Quality of life in patients with hypersensitivity pneumonitis and with idiopathic pulmonary fibrosis in real-life clinical practice

Introduction

Background

Interstitial lung diseases (ILDs) represent a heterogeneous group of disorders characterized by varying degrees of pulmonary fibrosis and inflammation, significant clinical symptoms, high mortality rates, and substantial impacts on patients’ quality of life (QoL) (1). In both clinical practice and the majority of clinical studies involving ILD patients, disease progression is typically assessed through pulmonary function tests (2). While physiological parameters play a key role in objectively evaluating disease severity and progression, they often correlate poorly with patients’ functional capacity and overall well-being (3,4). From this perspective, the study of health-related QoL (HRQL), which reflects patients’ self-assessment of their physical, social, emotional, and cognitive states in response to illness, is gaining increasing relevance and is frequently utilized as an endpoint in clinical trials (5-7).

Idiopathic pulmonary fibrosis (IPF) is a chronic fibrosing lung disease predominantly diagnosed in older individuals, characterized by progressive dyspnea, high mortality, and a significant impact on patients’ HRQL (8-10). Hypersensitivity pneumonitis (HP) is an immune-mediated disease that develops in susceptible individuals following exposure to inhaled antigens and is often associated with progressive pulmonary fibrosis, significantly affecting patients’ HRQL (11,12).

Rationale and knowledge gap

Despite differences in the pathophysiological mechanisms of IPF and HP, as well as variations in patient age and comorbidities, both conditions exert a comparable impact on daily life activities and HRQL (13). To date, significantly fewer studies have investigated HRQL in HP compared to IPF (2,13,14). Lubin et al. demonstrated that patients with chronic HP experience lower HRQL and more severe clinical symptoms than those with IPF (13). Currently, numerous methods exist for assessing HRQL, but no standardized approach has been established for ILD evaluation. One of the most widely used instruments is the King’s Brief Interstitial Lung Disease Questionnaire (K-BILD), a validated HRQL assessment tool specifically designed for ILD patients (15). Sinha et al. have shown that K-BILD correlates with the St. George’s Respiratory Questionnaire (SGRQ) in patients with IPF and other ILDs, such as HP and nonspecific interstitial pneumonia, as these diseases share similar clinical manifestations, functional impairments, and progressive courses in most cases (16). Additionally, the EuroQoL 5-Dimension 5-Level Questionnaire (EQ-5D-5L) is frequently used in HRQL research on ILD. This instrument has been validated for assessing HRQL in both respiratory and non-respiratory diseases (17-22).

Objective

This study aimed to evaluate HRQL in patients with IPF and HP using both K-BILD and EQ-5D-5L questionnaires, as well as to identify potential factors significantly influencing HRQL in these patient populations. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1042/rc).

Methods

Study design

This longitudinal, observational, non-interventional study was conducted at the Clinic of Pulmonology and Respiratory Medicine of Sechenov University (Moscow). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethic Committee of Sechenov University (Protocol No. 19-23). Written informed consent was obtained from all participants.

Patients

The study size was determined by the number of patients who met the inclusion criteria and were consecutively enrolled in the study during routine clinical practice at a university hospital between November 1, 2023 and February 1, 2025.

The study included adult patients (over 18 years of age) who had a confirmed diagnosis of HP or IPF. The HP group was subdivided into fibrotic HP (fHP) and non-fibrotic HP (nfHP), with a separate subgroup of fHP patients exhibiting the high-resolution computed tomography (HRCT) pattern of usual interstitial pneumonia (UIP). Patients were not included if they had any of the following conditions: obstructive lung diseases, decompensated cardiovascular disorders, active cancer or pulmonary metastases, morbid obesity (body mass index >40 kg/m²), or pneumonia. Definitions of fHP and nfHP phenotypes were based on the American Thoracic Society/Japanese Respiratory Society/Asociación Latinoamericana del Tórax (ATS/JRS/ALAT) guideline (12). The diagnosis of HP was made by a multidisciplinary team based on clinical symptoms, the patient history, HRCT findings, and, when available, histological pattern and/or bronchoalveolar lavage analysis. Specifically, the diagnosis of fHP required the presence of parenchymal and small airway involvement typical for HP, combined with radiologic features of pulmonary fibrosis such as reticulation with architectural distortion and traction bronchiectasis and/or honeycombing (12). The UIP pattern was defined according to the ATS/European Respiratory Society (ERS)/JRS/ALAT guideline (23). Patients classified as nfHP had a chronic disease course. At the time of inclusion, all nfHP patients had no ongoing exposure to the inciting antigen and demonstrated persistent abnormalities on HRCT typical for nfHP (12) and pulmonary function testing despite prior therapy.

