Evidence map›Paper›PMID 38534505›Full record

ArticleBioengineering (Basel, Switzerland)2024

Biomedical Evaluation of Early Chronic Kidney Disease in the Air Force: Building a Predictive Model from the Taiwan Military Health Service.

Po-Jen Hsiao, Ruei-Lin Wang, Fu-Kang Hu, Fu-Ru Tsai, Chih-Chien Chiu, Wen-Fang Chiang, Kun-Lin Wu, Yuan-Kuei Li, Jenq-Shyong Chan, Chi-Ming Chu and 1 more

Open access · goldAbstract read
In one paragraph

Article in Bioengineering (Basel, Switzerland), 2024. 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
1.1field-weighted citation impact, top 23% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  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

11 authors at 6 institutions in 1 country.

Po-Jen HsiaoDivision of Nephrology, Department of Medicine, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.ORCID 0000-0003-4965-0285
Ruei-Lin WangDepartment of Medicine, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.
Fu-Kang HuSchool of Public Health, National Defense Medical Center, Taipei 114, Taiwan.
Fu-Ru TsaiDepartment of Nursing, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.
Chih-Chien ChiuDivision of Infectious Disease, Department of Internal Medicine, Taoyuan Armed Forces General Hospital, Taoyuan 325, Taiwan.
Wen-Fang ChiangDivision of Nephrology, Department of Medicine, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.ORCID 0000-0002-5192-6706
Kun-Lin WuDivision of Nephrology, Department of Medicine, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.
Yuan-Kuei LiDivision of Colorectal Surgery, Department of Surgery, Taoyuan Armed Forces General Hospital, Taoyuan 325, Taiwan.ORCID 0000-0003-4919-286X
Jenq-Shyong ChanDivision of Nephrology, Department of Medicine, Armed Forces Taoyuan General Hospital, Taoyuan 325, Taiwan.
Chi-Ming ChuSchool of Public Health, National Defense Medical Center, Taipei 114, Taiwan.ORCID 0000-0001-8563-1378
Chi-Wen ChangSchool of Nursing, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan.ORCID 0000-0001-8718-7522
Tri-Service General Hospital · TWChang Gung University · TWFu Jen Catholic University · TWNational Central University · TWNational Defense Medical Center · TWTaoyuan Armed Forces General Hospital · TW

Funding

Taoyuan Armed Forces General Hospital TYAFGH-D-111024Taoyuan Armed Forces General Hospital TYAFGH-D-111038
6 · The paper itself

Abstract

objectiveChronic kidney disease (CKD) is one of the most common diseases worldwide. The increasing prevalence and incidence of CKD have contributed to the critical problem of high medical costs. Due to stressful environments, aircrew members may have a high risk of renal dysfunction. A better strategy to prevent CKD progression in Air Force personnel would be to diagnosis CKD at an early stage. Since few studies have been conducted in Taiwan to examine the long-term trends in early CKD in Air Force aircrew members, this study is highly important. We investigated the prevalence of CKD and established a predictive model of disease variation among aircrew members. MATERIALS AND

methodsIn this retrospective study, we included all subjects who had received physical examinations at a military hospital from 2004 to 2010 and who could be tracked for four years. The Abbreviated Modification of Diet in Renal Disease Formula (aMDRD) was used to estimate the glomerular filtration rate (GFR) and was combined with the National Kidney Foundation/ Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI) to identify CKD patients.

resultsA total of 212 aircrew members were assessed. The results showed that the prevalence of CKD was 3.8%, 9.4%, 9.0%, and 9.4% in each of the four years. According to the logistic regression analysis, abnormal urobilinogen levels, ketones, and white blood cell (WBC) counts in urine and a positive urine occult blood test increased the risk of CKD. A positive urine occult blood test can be used to predict the future risk of CKD. Moreover, the generalized estimating equation (GEE) model showed that a greater risk of CKD with increased examination time, age and seniority had a negative effect. In conclusion, abnormal urobilinogen levels, ketones, and urine WBC counts in urine as well as a positive urine occult blood test might serve as independent predictors for CKD.

conclusionIn the future, we can focus not only on annual physical examinations but also on simple and accurate examinations, such as urine occult blood testing, to determine the risk of CKD and prevent its progression in our aircrew members.

Indexed as

Air Forcechronic kidney diseasemilitary healthpredictive model

Identifiers

PMID38534505
PMCPMC10968100
OpenAlexW4392233892

What OpenQuestion holds

Textmetadata
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.