Opportunistic Computed Tomography Imaging for the Assessment of Fatty Muscle Fraction Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement

CT-derived Epicardial Adipose Tissue Inflammation Predicts Outcome in Patients Undergoing Transcatheter Aortic Valve Replacement

PURPOSE

Inflammatory changes in epicardial (EAT) and pericardial adipose tissue (PAT) are associated with increased overall cardiovascular risk. Using routine, preinterventional cardiac CT data, we examined the predictive value of quantity and quality of EAT and PAT for outcome after transcatheter aortic valve replacement (TAVR).

MATERIALS AND METHODS

Cardiac CT data of 1197 patients who underwent TAVR at the in-house heart center between 2011 and 2020 were retrospectively analyzed. The amount and density of EAT and PAT were quantified from single-slice CT images at the level of the aortic valve. Using established risk scores and known independent risk factors, a clinical benchmark model (BMI, Chronic kidney disease stage, EuroSCORE 2, STS Prom, year of intervention) for outcome prediction (2-year mortality) after TAVR was established. Subsequently, we tested whether the additional inclusion of area and density values of EAT and PAT in the clinical benchmark model improved prediction. For this purpose, the cohort was divided into a training (n=798) and a test cohort (n=399).

RESULTS

Within the 2-year follow-up, 264 patients died. In the training cohort, particularly the addition of EAT density to the clinical benchmark model showed a significant association with outcome (hazard ratio 1.04, 95% CI: 1.01-1.07; P =0.013). In the test cohort, the outcome prediction of the clinical benchmark model was also significantly improved with the inclusion of EAT density (c-statistic: 0.589 vs. 0.628; P =0.026).

CONCLUSIONS

EAT density as a surrogate marker of EAT inflammation was associated with 2-year mortality after TAVR and may improve outcome prediction independent of established risk parameters.

Adverse plasma branched-chain amino acid profile mirrors fatty muscle degeneration in diabetic heart failure patients

AIMS

Elevated plasma branched-chain amino acids (BCAAs) are tightly linked to incident diabetes and its complications, while lower BCAAs are associated with adverse outcomes in the elderly and heart failure (HF) patients. The interplay between body compositions and plasma BCAAs, especially under the influence of co-morbid diabetes in HF patients, is not well understood. Here, we examined the impact of diabetes on the prognostic value of plasma BCAA and its association with body compositions in HF patients.

METHODS AND RESULTS

We retrospectively examined 301 HF patients (70 ± 15 years old; 59% male), among which 36% had diabetes. Blood samples for plasma BCAA measurements were collected in a fasting state after stabilization of HF and analysed using ultraperformance liquid chromatography. A dual-energy X-ray absorptiometry scan assessed regional body compositions, and muscle wasting was defined as appendicular skeletal muscle mass index (ASMI) < 7.00 and <5.40 kg/m2 for males and females, respectively, according to the criteria of the Asian Working Group for Sarcopenia. Although analyses of covariance revealed that plasma BCAAs were significantly higher in diabetic patients, low valine (<222.1 nmol/mL) similarly predicted adverse events defined by HF hospitalization, lethal arrhythmia, or all-cause death in both diabetic and non-diabetic patients independently of age, sex, and NT-proBNP (adjusted hazard ratio [HR] 3.1, 95% confidence interval [CI] of 1.1-8.6 and adjusted HR 2.67, 95% CI 1.1-6.5, respectively; P for interaction 0.88). In multivariate linear regression analyses comprising age, sex, and regional body compositions as explanatory variables, plasma BCAAs were positively correlated with visceral adipose tissue area in non-diabetic patients (standardized β coefficients [β] = 0.44, P < 0.001). In contrast, in diabetic patients, plasma BCAAs were correlated positively with ASMI (β = 0.49, P = 0.001) and negatively with appendicular fat mass index (AFMI; β = -0.42, P = 0.004). Co-morbid diabetes was independently associated with muscle wasting (adjusted odds ratio 2.1, 95% CI 1.1-4.0) and significantly higher plasma 3-methylhistidine level, a marker of myofibrillar degradation. In diabetic patients, ASMI uniquely showed a J-shaped relationship with AFMI, and in a subgroup of HF patients with muscle wasting, diabetic patients showed 12% higher AFMI than non-diabetic patients despite comparable ASMI reductions.

CONCLUSIONS

Despite higher plasma BCAA levels in HF patients with diabetes, the prognostic value of low valine remained consistent regardless of diabetes status. However, low BCAAs were distinctly associated with fatty muscle degeneration in the extremities in diabetic patients, suggesting the importance of targeted interventions to prevent such tissue remodelling in this population.

