Skip to main content
Download PDF

Prognostic marker for severe acute exacerbation of chronic obstructive pulmonary disease: analysis of diffusing capacity of the lung for carbon monoxide (DLCO) and forced expiratory volume in one second (FEV1)

BMC Pulmonary Medicine volume 21, Article number: 152 (2021) Cite this article

Abstract

Background

It is important to assess the prognosis of patients with chronic obstructive pulmonary disease (COPD) and acute exacerbation of COPD (AECOPD). Recently, it was suggested that diffusing capacity of the lung for carbon monoxide (DLCO) should be added to multidimensional tools for assessing COPD. This study aimed to compare the DLCO and forced expiratory volume in one second (FEV1) to identify better prognostic factors for admitted patients with AECOPD.

Methods

We retrospectively analyzed 342 patients with AECOPD receiving inpatient treatment. We classified 342 severe AECOPD patients by severity of DLCO and FEV1 (≤ vs. > 50% predicted). We tested the association of FEV1 and DLCO with the following outcomes: in-hospital mortality, need for mechanical ventilation, need for intensive care unit (ICU) care. We analyzed the prognostic factors by multivariate analysis using logistic regression. In addition, we conducted a correlation analysis and receiver operating characteristic (ROC) curve analysis.

Results

In multivariate analyses, DLCO was associated with mortality (odds ratio = 4.408; 95% CI 1.070–18.167; P = 0.040) and need for mechanical ventilation (odds ratio = 2.855; 95% CI 1.216–6.704; P = 0.016) and ICU care (odds ratios = 2.685; 95% CI 1.290–5.590; P = 0.008). However, there was no statistically significant difference in mortality rate when using FEV1 classification (P = 0.075). In multivariate linear regression analyses, DLCO (B = − 0.542 ± 0.121, P < 0.001) and FEV1 (B = − 0.106 ± 0.106, P = 0.006) were negatively associated with length of hospital stay. In addition, DLCO showed better predictive ability than FEV1 in ROC curve analysis. The area under the curve (AUC) of DLCO was greater than 0.68 for all prognostic factors, and in contrast, the AUC of FEV1 was less than 0.68.

Conclusion

DLCO was likely to be as good as or better prognostic marker than FEV1 in severe AECOPD.

Peer Review reports

Background

Chronic obstructive pulmonary disease (COPD) is a chronic airway disease defined by persistent respiratory symptoms and irreversible airflow limitation [1,2,3]. Patients with COPD present with various symptoms, such as cough, sputum, and dyspnea, and these symptoms are closely related to the quality of life and prognosis [4, 5]. The global initiatives for chronic obstructive lung disease (GOLD) reports emphasize treatment based on patient history and symptoms, such as exacerbation history, the modified medical research council dyspnea scale (mMRC), and COPD assessment test (CAT) [6]. Forced expiratory volume in one second (FEV1) is still used to grade the severity of airflow obstruction, but the 'refined ABCD assessment tool' excludes FEV1 from the criteria for evaluating the 'ABCD' group. This is because the FEV1 value is weakly correlated with the patient's symptoms and health status [7, 8]. However, pulmonary function tests (PFT) are still important tests for diagnosing and treating COPD in the clinical field. Therefore, we want other PFT factors related to the patient's symptoms and health status rather than FEV1. Several studies have shown that the diffusing capacity of the lung for carbon monoxide (DLCO) among the various values of PFT is closely related to patient symptoms, prognosis, and oxygen demand in COPD [9, 10]. In addition, there was a recent opinion that DLCO should be added to multidimensional tools assessing COPD [11]. This study aimed to compare FEV1 and DLCO through the prognosis of severe acute exacerbations of COPD (AECOPD).

Method

Study population

We retrospectively analyzed the medical records of 342 patients admitted to Korea University Guro Hospital from January 2011 to May 2017. We searched our electronic medical records database with the keywords “COPD” and “Acute exacerbation.” This study was approved by the Institutional Review Board of Korea University Guro Hospital (KUGH16131-002). The requirement for informed consent from the patients was waived due to the retrospective nature of this study by the institutional review committee.