The diagnosis of IPF was made in accordance with the ATS/ERS/JRS/ALAT guideline and its 2022 update, considering patient history, laboratory tests, HRCT findings, and histopathological data when available (1,23). The disease progression was assessed based on the ATS/ERS/JRS/ALAT criteria (1).

Data collection

Demographic characteristics and clinical signs were analyzed including the disease duration, age, sex, body mass index, smoking history, dyspnea severity [using the modified Medical Research Council (mMRC) dyspnea scale] (24), cough severity [using a visual analog scale (VAS)] and cough-specific QoL [using the Leicester Cough Questionnaire (LCQ)] (25), comorbidities (Charlson Comorbidity Index) (26) and gender-age-physiology (GAP) index (27). We performed six-minute walk test (6MWT) and pulmonary function measurements. We also measured the right heart size, pulmonary artery systolic pressure (PAPsys), and tricuspid annular plane systolic excursion (TAPSE) using echocardiography. Laboratory findings included arterial blood gas parameters (PaO₂, PaCO₂, pH). Measurements were conducted at baseline and at 12 months of the follow-up. All HRCT scans were independently reviewed by two radiologists. This dual-review approach ensured a consistent and expert-based classification of fibrotic changes (1).

Pulmonary function tests

Spirometry and body plethysmography were performed according to the ERS recommendations (28). Both absolute values and percentages of predicted values for forced vital capacity (FVC), forced expiratory volume in 1 second (FEV₁), FEV₁/FVC ratio, total lung capacity (TLC), residual volume (RV), and functional residual capacity (FRC) were recorded, following ATS/ERS reference equations. Diffusing capacity of lungs for carbon monoxide (DLco) was measured using the single-breath carbon monoxide diffusion method, with results expressed as absolute values and percentages of predicted values, adjusted for hemoglobin concentration. Calculations adhered to the Global Lung Function Initiative standards (29). The composite physiologic index (CPI) was calculated using the formula: 91.0− (0.65× DLco %pred.)−(0.53× FVC %pred.) + (0.34× FEV₁ %pred.) (30).

QoL assessment

Patients’ HRQL and symptom burden were assessed prospectively using K-BILD and EQ-5D-5L questionnaires at baseline and in the first year of the follow-up. In our study, all questionnaires were administered at the beginning of each visit, prior to any clinical testing or interaction with study personnel.

The K-BILD questionnaire used the Russian version dated July 22, 2016 (K-BILD—Russia/Russian). The EQ-5D-5L instrument was used in its Russian translation provided by the EuroQol Research Foundation, Estonia (version 1.0). The K-BILD total score, ranging from 0 to 105, was derived from 15 questions with seven response options; the higher scores corresponded to the better HRQL (15). We considered the K-BILD total score <50 as a threshold for the poor prognosis (31).

The EQ-5D-5L questionnaire included five domains, such as mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, graded on five points each, followed by VAS scale ranging from 0 to 100, with 100 representing the best health status. Scores were converted into a 0-to-1 scale (with 1 indicating the highest HRQL) using a Russian population-specific valuation algorithm, and the overall value was summarized from the responses to all five questions (32,33).

Risk of bias

As this was an observational study, several efforts were made to minimize potential sources of bias. Consecutive patient enrollment was used to reduce selection bias. Data were collected prospectively according to a predefined protocol, and standardized assessment tools (K-BILD, EQ-5D-5L, LCQ, mMRC, etc.) were used to ensure consistency in data collection. Statistical analyses, including multivariable regression, were conducted to adjust for potential confounding variables. Additionally, data analysis was performed by independent researchers who were not involved in patient care to reduce information and observer bias.

