Skip to main content

Performance of a diagnostic algorithm for fibrotic hypersensitivity pneumonitis. A case–control study

Abstract

Background

The differential diagnosis fibrotic hypersensitivity pneumonitis (HP) versus idiopathic pulmonary fibrosis (IPF) is important but challenging. Recent diagnostic guidelines for HP emphasize including multidisciplinary discussion (MDD) in the diagnostic process, however MDD is not comprehensively available.

We aimed to establish the diagnostic accuracy and prognostic validity of a previously proposed HP diagnostic algorithm that foregoes MDD.

Methods

We tested the algorithm in patients with an MDD diagnosis of fibrotic HP or IPF (case control study) and determined diagnostic test performances for diagnostic confidences of ≥ 90% and ≥ 70%. Prognostic validity was established using Cox proportional hazards models.

Results

Thirty-one patients with fibrotic HP and 50 IPF patients were included. The algorithm-derived ≥ 90% confidence level for HP had high specificity (0.94, 95% confidence interval [CI] 0.83–0.99), but low sensitivity (0.35 [95%CI 0.19–0.55], J-index 0.29). Test performance was improved for the ≥ 70% confidence level (J-index 0.64) with a specificity of 0.90 (95%CI 0.78–0.97), and a sensitivity of 0.74 (95%CI 0.55–0.88). MDD fibrotic HP diagnosis was strongly associated with lower risk of death (adjusted hazard ratio [HR] 0.10 [0.01–0.92], p = 0.04), whereas the algorithm-derived ≥ 70% and ≥ 90% confidence diagnoses were not significantly associated with survival (adjusted HR 0.37 [0.07–1.80], p = 0.22, and adjusted HR 0.41 [0.05–3.25], p = 0.39, respectively).

Conclusion

The algorithm-derived ≥ 70% diagnostic confidence had satisfactory test performance for MDD-HP diagnosis, with insufficient sensitivity for ≥ 90% confidence. The lowest risk of death in the MDD-derived HP diagnosis validates the reference standard and suggests that a diagnostic algorithm not including MDD, might not replace the latter.

Background

Interstitial lung diseases (ILD) lead to significant symptoms, physical impairment, and early mortality [1, 2]. Fibrotic hypersensitivity pneumonitis (HP) is an ILD subtype caused by an ongoing or repetitive exposure to an inhaled antigen in sensitised and susceptible individuals [1]. The identification of patients with HP is crucial, particularly to prevent ongoing exposure to the causative antigen. However, in chronic cases where an inciting antigen cannot be identified [3], distinguishing fibrotic HP from idiopathic pulmonary fibrosis (IPF) is challenging with considerable diagnostic overlap between these fibrotic ILD subtypes [4, 5]. Patients with IPF have a worse prognosis than patients with fibrotic HP [6], and the immunosuppressive therapy that is frequently used in HP can be detrimental for IPF patients [7]. This emphasizes the importance of the differential diagnosis fibrotic HP versus IPF.

Due to the heterogeneity in clinical presentation among patients with fibrotic HP and IPF there is no single diagnostic test for accurate discrimination. The recent diagnostic guidelines for HP emphasize the importance of multidisciplinary discussion (MDD), and include MDD in the proposed diagnostic approach [5]. However MDDs are time and resource consuming and not widely available [8], simpler, accurate tools for HP diagnosis are urgently needed. A diagnostic HP algorithm that foregoes MDD has been developed by Morisset and group, with consensus criteria that were identified by a structured Delphi approach [9].

The main objective of this case control study was to validate the algorithm for the differential diagnosis fibrotic HP versus IPF. We aimed to establish the diagnostic accuracy of the algorithm, and to determine the prognostic validity of the algorithm and MDD derived HP diagnoses.

Methods

Study population, setting, and measurements

Patients with fibrotic ILD are prospectively recruited within our ongoing cohort study (Swiss Ethics Committee, Bern, KEK 246/15 PB_2016-01524). For this retrospective case control study, we included consecutive patients who were diagnosed with either fibrotic HP (cases) or IPF (controls) in our MDD between 03/2013 and 11/2018. Patients were included if the necessary data for a confident MDD diagnosis was available, including exposure history, high resolution computed tomography (HRCT) chest scans, and cytological or histological results if bronchoalveolar lavage (BAL) or lung biopsy were performed. All patients were questioned regarding exposure to a potentially inciting antigen using a questionnaire customized to our local practice. Antigen exposures that were considered relevant for HP included mould, hay, agricultural dusts, bird feathers and droppings, and the use of a hot tub, swimming pool, or air humidifier.