All patients included only patients who were followed up for more than 1 year in our hospital under the diagnosis of COPD. COPD and airflow limitation were diagnosed by synthesizing patient-reported respiratory symptoms, PFT (the ratio of FEV1 to forced vital capacity (FVC) was less than 70% in post-bronchodilator spirometry), chest image, and patient`s history (smokers with at least ten pack-years of tobacco exposure, etc.) by an experienced pulmonologist [6]. AECOPD was defined as worsening of the patient’s respiratory symptoms beyond normal day-to-day variation. Severe AECOPD was defined as ‘if the patient needs hospitalization due to AECOPD.’ The spirometry data used in the analysis was previously performed in the outpatient clinic during the stable period. Spirometry value that was measured within 1 year from the hospitalization day were used. Patients were excluded with the following criteria: (1) the cause of admission was not AECOPD; for example, acute heart failure, acute pulmonary edema, acute pulmonary embolism, pneumothorax, and arrhythmia (These diseases were excluded through cardiac enzyme, electrocardiogram, echocardiogram and chest image.), (2) the patient had undergoing active cancer treatment, (3) the patient received a major operation within 3 months, (4) the patient had an acute coronary syndrome, brain hemorrhage, or brain infarction within 3 months, (5) the patient had previously been diagnosed with asthma, and (6) the patient had no DLCO results. All patients were 40 years old or older. We retrospectively analyzed the charts by two experienced pulmonologists to exclude various exclusion factors. "events" is synonymous with "patients" in this study.

We classified 342 severe AECOPD patients by severity of DLCO and FEV1 (≤ vs. > 50% predicted). When the DLCO value is more than 50 (% of predicted value), it is defined as the 'DLCO normal group' and when it is 50 (% of predicted value) or less, it is defined as the 'DLCO impaired group' [11]. Likewise, when the FEV1 value is more than 50 (% of predicted value), it is defined as the ‘FEV1 normal group' and when it is 50 (% of predicted value) or less, it is defined as the ‘FEV1 impaired group' (Fig. 1).

Fig. 1
figure 1

Study design

Full size image

Data collection

We tested the association of FEV1 and DLCO with the following outcomes: in-hospital mortality, need for mechanical ventilation, need for intensive care unit (ICU) care. When the patient was hospitalized more than once, only the first hospitalized events were included, and the others were excluded. The following medical data were analyzed: age, sex, smoking history, comorbidities, baseline spirometry, inhaler and oral medication before admission, length of hospital stay, hospital mortality, experience of mechanical ventilation, and experience of ICU care in hospital.

Statistical analysis

Data were analyzed using SPSS 20 software (SPSS for Windows, SPSS Inc., Chicago, IL, USA). Data are presented as average ± standard deviation or number (percentage). We performed a statistical analysis in two directions. First, two groups were classified using DLCO and FEV1 and analyzed statistically. Continuous variables were compared using the independent t-test, and categorical variables were compared using the chi-squared test. We analyzed the prognostic factors (except length of hospital stay) by multivariate analysis through logistic regression. Multivariate analysis was conducted for variables with a P value of less than 0.05 in the univariate analysis, except for baseline spirometry (DLCO and FEV1). In the case of DLCO, multivariate analysis included sex, previous TB history, cerebrovascular accident, inhaler use before admission, oral β2 adrenoreceptor agonist, roflumilast, and mucolytic agent. In the case of FEV1, multivariate analysis included age, sex, previous TB history, inhaler use before admission, roflumilast, and mucolytic agent. Multivariate analysis was conducted using a backward elimination procedure and was assessed by the Hosmer–Lemeshow test.

Second, the linear correlation between spirometry factors (DLCO and FEV1) and length of hospital stay were analyzed. In univariate analysis, the correlation coefficients between spirometry factors and length of hospital stay were analyzed using the Pearson correlation analysis. In addition, we performed a multivariate linear regression analysis that included variables with a P value of less than 0.05 in the univariate analysis, except baseline spirometry. In addition, multivariate linear regression analysis was conducted using a backward elimination procedure. In the multivariate analysis, B was the regression coefficient, and a negative sign of the regression coefficient meant that the variables were negatively associated.

