Skip to main content

Main menu

  • Home
  • Current issue
  • Early View
  • Archive
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • Institutional open access agreements
    • Peer reviewer login
  • Alerts
  • Subscriptions
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

User menu

  • Log in
  • Subscribe
  • Contact Us
  • My Cart

Search

  • Advanced search
  • ERS Publications
    • European Respiratory Journal
    • ERJ Open Research
    • European Respiratory Review
    • Breathe
    • ERS Books
    • ERS publications home

Login

European Respiratory Society

Advanced Search

  • Home
  • Current issue
  • Early View
  • Archive
  • Authors/reviewers
    • Instructions for authors
    • Submit a manuscript
    • Institutional open access agreements
    • Peer reviewer login
  • Alerts
  • Subscriptions

Body mass index and in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis

Nobuyasu Awano, Taisuke Jo, Hideo Yasunaga, Minoru Inomata, Naoyuki Kuse, Mari Tone, Kojiro Morita, Hiroki Matsui, Kiyohide Fushimi, Takahide Nagase, Takehiro Izumo
ERJ Open Research 2021 7: 00037-2021; DOI: 10.1183/23120541.00037-2021
Nobuyasu Awano
1Dept of Respiratory Medicine, Japanese Red Cross Medical Center, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nobuyasu Awano
  • For correspondence: awanobu0606@hotmail.co.jp
Taisuke Jo
2Dept of Health Services Research, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
3Dept of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Taisuke Jo
Hideo Yasunaga
4Dept of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Minoru Inomata
1Dept of Respiratory Medicine, Japanese Red Cross Medical Center, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Minoru Inomata
Naoyuki Kuse
1Dept of Respiratory Medicine, Japanese Red Cross Medical Center, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mari Tone
1Dept of Respiratory Medicine, Japanese Red Cross Medical Center, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kojiro Morita
4Dept of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan
5Dept of Health Services Research, Faculty of Medicine, University of Tsukuba, Ibaraki, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Hiroki Matsui
4Dept of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Kiyohide Fushimi
6Dept of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School of Medicine, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Takahide Nagase
3Dept of Respiratory Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Takehiro Izumo
1Dept of Respiratory Medicine, Japanese Red Cross Medical Center, Tokyo, Japan
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data
  • Info & Metrics
  • PDF
Loading

Abstract

Background Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease characterised by chronic fibrosis, and acute exacerbation of IPF (AE-IPF) is the leading cause of death in patients with IPF. Data on the association between the body mass index (BMI) and prognosis of AE-IPF are lacking. This study was performed to evaluate the association between BMI and in-hospital mortality in patients who developed AE-IPF using a national inpatient database.

Methods Using the Japanese Diagnosis Procedure Combination database, we retrospectively collected data of inpatients with AE-IPF from 1 July, 2010 to 31 March, 2018. We performed a multivariable logistic regression analysis to evaluate the association between all-cause in-hospital mortality and BMI, categorised as underweight (<18.5 kg·m−2), low-normal weight (18.5–22.9 kg·m−2), high-normal weight (23.0–24.9 kg·m−2), overweight (25.0–29.9 kg·m−2) and obese (≥30.0 kg·m−2).

Results In total, 14 783 patients were eligible for this study. The in-hospital mortality rate was 59.0%, 55.0%, 53.8%, 54.8% and 46.0% in the underweight, low-normal weight, high-normal weight, overweight and obese groups, respectively. Underweight patients had a significantly higher mortality rate (OR 1.25, 95% CI 1.10–1.42) and obese patients had a significantly lower mortality rate (OR 0.71, 95% CI 0.54–0.94) than low-normal weight patients.

Conclusion Among patients with AE-IPF, the underweight group had higher mortality and the obese group had lower mortality.

Abstract

Among patients with acute exacerbation of idiopathic pulmonary fibrosis, underweight patients have higher mortality and obese patients lower mortality https://bit.ly/3eoVMOR

Introduction

Patients with idiopathic pulmonary fibrosis (IPF), an interstitial lung disease characterised by chronic fibrosis, have a poor prognosis with an average survival time of 3 to 4 years [1]. A previous study showed that acute exacerbation of IPF (AE-IPF) was associated with high mortality with a mean survival time of <1 year and a 90-day mortality rate of ∼50% after AE-IPF [2]. Risk factors for AE-IPF include oxygen administration, use of antacids, smoking, low lung function, a high serum Krebs von den Lungen-6 concentration, secondary pulmonary hypertension and seasonality [3–5].