Aiming to evaluate the diagnostic accuracy and prognostic validity of a fibrotic HP diagnostic algorithm [9], we included patients with IPF as a control group, since IPF is the most challenging differential diagnosis. We defined MDD diagnosis as our reference standard [1, 2, 5, 8]. As previously described, our MDD meetings include experienced chest radiologists, pathologists, and specialized ILD physicians [8, 10]. For the purpose of this study, a chest radiologist who was blinded for the diagnosis (LE) reanalysed the chest CT scans of all IPF and fibrotic HP cases.

Patient demographics and baseline characteristics were collected from the registry. All measurements were accomplished within 3 months of MDD diagnosis. Pulmonary function tests were conducted according to established protocols [11, 12]. We calculated the Composite Physiologic Index (CPI), which was developed to predict radiological extent of fibrosis by aggregating forced vital capacity (FVC), forced expiratory volume in 1 s (FEV1), and diffusing capacity of the lung for carbon monoxide (DLCO).[13] Serum IgG against specific antigens were measured in some patients, however results were not included in the proposed algorihm [9], and not considered for this study.

Algorithm for the diagnosis of fibrotic HP

We applied a slightly adapted version of the HP diagnostic algorithm proposed by Morisset and group [9]. Every case was approached in a stepwise manner as outlined in the algorithm, starting with stratification by exposure to an inciting antigen (Fig. 1). As proposed, a HRCT pattern was considered typical for HP if either a combination of mosaic attenuation, ground-glass and normal lung, or a combination of mosaic attenuation and signs of fibrosis were present [9]. Usual interstitial pneumonia (UIP) and probable UIP were classified based on the most recent diagnostic criteria for IPF [2]. This in slight contrast to the Morisset algorithm, which included possible instead of probable UIP [14]. We also included other HRCT patterns as a third category, since in our cohort some HRCT pattern were neither classified as (probable) UIP nor as typical for HP. The threshold for lymphocytosis in BAL was set at 40% as suggested [9]. The histopathological features for HP included chronic bronchiolocentric inflammation, poorly formed non-necrotizing granulomas, giant cells, airway-centred interstitial fibrosis, and the absence of an alternative diagnosis [15]. We considered provisional diagnosis with high confidence (diagnostic confidence 70–89%) and a confident diagnosis (diagnostic confidence ≥ 90%) as diagnostically meaningful for the differential diagnosis fibrotic HP versus IPF. Low confidence diagnosis (diagnostic confidence 50–69%) was not evaluated for diagnostic test performance [9, 16].

Fig. 1
figure1

Diagnostic algorithm for fibrotic HP with patients stratified by presence (red) or absence (blue) of an identified antigen exposure. *Radiological HP pattern: Combination of mosaic attenuation, ground-glass and normal lung, or a combination of mosaic attenuation and signs of fibrosis. Radiological definite and probable usual interstitial pneumonia (UIP) based on the 2018 diagnostic criteria for IPF. Pathological HP pattern: Chronic bronchiolocentric inflammation, poorly formed non-necrotizing granulomas, giant cells, airway-centred interstitial fibrosis, absence of an alternative diagnosis. BAL bronchoalveolar lavage, HP hypersensitivity pneumonitis, IPF idiopathic pulmonary fibrosis; UIP usual interstitial pneumonia

Statistical analysis

Baseline patient data are reported as mean (standard deviation) or median (interquartile range).