Third, we used receiver operating characteristic (ROC) curve analysis to predict the sensitivity and specificity of DLCO, FEV1 and DLCO + FEV1 as prognostic markers in severe AECOPD. When analyzing the ROC curve, DLCO, FEV1 and DLCO + FEV1 were analyzed as continuous variables. A P value of less than 0.05 was considered statistically significant.

Results

Characteristics of studied subjects

Among the 342 events, the DLCO normal group comprised 227 events (the DLCO value was more than 50% of the predicted value), and 115 in the DLCO impaired group. In the FEV1 normal group (the FEV1 value was more than 50% of the predicted value), there was 173 events, and the FEV1 impaired group had 169 events. The average age was 71.5 ± 9.2 years. A total of 238 (69.6%) events were male and 104 (30.4%) were female. Sixty-three (18.4%) events were current smokers and the average pack/year history was 41.3 ± 17.1 years. A total of 225 (65.38) events were using inhalers, and 165 (48.2%) were taking respiratory-related oral medications. Averaged FEV1 was 1.3 ± 0.5 L (54.0 ± 19.3%) and DLCO was 10.6 ± 4.8 L (59.3 ± 21.4%). (Table 1) In both groups, the average length of hospital stay was 10.0 ± 5.1 days. The mortality rate was 11 (3.2%), the experience of ventilator care was 29 (8.5%), and the experience of ICU care was 39 (11.4%).

Table 1 Baseline characteristics of patients with AECOPD
Full size table

Prognostic factor analysis classified using DLCO and FEV1

When classified through DLCO, the DLCO impaired group showed a poor prognosis in all four factors by univariate analysis (Fig. 2). When classified through FEV1, the FEV1 impaired group showed a poor prognosis in three factors by univariate analysis (Fig. 3). However, there was no statistically significant mortality rate when classified as FEV1 (P value = 0.116) (Fig. 3B).

Fig. 2
figure 2

Prognosis analysis for severe AECOPD according to DLCO classification. a Length of hospital stay (days), b mortality in hospital, c mechanical ventilation, and d intensive care unit. AECOPD, acute exacerbations of chronic obstructive pulmonary disease; DLCO, diffusing capacity of the lung for carbon monoxide

Full size image
Fig. 3
figure 3

Prognosis analysis for severe AECOPD according to FEV1 classification. a Length of hospital stay (days), b mortality in hospital, c mechanical ventilation, and d intensive care unit. AECOPD, acute exacerbations of chronic obstructive pulmonary disease; FEV1, forced expiratory volume in one second

Full size image

In multivariate analyses, DLCO was associated with mortality (odds ratio = 4.408; 95% CI 1.070–18.167; P = 0.040) and need for mechanical ventilation (odds ratio = 2.855; 95% CI 1.216–6.704; P = 0.016) and ICU care (odds ratios = 2.685; 95% CI 1.290–5.590; P = 0.008). In severe AECOPD, DLCO has been shown to predict mortality rate, ventilator, and ICU possibilities. When classified as FEV1, the experience of mechanical ventilation and ICU showed statistical significance. However, there was no significant difference in mortality rate (P = 0.075) (Table 2).

Table 2 Prognosis analysis for severe AECOPD
Full size table

Correlation analysis between spirometer factors and length of hospital stay

The length of hospital stay of the DLCO normal group was 7.3 ± 5.0 days and the DLCO impaired group was 12.4 ± 13.2 days. The length of hospital stay of the FEV1 normal group was 7.7 ± 5.4 days and the FEV1 impaired group was 10.4 ± 11.4 days. In the Pearson correlation analysis, both DLCO and FEV1 showed a negative correlation. In multivariate linear regression analyses, DLCO (B = − 0.542 ± 0.121, P < 0.001) and FEV1 (B = − 0.106 ± 0.106, P = 0.006) were negatively associated with length of hospital stay. Additionally, the regression coefficient was more pronounced in the DLCO analysis (Table 3).

Table 3 Correlation analysis of length of hospital stay
Full size table

ROC curve analysis of DLCO and FEV1

When analyzing the sensitivity and specificity using the ROC curve, DLCO showed better predictive ability than FEV1 (Table 4). When analyzing three prognostic factors (mortality in hospital, mechanical ventilation, and ICU care) through ROC curve analysis, area under the curve (AUC) was greater than 0.68 in all cases of DLCO (Fig. 4). In contrast, the AUCs of FEV1 were below 0.68 in all three prognostic factors. In addition, the sensitivity and specificity of DLCO were more than 64.1%, which was generally higher than FEV1. DLCO + FEV1 showed similar values to DLCO.

