programs if there is a lack of specific ILD specialty providers or long wait times. Third, our study lacked age-matched controls to use a comparator group. Fourth, we relied on billing codes from a single encounter [39] to find our initial cohort, which, as noted above, can be inaccurate. The decision to use a single encounter was made to collect a population as comprehensive as possible with the limitation that this would likely increase our coding inaccuracies. Lastly, veteran deaths outside the VHA system are not generally recorded in the VHA data warehouse, thus potentially affecting our ability to accurately define mortality. Despite these limitations, we believe our results provide insight into the real-world epidemiology of interstitial lung disease and its impact on veterans’ health.

Conclusion

Here we report the epidemiology of a large distinct cohort of VHA patients with interstitial lung disease. Identifying patients by diagnostic codes followed by a detailed EMR review allow us, for the first time, to define the types and characteristics of ILD in a VHA population. Based on this analysis, veterans are at a substantially higher risk for developing ILDs than other non-VA cohorts. It highlights that ILD is a critical issue for veterans’ health, and requires increased attention and awareness for ILD within the VA.

Supporting information

S1 Data

(CSV)

Click here for additional data file. (1.1MB, csv)
S1 Table. ICD-9 codes.

(JPG)

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S2 Table. Characteristics of patient population.

(JPG)

Click here for additional data file. (155.7KB, jpg)
S3 Table. Characteristics of patient population stratified by ICD-9 code.

(JPG)

Click here for additional data file. (594KB, jpg)
S1 Fig. CONSORT diagram of the VISN6 ILD cohort based on ICD-9 code.

(JPG)

Click here for additional data file. (62.9KB, jpg)

Acknowledgments

JCB performed the initial subject identification and data extraction. RK assisted with data warehouse development, data cleaning, and data extraction. AB, RAP, RMT and AR performed chart abstraction and review for quality. LH performed the statistical analysis. DS and KW assisted with data interpretation. AB and RMT drafted the manuscript. RAP and RMT conceived of the overall project.

Data Availability

All relevant data are within the manuscript and its Supporting information files.

Funding Statement

This research was supported by an investigator-initiated grant from Boehringer Ingelheim Pharmaceuticals, Inc. (BIPI). BIPI had no role in the design, analysis or interpretation of the results in this study. BIPI was given the opportunity to review the manuscript for medical and scientific accuracy as it relates to BIPI substances, as well as intellectual property considerations.

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Decision Letter 0

Mehrdad Arjomandi

7 Dec 2020

PONE-D-20-27587

Interstitial Lung Disease in a Veterans Affairs Regional Network; a Retrospective Cohort Study

PLOS ONE

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Reviewer #1: General: This is an interesting study that seeks to establish the burden of ILD in the Veteran population. The comments made below are for additional clarification. I hope that the authors find them useful.

Abstract

1. Incidence should be reported as person-time (example per 100,000 person-years)

2. It would be helpful to also include incidence and prevalence as a percentage in parenthesis. For example, 256 per 100,000 (0.26%).

Introduction

1 The intro refers to morbidity mortality of 3.75 per 100,000 people. It is unclear what this is referencing – IPF, ILD? As an attributable fraction mortality of the general population? Please clarify. It seems like a low morbidity and mortality rate for IPF.

2 The authors report that the incidence and prevalence of ILD is poorly characterized. This is not quite accurate as there has been substantial epidemiologic work in this area. However, as the authors point out, not much has been published from the VA. I would suggest reframing paragraph 2 of the introduction in a different way – that there is a gap in knowledge about epidemiology of ILD in the VA. As it is currently written, paragraph 2, line 84 suggests that the VA can fil the general ILD epidemiology knowledge gap. This is not necessarily true as the VA is a very unique population that may not be generalizable to other populations.

Methods

1. The ICD-9 to ICD-10 conversion happened in 10/2015. The authors refer to study period as 1/1/2008 to 12/31/2015 – please clarify

2. Additional discussion on how the ILD ICD codes were selected would be helpful. Prior literature references? Understanding the authors algorithm approach would be helpful to the reader.

