Evidence map›Paper›PMID 41262849›Full record

ArticleClinical kidney journal2025

The BestShape artificial intelligence system for real-time prediction of intradialytic hypotension-clinical outcomes after four-year follow-up.

Cheng-Jui Lin, Hong-Mou Shih, Ying-Ying Chen, Shiow-Huey Chen, Hui-Fang Su, Homtin Chen, Chih-Jen Wu

Abstract read
In one paragraph

Article in Clinical kidney journal, 2025. 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. Review
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.

Cheng-Jui LinDivision of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.
Hong-Mou ShihDivision of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.
Ying-Ying ChenDivision of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.
Shiow-Huey ChenDivision of Hemodialysis and Peritoneal Dialysis, Department of Nursing, MacKay Memorial Hospital, Taipei and New Taipei city, Taiwan.
Hui-Fang SuDivision of Hemodialysis and Peritoneal Dialysis, Department of Nursing, MacKay Memorial Hospital, Taipei and New Taipei city, Taiwan.
Homtin ChenDepartment of Information Technology, MacKay Memorial Hospital, Taipei, Taiwan.
Chih-Jen WuDivision of Nephrology, Department of Internal Medicine, MacKay Memorial Hospital, Taipei, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Intradialytic hypotension (IDH) is a serious complication of chronic hemodialysis (HD). BestShape is a novel artificial intelligence-driven system developed recently for real-time prediction of IDH. This study aimed to report the clinical results and health-economic benefits following the implementation of BestShape. Methods: Medical records from two institutions in Taiwan that incorporated BestShape into all HD practices were retrospectively reviewed. Data from HD sessions from January 2020 to December 2023, following BestShape implementation, constituted the "BestShape group." Data from January 2016 through the end of 2019 served as the historical control group. The primary outcome was IDH frequency, and secondary outcomes were rates of cardiopulmonary resuscitation (CPR), falls, mortality, medical personnel satisfaction, and estimated cost reduction. Results: In total, 213 071 HD sessions of 18 141 patients were included (BestShape 152 792 sessions; control 60 279 sessions). The mean monthly IDH rate significantly reduced from 27% in 2019 to 21% in 2023 ( Conclusion: Preliminary clinical data show BestShape is associated with reductions in IDH, CPR, and falls in patients undergoing HD, and a reduction in healthcare costs and high medical personnel satisfaction.

Indexed as

artificial intelligenceBestShapeintradialytic hypotensionprediction

Identifiers

PMID41262849
PMCPMC12624859

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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.