Diagnostic test characteristics with 95% confidence intervals (95% CI) were calculated for an algorithm derived diagnostic confidence ≥ 90% and ≥ 70% with an MDD diagnosis of fibrotic HP as reference standard. Sensitivity, specificity, positive and negative predictive values (PPV, NPV) were derived from two-by-two tables. Youden’s (J) index summarizes sensitivity and specificity, and is thus affected by disease prevalence, whereas diagnostic accuracy is the proportion of correctly classified cases among all cases, and not affected by disease prevalence. Based on the assumption that patients with a “true” diagnosis of fibrotic HP have a more favorable prognosis than patients with IPF [6, 17], we determined the prognostic validity of the MDD diagnosis and the algorithm derived diagnosis. Cox proportional hazards models were used to determine risk of mortality associated with baseline variables and fibrotic HP diagnoses (MDD versus algorithm derived). Adjusted models included radiological definite or probable UIP pattern, and CPI to account for potential confounding by ILD severity. Harrell’s C-statistic was used to indicate discrimination of the models. The proportional hazards assumption for all Cox regression models was tested based on weighted residuals. A two-sided p < 0.05 was used to indicate statistical significance. Data were analysed using R version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria) [18].

Results

Thirty-one and 50 patients with an MDD diagnosis of fibrotic HP or IPF respectively were included in this study (Table 1). 78% of the cohort were men with a mean (standard deviation) age of 67 (10) years, and a moderately reduced FVC of 68 (19) %-predicted, and a DLCO of 51 (20) %-predicted. 40 patients (49%) reported exposures to potentially causative antigens (74% and 34% of MDD HP and IPF patients, respectively). A radiological probable or definite UIP pattern was present in 29% and 86% of MDD HP and IPF patients, respectively, with 68% of the HP and 8% of the IPF patients showing a radiological pattern compatible with HP. Of the patients with MDD diagnosed HP 26 (84%) had BAL available with BAL lymphocytosis present in 14 of these cases. BAL was available in 21 (42%) of MDD diagnosed IPF cases, with lymphocytosis in 3 patients. Surgical lung biopsy was available in 22% of IPF and 23% of HP patients.

Table 1 Baseline characteristics by MDD diagnoses

Diagnostic accuracy

A diagnostic confidence for fibrotic HP of ≥ 90% and a diagnostic confidence 70–89% were reached in 14 cases each (Fig. 1). The algorithm-derived ≥ 90% confidence level had a high specificity (0.94 [95% CI 0.83–0.99]) but low sensitivity (0.35 [95% CI 0.19–0.55]) for MDD diagnosis of fibrotic HP, with an overall insufficient test performance (J-index 0.29 [95% CI 0.03–0.53]). The algorithm-derived ≥ 70% confidence level showed higher overall test performance (J-index 0.64 [95% CI 0.34–0.85]) with improved sensitivity (0.74 [95% CI 0.55–0.88]), positive (0.82 [95% CI 0.63–0.94]), and negative predictive values (0.85 [95% CI 0.72–0.93]), with marginally lower specificity (0.90 [95% CI 0.78–0.97]), (Table 2).

Table 2 Diagnostic test characteristics for algorithm derived HP diagnoses

Prognostic validity

Over a median time of 16 months (interquartile range 6–30), 28 patients deceased.

Per one % increase in DLCO patients had a 5% decrease in risk of death (hazard ratio [HR] 0.95, 95%CI 0.92–0.98, p = 0.007). FVC %-predicted was borderline associated with mortality (HR 0.98, 95%CI 0.96–1.00, p = 0.07), and CPI was significantly associated with mortality (HR 1.06, 95%CI 1.01–1.10, p = 0.01). Patients with a definite/probable radiological UIP pattern had a 5.4 higher risk of death (95% CI 1.62–17.8, p = 0.006). Age, sex, smoking status, body mass index, identification of the inciting antigen, and availability of surgical lung biopsy were not associated with survival.

MDD diagnosis of fibrotic HP was strongly associated with a lower risk of death on unadjusted analysis and with adjustment for potential confounding by disease severity (CPI) and a radiological UIP pattern (HR 0.10 [0.01–0.92], p = 0.04). The algorithm derived ≥ 70% confidence diagnosis showed a significant association with lower risk of death on unadjusted analysis, which lost statistical significance on adjusted analysis (HR 0.37 [0.07–1.80], p = 0.22). The algorithm derived ≥ 90% confidence diagnosis was not significantly associated with survival on unadjusted and adjusted analysis (HR 0.41 [0.05–3.25], p = 0.39), (Table 3, Fig. 2).