Statistical analysis

Data normality was assessed using the Shapiro-Wilk test. Descriptive statistics included the median (Me) and interquartile range (IQR) or the mean and the standard deviation (SD) for continuous variables, and percentages for categorical variables. When necessary, some variables were categorized to aid clinical interpretation; for example, GAP stages were used as categorical indicators of disease severity (I, II, III) based on established thresholds. Comparisons of continuous variables between the groups were made using the Mann-Whitney U test for two independent samples and the Wilcoxon test for paired samples. The Kruskal-Wallis test was used for multiple comparisons. When comparing three or more groups using the Kruskal-Wallis test, categorical variables were analyzed using Fisher’s exact test or Pearson’s chi-square test. Correlations were assessed using Spearman’s method. Furthermore, the internal consistency was assessed for the EQ-5D-5L domains and the total score using the Cronbach’s alpha coefficient. A linear regression analysis was performed to explore an association between HRQL domains, demographic data, clinical symptoms and lung function findings. Prior to being included in the multiple regression model, all variables were assessed for multicollinearity. Receiver operator characteristic (ROC) curves were used to calculate sensitivity and specificity of predicting HRQL score less than 50 points on the K-BILD scale by risk factors and to determine optimal cutoff values (Youden method). Results of the ROC analysis were presented as the area under the curve (AUC), 95% confidence interval (CI), and diagnostic significance level (P). The significance level was considered at P<0.05. Statistical processing of the data was done using IBM SPSS Statistics, version 26 (SPSS, Chicago, IL, USA).

Results

General characteristics

IPF and HP

A total of 153 patients were included in the study: 48 were diagnosed with IPF and 105 with HP. Among HP patients, 72 (68.6%) exhibited fHP phenotype, of whom 32 (30.5%) demonstrated HRCT-pattern of UIP, while 33 (31.4%) presented with nfHP phenotype.

The main characteristics for patients with IPF, HP and separately for fHP and fHP + UIP are presented in Tables 1,2.

Dyspnea severity assessed by mMRC scale was significantly higher in IPF patients compared to HP patients, but no significant differences in dyspnea severity were found when comparing IPF with fHP subgroup. Additionally, no statistically significant difference was observed in cough severity (VAS scale) or the impact of cough on HRQL (LCQ) between IPF and HP patients. The distance covered in 6MWT (6MWD) was significantly lower in the IPF group compared to HP patients. However, no significant difference was found between the groups regarding SpO₂ values during the 6MWT or arterial blood gas analysis.

No significant difference was found between IPF and HP groups in FVC or TLC; FEV₁ was significantly lower in HP patients, whereas the FEV₁/FVC ratio did not differ significantly between these groups. In contrast, DLco was significantly lower in IPF patients compared to HP patients, although no significant differences were observed between IPF and the fHP subgroup.

Fibrotic and nfHP

A comparative analysis revealed a significant difference between fHP and nfHP across multiple parameters such as age, prevalence of cardiovascular diseases, Charlson Comorbidity Index, GAP score, dyspnea and lung function parameters.

It is important to note that patients with fHP + UIP pattern differed significantly in disease duration [84 (IQR: 36–192) months in fHP + UIP vs. 37 (IQR: 21–81) months in fHP without UIP, P=0.01] and the period between the disease onset and the time of diagnosis [45 (IQR: 18–120) months in fHP + UIP vs. 24 (IQR: 6–40) months in fHP without UIP, P=0.02]. Patients with fHP + UIP demonstrated significantly lower pulmonary function compared to fHP without UIP.

QoL

K-BILD

HRQL according to the K-BILD questionnaire did not differ significantly between IPF patients and the entire group of HP patients as well as between patients with fHP and those with fHP + UIP. However, HRQL was significantly lower in the fHP subgroup compared to nfHP (Table 3) (Figure 1).

