Cardiometabolic risk estimation using exposome data and machine learning.

BACKGROUND: The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. OBJECTIVE: Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. METHODS: From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. RESULTS: The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. CONCLUSIONS: We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.

Anestesia para transplante renal: experiencia de tres años / Anesthesia for kidney transplantation: three years experience

El desarrollo de nuevos agentes anestésicos en la última década ha hecho posible que el Anestesiólogo prefiera la anestesia general sobre la anestesia regional para el transplante renal (TR). La mayor comprensión de los cuidados preoperatorios, transanestésicos y postoperatorios en el Hospital de Especialidades del Centro Médico Nacional Siglo XXI del Instituto Mexicano del Seguro Social (IMSS), ha hecho posible que la sobrevida a un año para el donador vivo relacionado sea del 85 por ciento y un 70 por ciento para el donador de cadáver y donador vivo relacionado emocionalmente. En un lapso de 3 años (Enero de 1991 a Diciembre de 1993). Se informa la experiencia de 133 pacientes, portadores de insuficiencia renal crónica (IRC). Se les administró anestesia general a pacientes para TR. En este grupo se registraron, peso, sexo, edad, riesgo anestésico-quirúrgico, fuente de donación, causa primaria de IRC, anormalidades bioquímicas, comportamiento hemodinámico y complicaciones transanestésicas. Con la productividad señalada se coloca al Hospital de Especialidades a la vanguardia. En el presente artículo se discuten los aspectos del manejo anestésico en el paciente que será sometido a Transplante Renal