Table 3 Prognostic validity of MDD and algorithm derived HP diagnoses
Fig. 2
figure2

Survival by multidisciplinary discussion a and algorithm derived ≥ 70% diagnostic confidence b diagnoses for fibrotic HP versus IPF. Survival curves from Cox Proportional Hazard models adjusted for Composite Physiological Index and radiological pattern of definite or probable UIP pattern. HP hypersensitivity pneumonitis, IPF idiopathic pulmonary fibrosis, UIP usual interstitial pneumonia

Discussion

With this case control study, we validate a previously developed diagnostic algorithm for fibrotic HP [9], by assessing its diagnostic test performance in a cohort of patients with fibrotic HP and a control cohort of patients with IPF, using MDD as the reference standard. The algorithm derived diagnostic confidence ≥ 70% demonstrated a high specificity, acceptable sensitivity, and a diagnostic accuracy of 84%. An algorithm derived diagnostic confidence level ≥ 90% showed poor sensitivity and lower diagnostic accuracy (71%) with an expectedly higher specificity than the ≥ 70% confidence level. Overall, if a confidence level ≥ 70% was considered diagnostic for fibrotic HP, the algorithm performed well in our cohort, whereas diagnostic performance for the ≥ 90% threshold was insufficient.

MDD diagnosis of fibrotic HP showed a stronger association with survival than the algorithm derived diagnoses on unadjusted analysis, and with adjustment for disease severity and UIP pattern, which have been shown to impact survival in IPF and fibrotic HP beyond diagnosis [6, 17]. This observation strengthens the validity of MDD diagnosis as the reference standard in our cohort.

Identification of the inciting antigen is crucial for HP diagnosis and management. In patients with an identified antigen diagnostic confidence for HP is higher [9], the danger of IPF misclassification is lower [4], and prognosis is more favorable [3]. However, in less than 50% of patients an antigen can be identified [3]. We confirm that a systematic exposure assessment is helpful for antigen identification. With a questionnaire tailored to local practice we identified an antigen exposure in 74% of HP cases, but also in 34% of IPF cases, which underlines the low specificity of patient-reported antigen exposure, and the need for estimation of potential causality by the clinician [19]. Testing of specific serum IgG levels can confirm exposure to the antigen at some point in the patient’s life, without prove of causality to the ILD [5].

BAL lymphocytosis is important for HP diagnosis, either as a piece of information to the MDD or as a formal diagnostic criterion or integrated in an algorithm. However, the optimal BAL lymphocytosis cut-off still needs to be determined: A recent meta-analysis demonstrated an optimized sensitivity (71%) and specificity (68%) for a 21% lymphocytosis threshold [20], and a threshold of 20% has been proposed to decide if a surgical lung biopsy for suspected HP is needed [21]. Conversely, most ILD experts indicated that they find a threshold of 40% to be useful for the diagnosis of chronic HP [9], and experts contributing to the new diagnostic guidelines indicated that they find a threshold of 30% reasonable [5]. Likely different thresholds are valid for different positions of BAL in a diagnostic algorithm.

Recent guidelines suggested BAL, transbronchial cryobiopsy, and surgical lung biopsy for the diagnosis of fibrotic HP with very low confidence [5]. In 2 (6%) of our patients with an MDD diagnosis of HP and in 19 (35%) of the MDD diagnosed IPF controls, no surgical lung biopsy or BAL was performed. In the individual patient, the risk–benefit ratio of these invasive procedures is challenging to estimate. Aside from the diagnostic confidence before biopsy (which can be estimated with a diagnostic algorithm), the individual procedural risk likely determines the role of biopsy in the diagnostic algorithm. Consequently, a diagnostic HP algorithm might need tailoring for patients with advanced disease, important comorbidities, and frailty.

The multi-component diagnostic process emphasizes the importance of MDDs for diagnostic decision making in complex cases. The overlap between fibrotic ILDs and the heterogeneity within fibrotic HP and IPF might not be sufficiently accounted for by an algorithm in every case, and MDD discussion likely provides additional granularity for the assessment of complex cases. The prognostic validity of chronic HP MDD diagnosis in this study further supports this approach. In line, the recent diagnostic guidelines for HP confirm the central role of MDD in the diagnostic evaluation of patients with possible HP [5].