Table 4 ROC curve analysis of DLCO, FEV1, and DLCO + FEV1
Full size table
Fig. 4
figure 4

ROC curve of DLCO, FEV1, and DLCO + FEV1. a Mortality in hospital, b mechanical ventilation, and c intensive care unit. ROC, receiver operating characteristics; FEV1, forced expiratory volume in one second; DLCO, diffusing capacity of the lung for carbon monoxide

Full size image

Discussion

This is the study to compare FEV1 and DLCO as prognostic markers in severe patients with AECOPD in Korea. In our study, the factors of prognosis were defined as the length of hospital stay, mortality rate in the hospital, experience of ventilation, and experience of ICU care. Classification by DLCO showed significant differences in all prognostic factors. However, classification by FEV1 did not show a statistically significant mortality rate. The number of deaths was small, so caution is needed in the interpretation about death (the 95% confidence interval of the odds ratio was large and the P value was marginal). In the correlation analysis, both DLCO and FEV1 showed a negative correlation with the length of hospital stay. The correlation coefficient was more pronounced in the DLCO classification. In addition, when analyzing the ROC curve, DLCO showed better predictive ability than FEV1. Of course, some odds ratio values were better when classified as FEV1 in our study. However, DLCO was better in various analysis methods (correlation analysis, ROC curve analysis), which was likely to be as good as or better than FEV1.

The PFT has various parameters. In general, we used FEV1 to grade COPD and select the inhaler. In addition to FEV1, DLCO is an important prognostic factor. In a study of smokers who did not show an obstruction pattern in PFT, a low DLCO group showed quickly decreased pulmonary function and COPD progression [12]. Studies have shown that DLCO is a more accurate prognostic factor than FEV1 when assessing postoperative risk [13, 14]. In addition, DLCO is known to accurately represent the actual emphysema level and performance status [15, 16]. These results suggest that DLCO can be a good predictor of early pulmonary dysfunction and prognosis.

If we know the prognosis of the patient early, we can focus on high-risk patients and improve the prognosis. The prognostic factors that can be used in the clinic are laboratory findings, scoring systems such as CAT or mMRC, and baseline spirometry [17, 18]. In some studies, high-C-reactive protein, eosinopenia, and thrombocytopenia are associated with poor outcomes in AECOPD [19,20,21]. Although various scoring systems—such as St. George's Respiratory Questionnaire, mMRC, and CAT, are useful—patients with severe symptoms may not be graded or might have similar scores, making them difficult to use. Instead, we focused on baseline spirometry and confirmed that DLCO is more accurate in evaluating the prognosis of hospitalized patients than FEV1. If a grading system that considers both DLCO and FEV1 is developed, the prognosis can be predicted more accurately.

Our study was limited because it was a retrospective single-center study. We were unable to analyze including important prognostic factors such as frequent exacerbations, obstructive sleep apnea, and body mass index. As this study is a retrospective study, data on these factors were not available or inaccurate. To compensate for this, we carefully analyzed the charts by two experienced pulmonologists. Also, we included as many factors as possible in baseline characteristics and multivariate analysis. In addition, the treatment received during the hospitalization period and the prognosis after discharge were not evaluated. Large prospective clinical studies that include information on treatment during hospitalization and post discharge may be required.

Conclusion

DLCO was likely to be as good as or better as a prognostic marker than FEV1 in severe AECOPD. Accurate classification using DLCO may help to treat severe ACEOPD patients.

Availability of data and materials

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

Abbreviations

COPD:

Chronic obstructive pulmonary disease

AECOPD:

Acute exacerbation of chronic obstructive pulmonary disease

DLCO :

Diffusing capacity of the lung for carbon monoxide

FEV1 :

Forced expiratory volume in one second

References

  1. Leidy NK, Sexton CC, Jones PW, Notte SM, Monz BU, Nelsen L, Goldman M, Murray LT, Sethi S. Measuring respiratory symptoms in clinical trials of COPD: reliability and validity of a daily diary. Thorax. 2014;69(5):443–9.

