Predictive ability of longitudinal changes in Kerbs von Lungren 6 for acute exacerbation of fibrotic interstitial lung disease: Database research

Predictive ability of longitudinal changes in Kerbs von Lungren 6 for acute exacerbation of fibrotic interstitial lung disease: Database research | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Predictive ability of longitudinal changes in Kerbs von Lungren 6 for acute exacerbation of fibrotic interstitial lung disease: Database research Akihiro Shiroshita, Keisuke Anan, Chigusa Shirakawa, Hiroshi Shiba, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3982411/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Acute exacerbation (AE) in fibrotic interstitial lung diseases (ILDs) poses a significant challenge, with limited available evidence for predicting such events. Kerbs von Lungren 6 (KL-6) is a proposed predictive marker for prospective AE; however, only its baseline value has been evaluated. To address this gap, this study investigates the association between the patient-specific evolution of serum KL-6 levels and the AE risk of fibrotic ILD. Methods This study used data from the Japanese Database of Health, Clinic, and Education Information Evaluation Institute and JMDC Inc. The included patients had fibrotic ILDs and antifibrotic therapy with ≥ 2 measurements of serum KL-6 from the index date to the end of follow-up. The outcome was AE of acute or chronic fibrotic ILDs defined based on the combination of its primary diagnoses, emergency admission, and pulse/high-dose steroids on the day of or after admission. We used a joint regression model for each patient’s serial serum KL-6 measurements and a proportional hazards regression model for the hazard ratio of AE. Results The study included 939 patients with fibrotic ILDs, and 194 (21%) experienced AE during the follow-up (event rate, 0.13/person-year; 1-year incidence, 35%). The hazard ratio of AE comparing patients differing in cumulative serum KL-6 was 1.54 (95% confidence interval: 1.20–1.98, p < 0.001). Conclusions A high baseline KL-6 level and its upward evolution can predict the AE of fibrotic ILD. Longitudinal KL -6 measurement should be considered, especially for patients who cannot perform pulmonary function tests well. Our study could help develop strategies for improving fibrotic ILD prognosis and treatment. Acute exacerbation fibrotic interstitial lung disease interstitial lung disease exacerbation interstitial pneumonia serum Kerbs von Lungren 6 Figures Figure 1 Figure 2 Figure 3 BACKGROUND Fibrotic interstitial lung disease (ILD) is a heterogeneous group of ILDs characterized by inflammation and fibrosis of the lung parenchyma [ 1 ]. It includes idiopathic pulmonary fibrosis (IPF) and other categories of idiopathic interstitial pneumonia, unclassifiable ILD, connective tissue disease-associated ILD (CTD-ILD), fibrotic hypersensitivity pneumonitis, sarcoidosis, organizing pneumonia, drug-induced interstitial lung disorders, and ILD associated with occupational exposures. Approximately 20–30% of patients with ILD have fibrotic ILD [ 2 – 4 ]; these patients usually experience progressive fibrosis accompanied by worsening lung function, dyspnea, poor physical performance, and poor quality of life [ 5 ]. Acute exacerbation (AE) is a life-threatening risk factor for subsequent disease progression [ 6 , 7 ]. However, no established markers have been identified to predict prospective AE. Previous studies have primarily evaluated patients’ baseline characteristics, such as dyspnea, quality of life, body mass index, oxygenation, serum biomarkers, and pulmonary function tests [ 8 – 12 ]. Baseline characteristics are just one aspect of the disease, and as time progresses, additional information is added for the patient. Although a recent decline in forced vital capacity and diffusing capacity of the lung for carbon monoxide predicted AE in IPF, it could be applied only to selected patients, and validating its predictive ability using real-world data is challenging [ 8 , 9 , 11 ]. Older patients with cognitive impairment, apraxia, or hypoxia may have difficulty undergoing pulmonary function tests [ 13 , 14 ]. In daily practice, the schedule of pulmonary function tests is not standardized because AE occurrence and other medical conditions related to ILD progression preclude pulmonary function measurement, leading to a fundamental violation of the missing data assumption in many statistical models [ 15 ]. Kerbs von Lungren 6 (KL-6) is a high-molecular-weight glycoprotein found in circulation, expressed by type II and bronchial epithelial cells. Elevated serum levels of KL-6 are observed in various ILDs [ 16 ]. KL-6 was originally identified in Japan as a cancer biomarker, and it has since been used for ILD diagnosis. Japanese clinicians have frequently measured it in daily practice, at baseline, and during follow-up since its insurance coverage in 1999 [ 16 ]. Therefore, this study used large-scale real-world data from Japan. Serum KL-6 can be measured via a simple blood test completed within an hour [ 17 ]. Initially proposed as a potential diagnostic factor for ILD, the baseline serum KL-6 level has recently been reported to predict future AE and disease activity [ 17 , 18 ]. However, limited evidence exists regarding the longitudinal role of serum KL-6 level in predicting AE [ 17 ]. This study evaluated the association between the patient-specific evolution of serum KL-6 levels and the risk of AE in patients with fibrotic ILD. The findings of this study could help improve the management of fibrotic ILD. METHODS Study design This database research utilized the anonymized commercial data from the Health, Clinic, and Education Information Evaluation Institute (Kyoto, Japan) and JMDC Inc. (Tokyo, Japan); it included the electronic medical records (e.g., demographic, pharmacy, and laboratory data) and administrative claims data (e.g., disease name and procedure data) of approximately 20 million patients from over 200 medical institutions in Japan. This study followed the Declaration of Helsinki [ 19 ] and the REporting of studies Conducted using Observational Routinely-collected Data statement (Additional file 1) [ 20 ]. The institutional review board of Showa University approved this study (approval number: 2023-119-B), and the requirement for written informed consent was waived because the dataset had been de-identified. Patient selection The target population comprised patients with fibrotic ILD. First, we selected patients with acute or chronic ILDs based on the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems: IPF, other idiopathic interstitial pneumonia, CTD-ILD, hypersensitivity pneumonitis, occupational lung diseases, drug-induced lung injury, radiation pneumonitis, Langerhans cell histiocytosis, pulmonary alveolar proteinosis, and sarcoidosis (Additional file 2). Second, we only included the number of patients who received antifibrotic therapy to enhance the validity of patient selection. Nintedanib and pirfenidone (Anatomical Therapeutic Chemical [ATC] Classification of L01EX09 and L04AX05) have shown therapeutic benefits in IPF and progressive fibrotic ILDs other than IPF (progressive fibrosing ILD) [ 6 , 21 ]. In Japan, nintedanib was approved for treating IPF in 2015, ILD caused by systemic sclerosis in 2019, and progressive fibrosing ILDs in 2020. Pirfenidone was approved for treating IPF in 2008. The first day of dispensing antifibrotic therapy was set as the index date. From the index date, we longitudinally followed up patients at the same medical institute until AE of fibrotic ILD or censoring (Fig. 1 ). Individuals with serum KL-6 levels measured ≤ 1 time from the index date to the end of the follow-up and those diagnosed with malignancy within 1 year before the index date were excluded. Patients with missing covariates were also excluded patients from the model. Data extraction The definitions of the variables are summarized in Additional file 2. The following baseline patient characteristics were extracted from the electronic medical data: age (continuous), sex (male vs. female), the disease name used for dispensing antifibrotic therapy, home oxygen therapy within 6 months before the index date, and the baseline use of systemic steroids and immunosuppressive agents. The baseline use of systemic steroids was defined as oral or parenteral corticosteroid administration (ATC Classification of H02AB) with methylprednisolone equivalent dose of ≥ 5 mg for ≥ 2 weeks within 1 month preceding the index date. The baseline use of immunosuppressive agents was defined as any oral or parenteral immunosuppressive agent usage (ATC Classification of L04A and L01FA01) for ≥ 2 weeks within 1 month before the index date. The baseline body mass index, dyspnea score (Hugh–Johns classification), activities of daily living (Barthel index), and comorbidity at the time of admission were extracted from the “Yoshiki 1” file submitted to the government for reimbursement of in-hospital medical fees. Information on these baseline characteristics was collected when patient admission within 12 months before the index date was identified from the “Yoshiki 1” file. We calculated the Charlson Comorbidity Index based on the International Classification