Deep learning-based assessment of CT markers of sarcopenia and myosteatosis for outcome assessment in patients with advanced pancreatic cancer after high-intensity focused ultrasound treatment

OBJECTIVES

To evaluate the prognostic value of CT-based markers of sarcopenia and myosteatosis in comparison to the Eastern Cooperative Oncology Group (ECOG) score for survival of patients with advanced pancreatic cancer treated with high-intensity focused ultrasound (HIFU).

MATERIALS AND METHODS

For 142 retrospective patients, the skeletal muscle index (SMI), skeletal muscle radiodensity (SMRD), fatty muscle fraction (FMF), and intermuscular fat fraction (IMFF) were determined on superior mesenteric artery level in pre-interventional CT. Each marker was tested for associations with sex, age, body mass index (BMI), and ECOG. The prognostic value of the markers was examined in Kaplan-Meier analyses with the log-rank test and in uni- and multivariable Cox proportional hazards (CPH) models.

RESULTS

The following significant associations were observed: Male patients had higher BMI and SMI. Patients with lower ECOG had lower BMI and SMI. Patients with BMI lower than 21.8 kg/m2 (median) also showed lower SMI and IMFF. Patients younger than 63.3 years (median) were found to have higher SMRD, lower FMF, and lower IMFF. In the Kaplan-Meier analysis, significantly lower survival times were observed in patients with higher ECOG or lower SMI. Increased patient risk was observed for higher ECOG, lower BMI, and lower SMI in univariable CPH analyses for 1-, 2-, and 3-year survival. Multivariable CPH analysis for 1-year survival revealed increased patient risk for higher ECOG, lower SMI, lower IMFF, and higher FMF. In multivariable analysis for 2- and 3-year survival, only ECOG and FMF remained significant.

CONCLUSION

CT-based markers of sarcopenia and myosteatosis show a prognostic value for assessment of survival in advanced pancreatic cancer patients undergoing HIFU therapy.

CLINICAL RELEVANCE STATEMENT

The results indicate a greater role of myosteatosis for additional risk assessment beyond clinical scores, as only FMF was associated with long-term survival in multivariable CPH analyses along ECOG and also showed independence to ECOG in group analysis.

KEY POINTS

• This study investigates the prognostic value of CT-based markers of sarcopenia and myosteatosis for patients with pancreatic cancer treated with high-intensity focused ultrasound. • Markers for sarcopenia and myosteatosis showed a prognostic value besides clinical assessment of the physical status by the Eastern Cooperative Oncology Group score. In contrast to muscle size measurements, the myosteatosis marker fatty muscle fraction demonstrated independence to the clinical score. • The results indicate that myosteatosis might play a greater role for additional patient risk assessments beyond clinical assessments of physical status.

Opportunistic CT-derived analysis of fat and muscle tissue composition predicts mortality in patients with cardiogenic shock

Prognosis estimation in patients with cardiogenic shock (CS) is important to guide clinical decision making. Aim of this study was to investigate the predictive value of opportunistic CT-derived body composition analysis in CS patients. Amount and density of fat and muscle tissue of 152 CS patients were quantified from single-slice CT images at the level of the intervertebral disc space L3/L4. Multivariable Cox regression and Kaplan-Meier survival analyses were performed to evaluate the predictive value of opportunistically CT-derived body composition parameters on the primary endpoint of 30-day mortality. Within the 30-day follow-up, 90/152 (59.2%) patients died. On multivariable analyses, lactate (Hazard Ratio 1.10 [95% Confidence Interval 1.04-1.17]; p = 0.002) and patient age (HR 1.04 [95% CI 1.01-1.07], p = 0.017) as clinical prognosticators, as well as visceral adipose tissue (VAT) area (HR 1.004 [95% CI 1.002-1.007]; p = 0.001) and skeletal muscle (SM) area (HR 0.987 [95% CI 0.975-0.999]; p = 0.043) as imaging biomarkers remained as independent predictors of 30-day mortality. Kaplan-Meier survival analyses showed significantly increased 30-day mortality in patients with higher VAT area (p = 0.015) and lower SM area (p = 0.035). CT-derived VAT and SM area are independent predictors of dismal outcomes in CS patients and have the potential to emerge as new imaging biomarkers available from routine diagnostic CT.

Direct deep learning-based survival prediction from pre-interventional CT prior to transcatheter aortic valve replacement

PURPOSE

To investigate survival prediction in patients undergoing transcatheter aortic valve replacement (TAVR) using deep learning (DL) methods applied directly to pre-interventional CT images and to compare performance with survival models based on scalar markers of body composition.

METHOD

This retrospective single-center study included 760 patients undergoing TAVR (mean age 81 ± 6 years; 389 female). As a baseline, a Cox proportional hazards model (CPHM) was trained to predict survival on sex, age, and the CT body composition markers fatty muscle fraction (FMF), skeletal muscle radiodensity (SMRD), and skeletal muscle area (SMA) derived from paraspinal muscle segmentation of a single slice at L3/L4 level. The convolutional neural network (CNN) encoder of the DL model for survival prediction was pre-trained in an autoencoder setting with and without a focus on paraspinal muscles. Finally, a combination of DL and CPHM was evaluated. Performance was assessed by C-index and area under the receiver operating curve (AUC) for 1-year and 2-year survival. All methods were trained with five-fold cross-validation and were evaluated on 152 hold-out test cases.