3. Please include details about how comorbidity data, bronchoscopy, CT scan etc was extracted … was it by ICD/CPT code? If so, include those codes in the supplement. What was the look back period for comorbidities (ex. 2 outpatient comorbidity codes over the year prior to ILD diagnosis).

4. For period prevalence from 2013 – 2015, it seems like the authors excluded patients who died before 12/31/2015. If I am understanding this correctly, that means that someone who was diagnosed with ILD and died 11/2015 would not be counted in the period prevalence calculation? Thus, their contribution to prevalence for 2013 and 2014 would be lost? Is that correct? This seems like it would lead to under estimation of prevalence. Please clarify.

5. For incidence calculation, did authors exclude patients who had prior ILD ICD codes in previous years? What was the lookback period?

6. More discussion is needed on chart review. How was the diagnosis of ILD confirmed on chart review? Physician note? Multidisciplinary conference? CT with fibrosis on report?

7. Could patients who had 515 codes then go on to have 516.3 codes in subsequent years? In other words, could the same patient be part of multiple different samples? This would be important to note if the cohorts outlined are or are not mutually exclusive.

8. The authors report that in many cases, there was no PFT data recorded in EMR. Did authors look at PIT (non-VA community care paid for by the VA) data?

Results

1. For Figure E1 (CONSORT diagram), it would be helpful to know what codes were represented in the “no ILD on chart review” and #/% breakdown.

2. The low use of antifibrotic does not seem appropriate for this study – pirfenidone and nintedanib were approved towards the end of the study period and thus the low rates of utilization may be biased as only a very small fraction would have been eligible.

3. Please see questions about incidence and prevalence calculations above in methods section.

4. It appears that the authors did quite extensive chart review, which is quite impressive. However, something that is missing from the results is a discussion of the accuracy of the diagnosis when charts were reviewed. Although the authors do account for yes/no ILD, there seems to be an opportunity to also state whether the ILD codes used reflect specific ILD diagnostic accuracy. For example, did someone diagnosed with an ICD code for sarcoid truly have sarcoid? Is this data available?

5. For patients who had no ILD on chart review, could it be because they received part of their care outside the VA?

6. For those who did not have ILD but had an ICD code for ILD, it would be helpful to provide additional details here about how that misclassification occurred. What were those ICD codes associated with? Imaging? Miscoding on outpatient notes?

Discussion

1. Please specify in the first sentence that the study represents the Mid-Atlantic region.

2. The authors state in line 245 that they had a large number of individuals coded with ILD but with no observed ILD on chart review. I would suggest removing the world large and specify the percentage. 26% misclassification is actually lower than what has been noted in some other databases which have reported up to 50% misclassifications.

3. Lines 285 – 287 seem somewhat misplaced as it does not seem like the authors were specifically evaluating diagnostic disagreement at the clinical level.

4. I would suggest reframing the discussion lines 290 – 302 about 515 vs 515.3. Other studies have suggested that the 515 code is often given in the initial workup of IPF and many patients with IPF may have only these diagnosis codes. My suggestion would be to frame it as in the Veteran population, based on demographics, there is a high pre test probability of IPF and thus even general 515 diagnostic codes may be capturing IPF.

5. Could the possibility that CPFE may be captured by the 515 code explain the difference in survival between the cohorts? Does this represent a unique subgroup of patients with IPF + emphysema?

6. The comment about Veteran death not being recorded in VHA data warehouse is surprising. As I understand it, death date is captured in CDW even if the patient’s death occurred outside the VA system. Please confirm.

7. One additional limitation to mention is that the data includes one geographic region and may not be generalizable to the rest of the US (ex. due to differences in exposures, age breakdown etc.)