Generally, undernutrition is a potential prognostic factor in patients with respiratory diseases such as COPD [6] and pulmonary tuberculosis [7]. Moreover, protective effects of adipose tissue, referred to as the “obesity paradox”, are known in many chronic diseases including cardiovascular disease [8], chronic heart failure [9] and COPD [10]. In one study of patients with IPF, one-third of the patients were undernourished [11], and a lower body mass index (BMI) at the time of diagnosis has been proposed as a prognostic factor [12–16]. To the best of our knowledge, however, no study has focused on the association between BMI and prognosis of AE-IPF.

The present study was performed using a nationwide inpatient database to evaluate the association between BMI and in-hospital mortality in patients who developed AE-IPF.

Patients and methods

Data source

Inpatient data were extracted from the Japanese Diagnosis Procedure Combination database, the details of which have been reported elsewhere [17]. More than 1000 hospitals voluntarily contribute to the database, representing ∼50% of all discharges from acute care hospitals in Japan. The data used in the present study included sex and age; body weight and height; smoking index; severity of dyspnoea based on the Hugh–Jones dyspnoea scale [18]; consciousness level on admission; intensive care unit (ICU) and/or emergency ward admission during hospitalisation; dates of hospitalisation and discharge; main diagnoses and pre-existing comorbidities on admission recoded by the attending physicians with the International Classification of Diseases, 10th revision (ICD-10) codes accompanied by text in Japanese; surgical and nonsurgical procedures and dates of the procedures performed; dates and doses of drugs administered during hospitalisation; and discharge status.

The Institutional Review Board of The University of Tokyo approved this study. The requirement for informed consent was waived because of the anonymous nature of the data.

Patient selection

This study used data from July 1, 2010 to March 31, 2018. The inclusion criteria were an age of ≥15 years, diagnosis of interstitial pneumonia (ICD-10 codes J84.1, J84.8 and J84.9), examination by computed tomography within 1 day after admission, and treatment with methylprednisolone at 500 to 1000 mg·day−1 intravenously for 3 days starting within 4 days after admission [19]. Patients with IPF were selected as follows. First, patients with idiopathic interstitial pneumonias (IIPs) other than IPF, such as idiopathic nonspecific interstitial pneumonia, respiratory bronchiolitis-associated interstitial lung disease, cryptogenic organising pneumonia, acute interstitial pneumonia, desquamative interstitial pneumonia, lymphoid interstitial pneumonia, idiopathic pleuroparenchymal fibroelastosis and unclassifiable idiopathic interstitial pneumonia, were excluded using the diagnoses in Japanese. Then, we excluded patients with the following secondary interstitial lung diseases identified using ICD-10 codes: hypersensitivity pneumonitis (J67), connective tissue disease associated with interstitial lung disease (M05, M06 and M30–35), sarcoidosis (D86), amyloidosis (E85), drug-induced lung disease (J70), radiation pneumonitis (J70), Pneumocystis jirovecii pneumonia (B59), pneumoconiosis (J60–65), pulmonary alveolar proteinosis (J84.0), eosinophilic pneumonia (J82), Langerhans cell histiocytosis (C96) and lymphangioleiomyomatosis (D21.9). We then excluded patients who received any of the following medications related to acute heart failure within 1 day after admission: furosemide, azosemide, carperitide, landiolol hydrochloride, digoxin, deslanoside and tolvaptan [20]. We also excluded patients who underwent intra-aortic balloon pump therapy during hospitalisation. The remaining patients were assumed to have IPF. Finally, we excluded patients with missing data regarding consciousness and those who died within 4 days after admission.

Patient characteristics and BMI categories

The patient characteristics evaluated in this study were BMI; age; sex; Hugh–Jones dyspnoea scale class on admission; consciousness on admission; smoking index; comorbidities; Charlson comorbidity index; surgical and nonsurgical procedures including tracheostomy, mechanical ventilation and use of medications for IPF during hospitalisation; and continuous renal replacement therapy within 1 day after admission. Consciousness on admission was evaluated using the Japan Coma Scale [21, 22], which is widely used in Japan and has been shown to be well correlated with the Glasgow Coma Scale assessment [23]. The following comorbidities were identified using ICD-10 codes: lung cancer (C34), COPD (J44), pneumonia (J18), aspiration pneumonia (J69), pulmonary embolism (I26), chronic heart failure (I50), chronic renal failure (N18) and diabetes mellitus (E11). The Charlson comorbidity index was classified into five groups: 0, 1, 2, 3–5 and ≥6.

