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Just IA, Schoenrath F, Roehrich L, Heil E, Stein J, Auer TA, Fehrenbach U, Potapov E, Solowjowa N, Balzer F, Geisel D, Braun J, Boening G. Artificial intelligence-based analysis of body composition predicts outcome in patients receiving long-term mechanical circulatory support. J Cachexia Sarcopenia Muscle 2024; 15:270-280. [PMID: 38146680 PMCID: PMC10834347 DOI: 10.1002/jcsm.13402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 10/13/2023] [Accepted: 11/02/2023] [Indexed: 12/27/2023] Open
Abstract
BACKGROUND Obesity is a known cardiovascular risk factor and associated with higher postoperative complication rates in patients undergoing cardiac surgery. In heart failure (HF), conflicting evidence in terms of survival has been reported, whereas sarcopenia is associated with poor prognosis. An increasing number of HF patients require left ventricular assist device (LVAD) implantations. The postoperative mortality has improved in recent years but is still relatively high. The impact of body composition on outcome in this population remains unclear. The aim of this investigation was to examine the preoperative computed tomography (CT) body composition as a predictor of the postoperative outcome in advanced HF patients, who receive LVAD implantations. METHODS Preoperative CT scans of 137 patients who received LVADs between 2015 and 2020 were retrospectively analysed using an artificial intelligence (AI)-powered automated software tool based on a convolutional neural network, U-net, developed for image segmentation (Visage Version 7.1, Visage Imaging GmbH, Berlin, Germany). Assessment of body composition included visceral and subcutaneous adipose tissue areas (VAT and SAT), psoas and total abdominal muscle areas and sarcopenia (defined by lumbar skeletal muscle indexes). The body composition parameters were correlated with postoperative major complication rates, survival and postoperative 6-min walk distance (6MWD) and quality of life (QoL). RESULTS The mean age of patients was 58.21 ± 11.9 years; 122 (89.1%) were male. Most patients had severe HF requiring inotropes (Interagency Registry for Mechanically Assisted Circulatory Support [INTERMACS] profile I-III, 71.9%) secondary to coronary artery diseases or dilated cardiomyopathy (96.4%). Forty-four (32.1%) patients were obese (body mass index ≥ 30 kg/m2 ), 96 (70.1%) were sarcopene and 19 (13.9%) were sarcopene obese. Adipose tissue was associated with a significantly higher risk of postoperative infections (VAT 172.23 cm2 [54.96, 288.32 cm2 ] vs. 124.04 cm2 [56.57, 186.25 cm2 ], P = 0.022) and in-hospital mortality (VAT 168.11 cm2 [134.19, 285.27 cm2 ] vs. 135.42 cm2 [49.44, 227.91 cm2 ], P = 0.033; SAT 227.28 cm2 [139.38, 304.35 cm2 ] vs. 173.81 cm2 [97.65, 254.16 cm2 ], P = 0.009). Obese patients showed no improvement of 6MWD and QoL within 6 months postoperatively (obese: +0.94 ± 161.44 months, P = 0.982; non-obese: +166.90 ± 139.00 months, P < 0.000; obese: +0.088 ± 0.421, P = 0.376; non-obese: +0.199 ± 0.324, P = 0.002, respectively). Sarcopenia did not influence the postoperative outcome and survival within 1 year after LVAD implantation. CONCLUSIONS Preoperative AI-based CT body composition identifies patients with poor outcome after LVAD implantation. Greater adipose tissue areas are associated with an increased risk for postoperative infections, in-hospital mortality and impaired 6MWD and QoL within 6 months postoperatively.
