Evidence map›Paper›PMID 41726465›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Data-Driven Evidence-Based Patient-Centered Optimal Initiation Time for Dialysis Treatment.

Eva K Lee, Di Liu, Jeffrey Hoffman

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

3 authors.

Eva K LeeCenter for Operations Research in Medicine and Healthcare, Data and Analytics Innovation Institute, Atlanta, Georgia, USA.
Di LiuGeorgia Institute of Technology, Atlanta, Georgia, USA.
Jeffrey HoffmanCambridge Health Alliance, Boston, Massachusetts, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this study, we propose a novel decision-making framework based on natural-language processing, machine learning and stochastic modeling for the purpose of providing a data-driven perspective to optimize the initiation time for dialysis treatment. When to start dialysis treatment is an important decision for end-stage chronic kidney disease (CKD) care. In the absence of a national guideline, determining the best time to start dialysis remains a challenge. Our decision support framework for optimizing the initiation time includes (a) a comprehensive, efficient "pipeline" for extracting, de-identifying, and standardizing EHR data; (b) an informatics toolkit that couples natural language processing and event mapping, clustering and machine learning to uncover the disease prognosis and treatment effects, and deduce the symptoms and utility rewards for each disease-action stage; (c) a first-of-its-kind, personalized, dialysis-timing stochastic model to determine the optimal initiation time; and (d) a clinical practice guideline (CPG) for systematic testing and implementation in the clinical setting. We evaluate the results using utility rewards for each decision process and published financial costs for each CKD disease and treatment stage. Compared to current clinical policy, the optimal initiation-time policies offer a potential 20.0% to 54.7% mortality reduction, an increase of 6.4% to 14.7% in overall utility reward and a reduction of 9.6% to 16.7% in overall cost. Working with nephrologists and a CKD/ESRD care team, a CPG was developed and tested in the clinic. Initial usage shows promising results. Follow-on clinical trials will gauge the overall effectiveness and impact on patient care of our approach. The new CPG has the potential to become a national standard.

Indexed as

Decision Support TechniquesKidney Failure, ChronicRenal DialysisEvidence-Based MedicineHumansMachine LearningNatural Language ProcessingPatient-Centered CareStochastic ProcessesTime FactorsChronic kidney diseaseevidence-basedglomerular filtration ratehemodialysismachine learningMarkov Decision Processoptimal access policyoptimal initiation timeoptimizationperitoneal dialysispersonalized dialysis timingsimulation

Identifiers

PMID41726465
PMCPMC12919589

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Registered trials

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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.