of Diseases 10th Revision Codes of Comorbidities. Laboratory data were extracted from each hospital’s data storage. The enzyme immunoassay, electrochemical luminescence immune assay, and latex agglutination tests have been approved for measuring serum KL-6 levels in Japan. All serum KL-6 measurements during the follow-up period were extracted, and the number of days from the index date to each measurement was calculated. The outcome of interest was the AE of fibrotic ILD. The definition was the combination of the primary diagnoses related to AE of ILD (acute or chronic ILDs), emergency admissions, and the administration of pulse/high-dose steroids (≥ 50 mg/day of methylprednisolone equivalent) either on the admission day or the subsequent day (Additional file 2) [ 22 ]. For the sensitivity analysis, we used another definition of AE previously validated for AE of interstitial pneumonia and acute ILDs (Additional file 2) [ 22 ]. Statistical analysis Patient characteristics were presented as frequencies and proportions for categorical variables and as means with standard deviations or median and interquartile range, as appropriate, for continuous variables. The patient-specific trend of serum KL-6 was depicted in a Spaghetti plot, accompanied by a smoothing line representing the average trend. A Kaplan–Meier plot was used to display survival probability over time. To evaluate the association between patient-specific evolution of serum KL-6 levels and AE risk, we used a joint regression model [ 23 ] involving the development of a linear mixed-effects model for serial serum KL-6 measurements in each patient and a proportional hazard regression model for the AE hazard. The model also can consider measurement errors inherent in KL-6 assessments. The linear mixed-effects model included time-independent covariates (age, sex, season of the index date, baseline steroid use, and baseline immunosuppressive therapy) and a time-dependent covariate (days from the index date to each KL-6 measurement). Patient-specific random effects were incorporated into the intercept, as well as the number of days from the index date to each KL-6 measurement. The proportional hazards regression model was postulated using a Weibull baseline risk function and incorporated the same covariates as in the longitudinal model, as well as the history of serum KL-6 defined within the linear mixed-effects model. The adaptive Gaussian–Hermite rule was applied for numerical integration within the maximum likelihood function. Given the right-skewed and non-negative distribution of serum KL-6, it was divided by 100 and log-transformed. During the model development, the functional form selection (linear term vs. quadratic term of the days from the index date to each KL-6 measurement, random slope vs. random intercept, value parameterization vs. value + slope parameterization) was based on the Akaike Information Criterion. We demonstrated the dynamic predictions of conditional survival using a joint regression model. We simulated four extreme cases: a 65-year-old man not taking baseline steroids or immunosuppressants initiating first antifibrotic therapy in winter, with KL-6 measurements of 1) 500 U/mL at Day 0, 1000 U/mL at Day 300, and 2000 U/mL at Day 600 (upward trend); 2) 2000 U/mL at Day 0, 1000 U/mL at Day 300, and 500 U/mL at Day 600 (downward trend); 3) 3000 U/mL at Day 0, 3000 U/mL at Day 300, and 3000 U/mL at Day 600 (constantly high); and 4) 500 U/mL at Day 0, 500 U/mL at Day 300, and 500 U/mL at Day 600 (constantly low). Statistical significance was set at a two-sided p -value of < 0.05. We utilized the R software version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria) for statistical analyses. RESULTS Descriptive analysis Figure 2 summarizes the patient-selection process. We identified 41,658 patients with disease codes related to fibrotic ILD; among them, 1,112 were prescribed antifibrotic therapy. After excluding 173 patients with ≤ 1 measurement of KL-6, 939 with fibrotic ILDs were included in this study. Tables 1 and 2 summarize the baseline patient characteristics. A total of 312/939 (33%) patients had IPF, and 113/939 (12%) had CTD-ILD (rheumatoid arthritis, 4%; systemic sclerosis, 4%). Among the included patients, 194 (21%) experienced AE during the follow-up period (event rate: 0.13/person-year; 1-year incidence: 35%). Among patients without AE, 86 (12%) died from other causes within the first year of follow-up. No patients experienced malignancy before the index date and during the follow-up period. Table 1 Patient characteristics Never acute exacerbation (N = 745) Acute exacerbation (N = 194) Total (N = 939) Mean age (SD) 72 (8) 71 (9) 72 (8) Male 517 (69) 143 (74) 660 (70) Nintedanib 473 (64) 104 (54) 577 (61) Median of baseline KL-6 (IQR) 1076 (686–1619) 1172 (840–1745) 1100 (710–1623) Season of the index date Winter 200 (27) 51 (26) 251 (27) Spring 202 (27) 40 (21) 242 (26) Summer 161 (22) 53 (27) 214 (23) Fall 182 (24) 50 (26) 232 (25) BMI <18.5 34 (5) 5 (3) 39 (4) 18.5 to 25 102 (14) 44 (23) 146 (16) Missing 430 (58) 95 (49) 525 (56) Activity of daily living (Barthel index) <20 8 (1) 1 (1) 9 (1) 20 to 50 310 (42) 95 (49) 405 (43) Missing 421 (57) 93 (48) 514 (55) Charlson Comorbidity Index (median, IQR) 3 (2–3) 3 (2–3) 3 (2–3) Home oxygen therapy 118 (16) 38 (20) 156 (17) Dyspnea score (Hugh–Johns classification) <3 206 (28) 58 (30) 264 (28) ≥3 72 (10) 34 (18) 106 (11) Missing 467 (63) 102 (53) 569 (61) Daily steroid use at baseline 135 (18) 53 (27) 188 (20) Daily immunosuppressant use at baseline 29 (4) 16 (8) 45 (5) N, number; SD, standard deviation; KL-6, Kerbs von Lungren 6; IQR, interquartile range; BMI, body mass index; Mechanical ventilation included invasive/non-invasive ventilation, high-flow nasal cannula, and continuous positive airway pressure Table 2 Patients’ background interstitial lung disease Number of patients (%) Idiopathic interstitial pneumonia Idiopathic pulmonary fibrosis 312 (32.2) Usual interstitial pneumonia 5 (0.5) Nonspecific interstitial pneumonia 15 (1.6) Combined pulmonary fibrosis and Emphysema 10 (1.1) Cryptogenic organizing pneumonia 1 (0.1) Idiopathic interstitial pneumonia (unspecified) 156 (16.6) Connective tissue disease-associated ILD Rheumatoid arthritis 39 (4.15) Systemic sclerosis 37 (3.94) Sjogren syndrome 16 (1.7) Microscopic polyangiitis 7 (0.7) Connective tissue disease-associated ILD (unspecified) 9 (1.0) Systemic lupus erythematosus 3 (0.3) IgG4-related disease 2 (0.2) Hypersensitivity pneumonitis 15 (1.6) Eosinophilic pneumonia 1 (0.1) Others Interstitial pneumonia (unspecified) 261 (27.8) Lung fibrosis (unspecified) 25 (2.7) Diffuse interstitial pneumonia (unspecified) 17 (1.8) Acute interstitial pneumonia (unspecified) 6 (0.6) Pulmonary hemorrhage 2 (0.2) ILD, interstitial lung disease; IgG, immunoglobulin At baseline, serum KL-6 levels were as follows: minimum, 265 U/mL; first quartile, 735 U/mL; median, 1,134 U/mL; mean, 1,406 U/mL; third quartile, 1,680 U/mL; and maximum, 12,408 U/mL. KL-6 measurements were performed at 3–170 visits (median, 11; interquartile range, 6–20) from the index date until the end of the follow-up. The difference between two consecutive measurements varied widely (minimum: -4750 U/mL; first quartile, -94 U/mL; median: -11 U/mL; mean: -16 U/mL; third quartile, 58 U/mL; maximum: 6,730 U/mL). Figure 3 a shows the individual-level trajectories of KL-6 from the index date, accompanied by a smoothing line representing the average trend. Patients who experienced AEs had higher serum KL-6 levels over time than those without AE. Figure 3 b shows the individual-level trajectories of serum KL-6 at the end of the follow-up. A slight upward trend in serum KL-6 levels was observed before AE, whereas the levels before censoring remained relatively stable. The Kaplan–Meier curve showed a constant or slightly monotonic increase in AE risk over time (Additional file 3). Main analysis According to the Akaike Information Criterion, the joint regression model with the following longitudinal and event processes provided the best fit for the data: Longitudinal process: $${y}_{i}\left(t\right)={x}_{i}\left(t\right)\beta +{Z}_{i}\left(t\right){b}_{i}+{ϵ}_{i}\left(t\right)={m}_{i}\left(t\right)+{ϵ}_{i}\left(t\right)$$ , where \({m}_{i}\left(t\right)\) represents the true KL-6 value; \({b}_{i}\sim N\left(0,D\right)\) denotes random-effects parameters based on the patient and time; and \({ϵ}_{i}\left(t\right)\sim N\left(0,{\sigma }^{2}\right)\) signifies measurement error based on time. Event process: $${h}_{i}\left(t|{M}_{i}\left(t\right),{w}_{i}\right)={\text{l}\text{i}\text{m}}_{\varDelta t\to 0}P\left[t\le T<t+\varDelta t|T\ge t,{M}_{i}\left(t\right),{w}_{i}\right]/\varDelta t={h}_{0}\left(t\right)exp\left({w}_{i}\gamma +\alpha {m}_{i}\left(t\right)\right)$$ , where \({M}_{i}\left(t\right)=\left[{m}_{i}\left(s\right),0\le s<t\right]\) , representing KL-6 history; \({h}_{0}\left(t\right)\) denotes the Weibull baseline hazard function; \(\gamma\) signifies parameters for time-independent covariates; and \({w}_{i}\) \(exp\left(\alpha \right)\) indicates the relative increase in the risk of an event at time t resulting from a simultaneous one-unit increase in \({m}_{i}\left(t\right)\) . We observed a