Evidence map›Paper›PMID 31821152›Full record

ArticleJournal of medical Internet research2019

Detecting Lifestyle Risk Factors for Chronic Kidney Disease With Comorbidities: Association Rule Mining Analysis of Web-Based Survey Data.

Suyuan Peng, Feichen Shen, Andrew Wen, Liwei Wang, Yadan Fan, Xusheng Liu, Hongfang Liu

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
2.7field-weighted citation impact, top 9% 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

14 citing papers in PubMed, 28 citations in OpenAlex.

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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

7 authors at 4 institutions in 2 countries.

Suyuan PengCenter for Data Science in Health and Medicine, Peking University, Beijing, China.ORCID 0000-0002-8221-7574
Feichen ShenDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0002-7803-2331
Andrew WenDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-9090-8028
Liwei WangDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0001-9970-8604
Yadan FanInstitute for Health Informatics, University of Minnesota, Minneapolis, MN, United States.ORCID 0000-0002-7797-815X
Xusheng LiuThe Second Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.ORCID 0000-0002-0931-4925
Hongfang LiuDepartment of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.ORCID 0000-0003-2570-3741
Mayo Clinic in Florida · USGuangzhou University of Chinese Medicine · CNPeking University · CNUniversity of Minnesota · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe rise in the number of patients with chronic kidney disease (CKD) and consequent end-stage renal disease necessitating renal replacement therapy has placed a significant strain on health care. The rate of progression of CKD is influenced by both modifiable and unmodifiable risk factors. Identification of modifiable risk factors, such as lifestyle choices, is vital in informing strategies toward renoprotection. Modification of unhealthy lifestyle choices lessens the risk of CKD progression and associated comorbidities, although the lifestyle risk factors and modification strategies may vary with different comorbidities (eg, diabetes, hypertension). However, there are limited studies on suitable lifestyle interventions for CKD patients with comorbidities.

objectiveThe objectives of our study are to (1) identify the lifestyle risk factors for CKD with common comorbid chronic conditions using a US nationwide survey in combination with literature mining, and (2) demonstrate the potential effectiveness of association rule mining (ARM) analysis for the aforementioned task, which can be generalized for similar tasks associated with noncommunicable diseases (NCDs).

methodsWe applied ARM to identify lifestyle risk factors for CKD progression with comorbidities (cardiovascular disease, chronic pulmonary disease, rheumatoid arthritis, diabetes, and cancer) using questionnaire data for 450,000 participants collected from the Behavioral Risk Factor Surveillance System (BRFSS) 2017. The BRFSS is a Web-based resource, which includes demographic information, chronic health conditions, fruit and vegetable consumption, and sugar- or salt-related behavior. To enrich the BRFSS questionnaire, the Semantic MEDLINE Database was also mined to identify lifestyle risk factors.

resultsThe results suggest that lifestyle modification for CKD varies among different comorbidities. For example, the lifestyle modification of CKD with cardiovascular disease needs to focus on increasing aerobic capacity by improving muscle strength or functional ability. For CKD patients with chronic pulmonary disease or rheumatoid arthritis, lifestyle modification should be high dietary fiber intake and participation in moderate-intensity exercise. Meanwhile, the management of CKD patients with diabetes focuses on exercise and weight loss predominantly.

conclusionsWe have demonstrated the use of ARM to identify lifestyle risk factors for CKD with common comorbid chronic conditions using data from BRFSS 2017. Our methods can be generalized to advance chronic disease management with more focused and optimized lifestyle modification of NCDs.

Indexed as

Life StyleComorbidityDisease ProgressionFemaleHumansMaleMiddle AgedRenal Insufficiency, ChronicRisk FactorsSurveys and Questionnairesassociation rule miningBehavioral Risk Factor Surveillance Systemchronic kidney diseasenoncommunicable diseases

Identifiers

PMID31821152
PMCPMC6930505
OpenAlexW2981903452

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.