Usefulness of Body Composition CT Analysis in Patients with Idiopathic Pulmonary Fibrosis: A Pilot Study

Body mass index and weight loss as risk factors for poor outcomes in patients with idiopathic pulmonary fibrosis: a systematic review and meta-analysis

OBJECTIVE

The association between nutritional status and prognosis of idiopathic pulmonary fibrosis (IPF) remains unclear. This systematic review and meta-analysis aimed to explore the effect of body mass index (BMI) and weight loss on the prognosis of IPF patients.

METHODS

We accumulated studies on IPF, BMI, and weight loss from databases including PubMed, Embase, Web of science, Scopus, Ovid and Cochrane Library up to 4 August 2023. Using Cox proportional hazard regression model for subgroup analysis, hazard ratio (HR) and 95% confidence intervals (CI) for BMI in relation to mortality, acute exacerbation (AE), and hospitalization in IPF patients were calculated, and HR, odds ratio (OR), and 95% CI for weight loss corresponding to IPF patient mortality were assessed. Sensitivity analysis was peformed by eliminating every study one by one, and publication bias was judged by Egger's test and trim-and-fill method.

RESULTS

A total of 34 eligible studies involving 18,343 IPF patients were included in the meta-analysis. The pooled results by univariate Cox regression analysis showed that baseline BMI was a predictive factor for IPF mortality (HR = 0.93, 95%CI = [0.91, 0.94]). Furthermore, the results by the multivariable regression model indicated that baseline BMI was an independent risk factor for predicting IPF mortality (HR = 0.94, 95%CI = [0.91, 0.98]). Weight loss was identified as a risk factor for IPF mortality (HR = 2.74, 95% CI = [2.12, 3.54]; OR = 4.51, 95% CI = [1.72, 11.82]) and there was no predictive value of BMI for acute exacerbation (HR = 1.00, 95% CI= [0.93, 1.07]) or hospitalization (HR = 0.95, 95% CI = [0.89, 1.02]).

CONCLUSION

Low baseline BMI and weight loss in the course of IPF may indicate a high risk of mortality in patients with IPF, so it is meaningful to monitor and manage the nutritional status of IPF patients, and early intervention should be conducted for low BMI and weight loss.

Admission chest CT scan of intensive care patients with interstitial lung disease: Unveiling its limited predictive value through visual and automated analyses in a retrospective study (ILDICTO)

BACKGROUND

Clinical course prediction of patients with interstitial lung disease (ILD) admitted to the intensive care unit (ICU) for acute respiratory failure (ARF) can be challenging. This study aimed to characterize the prognostic value of admission chest CT-scan in this situation.

METHODS

We retrospectively included ILD patients admitted to a French ICU for acute respiratory failure requiring oxygen. Patients with lymphangitis carcinomatosis and ANCA vasculitis were excluded. We analyzed every admission chest CT-scan using two different approaches: a visual analysis (grading the extent of traction bronchiectasis, ground glass and honeycomb) and an automated analysis (grading the extent of ground glass and consolidation with a dedicated software). The primary outcome was ICU mortality.

RESULTS

Between January 2014 and October 2020, 81 patients presented an acute respiratory failure with ILD on the admission chest CT-scan. In univariate analysis, only the main pulmonary artery diameter differed between patients who survived and those who died in ICU (30 vs 32 mm, p = 0.021). In multivariate analysis, none of the radiological funding was associated with ICU mortality. Visual and automated analyses did not yield different results, with a strong correlation between the two methods. However, the identification of an UIP pattern (and the presence of honeycomb) was associated with a poorer response to corticosteroid therapy.

CONCLUSION

Our study showed that the extent of radiological findings and the severity of fibrosis indices on admission chest CT scans of ILD patients admitted to the ICU for ARF were not associated with subsequent deterioration.