    Article  CAS  Google Scholar 

  2. Liu Y, Pleasants RA, Croft JB, Wheaton AG, Heidari K, Malarcher AM, Ohar JA, Kraft M, Mannino DM, Strange C. Smoking duration, respiratory symptoms, and COPD in adults aged >/=45 years with a smoking history. Int J Chron Obstruct Pulmon Dis. 2015;10:1409–16.

    Article  CAS  Google Scholar 

  3. Jones RL, Noble PB, Elliot JG, James AL. Airway remodelling in COPD: it’s not asthma! Respirology. 2016;21(8):1347–56.

    Article  Google Scholar 

  4. Brusse-Keizer M, Klatte M, Zuur-Telgen M, Koehorst-Ter Huurne K, van der Palen J, VanderValk P. Comparing the 2007 and 2011 GOLD classifications as predictors of all-cause mortality and morbidity in COPD. COPD. 2017;14(1):7–14.

    Article  CAS  Google Scholar 

  5. Gruenberger JB, Vietri J, Keininger DL, Mahler DA. Greater dyspnea is associated with lower health-related quality of life among European patients with COPD. Int J Chron Obstruct Pulmon Dis. 2017;12:937–44.

    Article  Google Scholar 

  6. Vogelmeier CF, Criner GJ, Martinez FJ, Anzueto A, Barnes PJ, Bourbeau J, Celli BR, Chen R, Decramer M, Fabbri LM, et al. Global strategy for the diagnosis, management and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary. Respirology. 2017;22(3):575–601.

    Article  Google Scholar 

  7. Han MK, Muellerova H, Curran-Everett D, Dransfield MT, Washko GR, Regan EA, Bowler RP, Beaty TH, Hokanson JE, Lynch DA, et al. GOLD 2011 disease severity classification in COPDGene: a prospective cohort study. Lancet Respir Med. 2013;1(1):43–50.

    Article  Google Scholar 

  8. Jones PW. Health status and the spiral of decline. COPD. 2009;6(1):59–63.

    Article  Google Scholar 

  9. Lee HY, Kim JW, Lee SH, Yoon HK, Shim JJ, Park JW, Lee JH, Yoo KH, Jung KS, Rhee CK. Lower diffusing capacity with chronic bronchitis predicts higher risk of acute exacerbation in chronic obstructive lung disease. J Thorac Dis. 2016;8(6):1274–82.

    Article  Google Scholar 

  10. Enright MdP. Office-based DLCO tests help pulmonologists to make important clinical decisions. Respir Investig. 2016;54(5):305–11.

    Article  Google Scholar 

  11. Balasubramanian A, MacIntyre NR, Henderson RJ, Jensen RL, Kinney G, Stringer WW, Hersh CP, Bowler RP, Casaburi R, Han MK, et al. Diffusing capacity of carbon monoxide in assessment of COPD. Chest. 2019;156(6):1111–9.

    Article  Google Scholar 

  12. Harvey BG, Strulovici-Barel Y, Kaner RJ, Sanders A, Vincent TL, Mezey JG, Crystal RG. Risk of COPD with obstruction in active smokers with normal spirometry and reduced diffusion capacity. Eur Respir J. 2015;46(6):1589–97.

    Article  CAS  Google Scholar 

  13. Liptay MJ, Basu S, Hoaglin MC, Freedman N, Faber LP, Warren WH, Hammoud ZT, Kim AW. Diffusion lung capacity for carbon monoxide (DLCO) is an independent prognostic factor for long-term survival after curative lung resection for cancer. J Surg Oncol. 2009;100(8):703–7.

    Article  Google Scholar 

  14. Kim ES, Kim YT, Kang CH, Park IK, Bae W, Choi SM, Lee J, Park YS, Lee CH, Lee SM, et al. Prevalence of and risk factors for postoperative pulmonary complications after lung cancer surgery in patients with early-stage COPD. Int J Chron Obstruct Pulmon Dis. 2016;11:1317–26.

    Article  Google Scholar 

  15. Diaz AA, Pinto-Plata V, Hernandez C, Pena J, Ramos C, Diaz JC, Klaassen J, Patino CM, Saldias F, Diaz O. Emphysema and DLCO predict a clinically important difference for 6MWD decline in COPD. Respir Med. 2015;109(7):882–9.