BMI categories were assigned based on the World Health Organization classifications of underweight (<18.5 kg·m−2), normal weight (18.5–24.9 kg·m−2), overweight (25.0–29.9 kg·m−2) and obese (≥30.0 kg·m−2) individuals. Normal weight was further divided into low-normal (18.5–22.9 kg·m−2) and high-normal (23.0–24.9 kg·m−2) [24, 25].

Outcome

The primary outcome was all-cause in-hospital mortality.

Statistical analysis

Continuous variables are presented as mean±standard deviation or median (interquartile range). The Kruskal–Wallis test was used to compare these variables between the groups. Proportions of categorical variables were compared using the Chi-squared test.

Missing data were observed for age, BMI, Hugh–Jones dyspnoea scale class and smoking index. First, we performed a multiple imputation procedure to replace each missing value with a set of submitted plausible values using a Markov chain Monte Carlo algorithm known as imputation by chained equations [26], thereby creating 20 filled-in complete datasets. The multiple imputation method assumes that data are missing at random and that any systemic differences between the missing and observed values can be explained by differences in the observed data [27, 28]. We then performed multivariable logistic regression analyses fitted with generalised estimating equations to estimate the odds ratio of in-hospital mortality for each BMI category. We defined the low-normal weight group as the reference category. Finally, the results of the multivariable logistic regression analyses from the 20 datasets were combined using Rubin's rule.

Secondly, we conducted a complete-case analysis that excluded all patients with missing data. Multivariable logistic regression analysis for in-hospital mortality was performed to estimate the odds ratio for each BMI category with adjustment for other patient background factors while also adjusting for within-hospital clustering by means of a generalised estimating equation [29].

The threshold for significance was p<0.05. All statistical analyses were performed using STATA/MP version 16 software (STATA Corp., College Station, TX, USA).

Results

During the study period, 95 221 patients underwent computed tomography within 1 day after admission and received high-dose methylprednisolone for 3 days starting within 4 days after admission (figure 1). Among these 95 221 patients, 14 783 were eligible for this study. Their mean age was 75.0±9.7 years, and the proportion of men was 71.7% (n=10 594). Their mean BMI was 22.4±3.7 kg·m−2, and 8294 (56.1%) patients died during hospitalisation. The proportions of patients with missing data for age, BMI, Hugh–Jones dyspnoea scale class and smoking index were 0.6% (n=89), 11.0% (n=1629), 22.7% (n=3359) and 12.4% (n=1830) of all eligible patients, respectively.

FIGURE 1
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 1

Flow chart of patient selection. #: idiopathic nonspecific interstitial pneumonia, respiratory bronchiolitis-associated interstitial lung disease, cryptogenic organising pneumonia, acute interstitial pneumonia, desquamative interstitial pneumonia, lymphoid interstitial pneumonia, idiopathic pleuroparenchymal fibroelastosis and unclassifiable idiopathic interstitial pneumonia.

The patient characteristics for each BMI category are shown in table 1. The proportion of patients aged >80 years was higher in the underweight group but lower in the obese group. The proportion of females was higher in the underweight and obese groups. The proportion of patients with a poor level of consciousness on admission was higher in the underweight group than in the other groups. The proportion of patients with a Charlson comorbidity index of ≥6 was higher in the lower BMI groups. However, the obese group had the highest percentage of patients admitted to the ICU. The percentages of lung cancer and chronic renal failure were higher in the lower BMI categories. Conversely, the percentage of diabetes mellitus was higher in the higher BMI categories. The percentages of the following treatments and procedures were higher in the higher BMI categories: azithromycin, sulfamethoxazole trimethoprim, intravenous cyclophosphamide, cyclosporin, tacrolimus, pirfenidone, nintedanib, sivelestat sodium hydrate and mechanical ventilation.

View this table:
  • View inline
  • View popup
TABLE 1

Patients’ characteristics and comorbidities in relation to body mass index (BMI) category

Figure 2 shows the all-cause in-hospital mortality rate for each BMI category. The in-hospital mortality rate was 59.0%, 55.0%, 53.8%, 54.8% and 46.0% in the underweight, low-normal weight, high-normal weight, overweight and obese groups, respectively.