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Affiliation(s)
- Isabell Anna Just
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Felix Schoenrath
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Department of Cardiothoracic Surgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Luise Roehrich
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- German Heart Foundation, Frankfurt am Main, Germany
| | - Emanuel Heil
- Department of Cardiology, German Heart Center Berlin, Berlin, Germany
| | - Julia Stein
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Timo Alexander Auer
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Evgenij Potapov
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
| | - Natalia Solowjowa
- Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Dominik Geisel
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Juergen Braun
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Georg Boening
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
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Kolck J, Rako ZA, Beetz NL, Auer TA, Segger LK, Pille C, Penzkofer T, Fehrenbach U, Geisel D. Intermittent body composition analysis as monitoring tool for muscle wasting in critically ill COVID-19 patients. Ann Intensive Care 2023; 13:61. [PMID: 37421448 DOI: 10.1186/s13613-023-01162-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/03/2023] [Indexed: 07/10/2023] Open
Abstract
OBJECTIVES SARS-CoV-2 virus infection can lead to acute respiratory distress syndrome (ARDS), which can be complicated by severe muscle wasting. Until now, data on muscle loss of critically ill COVID-19 patients are limited, while computed tomography (CT) scans for clinical follow-up are available. We sought to investigate the parameters of muscle wasting in these patients by being the first to test the clinical application of body composition analysis (BCA) as an intermittent monitoring tool. MATERIALS BCA was conducted on 54 patients, with a minimum of three measurements taken during hospitalization, totaling 239 assessments. Changes in psoas- (PMA) and total abdominal muscle area (TAMA) were assessed by linear mixed model analysis. PMA was calculated as relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan. Cox regression was applied to analyze associations with survival. Receiver operating characteristic (ROC) analysis and Youden index were used to define a decay cut-off. RESULTS Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p < 0.001) and maximum muscle decay of 5.48% (vs. 3.66%, p = 0.039) per day in non-survivors. The first available decay rate did not significantly differ between survival groups but showed significant associations with survival in Cox regression (p = 0.011). In ROC analysis, PMA loss averaged over the stay had the greatest discriminatory power (AUC = 0.777) for survival. A long-term PMA decline per day of 1.84% was defined as a threshold; muscle loss beyond this cut-off proved to be a significant BCA-derived predictor of mortality. CONCLUSION Muscle wasting in critically ill COVID-19 patients is severe and correlates with survival. Intermittent BCA derived from clinically indicated CT scans proved to be a valuable monitoring tool, which allows identification of individuals at risk for adverse outcomes and has great potential to support critical care decision-making.
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Affiliation(s)
- Johannes Kolck
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353, Berlin, Germany.
| | - Zvonimir A Rako
- Department of Pneumology and Intensive Care, Universities of Giessen and Giessen Lung Center (UGMLC), Member of the German Center for Lung Research (DZL), Berlin, Germany
| | - Nick L Beetz
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Timo A Auer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Laura K Segger
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Pille
- Department of Anesthesiology and Intensive Care Medicine | CCM | CVK, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Uli Fehrenbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Dominik Geisel
- Department of Radiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions. Diagnostics (Basel) 2023; 13:diagnostics13050968. [PMID: 36900112 PMCID: PMC10000509 DOI: 10.3390/diagnostics13050968] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/11/2023] [Accepted: 02/18/2023] [Indexed: 03/08/2023] Open
Abstract
CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.
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Prado CM, Ford KL, Gonzalez MC, Murnane LC, Gillis C, Wischmeyer PE, Morrison CA, Lobo DN. Nascent to novel methods to evaluate malnutrition and frailty in the surgical patient. JPEN J Parenter Enteral Nutr 2023; 47 Suppl 1:S54-S68. [PMID: 36468288 PMCID: PMC9905223 DOI: 10.1002/jpen.2420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2022] [Revised: 05/20/2022] [Accepted: 06/07/2022] [Indexed: 12/11/2022]
Abstract
Preoperative nutrition status is an important determinant of surgical outcomes, yet malnutrition assessment is not integrated into all surgical pathways. Given its importance and the high prevalence of malnutrition in patients undergoing surgical procedures, preoperative nutrition screening, assessment, and intervention are needed to improve postoperative outcomes. This narrative review discusses novel methods to assess malnutrition and frailty in the surgical patient. The Global Leadership Initiative for Malnutrition (GLIM) criteria are increasingly used in surgical settings although further spread and implementation are strongly encouraged to help standardize the diagnosis of malnutrition. The use of body composition (ie, reduced muscle mass) as a phenotypic criterion in GLIM may lead to a greater number of patients identified as having malnutrition, which may otherwise be undetected if screened by other diagnostic tools. Skeletal muscle loss is a defining criterion of malnutrition and frailty. Novel direct and indirect approaches to assess muscle mass in clinical settings may facilitate the identification of patients with or at risk for malnutrition. Selected imaging techniques have the additional advantage of identifying myosteatosis (an independent predictor of morbidity and mortality for surgical patients). Feasible pathways for screening and assessing frailty exist and may determine the cost/benefit of surgery, long-term independence and productivity, and the value of undertaking targeted interventions. Finally, the evaluation of nutrition risk and status is essential to predict and mitigate surgical outcomes. Nascent to novel approaches are the future of objectively identifying patients at perioperative nutrition risk and guiding therapy toward optimal perioperative standards of care.
