Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Association between six-minute walk test parameters and the health-related quality of life in patients with pulmonary Mycobacterium avium complex disease

  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 1,
  • 2,
  • 1,
  • 1 and
  • 3Email authorView ORCID ID profile
BMC Pulmonary Medicine201818:114

https://doi.org/10.1186/s12890-018-0686-5

  • Received: 7 April 2018
  • Accepted: 5 July 2018
  • Published:
Open Peer Review reports

Abstract

Background

Pulmonary Mycobacterium avium complex (pMAC) disease is a chronic, slowly progressive disease. The aim of the present study was to determine the association of six-minute walk test (6MWT) parameters with pulmonary function and the health-related quality of life (HRQL) in patients with pMAC disease.

Methods

This cross-sectional study included adult patients with pMAC and was conducted at Keio University Hospital. We investigated the relationship of 6MWT parameters with clinical parameters, including pulmonary function, and HRQL, which was assessed using the 36-Item Short Form Health Survey (SF-36) and St. George’s Respiratory Questionnaire (SGRQ).

Results

In total, 103 consecutive patients with pMAC participated in 6MWT (median age, 64 years; 80 women) and completed SF-36 and SGRQ. The six-minute walk distance (6MWD) showed significant negative and positive correlations with all SGRQ domain scores [ρ = (− 0.54)–(− 0.32)] and the physical component summary (PCS) score (ρ = 0.39) in SF-36, respectively; the opposite was observed for the final Borg scale (FBS) score (all SGRQ scores, ρ = 0.34–0.58; PCS score, ρ = − 0.50). The distance-saturation product showed significant negative and positive correlations with all SGRQ scores [ρ = (− 0.29)–(− 0.55)] and the PCS score (ρ = 0.40), respectively. Multivariate analysis revealed that 6MWD and the FBS score were significant predictors of HRQL.

Conclusions

Our findings suggest that 6MWD and the FBS score are useful parameters for evaluating HRQL in patients with pMAC. Further studies should investigate the impact of 6WMT parameters on disease progression, treatment responses, and prognosis.

Keywords

  • Nontuberculous mycobacteria
  • Mycobacterium avium complex
  • Six-minute walk test
  • Health-related quality of life
  • 36-item short form health survey
  • St. George’s respiratory questionnaire

Background

The increasing prevalence of pulmonary infections due to nontuberculous mycobacteria (NTM) is an emerging public health concern worldwide [1, 2]. Pulmonary Mycobacterium avium complex (pMAC) disease, the most common form of NTM infection, presents as a chronic, slowly progressive disease in immunocompetent patients [3]. It causes chronic pulmonary diseases such as asthma, chronic obstructive pulmonary disease (COPD), and interstitial lung disease (ILD) and is generally incurable, requires long-term antimicrobial therapy, and has a high recurrence rate after treatment discontinuation [3]. Because of the increasing chronicity of pMAC disease, patient-reported outcome measures that represent the health-related quality of life (HRQL) have become increasingly important to monitor the overall health status of affected patients [4]. It has been reported that HRQL, particularly the physical component, is impaired in patients with pMAC [5].

The six-minute walk test (6MWT) has become a useful tool for assessing the functional status and predicting the prognosis of patients with various pulmonary diseases, including COPD, ILD, sarcoidosis, and primary pulmonary hypertension [69]. It is also used as a standardized exercise test for the assessment of lung disease because of its simplicity, low cost, non-invasiveness, ease of performance, and reproducibility [10, 11]. Recently, a randomized study of inhaled liposomal amikacin revealed an improvement in 6MWT parameters in patients with pNTM disease [12]. With regard to the clinical significance of 6MWT for pMAC disease, only one study has assessed the relationship between the six-minute walk distance (6MWD) and HRQL evaluated using St. George’s Respiratory Questionnaire (SGRQ) [13]. However, the correlations between HRQL and various 6MWT parameters remain unknown.

The aim of the present study was to investigate the relationship of 6MWT parameters with clinical parameters, including pulmonary function test (PFT) findings, and HRQL. The tested hypothesis was that 6MWT parameters could be useful for evaluating HRQL in patients with pMAC disease.

Methods

Study design and study population

This cross-sectional study included adult patients with pMAC who were recruited from the prospective, observational cohort registry at Keio University Hospital (UMIN000007546). PMAC was diagnosed on the basis of statements published by the American Thoracic Society (ATS) and Infectious Disease Society of America in 2007 [3]. The Keio University Hospital institutional review board approved the study protocol (# 20110267), and written informed consent was obtained from each patient. All patients with pMAC who participated in 6MWT and completed questionnaires assessing HRQL between May 2012 and November 2013 were included.

6MWT

All patients were instructed to walk in a hallway for 6 min according to ATS guidelines [10]. Oxygen saturation by pulse oximetry (SpO2) and heart rate (HR) were monitored throughout the 6-min walk. The total distance walked and the oxygen saturation and HR (per minute) measured from start to finish were recorded. The patients were assessed using the Borg Dyspnea Scale at the end of the test [14]. Other parameters such as the distance-saturation product (DSP) [6] and desaturation area (DA) [15] were also calculated.

