Evidence map›Paper›PMID 36896264›Full record

ArticleFrontiers in surgery2023

Failure to rescue following anatomical lung resection. Analysis of a prospective nationwide database.

María Teresa Gómez-Hernández, Cristina Rivas, Nuria Novoa, Marcelo F Jiménez, Spanish Group of Video-assisted Thoracic Surgery (GEVATS)

Abstract read
In one paragraph

Article in Frontiers in surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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.

María Teresa Gómez-HernándezService of Thoracic Surgery, Salamanca University Hospital, Salamanca, Spain.
Cristina RivasService of Thoracic Surgery, Salamanca University Hospital, Salamanca, Spain.
Nuria NovoaService of Thoracic Surgery, Salamanca University Hospital, Salamanca, Spain.
Marcelo F JiménezService of Thoracic Surgery, Salamanca University Hospital, Salamanca, Spain.
Spanish Group of Video-assisted Thoracic Surgery (GEVATS)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Rescue failure has been described as an important factor that conditions postoperative mortality after surgical interventions. The objective of this study is to determine the incidence and main determinants of failure to rescue after anatomical lung resections. Methods: Prospective multicenter study that included all patients undergoing anatomical pulmonary resection between December 2016 and March 2018 and registered in the Spanish nationwide database GEVATS. Postoperative complications were classified as minor (grades I and II) and major (grades IIIa to V) according to the Clavien-Dindo standardized classification. Patients that died after a major complication were considered rescue failure. A stepwise logistic regression model was created to identify predictors of failure to rescue. Results: 3,533 patients were analyzed. In total, 361 cases (10.2%) had major complications, of which 59 (16.3%) could not be rescued. The variables associated with rescue failure were: ppoDLCO% (OR, 0.98; 95% CI, 0.96-1; Conclusion: A significant percentage of patients who presented major complications after anatomical lung resection did not survive to discharge. Pneumonectomy and annual surgical volume are the risk factors most closely related to rescue failure. Complex thoracic surgical pathology should be concentrated in high-volume centers to obtain the best results in potentially high-risk patients.

Indexed as

failure to rescue (FTR)hospital volumelung resectionmortalitypostoperative complicationrisk factor

Identifiers

PMID36896264
PMCPMC9989191

What OpenQuestion holds

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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.