Computed Tomography–Defined Abdominal Adiposity Is Associated With Acute Kidney Injury in Critically Ill Trauma Patients*

Shashaty, Michael G. S.; Kalkan, Esra; Bellamy, Scarlett L.; Reilly, John P.; Holena, Daniel N.; Cummins, Kathleen; Lanken, Paul N.; Feldman, Harold I.; Reilly, Muredach P; Udupa, Jayaram K.; Christie, Jason D.

doi:10.1097/ccm.0000000000000306

Cited by 37 publications

(33 citation statements)

References 45 publications

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“…In a matched-controlled study, van den Borst et al 19 showed that excessive abdominal visceral fat was associated with higher mortality rate in older chronic obstructive pulmonary disease (COPD) patients. In line with these findings, recent studies 20,62 showed that abdominal obesity, estimated by sagittal abdominal diameter or computerized tomography scan, was associated with increased mortality. Although limited in number, these studies highlight the relevance of defining the type of obesity in predicting the outcomes of critical illness and unanimously link abdominal obesity with higher odds of death.…”

Section: Abdominal Obesity and Icu Mortalitymentioning

confidence: 59%

“…19,20,61,62 First, a prospective controlled study by Paolini et al 61 reported that among obese patients with comparable BMI, those with higher abdominal obesity died at a higher rate (44%) than those with peripheral obesity (23%). The authors concluded that measurement of abdominal obesity was a better predictor of ICU outcomes than BMI.…”

Section: Abdominal Obesity and Icu Mortalitymentioning

confidence: 98%

“…Another more sophisticated procedure is based on computerized tomography scan to estimate visceral fat area. 19,20 Numerous investigations have shown that for any given amount of total body fat, the subgroup of individuals with visceral obesity is at substantially higher risk of obesity-associated diabetes and CVD than those with peripheral obesity. 18,21 Because of its anatomic position and proximity to vital organs in the abdomen, visceral fat could interact with organs and modulate their function.…”

Section: Physiologic and Pathologic Consequences Of Obesity Inflammatmentioning

confidence: 99%

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The obesity factor in critical illness

Gharib

Kaul

LoCurto

et al. 2015

Journal of Trauma and Acute Care Surgery

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T he prevalence of obesity has increased starkly during the last decades, and this trend includes every age, sex, race and socioeconomic group. 1,2 According to the most recent epidemiologic data, 1,2 approximately two thirds of the US population are either overweight or obese, of whom approximately 30% are obese and more than 5% are morbidly obese. Although some reports suggest that the trend of obesity may have begun to stabilize within some segments of the US population, 3 other studies project that obesity prevalence will continue to worsen, with as many as 50% of Americans potentially being obese by the year 2030. 4 Obesity has been linked to increased mortality resulting from acute and chronic comorbidities including diabetes, stroke, and cardiovascular diseases (CVDs). 5 The epidemic of obesity also has reached the intensive care unit (ICU), such that 33% of ICU patients are obese and 7% are morbidly obese. 6 Consequently, obesity complicates all aspects of health care in the ICU by increasing the complexity of management, nutritional support, and changes in the pattern of comorbidities. 7 Furthermore, obesity induces anatomic and physiologic changes that may interfere with the body response to injury and complicate any hospitalization. 7 In this article, we will review recent studies that have examined the impact of obesity in critical illness. PATIENTS METHODSA MEDLINE/PubMed search from 1990 TO 2014 was conducted using the National Library of Medicine MeSH search terms obesity, abdominal obesity, body mass index, critical illness, mortality, and organ failure. All publication types were searched, and relevant English-language studies, case reports, meta-analysis review articles, and short communications were then extracted and manually cross-referenced. Only clinical studies using explicit definitions of obesity, morbid obesity, or body mass index (BMI) were included. Throughout the review, obesity is defined as a BMI of 30 kg/m 2 or greater, morbid obesity as a BMI of 40 kg/m 2 or greater, and overweight as a BMI of 25 kg/m 2 to 29.9 kg/m 2 , unless specified otherwise. Underweight and healthy BMIs were defined as BMI less than 18.5 kg/m 2 and 18.5 kg/m 2 to 24.9 kg/m 2 , respectively. Studies pertaining to critical illness included an ICU length of stay (LOS) of 24 hours or longer. Although the main focus of this review is the impact of obesity on critical illness, an appreciation of fundamental physiologic derangements associated with obesity is essential to evaluate the response of obese patients to critical illness. We have thus structured our review as follows. We begin by discussing the development of pathologic conditions-associated with obesity. We then review available data on the impact of obesity on critical illness outcomes. When used for discussion, the term obesity refers to BMI of 30 kg/m 2 or greater. In many instances, data are available for one demographic group but not the other, and care is taken to make this distinction clear throughout the review.