Reviewer #2: The manuscript described the incidence/prevalence, clinical characteristics and outcomes of ILD patients within veteran’s administration Mid Atlantic Health Care Network (VISN6) from January 1, 2008 to December 31st, 2015. Patients were identified by at least one visit encounter with a 515, 516, or other ILD ICD-9 cod. The study identified 3293 subjects met the inclusion criteria. 879 subjects (26%) had no evidence of ILD following manual medical record review. Overall estimated prevalence in verified ILD subjects was 256 per 100,000 people with a mean incidence across the years of 70 per 100,000 people. The prevalence and mean incidence when focusing on people with an ILD diagnostic code who had a HRCT scan or a bronchoscopic or surgical lung biopsy was 237 per 100,000 people and 63 per 100,000 people respectively. The median survival was 76.9 months for 515 codes, 103.4 months for 516 codes, and 83.6 months for 516.31. The study concluded from this retrospective cohort data that there are high ILD incidence/prevalence within the VA. Therefore, ILD is an important VA health concern.

General comments

I believe that this is an interesting study with comprehensive data of ILD among Veteran population. There are some difficulties in conducting such a study which I believe the authors have addressed reasonably well. Prospective study addressing ILDs among Veteran population is highly needed to address multiple question which was difficult to address in the current study.

Major Critiques:

1) Certain types of ILD like chronic hypersensitivity pneumonitis and connective disease (CTD-ILD) which represents a large portion of ILD among Veteran was not addressed in current study

2) Inhalational and environmental exposure during military deployment is very common among Veteran and consider a risk for developing ILDs. The authors did not address veteran occupational and environmental exposure.

3) It is not clear if autoimmune work up for CTD was part of ILD work up in this ILD Cohort

4) HRCT chest was done in large number of ILD patients with Code 515 (87%), 516 (73%), other ILD (92%). It would be interesting if the author would use chest HRCT data to define the type of ILD and correlate this data with ICD-9 code.

Minor Critiques:

Abstract/introduction

1) There is contradiction between research question in the abstract and primary objective of this study described in last 3 lines of the introduction section “ The primary objective of this study is to describe the characteristics, diagnosis, management, and outcomes of the patients with ILD”

The management of ILD patient was not clearly described in the current study as most of these ILD patients was managed outside VA systems. The study did not discuss for example steroid/ Azathioprine and N-acetyl-cysteine which were commonly used in the period from 2008-2014. The study did not discuss the use of pulmonary rehabilitation and lung transplant as modalities of ILD treatment.

Result section

1) Line 185-186: There was a moderate use of corticosteroids and low rates of IPF therapy use across the cohort. Please explain low rate of IPF therapy use (antifibrotics or other).

2) The study examined ILD patients from 2008 till 2015 where most of patient were treated with corticosteroid and Azathioprine and N-acetylcysteine. Since antifibrotics drugs (Pirfenidone and Nintedanib) were approved by FDA in October 2014 for use in IPF. It is possible that low survival rate due to the use of steroid and lack of antifibrotic therapy.

3) Table E2: Mixed connective tissue disease was present in 1% of entire cohort. Please explain the association between CTD and ILD.

Discussion

Line 294-295: “it is possible that IPF cases were misclassified as a 515 code” To confirm this statement, is any possibility the authors look to the clinical and radiologic data to confirm the diagnosis of ICD-9 code especially the authors were able to exclude 879 subjects (26%) of cohort after finding no radiologic and clinical evidence of ILD.

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Reviewer #1: No

Reviewer #2: Yes: Nadia A Hasaneen

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PLoS One. 2021 Mar 18;16(3):e0247316. doi: 10.1371/journal.pone.0247316.r002

Author response to Decision Letter 0

7 Jan 2021

Response to Reviewer Comments to the Author

Reviewer #1:

General: This is an interesting study that seeks to establish the burden of ILD in the Veteran population. The comments made below are for additional clarification. I hope that the authors find them useful.

a. We thank the review for the positive appreciation of the study.

Abstract

1. Incidence should be reported as person-time (example per 100,000 person-years)

a. We have made this change in the manuscript to address this concern. All incidence was calculated per year (with one year being the interval). Therefore, the data per 100,000 people and per 100,000 person-years is equivalent in this setting.