Figure 1 K-BILD total score in IPF and HP groups, nfHP and fHP groups, fHP without UIP and fHP + UIP groups. *, P<0.05. fHP, fibrotic hypersensitivity pneumonitis; fHP + UIP, fibrotic hypersensitivity pneumonitis with UIP pattern; HP, hypersensitivity pneumonitis; IPF, idiopathic pulmonary fibrosis; K-BILD, King’s Brief Interstitial Lung Disease Questionnaire; nfHP, non-fibrotic hypersensitivity pneumonitis; UIP, usual interstitial pneumonia.

Higher GAP stage was associated with lower HRQL according to K-BILD questionnaire, with median score of 63 (IQR: 51–78) in GAP stage I, 46 (IQR: 43–57) in GAP stage II, and 44 (IQR: 36–52) in GAP stage III (P<0.001) in HP group. Also, in IPF group, the lower K-BILD score, the higher GAP stage: 54 (IQR: 40–61) in GAP stage II vs. 42 (IQR: 31–55) in GAP stage III, P=0.14.

EQ-5D-5L

EQ-5D-5L score significantly differed between patients with IPF and those with HP across several domains, as well as in the total score and VAS score. The self-care domain and the usual activity domain showed more severe impairment in IPF group compared to the HP group. The VAS score and the EQ-5D-5L value were significantly lower in IPF group compared to the HP group (P=0.002) (Table 3) (Figure 2).

Figure 2 EQ-5D-5L value in IPF and HP groups, nfHP and fHP groups, fHP without UIP and fHP + UIP groups. *, P<0.05. EQ-5D-5L, EuroQoL 5-Dimension 5-Level Questionnaire; fHP, fibrotic hypersensitivity pneumonitis; fHP + UIP, fibrotic hypersensitivity pneumonitis with UIP pattern; HP, hypersensitivity pneumonitis; IPF, idiopathic pulmonary fibrosis; nfHP, non-fibrotic hypersensitivity pneumonitis; UIP, usual interstitial pneumonia.

Among patients with IPF, the most impaired EQ-5D-5L domain was usual activity domain [4 (IQR: 3–4)], whereas the least affected domain was discomfort [2.5 (IQR: 2–3), P<0.001]. In contrast, in the HP group, including both fHP and fHP + UIP subgroups, the most affected domain was mobility [3 (IQR: 3–4)], whereas the least affected domain was anxiety/depression, with median scores of 2 (IQR: 1–3), P<0.001.

No statistically significant difference was identified in EQ-5D-5L score between IPF patients and patients with fHP (Table 3).

Comparing fHP and nfHP groups, patients in the fHP group demonstrated lower HRQL in the following EQ-5D-5L categories: “mobility”, 3 (IQR: 3–4) vs. 3 (IQR: 2–3), respectively, P=0.04; “self-care”, 3 (IQR: 2–3) vs. 1 (IQR: 1–2), respectively, P=0.002; and “usual activities”, 3 (IQR: 2.5–4) vs. 2 (IQR: 2–3), respectively, P=0.02. Additionally, the EQ-5D-5L VAS scale was significantly lower in the fHP group [60 (IQR: 45–70) vs. 70 (IQR: 60–80) in the nfHP group (P=0.003)]. The EQ-5D-5L value was 0.695 (IQR: 0.606–0.789) in the fHP group compared to 0.818 (IQR: 0.737–0.906) in the nfHP group (P=0.01) (Figure 2).

No significant difference was detected in HRQL between fHP + UIP and fHP based on EQ-5D-5L questionnaire.

IPF patients were stratified into GAP stage II or III, with no significant difference in HRQL between the stages. HP patients significantly differed depending on GAP stage in the EQ-5D-5L “mobility” domain [3 (IQR: 2–3) in GAP stage I; 3 (IQR: 2–4) in GAP stage II; and 3.5 (IQR: 3–4) in GAP stage III (P=0.04)]. For the “self-care” domain, scores were 2 (IQR: 1–3) in GAP stage I; 3 (IQR: 2–3) in GAP stage II; and 3 (IQR: 2–3.5) in GAP stage III (P=0.048), indicating a progressive decline in the perceived health status across GAP stages I, II, and III.

Comparison of HRQL questionnaires: K-BILD and EQ5D-5L

When comparing the results of different HRQL questionnaires, a strong correlation was identified between individual categories and EQ-5D-5L value, as well as between the EQ-5D-5L and K-BILD scores.