HRQL assessments

For the evaluation of HRQL, all patients completed the 36-Item Short Form Health Survey (SF-36), version 2 [16] and SGRQ in Japanese [17]. Both these questionnaires have been validated for use in patients with pMAC disease [5]. The SF-36 comprises physical, mental, and role/social domains, and the respective summary scores were adjusted for Japanese patients [18] before analysis. All scores were transformed to fit a norm-based score of 50 and standard deviation of 10, and lower scores indicated a poorer health status. With regard to SGRQ (range, 0–100), we calculated the total score as well as scores for Symptoms, Activity, and Impacts domains, which evaluate respiratory symptoms, physical activity impairment, and social and psychological disturbances, respectively. Lower scores indicated a better health status.

Assessment of clinical parameters

Clinical parameters, including sex, age at diagnosis, disease duration, body mass index (BMI), smoking status, underlying pulmonary diseases, comorbidities assessed using the age-adjusted Charlson comorbidity index (CCI) [19], and sputum smear and culture findings for MAC within the previous year were recorded at the time of enrolment. PFT was performed using an electronic spirometer (Chestac-9800 or HI-801; Chest M.I., Tokyo, Japan) when the patient was in a stable condition after study enrolment. MAC isolates were identified as previously described [20]. High-resolution computed tomography (HRCT) images were evaluated for cavitary lesions and the radiological pattern: nodular/bronchiectatic (NB), fibrocavitary (FC), NB + FC, and unclassified [21].

Statistical analysis

Correlations between two continuous variables were analyzed using Spearman’s correlation coefficients. Comparisons between two groups were conducted using the Wilcoxon rank sum test. To identify 6MWT parameters that predicted HRQL, parameters that showed a significant association with SGRQ and SF-36 scores in univariate analysis were entered into a stepwise forward and backward multiple regression model for multivariate analysis. All P-values were two-tailed, and a value of < 0.05 was considered statistically significant. All statistical analyses were conducted using JMP v11.0 (SAS Institute Japan Ltd., Tokyo, Japan).

Results

Patient characteristics and SF-36 and SGRQ scores

In total, 103 patients with pMAC were enrolled. Table 1 shows the clinical characteristics of the patients. The median [interquartile range (IQR)] age of patients was 68 (64–75) years. Eighty (78%) patients were women and 92 (89%) patients were never-smokers. Underlying pulmonary diseases were present in 16 (15%) patients. The median PFT values were within the normal range. On HRCT, 31 (30.1%) patients showed cavitary lesions, and the NB pattern was most commonly observed (81 patients, 78.6%). With regard to HRQL, the median scores for the physical and role/social domains in SF-36 were decreased, whereas the median Symptoms, Activity, and total scores in SGRQ were increased.
Table 1

Clinical characteristics of patients with pulmonary Mycobacterium avium complex disease (n = 103)

Variable

Age, years

68 (64–75)

Sex, Male/Female

23 (22)/80 (78)

Disease duration, years

5.8 (2.3–10.1)

BMI, kg/m2

19.2 (17.5–20.4)

Smoking status

 Never/ Former/ Current

92 (89)/11 (11)/0 (0)

Charlson comorbidity index

4 (4–5)

Underlying pulmonary diseases

 Old pulmonary tuberculosis

11 (11)

 Bronchial asthma

4 (4)

 Lung cancer

1 (1)

Sputum findings for NTM infection within the previous year

 Smear/culture positivity

31 (30)/62 (60)

%FVC, %

94 (80–107)

%FEV1, %

87 (73–98)

FEV1/FVC < 70%

35 (34)

%FEV1 < 80%

39 (38)

Presence of cavitary lesions

31 (30.1)

Radiological pattern

 NB/FC/NB + FC/unclassified

81 (78.6)/3 (2.9)/15 (14.5)/4 (3.9)

SF-36 scores

 PCS

48 (38–54)

 MCS

51 (42–56)

 RCS

48 (43–54)

SGRQ scores

 Symptoms

31 (15–48)

 Activity

24 (6–48)

 Impacts

9 (3–29)

 Total

19 (9–36)

Data are shown as number (%) of patients or medians (interquartile ranges)

BMI body mass index, FC fibrocavitary, FVC forced volume capacity, FEV1 forced expiratory volume in 1 s, NB nodular/bronchiectatic, MCS mental component summary, NTM nontuberculous mycobacteria, PCS physical component summary, RCS role/social component summary, SF-36 36-Item Short Form Health Survey, SGRQ St. George’s Respiratory Questionnaire

6MWT parameters

Table 2 shows the findings of 6WMT for the 103 patients. The median (IQR) 6MWD)was 410 (365–450) m. The median (IQR) initial and lowest SpO2 values were 96% (96–97%) and 94% (92–95%), respectively, while the median (IQR) initial and final HR values were 76 (67–85) and 106 (97–116) beats/minute, respectively. The median (IQR) final Borg scale (FBS) score, DSP, and DA were 0.5 (0.5–2), 385 (338–423) m%, and 32 (27–40) units.
Table 2