2. It would be helpful to also include incidence and prevalence as a percentage in parenthesis. For example, 256 per 100,000 (0.26%).

a. We added the percentages in parenthesis to the text.

Introduction

1. The intro refers to morbidity mortality of 3.75 per 100,000 people. It is unclear what this is referencing – IPF, ILD? As an attributable fraction mortality of the general population? Please clarify. It seems like a low morbidity and mortality rate for IPF.

a. We apologize for the confusion. This was a reference to males with IPF. It came from a 2018 study looking at WHO data of European Union IPF data from 2001-2013. We clarified this in the text specifying that this refers to median mortality of IPF in males standardized to the 2013 European population.

2. The authors report that the incidence and prevalence of ILD is poorly characterized. This is not quite accurate as there has been substantial epidemiologic work in this area. However, as the authors point out, not much has been published from the VA. I would suggest reframing paragraph 2 of the introduction in a different way – that there is a gap in knowledge about epidemiology of ILD in the VA. As it is currently written, paragraph 2, line 84 suggests that the VA can fil the general ILD epidemiology knowledge gap. This is not necessarily true as the VA is a very unique population that may not be generalizable to other populations.

a. We appreciate the feedback and have reformatted the text to try and address the concern. We do feel that there is a relative paucity of epidemiology data on ILD. The majority of this data focuses exclusively on IPF or on specific types of ILD (though to a lesser extent in these subgroups). Additionally, the data from ILD tends to be from single site centers. Therefore, larger databases such as the VA may offer some additional information on ILD epidemiology that is not presently available. We do agree with the reviewer that the veteran population is unique and therefore has some unique characteristics from a generalized population. We highlighted this in the limitations of the manuscript. However, we also feel that the data available from the VA including nationwide VA data could offer insights about ILD overall that are presently not available from single center and/or Medicare claims databases.

Methods

1. The ICD-9 to ICD-10 conversion happened in 10/2015. The authors refer to study period as 1/1/2008 to 12/31/2015 – please clarify.

a. We studied subjects who were coded with ILD with ICD-9 during the aforementioned years, then followed them forward through till January 2019. We did not capture ICD10 codes from 10/01/2015-12/31/2015. This is now clarified in the manuscript.

2. Additional discussion on how the ILD ICD codes were selected would be helpful. Prior literature references? Understanding the authors algorithm approach would be helpful to the reader.

a. The goal of the present study was to be inclusive of any ILD ICD-9 codes. Our experimental design was to identify any potential patients with ILD and then use the chart review to clarify the diagnosis. The ILD ICD codes were selected by the research team, primarily pulmonologists who specialize in ILD, based upon review of the available ICD-9 codes for ILD. We did not restrict ILD ICD-9 codes for this study. This is now clarified in the text.

3. Please include details about how comorbidity data, bronchoscopy, CT scan etc was extracted … was it by ICD/CPT code? If so, include those codes in the supplement. What was the look back period for comorbidities (ex. 2 outpatient comorbidity codes over the year prior to ILD diagnosis)?

a. Comorbidities, procedures, and imaging were principally extracted from the corporate data warehouse through ICD/CPT codes. These were then confirmed during the manual chart abstraction, and comorbidities were added if noted in the progress notes but not specifically coded as an ICD (e.g. GERD). To be as inclusive as possible, there was no limit to the lookback period as long as there were available records in the VA EMR. The prospective data collection went through January 2019 when the database was locked.

4. For period prevalence from 2013 – 2015, it seems like the authors excluded patients who died before 12/31/2015. If I am understanding this correctly, that means that someone who was diagnosed with ILD and died 11/2015 would not be counted in the period prevalence calculation? Thus, their contribution to prevalence for 2013 and 2014 would be lost? Is that correct? This seems like it would lead to under estimation of prevalence. Please clarify.

a. We appreciate the close review and apologize for the confusion, we did not restrict mortality events. We recorded all deaths that occurred between 2013 and January 2019 for all subjects if the data was available. This is now clarified in the methods section.