Acknowledging the limited availability of resources for MDD, a diagnostic algorithm might be valuable to pre-select typical cases where MDD discussion is less important. Future studies might investigate the implementation of diagnostic decision trees including the option of MDD discussion. Our findings suggest that an algorithm might be a valid tool for fibrotic HP diagnosis. The applicability of the algorithm might further be improved with adaptations according to local practice. Furthermore, future integration new diagnostic tools, e.g. blood-based biomarkers and genomic classifiers might improve the discriminative ability of diagnostic algorithms [22, 23].

Our local single centre study limits generalizability of the findings to populations with similar demographics and antigen exposure patterns. In our experience IPF is the most challenging and impactful differential diagnosis of fibrotic HP. However, our results do not apply for other differential diagnoses. A recent Japanese study reported the chronic HP algorithm to be useful in a heterogeneous ILD cohort, with potential avoidance of surgical lung biopsy in some of their ILD patients. However, 59% of cases were excluded from the algorithm, mainly because they did not have radiological features consistent with UIP or with HP [24]. By adding the “other radiological pattern” category to the algorithm, we ensured that every case was captured by the algorithm. Furthermore, prevalence of fibrotic ILD subtypes varies between cohorts, which can impact on PPV, NPV, and diagnostic accuracy. Sensitivity, specificity, and Youden’s index are not influenced by disease prevalence. Our adaptations to the proposed algorithm were minor, but necessary to include all cases and controls in the algorithm. With the application of a customized antigen exposure questionnaire and detailed interviews, we tried to capture all relevant exposures, however missing exposures are still possible. Incorporation bias is an inherent problem to many diagnostic test studies in ILD. The diagnostic pieces of the algorithm are also included in MDD discussions, which likely inflates the agreement between the two assessments. Furthermore, there is no “gold standard” for fibrotic HP diagnosis. We tried to account for this by demonstrating the prognostic validity of our reference standard.

Conclusion

Current diagnostic guidelines for HP recommend a diagnostic approach that includes MDD [5]. In this study, we validate a proposed diagnostic algorithm for fibrotic HP that foregoes MDD. We demonstrate acceptable test performance of the ≥ 70% diagnostic confidence level compared to the reference standard MDD, and consequently endorse the incorporation of algorithms to fibrotic HP diagnosis. Due to the low sensitivity and potential danger of missing HP cases, we suggest not relying on the current algorithm’s ≥ 90% diagnostic confidence level for the differential diagnosis HP versus IPF. Algorithms likely need to be adapted to regionally distinct exposures, local practice, and availability of diagnostic tests, with prospective validation in corresponding cohorts. Furthermore, algorithms might not be sufficiently accurate for complex cases and are unlikely to replace MDDs entirely.

Availability of data and materials

The datasets used and analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

BAL:

Bronchoalveolar lavage

CI:

Confidence interval

C-index:

Harrell’s C-statistic

CPI:

Composite Physiologic Index

DLCO:

Diffusing capacity of the lung for carbon monoxide

FEV1:

Forced expiratory volume in 1 s

FVC:

Forced vital capacity

HP:

Hypersensitivity pneumonitis

HR:

Hazard ratio

HRCT:

High resolution computed tomography

ILD:

Interstitial lung disease

IPF:

Idiopathic pulmonary fibrosis

J-index:

Youden’s index

MDD:

Multidisciplinary discussion

NPV:

Negative predictive value

NSIP:

Nonspecific interstitial pneumonia

PPV:

Positive predictive value

UIP:

Usual interstitial pneumonia

References

  1. 1.

    Travis WD, Costabel U, Hansell DM, King TE Jr, Lynch DA, Nicholson AG, Ryerson CJ, Ryu JH, Selman M, Wells AU, et al. An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias. Am J Respir Crit Care Med. 2013;188:733–48.

    Article  Google Scholar 

  2. 2.

    Raghu G, Remy-Jardin M, Myers JL, Richeldi L, Ryerson CJ, Lederer DJ, Behr J, Cottin V, Danoff SK, Morell F, et al. Diagnosis of idiopathic pulmonary fibrosis. An official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2018;198:e44–68.

    Article  Google Scholar 

  3. 3.

    Fernandez Perez ER, Swigris JJ, Forssen AV, Tourin O, Solomon JJ, Huie TJ, Olson AL, Brown KK. Identifying an inciting antigen is associated with improved survival in patients with chronic hypersensitivity pneumonitis. Chest. 2013;144:1644–51.