FIGURE 2
  • Download figure
  • Open in new tab
  • Download powerpoint
FIGURE 2

All-cause in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis in relation to body mass index category.

Table 2 shows the results of the multivariable logistic regression analysis for all-cause in-hospital mortality using the multiple imputation method for missing data. The mortality rate in the underweight group was significantly higher than that in the reference low-normal weight group (OR 1.25, 95% CI 1.10–1.42). In contrast, the mortality rate in the obese group was significantly lower than that in the reference low-normal weight group (OR 0.71, 95% CI 0.54–0.94). Older age, male sex, more severe dyspnoea scores and a higher Charlson comorbidity index were significantly associated with higher mortality. In contrast, ICU admission, emergency unit admission and care at an academic hospital were associated with lower mortality. With respect to comorbidities, lung cancer and chronic renal failure were associated with higher mortality, whereas COPD was associated with lower mortality. The following treatments and procedures were associated with higher mortality: intravenous or oral cyclophosphamide, cyclosporin, azathioprine, sivelestat sodium hydrate, thrombomodulin α, mechanical ventilation and tracheotomy. In contrast, azithromycin and sulfamethoxazole trimethoprim were associated with lower mortality.

View this table:
  • View inline
  • View popup
TABLE 2

Multivariable logistic regression analysis for all-cause in-hospital mortality

In the complete-case multivariable logistic regression analysis, the OR (95% CI) with reference to the low-normal weight group were 1.25 (1.06–1.46), 0.94 (0.83–1.07), 1.01 (0.90–1.15) and 0.75 (0.54–0.94) for the underweight, high-normal weight, overweight and obese groups, respectively.

Discussion

Using a nationwide inpatient database in Japan, we investigated the association between BMI and mortality in patients with AE-IPF. Patients in the underweight group had a significantly higher mortality rate and those in the obese group had a significantly lower mortality rate than patients in the other weight groups. To our knowledge, the present study is the first to demonstrate a relationship between BMI and mortality in patients with AE-IPF.

Studies have been performed to evaluate the relationship between patients with IPF and body weight. A previous study showed that patients who lost ≥5% of body weight during the first year after diagnosis of IPF had a poorer prognosis than those who did not [12]. Moreover, staging based on annual body weight loss is reportedly a useful predictor of the prognosis of IPF [16]. These studies have suggested a detrimental impact of a lower BMI on patients with IPF, whereas other studies have, although indirectly, depicted a detrimental impact of obesity on patients with IPF. For example, one study showed that a decline in the forced vital capacity was a prognostic factor for patients with IPF [30], but others showed that an increased BMI was associated with lower vital capacity [31] and forced vital capacity [32] in the general population. Data regarding the impact of BMI on AE-IPF are inconsistent. One report indicated that BMI was not a risk factor for developing acute exacerbation [4], whereas another study showed that high BMI was a risk factor for developing acute exacerbation [33]. To our knowledge, however, no previous study has examined the relationship between BMI and mortality in patients with AE-IPF. The in-hospital mortality rate for all patients with AE-IPF in the current study was 56.1%, which is similar to previously reported rates [2]. The underweight group had the highest mortality rate, and the obese group had the lowest. A British database study demonstrated that the association between BMI and mortality varied among diseases [34]. Some diseases had a J-shaped association with BMI and other diseases had an inverse linear association with BMI. The results of our study were similar to the association between BMI and mortality of lung cancer in that study. Obesity may be a risk factor for developing AE-IPF, but it may be favourable in patients who developed AE-IPF. The mechanism by which obese patients with AE-IPF have favourable outcomes remains unknown.

BMI can be influenced by a patient's background factors, such as ethnic characteristics. Reports have suggested that Asian ethnic populations have different associations between BMI and health risks than Western populations [35]. Additionally, Asian ethnic populations generally have a higher percentage of body fat than Caucasians of the same age, sex and BMI, which may contribute to the difference in the properties of fat, including adipocytokines such as adiponectin, leptin and resistin [35, 36]. The BMI of patients with IPF in the present Japanese study was lower than that reported from other countries [14]. Such a difference in BMI distribution between Asian and Caucasian patients with IPF has been observed in previous studies [15, 37]. The association between BMI and prognosis in patients with AE-IPF may therefore vary among different ethnic groups.