K-BILD score was moderately correlated to the following EQ-5D-5L domains: mobility (r =−0.508, P<0.001), self-care (r =−0.557, P<0.001), usual activities (r =−0.515, P<0.001), pain/discomfort (r =−0.385, P<0.001), anxiety/depression (r =−0.535, P<0.001), as well as with VAS score (r =0.469, P<0.001) and the overall calculated index value of the EQ-5D-5L (r =0.685, P<0.001).

A scatter plot demonstrated a statistically significant relationship between the K-BILD total score and the EQ-5D-5L value (R² =0.351, P<0.001) (Figure 3).

Figure 3 Scatter plot between K-BILD total score and EQ-5D-5L value. EQ-5D-5L, EuroQoL 5-Dimension 5-Level Questionnaire; K-BILD, King’s Brief Interstitial Lung Disease Questionnaire.

In the overall HRQL assessment using EQ-5D-5L, the contribution of each domain was equal, as confirmed by the multiple linear regression model (R² =0.870, P<0.001): EQ-5D-5L mobility (P<0.001), EQ-5D-5L self-care (P=0.002), EQ-5D-5L pain/discomfort (P<0.001), EQ-5D-5L usual activities (P<0.001), EQ-5D-5L anxiety/depression (P<0.001), while the VAS score did not contribute significantly to the EQ-5D-5L value.

The Cronbach’s alpha coefficient for EQ-5D-5L was 0.827, with the Cronbach’s alpha scores when excluding each domain as follows: when excluding the mobility domain, 0.787; when excluding the self-care domain, 0.758; when excluding the usual activities domain, 0.754; when excluding the pain/discomfort domain, 0.815; and when excluding the anxiety/depression domain, 0.839, indicating slightly greater significance of the mobility, self-care, and usual activities domains.

Follow-up assessment

Prospective data were obtained for 20 out of 48 (42%) IPF patients and for 69 out of 105 (66%) HP patients.

In 12 months of the follow-up, differences between patients with IPF and HP in key clinical and functional parameters diminished. After 12 months, the statistically significant difference between the groups was observed only for DLco [41 (IQR: 36–44)% pred. in IPF group vs. 51 (IQR: 41–63)% pred. in HP group, P=0.02].

Over the 6-month mark, HRQL still differed between patients with IPF and HP (EQ-5D-5L overall value 0.561 (IQR: 0.540–0.668) vs. 0.839 (IQR: 0.715–0.890), respectively, P=0.01; EQ-5D-5L pain/discomfort 0.128 (IQR: 0.06–0.19) vs. 0.053 (IQR: 0–0.066), respectively, P=0.04). However, after 12 months, no statistically significant difference in HRQL was found between the groups across all domains of both questionnaires.

Over the 1-year observation period, no statistically significant difference was found in disease progression between IPF and HP patients. Mortality rate was higher in IPF group compared to HP (25% vs. 7%, P=0.02), but no difference in mortality rate was found between IPF and fHP groups (25% vs. 1%, P=0.09), as well as between IPF and fHP + UIP groups (25% vs. 14%, P=0.37).

Relationship between HRQL and clinical and functional parameters

During univariate linear regression analysis, it was shown that the HRQL according to the K-BILD questionnaire depended on the following parameters: the presence of fibrosis on HRCT of the chest (R² =0.066, P=0.01), GAP scores (R² =0.079, P=0.007), a history of cardiovascular diseases (R² =0.039, P=0.054), the cough score on the VAS (R² =0.115, P=0.002), LCQ scale scores (R² =0.303, P=0.001), the assessment of dyspnea on the mMRC scale (R² =0.198, P=0.001), 6MWD (R² =0.171, P=0.001), FVC % pred. (R² =0.081, P=0.007), FEV₁ % pred. (R² =0.065, P=0.02), TLC % pred. (R² =0.066, P=0.02), DLco % pred. (R² =0.106, P=0.002), and the CPI index (R² =0.131, P=0.001).