5. For incidence calculation, did authors exclude patients who had prior ILD ICD codes in previous years? What was the lookback period?

a. We did not exclude patients who had prior ILD codes in the previous years. Once identified a subject with at least one ILD encounter, we went back as far as possible in CPRS for those subjects to collect data. Therefore, in some incidences the subject was identified in the study period by and encounter but their date of diagnosis was before the specified period. The purpose of defining individuals this way not to limit the cohort but rather to define all individuals with an ILD encounter code and then define the characteristics of those individuals regardless of the date of their initial diagnosis, etc. We hope this clarifies the reviewer’s concern and discussion about this was added to the methods section.

6. More discussion is needed on chart review. How was the diagnosis of ILD confirmed on chart review? Physician note? Multidisciplinary conference? CT with fibrosis on report?

a. We agree that this is an important consideration particularly since we hypothesized that there could be significant inaccuracy between the diagnostic code and the actual ILD diagnosis. Therefore, the ICD-9 diagnosis was confirmed based on a thorough chart review. The data from this chart review included physician notes, and CT scan reports. This was reviewed by the study team to confirm or refute the diagnosis. As the study included VA’s not associated with academic centers, there was not universal availability of a multidisciplinary conference for review of the ILD diagnosis at these sites. We have clarified this in the text.

7. Could patients who had 515 codes then go on to have 516.3 codes in subsequent years? In other words, could the same patient be part of multiple different samples? This would be important to note if the cohorts outlined are or are not mutually exclusive.

a. There were instances where patients were coded with both 515 and 516 codes. In this setting, the study team used the physician notes, exam, laboratory values and CT scan report to clarify which of the ICD-9 codes were accurate and this was then used this to define their ICD-9 group. This typically occurred in the 515 group and led to either refinement to a 516 group or determination that the subject did not have an ILD (typically miscoded based on being lung granuloma). No individual ended up in two cohorts in this study. To address this potential confusion, we have clarified this in the text.

8. The authors report that in many cases, there was no PFT data recorded in EMR. Did authors look at PIT (non-VA community care paid for by the VA) data?

a. We thank the reviewer for the astute observation. We did not look at PIT data. We acknowledge this as a limitation of our study in the Discussion section. Unfortunately, we did not have complete access to the documentation (notes, results, imaging) of non-VA community care. As these records were not consistently available and we were concerned about including potentially limited and inaccurate datasets.

Results

1. For Figure E1 (CONSORT diagram), it would be helpful to know what codes were represented in the “no ILD on chart review” and #/% breakdown.

a. We thank the reviewer for this suggestion. We have now updated this graphic to include this information.

2. The low use of antifibrotic does not seem appropriate for this study – pirfenidone and nintedanib were approved towards the end of the study period and thus the low rates of utilization may be biased as only a very small fraction would have been eligible.

a. We appreciate the astute observation. Though pirfenidone and nintedanib were approved towards the end of the study period, there was an additional delay as the VA went through a process of authorization of use and then the local sites had to develop the appropriate processes for distribution. We agree that this could introduce bias. We now add this concern to the Discussion. However, we feel it is important to keep this information in the manuscript as it will inform if this will increase over time as they are available in the VA and also if there are differences in use based on regional VA sites despite a known population of veterans with ILDs qualifying their use.

3. Please see questions about incidence and prevalence calculations above in methods section.

a. This has been addressed above

4. It appears that the authors did quite extensive chart review, which is quite impressive. However, something that is missing from the results is a discussion of the accuracy of the diagnosis when charts were reviewed. Although the authors do account for yes/no ILD, there seems to be an opportunity to also state whether the ILD codes used reflect specific ILD diagnostic accuracy. For example, did someone diagnosed with an ICD code for sarcoid truly have sarcoid? Is this data available?

a. The reviewer raises an excellent point. The present study focused on the overall characteristics of the cohort and asked the initial question of how often ILD was coded but did not have clinical evidence supporting an ILD diagnosis. For this, we relied on the physician notes and CT scan reports to assess the accuracy of a yes/no for the diagnosis. The question of the diagnostic accuracy of specific ILD subtype is one we are very interested in assessing. This specific question is a focus of future analysis that is being presently performed. This will focus on the accuracy of the original physician diagnosis with centralized reviews of CT scans, etc. The data is available, but is beyond the scope of this initial report of our dataset.