Several limitations of this study should be acknowledged. Because the database does not include data on laboratory examinations, pulmonary function tests, performance status and radiological findings, the diagnosis and severity of IPF could not be precisely evaluated in this study. Additionally, the accuracy of the IPF diagnosis was not confirmed by radiological and pathological analyses because we based the diagnosis on physician-diagnosed IPF. To classify IPF, all cases of IIPs other than IPF and secondary interstitial pneumonia were excluded using the diagnoses in Japanese or ICD-10 codes, because the specificity of diagnoses in the Diagnosis Procedure Combination (DPC) data are high in general [38].

In conclusion, this study has demonstrated that the underweight group had higher mortality and the obese group had lower mortality in patients with AE-IPF.

Footnotes

  • Author contributions: N. Awano designed the study, analysed and interpreted the data, and prepared the manuscript. T. Jo designed the study, analysed and interpreted the data, and prepared the manuscript. H. Yasunaga analysed and interpreted the data and prepared the manuscript. M. Inomata interpreted the data. N. Kuse interpreted the data. M. Tone interpreted the data. K. Morita collected and interpreted the data. H. Matsui collected the data. K. Fushimi collected the data. T. Nagase interpreted the data and prepared the manuscript. T. Izumo interpreted the data and prepared the manuscript. All authors approved the final manuscript.

  • Conflict of interest: N. Awano has nothing to disclose.

  • Conflict of interest: T. Jo has nothing to disclose.

  • Conflict of interest: H. Yasunaga reports grants from The Ministry of Health, Labour and Welfare, Japan, and The Ministry of Education, Culture, Sports, Science and Technology, Japan, during the conduct of the study.

  • Conflict of interest: M. Inomata has nothing to disclose.

  • Conflict of interest: N. Kuse has nothing to disclose.

  • Conflict of interest: M. Tone has nothing to disclose.

  • Conflict of interest: K. Morita has nothing to disclose.

  • Conflict of interest: H. Matsui has nothing to disclose.

  • Conflict of interest: K. Fushimi has nothing to disclose.

  • Conflict of interest: T. Nagase has nothing to disclose.

  • Conflict of interest: T. Izumo has nothing to disclose.

  • Support statement: This work was supported by grants from the Ministry of Health, Labour and Welfare, Japan (19AA2007 and H30-Policy-Designated-004), and a Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology, Japan (17H04141). The funding bodies had no role in the design of the study; collection, analysis, or interpretation of the data; or writing of the manuscript. Funding information for this article has been deposited with the Crossref Funder Registry.

  • Received January 19, 2021.
  • Accepted April 12, 2021.
  • Copyright ©The authors 2021
http://creativecommons.org/licenses/by-nc/4.0/