Validity of bioelectric impedance analysis for body composition assessment in interstitial lung disease patients

Introduction

Background: changes in body composition (BC) are common in interstitial lung disease, which leads to an increased risk of complications and infections, and are associated with poor quality of life and worse outcomes. BC assessment is important to identify malnutrition and sarcopenia. However, gold-standard techniques are not available in all clinical settings. Aims: this study aimed to evaluate the agreement and reliability of body composition estimated by bioelectric impedance analysis (BIA) and measured using dual-energy X-ray absorptiometry (DEXA) in women with interstitial lung disease. Methods: this is a cross-sectional study. BC (fat mass and appendicular skeletal muscle mass) were assessed using BIA multifrequency and DEXA in standardized conditions. Agreement and reliability between techniques were evaluated using Bland-Altman plots and the intraclass correlation coefficient (ICC). Results: a total of 50 women were evaluated. No differences were observed for FM (BIA, 25.8 ± 10.2 kg and DEXA, 26.3 ± 10.0 kg, p = 0.77) and ASMM (BIA, 14.1 ± 2.7 kg and DEXA, 13.9 ± 2.3 kg, p = 0.83). Based on ICC, good reliability was observed for FM (ICC, 0.98) and ASMM (ICC, 0.93). Conclusion: BC estimated by BIA showed good agreement and reliability with DEXA measurements. In the absence of this method, BIA can replace the DEXA technique for body composition assessment.

IA-Body Composition CT at T12 in Idiopathic Pulmonary Fibrosis: Diagnosing Sarcopenia and Correlating with Other Morphofunctional Assessment Techniques

BACKGROUND

Body composition (BC) techniques, including bioelectrical impedance analysis (BIVA), nutritional ultrasound® (NU), and computed tomography (CT), can detect nutritional diagnoses such as sarcopenia (Sc). Sc in idiopathic pulmonary fibrosis (IPF) is associated with greater severity and lower survival. Our aim was to explore the correlation of BIVA, NU and functional parameters with BC at T12 level CT scans in patients with IPF but also its relationship with degree of Sc, malnutrition and mortality.

METHODS

This bicentric cross-sectional study included 60 IPF patients (85.2% male, 70.9 ± 7.8 years). Morphofunctional assessment (MFA) techniques included BIVA, NU, CT at T12 level (T12-CT), handgrip strength, and timed up and go. CT data were obtained using FocusedON®. Statistical analysis was conducted using JAMOVI version 2.3.22 to determine the cutoff points for Sc in T12-CT and to analyze correlations with other MFA techniques.

RESULTS

the cutoff for muscle area in T12-CT was ≤77.44 cm2 (area under the curve (AUC) = 0.734, sensitivity = 41.7%, specificity = 100%). The skeletal muscle index (SMI_T12CT) cutoff was ≤24.5 cm2/m2 (AUC = 0.689, sensitivity = 66.7%, specificity = 66.7%). Low SMI_T12CT exhibited significantly reduced median survival and higher risk of mortality compared to those with normal muscle mass (SMI cut off ≥ 28.8 cm/m2). SMI_T12CT was highly correlated with body cell mass from BIVA (r = 0.681) and rectus femoris cross-sectional area (RF-CSA) from NU (r = 0.599). Cronbach's α for muscle parameters across different MFA techniques and CT was 0.735, confirming their validity for evaluating muscle composition.

CONCLUSIONS

T12-CT scan is a reliable technique for measuring low muscle mass in patients with IPF, specifically when the L3 vertebrae are not captured. An SMI value of <28.8 is a good predictor of low lean mass and 12-month mortality in IPF patients.

Automated 3D-Body Composition Analysis as a Predictor of Survival in Patients With Idiopathic Pulmonary Fibrosis

PURPOSE

Idiopathic pulmonary fibrosis (IPF) is the most common interstitial lung disease, with a median survival time of 2 to 5 years. The focus of this study is to establish a novel imaging biomarker.

MATERIALS AND METHODS

In this study, 79 patients (19% female) with a median age of 70 years were studied retrospectively. Fully automated body composition analysis (BCA) features (bone, muscle, total adipose tissue, intermuscular, and intramuscular adipose tissue) were combined into Sarcopenia, Fat, and Myosteatosis indices and compared between patients with a survival of more or less than 2 years. In addition, we divided the cohort at the median (high=≥ median, low=

RESULTS

A high Sarcopenia and Fat index and low Myosteatosis index were associated with longer median survival (35 vs. 16 mo for high vs. low Sarcopenia index, P=0.066; 44 vs. 14 mo for high vs. low Fat index, P<0.001; and 33 vs. 14 mo for low vs. high Myosteatosis index, P=0.0056) and better 5-year survival rates (34.0% vs. 23.6% for high vs. low Sarcopenia index; 47.3% vs. 9.2% for high vs. low Fat index; and 11.2% vs. 42.7% for high vs. low Myosteatosis index). Adjusted multivariate Cox regression showed a significant impact of the Fat (HR=0.71, P=0.01) and Myosteatosis (HR=1.12, P=0.005) on overall survival.

