Evidence map›Paper›PMID 34003134›Full record

ArticleJMIR research protocols2021

Using Computational Methods to Improve Integrated Disease Management for Asthma and Chronic Obstructive Pulmonary Disease: Protocol for a Secondary Analysis.

Gang Luo, Bryan L Stone, Xiaoming Sheng, Shan He, Corinna Koebnick, Flory L Nkoy

Abstract read
In one paragraph

Article in JMIR research protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

6 authors.

Gang LuoDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States.ORCID https://orcid.org/0000-0001-7217-4008
Bryan L StoneDepartment of Pediatrics, University of Utah, Salt Lake City, UT, United States.ORCID https://orcid.org/0000-0002-0912-5227
Xiaoming ShengCollege of Nursing, University of Utah, Salt Lake City, UT, United States.ORCID https://orcid.org/0000-0001-6576-3699
Shan HeCare Transformation and Information Systems, Intermountain Healthcare, West Valley City, UT, United States.ORCID https://orcid.org/0000-0002-7982-9625
Corinna KoebnickDepartment of Research & Evaluation, Kaiser Permanente Southern California, Pasadena, CA, United States.ORCID https://orcid.org/0000-0001-8274-0309
Flory L NkoyDepartment of Pediatrics, University of Utah, Salt Lake City, UT, United States.ORCID https://orcid.org/0000-0002-8954-8288

Funding

Using Computational Approaches to Optimize Asthma Care ManagementR01HL142503 · NHLBI · UNIVERSITY OF WASHINGTON · PI LUO, GANG · 2018 to 2021
$3.3M
NHLBI NIH HHS R01 HL142503
6 · The paper itself

Abstract

backgroundAsthma and chronic obstructive pulmonary disease (COPD) impose a heavy burden on health care. Approximately one-fourth of patients with asthma and patients with COPD are prone to exacerbations, which can be greatly reduced by preventive care via integrated disease management that has a limited service capacity. To do this well, a predictive model for proneness to exacerbation is required, but no such model exists. It would be suboptimal to build such models using the current model building approach for asthma and COPD, which has 2 gaps due to rarely factoring in temporal features showing early health changes and general directions. First, existing models for other asthma and COPD outcomes rarely use more advanced temporal features, such as the slope of the number of days to albuterol refill, and are inaccurate. Second, existing models seldom show the reason a patient is deemed high risk and the potential interventions to reduce the risk, making already occupied clinicians expend more time on chart review and overlook suitable interventions. Regular automatic explanation methods cannot deal with temporal data and address this issue well.

objectiveTo enable more patients with asthma and patients with COPD to obtain suitable and timely care to avoid exacerbations, we aim to implement comprehensible computational methods to accurately predict proneness to exacerbation and recommend customized interventions.

methodsWe will use temporal features to accurately predict proneness to exacerbation, automatically find modifiable temporal risk factors for every high-risk patient, and assess the impact of actionable warnings on clinicians' decisions to use integrated disease management to prevent proneness to exacerbation.

resultsWe have obtained most of the clinical and administrative data of patients with asthma from 3 prominent American health care systems. We are retrieving other clinical and administrative data, mostly of patients with COPD, needed for the study. We intend to complete the study in 6 years.

conclusionsOur results will help make asthma and COPD care more proactive, effective, and efficient, improving outcomes and saving resources. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/27065.

Indexed as

asthmachronic obstructive pulmonary diseasedecision support techniquesforecastingmachine learning

Identifiers

PMID34003134
PMCPMC8170556

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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.