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Section: Abdominal Obesity and Icu Mortalitymentioning

confidence: 59%

Section: Abdominal Obesity and Icu Mortalitymentioning

confidence: 98%

Section: Physiologic and Pathologic Consequences Of Obesity Inflammatmentioning

confidence: 99%

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The obesity factor in critical illness

Gharib

Kaul

LoCurto

et al. 2015

Journal of Trauma and Acute Care Surgery

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“…The systemic proinflammatory response state in obese trauma patients is due to abundant adipose tissue which releases elevated amounts of inflammatory mediators, including tumor necrosis factor-a (TNF-a), interleukin-6 (IL-6), and leptin [12,17,18]. Shashaty et al [19] also implicated abdominal adiposity as an independent risk factor for the development of acute kidney injury (AKI) in critically ill trauma patients. Several mechanisms have been theorized to link obesity to AKI, such as, a potentiation of inflammatory mediators (interleukin-6, tumor necrosis factor-a, monocyte chemoattractant protein-1) by adipose tissue and subclinical chronic kidney disease due to an abnormal glomerular architecture seen in morbidly obese patients [19].…”

Section: Discussionmentioning

confidence: 97%

“…Shashaty et al [19] also implicated abdominal adiposity as an independent risk factor for the development of acute kidney injury (AKI) in critically ill trauma patients. Several mechanisms have been theorized to link obesity to AKI, such as, a potentiation of inflammatory mediators (interleukin-6, tumor necrosis factor-a, monocyte chemoattractant protein-1) by adipose tissue and subclinical chronic kidney disease due to an abnormal glomerular architecture seen in morbidly obese patients [19].…”

Section: Discussionmentioning

confidence: 98%

Utilizing quantitative measures of visceral adiposity in evaluating trauma patient outcomes

Docimo

Lamparello

Cohen

et al. 2015

International Journal of Surgery

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ABCNet: A new efficient 3D dense‐structure network for segmentation and analysis of body tissue composition on body‐torso‐wide CT images

et al. 2020

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Purpose: Quantification of body tissue composition is important for research and clinical purposes, given the association between the presence and severity of several disease conditions, such as the incidence of cardiovascular and metabolic disorders, survival after chemotherapy, etc., with the quantity and quality of body tissue composition. In this work, we aim to automatically segment four key body tissues of interest, namely subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, and skeletal structures from body-torso-wide low-dose computed tomography (CT) images. Method: Based on the idea of residual Encoder-Decoder architecture, a novel neural network design named ABCNet is proposed. The proposed system makes full use of multiscale features from four resolution levels to improve the segmentation accuracy. This network is built on a uniform convolutional unit and its derived units, which makes the ABCNet easy to implement. Several parameter compression methods, including Bottleneck, linear increasing feature maps in Dense Blocks, and memory-efficient techniques, are employed to lighten the network while making it deeper. The strategy of dynamic soft Dice loss is introduced to optimize the network in coarse-to-fine tuning. The proposed segmentation algorithm is accurate, robust, and very efficient in terms of both time and memory. Results: A dataset composed of 38 low-dose unenhanced CT images, with 25 male and 13 female subjects in the age range 31-83 yr and ranging from normal to overweight to obese, is utilized to evaluate ABCNet. We compare four state-of-the-art methods including DeepMedic, 3D U-Net, V-Net, Dense V-Net, against ABCNet on this dataset. We employ a shuffle-split fivefold cross-validation strategy: In each experimental group, 18, 5, and 15 CT images are randomly selected out of 38 CT image sets for training, validation, and testing, respectively. The commonly used evaluation metricsprecision, recall, and F1-score (or Dice)are employed to measure the segmentation quality. The results show that ABCNet achieves superior performance in accuracy of segmenting body tissues from body-torso-wide low-dose CT images compared to other state-of-the-art methods, reaching 92-98% in common accuracy metrics such as F1-score. ABCNet is also time-efficient and memory-efficient. It costs about 18 h to train and an average of 12 sec to segment four tissue components from a body-torso-wide CT image, on an ordinary desktop with a single ordinary GPU. Conclusions: Motivated by applications in body tissue composition quantification on large population groups, our goal in this paper was to create an efficient and accurate body tissue segmentation method for use on body-torso-wide CT images. The proposed ABCNet achieves peak performance in both accuracy and efficiency that seems hard to improve any more. The experiments performed demonstrate that ABCNet can be run on an ordinary desktop with a single ordinary GPU, with practical times for both training and testing, and achieves superior accuracy compa...

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Computed Tomography–Defined Abdominal Adiposity Is Associated With Acute Kidney Injury in Critically Ill Trauma Patients*

Cited by 37 publications

References 45 publications

The obesity factor in critical illness

The obesity factor in critical illness

Utilizing quantitative measures of visceral adiposity in evaluating trauma patient outcomes

ABCNet: A new efficient 3D dense‐structure network for segmentation and analysis of body tissue composition on body‐torso‐wide CT images

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