When performing multiple stepwise linear regression analyses, factors that showed significance in the univariate regression analysis were included, taking into account the collinearity of factors. The best model (R² =0.720, P=0.001) describing the dependence of HRQL according to K-BILD included the following factors: LCQ scale scores, 6MWD, and the assessment of dyspnea on the mMRC scale (Table 4).

According to the results of ROC analysis, the following were identified as predictors of HRQL score less than 50 points on the K-BILD scale: a cough score<13 points on the LCQ [Sensitivity (Se) 68.2%, Specificity (Sp) 60.0%; AUC 0.721 (95% CI: 0.557–0.885), P=0.008] and a dyspnea score of ≥3 on the mMRC scale [Se 86.5%, Sp 62.5%; AUC 0.652 (95% CI: 0.537–0.767), P=0.01].

The assessment of HRQL according to EQ-5D-5L during univariate linear regression analysis depended on the following variables: diagnosis (R² =0.076, P=0.008), fibrosis on HRCT of the chest (R² =0.06, P=0.02), age (R² =0.06, P=0.02), GAP scale scores (R² =0.096, P=0.004), a history of cardiovascular diseases (R² =0.062, P=0.02), the Charlson index (R² =0.045, P=0.05), the cough score on the VAS (R² =0.072, P=0.04), the LCQ score (R² =0.061, P=0.056), the assessment of dyspnea on the mMRC scale (R² =0.144, P=0.001), 6MWD (R² =0.187, P=0.001), SpO₂ measured at the beginning of the 6MWT (R² =0.059, P=0.004), DLco % pred. (R² =0.054, P=0.02), the CPI index (R² =0.060, P=0.004), TAPSE (R² =0.060, P=0.04), and the need for long-term oxygen therapy (R² =0.053, P=0.03).

Using multiple linear regression analysis with stepwise inclusion of variables, the optimal model (R² =0.321, P=0.01) determining the dependence of HRQL according to EQ-5D-5L included the following patient characteristics: 6MWD and LCQ scale scores (Table 4).

During the study, we separately examined the factors influencing HRQL in the HP and IPF groups.

When studying the factors determining HRQL according to K-BILD and EQ-5D-5L (value) using multiple regression analysis separately in the IPF group, a satisfactory model could not be obtained, which is likely due to the sample size.

However, among patients with HP, a multiple regression model was constructed using stepwise variable inclusion (R² =0.764, P=0.001), which included the following factors: LCQ scale scores, 6MWD, and FVC % pred. (Table 4).

Among patients with HP, predictors of a K-BILD score less than 50 points included an FVC lower than 55% pred. [Se 59.0%, Sp 58.3%; AUC 0.651 (95% CI: 0.510–0.792), P=0.03], and an LCQ total score less than 13 points [Se 73.3%, Sp 61.5%; AUC 0.738 (95% CI: 0.553–0.924), P=0.01].

In the HP group, in the multiple regression analysis for HRQL according to EQ-5D-5L, it was found that the final model (R² =0.170, P=0.003) included only one factor: 6MWD (Table 4).

Discussion

According to the results of our study, patients with HP were characterized by the predominance of fibrosis, a high prevalence of the UIP pattern on HRCT, prominent clinical signs and symptoms, and functional impairments compatible with those of IPF patients. In our cohort, 44% of patients with fHP demonstrated a typical UIP pattern on HRCT, which is in line with the findings of a cohort study including patients with IPF or fHP in the US in 2003 to 2019, where 30.8% of fHP patients exhibited honeycombing on HRCT (34). Patients with fHP and UIP pattern on HRCT had significantly longer disease duration and more functional impairments compared to IPF patients. These findings are attributable to the delayed diagnosis of HP and pulmonary fibrosis among these patients.

Comparison of HRQL between IPF and HP groups

It is noteworthy that a significant difference between patients with IPF and HP was found only for some EQ-5D-5L domains and for the VAS scale, while no significant intergroup differences were observed with the K-BILD questionnaire. Regression analysis revealed that HRQL measured by EQ-5D-5L depended on diagnosis (IPF or HP), whereas K-BILD scores were not significantly influenced by diagnosis.