RESULTS

The CNN for direct image-based survival prediction, pre-trained in a focussed autoencoder scenario, outperformed the baseline CPHM (CPHM: C-index = 0.608, 1Y-AUC = 0.606, 2Y-AUC = 0.594 vs. DL: C-index = 0.645, 1Y-AUC = 0.687, 2Y-AUC = 0.692). Combining DL and CPHM led to further improvement (C-index = 0.668, 1Y-AUC = 0.713, 2Y-AUC = 0.696).

CONCLUSIONS

Direct DL-based survival prediction shows potential to improve image feature extraction compared to segmentation-based scalar markers of body composition for risk assessment in TAVR patients.

Population Health Management in Radiology: Economic Considerations

There is a growing emphasis on population health management (PHM) in the United States, in part because it has the worst health outcomes indices among high-income countries despite spending by far the most on health care. Successful PHM is expected to lead to a healthier population with reduced health care utilization and cost. The role of radiology in PHM is increasingly being recognized, including efforts in care coordination, secondary prevention, and appropriate imaging utilization, among others. To further discuss economic considerations for PHM, we must understand the evolving health care payer environment, which combines fee-for-service and increasingly, an alternative payment model framework developed by the Health Care Payment Learning and Action Network. In considering the term "value-based care," perceived value needs to accrue to those who ultimately pay for care, which is more commonly employers and the government. This perspective drives the design of alternative payment models and thus should be taken into consideration to ensure sustainable practice models.

Sarcopenia and Cardiovascular Diseases

Sarcopenia is the loss of muscle strength, mass, and function, which is often exacerbated by chronic comorbidities including cardiovascular diseases, chronic kidney disease, and cancer. Sarcopenia is associated with faster progression of cardiovascular diseases and higher risk of mortality, falls, and reduced quality of life, particularly among older adults. Although the pathophysiologic mechanisms are complex, the broad underlying cause of sarcopenia includes an imbalance between anabolic and catabolic muscle homeostasis with or without neuronal degeneration. The intrinsic molecular mechanisms of aging, chronic illness, malnutrition, and immobility are associated with the development of sarcopenia. Screening and testing for sarcopenia may be particularly important among those with chronic disease states. Early recognition of sarcopenia is important because it can provide an opportunity for interventions to reverse or delay the progression of muscle disorder, which may ultimately impact cardiovascular outcomes. Relying on body mass index is not useful for screening because many patients will have sarcopenic obesity, a particularly important phenotype among older cardiac patients. In this review, we aimed to: (1) provide a definition of sarcopenia within the context of muscle wasting disorders; (2) summarize the associations between sarcopenia and different cardiovascular diseases; (3) highlight an approach for a diagnostic evaluation; (4) discuss management strategies for sarcopenia; and (5) outline key gaps in knowledge with implications for the future of the field.

[Routine computed tomography body composition analysis-experience in intensive care patients]

The assessment of the nutritional status of patients in the intensive care unit is recommended in current guidelines and should include the assessment of muscle status. A suitable method is the analysis of routine computed tomography (CT) scans, which are frequently performed in critically ill patients. With the help of special software, individual CT slices are processed and various parameters such as muscle area, muscle density or even the percentage of adipose tissue are displayed and quantified. It has been shown that cross-sectional acquisition of skeletal muscle in the lumbar spine correlates very well with total body muscle. There are defined, albeit population-based, cut-off values that can be used to establish diagnosis of sarcopenia. Monitoring of individualized nutritional therapy can be accomplished by assessment of repetitive CT examinations. The steadily growing body of data confirms that the method can make a valuable contribution to the assessment of body composition in intensive care medicine. Most of the currently available software requires time-consuming processing of the CT. Automated programs, which are now occasionally available and eliminate the need for most manual processing, may make the method even more attractive in the future. Ultimately, the risk of intensive transport to the CT or radiation exposure may be only justified for medical indications. Nevertheless, whenever CT is available for medical reasons, it should also be exploited for composition analysis.

Computed tomography derived cervical fat-free muscle fraction as an imaging-based outcome marker in patients with acute ischemic stroke: a pilot study

BACKGROUND

Outcome assessment in stroke patients is essential for evidence-based stroke care planning. Computed tomography (CT) is the mainstay of diagnosis in acute stroke. This study aimed to investigate whether CT-derived cervical fat-free muscle fraction (FFMF) as a biomarker of muscle quality is associated with outcome parameters after acute ischemic stroke.