Results of the six-minute walk test for patients with pulmonary Mycobacterium avium complex disease (n = 103)

Variables

6MWD, m

410 (365–450)

Initial SpO2, %

96 (96–97)

Lowest SpO2, %

94 (92–95)

Initial heart rate, beats/minute

76 (67–85)

Final heart rate, beats/minute

106 (97–116)

Final Borg scale score

0.5 (0.5–2)

DSP, m%

385 (338–423)

DA, units

32 (27–40)

Data are shown as medians (interquartile ranges)

6MWD six-minute walk distance, DA desaturation area, DSP distance-saturation product, SpO2 oxygen saturation by pulse oximetry

Correlations among 6MWT parameters and clinical parameters

Table 3 shows Spearman’s correlations among 6MWT parameters and clinical parameters for the 103 patients. 6MWD showed a strong correlation with DSP, DA, and the lowest SpO2. The initial SpO2 was moderately correlated with the lowest SpO2 or DA and the initial and final HRs. Age was significantly correlated with 6MWD, the FBS score, and DSP. Disease duration and age-adjusted CCI were also weakly correlated with the FBS score. Finally, the percentage functional volume capacity (%FVC) and percentage forced expiratory volume in 1 s (%FEV1) showed significant but weak correlations with the initial and lowest SpO2 values, DSP, and DA.
Table 3

Spearman’s correlations among six-minute walk test parameters and clinical parameters for patients with pulmonary Mycobacterium avium complex disease (n = 103)

 

Age

Disease duration

BMI

CCI

%FVC

%FEV1

6MWD

Initial SpO2

Lowest SpO2

Initial HR

Final HR

FBS

DSP

DA

Age

              

Disease duration

− 0.09

             

BMI

0.01

0.10

            

CCI

0.32

− 0.04

0.09

           

%FVC

− 0.06

− 0.08

0.21*

− 0.05

          

%FEV1

0.05

−0.19

0.02

− 0.01

0.78§

         

6MWD

−0.34

−0.03

− 0.16

− 0.16

0.18

0.21*

        

Initial SpO2

−0.03

−0.18

−0.12

−0.05

0.21*

0.32

0.08

       

Lowest SpO2

−0.07

−0.11

− 0.05

− 0.08

0.23*

0.27

0.03

0.44§

      

Initial HR

−0.06

0.06

− 0.24*

− 0.11

−0.13

−0.05

−0.01

−0.17

−0.17

     

Final HR

−0.16

−0.04

−0.07

−0.13

−0.18

−0.11

0.18

−0.17

−0.17

0.56§

    

FBS

0.28

0.23*

− 0.09

0.28

−0.22*

− 0.12

−0.17

−0.05

−0.08

−0.002

−0.04

   

DSP

−0.35

−0.05

−0.15

−0.17

0.23*

0.26

0.99§

0.14

0.16

−0.03

0.16

−0.17

  

DA

0.06

0.17

0.07

0.08

−0.25*

− 0.29

−0.01

−0.54§

−0.95§

0.20*

0.24*

0.11

−0.14

 

6MWD six-minute walk distance, 6MWT six-minute walk test, BMI body mass index, BS Borg scale, CCI Charlson comorbidity index, DA Desaturation area, DSP Distance-saturation product, FBS final Borg scale, FEV1 forced expiratory volume in 1 s, FVC forced volume capacity, HR heart rate, SpO2 oxygen saturation by pulse oximetry

*P < 0.05, P < 0.01, P < 0.001, §P < 0.0001

Correlations among 6MWT parameters or clinical parameters and SF-36 and SGRQ scores

Table 4 shows the correlations among 6MWT parameters or clinical parameters and SF-36 and SGRQ scores for the 103 patients. The physical component summary (PCS) score in SF-36 was strongly correlated with the Activity and total scores in SGRQ. The mental component summary (MCS) and role/social component summary (RCS) scores were also significantly correlated with the SGRQ scores, although the correlations were weaker than that of the PCS score. All SGRQ scores showed a significant negative correlation with 6MWD [ρ = (− 0.54)–(− 0.32)] and DSP [ρ = (− 0.29)–(− 0.55)] and a significant positive correlation with the FBS score (ρ = 0.34–0.58). The PCS and RCS scores in SF-36 showed a significant positive correlation with 6MWD (ρ = 0.39 and ρ = 0.20, respectively) and a significant negative correlation with the FBS score (ρ = − 0.50 and ρ = − 0.24, respectively). The PCS score also showed a significant positive correlation with DSP (ρ = 0.40). Regarding the correlations among clinical parameters and SF-36 and SGRQ scores, all SGRQ scores showed a significant negative correlation with %FVC [ρ = (− 0.43)–(− 0.28)] and %FEV1 [ρ = (− 0.35)–(− 0.26)]. The Activity, Impacts, and Total scores in SGRQ showed significant positive correlation (ρ = 0.22–0.45) and the PCS score in SF-36 showed significant negative correlation (ρ = − 0.51) with age. Moreover, the Symptoms, Impacts, and Total scores in SGRQ showed significant negative correlation [ρ = (− 0.22)–(− 0.30)] and the RCS score in SF-36 showed significant positive correlation (ρ = 0.20) with BMI. The Activity score in SGRQ showed significant positive correlation (ρ = 0.33) and the PCS score in SF-36 showed significant negative correlation (ρ = − 0.35) with age-adjusted CCI. We also analyzed the data for the never smoker group (n = 92) alone in Additional file 1: Table S4. Correlations among 6MWT parameters or clinical parameters and SF-36 and SGRQ scores were the same as that for all patients including former-smoker patients (n = 103).
Table 4

