Evidence map›Paper›PMID 41836202›Full record

ArticleJournal of cardiovascular and thoracic research2025

Myocardial injury after lung cancer surgery as a predictor of postoperative long-term mortality: A prospective cohort study.

Konstantin Protasov, Olga Barakhtenko, Elena Batunova, Roman Zubkov, Pavel Ulybin

Abstract read
In one paragraph

Article in Journal of cardiovascular and thoracic research, 2025. 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

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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

5 authors.

Konstantin ProtasovCardiology and Functional Diagnostics Department, Irkutsk State Medical Academy of Postgraduate Education, Irkutsk, Russia.ORCID https://orcid.org/0000-0002-6516-3180
Olga BarakhtenkoThoracic Surgery Department, Irkutsk Regional Oncological Hospital, Irkutsk, Russia.ORCID https://orcid.org/0000-0002-8993-2503
Elena BatunovaScientific Research Laboratory, Irkutsk State Medical Academy of Postgraduate Education, Irkutsk, Russia.ORCID https://orcid.org/0000-0001-8665-4969
Roman ZubkovOncology Department, Irkutsk State Medical Academy of Postgraduate Education, Irkutsk, Russia.ORCID https://orcid.org/0000-0001-7083-2695
Pavel UlybinThoracic Surgery Department, Irkutsk Regional Oncological Hospital, Irkutsk, Russia.ORCID https://orcid.org/0000-0003-3064-8441

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Myocardial injury after non-cardiac surgery (MINS), characterized by cardiac troponin (cTn) elevation, is a marker of poor postoperative prognosis. The predictive value of MINS in thoracic oncosurgery remains unclear. The aim of the study was to determine the prognostic value of acute myocardial injury after surgical lung resection for 1-year all-cause mortality in patients with non-small-cell lung cancer (NSCLC). Methods: In this prospective cohort study, 101 consecutive men aged 63.0 (58;67) years who underwent surgical lung resection for NSCLC, were enrolled. Serum cTnI concentration was measured preoperatively and in 24 and 48 hours postoperatively. MINS was defined by at least one postoperative cTnI value that exceeds the 99th percentile upper reference limit, as a result of a presumed ischemic mechanism. The primary endpoint was 1-year all-cause mortality. Results: MINS was diagnosed in 37 patients (36.6%). During the follow-up, three patients were lost, and 28 (27.7%) died. Multivariate Cox regression analysis identified MINS as an independent predictor of all-cause mortality (adjusted hazard ratio [HR] 2.98, 95% confidence interval [CI] 1.29-6.89, Conclusion: Myocardial injury within the first 72 hours after surgical lung resection was found as an independent predictor of 1-year all-cause mortality in patients with NSCLC.

Indexed as

MortalityNon-small-cell lung carcinomaRisk assessmentSurgical oncologyTroponin

Identifiers

PMID41836202
PMCPMC12980097

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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.