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

References

  1. ↵
    1. Raghu G,
    2. Collard HR,
    3. Egan JJ, et al.
    An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management. Am J Respir Crit Care Med 2011; 183: 788–824. doi:10.1164/rccm.2009-040GL
    OpenUrlCrossRefPubMed
  2. ↵
    1. Natsuizaka M,
    2. Chiba H,
    3. Kuronuma K, et al.
    Epidemiologic survey of Japanese patients with idiopathic pulmonary fibrosis and investigation of ethnic differences. Am J Respir Crit Care Med 2014; 190: 773–779. doi:10.1164/rccm.201403-0566OC
    OpenUrlCrossRefPubMed
  3. ↵
    1. Collard HR,
    2. Richeldi L,
    3. Kim DS, et al.
    Acute exacerbations in the INPULSIS trials of nintedanib in idiopathic pulmonary fibrosis. Eur Respir J 2017; 49: 1601339. doi:10.1183/13993003.01339-2016
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Qiu M,
    2. Chen Y,
    3. Ye Q
    . Risk factors for acute exacerbation of idiopathic pulmonary fibrosis: a systematic review and meta-analysis. Clin Respir J 2018; 12: 1084–1092. doi:10.1111/crj.12631
    OpenUrlCrossRefPubMed
  5. ↵
    1. Simon-Blancal V,
    2. Freynet O,
    3. Nunes H, et al.
    Acute exacerbation of idiopathic pulmonary fibrosis: outcome and prognostic factors. Respiration 2012; 83: 28–35. doi:10.1159/000329891
    OpenUrlCrossRefPubMed
  6. ↵
    1. Landbo C,
    2. Prescott E,
    3. Lange P, et al.
    Prognostic value of nutritional status in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1999; 160: 1856–1861. doi:10.1164/ajrccm.160.6.9902115
    OpenUrlCrossRefPubMed
  7. ↵
    1. Miyata S,
    2. Tanaka M,
    3. Ihaku D
    . The prognostic significance of nutritional status using malnutrition universal screening tool in patients with pulmonary tuberculosis. Nutr J 2013; 12: 42. doi:10.1186/1475-2891-12-42
    OpenUrlCrossRefPubMed
  8. ↵
    1. Romero-Corral A,
    2. Montori VM,
    3. Somers VK, et al.
    Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006; 368: 666–678. doi:10.1016/S0140-6736(06)69251-9
    OpenUrlCrossRefPubMed
  9. ↵
    1. Oreopoulos A,
    2. Padwal R,
    3. Kalantar-Zadeh K, et al.
    Body mass index and mortality in heart failure: a meta-analysis. Am Heart J 2008; 156: 13–22. doi:10.1016/j.ahj.2008.02.014
    OpenUrlCrossRefPubMed
  10. ↵
    1. Yamauchi Y,
    2. Hasegawa W,
    3. Yasunaga H, et al.
    Paradoxical association between body mass index and in-hospital mortality in elderly patients with chronic obstructive pulmonary disease in Japan. Int J Chron Obstruct Pulmon Dis 2014; 9: 1337–1346. doi:10.2147/COPD.S75175
    OpenUrlCrossRefPubMed
  11. ↵
    1. Jouneau S,
    2. Kerjouan M,
    3. Rousseau C, et al.
    What are the best indicators to assess malnutrition in idiopathic pulmonary fibrosis patients? A cross-sectional study in a referral center. Nutrition 2019; 62: 115–121. doi:10.1016/j.nut.2018.12.008
    OpenUrl
  12. ↵
    1. Nakatsuka Y,
    2. Handa T,
    3. Kokosi M, et al.
    The clinical significance of body weight loss in idiopathic pulmonary fibrosis patients. Respiration 2018; 96: 338–347. doi:10.1159/000490355
    OpenUrl
    1. Kim JH,
    2. Lee JH,
    3. Ryu YJ, et al.
    Clinical predictors of survival in idiopathic pulmonary fibrosis. Tuberc Respir Dis (Seoul) 2012; 73: 162–168. doi:10.4046/trd.2012.73.3.162
    OpenUrlCrossRefPubMed
  13. ↵
    1. Alakhras M,
    2. Decker PA,
    3. Nadrous HF, et al.
    Body mass index and mortality in patients with idiopathic pulmonary fibrosis. Chest 2007; 131: 1448–1453. doi:10.1378/chest.06-2784
    OpenUrlCrossRefPubMed
  14. ↵
    1. Mura M,
    2. Porretta MA,
    3. Bargagli E, et al.
    Predicting survival in newly diagnosed idiopathic pulmonary fibrosis: a 3-year prospective study. Eur Respir J 2012; 40: 101–109. doi:10.1183/09031936.00106011
    OpenUrlAbstract/FREE Full Text
  15. ↵
    1. Kishaba T,
    2. Nagano H,
    3. Nei Y, et al.
    Body mass index-percent forced vital capacity-respiratory hospitalization: new staging for idiopathic pulmonary fibrosis patients. J Thorac Dis 2016; 8: 3596–3604. doi:10.21037/jtd.2016.12.49
    OpenUrlPubMed
  16. ↵
    1. Yasunaga H
    . Real World Data in Japan: Chapter II the diagnosis procedure combination database. Ann Clin Epidemiol 2019; 1: 76–79. doi:10.37737/ace.1.3_76
    OpenUrl
  17. ↵
    1. Hugh-Jones P,
    2. Lambert AV
    . A simple standard exercise test and its use for measuring exertion dyspnoea. Br Med J 1952; 1: 65–71. doi:10.1136/bmj.1.4749.65
    OpenUrlFREE Full Text
  18. ↵
    1. Aso S,
    2. Matsui H,
    3. Fushimi K, et al.
