In European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
OBJECTIVES : To investigate the impact of machine-learning derived baseline lean psoas muscle area (LPMA) for patients undergoing thoracic endovascular aortic repair.
METHODS : A retrospective study was undertaken of acute and subacute complicated type B aortic dissection patients who underwent endovascular treatment from 2010 to 2017. LPMA (a marker of frailty) was calculated by multiplying psoas muscle area and density measured at L3 level from the computed tomography. The optimal cut-off value of LPMA was determined by the Cox hazard model with restricted cubic spline.
RESULTS : A total of 428 patients who met the inclusion criteria were included in this study. Patients were classified into low LPMA group (n = 218) and high LPMA group (n = 210) using the cut-off value of 395 cm2·HU. An automatic muscle segmentation algorithm was developed based on U-Net architecture. There was high correlation between machine-learning method and manual measurement for psoas muscle area (r = 0.91, P < 0.001) and density (r = 0.90, P < 0.001). Multivariable regression analyses revealed that baseline low LPMA (<395 cm2·HU) was an independent positive predictor for 30-day (Odds ratio 5.62, 95% confidence interval [CI] 1.20-26.23, P = 0.028) and follow-up (Hazard ratio [HR] 5.62, 95% CI 2.68-11.79, P < 0.001) mortality. Propensity-score matching and subgroup analysis based on age (<65 vs ≥ 65 years) confirmed the independent association between baseline LPMA and follow-up mortality.
CONCLUSIONS : Baseline LPMA could profoundly affect the prognosis of patients undergoing thoracic endovascular aortic repair. It was feasible to integrate the automatic muscle measurements into clinical routine.
Liu Jitao, Su Sheng, Liu Weijie, Xie Enmin, Hu Xiaolu, Lin Wenhui, Ding Huanyu, Luo Songyuan, Liu Yuan, Huang Wenhui, Li Jie, Yang Fan, Luo Jianfang
lean psoas muscle area, machine-learning, outcomes, type B aortic dissection