Nomogram and Validity of a Model for Predicting Malnutrition in Patients on Liver Transplant Lists

Establishment and validation of a nomogram model for riskprediction of hepatic encephalopathy: a retrospective analysis

To establish a high-quality, easy-to-use, and effective risk prediction model for hepatic encephalopathy, to help healthcare professionals with identifying people who are at high risk of getting hepatic encephalopathy, and to guide them to take early interventions to reduce the occurrence of hepatic encephalopathy. Patients (n = 1178) with decompensated cirrhosis who attended the First Affiliated Hospital of Guangxi University of Chinese Medicine between January 2016 and June 2022 were selected for the establishment and validation of a nomogram model for risk prediction of hepatic encephalopathy. In this study, we screened the risk factors for the development of hepatic encephalopathy in patients with decompensated cirrhosis by univariate analysis, LASSO regression and multifactor analysis, then established a nomogram model for predicting the risk of getting hepatic encephalopathy for patients with decompensated cirrhosis, and finally performed differentiation analysis, calibration analysis, clinical decision curve analysis and validation of the established model. A total of 1178 patients with decompensated cirrhosis who were hospitalized and treated at the First Affiliated Hospital of Guangxi University of Chinese Medicine between January 2016 and June 2022 were included for modeling and validation. Based on the results of univariate analysis, LASSO regression analysis and multifactor analysis, a final nomogram model with age, diabetes, ascites, spontaneous peritonitis, alanine transaminase, and blood potassium as predictors of hepatic encephalopathy risk prediction was created. The results of model differentiation analysis showed that the AUC of the model of the training set was 0.738 (95% CI 0.63-0.746), while the AUC of the model of the validation set was 0.667 (95% CI 0.541-0.706), and the two AUCs indicated a good discrimination of this nomogram model. According to the Cut-Off value determined by the Jorden index, when the Cut-Off value of the training set was set at 0.150, the sensitivity of the model was 72.8%, the specificity was 64.8%, the positive predictive value was 30.4%, and the negative predictive value was 91.9%; when the Cut-Off value of the validation set was set at 0.141, the sensitivity of the model was 69.7%, the specificity was 57.3%, the positive predictive value was 34.5%, and the negative predictive value was 84.7%. The calibration curve and the actual events curve largely overlap at the diagonal, indicating that the prediction with this model has less error. The Hosmer-Lemeshow test for goodness of fit was also applied, and the results showed that for the training set, χ2 = 1.237587, P = 0.998, and for the validation set, χ2 = 31.90904, P = 0.0202, indicating that there was no significant difference between the predicted and actual observed values. The results of the clinical decision curve analysis showed that the model had a good clinical benefit, compared with the two extreme clinical scenarios (all patients treated or none treated), and the model also had a good clinical benefit in the validation set. This study showed that aged over 55 years, complications of diabetes, ascites, and spontaneous bacterial peritonitis, abnormal glutamate aminotransferase and abnormal blood potassium are independent risks indicators for the development of hepatic encephalopathy in patients with decompensated cirrhosis. The nomogram model based on the indicators mentioned above can effectively and conveniently predict the risk of developing hepatic encephalopathy in patients with decompensated cirrhosis. The nomogram model established on this study can help clinical healthcare professionals to timely and early identify patients with high risk of developing hepatic encephalopathy.

A nomogram model for predicting malnutrition among older hospitalized patients with type 2 diabetes: a cross-sectional study in China

BACKGROUND

Malnutrition remains a pervasive issue among older adults, a prevalence that is markedly higher among those diagnosed with diabetes. The primary objective of this study was to develop and validate a risk prediction model that can accurately identify instances of malnutrition among elderly hospitalized patients with type 2 diabetes mellitus (T2DM) within a Chinese demographic.

METHODS

This cross-sectional study was conducted between August 2021 and August 2022, we enrolled T2DM patients aged 65 years and above from endocrinology wards. The creation of a nomogram for predicting malnutrition was based on risk factors identified through univariate and multivariate logistic regression analyses. The predictive accuracy of the model was evaluated by the receiver operating characteristic curve (ROC),the area under the ROC (AUC), the concordance index (C-index), and calibration curves.

