Evidence map›Paper›PMID 25954392›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2014

COPD Hospitalization Risk Increased with Distinct Patterns of Multiple Systems Comorbidities Unveiled by Network Modeling.

Young Ji Lee, Andrew D Boyd, Jianrong John Li, Vincent Gardeux, Colleen Kenost, Don Saner, Haiquan Li, Ivo Abraham, Jerry A Krishnan, Yves A Lussier

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Young Ji LeeDepartment of Medicine, University of Illinois at Chicago, Chicago, IL.
Andrew D BoydInstitute for Translational Health Informatics, University of Illinois at Chicago, Chicago, IL ; Departments of Biomedical and Health Information Sciences, University of Illinois at Chicago, Chicago, IL ; University of Illinois Hospital and Health Science System, University of Illinois at Chicago, Chicago, IL.
Jianrong John LiDepartment of Medicine, The University of Arizona, Tucson, AZ, USA.
Vincent GardeuxDepartment of Medicine, The University of Arizona, Tucson, AZ, USA.
Colleen KenostDepartment of Medicine, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA.
Don SanerCancer Center, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA.
Haiquan LiDepartment of Medicine, The University of Arizona, Tucson, AZ, USA.
Ivo AbrahamDepartment of Pharmacy Practice and Science, The University of Arizona, Tucson, AZ, USA.
Jerry A KrishnanDepartment of Medicine, University of Illinois at Chicago, Chicago, IL ; University of Illinois Hospital and Health Science System, University of Illinois at Chicago, Chicago, IL.
Yves A LussierDepartment of Medicine, The University of Arizona, Tucson, AZ, USA ; Cancer Center, The University of Arizona, Tucson, AZ, USA ; Biomedical Informatics Service Group, Arizona Health Science Center, The University of Arizona, Tucson, AZ, USA ; Interdisciplinary Program in Statistics, The University of Arizona, Tucson, AZ, USA.

Funding

CTSA INFRASTRUCTURE FOR PEDIATRIC RESEARCHUL1RR029879 · NCRR · UNIVERSITY OF ILLINOIS AT CHICAGO · PI MAZZONE, THEODORE · 2009 to 2011
$11.0M
Center for Clinical and Translational ScienceUL1TR000050 · NCATS · UNIVERSITY OF ILLINOIS AT CHICAGO · PI AZAR, DIMITRI T, MERMELSTEIN, ROBIN J. · 2012 to 2014
$9.8M
NCATS NIH HHS UL1 TR000050NCATS NIH HHS UL1TR000050NCRR NIH HHS 1UL1RR029879NCRR NIH HHS UL1 RR029879
6 · The paper itself

Abstract

Earlier studies on hospitalization risk are largely based on regression models. To our knowledge, network modeling of multiple comorbidities is novel and inherently enables multidimensional scoring and unbiased feature reduction. Network modeling was conducted using an independent validation design starting from 38,695 patients, 1,446,581 visits, and 430 distinct clinical facilities/hospitals. Odds ratios (OR) were calculated for every pair of comorbidity using patient counts and compared their tendency with hospitalization rates and ED visits. Network topology analyses were performed, defining significant comorbidity associations as having OR≥5 & False-Discovery-Rate≤10(-7). Four COPD-associated comorbidity sub-networks emerged, incorporating multiple clinical systems: (i) metabolic syndrome, (ii) substance abuse and mental disorder, (iii) pregnancy-associated conditions, and (iv) fall-related injury. The latter two have not been reported yet. Features prioritized from the network are predictive of hospitalizations in an independent set (p<0.004). Therefore, we suggest that network topology is a scalable and generalizable method predictive of hospitalization.

Indexed as

ComorbidityHospitalizationPulmonary Disease, Chronic ObstructiveAccidental FallsDiabetes MellitusFemaleHumansPregnancyPregnancy ComplicationsRiskSubstance-Related Disorders

Identifiers

PMID25954392
PMCPMC4419951

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.