Association between specific presurgical anthropometric indexes and morbidity in patients undergoing rectal cancer resection.


Journal

Nutrition (Burbank, Los Angeles County, Calif.)
ISSN: 1873-1244
Titre abrégé: Nutrition
Pays: United States
ID NLM: 8802712

Informations de publication

Date de publication:
Historique:
received: 27 10 2019
revised: 27 01 2020
accepted: 28 01 2020
pubmed: 9 4 2020
medline: 24 6 2021
entrez: 9 4 2020
Statut: ppublish

Résumé

Baseline body composition has been associated with dismal outcomes in patients undergoing a variety of major abdominal operations. Whether specific anthropometric indexes can predict morbidity after rectal resection has been poorly investigated. The aims of this study were to assess whether there is a relationship between body mass index and the different computed tomography-assessed body composition indexes, and whether the analysis of different body compartments could be predictive of short-term outcomes in patients undergoing curative surgery for rectal cancer. Computed tomography-derived measures of skeletal muscle and adipose tissue areas of patients undergoing surgery for rectal cancer between January 2009 and December 2016 were used to calculate population-specific thresholds of sarcopenia, subcutaneous adiposity, visceral adiposity, visceral obesity, sarcopenic obesity, and myosteatosis. Association between the aforementioned body composition features were related with overall complication, infection, and anastomotic leak. During the study period, 311 patients received surgery and 173 were eligible for an accessible preoperative computed tomography imaging. After surgery, 59 (34.1%) patients experienced a complication, 29 an infection, and 10 an anastomotic failure. The overall morbidity rate was observed more frequently in patients with sarcopenia than in those without sarcopenia (39% versus 17.5%; P = 0.002) and infections (41.4% versus 21.5% respectively; P = 0.024). The presence of myosteatosis also was associated with a higher incidence of overall morbidity (33.9% versus 20.2% in patients without myoteatosis; P = 0.048). Anastomotic failure occurred in 6 of 10 patients with visceral obesity and in 24 of 112 (21.4%) patients without this condition (P = 0.007). Some anthropometric indexes are accurate predictors of specific types of morbidity. These findings may allow a more accurate preoperative risk stratification.

Identifiants

pubmed: 32268263
pii: S0899-9007(20)30062-9
doi: 10.1016/j.nut.2020.110779
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

110779

Informations de copyright

Copyright © 2020 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Alessandro Giani (A)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Surgery, San Gerardo Hospital, Monza, Italy.

Simone Famularo (S)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Surgery, San Gerardo Hospital, Monza, Italy.

Luca Riva (L)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Radiology, San Gerardo Hospital, Monza, Italy.

Nicolò Tamini (N)

Department of Surgery, San Gerardo Hospital, Monza, Italy.

Davide Ippolito (D)

Department of Radiology, San Gerardo Hospital, Monza, Italy.

Luca Nespoli (L)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Surgery, San Gerardo Hospital, Monza, Italy.

Paola Conconi (P)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.

Sandro Sironi (S)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.

Marco Braga (M)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Surgery, San Gerardo Hospital, Monza, Italy.

Luca Gianotti (L)

School of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy; Department of Surgery, San Gerardo Hospital, Monza, Italy. Electronic address: luca.gianotto@unimib.it.

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Classifications MeSH