Spearman’s correlations among six-minute walk test parameters or clinical parameters and 36-Item Short Form Health Survey and St George’s Respiratory Questionnaire scores for patients with pulmonary Mycobacterium avium complex disease (n = 103)

  

SGRQ

SF-36

Symptoms

Activity

Impacts

Total

PCS

MCS

RCS

SGRQ

Symptoms

       

Activity

0.51§

      

Impacts

0.68§

0.67§

     

Total

0.77§

0.88§

0.91§

    

SF-36

PCS

−0.44§

−0.73§

−0.60§

−0.70§

   

MCS

−0.32

−0.27

−0.28

−0.29

0.08

  

RCS

−0.28

−0.42§

−0.40§

−0.44§

0.25*

0.15

 

6WMT

6MWD

−0.27

−0.54§

−0.32

−0.44§

0.39§

0.08

0.20*

Initial SpO2

0.0002

−0.12

−0.08

−0.09

0.05

−0.15

−0.09

Lowest SpO2

−0.15

−0.14

−0.21

−0.19

0.10

−0.01

−0.05

Initial HR

0.16

0.03

0.15

0.11

−0.09

0.03

−0.06

Final HR

0.11

−0.04

0.04

0.03

0.07

0.00

−0.02

FBS

0.34

0.56§

0.54§

0.58§

−0.50§

−0.17

−0.24*

DSP

−0.29

−0.55§

−0.33

−0.46§

0.40§

0.06

0.19

DA

0.14

0.12

0.19

0.17

−0.09

0.04

0.07

Clinical parameters

Age

0.09

0.45§

0.22*

0.36

−0.51§

0.10

−0.18

Disease duration

0.16

0.13

0.11

0.18

−0.06

− 0.02

−0.01

BMI

−0.30

−0.08

− 0.27

−0.22*

0.14

0.07

0.20*

CCI

−0.09

0.33

0.13

0.19

−0.35

0.04

−0.05

%FVC

−0.41§

−0.28

− 0.43§

−0.43§

0.15

−0.03

0.07

%FEV1

−0.34

−0.26

− 0.32

−0.35

0.07

−0.03

− 0.003

6MWD six-minute walk distance, 6MWT six-minute walk test, BMI body mass index, CCI Charlson comorbidity index, DA Desaturation area, DSP Distance-saturation product, FBS final Borg scale, FEV1 forced expiratory volume in 1 s, FVC forced volume capacity, MCS mental component summary, PCS physical component summary, RCS role/social component score, SF-36 36-Item Short Form Health Survey, SGRQ St. George’s Respiratory Questionnaire, SpO2 oxygen saturation by pulse oximetry

*P < 0.05, P < 0.01, P < 0.001, §P < 0.0001

Comparisons of 6MWT parameters and questionnaire scores between patients with cavitary lesions and those without

We also evaluated whether 6MWT parameters and the questionnaire scores differed between patients with cavitary lesions (n = 31, 30.1%) on HRCT and those without (n = 72, 69.9%; Additional file 1: Table S6), because cavitary lesions have been reported as a prognostic factor for pMAC disease [22, 23]. While 6MWT parameters except the initial HR showed no significant differences between the two groups, the Symptoms and Impacts scores in SGRQ were significantly higher in patients with cavitary lesions than in those without.

Multivariate analysis for predictors of SGRQ and SF-36 scores

We performed stepwise multiple regression analysis to determine the association of SGRQ and SF-36 scores with age, sex, age-adjusted CCI, BMI, smoking status, disease duration, underlying pulmonary diseases, positive sputum smear or culture findings, presence of cavitary lesions, pulmonary function, 6MWD, DSP, and the FBS score (Table 5). The MCS and RCS scores were excluded because they only showed weak correlations with 6WMT parameters. We chose 6MWT parameters and clinical parameters for stepwise multiple regression analysis that showed significant correlations with SGRQ or SF-36 scores as shown in Table 4 or that were thought to be clinically important from previous studies. %FVC, sex, and BMI were found to be significant predictors of the Symptoms score in SGRQ, accounting for 21.2% variance, while DSP, the FBS score, and BMI were found to be significant predictors of the Activity score in SGRQ, accounting for 54.0% variance. The FBS score, %FVC, 6MWD, and BMI were found to be significant predictors of the Impacts score in SGRQ, accounting for 38.9% variance. The FBS score, 6MWD, BMI, and %FVC were found to be significant predictors of the SGRQ total score, accounting for 49.4% variance. Finally, the FBS score, 6MWD, age, sex, and disease duration were found to be significant predictors of the physical component summary (PCS) score in SF-36, accounting for 49.2% variance. We also analyzed the data for the never smoker group (n = 92) alone in Additional file 1: Table S5. Correlations among 6MWT parameters and SF-36 and SGRQ scores were the same as that of all patients including former-smoker patients (n = 103). Hence, it was concluded that 6MWD and the FBS score are useful 6MWT parameters for the prediction of HRQL in patients with pMAC disease in all patients, including the never smoker group (n = 92) and former-smoker patients (n = 103) (Table 5).
Table 5