5. For patients who had no ILD on chart review, could it be because they received part of their care outside the VA?

a. We agree that this is a possibility and acknowledged it as a potential limitation. A large number of these individuals had some clinical data typically including radiographs that did not support an ILD diagnosis. However, it is possible that the reviewer is correct.

6. For those who did not have ILD but had an ICD code for ILD, it would be helpful to provide additional details here about how that misclassification occurred. What were those ICD codes associated with? Imaging? Miscoding on outpatient notes?

a. We agree with the reviewer. Unfortunately, we have limited data for these patients. The original extraction used encounter codes to identify these patients. However, in general, the patients lacked radiographic data or outpatient notes supporting the use of the encounter codes. Alternatively, in several cases the ILD encounter code was associated with a radiograph revealing lung granulomas on the report. We expected miscoding based on our study design, as the goal was to identify as many ILD as possible and then confirm accurate diagnosis based on the chart review.

Discussion

1. Please specify in the first sentence that the study represents the Mid-Atlantic region.

a. We have added this clarification.

2. The authors state in line 245 that they had a large number of individuals coded with ILD but with no observed ILD on chart review. I would suggest removing the world large and specify the percentage. 26% misclassification is actually lower than what has been noted in some other databases which have reported up to 50% misclassifications.

a. We thank the reviewer for this comment. We have made this change.

3. Lines 285 – 287 seem somewhat misplaced as it does not seem like the authors were specifically evaluating diagnostic disagreement at the clinical level.

a. We appreciate the feedback and have made the change.

4. I would suggest reframing the discussion lines 290 – 302 about 515 vs 515.3. Other studies have suggested that the 515 code is often given in the initial workup of IPF and many patients with IPF may have only these diagnosis codes. My suggestion would be to frame it as in the Veteran population, based on demographics, there is a high pretest probability of IPF and thus even general 515 diagnostic codes may be capturing IPF.

a. We agree with the review. Our pretest probability was that there would be IPF patients coded as 515. During the course of the chart review, if it was determined that they did have IPF, then this was converted to the appropriate code. However, there could still be inaccuracy based on the clinical and radiologic interpretation of the treating physicians. We appreciate the feedback and have made that change to clarify this concern.

5. Could the possibility that CPFE may be captured by the 515 code explain the difference in survival between the cohorts? Does this represent a unique subgroup of patients with IPF + emphysema?

a. We are uncertain about the reason for the difference in the survival between the cohorts. It was not an expected finding. We did observe CPFE in the VA ILD cohort. The reviewer asks an interesting question and one we have actively been working on. We are currently performing an analysis of CPFE and hopefully will publish on this question soon. We felt that it was beyond the scope of this initial manuscript.

6. The comment about Veteran death not being recorded in VHA data warehouse is surprising. As I understand it, death date is captured in CDW even if the patient’s death occurred outside the VA system. Please confirm.

a. Death is a difficult topic in the Electronic Health Record. The federal government attempts to track all deaths across the US via its own mechanisms (social security) but also through state mechanisms. The VHA receives various files but not all death files are uploaded into the CDW. There can also be a delay in when this information becomes available in the HER.

7. One additional limitation to mention is that the data includes one geographic region and may not be generalizable to the rest of the US (ex. due to differences in exposures, age breakdown etc.)

a. We agree that this is a potential concern. Future studies looking at larger datasets will help to address this question. We have added this as a limitation to the text.