CONCLUSION

The fully automated BCA provides biomarkers with a predictive value for the overall survival in patients with IPF.

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Affiliation(s)

Luca Salhöfer Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Francesco Bonella Department of Pneumology, Center for Interstitial and Rare Lung Diseases, University Hospital Essen, Essen, Germany
Mathias Meetschen Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Lale Umutlu Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Michael Forsting Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Benedikt Michael Schaarschmidt Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Marcel Klaus Opitz Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
Jens Kleesiek Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Rene Hosch Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Sven Koitka Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Vicky Parmar Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Felix Nensa Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany
Johannes Haubold Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany

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Association of musculoskeletal involvement with lung function and mortality in patients with idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a progressive disease associated with high mortality. Low muscle mass, frailty and sarcopenia lead to functional impairment that negatively impact quality of life and survival but are not used in clinical practice. We aimed to determine the association of Fat-free mass index (FFMI) and frailty with lung function, exercise tolerance and survival in patients with IPF. In this study, 70 patients with IPF underwent assessment of body composition, lung function, 6-min walk distance (6MWD) testing, hand grip strength, quality of life (QoL) assessment by St. George's Respiratory questionnaire (SGRQ) and frailty assessment using the SHARE-FI tool. FFMI was calculated using pectoralis muscle cross-sectional area (PM-CSA) on CT chest images and the lowest quartile defined reduced muscle mass. Sarcopenia was defined as low FFMI and handgrip strength. Regression analyses were conducted to determine predictive value of frailty, low FFMI and sarcopenia on clinical outcomes. The Cox proportional hazards model was used to analyze the impact of FFMI and frailty score on survival. The mean age was 70 years with moderate impairment in lung function (mean ppFVC 68.5%, ppDLCO 45.6%). Baseline forced vital capacity (p < 0.001), diffusion capacity of lung for carbon monoxide (p =  < 0.01), 6WMD (p < 0.05) were significantly lower in frail patients compared to non-frail patients. BMI was found to closely correlate with FFMI (r = 0.79, p < 0.001), but not with frailty score (r = - 0.2, p = 0.07). Frailty was a significant predictor of FVC, DLCO, 6MWD, SGRQ scores when adjusted for age and gender. Muscle mass and sarcopenia were significant predictors of FVC, DLCO, but not 6MWD or QoL scores. Multivariate cox-proportional hazards ratio model adjusting for age and gender showed that frailty was significantly associated with increased mortality (HR = 2.6, 95% CI 1.1-6.1). Low FFMI (HR = 1.3, 95% CI 0.6-2.8), and sarcopenia (HR = 2.1, 95% CI 0.8-5.3), though associated with a trend to increased mortality, were not statistically significant. Frailty is associated with lower lung function and higher mortality in patients with IPF. Longitudinal evaluations are necessary to further determine the associations between low FFMI, sarcopenia and frailty with outcomes in IPF.

Ectopic and visceral fat deposition in aging, obesity, and idiopathic pulmonary fibrosis: an interconnected role

Idiopathic pulmonary fibrosis (IPF) is considered an age-related disease. Age-related changes, along with other factors such as obesity, hormonal imbalances, and various metabolic disorders, lead to ectopic fat deposition (EFD). This accumulation of fat outside of its normal storage sites is associated with detrimental effects such as lipotoxicity, oxidative stress, inflammation, and insulin resistance. This narrative review provides an overview of the connection between ectopic and visceral fat deposition in aging, obesity, and IPF. It also elucidates the mechanism by which ectopic fat deposition in the airways and lungs, pericardium, skeletal muscles, and pancreas contributes to lung injury and fibrosis in patients with IPF, directly or indirectly. Moreover, the review discusses the impact of EFD on the severity of the disease, quality of life, presence of comorbidities, and overall prognosis in IPF patients. The review provides detailed information on recent research regarding representative lipid-lowering drugs, hypoglycemic drugs, and lipid-targeting drugs in animal experiments and clinical studies. This may offer new therapeutic directions for patients with IPF.

