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Novel equations better predict lung age: a retrospective analysis using two cohorts of participants with medical check-up examinations in Japan.

Ishida Y, Ichikawa YE, Fukakusa M, Kawatsu A, Masuda K - NPJ Prim Care Respir Med (2015)

Bottom Line: As a result of the linear regression analysis for forced expiratory volume in 1 s (FEV1), spirometric variables using forced vital capacity (FVC) improved the adjusted R(2) values to greater than 0.8.On the basis of the scatter plots between chronological age and SDL age, the best model included the equations using FEV1 and %FVC in females and males (R(2)=0.66 and 0.55, respectively), which was confirmed by the validation cohort.This study produced novel SDL age equations for Japanese adults using data from a large number of healthy never-smokers with both normal spirometric measurements and BMIs.

View Article: PubMed Central - PubMed

Affiliation: Pediatric Medical Center, Ehime Prefectural Central Hospital, Ehime, Japan.

ABSTRACT

Background: The lung age equations developed by the Japanese Respiratory Society encounter several problems when being applied in a clinical setting.

Aims: To establish novel spirometry-derived lung age (SDL age) equations using data from a large number of Japanese healthy never-smokers with normal spirometric measurements and normal body mass indices (BMIs).

Methods: The participants had undergone medical check-ups at the Center for Preventive Medicine of St Luke's International Hospital between 2004 and 2012. A total of 15,238 Japanese participants (5,499 males and 9,739 females) were chosen for the discovery cohort. The other independent 2,079 individuals were selected for the validation cohort. The original method of Morris and Temple was applied to the discovery cohort.

Results: As a result of the linear regression analysis for forced expiratory volume in 1 s (FEV1), spirometric variables using forced vital capacity (FVC) improved the adjusted R(2) values to greater than 0.8. On the basis of the scatter plots between chronological age and SDL age, the best model included the equations using FEV1 and %FVC in females and males (R(2)=0.66 and 0.55, respectively), which was confirmed by the validation cohort. The following equations were developed: SDL age (females)=0.84×%FVC+50.2-40×FEV1 (l) and SDL age (males)=1.00×%FVC+50.7-33.3×FEV1 (l).

Conclusions: This study produced novel SDL age equations for Japanese adults using data from a large number of healthy never-smokers with both normal spirometric measurements and BMIs.

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Related in: MedlinePlus

Scatter plots between chronological age (years) and SDL age (years) in Group 1. Scatter plots showing the relationship between chronological age and SDL age in Group 1. SDL age was determined in females and males according to the JRS equations (a and b, respectively), model 2 equations (c and d, respectively) and model 3 equations (e and f, respectively). The lines of best fit for SDL age (y) against chronological age (x) are expressed by solid lines. Dotted lines indicate identity lines (y–x).
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fig2: Scatter plots between chronological age (years) and SDL age (years) in Group 1. Scatter plots showing the relationship between chronological age and SDL age in Group 1. SDL age was determined in females and males according to the JRS equations (a and b, respectively), model 2 equations (c and d, respectively) and model 3 equations (e and f, respectively). The lines of best fit for SDL age (y) against chronological age (x) are expressed by solid lines. Dotted lines indicate identity lines (y–x).

Mentions: We developed the SDL age estimation equations according to the models described above and shown in Table 2. The representative scatter plots between chronological age and SDL age are shown in Figure 2. We compared models 2 and 3 with the original JRS model. On the basis of the R2 values, the slope (nearly equal to 1) and its simplicity (only two predictive variables), model 3 was considered the best model in both females (R2=0.66) and males (R2=0.55).


Novel equations better predict lung age: a retrospective analysis using two cohorts of participants with medical check-up examinations in Japan.

Ishida Y, Ichikawa YE, Fukakusa M, Kawatsu A, Masuda K - NPJ Prim Care Respir Med (2015)

Scatter plots between chronological age (years) and SDL age (years) in Group 1. Scatter plots showing the relationship between chronological age and SDL age in Group 1. SDL age was determined in females and males according to the JRS equations (a and b, respectively), model 2 equations (c and d, respectively) and model 3 equations (e and f, respectively). The lines of best fit for SDL age (y) against chronological age (x) are expressed by solid lines. Dotted lines indicate identity lines (y–x).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373493&req=5

fig2: Scatter plots between chronological age (years) and SDL age (years) in Group 1. Scatter plots showing the relationship between chronological age and SDL age in Group 1. SDL age was determined in females and males according to the JRS equations (a and b, respectively), model 2 equations (c and d, respectively) and model 3 equations (e and f, respectively). The lines of best fit for SDL age (y) against chronological age (x) are expressed by solid lines. Dotted lines indicate identity lines (y–x).
Mentions: We developed the SDL age estimation equations according to the models described above and shown in Table 2. The representative scatter plots between chronological age and SDL age are shown in Figure 2. We compared models 2 and 3 with the original JRS model. On the basis of the R2 values, the slope (nearly equal to 1) and its simplicity (only two predictive variables), model 3 was considered the best model in both females (R2=0.66) and males (R2=0.55).

Bottom Line: As a result of the linear regression analysis for forced expiratory volume in 1 s (FEV1), spirometric variables using forced vital capacity (FVC) improved the adjusted R(2) values to greater than 0.8.On the basis of the scatter plots between chronological age and SDL age, the best model included the equations using FEV1 and %FVC in females and males (R(2)=0.66 and 0.55, respectively), which was confirmed by the validation cohort.This study produced novel SDL age equations for Japanese adults using data from a large number of healthy never-smokers with both normal spirometric measurements and BMIs.

View Article: PubMed Central - PubMed

Affiliation: Pediatric Medical Center, Ehime Prefectural Central Hospital, Ehime, Japan.

ABSTRACT

Background: The lung age equations developed by the Japanese Respiratory Society encounter several problems when being applied in a clinical setting.

Aims: To establish novel spirometry-derived lung age (SDL age) equations using data from a large number of Japanese healthy never-smokers with normal spirometric measurements and normal body mass indices (BMIs).

Methods: The participants had undergone medical check-ups at the Center for Preventive Medicine of St Luke's International Hospital between 2004 and 2012. A total of 15,238 Japanese participants (5,499 males and 9,739 females) were chosen for the discovery cohort. The other independent 2,079 individuals were selected for the validation cohort. The original method of Morris and Temple was applied to the discovery cohort.

Results: As a result of the linear regression analysis for forced expiratory volume in 1 s (FEV1), spirometric variables using forced vital capacity (FVC) improved the adjusted R(2) values to greater than 0.8. On the basis of the scatter plots between chronological age and SDL age, the best model included the equations using FEV1 and %FVC in females and males (R(2)=0.66 and 0.55, respectively), which was confirmed by the validation cohort. The following equations were developed: SDL age (females)=0.84×%FVC+50.2-40×FEV1 (l) and SDL age (males)=1.00×%FVC+50.7-33.3×FEV1 (l).

Conclusions: This study produced novel SDL age equations for Japanese adults using data from a large number of healthy never-smokers with both normal spirometric measurements and BMIs.

Show MeSH
Related in: MedlinePlus