    Effect of cyclosporine A on mortality after acute exacerbation of idiopathic pulmonary fibrosis. J Thorac Dis 2018; 10: 5275–5282. doi:10.21037/jtd.2018.08.08
    OpenUrl
  19. ↵
    1. Collard HR,
    2. Ryerson CJ,
    3. Corte TJ, et al.
    Acute exacerbation of idiopathic pulmonary fibrosis an international working group report. Am J Respir Crit Care Med 2016; 194: 265–275. doi:10.1164/rccm.201604-0801CI
    OpenUrlCrossRefPubMed
  20. ↵
    1. Ohta T,
    2. Waga S,
    3. Hajime H, et al.
    New grading of level of disordered consciousness (author's translation). No Shinkei Geka 1974; 2: 623-627.
    OpenUrlPubMed
  21. ↵
    1. Shigematsu K,
    2. Nakano H,
    3. Watanabe Y
    . The eye response test alone is sufficient to predict stroke outcome reintroduction of Japan Coma Scale: a cohort study. BMJ Open 2013; 3: e002736.
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Ono K,
    2. Wada K,
    3. Takahara T, et al.
    Indications for computed tomography in patients with mild head injury. Neurol Med Chir (Tokyo) 2007; 47: 291–297. doi:10.2176/nmc.47.291
    OpenUrlCrossRefPubMed
  23. ↵
    1. World Health Organization
    . Global Database on Body Mass Index. World Health Organization; 2014. www.who.int/nutrition/databases/bmi/en/ Date last accessed: 31 October 2020.
  24. ↵
    1. Hasegawa W,
    2. Yamauchi Y,
    3. Yasunaga H, et al.
    Factors affecting mortality following emergency admission for chronic obstructive pulmonary disease. BMC Pulm Med 2014; 14: 151. doi:10.1186/1471-2466-14-151
    OpenUrl
  25. ↵
    1. Rubin DB,
    2. Schenker N
    . Multiple imputation in healthcare databases: an overview and some applications. Stat Med 1991; 10: 585–598. doi:10.1002/sim.4780100410
    OpenUrlCrossRefPubMed
  26. ↵
    1. Sterne JAC,
    2. White IR,
    3. Carlin JB, et al.
    Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ 2009; 338: b2393. doi:10.1136/bmj.b2393
    OpenUrlFREE Full Text
  27. ↵
    1. Li P,
    2. Stuart EA,
    3. Allison DB
    . Multiple imputation: a flexible tool for handling missing data. JAMA 2015; 314: 1966–1967. doi:10.1001/jama.2015.15281
    OpenUrlCrossRefPubMed
  28. ↵
    1. Hubbard AE,
    2. Ahern J,
    3. Fleischer NL, et al.
    To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health. Epidemiology 2010; 21: 467–474. doi:10.1097/EDE.0b013e3181caeb90
    OpenUrlCrossRefPubMed
  29. ↵
    1. Taniguchi H,
    2. Kondoh Y,
    3. Ebina M, et al.
    Pirfenidone Clinical Study Group in Japan. The clinical significance of 5% change in vital capacity in patients with idiopathic pulmonary fibrosis: extended analysis of the pirfenidone trial. Respir Res 2011; 12: 93. doi:10.1186/1465-9921-12-93
    OpenUrlCrossRefPubMed
  30. ↵
    1. Jones RL,
    2. Nzekwu MM
    . The effects of body mass index on lung volumes. Chest 2006; 130: 827–833. doi:10.1378/chest.130.3.827
    OpenUrlCrossRefPubMed
  31. ↵
    1. Wang S,
    2. Sun X,
    3. Hsia TC, et al.
    The effects of body mass index on spirometry tests among adults in Xi'an, China. Medicine (Baltimore) 2017; 96: e6596. doi:10.1097/MD.0000000000006596
    OpenUrl
  32. ↵
    1. Kondoh Y,
    2. Taniguchi H,
    3. Katsuta T, et al.
    Risk factors of acute exacerbation of idiopathic pulmonary fibrosis. Sarcoidosis Vasc Diffuse Lung Dis 2010; 27: 103–110.
    OpenUrlCrossRefPubMed
  33. ↵
    1. Bhaskaran K,
    2. Dos-Santos-Silva I,
    3. Leon DA, et al.
    Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3.6 million adults in the UK. Lancet Diabetes Endocrinol 2018; 6: 944–953. doi:10.1016/S2213-8587(18)30288-2
    OpenUrl
  34. ↵
    1. WHO Expert Consultation
    . Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 363: 157–163. doi:10.1016/S0140-6736(03)15268-3
    OpenUrlCrossRefPubMed
  35. ↵
    1. Gallagher D,
    2. Heymsfield SB,
    3. Heo M, et al.
    Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000; 72: 694–701. doi:10.1093/ajcn/72.3.694
    OpenUrlAbstract/FREE Full Text
  36. ↵
    1. Brown AW,
    2. Shlobin OA,
    3. Weir N, et al.
    Dynamic patient counseling: a novel concept in idiopathic pulmonary fibrosis. Chest 2012; 142: 1005–1010. doi:10.1378/chest.12-0298
    OpenUrl
  37. ↵
    1. Yamana H,
    2. Moriwaki M,
    3. Horiguchi H, et al.
    Validity of diagnoses, procedures, and laboratory data in Japanese administrative data. J Epidemiol 2017; 27: 476–482. doi:10.1016/j.je.2016.09.009
    OpenUrlCrossRefPubMed
PreviousNext
Back to top
Vol 7 Issue 2 Table of Contents
ERJ Open Research: 7 (2)
  • Table of Contents
  • Index by author
Email