METHODS

In this retrospective study, 66 patients (mean age: 76 ± 13 years, 30 female) with acute ischemic stroke in the anterior circulation who underwent CT, including CT-angiography, and endovascular mechanical thrombectomy of the middle cerebral artery between August 2016 and January 2020 were identified. Based on densitometric thresholds, cervical paraspinal muscles covered on CT-angiography were separated into areas of fatty and lean muscle and FFMF was calculated. The study cohort was binarized based on median FFMF (cutoff value: < 71.6%) to compare clinical variables and outcome data between two groups. Unpaired t test and Mann-Whitney U test were used for statistical analysis.

RESULTS

National Institute of Health Stroke Scale (NIHSS) (12.2 ± 4.4 vs. 13.6 ± 4.5, P = 0.297) and modified Rankin scale (mRS) (4.3 ± 0.9 vs. 4.4 ± 0.9, P = 0.475) at admission, and pre-stroke mRS (1 ± 1.3 vs. 0.9 ± 1.4, P = 0.489) were similar between groups with high and low FFMF. NIHSS and mRS at discharge were significantly better in patients with high FFMF compared to patients with low FFMF (NIHSS: 4.5 ± 4.4 vs. 9.5 ± 6.7; P = 0.004 and mRS: 2.9 ± 2.1 vs.3.9 ± 1.8; P = 0.049). 90-day mRS was significantly better in patients with high FFMF compared to patients with low FFMF (3.3 ± 2.2 vs. 4.3 ± 1.9, P = 0.045).

CONCLUSION

Cervical FFMF obtained from routine clinical CT might be a new imaging-based muscle quality biomarker for outcome prediction in stroke patients.

Assessment of anterior thigh muscle size and fat infiltration using single-slice CT imaging versus automated MRI analysis in adults

OBJECTIVES

We examined the longitudinal and cross-sectional relationship between automated MRI-analysis and single-slice axial CT imaging for determining muscle size and muscle fat infiltration (MFI) of the anterior thigh.

METHODS

Twenty-two patients completing sex-hormone treatment expected to result in muscle hypertrophy (n = 12) and atrophy (n = 10) underwent MRI scans using 2-point Dixon fat/water-separated sequences and CT scans using a system operating at 120 kV and a fixed flux of 100 mA. At baseline and 12 months after, automated volumetric MRI analysis of the anterior thigh was performed bilaterally, and fat-free muscle volume and MFI were computed. In addition, cross-sectional area (CSA) and radiological attenuation (RA) (as a marker of fat infiltration) were calculated from single slice axial CT-images using threshold-assisted planimetry. Linear regression models were used to convert units.

RESULTS

There was a strong correlation between MRI-derived fat-free muscle volume and CT-derived CSA (R = 0.91), and between MRI-derived MFI and CT-derived RA (R = -0.81). The 95% limits of agreement were ±0.32 L for muscle volume and ±1.3% units for %MFI. The longitudinal change in muscle size and MFI was comparable across imaging modalities.

CONCLUSIONS

Both automated MRI and single-slice CT-imaging can be used to reliably quantify anterior thigh muscle size and MFI.

ADVANCES IN KNOWLEDGE

This is the first study examining the intermodal agreement between automated MRI analysis and CT-image assessment of muscle size and MFI in the anterior thigh muscles. Our results support that both CT- and MRI-derived measures of muscle size and MFI can be used in clinical settings.

Skeletal muscle atrophy and myosteatosis are not related to long-term aneurysmal subarachnoid hemorrhage outcome

The prognosis of aneurysmal subarachnoid hemorrhage (aSAH) is highly variable. This study aims to investigate whether skeletal muscle atrophy and myosteatosis are associated with poor outcome after aSAH. In this study, a cohort of 293 consecutive aSAH-patients admitted during a 4-year period was retrospectively analyzed. Cross-sectional muscle measurements were obtained at the level of the third cervical vertebra. Muscle atrophy was defined by a sex-specific cutoff value. Myosteatosis was defined by a BMI-specific cutoff value. Poor neurological outcome was defined as modified Rankin Scale 4–6 at 2 and 6-month follow-up. Patient survival state was checked until January 2021. Generalized estimating equation was performed to assess the effect of muscle atrophy / myosteatosis on poor neurological outcome after aSAH. Cox regression was performed to analyze the impact of muscle atrophy and myosteatosis on overall survival. The study found that myosteatosis was associated with poor neurological condition (WFNS 4–5) at admission after adjusting for covariates (odds ratio [OR] 2.01; 95%CI 1.05,3.83; P = .03). It was not associated with overall survival (P = .89) or with poor neurological outcomes (P = .18) when adjusted for other prognostic markers. Muscle atrophy was not associated with overall survival (P = .58) or neurological outcome (P = .32) after aSAH. In conclusion, myosteatosis was found to be associated with poor physical condition directly after onset of aSAH. Skeletal muscle atrophy and myosteatosis were however irrelevant to outcome in the Western-European aSAH patient. Future studies are needed to validate these finding.