strong association between the log of serum KL-6 levels and AE occurrence over time (Table 3 ). The hazard ratio for AE comparing patients with a cumulative serum KL-6 difference of 100*exp(1) = 272 U/mL was 1.54 (95% confidence interval: 1.20–1.98, p < 0.001). As shown in Additional file 4, patients with an increasing trend of serum KL-6 levels and constantly high serum KL-6 levels had an unfavorable predicted survival compared with those with a decreasing trend or consistently low serum KL-6 values. Sensitivity analysis using an alternate algorithm for detecting AE yielded similar results (hazard ratio: 1.26, 95% confidence interval: 1.01–1.56, p < 0.04). Table 3 Summary of analysis results Estimate 95% Confidence interval p -value Longitudinal process Days from the index date to KL-6 Measurement 1.001 0.999–1.002 < 0.001 Baseline daily steroid use 1.115 1.010–1.230 0.031 Baseline daily immunosuppressant Use 0.979 0.814–1.178 0.824 Age 0.989 0.985–0.993 < 0.001 Sex 0.975 0.900–1.055 0.528 Season 0.992 0.960–1.025 0.642 Event process (hazard ratio) Baseline steroid 1.791 1.267–2.532 < 0.001 Baseline immunosuppressant 1.700 0.964–2.996 0.067 Age 1.018 0.999–1.036 0.062 Sex 1.494 1.070–2.085 0.018 Season 1.063 0.939–1.204 0.337 Association of log (KL-6/100) 1.539 1.193–1.984 < 0.001 KL-6, Kerbs von Lungren 6 DISCUSSION Limited evidence exists regarding the dynamic prediction of AE. This study is the first to demonstrate a strong association between the patient-specific evolution of serum KL-6 levels and the risk of AE among patients with fibrotic ILD. The trajectories of serum KL-6 exhibited high heterogeneity and fluctuation; however, physicians should be vigilant when observing an upward trend of KL-6 or constantly high serum KL-6 values, as they may indicate prospective AE. Serum KL-6 is a simple and fast blood test that is especially beneficial for patients who struggle to perform pulmonary function tests effectively. Previous studies primarily evaluated patients’ baseline characteristics, identifying sex, age, compromised pulmonary function, and radiological and histological patterns as potential predictors of poor prognosis, including AE [ 2 , 24 – 26 ]. A high baseline serum KL-6 level is proposed as a predictor of disease progression, AE, and poor outcomes of ILDs [ 27 ]. In contrast, our study showed that the serum KL-6 level exhibited a considerable fluctuation, and the timing of fibrotic ILD diagnosis influenced the predictive value of baseline KL-6. Some smaller-scale studies suggested that longitudinally increased serum KL-6 levels may predict a decline in forced vital capacity and disease progression in IPF [ 28 , 29 ]. The authors evaluated the correlation between serial KL-6 levels and patient outcomes but excluded patients when either KL-6 or pulmonary function test results were missing. This exclusion strategy assumes missing-completely-at-random; the probability of an outcome being missing is independent of both observed and unobserved factors [ 15 ]. In a non-standardized protocol, a patient's medical condition could highly influence missing data, especially in pulmonary results. In addition, their analyses did not consider KL-6 measurement errors. Notably, small sample sizes in research may inadvertently yield significant results by chance. This study addressed AE, an outcome often omitted in previous longitudinal studies, and incorporated a large patient cohort with numerous measurement time points. To address methodological limitations, we employed a joint regression model with a longitudinal component that accommodates missing outcome data by assuming missing data not at random; this means that the probability of an outcome being missed may depend on any other observed covariates and previous KL-6 measurements. The model accounts for the impact of AE on the interruption of pre-AE KL-6 measurements. Furthermore, the linear mixed-effect model incorporated the measurement error inherent in KL-6 assessments. Finally, we expanded the disease scope to include fibrotic IPF, encompassing a range of heterogeneous ILDs, broadening KL-6’s applicability in treating ILDs. This study has limitations. First, the accuracy of the data captured in this study remains uncertain. Although we used modified versions of a validated algorithm, the original algorithm solely evaluated the positive predictive value for AE rather than the sensitivity and specificity of the algorithm. In addition, in the Real World Data database, longitudinal follow-ups on patients were conducted longitudinally only at the same hospital; this could have skewed associations toward null, potentially increasing the likelihood of missing AE more than overdiagnosing these events. Second, our small sample size limited our ability to develop a more intricate and flexible model. For instance, we could not evaluate the contribution of the change rate in the patient-specific evolution of KL-6. Third, our study was primarily based on administrative claim data and selected data from electronic medical records; thus, we could not incorporate all variables. Incorporating more time-independent and time-dependent covariates, such as pulmonary function test results, dyspnea scores, physical function, and imaging results, could enhance predictive accuracy. Lastly, our study focused on the Japanese population. The genotype distribution may cause differences in KL-6 levels [ 30 , 31 ]. Further large-scale studies are needed to validate our findings and to develop a more accurate dynamic prediction model for AE. CONCLUSIONS Our study showed that high baseline KL-6 levels and an upward trend can predict AE in fibrotic ILD. Physicians should consider longitudinal KL-6 measurements, particularly in patients who struggle to perform pulmonary function tests effectively. Abbreviations AE , acute exacerbation; CTD-ILD, connective tissue disease-associated ILD; ILD, interstitial lung diseases; IPF, idiopathic pulmonary fibrosis; KL-6, Kerbs von Lungren 6 Declarations Ethics approval and consent to participate The institutional review board of Showa University approved this study (approval number: 2023-119-B), and the requirement for written informed consent was waived because the dataset had been de-identified. Consent for publication Not applicable. Acknowledgments The authors would like to thank the Health, Clinic, and Education Information Evaluation Institute for developing the database used in this study. Availability of data and materials The data supporting the findings of this study are available upon request from the corresponding author. The data are not publicly available because of the restrictions imposed by Real World Data Co., Ltd. Competing interests AS received funding from the Japan Society for the Promotion of Science and Pfizer Health Research Foundation. Other authors have nothing to declare . Funding This study was funded by the Japan Society for the Promotion of Science and Pfizer Health Research Foundation. The Japanese Society for the Promotion of Science provided English editing and publication fees (Grants-in-Aid for Scientific Research [Kakenhi]; grant number 23K09582). The Pfizer Health Research Foundation supported the cost of data acquisition. The funders played no role in the study design, execution, analyses, interpretation, or decision to submit the results. Authors’ contributions AS, KA, CS, HS, and NY contributed to the conception and design of the study. AS managed the database and performed the data analysis. All authors confirmed the validity of the data analysis and contributed to the data interpretation. AS and KA drafted the manuscript. All authors critically revised and approved the final version of the manuscript. AS, KA, CS, HS, and NY consent to be accountable for all aspects of this study. References Wong AW, Ryerson CJ, Guler SA. Progression of fibrosing interstitial lung disease. Respir Res. 2020;21:32. Zamora-Legoff JA, Krause ML, Crowson CS, Ryu JH, Matteson EL. Progressive decline of lung function in rheumatoid arthritis-associated interstitial lung disease. Arthritis Rheumatol. 2017;69:542-549. Reiseter S, Gunnarsson R, Mogens Aaløkken T, Lund MB, Mynarek G, Corander J, et al. 