Evidence map›Paper›PMID 42506092›Full record

ReviewJournal of personalized medicine2026

Integration of Precision Medicine into ERAS Pathways: A Conceptual Framework, Current Feasibility and Challenges.

Berkan Aliev, Boyko Atanasov

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2026. 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

2 authors.

Berkan AlievDepartment of Anesthesiology, Emergency and Intensive Care Medicine, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.ORCID 0009-0009-7786-6164
Boyko AtanasovDepartment of Propedeutics of Surgical Diseases, Section General Surgery, Medical University of Plovdiv, 4002 Plovdiv, Bulgaria.ORCID 0000-0003-3000-681X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Enhanced Recovery After Surgery (ERAS) pathways have improved perioperative outcomes by standardizing evidence-based interventions across the surgical continuum. However, substantial variability in postoperative recovery persists, even within well-implemented ERAS programs. This heterogeneity reflects differences in clinical risk, functional reserve, biological response to surgical stress, treatment responsiveness, and contextual factors that are not fully captured by uniform protocols. Precision medicine provides a potential framework for refining ERAS by integrating patient-specific data into perioperative risk assessment, intervention selection, patient monitoring, and recovery planning. Nevertheless, most precision medicine tools remain insufficiently validated for routine ERAS implementation, and their clinical utility is limited by heterogeneous evidence, data integration challenges, costs, workflow complexity, and equity concerns. Future progress will require prospective validation, pragmatic implementation studies, interoperable data systems, and evaluation of patient-centered outcomes. This narrative review examines the emerging role of precision medicine tools in perioperative practice and proposes an idealized conceptual model of "precision ERAS" in which standardized evidence-based care is preserved as the foundation, while selected interventions are adapted according to individual risk, biological phenotype, and recovery trajectory.

Indexed as

artificial intelligencedigital healthEnhanced Recovery After Surgery (ERAS)multi-omicsprecision medicineprecision perioperative carepredictive analyticsrisk stratification

Identifiers

PMID42506092
PMCPMC13412564

What OpenQuestion holds

Textmetadata
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.