Browsing by Keyword "prediction"
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Publication Biomarkers in DILI: One More Step Forward.(2016-08-22) Robles-Díaz, Mercedes; Medina-Caliz, Inmaculada; Stephens, Camilla; Andrade, Raúl J; Lucena, M IsabelDespite being relatively rare, drug-induced liver injury (DILI) is a serious condition, both for the individual patient due to the risk of acute liver failure, and for the drug development industry and regulatory agencies due to associations with drug development attritions, black box warnings, and postmarketing withdrawals. A major limitation in DILI diagnosis and prediction is the current lack of specific biomarkers. Despite refined usage of traditional liver biomarkers in DILI, reliable disease outcome predictions are still difficult to make. These limitations have driven the growing interest in developing new more sensitive and specific DILI biomarkers, which can improve early DILI prediction, diagnosis, and course of action. Several promising DILI biomarker candidates have been discovered to date, including mechanistic-based biomarker candidates such as glutamate dehydrogenase, high-mobility group box 1 protein and keratin-18, which can also provide information on the injury mechanism of different causative agents. Furthermore, microRNAs have received much attention lately as potential non-invasive DILI biomarker candidates, in particular miR-122. Advances in "omics" technologies offer a new approach for biomarker exploration studies. The ability to screen a large number of molecules (e.g., metabolites, proteins, or DNA) simultaneously enables the identification of 'toxicity signatures,' which may be used to enhance preclinical safety assessments and disease diagnostics. Omics-based studies can also provide information on the underlying mechanisms of distinct forms of DILI that may further facilitate the identification of early diagnostic biomarkers and safer implementation of personalized medicine. In this review, we summarize recent advances in the area of DILI biomarker studies.Publication Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.(2022-05-17) Klén, Riku; Purohit, Disha; Gómez-Huelgas, Ricardo; Casas-Rojo, José Manuel; Antón-Santos, Juan Miguel; Núñez-Cortés, Jesús Millán; Lumbreras, Carlos; Ramos-Rincón, José Manuel; García Barrio, Noelia; Pedrera-Jiménez, Miguel; Lalueza Blanco, Antonio; Martin-Escalante, María Dolores; Rivas-Ruiz, Francisco; Onieva-García, Maria Ángeles; Young, Pablo; Ramirez, Juan Ignacio; Titto Omonte, Estela Edith; Gross Artega, Rosmery; Canales Beltrán, Magdy Teresa; Valdez, Pascual Ruben; Pugliese, Florencia; Castagna, Rosa; Huespe, Ivan A; Boietti, Bruno; Pollan, Javier A; Funke, Nico; Leiding, Benjamin; Gómez-Varela, DavidNew SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.