Automated major psoas muscle volumetry in computed tomography using machine learning algorithms

Purpose

The psoas major muscle (PMM) volume serves as an opportunistic imaging marker in cross-sectional imaging datasets for various clinical applications. Since manual segmentation is time consuming, two different automated segmentation methods, a generative adversarial network architecture (GAN) and a multi-atlas segmentation (MAS), as well as a combined approach of both, were investigated in terms of accuracy of automated volumetrics in given CT datasets.

Materials and methods

The bilateral PMM was manually segmented by a radiologist in 34 abdominal CT scans, resulting in 68 single 3D muscle segmentations as training data. Three different methods were tested for their ability to generate automated image segmentations: a GAN- and MAS-based approach and a combined approach of both methods (COM). Bilateral PMM volume (PMMV) was calculated in cm³ by each algorithm for every CT. Results were compared to the corresponding ground truth using the Dice similarity coefficient (DSC), Spearman’s correlation coefficient and Wilcoxon signed-rank test.

Results

Mean PMMV was 239 ± 7.0 cm³ and 308 ± 9.6 cm³, 306 ± 9.5 cm³ and 243 ± 7.3 cm³ for the CNN, MAS and COM, respectively. Compared to the ground truth the CNN and MAS overestimated the PMMV significantly (+ 28.9% and + 28.0%, p < 0.001), while results of the COM were quite accurate (+ 0.7%, p = 0.33). Spearman’s correlation coefficients were 0.38, 0.62 and 0.73, and the DSCs were 0.75 [95%CI: 0.56–0.88], 0.73 [95%CI: 0.54–0.85] and 0.82 [95%CI: 0.65–0.90] for the CNN, MAS and COM, respectively.

Conclusion

The combined approach was able to efficiently exploit the advantages of both methods (GAN and MAS), resulting in a significantly higher accuracy in PMMV predictions compared to the isolated implementations of both methods. Even with the relatively small set of training data, the segmentation accuracy of this hybrid approach was relatively close to that of the radiologist.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02539-2.

Baseline PA/BSA ratio in patients undergoing transcatheter aortic valve replacement - A novel CT-based marker for the prediction of pulmonary hypertension and outcome

BACKGROUND

Pulmonary hypertension (pH) has a prognostic impact on patients undergoing transcatheter aortic valve replacement (TAVR). Pulmonary artery (PA) dilatation assessed by multidetector computed tomography (MDCT) has the potential to predict PH. The aim of the study was to evaluate the clinical parameters associated with PA dilatation and to investigate its prognostic relevance in patients undergoing TAVR.

METHODS

In 770 patients undergoing TAVR between February 2016 and July 2019, PA diameter was measured by MDCT before TAVR. Additionally, PA diameter divided by ascending aorta diameter or body surface area (BSA) was calculated.

RESULTS

Of all the CT-derived parameters compared with a receiver operating characteristic curve, the value for PA/BSA with a median of 1.68 (IQR 1.47, 1.91) cm/m2 showed the greatest area-under-the-curve (0.75) for predicting PH at baseline. Based on this median, patients were assigned to a small PA/BSA (n = 386) or a large PA/BSA (n = 384) group. Hereby, a large PA/BSA was independently associated with PH at baseline (OR:8.39 [5.36-13.14], p < 0.001) and after TAVR (OR:1.73 [1.18-2.53], p = 0.005). A large PA/BSA was associated with a significantly higher cumulative two-year all-cause mortality compared to small PA/BSA (30.0% vs. 13.7%, p < 0.001), which was supported in the multivariable model (HR:1.87; 95%CI, 1.12-3.04; p = 0.017).

CONCLUSION

Patients with a large PA/BSA on MDCT are more likely to have PH at baseline and after TAVR. Large PA/BSA is associated with an increased risk of mortality and could provide additional information for risk stratification in patients undergoing TAVR.

Deep Learning-Based Body Composition Analysis Predicts Outcome in Melanoma Patients Treated with Immune Checkpoint Inhibitors

