ArticleJournal of cachexia, sarcopenia and muscle2024
Artificial intelligence-based analysis of body composition predicts outcome in patients receiving long-term mechanical circulatory support.
Article in Journal of cachexia, sarcopenia and muscle, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- The Application of Artificial Intelligence and Machine Learning in Left Ventricular Assist Device Implantation: A Systematic Review.Artificial organs · 2025Pooled it
- Indication for durable mechanical circulatory support: an expert opinion review.ESC heart failure · 2026Review
- Changes of bone, adipose, and muscle-related body compositions in gastric cancers after gastrectomy using deep learning based automatic segmentation.BMC gastroenterology · 2025Article
- Analysis of the current status of computed tomography diagnosis of sarcopenia.Archives of medical science : AMS · 2025Article
- The Role of Frailty and Myosteatosis in Predicting All-Cause Mortality in Older Adults with Cancer.Current oncology (Toronto, Ont.) · 2024Article
- The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence-based analysis of body composition predicts outcome in patients receiving long-term mechanical circulatory support.Journal of cachexia, sarcopenia and muscle · 2024Article
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundObesity 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.
methodsPreoperative 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).
resultsThe 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
conclusionsPreoperative 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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