Evidence map›Paper›PMID 30779441›Full record

ArticleHepatology (Baltimore, Md.)2019

Predicting Postoperative Liver Dysfunction Based on Blood-Derived MicroRNA Signatures.

Patrick Starlinger, Hubert Hackl, David Pereyra, Susanna Skalicky, Elisabeth Geiger, Michaela Finsterbusch, Dietmar Tamandl, Christine Brostjan, Thomas Grünberger, Matthias Hackl and 1 more

Open access · hybridAbstract read
In one paragraph

Article in Hepatology (Baltimore, Md.), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 14% of its field
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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.

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  4. Subclinical Hepatic Injury and Dysfunction Following Surgery: Evidence From a Young and Aged Laparotomy Murine Model.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 1 country.

Patrick StarlingerDepartment of Surgery, Medical University of Vienna, General Hospital, Vienna, Austria.ORCID 0000-0002-4666-5337
Hubert HacklDivision of Bioinformatics, Biocenter, Medical University of Innsbruck, Innsbruck, Austria.
David PereyraDepartment of Surgery, Medical University of Vienna, General Hospital, Vienna, Austria.
Susanna SkalickyTAmiRNA GmbH, Vienna, Austria.
Elisabeth GeigerTAmiRNA GmbH, Vienna, Austria.
Michaela FinsterbuschDepartment of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
Dietmar TamandlDepartment of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
Christine BrostjanDepartment of Surgery, Medical University of Vienna, General Hospital, Vienna, Austria.
Thomas GrünbergerDepartment of Surgery I, Rudolfstiftung Hospital, Vienna, Austria.
Matthias HacklTAmiRNA GmbH, Vienna, Austria.
Alice AssingerDepartment of Physiology and Pharmacology, Medical University of Vienna, Vienna, Austria.
Medical University of Vienna · ATVienna General Hospital · ATInnsbruck Medical University · ATRudolfinerhaus Hospital · AT

Funding

Austrian Research Promoting Agency (FFG) 855576
6 · The paper itself

Abstract

There is an urgent need for an easily assessable preoperative test to predict postoperative liver function recovery and thereby determine the optimal time point of liver resection, specifically as current markers are often expensive, time consuming, and invasive. Emerging evidence suggests that microRNA (miRNA) signatures represent potent diagnostic, prognostic, and treatment-response biomarkers for several diseases. Using next-generation sequencing as an unbiased systematic approach, 554 miRNAs were detected in preoperative plasma of 21 patients suffering from postoperative liver dysfunction (LD) after liver resection and 27 matched controls. Subsequently, we identified a miRNA signature-consisting of miRNAs 151a-5p, 192-5p, and 122-5p-that highly correlated with patients developing postoperative LD after liver resection. The predictive potential for postoperative LD was subsequently confirmed using real-time PCR in an independent validation cohort of 98 patients. Ultimately, a regression model of the two miRNA ratios 151a-5p to 192-5p and 122-5p to 151a-5p was found to reliably predict postoperative LD, severe morbidity, prolonged intensive care unit and hospital stays, and even mortality before an operation with a remarkable accuracy, thereby outperforming established markers of postoperative LD. Ultimately, we documented that miRNA ratios closely followed liver function recovery after partial hepatectomy. Conclusion: Our data demonstrate the clinical utility of an miRNA-based biomarker to support the selection of patients undergoing partial hepatectomy. The dynamical changes during liver function recovery indicate a possible role in individualized patient treatment. Thereby, our data might help to tailor surgical strategies to the specific risk profile of patients.

Indexed as

TranscriptomeAdultAgedAged, 80 and overBiomarkers, TumorCase-Control StudiesCohort StudiesFemaleHepatectomyHumansLiver DiseasesLiver NeoplasmsMaleMicroRNAsMiddle AgedPostoperative ComplicationsBiomarkers, TumorMicroRNAs

Identifiers

PMID30779441
PMCPMC6593830
OpenAlexW2916910687

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.