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Affiliation(s)
- Carla M. Prado
- Department of Agricultural, Food & Nutritional ScienceUniversity of AlbertaEdmontonAlbertaCanada
| | - Katherine L. Ford
- Department of Agricultural, Food & Nutritional ScienceUniversity of AlbertaEdmontonAlbertaCanada
| | - M. Cristina Gonzalez
- Postgraduate Program in Health and BehaviorCatholic University of PelotasPelotasBrazil
| | - Lisa C. Murnane
- School of Allied Health, Human Services and SportLa Trobe UniversityMelbourneVictoriaAustralia
- Department of Nutrition and DieteticsAlfred HealthMelbourneVictoriaAustralia
| | - Chelsia Gillis
- School of Human NutritionMcGill UniversityMontrealQuebecCanada
| | - Paul E. Wischmeyer
- Departments of Anesthesiology and SurgeryDuke University School of MedicineDurhamNorth CarolinaUSA
| | - Chet A. Morrison
- Department of SurgeryCentral Michigan UniversitySaginawMichiganUSA
| | - Dileep N. Lobo
- Gastrointestinal SurgeryNottingham Digestive Diseases Centre and National Institute for Health Research (NIHR) Nottingham Biomedical Research CentreNottingham University Hospitals NHS Trust and University of Nottingham, Queen's Medical CentreNottinghamUK
- MRC Versus Arthritis Centre for Musculoskeletal Ageing Research, School of Life SciencesUniversity of Nottingham, Queen's Medical CentreNottinghamUK
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Influence of Baseline CT Body Composition Parameters on Survival in Patients with Pancreatic Adenocarcinoma. J Clin Med 2022; 11:jcm11092356. [PMID: 35566483 PMCID: PMC9105849 DOI: 10.3390/jcm11092356] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 04/20/2022] [Accepted: 04/21/2022] [Indexed: 01/06/2023] Open
Abstract
Pancreatic cancer is the seventh leading cause of cancer death in both sexes. The aim of this study is to analyze baseline CT body composition using artificial intelligence to identify possible imaging predictors of survival. We retrospectively included 103 patients. First, the presence of surgical treatment and cut-off values for sarcopenia and obesity served as independent variates. Second, the presence of surgery, subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle index (SMI) served as independent variates. Cox regression analysis was performed for 1-year, 2-year, and 3-year survival. Possible differences between patients undergoing surgical versus nonsurgical treatment were analyzed. Presence of surgery significantly predicted 1-year, 2-year, and 3-year survival (p = 0.01, <0.001, and <0.001, respectively). Across the follow-up periods of 1-year, 2-year, and 3-year survival, the presence of sarcopenia became an equally important predictor of survival (p = 0.25, 0.07, and <0.001, respectively). Additionally, increased VAT predicted 2-year and 3-year survival (p = 0.02 and 0.04, respectively). The impact of sarcopenia on 3-year survival was higher in the surgical treatment group (p = 0.02 and odds ratio = 2.57) compared with the nonsurgical treatment group (p = 0.04 and odds ratio = 1.92). Fittingly, a lower SMI significantly affected 3-year survival only in patients who underwent surgery (p = 0.02). Especially if surgery is performed, AI-derived sarcopenia and reduced muscle mass are unfavorable imaging predictors.
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Beetz NL, Geisel D, Shnayien S, Auer TA, Globke B, Öllinger R, Trippel TD, Schachtner T, Fehrenbach U. Effects of Artificial Intelligence-Derived Body Composition on Kidney Graft and Patient Survival in the Eurotransplant Senior Program. Biomedicines 2022; 10:biomedicines10030554. [PMID: 35327356 PMCID: PMC8945723 DOI: 10.3390/biomedicines10030554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/21/2022] [Accepted: 02/24/2022] [Indexed: 11/16/2022] Open
Abstract
The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival.
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Affiliation(s)
- Nick Lasse Beetz
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany; (D.G.); (S.S.); (T.A.A.); (U.F.)
- DZHK (German Center for Cardiovascular Research), 10785 Berlin, Germany;
- Correspondence: ; Tel.: +49-30-45-065-7278
| | - Dominik Geisel
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany; (D.G.); (S.S.); (T.A.A.); (U.F.)
| | - Seyd Shnayien
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany; (D.G.); (S.S.); (T.A.A.); (U.F.)
| | - Timo Alexander Auer
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany; (D.G.); (S.S.); (T.A.A.); (U.F.)
- Berlin Institute of Health, 10178 Berlin, Germany;
| | - Brigitta Globke
- Berlin Institute of Health, 10178 Berlin, Germany;
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany;
| | - Robert Öllinger
- Department of Surgery, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany;
| | - Tobias Daniel Trippel
- DZHK (German Center for Cardiovascular Research), 10785 Berlin, Germany;
- Department of Internal Medicine—Cardiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany
| | - Thomas Schachtner
- Division of Nephrology, University Hospital Zurich, 8091 Zürich, Switzerland;
| | - Uli Fehrenbach
- Department of Radiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany; (D.G.); (S.S.); (T.A.A.); (U.F.)
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