Decreased skeletal muscle density is an independent predictor of mortality in necrotizing pancreatitis- A single tertiary center experience in 507 patients

Low skeletal muscle quality extracted from CT is associated with poor outcomes in severe acute pancreatitis patients

PURPOSE

To evaluate the association between body composition parameters derived from computed tomography (CT) scans and clinical outcomes in patients with severe acute pancreatitis (AP).

METHODS

Patients who have been diagnosed AP with a CT scan at ICU admission were included. Body composition parameters were measured on a single slice at L2-3 of the unenhanced CT scans. The intermuscular adipose tissue (IMAT), visceral adipose tissue (VAT), skeletal muscle area (SMA) and skeletal muscle density (SMD) were assessed using HUs by image analysis software. Univariable and multivariable analyses were performed to analyze the association between body composition parameters and clinical outcomes including all-cause mortality or prolonged ICU stay. The area under the curve (AUC) of a receiver operating characteristic curve was used to explore the predictive value of the body composition on treatment clinical outcomes.

RESULTS

A total of 158 patients were included. The IMAT (8.3 cm2 vs 6.0 cm2, P = 0.001) and VAT (190.3 cm2 vs 143.7 cm2, P < 0.001) were significantly higher in the severe AP group than in the moderately severe group, but were notassociatedwithoutcomes. For 1 HU of SMD increased, the risk of poor clinical outcomes decresed 11 % (adjusted OR 0.892, 95 %CI 0.806-0.987, P = 0.026), while an SMD below the median value (32.1 HU for males and 28.5 HU for females) was independently associated with worse outcomes in the multivariable analysis (adjusted OR 8.868, 95 % CI 2.146-36.650, P = 0.003). The SMD had a good predictive ability for clinical outcomes, AUC was 0.824 (95 % CI, 0.715-0.933) for males and 0.803 (95 % CI, 0.639-0.967) for females.

CONCLUSION

Low SMD was associated with poor outcomes in patients with severe and moderately severe AP and might be used as a novel marker to predict outcomes in patients suffering from severe and moderately severe AP.

Sarcopenia and Sarcopenic Obesity on Body Composition Analysis is a Significant Predictor of Mortality in Severe Acute Pancreatitis: A Longitudinal Observational Study

BACKGROUND

The prevalence and impact of sarcopenia and sarcopenic obesity noted on body composition analysis in severe acute pancreatitis (SAP) is unknown. This study investigates the prevalence of sarcopenia at different timepoints and its effect on post-pancreatitis complications and mortality.

METHODS

A prospective database of SAP admissions with organ failure at a single institution from 2015 to 2019 were analysed. Sarcopenia was determined by IMAGE J software on CT. Database was further queried for post-pancreatitis complications and mortality.

RESULTS

141 patients with a median age of 59 (range 18-88) and M:F ratio 1.52:1 of were analysed. Sarcopenia was present in 111/141 (79%) patients at admission, 78/79 (99%) at 3 months and 26/36 (72%) at 12 months. 67/111 patients with sarcopenia on admission had sarcopenic obesity. The mortality at 30 days, 3 months and 12 months was 16/141 (11%), 30/141 (21%) and 42/141 (30%) respectively. Mortality was significantly higher in sarcopenic patients at admission (35.14%) compared to the non-sarcopenic group (10%), P = 0.008). Mortality in the sarcopenic obesity group was significantly higher (45%) compared to the sarcopenic non-obese group (20%), P = 0.009) at admission. Multivariate logistic regression identified sarcopenic obesity (OR: 2.880), age (OR: 1.048) and number of organ failures (OR: 3.225) as significant predictors of mortality.

CONCLUSIONS

Sarcopenia and Sarcopenic obesity are highly prevalent in SAP patients on admission and during follow up. Furthermore, sarcopenic obesity was shown to be a significant predictor of mortality at admission, suggesting that body composition analysis could be a potential predictive marker of mortality in SAP patients.

Role of Machine Learning-Based CT Body Composition in Risk Prediction and Prognostication: Current State and Future Directions

CT body composition analysis has been shown to play an important role in predicting health and has the potential to improve patient outcomes if implemented clinically. Recent advances in artificial intelligence and machine learning have led to high speed and accuracy for extracting body composition metrics from CT scans. These may inform preoperative interventions and guide treatment planning. This review aims to discuss the clinical applications of CT body composition in clinical practice, as it moves towards widespread clinical implementation.

Clinical characteristics and risk factors of organ failure and death in necrotizing pancreatitis

BACKGROUND

Organ failure (OF) and death are considered the most significant adverse outcomes in necrotizing pancreatitis (NP). However, there are few NP-related studies describing the clinical traits of OF and aggravated outcomes.