Thank you for your interest in spreading the word on European Respiratory Society .

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Body mass index and in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis
(Your Name) has sent you a message from European Respiratory Society
(Your Name) thought you would like to see the European Respiratory Society web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Print
Citation Tools
Body mass index and in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis
Nobuyasu Awano, Taisuke Jo, Hideo Yasunaga, Minoru Inomata, Naoyuki Kuse, Mari Tone, Kojiro Morita, Hiroki Matsui, Kiyohide Fushimi, Takahide Nagase, Takehiro Izumo
ERJ Open Research Apr 2021, 7 (2) 00037-2021; DOI: 10.1183/23120541.00037-2021

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Body mass index and in-hospital mortality in patients with acute exacerbation of idiopathic pulmonary fibrosis
Nobuyasu Awano, Taisuke Jo, Hideo Yasunaga, Minoru Inomata, Naoyuki Kuse, Mari Tone, Kojiro Morita, Hiroki Matsui, Kiyohide Fushimi, Takahide Nagase, Takehiro Izumo
ERJ Open Research Apr 2021, 7 (2) 00037-2021; DOI: 10.1183/23120541.00037-2021
del.icio.us logo Digg logo Reddit logo Technorati logo Twitter logo CiteULike logo Connotea logo Facebook logo Google logo Mendeley logo
Full Text (PDF)

Jump To

  • Article
    • Abstract
    • Abstract
    • Introduction
    • Patients and methods
    • Results
    • Discussion
    • Footnotes
    • References
  • Figures & Data
  • Info & Metrics
  • PDF

Subjects

  • Interstitial and orphan lung disease
  • Tweet Widget
  • Facebook Like
  • Google Plus One

More in this TOC Section

Original articles

  • Endobronchial autologous BM-MSCs in IPF patients
  • Effect of β-blockers on the risk of COPD exacerbations
  • Recurrence of symptoms after childhood LRTI
Show more Original articles

Interstitial lung disease

  • Clinical trial simulations in pulmonary fibrosis
  • Cough perspectives in interstitial lung disease
  • Mood disorder in idiopathic pulmonary fibrosis
Show more Interstitial lung disease

Related Articles

Navigate

  • Home
  • Current issue
  • Archive

About ERJ Open Research

  • Editorial board
  • Journal information
  • Press
  • Permissions and reprints
  • Advertising

The European Respiratory Society

  • Society home
  • myERS
  • Privacy policy
  • Accessibility

ERS publications

  • European Respiratory Journal
  • ERJ Open Research
  • European Respiratory Review
  • Breathe
  • ERS books online
  • ERS Bookshop

Help

  • Feedback

For authors

  • Instructions for authors
  • Publication ethics and malpractice
  • Submit a manuscript

For readers

  • Alerts
  • Subjects
  • RSS

Subscriptions

  • Accessing the ERS publications

Contact us

European Respiratory Society
442 Glossop Road
Sheffield S10 2PX
United Kingdom
Tel: +44 114 2672860
Email: journals@ersnet.org

ISSN

Online ISSN: 2312-0541

Copyright © 2023 by the European Respiratory Society