Multivariate analysis for predictors of 36-Item Short Form Health Survey and St George’s Respiratory Questionnaire scores for patients with pulmonary Mycobacterium avium complex disease (n = 103)

HRQL

Determinants

P-value

Cumulative R2, %

SGRQ scores

 Symptoms

%FVC

0.0002

14.6

Sex

0.0076

21.2

BMI

0.0303

27.9

 Activity

DSP

< 0.0001

33.5

FBS

< 0.0001

51.3

BMI

0.0248

54.0

 Impacts

FBS

< 0.0001

20.6

%FVC

0.0020

28.7

6MWD

0.0192

33.0

BMI

0.0048

38.9

 Total

FBS

< 0.0001

24.3

6MWD

< 0.0001

39.2

BMI

0.0005

47.0

%FVC

0.0483

49.4

SF-36

 PCS

FBS

< 0.0001

22.4

6MWD

< 0.0001

36.2

Age

0.0014

43.2

Sex

0.0179

46.8

Disease duration

0.0462

49.2

6MWD six-minute walk distance, 6MWT six-minute walk test, BMI body mass index, DA desaturation area, DSP distance-saturation product, FEV1 forced expiratory volume in 1 s, FBS final Borg scale, FVC forced volume capacity, PCS physical component summary, SGRQ St. George’s Respiratory Questionnaire, SF-36 36-Item Short Form Health Survey, SpO2 oxygen saturation by pulse oximetry

Discussion

In the present study, we evaluated the relationship of 6MWT parameters with clinical parameters, including PFT findings, and HRQL in patients with pMAC disease. The findings revealed that 6MWD and the FBS score showed strong correlations with the PCS score in SF-36 and all SGRQ scores except for the Symptoms score. Furthermore, %FVC and BMI, which weakly correlated with 6MWD and the FBS score, were important predictors of the SGRQ scores.

The relationship between 6MWD, which is classically used as the primary outcome of 6MWT, and HRQL assessed using different tools (including SGRQ and SF-36) has been determined in patients with various chronic pulmonary diseases [24, 25], and it was found to correlate negatively with the total SGRQ score in patients with COPD and ILD [2628]. A previous study on pMAC disease reported that 6WMD significantly correlated with the Activity (ρ = − 0.45) and total (ρ = − 0.31) scores in SGRQ [13], while in another study, 6MWD showed a significant positive correlation with the total SF-36 score (ρ = 0.37) in patients with sarcoidosis [29]. Consistent with findings in previous studies, our data demonstrated that 6MWD was significantly correlated with SGRQ scores and the PCS score in SF-36 and was one of the important predictors of HRQL according to multivariate analysis.

In addition to 6WMD, we found that the FBS score was also an important predictor of SGRQ scores and the PCS score in SF-36. Dyspnea reflects the physiology of exercise limitation as well as the impact of exercise limitation on daily life [30]. In addition, scores for dyspnea assessed using the Borg scale exhibit good reliability and are used as a marker of patient-reported fatigue that reflects a local muscle phenomenon as well as general fatigue in patients with chronic pulmonary diseases [14]. Previous studies reported a significant correlation between the FBS score and all SGRQ scores (ρ = 0.33–0.56) in patients with COPD [27] and the total SF-36 score (ρ = 0.45) in patients with sarcoidosis [29].

Our multivariate analysis revealed that both 6MWD and the FBS score were important predictors of HRQL parameters. 6MWD is an objective parameter showing significant correlations with measures of peak work capacity in cardiopulmonary exercise tests [24]. Furthermore, a study on COPD revealed that 6MWD was associated with not only lung function but also lower limb strength, including quadriceps strength and the lean leg mass [31]. On the other hand, the Borg scale is a subjective parameter reflecting subjective breathlessness, perceived exertion, and fatigue [14, 24, 32]. A previous study on severe COPD and asthma indicated that the Borg scale could not be reliably predicted by desaturation and PFT findings [32]. In fact, a study on COPD indicated that both 6MWD and the FBS score were significant independent prognostic factors [33]. Thus, 6MWD reflects objective exercise tolerance better than does the FBS score, and both are useful parameters for the assessment of patients with pMAC disease.

