Myosteatosis in a systemic inflammation-dependent manner predicts favorable survival outcomes in locally advanced esophageal cancer

Increased adiposity and its attendant metabolic features as well as systemic inflammation have been associated with prognosis in locally advanced esophageal cancer (LAEC). However, whether myosteatosis and its combination with systemic inflammatory markers are associated with prognosis of esophageal cancer is unknown. Our study aimed to investigate the influence of myosteatosis and its association with systemic inflammation on progression-free survival (PFS) and overall survival (OS) in LAEC patients treated with definitive chemoradiotherapy (dCRT). We retrospectively gathered information on 123 patients with LAEC submitted to dCRT at the University of Campinas Hospital. Computed tomography (CT) images at the level of L3 were analyzed to assess muscularity and adiposity. Systemic inflammation was mainly measured by calculating the neutrophil-to-lymphocyte ratio (NLR). Median PFS for patients with myosteatosis (n = 72) was 11.0 months vs 4.0 months for patients without myosteatosis (n = 51) (hazard ratio [HR]: 0.53; 95% confidence interval [CI], 0.34-0.83; P = .005). Myosteatosis was also independently associated with a favorable OS. Systemic inflammation (NLR > 2.8) was associated with a worse prognosis. The combination of myosteatosis with systemic inflammation revealed that the subgroup of patients with myosteatosis and without inflammation presented less than half the risk of disease progression (HR: 0.47; 95% CI: 0.26-0.85; P = .013) and death (HR: 0.39; 95% CI, 0.21-0.72; P = .003) compared with patients with inflammation. This study demonstrated that myosteatosis without systemic inflammation was independently associated with favorable PFS and OS in LAEC patients treated with dCRT81669676976FAPESP – Fundação de Amparo à Pesquisa Do Estado De São Paulo2018/23428-

The Role of Registers in Increasing Knowledge and Improving Management of Children and Adolescents Affected by Familial Hypercholesterolemia: the LIPIGEN Pediatric Group

Pathology registers can be a useful tool to overcome obstacles in the identification and management of familial hypercholesterolemia since childhood. In 2018, the LIPIGEN pediatric group was constituted within the Italian LIPIGEN study to focus on FH subjects under 18 years. This work aimed at discussing its recent progress and early outcomes. Demographic, biochemical, and genetic baseline characteristics were collected, with an in-depth analysis of the genetic defects. The analysis was carried out on 1,602 children and adolescents (mean age at baseline 9.9 ± 4.0 years), and almost the whole cohort underwent the genetic test (93.3%). Overall, the untreated mean value of LDL-C was 220.0 ± 97.2 mg/dl, with an increasing gradient from subjects with a negative (N = 317; mean untreated LDL-C = 159.9 ± 47.7 mg/dl), inconclusive (N = 125; mean untreated LDL-C = 166.4 ± 56.5 mg/dl), or positive (N = 1,053; mean untreated LDL-C = 246.5 ± 102.1 mg/dl) genetic diagnosis of FH. In the latter group, the LDL-C values presented a great variability based on the number and the biological impact of involved causative variants. The LIPIGEN pediatric group represents one of the largest cohorts of children with FH, allowing the deepening of the characterization of their baseline and genetic features, providing the basis for further longitudinal investigations for complete details

Myosteatosis in a systemic inflammation‐dependent manner predicts favorable survival outcomes in locally advanced esophageal cancer

Abstract Increased adiposity and its attendant metabolic features as well as systemic inflammation have been associated with prognosis in locally advanced esophageal cancer (LAEC). However, whether myosteatosis and its combination with systemic inflammatory markers are associated with prognosis of esophageal cancer is unknown. Our study aimed to investigate the influence of myosteatosis and its association with systemic inflammation on progression‐free survival (PFS) and overall survival (OS) in LAEC patients treated with definitive chemoradiotherapy (dCRT). We retrospectively gathered information on 123 patients with LAEC submitted to dCRT at the University of Campinas Hospital. Computed tomography (CT) images at the level of L3 were analyzed to assess muscularity and adiposity. Systemic inflammation was mainly measured by calculating the neutrophil‐to‐lymphocyte ratio (NLR). Median PFS for patients with myosteatosis (n = 72) was 11.0 months vs 4.0 months for patients without myosteatosis (n = 51) (hazard ratio [HR]: 0.53; 95% confidence interval [CI], 0.34‐0.83; P = .005). Myosteatosis was also independently associated with a favorable OS. Systemic inflammation (NLR > 2.8) was associated with a worse prognosis. The combination of myosteatosis with systemic inflammation revealed that the subgroup of patients with myosteatosis and without inflammation presented less than half the risk of disease progression (HR: 0.47; 95% CI: 0.26‐0.85; P = .013) and death (HR: 0.39; 95% CI, 0.21‐0.72; P = .003) compared with patients with inflammation. This study demonstrated that myosteatosis without systemic inflammation was independently associated with favorable PFS and OS in LAEC patients treated with dCRT

Construction of a nomogram for predicting COVID-19 in-hospital mortality: A machine learning analysis

Background and objectives: We aim to verify the use of ML algorithms to predict patient outcome using a relatively small dataset and to create a nomogram to assess in-hospital mortality of patients with COVID-19. Methods: A database of 200 COVID-19 patients admitted to the Clinical Hospital of State University of Campinas (UNICAMP) was used in this analysis. Patient features were divided into three categories: clinical, chest abnormalities, and body composition characteristics acquired by computerized tomography. These features were evaluated independently and combined to predict patient outcomes. To minimize performance fluctuations due to low sample number, reduce possible bias related to outliers, and evaluate the uncertainties generated by the small dataset, we developed a shuffling technique, a modified version of the Monte Carlo Cross Validation, creating several subgroups for training the algorithm and complementary testing subgroups. The following ML algorithms were tested: random forest, boosted decision trees, logistic regression, support vector machines, and neural networks. Performance was evaluated by analyzing Receiver operating characteristic (ROC) curves. The importance of each feature in the determination of the outcome predictability was also studied and a nomogram was created based on the most important features selected by the exclusion test. Results: Among the different sets of features, clinical variables age, lymphocyte number and weight were the most valuable features for prognosis prediction. However, we observed that skeletal muscle radiodensity and presence of pleural effusion were also important for outcome determination. Integrating these independent predictors was successfully developed to accurately predict mortality in COVID-19 in hospital patients. A nomogram based on these five features was created to predict COVID-19 mortality in hospitalized patients. The area under the ROC curve was 0.86 ± 0.04. Conclusion: ML algorithms can be reliable for the prediction of COVID-19-related in-hospital mortality, even when using a relatively small dataset. The success of ML techniques in smaller datasets broadens the applicability of these methods in several problems in the medical area. In addition, feature importance analysis allowed us to determine the most important variables for the prediction tasks resulting in a nomogram with good accuracy and clinical utility in predicting COVID-19 in-hospital mortality