Effect of mild cortisol cosecretion on body composition and metabolic parameters in patients with primary hyperaldosteronism.

OBJECTIVE: To investigate the effects of simultaneous cortisol cosecretion (CCS) on body composition in computed tomography (CT)-imaging and metabolic parameters in patients with primary aldosteronism (PA) with the objective of facilitating early detection. DESIGN: Retrospective cohort study. PATIENTS: Forty-seven patients with PA and CCS confirmed by 1-mg dexamethasone suppression test (DST) with a cutoff of ≥1.8 µg/dL were compared with PA patients with excluded CCS (non-CCS, n = 47) matched by age and sex. METHODS: Segmentation of the fat compartments and muscle area at the third lumbar region was performed on non-contrast-enhanced CT images with dedicated segmentation software. Additionally, liver, spleen, pancreas and muscle attenuation were compared between the two groups. RESULTS: Mean cortisol after DST was 1.2 µg/dL (33.1 nmol/L) in the non-CCS group and 3.2 µg/dL (88.3 nmol/L) in the CCS group with mild autonomous cortisol excess (MACE). No difference in total, visceral and subcutaneous fat volumes was observed between the CCS and non-CCS group (p = .7, .6 and .8, respectively). However, a multivariable regression analysis revealed a significant correlation between total serum cholesterol and results of serum cortisol after 1-mg DST (p = .026). Classification of the patients based on visible lesion on CT and PA-lateralization via adrenal venous sampling also did not show any significant differences in body composition. CONCLUSION: MACE in PA patients does not translate into body composition changes on CT-imaging. Therefore, early detection of concurrent CCS in PA is currently only attainable through biochemical tests. Further investigation of the long-term clinical adverse effects of MACE in PA is necessary.

Integration of clinical parameters and CT-based radiomics improves machine learning assisted subtyping of primary hyperaldosteronism.

Objectives: The aim of this study was to investigate an integrated diagnostics approach for prediction of the source of aldosterone overproduction in primary hyperaldosteronism (PA). Methods: 269 patients from the prospective German Conn Registry with PA were included in this study. After segmentation of adrenal glands in native CT images, radiomic features were calculated. The study population consisted of a training (n = 215) and a validation (n = 54) cohort. The k = 25 best radiomic features, selected using maximum-relevance minimum-redundancy (MRMR) feature selection, were used to train a baseline random forest model to predict the result of AVS from imaging alone. In a second step, clinical parameters were integrated. Model performance was assessed via area under the receiver operating characteristic curve (ROC AUC). Permutation feature importance was used to assess the predictive value of selected features. Results: Radiomics features alone allowed only for moderate discrimination of the location of aldosterone overproduction with a ROC AUC of 0.57 for unilateral left (UL), 0.61 for unilateral right (UR), and 0.50 for bilateral (BI) aldosterone overproduction (total 0.56, 95% CI: 0.45-0.65). Integration of clinical parameters into the model substantially improved ROC AUC values (0.61 UL, 0.68 UR, and 0.73 for BI, total 0.67, 95% CI: 0.57-0.77). According to permutation feature importance, lowest potassium value at baseline and saline infusion test (SIT) were the two most important features. Conclusion: Integration of clinical parameters into a radiomics machine learning model improves prediction of the source of aldosterone overproduction and subtyping in patients with PA.

Serological prevalence of antibodies to SARS-CoV-2 in a prison in the Peruvian Amazon / Prevalencia serológica de anticuerpos al SARS-CoV-2 en una prisión de la amazonia del Perú

Objectives: The objective of this research was to describe the prevalence of antibodies to COVID19 in a prison in Ucayali, Peru. Methods: 325 inmates were selected from the Penitentiary Centerin Pucallpa, Peru through simple random sampling. On 06/25/20 and 07/24/20, the fingerpuncture samples were taken from the inmates for COVID-19, the blood sample was obtainedfor the chemo-luminescent microparticle immunoassay tests. Descriptive statistics were usedfor data analysis with InfoStat software. Results: Of the 325 inmates evaluated, 80 were women(25%) and 245 men (75%), the mean age was 43 years (SD 12.44). Regarding the serologicalresults, 137 (42%) were found with positive IgG and 188 (58%) negative IgG. Regarding positiveIgM 145 (45%) and negative IgM 180 (55%). Regarding the distribution of positive cases by sex,we have 47 women and 141 men with a positive result for IgM or IgG. Conclusion: It is concludedthat there is a high number of inmates who have antibodies against COVID-19.

Objetivos: El objetivo de esta investigación fue el de describir la prevalencia de anticuerpos para COVID-19 en una cárcel de Ucayali, Perú. Métodos: Se seleccionaron 325 reclusos del Centro penitenciario en Pucallpa, Perú mediante un muestreo aleatorio simple. Del 25/06/20 y el 24/07/20 se tomaron las muestras punción dactilar de los reclusos para COVID-19, se obtuvo la muestra de sangre para las pruebas de inmunoensayo de micropartículas quimio luminiscentes. Se utilizó estadística descriptiva para en análisis de los datos con el software InfoStat. Resultados: De los 325 reclusos evaluados 80 eran mujeres (25%) y 245 varones (75%), la media de la edad fue de 43 años (DE 12,44). En cuantos a los resultados serológicos se encontraron a con IgG positivo a 137 (42%) e IgG negativo 188 (58%). En cuanto a IgM positivo 145 (45%) e IgM negativo 180 (55%). En cuanto a la distribución de casos positivos por sexo contamos con 47 mujeres y 141 varones con resultado positivo para IgM o IgG. Conclusión: Se concluye con que existe un elevado número de reclusos que tienen anticuerpos contra la COVID-19.