Our multivariate analysis also found that %FVC and BMI, which have been reported as important clinical factors in previous studies, were predictors of SGRQ scores [13, 20, 23], [34, 35]. In a previous study, FVC showed a negative correlation with SGRQ scores [13], consistent with the findings in this study. Moreover, a substantial decline in FEV1 and FVC was associated with treatment failure in another study [34]. BMI has been reported as a poor prognostic factor for pulmonary NTM infection [20, 23, 35]. Notably, our results indicated partially significant but weak correlations among BMI, 6WMD or the FBS score, and PFT findings. Therefore, %FVC and BMI, in addition to 6MWD and the FBS score, may be useful parameters for clinical assessments, including HRQL evaluation.

Our study has several potential limitations. First, we may have excluded patients with more severe pMAC disease, considering we only included patients who could complete PFT, 6MWT, and HRQL questionnaires. Second, this was a cross-sectional study; therefore, we could not determine causal associations, particularly with regard to the influence of treatment. Third, our study mainly included female patients, consistent with the participants of previous studies about pMAC disease [1, 3537]. Comparison between sexes shows significantly lower BMI in women (Additional file 1: Table S7); this may have affected our results, because BMI is a significant predictor of all SGRQ scores (Table 5). Moreover, we could not evaluate the impact of 6MWT parameters on the prognosis. However, a shorter 6MWD is reported to be strongly correlated with an increased risk of mortality in patients with other chronic pulmonary diseases, including COPD, ILD, and pulmonary hypertension [24, 25, 3840]. Therefore, we believe that 6MWT may be useful for the assessment of prognosis in patients with pMAC disease.

Conclusions

Our findings suggest that 6MWD and the FBS score are useful 6MWT parameters for the prediction of HRQL in patients with pMAC disease. However, further studies are needed to investigate the impact of 6WMT parameters on disease progression, treatment responses, and prognosis.

Abbreviations

6MWD: 

six-minute walk distance

6MWT: 

six-minute walk test

ATS: 

American Thoracic Society

BMI: 

body mass index

CCI: 

Charlson comorbidity index

COPD: 

chronic obstructive pulmonary disease

DA: 

desaturation area

DSP: 

distance-saturation product

FC: 

fibrocavitary

FEV1

forced expiratory volume in 1 s

FVC: 

functional volume capacity

HRQL: 

health-related quality of life

ILD: 

interstitial lung disease

IQR: 

interquartile range

MAC: 

Mycobacterium avium complex

MCS: 

mental component summary

NB: 

nodular/bronchiectatic

PCS: 

physical component summary

PFT: 

pulmonary function test

RCS: 

role/social component summary

SF-36: 

36-Item Short Form Health Survey

SGRQ: 

St. George’s Respiratory Questionnaire

SpO2

oxygen saturation by pulse oximetry

Declarations

Acknowledgements

We thank Shoko Takahashi and Chiyomi Uemura for their assistance with collecting data.

Availability of data and materials

The data will not be shared with participant confidentiality.

Authors’ contributions

KY, TA, and NH designed the study; acquired and interpreted data; and wrote, revised, and approved the final manuscript. HN, SS, and MI designed the study; acquired and interpreted data; and revised and approved the final manuscript. TA, SO, TK, YF, HK, TN, and TB acquired and interpreted data and revised and approved the final manuscript.

Ethics approval and consent to participate

The Keio University Hospital ethics review board approved the study protocol (# 20110267). All patients provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.

Authors’ Affiliations

(1)
Division of Pulmonary Medicine, Department of Medicine, Keio University School of Medicine, Tokyo, Japan
(2)
Keio University Health Center, Tokyo, Japan
(3)
Center for Infectious Diseases and Infection Control, Keio University School of Medicine, 35 Shinanomachi Shinjuku, Tokyo 160-8582, Japan

