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Machine Learning and Prediction of All-Cause Mortality in COPD.
Moll, Matthew; Qiao, Dandi; Regan, Elizabeth A; Hunninghake, Gary M; Make, Barry J; Tal-Singer, Ruth; McGeachie, Michael J; Castaldi, Peter J; San Jose Estepar, Raul; Washko, George R; Wells, James M; LaFon, David; Strand, Matthew; Bowler, Russell P; Han, MeiLan K; Vestbo, Jorgen; Celli, Bartolome; Calverley, Peter; Crapo, James; Silverman, Edwin K; Hobbs, Brian D; Cho, Michael H.
  • Moll M; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
  • Qiao D; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA.
  • Regan EA; Division of Pulmonary and Critical Care Medicine, University of Colorado, Denver, CO.
  • Hunninghake GM; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
  • Make BJ; Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, CO.
  • Tal-Singer R; GlaxoSmithKline Research and Development, Collegeville, PA.
  • McGeachie MJ; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA.
  • Castaldi PJ; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA.
  • San Jose Estepar R; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA.
  • Washko GR; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA; Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, MA.
  • Wells JM; Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL.
  • LaFon D; Division of Pulmonary, Allergy and Critical Care Medicine, University of Alabama at Birmingham, Birmingham, AL.
  • Strand M; Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, CO.
  • Bowler RP; Division of Pulmonary and Critical Care Medicine, University of Colorado, Denver, CO; Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, CO.
  • Han MK; Division of Pulmonary and Critical Care Medicine, University of Michigan Health System, Ann Arbor, MI.
  • Vestbo J; Division of Infection, Immunity and Respiratory Medicine, Manchester Academic Health Sciences Centre, The University of Manchester and the Manchester University NHS Foundation Trust, Manchester, England.
  • Celli B; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
  • Calverley P; Department of Medicine, University of Liverpool, Liverpool, England.
  • Crapo J; Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, CO.
  • Silverman EK; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
  • Hobbs BD; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA.
  • Cho MH; Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA; Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA. Electronic address: remhc@channing.harvard.edu.
Chest ; 158(3): 952-964, 2020 09.
Article in English | MEDLINE | ID: covidwho-987243
ABSTRACT

BACKGROUND:

COPD is a leading cause of mortality. RESEARCH QUESTION We hypothesized that applying machine learning to clinical and quantitative CT imaging features would improve mortality prediction in COPD. STUDY DESIGN AND

METHODS:

We selected 30 clinical, spirometric, and imaging features as inputs for a random survival forest. We used top features in a Cox regression to create a machine learning mortality prediction (MLMP) in COPD model and also assessed the performance of other statistical and machine learning models. We trained the models in subjects with moderate to severe COPD from a subset of subjects in Genetic Epidemiology of COPD (COPDGene) and tested prediction performance in the remainder of individuals with moderate to severe COPD in COPDGene and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE). We compared our model with the BMI, airflow obstruction, dyspnea, exercise capacity (BODE) index; BODE modifications; and the age, dyspnea, and airflow obstruction index.

RESULTS:

We included 2,632 participants from COPDGene and 1,268 participants from ECLIPSE. The top predictors of mortality were 6-min walk distance, FEV1 % predicted, and age. The top imaging predictor was pulmonary artery-to-aorta ratio. The MLMP-COPD model resulted in a C index ≥ 0.7 in both COPDGene and ECLIPSE (6.4- and 7.2-year median follow-ups, respectively), significantly better than all tested mortality indexes (P < .05). The MLMP-COPD model had fewer predictors but similar performance to that of other models. The group with the highest BODE scores (7-10) had 64% mortality, whereas the highest mortality group defined by the MLMP-COPD model had 77% mortality (P = .012).

INTERPRETATION:

An MLMP-COPD model outperformed four existing models for predicting all-cause mortality across two COPD cohorts. Performance of machine learning was similar to that of traditional statistical methods. The model is available online at https//cdnm.shinyapps.io/cgmortalityapp/.
Subject(s)
Keywords

Full text: Available Collection: International databases Database: MEDLINE Main subject: Pulmonary Disease, Chronic Obstructive / Machine Learning Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Language: English Journal: Chest Year: 2020 Document Type: Article Affiliation country: J.chest.2020.02.079

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pulmonary Disease, Chronic Obstructive / Machine Learning Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Limits: Female / Humans / Male / Middle aged Language: English Journal: Chest Year: 2020 Document Type: Article Affiliation country: J.chest.2020.02.079