Proposal for a computed tomography score to predict major complications requiring hospitalization after percutaneous lung biopsy: a single-center retrospective study.

Computed tomography (CT) interventional radiology percutaneous lung biopsy (PLB) predictive score

Journal

Quantitative imaging in medicine and surgery
ISSN: 2223-4292
Titre abrégé: Quant Imaging Med Surg
Pays: China
ID NLM: 101577942

Informations de publication

Date de publication:
01 Sep 2024
Historique:
received: 28 04 2023
accepted: 12 12 2023
medline: 17 9 2024
pubmed: 17 9 2024
entrez: 16 9 2024
Statut: ppublish

Résumé

Image-guided percutaneous lung biopsy (PLB) may lead to major complications requiring hospitalization. This study aims to evaluate the rate of major PLB complications and determine a predictive computed tomography (CT) score to define patients requiring hospitalization due to these complications. This single-center retrospective study included all PLBs performed from July 2019 to December 2020 in Nimes University Hospital, France. Patients who were undergoing thermo-ablation during the same procedure or for whom PLB procedure data were not available were excluded. All major complications leading to hospitalization were recorded. A Percutaneous Image-guided Lung biopsy In/out Patient score (PILIP) based on variables significantly associated with major complications was calculated by multivariate analysis. A total of 240 consecutive patients (160 men, 80 women; mean age: 67.3±10.5 years) were included. The major complication rate was 10.4%. Length of lung parenchyma traversed <20 PLB showed a major complication rate of 10.4%. The PILIP is an easy-to-use CT score for differentiating patients at a low or high risk of complications requiring hospitalization.

Sections du résumé

Background UNASSIGNED
Image-guided percutaneous lung biopsy (PLB) may lead to major complications requiring hospitalization. This study aims to evaluate the rate of major PLB complications and determine a predictive computed tomography (CT) score to define patients requiring hospitalization due to these complications.
Methods UNASSIGNED
This single-center retrospective study included all PLBs performed from July 2019 to December 2020 in Nimes University Hospital, France. Patients who were undergoing thermo-ablation during the same procedure or for whom PLB procedure data were not available were excluded. All major complications leading to hospitalization were recorded. A Percutaneous Image-guided Lung biopsy In/out Patient score (PILIP) based on variables significantly associated with major complications was calculated by multivariate analysis.
Results UNASSIGNED
A total of 240 consecutive patients (160 men, 80 women; mean age: 67.3±10.5 years) were included. The major complication rate was 10.4%. Length of lung parenchyma traversed <20
Conclusions UNASSIGNED
PLB showed a major complication rate of 10.4%. The PILIP is an easy-to-use CT score for differentiating patients at a low or high risk of complications requiring hospitalization.

Identifiants

pubmed: 39281132
doi: 10.21037/qims-23-500
pii: qims-14-09-6830
pmc: PMC11400643
doi:

Types de publication

Journal Article

Langues

eng

Pagination

6830-6842

Informations de copyright

2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Déclaration de conflit d'intérêts

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-500/coif). The authors have no conflicts of interest to declare.

Auteurs

Satcha Ortmans (S)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Fabien de Oliveira (F)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Chris Serrand (C)

Department of Biostatistics, Clinical Epidemiology, Public Health, and Innovation in Methodology (BESPIM), Hospital University Center, Nîmes, France.

Tarek Kammoun (T)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Joel Greffier (J)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Djamel Dabli (D)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Hélène de Forges (H)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Cécile Rieux (C)

Department of Pneumology, Hospital University Center of Nîmes, Hôpital Caremeau, Rue du Pr Debré, Nîmes Cedex, France.

Jean-Paul Beregi (JP)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Julien Frandon (J)

Department of Medical Imaging, PRIM Platform, Nîmes University Hospital, University of Montpellier, Medical Imaging Group Nîmes, IMAGINE, Nîmes, France.

Classifications MeSH