    Article  Google Scholar 

  4. 4.

    Morell F, Villar A, Montero MA, Munoz X, Colby TV, Pipvath S, Cruz MJ, Raghu G. Chronic hypersensitivity pneumonitis in patients diagnosed with idiopathic pulmonary fibrosis: a prospective case-cohort study. Lancet Respir Med. 2013;1:685–94.

    Article  Google Scholar 

  5. 5.

    Raghu G, Document S, Remy-Jardin M, Ryerson CJ, Myers JL, Kreuter M, Vasakova M, Bargagli E, Chung JH, Collins BF, et al. Diagnosis of hypersensitivity pneumonitis in adults: an official ATS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2020. https://doi.org/10.1164/rccm.202005-2032ST.

    Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Ryerson CJ, Vittinghoff E, Ley B, Lee JS, Mooney JJ, Jones KD, Elicker BM, Wolters PJ, Koth LL, King TE Jr, Collard HR. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145:723–8.

    Article  Google Scholar 

  7. 7.

    Idiopathic Pulmonary Fibrosis Clinical Research N, Raghu G, Anstrom KJ, King TE, Lasky JA, Martinez FJ. Prednisone, azathioprine, and N-acetylcysteine for pulmonary fibrosis. N Engl J Med. 2012;366:1968–77.

    Article  Google Scholar 

  8. 8.

    Walsh SL, Wells AU, Desai SR, Poletti V, Piciucchi S, Dubini A, Nunes H, Valeyre D, Brillet PY, Kambouchner M, et al. Multicentre evaluation of multidisciplinary team meeting agreement on diagnosis in diffuse parenchymal lung disease: a case-cohort study. Lancet Respir Med. 2016;4:557–65.

    Article  Google Scholar 

  9. 9.

    Morisset J, Johannson KA, Jones KD, Wolters PJ, Collard HR, Walsh SLF, Ley B. Identification of diagnostic criteria for chronic hypersensitivity pneumonitis: an International Modified Delphi Survey. Am J Respir Crit Care Med. 2018;197:1036–44.

    Article  Google Scholar 

  10. 10.

    Guler SA, Berezowska SA, Christe A, Geiser T, Funke-Chambour M. Multidisciplinary discussion for diagnosis of interstitial lung disease in real life. Swiss Med Wkly. 2016;146:w14318.

    PubMed  Google Scholar 

  11. 11.

    Macintyre N, Crapo RO, Viegi G, Johnson DC, van der Grinten CP, Brusasco V, Burgos F, Casaburi R, Coates A, Enright P, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J. 2005;26:720–35.

    CAS  Article  Google Scholar 

  12. 12.

    Wanger J, Clausen JL, Coates A, Pedersen OF, Brusasco V, Burgos F, Casaburi R, Crapo R, Enright P, van der Grinten CP, et al. Standardisation of the measurement of lung volumes. Eur Respir J. 2005;26:511–22.

    CAS  Article  Google Scholar 

  13. 13.

    Wells AU, Desai SR, Rubens MB, Goh NS, Cramer D, Nicholson AG, Colby TV, du Bois RM, Hansell DM. Idiopathic pulmonary fibrosis: a composite physiologic index derived from disease extent observed by computed tomography. Am J Respir Crit Care Med. 2003;167:962–9.

    Article  Google Scholar 

  14. 14.

    Raghu G, Collard HR, Egan JJ, Martinez FJ, Behr J, Brown KK, Colby TV, Cordier JF, Flaherty KR, Lasky JA, et al. An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med. 2011;183:788–824.

    Article  Google Scholar 

  15. 15.

    Miller R, Allen TC, Barrios RJ, Beasley MB, Burke L, Cagle PT, Capelozzi VL, Ge Y, Hariri LP, Kerr KM, et al. Hypersensitivity pneumonitis a perspective from members of the pulmonary pathology society. Arch Pathol Lab Med. 2018;142:120–6.

    CAS  Article  Google Scholar 

  16. 16.