Previous studies suggest an impact of body composition on outcome in melanoma patients. We aimed to determine the prognostic value of CT-based body composition assessment in patients receiving immune checkpoint inhibitor therapy for treatment of metastatic disease using a deep learning approach. One hundred seven patients with staging CT examinations prior to initiation of checkpoint inhibition between January 2013 and August 2019 were retrospectively evaluated. Using an automated deep learning-based body composition analysis pipeline, parameters for estimation of skeletal muscle mass (skeletal muscle index, SMI) and adipose tissue compartments (visceral adipose tissue index, VAI; subcutaneous adipose tissue index, SAI) were derived from staging CT. The cohort was binarized according to gender-specific median cut-off values. Patients below the median were defined as having low SMI, VAI, or SAI, respectively. The impact on outcome was assessed using the Kaplan-Meier method with log-rank tests. A multivariable logistic regression model was built to test the impact of body composition parameters on 3-year mortality. Patients with low SMI displayed significantly increased 1-year (25% versus 9%, p = 0.035), 2-year (32% versus 13%, p = 0.017), and 3-year mortality (38% versus 19%, p = 0.016). No significant differences with regard to adipose tissue compartments were observed (3-year mortality: VAI, p = 0.448; SAI, p = 0.731). On multivariable analysis, low SMI (hazard ratio (HR), 2.245; 95% confidence interval (CI), 1.005-5.017; p = 0.049), neutrophil-to-lymphocyte ratio (HR, 1.170; 95% CI, 1.076-1.273; p < 0.001), and Karnofsky index (HR, 0.965; 95% CI, 0.945-0.985; p = 0.001) remained as significant predictors of 3-year mortality. Lowered skeletal muscle index as an indicator of sarcopenia was associated with worse outcome in patients with metastatic melanoma receiving immune checkpoint inhibitor therapy.

Muscle fat index is associated with frailty and length of hospital stay following transcatheter aortic valve replacement in high-risk patients

BACKGROUND

Aging is associated with progressive loss of muscle mass, as well as replacement of muscle with fat and fibrous tissue. We studied the contribution of muscle fat content, a surrogate marker of biological aging, to frailty and Length of Hospital Stay (LOS) following Transcatheter Aortic Valve Replacement (TAVR).

METHODS

We evaluated 415 patients who underwent TAVR from February 2012 to December 2016 at Mayo Clinic, MN, USA. Densities between -190 to -30 Hounsfield Units within the abdominal muscle area were determined as muscle fat. Muscle Fat Index (MFI) was defined as muscle fat mass divided by height squared. LOS was considered as the primary outcome. Stepwise multivariable linear regression was used to identify the predictors of LOS.

RESULTS

Mean age ± SD of the study population was 81.2 ± 9.6 years and 58.07% were male. Seventy-two patients (17.35%) had frailty. Median (IQR) LOS was 4 (3-6) days. MFI was higher in patients with frailty (median (IQR); 18.1 [13.8-24.2] vs 14.4 [10.6-18.7], p < 0.001) and was positively correlated with LOS (r = 0.129, p = 0.009). In multivariable analysis of predictors of LOS, MFI (β = 0.06, p = 0.022), pre-TAVR atrial fibrillation/flutter (β = 0.5, p = 0.015), and post-TAVR complications (β = 0.91, p < 0.001) were directly, and femoral access route (β = -1.13, p < 0.001) and pre-TAVR hemoglobin (β = -0.35, p = 0.002) were inversely associated with LOS.

CONCLUSIONS

MFI can be determined from pre-TAVR CT scans and is a novel predictor of LOS following TAVR. This objective indicator can potentially be used in a pre-TAVR clinic to plan for rehabilitation programs in selected patients.

Association between single-slice and whole heart measurements of epicardial and pericardial fat in cardiac MRI

BACKGROUND

Epicardial (ECF) and pericardial fat (PCF) are important prognostic markers for various cardiac diseases. However, volumetry of the fat compartments is time-consuming.

PURPOSE

To investigate whether total volume of ECF and PCF can be estimated by axial single-slice measurements and in a four-chamber view.

MATERIAL AND METHODS

A total of 113 individuals (79 patients and 34 healthy) were included in this retrospective magnetic resonance imaging (MRI) study. The total volume of ECF and PCF was determined using a 3D-Dixon sequence. Additionally, the area of ECF and PCF was obtained in single axial layers at five anatomical landmarks (left coronary artery, right coronary artery, right pulmonary artery, mitral valve, coronary sinus) of the Dixon sequence and in a four-chamber view of a standard cine sequence. Pearson's correlation coefficient was calculated between the total volume and each single-slice measurement.

RESULTS

Axial single-slice measurements of ECF and PCF correlated strongly with the total fat volumes at all landmarks (ECF: r  =  0.85-0.94, P < 0.001; PCF: r  =  0.89-0.94, P < 0.001). The best correlation was found at the level of the left coronary artery for ECF and PCF (r  =  0.94, P < 0.001). Correlation between single-slice measurement in the four-chamber view and the total ECF and PCF volume was lower (r  =  0.75 and r  =  0.8, respectively, P < 0.001).

CONCLUSION

Single-slice measurements allow an estimation of ECF and PCF volume. This time-efficient analysis allows studies of larger patient cohorts and the opportunistic determination of ECF/PCF from routine examinations.

End-to-end automated body composition analyses with integrated quality control for opportunistic assessment of sarcopenia in CT

OBJECTIVES

To develop a pipeline for automated body composition analysis and skeletal muscle assessment with integrated quality control for large-scale application in opportunistic imaging.