References

  1. Prevots DR, Marras TK. Epidemiology of human pulmonary infection with nontuberculous mycobacteria: a review. Clin Chest Med. 2015;36(1):13–34.View ArticlePubMedGoogle Scholar
  2. Namkoong H, Kurashima A, Morimoto K, Hoshino Y, Hasegawa N, Ato M, et al. Epidemiology of pulmonary nontuberculous mycobacterial disease, Japan(1). Emerg Infect Dis. 2016;22(6):1116–7.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Griffith DE, Aksamit T, Brown-Elliott BA, Catanzaro A, Daley C, Gordin F, et al. An official ATS/IDSA statement: diagnosis, treatment, and prevention of nontuberculous mycobacterial diseases. Am J Respir Crit Care Med. 2007;175(4):367–416.View ArticlePubMedGoogle Scholar
  4. Satta G, McHugh TD, Mountford J, Abubakar I, Lipman M. Managing pulmonary nontuberculous mycobacterial infection. Time for a patient-centered approach. Ann Am Thorac Soc. 2014;11(1):117–21.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Asakura T, Funatsu Y, Ishii M, Namkoong H, Yagi K, Suzuki S, et al. Health-related quality of life is inversely correlated with C-reactive protein and age in Mycobacterium avium complex lung disease: a cross-sectional analysis of 235 patients. Respir Res. 2015;16:145.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Lettieri CJ, Nathan SD, Browning RF, Barnett SD, Ahmad S, Shorr AF. The distance-saturation product predicts mortality in idiopathic pulmonary fibrosis. Respir Med. 2006;100(10):1734–41.View ArticlePubMedGoogle Scholar
  7. Miyamoto S, Nagaya N, Satoh T, Kyotani S, Sakamaki F, Fujita M, et al. Clinical correlates and prognostic significance of six-minute walk test in patients with primary pulmonary hypertension. Comparison with cardiopulmonary exercise testing. Am J Respir Crit Care Med. 2000;161(2 Pt 1):487–92.View ArticlePubMedGoogle Scholar
  8. Sciurba F, Criner GJ, Lee SM, Mohsenifar Z, Shade D, Slivka W, et al. Six-minute walk distance in chronic obstructive pulmonary disease: reproducibility and effect of walking course layout and length. Am J Respir Crit Care Med. 2003;167(11):1522–7.View ArticlePubMedGoogle Scholar
  9. Alhamad EH, Shaik SA, Idrees MM, Alanezi MO, Isnani AC. Outcome measures of the 6 minute walk test: relationships with physiologic and computed tomography findings in patients with sarcoidosis. BMC Pulm Med. 2010;10:42.View ArticlePubMedPubMed CentralGoogle Scholar
  10. American Thoracic Society. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002;166(1):111–7.Google Scholar
  11. American Thoracic Society. Erratum: ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2016;193(10):1185.Google Scholar
  12. Olivier KN, Griffith DE, Eagle G, McGinnis Ii JP, Micioni L, Liu K, et al. Randomized trial of liposomal amikacin for inhalation in nontuberculous mycobacterial lung disease. Am J Respir Crit Care Med. 2017;195(6):814–23.Google Scholar
  13. Maekawa K, Ito Y, Oga T, Hirai T, Kubo T, Fujita K, et al. High-resolution computed tomography and health-related quality of life in Mycobacterium avium complex disease. Int J Tuberc Lung Dis. 2013;17(6):829–35.View ArticlePubMedGoogle Scholar
  14. Borg GA. Psychophysical bases of perceived exertion. Med Sci Sports Exerc. 1982;14(5):377-381.View ArticleGoogle Scholar
  15. Flaherty KR, Andrei AC, Murray S, Fraley C, Colby TV, Travis WD, et al. Idiopathic pulmonary fibrosis: prognostic value of changes in physiology and six-minute-walk test. Am J Respir Crit Care Med. 2006;174(7):803–9.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Fukuhara S, Bito S, Green J, Hsiao A, Kurokawa K. Translation, adaptation, and validation of the SF-36 health survey for use in Japan. J Clin Epidemiol. 1998;51(11):1037–44.View ArticlePubMedGoogle Scholar
  17. Jones PW, Quirk FH, Baveystock CM, Littlejohns P. A self-complete measure of health status for chronic airflow limitation. The St. George's respiratory questionnaire. Am Rev Respir Dis. 1992;145(6):1321–7.View ArticlePubMedGoogle Scholar
  18. Suzukamo Y, Fukuhara S, Green J, Kosinski M, Gandek B, Ware JE. Validation testing of a three-component model of short Form-36 scores. J Clin Epidemiol. 2011;64(3):301–8.View ArticlePubMedGoogle Scholar
  19. Charlson M, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–51.View ArticlePubMedGoogle Scholar
  20. Ueyama M, Asakura T, Morimoto K, Namkoong H, Matsuda S, Osawa T, et al. Pneumothorax associated with nontuberculous mycobacteria: a retrospective study of 69 patients. Medicine (Baltimore). 2016;95(29):e4246.View ArticlePubMed CentralGoogle Scholar
  21. Asakura T, Yamada Y, Namkoong H, Suzuki S, Niijima Y, Kamata H, et al. Impact of cavity and infiltration on pulmonary function and health-related quality of life in pulmonary Mycobacterium avium complex disease: a 3-dimensional computed tomographic analysis. Respir Med. 2017;126:9–16.View ArticlePubMedGoogle Scholar