Reviewer #2:

The manuscript described the incidence/prevalence, clinical characteristics and outcomes of ILD patients within veteran’s administration Mid Atlantic Health Care Network (VISN6) from January 1, 2008 to December 31st, 2015. Patients were identified by at least one visit encounter with a 515, 516, or other ILD ICD-9 cod. The study identified 3293 subjects met the inclusion criteria. 879 subjects (26%) had no evidence of ILD following manual medical record review. Overall estimated prevalence in verified ILD subjects was 256 per 100,000 people with a mean incidence across the years of 70 per 100,000 people. The prevalence and mean incidence when focusing on people with an ILD diagnostic code who had a HRCT scan or a bronchoscopic or surgical lung biopsy was 237 per 100,000 people and 63 per 100,000 people respectively. The median survival was 76.9 months for 515 codes, 103.4 months for 516 codes, and 83.6 months for 516.31. The study concluded from this retrospective cohort data that there are high ILD incidence/prevalence within the VA. Therefore, ILD is an important VA health concern.

General comments

I believe that this is an interesting study with comprehensive data of ILD among Veteran population. There are some difficulties in conducting such a study which I believe the authors have addressed reasonably well. Prospective study addressing ILDs among Veteran population is highly needed to address multiple question which was difficult to address in the current study.

a. We completely agree with the reviewer and appreciate their favorable comments. We always viewed the present study as being the first of many that are needed to understand ILD within the VA.

Major Critiques:

1. Certain types of ILD like chronic hypersensitivity pneumonitis and connective disease (CTD-ILD) which represents a large portion of ILD among Veteran was not addressed in current study.

a. We agree with the reviewer. We are presently formulating a manuscript that focuses specifically on CTD-ILD in our cohort to expand on this present study. Hypersensitivity Pneumonitis diagnosis (ICD9 495.9) is included in the “other category” in the present study. We agree that chronic hypersensitivity pneumonitis is common in veterans (based on our own clinical experience) and this is not well documented in the present dataset. However, this diagnosis is complicated and frequently overlaps with IPF. Accurate distinction requires a level of detail and expertise that is not present in the available clinic notes and radiology studies (for example, many of CT scans are high resolution but not with inspiratory and expiratory imaging). Future studies will need to consider understanding this in the VA but will likely need to be prospective studies with specific pulmonary and radiologic expertise.

2. Inhalational and environmental exposure during military deployment is very common among Veteran and consider a risk for developing ILDs. The authors did not address veteran occupational and environmental exposure.

a. We agree this is an important topic, particularly in Veteran populations. We do have this data presently but are planning to address this issue in a subsequent manuscript.

3. It is not clear if autoimmune work up for CTD was part of ILD work up in this ILD Cohort

a. We agree this is an important point about accurate diagnosis of a CTD-ILD. One of the overriding issues we feel this manuscript and future ones will demonstrate is the lack of consistency in the diagnostic evaluation of ILD within the VA. As a result, autoimmune work-ups are inconsistently performed depending on the regional site and the individual providers. This makes it difficult to provide a definitive answer to the reviewer’s important question.

4. HRCT chest was done in large number of ILD patients with Code 515 (87%), 516 (73%), other ILD (92%). It would be interesting if the author would use chest HRCT data to define the type of ILD and correlate this data with ICD-9 code.

a. We agree completely with the reviewer. This first manuscript was limited to the specific questions outlined. However, we agree this is an important question. A portion of the CT scans in this cohort were independently reviewed by a chest radiologist to evaluate their accuracy. This is a planned analysis that is currently in process.

Minor Critiques:

Abstract/introduction

1. There is contradiction between research question in the abstract and primary objective of this study described in last 3 lines of the introduction section “The primary objective of this study is to describe the characteristics, diagnosis, management, and outcomes of the patients with ILD”

a. We thank the reviewer for picking up this discrepancy. We have corrected this in the text.