Establishment and application of the BRP prognosis model for idiopathic pulmonary fibrosis

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is the most common idiopathic interstitial lung disease. Clinical models to accurately evaluate the prognosis of IPF are currently lacking. This study aimed to construct an easy-to-use and robust prediction model for transplant-free survival (TFS) of IPF based on clinical and radiological information.

METHODS

A multicenter prognostic study was conducted involving 166 IPF patients who were followed up for 3 years. The end point of follow-up was death or lung transplantation. Clinical information, lung function tests, and chest computed tomography (CT) scans were collected. Body composition quantification on CT was performed using 3D Slicer software. Risk factors in blood routine examination-radiology-pulmonary function (BRP) were identified by Cox regression and utilized to construct the "BRP Prognosis Model". The performance of the BRP model and the gender-age-physiology variables (GAP) model was compared using time-ROC curves, calibration curves, and decision curve analysis (DCA). Furthermore, histopathology fibrosis scores in clinical specimens were compared between the different risk stratifications identified by the BRP model. The correlations among body composition, lung function, serum inflammatory factors, and profibrotic factors were analyzed.

RESULTS

Neutrophil percentage > 68.3%, pericardial adipose tissue (PAT) > 94.91 cm3, pectoralis muscle radiodensity (PMD) ≤ 36.24 HU, diffusing capacity of the lung for carbon monoxide/alveolar ventilation (DLCO/VA) ≤ 56.03%, and maximum vital capacity (VCmax) < 90.5% were identified as independent risk factors for poor TFS among patients with IPF. We constructed a BRP model, which showed superior accuracy, discrimination, and clinical practicability to the GAP model. Median TFS differed significantly among patients at different risk levels identified by the BRP model (low risk: TFS > 3 years; intermediate risk: TFS = 2-3 years; high risk: TFS ≈ 1 year). Patients with a high-risk stratification according to the BRP model had a higher fibrosis score on histopathology. Additionally, serum proinflammatory markers were positively correlated with visceral fat volume and infiltration.

CONCLUSIONS

In this study, the BRP prognostic model of IPF was successfully constructed and validated. Compared with the commonly used GAP model, the BRP model had better performance and generalization with easily obtainable indicators. The BRP model is suitable for clinical promotion.

Estimating 3-D whole-body composition from a chest CT scan

BACKGROUND

Estimating whole-body composition from limited region-computed tomography (CT) scans has many potential applications in clinical medicine; however, it is challenging.

PURPOSE

To investigate if whole-body composition based on several tissue types (visceral adipose tissue [VAT], subcutaneous adipose tissue [SAT], intermuscular adipose tissue [IMAT], skeletal muscle [SM], and bone) can be reliably estimated from a chest CT scan only.

METHODS

A cohort of 97 lung cancer subjects who underwent both chest CT scans and whole-body positron emission tomography-CT scans at our institution were collected. We used our in-house software to automatically segment and quantify VAT, SAT, IMAT, SM, and bone on the CT images. The field-of-views of the chest CT scans and the whole-body CT scans were standardized, namely, from vertebra T1 to L1 and from C1 to the bottom of the pelvis, respectively. Multivariate linear regression was used to develop the computer models for estimating the volumes of whole-body tissues from chest CT scans. Subject demographics (e.g., gender and age) and lung volume were included in the modeling analysis. Ten-fold cross-validation was used to validate the performance of the prediction models. Mean absolute difference (MAD) and R-squared (R² ) were used as the performance metrics to assess the model performance.

RESULTS

The R² values when estimating volumes of whole-body SAT, VAT, IMAT, total fat, SM, and bone from the regular chest CT scans were 0.901, 0.929, 0.900, 0.933, 0.928, and 0.918, respectively. The corresponding MADs (percentage difference) were 1.44 ± 1.21 L (12.21% ± 11.70%), 0.63 ± 0.49 L (29.68% ± 61.99%), 0.12 ± 0.09 L (16.20% ± 18.42%), 1.65 ± 1.40 L (10.43% ± 10.79%), 0.71 ± 0.68 L (5.14% ± 4.75%), and 0.17 ± 0.15 L (4.32% ± 3.38%), respectively.

CONCLUSION

Our algorithm shows promise in its ability to estimate whole-body compositions from chest CT scans. Body composition measures based on chest CT scans are more accurate than those based on vertebra third lumbar.