RESULTS

The study included a total of 248 older T2DM patients, with a recorded malnutrition prevalence of 26.21%. The identified critical risk factors for malnutrition in this cohort were body mass index, albumin, impairment in activities of daily living, dietary habits, and glycosylated hemoglobin. The AUC of the nomogram model reached 0.914 (95% CI: 0.877-0.951), with an optimal cutoff value of 0.392. The model demonstrated a sensitivity of 80.0% and a specificity of 88.5%. Bootstrap-based internal verification results revealed a C-index of 0.891, while the calibration curves indicated a strong correlation between the actual and predicted malnutrition risks.

CONCLUSIONS

This study underscores the critical need for early detection of malnutrition in older T2DM patients. The constructed nomogram represents a practical and reliable tool for the rapid identification of malnutrition among this vulnerable population.

Individualised prognosis for risk of developing abdominal obesity in the paediatric population

AIMS

To develop and validate risk scores for predicting abdominal obesity in South American children and adolescents based on extrinsic and intrinsic variables.

METHODS

Children (n = 358) and adolescents (n = 369) from seven South American cities from the South American Youth Cardiovascular and Environmental (SAYCARE) Study. The primary outcome was abdominal obesity. Potential predictors were based on sociodemographic, maternal, environmental, and behavioural factors and nutritional status. In multilevel logistic models, associated variables were tested to build the scores, which were internally validated.

RESULTS

We identified 120 children and 98 adolescents who were abdominally obese. We found at least five variables associated with the outcome in children with unacceptable predictive capacity. However, in adolescents, we found that biological sex, age, maternal body mass index (BMI), active commuting by bike, soft drink consumption (for risk score A), and weight (for score B) can predict abdominal obesity. Both scores, A and B, showed acceptable performance in the ROC curve [areas under curve: 0.70 (95% CI: 0.56-0.82) and 0.95 (95% CI: 0.89-1.00), respectively].

CONCLUSION

The SAYCARE risk scores present accurate, individualised estimates for identifying adolescents who are at risk of developing abdominal obesity. However, these have not been externally validated.

NUTRITIONAL MANAGEMENT OF LIVER CIRRHOSIS AND ITS COMPLICATIONS IN HOSPITALIZED PATIENTS

BACKGROUND

Cirrhosis is a chronic and progressive liver disease that occurs from prolonged hepatocellular injury. Malnutrition causes complications in cirrhosis patients that worsen the condition to liver failure. Both are closely linked and increase the chances of morbidity and mortality. Regular nutritional screening and monitoring is prime concern for such patients including comprehensive dietary history, laboratory tests, and evaluation of muscle loss and strength capabilities to determine the degree of frailty. For efficient assessment of liver cirrhosis patients Subjective Global Assessment has been used worldwide. The nutritional objectives for such individuals should be to regain liver functions, to prevent complications associated, and to overcome nutritional deficiencies causing malnutrition.

METHODS

We conducted a literature review using PubMed, Google Scholar and Science Direct for this purpose, a total of 130 articles were reviewed out of which 80 (from the past 5 years) including originally published research, review articles and abstracts were also included. Exclusion criteria of the selected studies was year of publication, irrelevancy and animal studies based on the purpose of current study. The aim of this study was to check nutritional management in patients having complications of liver cirrhosis.

RESULTS

According to the guidelines, for the conservation of normal nutritional status of the malnourished patients', energy should be provided 35 kcal/kg/day while to prevent hypoalbuminemia and maintain the protein stores in the body, 1.5 g/kg/day protein has been recommended. Carbohydrates and fats for cirrhosis patients are recommended 50% to 60% and 10% to 20% of the total dietary intake respectively.

CONCLUSION

Initial identification and prevention of malnutrition have the probability to lead to better health outcomes, prevention of complications of the disease, and improving quality of life.