Median regression spline modeling of longitudinal FEV1 measurements in cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.

PMID- 29261779
OWN - NLM
STAT- In-Process
LR  - 20171231
IS  - 1932-6203 (Electronic)
IS  - 1932-6203 (Linking)
VI  - 12
IP  - 12
DP  - 2017
TI  - Median regression spline modeling of longitudinal FEV1 measurements in cystic
      fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients.
PG  - e0190061
LID - 10.1371/journal.pone.0190061 [doi]
AB  - RATIONALE: Clinical phenotyping, therapeutic investigations as well as genomic,
      airway secretion metabolomic and metagenomic investigations can benefit from
      robust, nonlinear modeling of FEV1 in individual subjects. We demonstrate the
      utility of measuring FEV1 dynamics in representative cystic fibrosis (CF) and
      chronic obstructive pulmonary disease (COPD) populations. METHODS: Individual
      FEV1 data from CF and COPD subjects were modeled by estimating median regression 
      splines and their predicted first and second derivatives. Classes were created
      from variables that capture the dynamics of these curves in both cohorts.
      RESULTS: Nine FEV1 dynamic variables were identified from the splines and their
      predicted derivatives in individuals with CF (n = 177) and COPD (n = 374). Three 
      FEV1 dynamic classes (i.e. stable, intermediate and hypervariable) were generated
      and described using these variables from both cohorts. In the CF cohort, the FEV1
      hypervariable class (HV) was associated with a clinically unstable,
      female-dominated phenotypes while stable FEV1 class (S) individuals were highly
      associated with the male-dominated milder clinical phenotype. In the COPD cohort,
      associations were found between the FEV1 dynamic classes, the COPD GOLD grades,
      with exacerbation frequency and symptoms. CONCLUSION: Nonlinear modeling of FEV1 
      with splines provides new insights and is useful in characterizing CF and COPD
      clinical phenotypes.
FAU - Conrad, Douglas J
AU  - Conrad DJ
AUID- ORCID: http://orcid.org/0000-0002-9344-3446
AD  - Department of Medicine, University of California, San Diego, United States of
      America.
FAU - Bailey, Barbara A
AU  - Bailey BA
AD  - Department of Mathematics and Statistics, San Diego State University, San Diego, 
      United States of America.
FAU - Hardie, Jon A
AU  - Hardie JA
AD  - Department of Clinical Science, University of Bergen, Bergen, Norway.
FAU - Bakke, Per S
AU  - Bakke PS
AD  - Department of Clinical Science, University of Bergen, Bergen, Norway.
FAU - Eagan, Tomas M L
AU  - Eagan TML
AD  - Department of Clinical Science, University of Bergen, Bergen, Norway.
AD  - Department of Thoracic Medicine, Haukeland University Hospital, Bergen, Norway.
FAU - Aarli, Bernt B
AU  - Aarli BB
AD  - Department of Clinical Science, University of Bergen, Bergen, Norway.
LA  - eng
PT  - Journal Article
DEP - 20171220
PL  - United States
TA  - PLoS One
JT  - PloS one
JID - 101285081
PMC - PMC5738083
EDAT- 2017/12/21 06:00
MHDA- 2017/12/21 06:00
CRDT- 2017/12/21 06:00
PHST- 2017/08/11 00:00 [received]
PHST- 2017/12/07 00:00 [accepted]
PHST- 2017/12/21 06:00 [entrez]
PHST- 2017/12/21 06:00 [pubmed]
PHST- 2017/12/21 06:00 [medline]
AID - 10.1371/journal.pone.0190061 [doi]
AID - PONE-D-17-29854 [pii]
PST - epublish
SO  - PLoS One. 2017 Dec 20;12(12):e0190061. doi: 10.1371/journal.pone.0190061.
      eCollection 2017.