2. The management of ILD patient was not clearly described in the current study as most of these ILD patients was managed outside VA systems. The study did not discuss for example steroid/ Azathioprine and N-acetyl-cysteine which were commonly used in the period from 2008-2014. The study did not discuss the use of pulmonary rehabilitation and lung transplant as modalities of ILD treatment.

a. The reviewer asks an important question. Much of the ILD care occurred within the VA in this cohort. As noted in Figure 2, there was steroid use in this cohort (though the majority did not appear to be sustained use but rather short steroid courses). In terms of azathioprine use, this was low in the VA cohort (~3%) with similar low percentage of use of mycophenolate. This suggests low use of steroid sparing agents and other measures common to ILD management in the VA. This includes lung transplant, which was very uncommon in the cohort. One of the issues we hope that our study and future studies will raise is the importance of ILD within the VA, thereby guiding efforts to improve the care of this disease.

Result section

1. Line 185-186: There was a moderate use of corticosteroids and low rates of IPF therapy use across the cohort. Please explain low rate of IPF therapy use (antifibrotics or other).

a. Much of the steroid use was for shorter courses of therapy (acute steroid tapers). However, it was difficult to distinguish these during the course of the chart review unless specifically spelled out by the clinician in the chart. We agree that the use of IPF therapies was low in this cohort. This was discussed in response to reviewer #1. We suspect this is a combination of the timing of the cohort (ala around the time of initial IPF therapy approval), delayed access to the medication and poor identification and treatment of IPF. We feel this data is important to raise this as a potential area of future research within the VA. We added a discussion about the low rates of IPF therapies in the Discusion section.

2. The study examined ILD patients from 2008 till 2015 where most of patient were treated with corticosteroid and Azathioprine and N-acetylcysteine. Since antifibrotics drugs (Pirfenidone and Nintedanib) were approved by FDA in October 2014 for use in IPF. It is possible that low survival rate due to the use of steroid and lack of antifibrotic therapy.

a. This is an excellent question. We agree that this cohort was developed during a period of transition in IPF care. As the results of the PANTHER trial supported that immunosuppressive regimens were associated with harm, while pirfenidone and nintedanib were approved as a treatment for IPF. This raises questions about the difference in survival rates. It is possible that this could account for the differences observed. We suspect that it reflects issues of treatment, diagnostic accuracy and appropriate care. Despite our best efforts, this was not able to be clarified in the cohort. We feel this data will highlight questions about the use of IPF and ILD treatments in the VA and direct future studies.

3. Table E2: Mixed connective tissue disease was present in 1% of entire cohort. Please explain the association between CTD and ILD.

a. We apologize for the confusion. For the sake of clarity, we removed this mixed connective tissue designation from the table. We are presently drafting a separate manuscript focused on individuals diagnosed with CTD-ILD and their characteristics.

Discussion

1. Line 294-295: “it is possible that IPF cases were misclassified as a 515 code” To confirm this statement, is any possibility the authors look to the clinical and radiologic data to confirm the diagnosis of ICD-9 code especially the authors were able to exclude 879 subjects (26%) of cohort after finding no radiologic and clinical evidence of ILD.

a. In the present study, we used the available clinical records, PFTs and radiology reports to assess the rationale behind the diagnostic code utilized. If there was a diagnosis of IPF in this evaluation, but a miscoding as 515 in this setting the record was corrected. However, we did not independently review the CT scan for this present study to assess this accuracy. For this reason, we agree that it is possible that there may be additional cases of IPF in the 515 group. Alternatively, for the no ILD group this assessment was possible based on the available data in the electronic record. We hope this clarifies the reviewer’s concern.

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Decision Letter 1

Mehrdad Arjomandi

5 Feb 2021

Interstitial Lung Disease in a Veterans Affairs Regional Network; a retrospective cohort study

PONE-D-20-27587R1

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Acceptance letter

Mehrdad Arjomandi

4 Mar 2021

PONE-D-20-27587R1

Interstitial Lung Disease in a Veterans Affairs Regional Network; a retrospective cohort study

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Associated Data

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    S1 Data

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    S3 Table. Characteristics of patient population stratified by ICD-9 code.

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    S1 Fig. CONSORT diagram of the VISN6 ILD cohort based on ICD-9 code.

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