  22. Gochi M, Takayanagi N, Kanauchi T, Ishiguro T, Yanagisawa T, Sugita Y. Retrospective study of the predictors of mortality and radiographic deterioration in 782 patients with nodular/bronchiectatic Mycobacterium avium complex lung disease. BMJ Open. 2015;5(8):e008058.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Asakura T, Hayakawa N, Hasegawa N, Namkoong H, Takeuchi K, Suzuki S, et al. Long-term outcome of pulmonary resection for nontuberculous mycobacterial pulmonary disease. Clin Infect Dis. 2017;65(2):244–51.View ArticlePubMedGoogle Scholar
  24. Singh SJ, Puhan MA, Andrianopoulos V, Hernandes NA, Mitchell KE, Hill CJ, et al. An official systematic review of the European Respiratory Society/American Thoracic Society: measurement properties of field walking tests in chronic respiratory disease. Eur Respir J. 2014;44(6):1447–78.View ArticlePubMedGoogle Scholar
  25. Holland AE, Spruit MA, Troosters T, Puhan MA, Pepin V, Saey D, et al. An official European Respiratory Society/American Thoracic Society technical standard: field walking tests in chronic respiratory disease. Eur Respir J. 2014;44(6):1428–46.View ArticlePubMedGoogle Scholar
  26. Brown CD, Benditt JO, Sciurba FC, Lee SM, Criner GJ, Mosenifar Z, et al. Exercise testing in severe emphysema: association with quality of life and lung function. COPD. 2008;5(2):117–24.View ArticlePubMedGoogle Scholar
  27. Katsura H, Yamada K, Wakabayashi R, Kida K. The impact of dyspnoea and leg fatigue during exercise on health-related quality of life in patients with COPD. Respirology. 2005;10(4):485–90.View ArticlePubMedGoogle Scholar
  28. du Bois RM, Weycker D, Albera C, Bradford WZ, Costabel U, Kartashov A, et al. Six-minute-walk test in idiopathic pulmonary fibrosis: test validation and minimal clinically important difference. Am J Respir Crit Care Med. 2011;183(9):1231–7.View ArticlePubMedGoogle Scholar
  29. Bourbonnais JM, Malaisamy S, Dalal BD, Samarakoon PC, Parikh SR, Samavati L. Distance saturation product predicts health-related quality of life among sarcoidosis patients. Health Qual Life Outcomes. 2012;10:67.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Callens E, Graba S, Gillet-Juvin K, Essalhi M, Bidaud-Chevalier B, Peiffer C, et al. Measurement of dynamic hyperinflation after a 6-minute walk test in patients with COPD. Chest. 2009;136(6):1466–72.View ArticlePubMedGoogle Scholar
  31. Hillman CM, Heinecke EL, Hii JW, Cecins NM, Jenkins SC, Eastwood PR. Relationship between body composition, peripheral muscle strength and functional exercise capacity in patients with severe chronic obstructive pulmonary disease. Intern Med J. 2012;42(5):578–81.View ArticlePubMedGoogle Scholar
  32. Mak VH, Bugler JR, Roberts CM, Spiro SG. Effect of arterial oxygen desaturation on six minute walk distance, perceived effort, and perceived breathlessness in patients with airflow limitation. Thorax. 1993;48(1):33–8.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Golpe R, Perez-de-Llano LA, Mendez-Marote L, Veres-Racamonde A. Prognostic value of walk distance, work, oxygen saturation, and dyspnea during 6-minute walk test in COPD patients. Respir Care. 2013;58(8):1329–34.View ArticlePubMedGoogle Scholar
  34. Park HY, Jeong BH, Chon HR, Jeon K, Daley CL, Koh WJ. Lung function decline according to clinical course in nontuberculous mycobacterial lung disease. Chest. 2016;150(6):1222–32.View ArticlePubMedGoogle Scholar
  35. Hayashi M, Takayanagi N, Kanauchi T, Miyahara Y, Yanagisawa T, Sugita Y. Prognostic factors of 634 HIV-negative patients with Mycobacterium avium complex lung disease. Am J Respir Crit Care Med. 2012;185(5):575–83.View ArticlePubMedGoogle Scholar
  36. Morimoto K, Hasegawa N, Izumi K, Namkoong H, Uchimura K, Yoshiyama T, et al. A laboratory-based analysis of nontuberculous mycobacterial lung disease in Japan from 2012 to 2013. Ann Am Thorac Soc. 2017;14(1):49–56.View ArticlePubMedGoogle Scholar
  37. Morimoto K, Iwai K, Uchimura K, Okumura M, Yoshiyama T, Yoshimori K, et al. A steady increase in nontuberculous mycobacteriosis mortality and estimated prevalence in Japan. Ann Am Thorac Soc. 2014;11(1):1–8.View ArticlePubMedGoogle Scholar
  38. Szekely LA, Oelberg DA, Wright C, Johnson DC, Wain J, Trotman-Dickenson B, et al. Preoperative predictors of operative morbidity and mortality in COPD patients undergoing bilateral lung volume reduction surgery. Chest. 1997;111(3):550–8.View ArticlePubMedGoogle Scholar
  39. Spruit MA, Polkey MI, Celli B, Edwards LD, Watkins ML, Pinto-Plata V, et al. Predicting outcomes from 6-minute walk distance in chronic obstructive pulmonary disease. J Am Med Dir Assoc. 2012;13(3):291–7.View ArticlePubMedGoogle Scholar
  40. Durheim MT, Smith PJ, Babyak MA, Mabe SK, Martinu T, Welty-Wolf KE, et al. Six-minute-walk distance and accelerometry predict outcomes in chronic obstructive pulmonary disease independent of global initiative for chronic obstructive lung disease 2011 group. Ann Am Thorac Soc. 2015;12(3):349–56.View ArticlePubMedPubMed CentralGoogle Scholar

Copyright

© The Author(s). 2018

Advertisement