Evidence map›Paper›PMID 40178620›Full record

ArticlePediatric nephrology (Berlin, Germany)2025

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

Jian-An Wang, Hsiang-Wei Hu, Yuan-Yow Chiou, Kuan-Yu Chen, Chun-Chuan Hsueh, Chih-Chia Chen

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Article in Pediatric nephrology (Berlin, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. A brief review of some artificial intelligence methods in nephrology.Pediatric nephrology (Berlin, Germany) · 2026
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4 · The record

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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

6 authors.

Jian-An WangAcusense Biomedical Technology Corp, Tainan, Taiwan.
Hsiang-Wei HuAcusense Biomedical Technology Corp, Tainan, Taiwan.
Yuan-Yow ChiouDepartment of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng-Li Rd, North District, Tainan, 70403, Taiwan.
Kuan-Yu ChenAcusense Biomedical Technology Corp, Tainan, Taiwan.
Chun-Chuan HsuehAcusense Biomedical Technology Corp, Tainan, Taiwan.
Chih-Chia ChenDepartment of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Sheng-Li Rd, North District, Tainan, 70403, Taiwan. chenmarki@gmail.com.

Funding

Ministry of Science and Technology, Taiwan NSTC 111-2926-B-006-001Ministry of Science and Technology, Taiwan NSTC 112-2926-B-006-001
6 · The paper itself

Abstract

backgroundIntradialytic hypotension (IDH) is associated with mortality in adults undergoing intermittent hemodialysis, but this relationship is unclear in critically ill children receiving continuous kidney replacement therapy (CKRT). We aim to evaluate the relationship between IDH and hospital mortality and if pressure data from dialysis machines could predict IDH.

methodsWe conducted a retrospective cohort study in a tertiary pediatric intensive care unit and NICU from December 2019 to July 2022, including 23 patients across 38 admissions (median age 10 years).

resultsIDH proportion was significantly associated with mortality (risk ratio [RR]: 4.40, 95% confidence interval [CI]: 1.22-15.90, p = 0.02). Random Forest models using Entropy or Gini criteria demonstrated high sensitivity. The CatBoost model achieved the highest average F1-score and area under the receiver operating characteristic (ROC) curve (AUC) (88.18% and 86.6% with and without dialysis settings, respectively). Local Interpretable Model-agnostic Explanations (LIME) indicated that dialysis machine-derived time-series pressure parameters were critical predictive features for IDH, whereas blood pressure-related variables were not among the top predictors.

conclusionsDialysis machine-derived pressure parameters may serve as effective predictive markers for IDH, which is associated with increased mortality. These findings support the potential of integrating pressure data in the early detection and management of IDH in pediatric CKRT patients.

Indexed as

Continuous Renal Replacement TherapyHypotensionMachine LearningAdolescentChildChild, PreschoolCritical IllnessFemaleHospital MortalityHumansInfantIntensive Care Units, PediatricMalePredictive Value of TestsRenal DialysisRetrospective StudiesContinuous kidney replacement therapyIntradialytic hypotensionMachine learningMortality

Identifiers

PMID40178620

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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.