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MUC1 gene polymorphisms are associated with serum KL-6 levels and pulmonary dysfunction in pulmonary alveolar proteinosis. Orphanet J Rare Dis. 2016;11:48. Additional Declarations No competing interests reported. Supplementary Files additionalfile1.docx additionalfile2.docx additionalfile3.docx additionalfile4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3982411","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275047259,"identity":"19b99e36-ae19-4e6f-8de1-84e200567c7c","order_by":0,"name":"Akihiro Shiroshita","email":"data:image/png;base64,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","orcid":"","institution":"Vanderbilt University School of Medicine","correspondingAuthor":true,"prefix":"","firstName":"Akihiro","middleName":"","lastName":"Shiroshita","suffix":""},{"id":275047260,"identity":"483c8268-72b0-48a5-aff7-1957afa28b5d","order_by":1,"name":"Keisuke Anan","email":"","orcid":"","institution":"Saiseikai Kumamoto Hospital","correspondingAuthor":false,"prefix":"","firstName":"Keisuke","middleName":"","lastName":"Anan","suffix":""},{"id":275047261,"identity":"3978c30b-ea64-45d0-95e0-c3cc1740494f","order_by":2,"name":"Chigusa Shirakawa","email":"","orcid":"","institution":"Kobe City Medical Center General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chigusa","middleName":"","lastName":"Shirakawa","suffix":""},{"id":275047262,"identity":"51e535a5-1c93-49ef-8161-ab61ea65c0a9","order_by":3,"name":"Hiroshi Shiba","email":"","orcid":"","institution":"Suwa Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Shiba","suffix":""},{"id":275047263,"identity":"48ec1f72-10a5-4a69-841c-1f5bd5430a49","order_by":4,"name":"Nobuyuki Yajima","email":"","orcid":"","institution":"Showa University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Nobuyuki","middleName":"","lastName":"Yajima","suffix":""}],"badges":[],"createdAt":"2024-02-23 15:51:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3982411/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3982411/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51777908,"identity":"db33c9ab-1aed-49aa-b646-ffce9f53317a","added_by":"auto","created_at":"2024-02-28 21:10:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":184004,"visible":true,"origin":"","legend":"\u003cp\u003eStudy overview\u003c/p\u003e\n\u003cp\u003eThe index date was when a patient with a disease code related to interstitial pneumonia was prescribed anti-fibrotic therapy. We tracked the patient until acute exacerbation or censoring and collected serial results of serum Kerbs von Lungren 6 (KL-6) levels until the end of follow-up. The evolution of serum KL-6 levels was modeled by a longitudinal analysis, and the association with the hazard of acute exacerbation was modeled with a survival analysis (joint regression modeling).\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/3589d411be1a4b25a03b3193.png"},{"id":51777265,"identity":"88b815d5-32fe-4dfe-a976-3627cfb5475c","added_by":"auto","created_at":"2024-02-28 21:02:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":720894,"visible":true,"origin":"","legend":"\u003cp\u003eA flow diagraph of the patient-selection approach.\u003c/p\u003e\n\u003cp\u003eWe identified 41,658 patients with a disease code of interstitial lung disease (ILD); 1,112 received antifibrotic therapy. We excluded 173 patients with ≤1 measurement of Kerbs von Lungren 6, and 939 with fibrotic ILDs were included.\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/474d2d39ab2f6e90b454678d.png"},{"id":51777264,"identity":"bf698b07-7cab-40ee-9833-c0e0b0833dde","added_by":"auto","created_at":"2024-02-28 21:02:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1399184,"visible":true,"origin":"","legend":"\u003cp\u003eSpaghetti plots of Kerbs von Lungren 6 and serum KL-6.\u003c/p\u003e\n\u003cp\u003ea. The individual-level trajectories of Kerbs von Lungren 6 (KL-6) from the index date, along with a smoothing line for the average trend. On average, patients with acute exacerbation (AE) have higher serum KL-6 values over time than those without.\u003c/p\u003e\n\u003cp\u003eb. Individual-level trajectories of serum KL-6 from the end of the follow-up. A slight upward trend in the serum KL-6 is observed before AE, and the trend before censoring is flat.\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/b0217fb45865301ed635ff65.png"},{"id":66181097,"identity":"274ff4cd-1b7e-43da-9fbd-e3a2257f0667","added_by":"auto","created_at":"2024-10-08 12:32:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3029082,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/db743dd3-dce7-4de7-ac47-374bc9655e40.pdf"},{"id":51777272,"identity":"a28998dc-498b-466a-93dc-58c1dccad572","added_by":"auto","created_at":"2024-02-28 21:02:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21252,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/2e88d0e4805985de42be58d0.docx"},{"id":51777271,"identity":"f18cf23e-d7c7-4e6d-94f8-510b91a7cc5d","added_by":"auto","created_at":"2024-02-28 21:02:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19276,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/f1238d8f5e389ea476168e32.docx"},{"id":51777317,"identity":"36e0f16b-5129-4090-92d6-ea6264b8c848","added_by":"auto","created_at":"2024-02-28 21:02:59","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":191178,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/360593aede989668cbf47055.docx"},{"id":51777320,"identity":"aa8682c5-155f-4f13-b2f9-925061d2907d","added_by":"auto","created_at":"2024-02-28 21:02:59","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":297403,"visible":true,"origin":"","legend":"","description":"","filename":"additionalfile4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3982411/v1/06eb63a6ce1647ae619b6cd5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive ability of longitudinal changes in Kerbs von Lungren 6 for acute exacerbation of fibrotic interstitial lung disease: Database research","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eFibrotic interstitial lung disease (ILD) is a heterogeneous group of ILDs characterized by inflammation and fibrosis of the lung parenchyma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It includes idiopathic pulmonary fibrosis (IPF) and other categories of idiopathic interstitial pneumonia, unclassifiable ILD, connective tissue disease-associated ILD (CTD-ILD), fibrotic hypersensitivity pneumonitis, sarcoidosis, organizing pneumonia, drug-induced interstitial lung disorders, and ILD associated with occupational exposures. Approximately 20\u0026ndash;30% of patients with ILD have fibrotic ILD [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]; these patients usually experience progressive fibrosis accompanied by worsening lung function, dyspnea, poor physical performance, and poor quality of life [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcute exacerbation (AE) is a life-threatening risk factor for subsequent disease progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, no established markers have been identified to predict prospective AE. Previous studies have primarily evaluated patients\u0026rsquo; baseline characteristics, such as dyspnea, quality of life, body mass index, oxygenation, serum biomarkers, and pulmonary function tests [\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Baseline characteristics are just one aspect of the disease, and as time progresses, additional information is added for the patient. Although a recent decline in forced vital capacity and diffusing capacity of the lung for carbon monoxide predicted AE in IPF, it could be applied only to selected patients, and validating its predictive ability using real-world data is challenging [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Older patients with cognitive impairment, apraxia, or hypoxia may have difficulty undergoing pulmonary function tests [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In daily practice, the schedule of pulmonary function tests is not standardized because AE occurrence and other medical conditions related to ILD progression preclude pulmonary function measurement, leading to a fundamental violation of the missing data assumption in many statistical models [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eKerbs von Lungren 6 (KL-6) is a high-molecular-weight glycoprotein found in circulation, expressed by type II and bronchial epithelial cells. Elevated serum levels of KL-6 are observed in various ILDs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. KL-6 was originally identified in Japan as a cancer biomarker, and it has since been used for ILD diagnosis. Japanese clinicians have frequently measured it in daily practice, at baseline, and during follow-up since its insurance coverage in 1999 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Therefore, this study used large-scale real-world data from Japan. Serum KL-6 can be measured via a simple blood test completed within an hour [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Initially proposed as a potential diagnostic factor for ILD, the baseline serum KL-6 level has recently been reported to predict future AE and disease activity [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, limited evidence exists regarding the longitudinal role of serum KL-6 level in predicting AE [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This study evaluated the association between the patient-specific evolution of serum KL-6 levels and the risk of AE in patients with fibrotic ILD. The findings of this study could help improve the management of fibrotic ILD.