Tian-Yi Zhang1, Dong-Aolei An2, Hang Zhou1, Zhaohui Ni1, Qin Wang1, Binghua Chen2, Renhua Lu1, Jiaying Huang1, Yin Zhou1, Doo Hee Kim3, Molly Wilson3, Lian-Ming Wu4, Shan Mou5. 1. Department of Nephrology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No.160 PuJian Road, Shanghai, 200127, People's Republic of China. 2. Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No.160 PuJian Road, Shanghai, 200127, People's Republic of China. 3. Department of Radiology, Wayne State University, Detroit, MI, 48201, USA. 4. Department of Radiology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No.160 PuJian Road, Shanghai, 200127, People's Republic of China. wlmssmu@126.com. 5. Department of Nephrology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, No.160 PuJian Road, Shanghai, 200127, People's Republic of China. moushanrenji@126.com.
Abstract
OBJECTIVES: To explore the diagnostic potential of texture analysis applied to native T1 maps obtained from cardiac magnetic resonance (CMR) images for the assessment of heart failure with preserved ejection fraction (HFpEF) among patients with end-stage renal disease (ESRD). METHODS: This study, conducted from June 2018 to November 2020, included 119 patients (35 on hemodialysis, 55 on peritoneal dialysis, and 29 with kidney transplants) in Renji Hospital. Native T1 maps were assessed with texture analysis, using a freely available software package, in participants who underwent cardiac MRI at 3.0 T. Four texture features, selected by dimension reduction specific to the diagnosis of HFpEF, were analyzed. Multivariate logistic regression was performed to examine the independent association between the selected features and HFpEF in ESRD patients. RESULTS: Seventy-six of 119 patients were diagnosed with HFpEF. Demographic, laboratory, cardiac MRI, and echocardiogram characteristics were compared between HFpEF and non-HFpEF groups. The four texture features that were analyzed showed statistically significant differences between groups. In multivariate analysis, age, left atrial volume index (LAVI), and sum average 4 (SA4) turned out to be independent predictors for HFpEF in ESRD patients. Combining the texture feature, SA4, with typical predictive factors resulted in higher C-index (0.923 vs. 0.898, p = 0.045) and a sensitivity and specificity of 79.2% and 95.2%, respectively. CONCLUSIONS: Texture analysis of T1 maps adds diagnostic value to typical clinical parameters for the assessment of heart failure with preserved ejection fraction in patients with end-stage renal disease. KEY POINTS: • Non-invasive assessment of HFpEF can help predict prognosis in ESRD patients and help them take timely preventative measures. • Texture analysis of native T1 maps adds diagnostic value to the typical clinical parameters for the assessment of HFpEF in patients with ESRD.
OBJECTIVES: To explore the diagnostic potential of texture analysis applied to native T1 maps obtained from cardiac magnetic resonance (CMR) images for the assessment of heart failure with preserved ejection fraction (HFpEF) among patients with end-stage renal disease (ESRD). METHODS: This study, conducted from June 2018 to November 2020, included 119 patients (35 on hemodialysis, 55 on peritoneal dialysis, and 29 with kidney transplants) in Renji Hospital. Native T1 maps were assessed with texture analysis, using a freely available software package, in participants who underwent cardiac MRI at 3.0 T. Four texture features, selected by dimension reduction specific to the diagnosis of HFpEF, were analyzed. Multivariate logistic regression was performed to examine the independent association between the selected features and HFpEF in ESRD patients. RESULTS: Seventy-six of 119 patients were diagnosed with HFpEF. Demographic, laboratory, cardiac MRI, and echocardiogram characteristics were compared between HFpEF and non-HFpEF groups. The four texture features that were analyzed showed statistically significant differences between groups. In multivariate analysis, age, left atrial volume index (LAVI), and sum average 4 (SA4) turned out to be independent predictors for HFpEF in ESRD patients. Combining the texture feature, SA4, with typical predictive factors resulted in higher C-index (0.923 vs. 0.898, p = 0.045) and a sensitivity and specificity of 79.2% and 95.2%, respectively. CONCLUSIONS: Texture analysis of T1 maps adds diagnostic value to typical clinical parameters for the assessment of heart failure with preserved ejection fraction in patients with end-stage renal disease. KEY POINTS: • Non-invasive assessment of HFpEF can help predict prognosis in ESRD patients and help them take timely preventative measures. • Texture analysis of native T1 maps adds diagnostic value to the typical clinical parameters for the assessment of HFpEF in patients with ESRD.
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