    Ryerson CJ, Corte TJ, Lee JS, Richeldi L, Walsh SLF, Myers JL, Behr J, Cottin V, Danoff SK, Flaherty KR, et al. A standardized diagnostic ontology for fibrotic interstitial lung disease an international working group perspective. Am J Respir Crit Care Med. 2017;196:1249–54.

    Article  Google Scholar 

  17. 17.

    Salisbury ML, Gu T, Murray S, Gross BH, Chughtai A, Sayyouh M, Kazerooni EA, Myers JL, Lagstein A, Konopka KE, et al. Hypersensitivity pneumonitis: radiologic phenotypes are associated with distinct survival time and pulmonary function trajectory. Chest. 2019;155:699–711.

    Article  Google Scholar 

  18. 18.

    R Core Team. R: A language and environment for statistical computing. 2019. https://www.R-project.org/. Accessed 19 Mar 2021.

  19. 19.

    Barnes H, Morisset J, Molyneaux P, Westall G, Glaspole I, Collard HR. A systematically derived exposure assessment instrument for chronic hypersensitivity pneumonitis. Chest. 2020. https://doi.org/10.1016/j.chest.2019.08.1507.

    Article  PubMed  Google Scholar 

  20. 20.

    Adderley N, Humphreys CJ, Barnes H, Ley B, Premji ZA, Johannson KA. Bronchoalveolar lavage fluid lymphocytosis in chronic hypersensitivity pneumonitis: a systematic review and meta-analysis. Eur Respir J. 2020. https://doi.org/10.1183/13993003.00206-2020.

    Article  PubMed  Google Scholar 

  21. 21.

    Salisbury ML, Myers JL, Belloli EA, Kazerooni EA, Martinez FJ, Flaherty KR. Diagnosis and treatment of fibrotic hypersensitivity pneumonia. Where we stand and where we need to go. Am J Respir Crit Care Med. 2016. https://doi.org/10.1164/rccm.201608-1675PP.

    Article  Google Scholar 

  22. 22.

    Richeldi L, Scholand MB, Lynch DA, Colby TV, Myers JL, Groshong SD, Chung JH, Benzaquen S, Nathan SD, Davis JR, et al. Utility of a molecular classifier as a complement to HRCT to identify usual interstitial pneumonia. Am J Respir Crit Care Med. 2020. https://doi.org/10.1164/rccm.202003-0877OC.

    Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Okamoto T, Fujii M, Furusawa H, Tsuchiya K, Miyazaki Y, Inase N. The usefulness of KL-6 and SP-D for the diagnosis and management of chronic hypersensitivity pneumonitis. Respir Med. 2015;109:1576–81.

    Article  Google Scholar 

  24. 24.

    Takei R, Yamano Y, Kataoka K, Yokoyama T, Matsuda T, Kimura T, Johkoh T, Tabata K, Fukuoka J, Kondoh Y. Usefulness of new diagnostic criteria for chronic hypersensitivity pneumonitis established on the basis of a Delphi survey: a Japanese cohort study. Respir Investig. 2020;58:52–8.

    Article  Google Scholar 

Download references

Acknowledgements

We thank the patients of our ILD clinic who contribute to the Swiss IIP registry. We acknowledge Michèle Tschiemer for her work and support as ILD nurse.

Funding

The Swiss IIP registry is funded by unrestricted grants from Boehringer Ingelheim and Roche.

Author information

Affiliations

Authors

Contributions

SAG and MF designed the study. SAG, EW, SB, TKG, LE, and MF contributed to data acquisition, analysis, and interpretation of the data. SAG, EW, and MF drafted the manuscript. All authors revised the manuscript for important intellectual content and approved the final version. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Sabina A. Guler.

Ethics declarations

Ethics approval and consent to participate

All participants provided written informed consent for participation in the current study (Swiss Ethics Committee, Bern, KEK 246/15 PB_2016-01524).

Competing interests

The authors report no relevant conflicts of interest related to this work.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Guler, S.A., Wohlfarth, E., Berezowska, S. et al. Performance of a diagnostic algorithm for fibrotic hypersensitivity pneumonitis. A case–control study. Respir Res 22, 120 (2021). https://doi.org/10.1186/s12931-021-01727-7

Download citation

Keywords

  • Pulmonary Fibrosis
  • Environmental Exposure
  • Algorithms
  • Diagnosis
  • Alveolitis
  • Extrinsic Allergic