학술논문
Artificial intelligence-based analysis of body composition predicts outcome in patients receiving long-term mechanical circulatory support.
Document Type
Academic Journal
Author
Just IA; Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.; Schoenrath F; 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.; Roehrich L; 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.; Heil E; Department of Cardiology, German Heart Center Berlin, Berlin, Germany.; Stein J; Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.; Auer TA; 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.; Fehrenbach U; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.; Potapov E; Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.; DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany.; Solowjowa N; Department of Cardiothoracic and Vascular Surgery, German Heart Center Berlin, Berlin, Germany.; Balzer F; Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.; Geisel D; Department of Radiology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany.; Braun J; 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.; Boening G; 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.
Source
Publisher: John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders Country of Publication: Germany NLM ID: 101552883 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2190-6009 (Electronic) Linking ISSN: 21905991 NLM ISO Abbreviation: J Cachexia Sarcopenia Muscle Subsets: MEDLINE
Subject
Language
English
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 cm 2 [54.96, 288.32 cm 2 ] vs. 124.04 cm 2 [56.57, 186.25 cm 2 ], P = 0.022) and in-hospital mortality (VAT 168.11 cm 2 [134.19, 285.27 cm 2 ] vs. 135.42 cm 2 [49.44, 227.91 cm 2 ], P = 0.033; SAT 227.28 cm 2 [139.38, 304.35 cm 2 ] vs. 173.81 cm 2 [97.65, 254.16 cm 2 ], 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.
(© 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)
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/m
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.
(© 2023 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.)