    Article  Google Scholar 

  16. Grydeland TB, Thorsen E, Dirksen A, Jensen R, Coxson HO, Pillai SG, Sharma S, Eide GE, Gulsvik A, Bakke PS. Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO. Respir Med. 2011;105(3):343–51.

    Article  Google Scholar 

  17. Chan HP, Mukhopadhyay A, Chong PL, Chin S, Wong XY, Ong V, Chan YH, Lim TK, Phua J. Role of BMI, airflow obstruction, St George’s Respiratory Questionnaire and age index in prognostication of Asian COPD. Respirology. 2017;22(1):114–9.

    Article  Google Scholar 

  18. Martin AL, Marvel J, Fahrbach K, Cadarette SM, Wilcox TK, Donohue JF. The association of lung function and St. George’s respiratory questionnaire with exacerbations in COPD: a systematic literature review and regression analysis. Respir Res. 2016;17:40.

    Article  Google Scholar 

  19. Chang C, Zhu H, Shen N, Han X, Chen Y, He B. Utility of the combination of serum highly-sensitive C-reactive protein level at discharge and a risk index in predicting readmission for acute exacerbation of COPD. J Bras Pneumol. 2014;40(5):495–503.

    Article  Google Scholar 

  20. Rahimi-Rad MH, Asgari B, Hosseinzadeh N, Eishi A. Eosinopenia as a marker of outcome in acute exacerbations of chronic obstructive pulmonary disease. Maedica (Buchar). 2015;10(1):10–3.

    Google Scholar 

  21. Rahimi-Rad MH, Soltani S, Rabieepour M, Rahimirad S. Thrombocytopenia as a marker of outcome in patients with acute exacerbation of chronic obstructive pulmonary disease. Pneumonol Alergol Pol. 2015;83(5):348–51.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by a Korea University Guro Hospital Grant (O1801541)

Funding

Not applicable.

Author information

Author notes

  1. Chin Kook Rhee and Kyung Hoon Min have contributed equally to this work

Authors and Affiliations

  1. Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Republic of Korea

    Juwhan Choi, Jae Kyeom Sim, Jee Youn Oh, Young Seok Lee, Gyu Young Hur, Sung Yong Lee, Jae Jeong Shim & Kyung Hoon Min

  2. Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea

    Chin Kook Rhee

Authors
  1. Juwhan Choi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jae Kyeom Sim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jee Youn Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Young Seok Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gyu Young Hur
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sung Yong Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jae Jeong Shim
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Chin Kook Rhee
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kyung Hoon Min
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JC performed data collection, interpretation and was major contributor in writing the manuscript. JKS, JYO, and YSL performed data collection and interpretation. GYH, SYL, and JJS performed data analysis and interpretation. CKR and KHM designed and supervised study. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Chin Kook Rhee or Kyung Hoon Min.

Ethics declarations

Ethics approval and consent to participate

This study was conducted in accordance with the 'Declaration of Helsinki' as a statement of ethical principles for medical research involving human subjects, including the study of identifiable human substances and data. This study was approved by the Institutional Review Board of Korea University Guro Hospital (KUGH16131-002) for all research-related matters prior to the start of the study and was conducted in compliance with the relevant research regulations throughout the study. This study is a study through retrospective data analysis, and since there is no reason to estimate the subject's refusal to consent and the risk to the subject is low even without consent, it was approved as a 'signature consent waiver study' by the institutional review committee. In the course of the research, all personally identifiable data were anonymized to further minimize the impact on the research subject.

Consent to publication

Not applicable.

Competing interests

There is no competing interest. All authors declare they have no competing interest.

Additional information

Publisher's Note

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

Rights and permissions

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

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Choi, J., Sim, J.K., Oh, J.Y. et al. Prognostic marker for severe acute exacerbation of chronic obstructive pulmonary disease: analysis of diffusing capacity of the lung for carbon monoxide (DLCO) and forced expiratory volume in one second (FEV1). BMC Pulm Med 21, 152 (2021). https://doi.org/10.1186/s12890-021-01519-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12890-021-01519-1

Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

Contact us