Cutoff Value of Psoas Muscle Area as Reduced Muscle Mass and Its Association with Acute Pancreatitis in China

Comparative Study of Different Inflammation Definition Methods of GLIM in the Diagnosis of Malnutrition in Patients with Acute Pancreatitis

Purpose

This study aims to investigate the influence of the Global Leadership Initiative on Malnutrition (GLIM) on diagnosing malnutrition in acute pancreatitis (AP) based on various inflammatory criteria.

Patients and Methods

A total of 258 AP patients admitted to a large medical center between June 2019 and January 2022 were retrospectively analyzed. All patients underwent evaluation using the original GLIM and GLIM criteria based on C-reactive protein (CRP), albumin, neutrophil/lymphocyte ratio, and CRP/albumin ratio (CAR). The study explored the impact of malnutrition diagnosis using different GLIM criteria on various clinical outcomes of AP patients and assessed the agreement of different GLIM criteria compared to the original GLIM.

Results

Thirty-seven (14.34%) patients were malnourished according to the original GLIM criteria. Using the other four criteria, malnutrition rates ranged from 6.59% to 12.40%. Malnutrition diagnosed by all GLIM criteria was associated with local complications. Malnutrition identified by the original, CRP-based, and CAR-based GLIM criteria was also associated with infectious complications and composite outcomes. Meanwhile, albumin-based malnutrition was associated with all adverse outcomes except organ failure. When considering all four GLIM criteria except the original one, malnourished patients exhibited longer lengths of stay than non-malnourished patients. Under the CRP- and albumin-based GLIM criteria, hospitalization costs were higher for malnourished patients. The sensitivity analyses demonstrated the robustness of the results. The agreement of the four GLIM criteria with the original GLIM criteria were consistent with the corresponding incidence of malnutrition.

Conclusion

This study validated the GLIM criteria for the first time in AP. Malnourished patients were more likely to experience local complications than non-malnourished AP patients. However, the inconsistency between GLIM criteria based on disease burden and various inflammatory markers was significant. The inflammatory marker-based GLIM criteria demonstrated a stronger predictive value than the original GLIM criteria in assessing prognosis in AP patients.

Psoas Muscle Index and Density as Prognostic Predictors in Patients Hospitalized with Acute Pancreatitis

Background: Early prognostication of acute pancreatitis (AP) patients for those at high risk of complications during hospitalization can facilitate clinical decision-making. Sarcopenia has been proven to be a risk factor for poor prognosis in patients with AP. We aimed to evaluate the association between the muscle parameters measured in computed tomography (CT) and the clinical outcomes of hospitalized patients with AP. Methods: A total of 132 consecutive patients hospitalized between 1 January 2015 and 31 December 2021 for AP with a valid CT scan were analyzed. The first CT conducted during hospitalization was analyzed for psoas muscle area (PMA), index (PMI), and density (PMD) at the L3 vertebral level. The main adverse outcomes indicating a worse prognosis were the development of extrapancreatic complications, infections, ICU transfer, in-hospital mortality, and hospitalization length. Results: The lowest tertile of PMI, as a surrogate for sarcopenia, was significantly correlated with increased rates of extrapancreatic complications, infections, and longer hospitalizations. It was additionally correlated with a worse CT severity index. The results for PMA and PMD also showed worse outcomes, largely mirroring the results for PMI. Although in-hospital mortality was relatively low, none of the patients died in the highest tertile of PMI. A clear cutoff with sufficient predictive capability could not be found. Conclusions: A low psoas muscle index can serve as an additional potential predictive marker for more severe disease and worse outcomes in hospitalized acute pancreatitis patients. More studies are needed to determine its combination with existing prediction tools.

Validation of the Global Leadership Initiative on Malnutrition Criteria for Predicting Adverse Outcomes in Acute Pancreatitis

Background and Aim

The Global Leadership Initiative on Malnutrition (GLIM) has proposed criteria for the diagnosis of malnutrition. No studies validated the GLIM criteria in acute pancreatitis (AP). The present study aimed to validate the predictive capacity of GLIM criteria for adverse outcomes in AP patients.

Patients and Methods

Clinical data of 269 patients with AP were analyzed retrospectively. The Nutritional Risk Screening 2002 (NRS2002) was chosen as the screening tool. Multivariate logistic regression analyses evaluated the adverse clinical outcomes in malnourished patients.

Results

Overall, 160 patients (59.5%) were at nutritional risk and 38 (14.1%) were malnourished. Reduced muscle mass/ low body mass index + inflammation combinations contributed most to malnutrition overall and in each subgroup. The malnourished group had lower hemoglobin, neutrophils, albumin, total cholesterol, and triglycerides than the well-nourished group. The malnourished group had higher hospitalization costs (CNY, 11319.34 vs 9258.22, p <0.001) and more local complications (34.2% vs 14.7%, p =0.009) than the well-nourished group. There was an interaction between malnutrition and overweight/obesity on local complications (p for interaction = 0.023). Multivariate logistic regression showed malnutrition was significantly associated with local complications (OR 12.2, 95% CI: 2.51-59.37), infectious complications (OR 9.95, 95% CI: 1.25-79.44) and composite adverse outcome (OR 4.78, 95% CI: 1.05-21.73) in the overweight/obesity subgroup. There was no association between malnutrition and the rate of various adverse outcomes in the non-overweight/obesity subgroup. Additionally, we observed an association between malnutrition and composite adverse outcome (OR 6.75, 95% CI: 1.49-30.68) in patients <70 years only in females.