To date, there are limited studies evaluating HRQL in HP, especially in comparison with IPF. According to Aronson et al., the K-BILD score in patients with HP was 54.8 (IQR: 46.5–61.0); in the study by Hasan et al., where HP predominated, the mean K-BILD score was 52, which is comparable to our findings of 52.0 (IQR: 45.0–65.0) (35,36).

According to the ILD-patient-reported outcome (PRO) Registry, patients with HP did not significantly differ in HRQL from those with other progressive pulmonary fibroses (37).

Lubin et al. demonstrated that patients with chronic HP had poorer HRQL than those with IPF according to the short form 36 health survey (SF-36) questionnaire (13).

An important focus of our study was to assess HRQL in patients with different HP phenotypes—fHP and nfHP—and specifically in patients with fHP + UIP. Patients with fHP demonstrated lower HRQL scores compared to nfHP patients, with differences found in both the K-BILD and EQ-5D-5L questionnaires. In patients with fHP HRQL was comparable to that in IPF patients.

At the same time, patients with fHP and honeycombing did not significantly differ in HRQL from patients with fHP without honeycombing.

Thus, it is crucial to consider disease phenotype when designing future studies on HRQL in HP.

In addition, we identified differences in HRQL between patients with HP at different stages according to the GAP questionnaire of the K-BILD and some EQ-5D-5L categories. Regression analysis also confirmed that GAP stage was a significant predictor of HRQL in both instruments. These findings support the notion that the GAP index reflects not only disease severity and prognosis but also HRQL. Several studies have also reported associations between GAP stage and HRQL in ILDs (18,38). Therefore, in addition to diagnosis and phenotype, disease stage according to the GAP index—which has demonstrated its utility across ILD subtypes—should be considered in HRQL assessment.

Different methods of HRQL assessment

One of the objectives of our study was to compare the results of HRQL assessment using the two most commonly employed questionnaires in ILDs—EQ-5D-5L and K-BILD.

We observed a strong correlation between various EQ-5D-5L domains and K-BILD scores, with the strongest correlation found between K-BILD total score and EQ-5D-5L index value. According to Szentes et al. and Wapenaar et al., moderate to strong associations exist between K-BILD and EQ-5D-5L scores, confirming the relevance of both tools for assessing HRQL in ILD patients (17,39).

Szentes et al. noted that EQ-5D-5L was more sensitive to sociodemographic variables (age, sex), whereas K-BILD was more responsive to lung function indicators (e.g., FVC % pred.), particularly in the domains “breathlessness and activities” and “chest symptoms” (17).

Our findings highlight several distinctions between these questionnaires: HRQL measured by K-BILD was more dependent on pulmonary function parameters (FVC, FEV₁, TLC, DLco), while HRQL as assessed by EQ-5D-5L was more influenced by SpO₂ levels, need for long-term oxygen therapy, certain echocardiographic parameters, history of cardiovascular disease, and Charlson Comorbidity Index.

Factors influencing QoL

Through regression analysis, we identified that the main factors determining HRQL in patients with IPF and HP were dyspnea severity according to the mMRC scale, the impact of cough on patients’ HRQL by the LCQ, and physical activity tolerance assessed by the 6MWT. These factors played a leading role in determining HRQL using both questionnaires (EQ-5D-5L and K-BILD), in the overall patient cohort as well as in the HP subgroup.

In ILDs, the 6MWT has been repeatedly used as a surrogate for physical functional capacity, aiding in assessing disease severity and prognosis. D’Souza et al. reported that in ILD patients, 6MWT desaturation was a major predictor of decreased HRQL as measured by both K-BILD and EQ-5D-5L (40). Prior et al. found weak to moderate correlations between K-BILD scores and 6MWD in IPF patients, with the strongest correlation observed for the total score and the breathlessness domain (38).

Dyspnea and cough are the most common symptoms in IPF and non-IPF fibrotic ILD patients. These symptoms significantly restrict daily physical activity, are associated with anxiety-depressive disorders, and influence prognosis and HRQL (31,41-43).

In our cohort, both HP and IPF patients experienced severe dyspnea. Our study demonstrated that mMRC dyspnea intensity had a substantial impact on HRQL, particularly using the K-BILD scale. An mMRC score >3 was a predictor of a K-BILD total score <50, associated with poor prognosis in IPF (31).