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eThis database research utilized the anonymized commercial data from the Health, Clinic, and Education Information Evaluation Institute (Kyoto, Japan) and JMDC Inc. (Tokyo, Japan); it included the electronic medical records (e.g., demographic, pharmacy, and laboratory data) and administrative claims data (e.g., disease name and procedure data) of approximately 20\u0026nbsp;million patients from over 200 medical institutions in Japan. This study followed the Declaration of Helsinki [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and the REporting of studies Conducted using Observational Routinely-collected Data statement (Additional file 1) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The institutional review board of Showa University approved this study (approval number: 2023-119-B), and the requirement for written informed consent was waived because the dataset had been de-identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatient selection\u003c/h2\u003e \u003cp\u003eThe target population comprised patients with fibrotic ILD. First, we selected patients with acute or chronic ILDs based on the 10th Revision of the International Statistical Classification of Diseases and Related Health Problems: IPF, other idiopathic interstitial pneumonia, CTD-ILD, hypersensitivity pneumonitis, occupational lung diseases, drug-induced lung injury, radiation pneumonitis, Langerhans cell histiocytosis, pulmonary alveolar proteinosis, and sarcoidosis (Additional file 2). Second, we only included the number of patients who received antifibrotic therapy to enhance the validity of patient selection. Nintedanib and pirfenidone (Anatomical Therapeutic Chemical [ATC] Classification of L01EX09 and L04AX05) have shown therapeutic benefits in IPF and progressive fibrotic ILDs other than IPF (progressive fibrosing ILD) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In Japan, nintedanib was approved for treating IPF in 2015, ILD caused by systemic sclerosis in 2019, and progressive fibrosing ILDs in 2020. Pirfenidone was approved for treating IPF in 2008. The first day of dispensing antifibrotic therapy was set as the index date. From the index date, we longitudinally followed up patients at the same medical institute until AE of fibrotic ILD or censoring (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Individuals with serum KL-6 levels measured\u0026thinsp;\u0026le;\u0026thinsp;1 time from the index date to the end of the follow-up and those diagnosed with malignancy within 1 year before the index date were excluded. Patients with missing covariates were also excluded patients from the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eThe definitions of the variables are summarized in Additional file 2. The following baseline patient characteristics were extracted from the electronic medical data: age (continuous), sex (male vs. female), the disease name used for dispensing antifibrotic therapy, home oxygen therapy within 6 months before the index date, and the baseline use of systemic steroids and immunosuppressive agents. The baseline use of systemic steroids was defined as oral or parenteral corticosteroid administration (ATC Classification of H02AB) with methylprednisolone equivalent dose of \u0026ge;\u0026thinsp;5 mg for \u0026ge;\u0026thinsp;2 weeks within 1 month preceding the index date. The baseline use of immunosuppressive agents was defined as any oral or parenteral immunosuppressive agent usage (ATC Classification of L04A and L01FA01) for \u0026ge;\u0026thinsp;2 weeks within 1 month before the index date. The baseline body mass index, dyspnea score (Hugh\u0026ndash;Johns classification), activities of daily living (Barthel index), and comorbidity at the time of admission were extracted from the \u0026ldquo;Yoshiki 1\u0026rdquo; file submitted to the government for reimbursement of in-hospital medical fees. Information on these baseline characteristics was collected when patient admission within 12 months before the index date was identified from the \u0026ldquo;Yoshiki 1\u0026rdquo; file. We calculated the Charlson Comorbidity Index based on the International Classification of Diseases 10th Revision Codes of Comorbidities.\u003c/p\u003e \u003cp\u003eLaboratory data were extracted from each hospital\u0026rsquo;s data storage. The enzyme immunoassay, electrochemical luminescence immune assay, and latex agglutination tests have been approved for measuring serum KL-6 levels in Japan. All serum KL-6 measurements during the follow-up period were extracted, and the number of days from the index date to each measurement was calculated.\u003c/p\u003e \u003cp\u003eThe outcome of interest was the AE of fibrotic ILD. The definition was the combination of the primary diagnoses related to AE of ILD (acute or chronic ILDs), emergency admissions, and the administration of pulse/high-dose steroids (\u0026ge;\u0026thinsp;50 mg/day of methylprednisolone equivalent) either on the admission day or the subsequent day (Additional file 2) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For the sensitivity analysis, we used another definition of AE previously validated for AE of interstitial pneumonia and acute ILDs (Additional file 2) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003ePatient characteristics were presented as frequencies and proportions for categorical variables and as means with standard deviations or median and interquartile range, as appropriate, for continuous variables. The patient-specific trend of serum KL-6 was depicted in a Spaghetti plot, accompanied by a smoothing line representing the average trend. A Kaplan\u0026ndash;Meier plot was used to display survival probability over time.\u003c/p\u003e \u003cp\u003eTo evaluate the association between patient-specific evolution of serum KL-6 levels and AE risk, we used a joint regression model [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] involving the development of a linear mixed-effects model for serial serum KL-6 measurements in each patient and a proportional hazard regression model for the AE hazard. The model also can consider measurement errors inherent in KL-6 assessments. The linear mixed-effects model included time-independent covariates (age, sex, season of the index date, baseline steroid use, and baseline immunosuppressive therapy) and a time-dependent covariate (days from the index date to each KL-6 measurement). Patient-specific random effects were incorporated into the intercept, as well as the number of days from the index date to each KL-6 measurement. The proportional hazards regression model was postulated using a Weibull baseline risk function and incorporated the same covariates as in the longitudinal model, as well as the history of serum KL-6 defined within the linear mixed-effects model. The adaptive Gaussian\u0026ndash;Hermite rule was applied for numerical integration within the maximum likelihood function. Given the right-skewed and non-negative distribution of serum KL-6, it was divided by 100 and log-transformed. During the model development, the functional form selection (linear term vs. quadratic term of the days from the index date to each KL-6 measurement, random slope vs. random intercept, value parameterization vs. value\u0026thinsp;+\u0026thinsp;slope parameterization) was based on the Akaike Information Criterion.\u003c/p\u003e \u003cp\u003eWe demonstrated the dynamic predictions of conditional survival using a joint regression model. We simulated four extreme cases: a 65-year-old man not taking baseline steroids or immunosuppressants initiating first antifibrotic therapy in winter, with KL-6 measurements of 1) 500 U/mL at Day 0, 1000 U/mL at Day 300, and 2000 U/mL at Day 600 (upward trend); 2) 2000 U/mL at Day 0, 1000 U/mL at Day 300, and 500 U/mL at Day 600 (downward trend); 3) 3000 U/mL at Day 0, 3000 U/mL at Day 300, and 3000 U/mL at Day 600 (constantly high); and 4) 500 U/mL at Day 0, 500 U/mL at Day 300, and 500 U/mL at Day 600 (constantly low). Statistical significance was set at a two-sided \u003cem\u003ep\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05. We utilized the R software version 4.3.0 (R Foundation for Statistical Computing, Vienna, Austria) for statistical analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarizes the patient-selection process. We identified 41,658 patients with disease codes related to fibrotic ILD; among them, 1,112 were prescribed antifibrotic therapy. After excluding 173 patients with \u0026le;\u0026thinsp;1 measurement of KL-6, 939 with fibrotic ILDs were included in this study. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarize the baseline patient characteristics. A total of 312/939 (33%) patients had IPF, and 113/939 (12%) had CTD-ILD (rheumatoid arthritis, 4%; systemic sclerosis, 4%). Among the included patients, 194 (21%) experienced AE during the follow-up period (event rate: 0.13/person-year; 1-year incidence: 35%). Among patients without AE, 86 (12%) died from other causes within the first year of follow-up. No patients experienced malignancy before the index date and during the follow-up period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever acute exacerbation\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;745)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute exacerbation\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;194)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;939)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean age (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e517 (69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e660 (70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNintedanib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473 (64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e577 (61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian of baseline KL-6 (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1076 (686\u0026ndash;1619)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1172 (840\u0026ndash;1745)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1100 (710\u0026ndash;1623)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason of the index date\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e251 (27)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242 (26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e214 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e182 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e232 (25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5 to \u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e229 (24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102 (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e430 (58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e525 (56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity of daily living (Barthel index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20 to \u0026lt;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (1.