METHODS

First, a convolutional neural network for extraction of a single slice at the L3/L4 lumbar level was developed on CT scans of 240 patients applying the nnU-Net framework. Second, a 2D competitive dense fully convolutional U-Net for segmentation of visceral and subcutaneous adipose tissue (VAT, SAT), skeletal muscle (SM), and subsequent determination of fatty muscle fraction (FMF) was developed on single CT slices of 1143 patients. For both steps, automated quality control was integrated by a logistic regression model classifying the presence of L3/L4 and a linear regression model predicting the segmentation quality in terms of Dice score. To evaluate the performance of the entire pipeline end-to-end, body composition metrics, and FMF were compared to manual analyses including 364 patients from two centers.

RESULTS

Excellent results were observed for slice extraction (z-deviation = 2.46 ± 6.20 mm) and segmentation (Dice score for SM = 0.95 ± 0.04, VAT = 0.98 ± 0.02, SAT = 0.97 ± 0.04) on the dual-center test set excluding cases with artifacts due to metallic implants. No data were excluded for end-to-end performance analyses. With a restrictive setting of the integrated segmentation quality control, 39 of 364 patients were excluded containing 8 cases with metallic implants. This setting ensured a high agreement between manual and fully automated analyses with mean relative area deviations of ΔSM = 3.3 ± 4.1%, ΔVAT = 3.0 ± 4.7%, ΔSAT = 2.7 ± 4.3%, and ΔFMF = 4.3 ± 4.4%.

CONCLUSIONS

This study presents an end-to-end automated deep learning pipeline for large-scale opportunistic assessment of body composition metrics and sarcopenia biomarkers in clinical routine.

KEY POINTS

• Body composition metrics and skeletal muscle quality can be opportunistically determined from routine abdominal CT scans. • A pipeline consisting of two convolutional neural networks allows an end-to-end automated analysis. • Machine-learning-based quality control ensures high agreement between manual and automatic analysis.

Decreased Bone Mineral Density Is a Predictor of Poor Survival in Critically Ill Patients

Alterations in bone mineral density (BMD) have been suggested as independent predictors of survival for several diseases. However, little is known about the role of BMD in the context of critical illness and intensive care medicine. We therefore evaluated the prognostic role of BMD in critically ill patients upon admission to an intensive care unit (ICU). Routine computed tomography (CT) scans of 153 patients were used to assess BMD in the first lumbar vertebra. Results were correlated with clinical data and outcomes. While median BMD was comparable between patients with and without sepsis, BMD was lower in patients with pre-existing arterial hypertension or chronic obstructive pulmonary disease. A low BMD upon ICU admission was significantly associated with impaired short-term ICU survival. Moreover, patients with baseline BMD < 122 HU had significantly impaired overall survival. The prognostic relevance of low BMD was confirmed in uni- and multivariate Cox-regression analyses including several clinicopathological parameters. In the present study, we describe a previously unrecognised association of individual BMD with short- and long-term outcomes in critically ill patients. Due to its easy accessibility in routine CT, BMD provides a novel prognostic tool to guide decision making in critically ill patients.

Sarcopenia in Patients Undergoing Transcatheter Aortic Valve Implantation (TAVI): A Systematic Review of the Literature

BACKGROUND

In older patients, sarcopenia is a prevalent disease associated with negative outcomes. Sarcopenia has been investigated in patients undergoing transcatheter aortic valve implantation (TAVI), but the criteria for diagnosis of the disease are heterogeneous. This systematic review of the current literature aims to evaluate the prevalence of sarcopenia in patients undergoing TAVI and to analyse the impact of sarcopenia on clinical outcomes.

METHODS

A comprehensive search of the literature has been performed in electronic databases from the date of initiation until March 2020. Using a pre-defined search strategy, we identified studies assessing skeletal muscle mass, muscle quality and muscle function as measures for sarcopenia in patients undergoing TAVI. We evaluated how sarcopenia affects the outcomes mortality at ≥1 year, prolonged length of hospital stay, and functional decline.

RESULTS

We identified 18 observational studies, enrolling a total number of 9'513 patients. For assessment of skeletal muscle mass, all included studies used data from computed tomography. Cut-off points for definition of low muscle mass were heterogeneous, and prevalence of sarcopenia varied between 21.0% and 70.2%. In uni- or multivariate regression analysis of different studies, low muscle mass was found to be a significant predictor of mortality, prolonged length of hospital stay, and functional decline. No interventional study was identified measuring the effect of nutritional or physiotherapy interventions on sarcopenia in TAVI patients.

CONCLUSIONS

Sarcopenia is highly prevalent among patients undergoing TAVI, and negatively affects important outcomes. Early diagnosis of this condition might allow a timely start of nutritional and physiotherapy interventions to prevent negative outcomes in TAVI patients.