Conclusion

Malnourished AP patients were more likely to have adverse outcomes than well-nourished patients. Malnutrition was associated with various adverse outcomes only in the overweight/obesity subgroups.

The role of sarcopenia in fragility fractures of the pelvis - is sarcopenia an underestimated risk factor?

BACKGROUND

Fragility fractures of the pelvis (FFPs) represent a significant health burden, particularly for the elderly. The role of sarcopenia, an age-related loss of muscle mass and function, in the development and impact of these fractures is not well understood. This study aims to investigate the prevalence and impact of osteoporosis and sarcopenia in patients presenting with FFPs.

METHODS

This retrospective study evaluated 140 elderly patients with FFPs. The diagnosis of sarcopenia was assessed by psoas muscle area (PMA) and the height-adjusted psoas muscle index (PMI) measured on computed tomography (CT) scans. Clinical data, radiological findings and functional outcomes were recorded and compared with the presence or absence of sarcopenia and osteoporosis.

RESULTS

Our study cohort comprised 119 female (85.0%) and 21 (15.0%) male patients. The mean age at the time of injury or onset of symptoms was 82.26 ± 8.50 years. Sarcopenia was diagnosed in 68.6% (n = 96) patients using PMA and 68.8% (n = 88) using PMI. 73.6% (n = 103) of our study population had osteoporosis and 20.0% (n = 28) presented with osteopenia. Patients with sarcopenia and osteoporosis had longer hospital stays (p < 0.04), a higher rate of complications (p < 0.048) and functional recovery was significantly impaired, as evidenced by a greater need for assistance in daily living (p < 0.03). However, they were less likely to undergo surgery (p < 0.03) and the type of FFP differed significantly (p < 0.04). There was no significant difference in mortality rate, pre-hospital health status, age or gender.

CONCLUSION

Our study highlights the important role of sarcopenia in FFPs in terms of the serious impact on health and quality of life in elderly patients especially when osteoporosis and sarcopenia occur together. Identifying and targeting sarcopenia in older patients may be an important strategy to reduce pelvic fractures and improve recovery. Further research is needed to develop effective prevention and treatment approaches that target muscle health in the elderly.

Predicting malnutrition in gastric cancer patients using computed tomography(CT) deep learning features and clinical data

OBJECTIVE

The aim of this study is using clinical factors and non-enhanced computed tomography (CT) deep features of the psoas muscles at third lumbar vertebral (L3) level to construct a model to predict malnutrition in gastric cancer before surgery, and to provide a new nutritional status assessment and survival assessment tool for gastric cancer patients.

METHODS

A retrospective analysis of 312 patients of gastric cancer were divided into malnutrition group and normal group based on Nutrition Risk Screening 2002(NRS-2002). 312 regions of interest (ROI) of the psoas muscles at L3 level of non-enhanced CT were delineated. Deep learning (DL) features were extracted from the ROI using a deep migration model and were screened by principal component analysis (PCA) and least-squares operator (LASSO). The clinical predictors included Body Mass Index (BMI), lymphocyte and albumin. Both deep learning model (including deep learning features) and mixed model (including selected deep learning features and selected clinical predictors) were constructed by 11 classifiers. The model was evaluated and selected by calculating receiver operating characteristic (ROC), area under curve (AUC), accuracy, sensitivity and specificity, calibration curve and decision curve analysis (DCA). The Cohen's Kappa coefficient (κ) was using to compare the diagnostic agreement for malnutrition between the mixed model and the GLIM in gastric cancer patients.

RESULT

The results of logistics multivariate analysis showed that BMI [OR = 0.569 (95% CI 0.491-0.660)], lymphocyte [OR = 0.638 (95% CI 0.408-0.998)], and albumin [OR = 0.924 (95% CI 0.859-0.994)] were clinically independent malnutrition of gastric cancer predictor(P < 0.05). Among the 11 classifiers, the Multilayer Perceptron (MLP)were selected as the best classifier. The AUC of the training and test sets for deep learning model were 0.806 (95% CI 0.7485-0.8635) and 0.769 (95% CI 0.673-0.863) and with accuracies were 0.734 and 0.766, respectively. The AUC of the training and test sets for the mixed model were 0.909 (95% CI 0.869-0.948) and 0.857 (95% CI 0.782-0.931) and with accuracies of 0.845 and 0.861, respectively. The DCA confirmed the clinical benefit of the both models. The Cohen's Kappa coefficient (κ) was 0.647 (P < 0.001). Diagnostic agreement for malnutrition between the mixed model and GLIM criteria was good. The mixed model was used to calculate the predicted probability of malnutrition in gastric cancer patients, which was divided into high-risk and low-risk groups by median, and the survival analysis showed that the overall survival time of the high-risk group was significantly lower than that of the low-risk group (P = 0.005).

CONCLUSION

Deep learning based on mixed model may be a potential tool for predicting malnutrition in gastric cancer patients.