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e310 (42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e405 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e421 (57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e514 (55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson Comorbidity Index (median, IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2\u0026ndash;3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHome oxygen therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyspnea score (Hugh\u0026ndash;Johns classification)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264 (28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106 (11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e467 (63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e569 (61)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily steroid use at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135 (18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e188 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily immunosuppressant use at baseline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eN, number; SD, standard deviation; KL-6, Kerbs von Lungren 6; IQR, interquartile range; BMI, body mass index; Mechanical ventilation included invasive/non-invasive ventilation, high-flow nasal cannula, and continuous positive airway pressure\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo; background interstitial lung disease\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of patients (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic interstitial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic pulmonary fibrosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e312 (32.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsual interstitial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5 (0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNonspecific interstitial pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined pulmonary fibrosis and Emphysema\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10 (1.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCryptogenic organizing pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdiopathic interstitial pneumonia (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156 (16.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease-associated ILD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRheumatoid arthritis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39 (4.15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic sclerosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37 (3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSjogren syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16 (1.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicroscopic polyangiitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConnective tissue disease-associated ILD (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic lupus erythematosus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgG4-related disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypersensitivity pneumonitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15 (1.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic pneumonia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1 (0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterstitial pneumonia (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e261 (27.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLung fibrosis (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (2.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiffuse interstitial pneumonia (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17 (1.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAcute interstitial pneumonia (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (0.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePulmonary hemorrhage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003eILD, interstitial lung disease; IgG, immunoglobulin\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAt baseline, serum KL-6 levels were as follows: minimum, 265 U/mL; first quartile, 735 U/mL; median, 1,134 U/mL; mean, 1,406 U/mL; third quartile, 1,680 U/mL; and maximum, 12,408 U/mL. KL-6 measurements were performed at 3\u0026ndash;170 visits (median, 11; interquartile range, 6\u0026ndash;20) from the index date until the end of the follow-up. The difference between two consecutive measurements varied widely (minimum: -4750 U/mL; first quartile, -94 U/mL; median: -11 U/mL; mean: -16 U/mL; third quartile, 58 U/mL; maximum: 6,730 U/mL). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea shows the individual-level trajectories of KL-6 from the index date, accompanied by a smoothing line representing the average trend. Patients who experienced AEs had higher serum KL-6 levels over time than those without AE. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb shows the individual-level trajectories of serum KL-6 at the end of the follow-up. A slight upward trend in serum KL-6 levels was observed before AE, whereas the levels before censoring remained relatively stable. The Kaplan\u0026ndash;Meier curve showed a constant or slightly monotonic increase in AE risk over time (Additional file 3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMain analysis\u003c/h2\u003e \u003cp\u003eAccording to the Akaike Information Criterion, the joint regression model with the following longitudinal and event processes provided the best fit for the data:\u003c/p\u003e \u003cp\u003eLongitudinal process:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${y}_{i}\\left(t\\right)={x}_{i}\\left(t\\right)\\beta +{Z}_{i}\\left(t\\right){b}_{i}+{ϵ}_{i}\\left(t\\right)={m}_{i}\\left(t\\right)+{ϵ}_{i}\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the true KL-6 value; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({b}_{i}\\sim N\\left(0,D\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes random-effects parameters based on the patient and time; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({ϵ}_{i}\\left(t\\right)\\sim N\\left(0,{\\sigma }^{2}\\right)\\)\u003c/span\u003e\u003c/span\u003e signifies measurement error based on time.\u003c/p\u003e \u003cp\u003eEvent process:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${h}_{i}\\left(t|{M}_{i}\\left(t\\right),{w}_{i}\\right)={\\text{l}\\text{i}\\text{m}}_{\\varDelta t\\to 0}P\\left[t\\le T\u0026lt;t+\\varDelta t|T\\ge t,{M}_{i}\\left(t\\right),{w}_{i}\\right]/\\varDelta t={h}_{0}\\left(t\\right)exp\\left({w}_{i}\\gamma +\\alpha {m}_{i}\\left(t\\right)\\right)$$\u003c/div\u003e\u003c/div\u003e,\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({M}_{i}\\left(t\\right)=\\left[{m}_{i}\\left(s\\right),0\\le s\u0026lt;t\\right]\\)\u003c/span\u003e\u003c/span\u003e, representing KL-6 history; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({h}_{0}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the Weibull baseline hazard function; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\gamma\\)\u003c/span\u003e\u003c/span\u003e signifies parameters for time-independent covariates; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({w}_{i}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(exp\\left(\\alpha \\right)\\)\u003c/span\u003e\u003c/span\u003e indicates the relative increase in the risk of an event at time t resulting from a simultaneous one-unit increase in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eWe observed a strong association between the log of serum KL-6 levels and AE occurrence over time (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The hazard ratio for AE comparing patients with a cumulative serum KL-6 difference of 100*exp(1)\u0026thinsp;=\u0026thinsp;272 U/mL was 1.54 (95% confidence interval: 1.20\u0026ndash;1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). As shown in Additional file 4, patients with an increasing trend of serum KL-6 levels and constantly high serum KL-6 levels had an unfavorable predicted survival compared with those with a decreasing trend or consistently low serum KL-6 values. Sensitivity analysis using an alternate algorithm for detecting AE yielded similar results (hazard ratio: 1.26, 95% confidence interval: 1.01\u0026ndash;1.56, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.04).