CT fatty muscle fraction as a new parameter for muscle quality assessment predicts outcome in venovenous extracorporeal membrane oxygenation

Impaired skeletal muscle quality is a major risk factor for adverse outcomes in acute respiratory failure. However, conventional methods for skeletal muscle assessment are inapplicable in the critical care setting. This study aimed to determine the prognostic value of computed tomography (CT) fatty muscle fraction (FMF) as a biomarker of muscle quality in patients undergoing extracorporeal membrane oxygenation (ECMO). To calculate FMF, paraspinal skeletal muscle area was obtained from clinical CT and separated into areas of fatty and lean muscle based on densitometric thresholds. The cohort was binarized according to median FMF. Patients with high FMF displayed significantly increased 1-year mortality (72.7% versus 55.8%, P = 0.036) on Kaplan–Meier analysis. A multivariable logistic regression model was built to test the impact of FMF on outcome. FMF was identified as a significant predictor of 1-year mortality (hazard ratio per percent FMF, 1.017 [95% confidence interval, 1.002–1.033]; P = 0.031), independent of anthropometric characteristics, Charlson Comorbidity Index, Simplified Acute Physiology Score, Respiratory Extracorporeal Membrane Oxygenation Survival Prediction Score, and duration of ECMO support. To conclude, FMF predicted 1-year mortality independently of established clinical prognosticators in ECMO patients and may have the potential to become a new muscle quality imaging biomarker, which is available from clinical CT.

Body composition analysis using CT and MRI: intra-individual intermodal comparison of muscle mass and myosteatosis

Computed tomography (CT) and magnetic resonance imaging (MRI) can quantify muscle mass and quality. However, it is still unclear if CT and MRI derived measurements can be used interchangeable. In this prospective study, fifty consecutive participants of a cancer screening program underwent same day low-dose chest CT and MRI. Cross-sectional areas (CSA) of the paraspinal skeletal muscles were obtained. CT and MRI muscle fat infiltration (MFI) were assessed by mean radiodensity in Hounsfield units (HU) and proton density fat fraction (MRI^PDFF), respectively. CSA and MFI were highly correlated between CT and MRI (CSA: r = 0.93, P < 0.001; MFI: r = - 0.90, P < 0.001). Mean CSA was higher in CT compared to MRI (46.6cm² versus 43.0cm²; P = 0.05) without significance. Based on MRI^PDFF, a linear regression model was established to directly estimate skeletal muscle fat content from CT. Bland-Altman plots showed a difference between measurements of - 0.5 cm² to 7.6 cm² and - 4.2% to 2.4% regarding measurements of CSA and MFI, respectively. In conclusion, the provided results indicate interchangeability of CT and MRI derived imaging biomarkers of skeletal muscle quantity and quality. Comparable to MRI^PDFF, skeletal muscle fat content can be quantified from CT, which might have an impact of analyses in larger cohort studies, particularly in sarcopenia patients.

Yttrium-90 radioembolization for hepatocellular carcinoma: Outcome prediction with MRI derived fat-free muscle area

PURPOSE

Sarcopenia is associated with adverse outcomes in several gastrointestinal malignancies and liver cirrhosis. We aimed to study the utility of magnetic resonance imaging (MRI) derived fat-free muscle area (FFMA) to predict clinical outcome in patients receiving yttrium-90 radioembolization (RE) for treatment of hepatocellular carcinoma (HCC).

METHODS

Fifty-eight patients with unresectable HCC and pre-interventional liver MRI undergoing salvage RE were retrospectively evaluated. Using axial T2-weighted turbo spin echo sequences, FFMA was calculated by subtraction of the intramuscular adipose tissue area from the total cross-sectional area of paraspinal skeletal muscles at the superior mesenteric artery level. FFMA values lower than 3582 mm² in male and 2301 mm² in female patients were defined as low FFMA. Main outcomes were progression-free survival (PFS) and overall survival (OS). For outcome analysis, the Kaplan-Meier method with log rank test and multivariate cox regression analysis were used.

RESULTS

Mean time from pre-interventional MRI to RE was 27 ± 20 days. Median OS and PFS after RE were 250 (range: 21-1230 days) and 156 days (range: 21-674 days), respectively. Patients with low FFMA showed significantly reduced OS (197 vs. 294 days, P = 0.024) and tended to have shortened PFS (109 vs. 185 days, P = 0.068). Low FFMA (HR 2.675; P = 0.011), estimated liver tumor burden (HR 4.058; P = 0.001), and Eastern Cooperative Oncology Group (ECOG) performance status (1.763; P = 0.009) were independent predictors of OS on multivariate analysis.

CONCLUSIONS

FFMA as a measure of sarcopenia predicts OS and might represent a promising new biomarker for survival prognosis in patients undergoing RE for treatment of unresectable HCC.