Evidence map›Paper›PMID 34230524›Full record

ArticleScientific reports2021

Prediction of non-responsiveness to pre-dialysis care program in patients with chronic kidney disease: a retrospective cohort analysis.

Emily K King, Ming-Han Hsieh, David R Chang, Cheng-Ting Lu, I-Wen Ting, Charles C N Wang, Pei-Shan Chen, Hung-Chieh Yeh, Hsiu-Yin Chiang, Chin-Chi Kuo

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 pooled it
0.3field-weighted citation impact, top 44% 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

2 citing papers in PubMed, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  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 at 3 institutions in 1 country.

Emily K KingDepartment of Medical Media Design and Application, Interpedia Incorporated, Taichung, Taiwan.
Ming-Han HsiehDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
David R ChangDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
Cheng-Ting LuDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
I-Wen TingDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
Charles C N WangDepartment of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan.
Pei-Shan ChenBig Data Center, China Medical University Hospital and College of Medicine, China Medical University, 2, Yude Rd., North Dist., Taichung City, 404, Taiwan.
Hung-Chieh YehDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan.
Hsiu-Yin ChiangBig Data Center, China Medical University Hospital and College of Medicine, China Medical University, 2, Yude Rd., North Dist., Taichung City, 404, Taiwan.
Chin-Chi KuoDivision of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan. chinchik@gmail.com.
China Medical University · TWChina Medical University Hospital · TWAsia University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The responsiveness of patients with chronic kidney disease (CKD) to nephrologists' care is unpredictable. We defined the longitudinal stages (LSs) 1-5 of estimated glomerular filtration rate (eGFR) by group-based trajectory modeling for repeated eGFR measurements of 7135 patients with CKD aged 20-90 years from a 13-year pre-end-stage renal disease (ESRD) care registry. Patients were considered nonresponsive to the pre-dialysis care if they had a more advanced eGFR LS compared with the baseline. Conversely, those with improved or stable eGFR LS were considered responsive. The proportion of patients with CKD stage progression increased with the increase in the baseline CKD stage (stages 1-2: 29.2%; stage 4: 45.8%). The adjusted times to ESRD and all-cause mortality in patients with eGFR LS-5 were 92% (95% confidence interval [CI] 86-96%) and 57% (95% CI 48-65%) shorter, respectively, than in patients with eGFR LS-3A. Among patients with baseline CKD stages 3 and 4, the adjusted times to ESRD and all-cause death in the nonresponsive patients were 39% (95% CI 33-44%) and 20% (95% CI 14-26%) shorter, respectively, than in the responsive patients. Our proposed Renal Care Responsiveness Prediction (RCRP) model performed significantly better than the conventional Kidney Failure Risk Equation in discrimination, calibration, and net benefit according to decision curve analysis. Non-responsiveness to nephrologists' care is associated with rapid progression to ESRD and all-cause mortality. The RCRP model improves early identification of responsiveness based on variables collected during enrollment in a pre-ESRD program. Urgent attention should be given to characterize the underlying heterogeneous responsiveness to pre-dialysis care.

Indexed as

Patient CareRenal DialysisAgedDisease ProgressionGlomerular Filtration RateHumansKidney Failure, ChronicLogistic ModelsMiddle AgedPrognosisRenal Insufficiency, ChronicRetrospective StudiesRisk Factors

Identifiers

PMID34230524
PMCPMC8260802
OpenAlexW3181116351

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.