Several studies have also confirmed the relationship between K-BILD and dyspnea severity in ILD (31,43). In our study, up to 83% of HP and IPF patients reported complaints of cough. The LCQ score was one of the most significant predictors of decreased HRQL as measured by both K-BILD and EQ-5D-5L.

Cough is an important clinical symptom in ILD, potentially reflecting the severity of functional impairment and serving as a prognostic factor (44-46). Recent studies have also confirmed the relationship between cough and HRQL in ILD (45,46).

Lung function parameters (FVC, DLco) play a key role in assessing disease severity, progression, and prognosis in ILD patients (47,48).

In our univariate regression analysis, K-BILD scores were associated with FVC % pred., FEV₁ % pred., TLC % pred., DLco % pred., and the calculated CPI. However, in the regression model, FVC % predicted had a significant impact on K-BILD only in HP patients. When using the EQ-5D-5L questionnaire, a significantly lower influence of respiratory function indicators on the HRQL of patients was revealed.

These findings emphasize that physiological markers of disease severity, unlike clinical symptoms and general physical performance indicators, do not fully capture the impact of the disease on patients’ HRQL.

Conflicting results regarding the relationship between lung function and HRQL have also been reported in other studies. Aronson et al. showed that patients with HP and FVC <50% predicted, as well as those with DLco ≤59% predicted, had significantly lower HRQL (35). Maqhuzu et al. demonstrated the significance of FVC % pred. and DLco % pred. for K-BILD HRQL assessment in various ILDs (10,49). In INBUILD trial, the adjusted mean change in total K-BILD score at week 52 was slightly better in the nintedanib group compared to placebo, though not statistically significant. This modest benefit in HRQL aligns with the overall effect of nintedanib on disease progression and supports the need for further investigation of PROs in fibrosing ILDs (6). At the same time, Lubin et al. demonstrated that HRQL in patients with IPF and HP did not depend on pulmonary function parameters (FVC, DLco), highlighting that HRQL is influenced by a complex interplay of physiological and psychological factors (13).

Limitations

The limitations of this study include a relatively short observation period, which complicates the interpretation of dynamic changes over time. Additionally, this was a single-center study and the study design did not include an analysis of patient treatment over time. Patient dropout during the follow-up period due to the inability of patients to return for in-person visits, often due to long travel distances from their place of residence, disease progression, clinical deterioration, or death, may have affected the completeness and interpretation of longitudinal data. It should also be noted that the IPF and HP groups initially differed in terms of age, comorbidities, and pulmonary function parameters, which may influence differences in HRQL. However, it is important to emphasize that the data obtained reflect real-world clinical practice and are consistent with previous research.

We did not evaluate depressive and anxiety symptoms using specific questionnaires, although depressive symptoms have been shown to correlate strongly with HRQL scores in many diseases and are likely to have a significant impact on HRQL in patients with IPF and HP

Conclusions

Patients with fHP in our study were comparable to those with IPF, and in some parameters even presented with more severe disease than IPF patients, indicating the clinical burden of HP patients in real-world practice. In the course of our study, we observed minimal differences in HRQL between patients with IPF and HP, and no significant differences were found when comparing IPF and fHP. Based on our findings, the use of both EQ-5D-5L and K-BILD questionnaires is justified for the assessment of HRQL in patients with IPF and HP. When comparing the results of the HRQL assessment using both questionnaires, it shows a slightly greater influence of comorbid pathology on the EQ-5D-5L indicators, and a greater influence of respiratory function indicators on the K-BILD results. The key determinants of HRQL in patients with HP and IPF were the severity of clinical symptoms and physical activity tolerance. An interesting finding was the role of the often underestimated cough syndrome, which significantly affects patients’ HRQL.

HRQL indicators are highly relevant for patients with ILDs, particularly considering the limited number of therapeutic options available, which are often associated with a high risk of adverse events. Therefore, further studies on HRQL remain a priority in this patient population.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1042/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1042/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1042/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1042/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are 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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethic Committee of Sechenov University (Protocol No. 19-23). A written informed consent was obtained from all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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