Evidence map›Paper›PMID 31905995›Full record

ArticleSensors (Basel, Switzerland)2019

Prediction of 30-Day Readmission for COPD Patients Using Accelerometer-Based Activity Monitoring.

Wen-Yen Lin, Vijay Kumar Verma, Ming-Yih Lee, Horng-Chyuan Lin, Chao-Sung Lai

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 16% of its field
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

10 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Pooled it
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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

5 authors at 3 institutions in 1 country.

Wen-Yen LinDepartment of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan.ORCID 0000-0002-5748-0151
Vijay Kumar VermaDepartment of Electrical Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan.ORCID 0000-0002-2032-7321
Ming-Yih LeeDivision of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan.ORCID 0000-0002-2031-0183
Horng-Chyuan LinDepartment of Thoracic Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan.
Chao-Sung LaiDepartment of Electronic Engineering, Center for Biomedical Engineering, Chang Gung University, Tao-Yuan 33302, Taiwan.
Chang Gung University · TWChang Gung Memorial Hospital · TWMing Chi University of Technology · TW

Funding

Chang Gung Memorial Hospital CIRPG5E0012 & CIRPD5E0012Chang Gung University BMRPC50 and BMRP138Ministry of Science and Technology, Taiwan, R.O.C. MOST 108-2221-E-182-054
6 · The paper itself

Abstract

Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk.

Indexed as

Patient ReadmissionAccelerometryEmergency Service, HospitalHumansMonitoring, PhysiologicPulmonary Disease, Chronic Obstructiveaccelerometersactigraphyactivity monitoringCOPDpredictionreadmission riskwearable devices

Identifiers

PMID31905995
PMCPMC6982816
OpenAlexW2997797506

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.