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% Confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongitudinal process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays from the index date to KL-6 Measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u0026ndash;1.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline daily steroid use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.010\u0026ndash;1.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline daily immunosuppressant Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.814\u0026ndash;1.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.985\u0026ndash;0.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.900\u0026ndash;1.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.960\u0026ndash;1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEvent process (hazard ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline steroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.267\u0026ndash;2.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBaseline immunosuppressant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.964\u0026ndash;2.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.999\u0026ndash;1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.070\u0026ndash;2.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeason\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.939\u0026ndash;1.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssociation of log (KL-6/100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.193\u0026ndash;1.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eKL-6, Kerbs von Lungren 6\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eLimited evidence exists regarding the dynamic prediction of AE. This study is the first to demonstrate a strong association between the patient-specific evolution of serum KL-6 levels and the risk of AE among patients with fibrotic ILD. The trajectories of serum KL-6 exhibited high heterogeneity and fluctuation; however, physicians should be vigilant when observing an upward trend of KL-6 or constantly high serum KL-6 values, as they may indicate prospective AE. Serum KL-6 is a simple and fast blood test that is especially beneficial for patients who struggle to perform pulmonary function tests effectively.\u003c/p\u003e \u003cp\u003ePrevious studies primarily evaluated patients\u0026rsquo; baseline characteristics, identifying sex, age, compromised pulmonary function, and radiological and histological patterns as potential predictors of poor prognosis, including AE [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. A high baseline serum KL-6 level is proposed as a predictor of disease progression, AE, and poor outcomes of ILDs [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In contrast, our study showed that the serum KL-6 level exhibited a considerable fluctuation, and the timing of fibrotic ILD diagnosis influenced the predictive value of baseline KL-6. Some smaller-scale studies suggested that longitudinally increased serum KL-6 levels may predict a decline in forced vital capacity and disease progression in IPF [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The authors evaluated the correlation between serial KL-6 levels and patient outcomes but excluded patients when either KL-6 or pulmonary function test results were missing. This exclusion strategy assumes missing-completely-at-random; the probability of an outcome being missing is independent of both observed and unobserved factors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In a non-standardized protocol, a patient's medical condition could highly influence missing data, especially in pulmonary results. In addition, their analyses did not consider KL-6 measurement errors. Notably, small sample sizes in research may inadvertently yield significant results by chance.\u003c/p\u003e \u003cp\u003eThis study addressed AE, an outcome often omitted in previous longitudinal studies, and incorporated a large patient cohort with numerous measurement time points. To address methodological limitations, we employed a joint regression model with a longitudinal component that accommodates missing outcome data by assuming missing data not at random; this means that the probability of an outcome being missed may depend on any other observed covariates and previous KL-6 measurements. The model accounts for the impact of AE on the interruption of pre-AE KL-6 measurements. Furthermore, the linear mixed-effect model incorporated the measurement error inherent in KL-6 assessments. Finally, we expanded the disease scope to include fibrotic IPF, encompassing a range of heterogeneous ILDs, broadening KL-6\u0026rsquo;s applicability in treating ILDs.\u003c/p\u003e \u003cp\u003eThis study has limitations. First, the accuracy of the data captured in this study remains uncertain. Although we used modified versions of a validated algorithm, the original algorithm solely evaluated the positive predictive value for AE rather than the sensitivity and specificity of the algorithm. In addition, in the Real World Data database, longitudinal follow-ups on patients were conducted longitudinally only at the same hospital; this could have skewed associations toward null, potentially increasing the likelihood of missing AE more than overdiagnosing these events. Second, our small sample size limited our ability to develop a more intricate and flexible model. For instance, we could not evaluate the contribution of the change rate in the patient-specific evolution of KL-6. Third, our study was primarily based on administrative claim data and selected data from electronic medical records; thus, we could not incorporate all variables. Incorporating more time-independent and time-dependent covariates, such as pulmonary function test results, dyspnea scores, physical function, and imaging results, could enhance predictive accuracy. Lastly, our study focused on the Japanese population. The genotype distribution may cause differences in KL-6 levels [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Further large-scale studies are needed to validate our findings and to develop a more accurate dynamic prediction model for AE.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eOur study showed that high baseline KL-6 levels and an upward trend can predict AE in fibrotic ILD. Physicians should consider longitudinal KL-6 measurements, particularly in patients who struggle to perform pulmonary function tests effectively.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAE\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eacute exacerbation; CTD-ILD, connective tissue disease-associated ILD; ILD, interstitial lung diseases; IPF, idiopathic pulmonary fibrosis; KL-6, Kerbs von Lungren 6\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe institutional review board of Showa University approved this study (approval number: 2023-119-B), and the requirement for written informed consent was waived because the dataset had been de-identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the Health, Clinic, and Education Information Evaluation Institute for developing the database used in this study.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting\u0026nbsp;the findings of this study are available upon request from the corresponding author. The data are not publicly available because of the restrictions imposed by Real World Data Co., Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS received funding from the Japan Society for the Promotion of Science and Pfizer Health Research Foundation. Other authors have nothing to declare\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by\u0026nbsp;the Japan Society for the Promotion of Science\u0026nbsp;and Pfizer Health Research Foundation.\u0026nbsp;The Japanese Society for the Promotion of Science provided English editing and publication fees (Grants-in-Aid for Scientific Research [Kakenhi]; grant number 23K09582). The\u0026nbsp;Pfizer Health Research Foundation\u0026nbsp;supported the cost of data acquisition.\u0026nbsp;The funders played no role in the study design, execution, analyses, interpretation, or decision to submit the results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAS, KA, CS, HS, and NY contributed to the conception and design of the study. AS managed the database and performed the data analysis. All authors confirmed the validity of the data analysis and contributed to\u0026nbsp;the data interpretation. AS\u0026nbsp;and KA drafted the manuscript. All authors critically revised and approved the final version of the manuscript. AS, KA, CS, HS, and NY consent to be accountable for all aspects of this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWong AW, Ryerson CJ, Guler SA. Progression of fibrosing interstitial lung disease. Respir Res. 2020;21:32.\u003c/li\u003e\n\u003cli\u003eZamora-Legoff JA, Krause ML, Crowson CS, Ryu JH, Matteson EL. Progressive decline of lung function in rheumatoid arthritis-associated interstitial lung disease. Arthritis Rheumatol. 2017;69:542-549.