PURPOSE

An improved insight into the details of OF and death will be helpful to the management of NP. Thus, in our research, we addressed the risk factors of OF and death in NP patients.

METHODS

We performed a study of 432 NP patients from May 2017 to December 2021. All patients with NP were followed up for 36 months. The primary end-points were risk factors of OF and death in NP patients. The risk factors were evaluated by logistic regression analysis.

RESULTS

NP patients with OF or death patients were generally older, had a higher APACHE II score, longer hospital stay, longer ICU stay, as well as a higher incidence of severe acute pancreatitis (SAP), shock and pancreatic necrosis. Independent risk factors related to OF included BMI, APACHE II score and SAP (P < 0.05). Age, shock and APACHE II score (P < 0.05) were the most significant factors correlated with the risk of death in NP patients. Notably, increased mortality was linked to the number of failed organs.

CONCLUSIONS

NP is a potentially fatal disease with a long hospital or ICU stay. Our study indicated that the incidence of OF and death in NP patients was 69.9% and 10.2%, respectively. BMI, SAP, APACHE II score, age and shock are potential risk factors of OF and death in NP patients. Clinicians should focus on these factors for early diagnosis and appropriate therapy.

Association Between the Growth Hormone/Insulin-Like Growth Factor-1 Axis and Muscle Density in Children and Adolescents of Short Stature

OBJECTIVE

To evaluate the association between the growth hormone (GH)/insulin-like growth factor-1 (IGF-1) axis and muscle density in children and adolescents of short stature.

METHODS

Participants were children and adolescents of short stature hospitalized in the Affiliated Hospital of Jining Medical University between January 2020 and June 2021. All participants had CT scan images available. We performed an analysis of the images to calculate the muscle density or skeletal muscle attenuation (SMA), skeletal muscle index (SMI), and fat mass index (FMI). Bioelectrical impedance analysis (BIA) was used to ensure that chest CT is a credible way of evaluating body composition.

RESULTS

A total of 297 subjects were included with the mean age of 10.00 ± 3.42 years, mean height standard deviation score (SDS) of -2.51 ± 0.53, and mean IGF-1 SDS of -0.60 ± 1.07. The areas of muscle and fat tissues at the fourth thoracic vertebra level in the CT images showed strong correlation with the total weights of the participants (R²  = 0.884 and 0.897, respectively). The peak of GH was negatively associated with FMI (r = - 0.323, P <.01) and IGF-1 SDS was positively associated with SMI (r = 0.303, P <.01). Both the peak GH and IGF-1 SDS were positively associated with SMA (r = 0.244, P <.01 and r = 0.165, P <.05, respectively). Multiple stepwise linear regression analysis demonstrated that the GH peak was the predictor of FMI (β = - 0.210, P < .01), the IGF-1 SDS was the predictor of SMI (β = 0.224, P < .01), and both the peak GH and IGF-1 SDS were predictors of SMA (β = 0.180, P < .01 and β = 0.222, P < .01).

CONCLUSIONS

A chest CT scan is a credible method of evaluating body composition in children and adolescents of short stature. In these patients, peak GH and IGF-1 SDS are independent predictors of muscle density and the GF/IGF-1 axis may regulate body composition through complex mechanisms.

Impacts of body composition parameters and liver cirrhosis on the severity of alcoholic acute pancreatitis

AIM

Liver cirrhosis and features of muscle or adipose tissues may affect the severity of acute pancreatitis (AP). We aimed to evaluate the impact of body composition parameters and liver cirrhosis on the severity of AP in patients with alcohol-induced AP (AAP).

METHODS

Patients with presumed AAP who underwent CT within one week after admission were retrospectively enrolled. L3 sectional areas of abdominal fat and muscle, and mean muscle attenuations (MMAs) were quantified. The presence of liver cirrhosis was determined using clinical and CT findings. Factors potentially associated with moderately severe or severe AP were included in the multivariable logistic regression analysis.

RESULTS

A total of 242 patients (47.0 ± 12.6 years, 215 males) with presumed AAP were included. The mild and moderately severe/severe (MSS) groups included 137 (56.6%) and 105 patients (43.4%), respectively. Patients in the MSS group had higher rates of liver cirrhosis, organ failure, and local complications. Among body composition parameters, mean MMA (33.4 vs 36.8 HU, P<0.0001) and abdominal muscle mass (126.5 vs 135.1 cm2, P = 0.029) were significantly lower in the MSS group. The presence of liver cirrhosis (OR, 4.192; 95% CI, 1.620-10.848) was found to be a significant risk factor for moderately severe or severe AP by multivariable analysis.

CONCLUSION

The results of this study suggest that liver cirrhosis has a significant impact on the severity of AAP. Of the body composition parameters examined, MMA and abdominal muscle mass showed potential as promising predictors.