Evidence map›Paper›PMID 41146036›Full record

ArticleBMC nephrology2025

A prescription-based model for predicting post-hemodialysis urea and electrolytes: a real-world validation study in acute hemodialysis.

Pablo Galindo, Cesar R Barrientos, Rosario G Hernández, Armando J Martínez-Rueda, Noemi Del Toro-Cisneros, Olynka Vega-Vega

Abstract readValidation Study
In one paragraph

Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Pablo GalindoDepartment of Nephrology, Hospital General de México "Dr Eduardo Liceaga", Mexico City, Mexico.ORCID 0000-0001-8617-333X
Cesar R BarrientosDepartment of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Sección XVI, Tlalpan, Mexico City, 14080, Mexico.ORCID 0009-0008-7567-5666
Rosario G HernándezDepartment of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Sección XVI, Tlalpan, Mexico City, 14080, Mexico.ORCID 0000-0001-6318-4781
Armando J Martínez-RuedaDepartment of Medical Sciences Integration, Facultad de Medicina, Universidad Nacional Autonoma de México, Mexico City, Mexico.ORCID 0000-0001-5765-4521
Noemi Del Toro-CisnerosDepartment of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Sección XVI, Tlalpan, Mexico City, 14080, Mexico.ORCID 0000-0001-5500-6678
Olynka Vega-VegaDepartment of Nephrology and Mineral Metabolism, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Vasco de Quiroga 15, Belisario Domínguez Sección XVI, Tlalpan, Mexico City, 14080, Mexico. olynkavega@hotmail.com.ORCID 0000-0001-5299-4842

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUrgent-start hemodialysis often involves severe biochemical abnormalities but is associated with a high risk of complications. Currently, no tool has been validated to predict post-hemodialysis urea and electrolytes in acute settings from prescription data alone.

methodsIn this ambispective, two-phase study, we developed and validated a model to predict post-hemodialysis blood urea nitrogen and electrolytes using only prescription parameters in patients with urgent hemodialysis indications and those at risk for dialysis disequilibrium syndrome, severe hyponatremia, or hyperkalemia. The model was validated through statistical assessment of correlation and agreement. The model was integrated into a free, user-friendly web application (Adequator app®).

resultsThe development cohort included 303 treatments in 42 chronic hemodialysis patients. Dialyzer clearance, which was calculated via formal urea kinetic modeling, demonstrated excellent correlation and agreement with the proposed model (r = 0.99; 95% CI: 0.99 to 0.99) and a bias of 0.23 ± 1.2 ml/min (95% CI: -2.17 to 2.63). The validation cohort included 44 urgent hemodialysis sessions in 30 patients with chronic kidney disease or acute kidney injury and severe electrolyte abnormalities or at risk of dialysis disequilibrium syndrome. The predicted post-hemodialysis, blood urea nitrogen, sodium, and potassium levels strongly correlated with the measured values: r = 0.96 (95% CI: 0.93 to 0.98), r = 0.96 (95% CI: 0.94 to 0.98), and r = 0.84 (95% CI: 0.73 to 0.91), respectively. Agreement was also high, with biases of 0.4 ± 9.4 mg/dL (95% CI: -19.0 to 18.1) for blood urea nitrogen, -0.6 ± 1.6 mEq/L (95% CI: -3.8 to 2.5) for sodium, and 0.17 ± 0.4 mEq/L (95% CI: 0.6 to 0.9) for potassium. Among patients at risk of dialysis disequilibrium syndrome, 92% achieved the target blood urea nitrogen reduction ( < 40%) without severe neurological events. The sodium correction remained below 6 mEq/L, and potassium normalized in all the cases.

conclusionsA prescription-based model can reliably predict post-Hemodialysis urea and electrolyte values in urgent dialysis settings. The Adequator App HD-Predictor ( https://adequatorapp.com/hd-predictor ) offers a non-invasive, accessible tool to guide individualized prescriptions, enhancing safety and precision in high-risk clinical scenarios.

Indexed as

ElectrolytesRenal DialysisUreaAcute Kidney InjuryAgedBlood Urea NitrogenFemaleHumansHyperkalemiaHyponatremiaMaleMiddle AgedWater-Electrolyte ImbalanceElectrolytesUreaDialysis disequilibrium syndromeDialysis prescription modelingElectrolyte predictionHemodialysisUrgent-start dialysis

Identifiers

PMID41146036
PMCPMC12560513

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.