\u003c/li\u003e\n\u003cli\u003eReiseter S, Gunnarsson R, Mogens Aal\u0026oslash;kken T, Lund MB, Mynarek G, Corander J, et al. Progression and mortality of interstitial lung disease in mixed connective tissue disease: a long-term observational nationwide cohort study. Rheumatology (Oxford). 2018;57:255-262.\u003c/li\u003e\n\u003cli\u003eGagliardi M, Berg DV, Heylen CE, Koenig S, Hoton D, Tamirou F, et al. Real-life prevalence of progressive fibrosing interstitial lung diseases. Sci Rep. 2021;11:23988.\u003c/li\u003e\n\u003cli\u003eCottin V, Wollin L, Fischer A, Quaresma M, Stowasser S, Harari S. Fibrosing interstitial lung diseases: knowns and unknowns. Eur Respir Rev. 2019;28:180100.\u003c/li\u003e\n\u003cli\u003eRaghu G, Remy-Jardin M, Richeldi L, Thomson CC, Inoue Y, Johkoh T, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med. 2022;205:e18-e47.\u003c/li\u003e\n\u003cli\u003eCollard HR, Ryerson CJ, Corte TJ, Jenkins G, Kondoh Y, Lederer DJ, et al. Acute exacerbation of idiopathic pulmonary fibrosis. An International Working Group Report. Am J Respir Crit Care Med. 2016;194:265-275.\u003c/li\u003e\n\u003cli\u003eKondoh Y, Taniguchi H, Katsuta T, Kataoka K, Kimura T, Nishiyama O, et al. Risk factors of acute exacerbation of idiopathic pulmonary fibrosis. Sarcoidosis Vasc Diffuse Lung Dis. 2010;27:103-110.\u003c/li\u003e\n\u003cli\u003eSong JW, Hong SB, Lim CM, Koh Y, Kim DS. Acute exacerbation of idiopathic pulmonary fibrosis: incidence, risk factors and outcome. Eur Respir J. 2011;37:356-363.\u003c/li\u003e\n\u003cli\u003eAlqalyoobi S, Adegunsoye A, Linderholm A, Hrusch C, Cutting C, Ma SF, et al. Circulating plasma biomarkers of progressive interstitial lung disease. Am J Respir Crit Care Med. 2020;201:250-253.\u003c/li\u003e\n\u003cli\u003eCollard HR, Yow E, Richeldi L, Anstrom KJ, Glazer C, IPFnet investigators. Suspected acute exacerbation of idiopathic pulmonary fibrosis as an outcome measure in clinical trials. Respir Res. 2013;14:73.\u003c/li\u003e\n\u003cli\u003eOhshimo S, Ishikawa N, Horimasu Y, Hattori N, Hirohashi N, Tanigawa K, et al. Baseline KL-6 predicts increased risk for acute exacerbation of idiopathic pulmonary fibrosis. Respir Med. 2014;108:1031-1039.\u003c/li\u003e\n\u003cli\u003eAllen SC, Baxter M. A comparison of four tests of cognition as predictors of inability to perform spirometry in old age. Age Ageing. 2009;38:537-541.\u003c/li\u003e\n\u003cli\u003eCarvalhaes-Neto N, Lorino H, Gallinari C, Escolano S, Mallet A, Zerah F, et al. Cognitive function and assessment of lung function in the elderly. Am J Respir Crit Care Med. 1995;152:1611-1615.\u003c/li\u003e\n\u003cli\u003eRubin DB. Inference and missing data. Biometrika. 1976;63:581-592.\u003c/li\u003e\n\u003cli\u003eKohno N, Akiyama M, Kyoizumi S, Hakoda M, Kobuke K, Yamakido M. Detection of soluble tumor-associated antigens in sera and effusions using novel monoclonal antibodies, KL-3 and KL-6, against lung adenocarcinoma. Jpn J Clin Oncol. 1988;18:203-216.\u003c/li\u003e\n\u003cli\u003ed\u0026rsquo;Alessandro M, Bergantini L, Cameli P, Vietri L, Lanzarone N, Alonzi V, et al. Krebs von den Lungen-6 as a biomarker for disease severity assessment in interstitial lung disease: a comprehensive review. Biomark Med. 2020;14:665-674.\u003c/li\u003e\n\u003cli\u003eWang C, Wang Q, Liu T, Zhu J, Zhang B. Krebs von den Lungen-6 (KL-6) as a diagnostic marker for pulmonary fibrosis: a systematic review and meta-analysis. Clin Biochem. 2023;114:30-38.\u003c/li\u003e\n\u003cli\u003eWorld Medical Association. World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull World Health Organ. 2001;79:373-374.\u003c/li\u003e\n\u003cli\u003eBenchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement. PLOS Med. 2015;12:e1001885.\u003c/li\u003e\n\u003cli\u003eBehr J, Prasse A, Kreuter M, Johow J, Rabe KF, Bonella F, et al. Pirfenidone in patients with progressive fibrotic interstitial lung diseases other than idiopathic pulmonary fibrosis (RELIEF): a double-blind, randomised, placebo-controlled, phase 2b trial. Lancet Respir\u003cem\u003e \u003c/em\u003eMed. 2021;9:476-486.\u003c/li\u003e\n\u003cli\u003eAnan K, Kataoka Y, Ichikado K, Kawamura K, Yasuda Y, Hisanaga J, et al. Algorithms identifying patients with acute exacerbation of interstitial pneumonia and acute interstitial lung diseases developed using Japanese administrative data. Cureus. 2004;16:e53073.\u003c/li\u003e\n\u003cli\u003eWulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. \u003cem\u003eBiometrics\u003c/em\u003e 1997;53:330-339.\u003c/li\u003e\n\u003cli\u003eGimenez A, Storrer K, Kuranishi L, Soares MR, Ferreira RG, Pereira CAC. Change in FVC and survival in chronic fibrotic hypersensitivity pneumonitis. Thorax. 2018;73:391-392.\u003c/li\u003e\n\u003cli\u003eRyerson CJ, Vittinghoff E, Ley B, Lee JS, Mooney JJ, Jones KD, et al. Predicting survival across chronic interstitial lung disease: the ILD-GAP model. Chest. 2014;145:723-728.\u003c/li\u003e\n\u003cli\u003eQiu M, Chen Y, Ye Q. Risk factors for acute exacerbation of idiopathic pulmonary fibrosis: A systematic review and meta-analysis. Clin Respir J. 2018;12:1084-1092.\u003c/li\u003e\n\u003cli\u003eZhang T, Shen P, Duan C, Gao L. KL-6 as an immunological biomarker predicts the severity, progression, acute exacerbation, and poor outcomes of interstitial lung disease: a systematic review and meta-analysis. Front Immunol. 2021;12:745233.\u003c/li\u003e\n\u003cli\u003eBergantini L, Bargagli E, Cameli P, Cekorja B, Lanzarone N, Pianigiani L, et al. Serial KL-6 analysis in patients with idiopathic pulmonary fibrosis treated with nintedanib. Respir Investig. 2019;57:290-291.\u003c/li\u003e\n\u003cli\u003eWakamatsu K, Nagata N, Kumazoe H, Oda K, Ishimoto H, Yoshimi M, et al. Prognostic value of serial serum KL-6 measurements in patients with idiopathic pulmonary fibrosis. Respir Investig. 2017;55:16-23.\u003c/li\u003e\n\u003cli\u003eHorimasu Y, Hattori N, Ishikawa N, Kawase S, Tanaka S, Yoshioka K, et al. Different MUC1 gene polymorphisms in German and Japanese ethnicities affect serum KL-6 levels. Respir Med. 2012;106:1756-1764.\u003c/li\u003e\n\u003cli\u003eBonella F, Long X, Ohshimo S, Horimasu Y, Griese M, Guzman J, et al. MUC1 gene polymorphisms are associated with serum KL-6 levels and pulmonary dysfunction in pulmonary alveolar proteinosis. Orphanet J Rare Dis. 2016;11:48.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Acute exacerbation, fibrotic interstitial lung disease, interstitial lung disease exacerbation, interstitial pneumonia, serum Kerbs von Lungren 6","lastPublishedDoi":"10.21203/rs.3.rs-3982411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3982411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAcute exacerbation (AE) in fibrotic interstitial lung diseases (ILDs) poses a significant challenge, with limited available evidence for predicting such events. Kerbs von Lungren 6 (KL-6) is a proposed predictive marker for prospective AE; however, only its baseline value has been evaluated. To address this gap, this study investigates the association between the patient-specific evolution of serum KL-6 levels and the AE risk of fibrotic ILD.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study used data from the Japanese Database of Health, Clinic, and Education Information Evaluation Institute and JMDC Inc. The included patients had fibrotic ILDs and antifibrotic therapy with \u0026ge;\u0026thinsp;2 measurements of serum KL-6 from the index date to the end of follow-up. The outcome was AE of acute or chronic fibrotic ILDs defined based on the combination of its primary diagnoses, emergency admission, and pulse/high-dose steroids on the day of or after admission. We used a joint regression model for each patient\u0026rsquo;s serial serum KL-6 measurements and a proportional hazards regression model for the hazard ratio of AE.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe study included 939 patients with fibrotic ILDs, and 194 (21%) experienced AE during the follow-up (event rate, 0.13/person-year; 1-year incidence, 35%). The hazard ratio of AE comparing patients differing in cumulative serum KL-6 was 1.54 (95% confidence interval: 1.20\u0026ndash;1.98, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA high baseline KL-6 level and its upward evolution can predict the AE of fibrotic ILD. Longitudinal KL -6 measurement should be considered, especially for patients who cannot perform pulmonary function tests well. Our study could help develop strategies for improving fibrotic ILD prognosis and treatment.\u003c/p\u003e","manuscriptTitle":"Predictive ability of longitudinal changes in Kerbs von Lungren 6 for acute exacerbation of fibrotic interstitial lung disease: Database research","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-28 21:02:01","doi":"10.21203/rs.3.rs-3982411/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91cb6fd0-788f-4e45-bee6-0fe31092418b","owner":[],"postedDate":"February 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-08T12:24:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-28 21:02:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3982411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3982411","identity":"rs-3982411","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}