Examinando por Autor "Garcia-Carretero, Rafael"

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Assessment of Classification Models and Relevant Features on Nonalcoholic Steatohepatitis Using Random Forest
(MDPI, 2021-06) Garcia-Carretero, Rafael; Holgado-Cuadrado, Roberto; Barquero-Perez, Oscar
Nonalcoholic fatty liver disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is the most common cause of chronic liver disease in developed countries. Certain conditions, including mild inflammation biomarkers, dyslipidemia, and insulin resistance, can trigger a progression to nonalcoholic steatohepatitis (NASH), a condition characterized by inflammation and liver cell damage. We demonstrate the usefulness of machine learning with a case study to analyze the most important features in random forest (RF) models for predicting patients at risk of developing NASH. We collected data from patients who attended the Cardiovascular Risk Unit of Mostoles University Hospital (Madrid, Spain) from 2005 to 2021. We reviewed electronic health records to assess the presence of NASH, which was used as the outcome. We chose RF as the algorithm to develop six models using different pre-processing strategies. The performance metrics was evaluated to choose an optimized model. Finally, several interpretability techniques, such as feature importance, contribution of each feature to predictions, and partial dependence plots, were used to understand and explain the model to help obtain a better understanding of machine learning-based predictions. In total, 1525 patients met the inclusion criteria. The mean age was 57.3 years, and 507 patients had NASH (prevalence of 33.2%). Filter methods (the chi-square and Mann–Whitney–Wilcoxon tests) did not produce additional insight in terms of interactions, contributions, or relationships among variables and their outcomes. The random forest model correctly classified patients with NASH to an accuracy of 0.87 in the best model and to 0.79 in the worst one. Four features were the most relevant: insulin resistance, ferritin, serum levels of insulin, and triglycerides. The contribution of each feature was assessed via partial dependence plots. Random forest-based modeling demonstrated that machine learning can be used to improve interpretability, produce understanding of the modeled behavior, and demonstrate how far certain features can contribute to predictions.
Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population
(Springer Science and Business Media LLC, 2017-12) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Barquero-Perez, Oscar; Goya-Esteban, Rebeca; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Ramos-Lopez, Javier
Differences in Trends in Admissions and Outcomes among Patients from a Secondary Hospital in Madrid during the COVID-19 Pandemic: A Hospital-Based Epidemiological Analysis (2020–2022)
(MDPI, 2023-07-24) Garcia-Carretero, Rafael; Vazquez-Gomez, Oscar; Ordoñez-Garcia, Maria; Garrido-Peño, Noelia; Gil-Prieto, Ruth; Gil-de-Miguel, Angel
Hospitalization burden and epidemiology of the COVID-19 pandemic in Spain (2020-2021)
(Springer Science and Business Media LLC, 2023-07-18) Garcia-Carretero, Rafael; Vazquez-Gomez, Oscar; Gil-Prieto, Ruth; Gil-de-Miguel, Angel
Background Spain had some of Europe’s highest incidence and mortality rates for coronavirus disease 2019 (COVID19). Here we describe the epidemiology and trends in hospitalizations, the number of critical patients, and deaths in Spain in 2020 and 2021. Methods We performed a descriptive, retrospective, nationwide study using an administrative database, the Minimum Basic Data Set at Hospitalization, which includes 95–97% of discharge reports for patients hospitalized in Spain in 2020 and 2021. We analyzed the number of hospitalizations, admissions to intensive care units, and deaths and their geographic distribution across regions of Spain. Results As of December 31, 2021, a total of 498,789 patients (1.04% of the entire Spanish population) had needed hospitalization. At least six waves of illness were identified. Men were more prone to hospitalization than women. The median age was 66. A total of 54,340 patients (10.9% of all hospitalizations) had been admitted to the intensive care unit. We identified 71,437 deaths (mortality rate of 14.3% among hospitalized patients). We also observed important differences among regions, with Madrid being the epicenter of hospitalizations and mortality. Conclusions We analyzed Spain’s response to COVID-19 and describe here its experiences during the pandemic in terms of hospitalizations, critical illness, and deaths. This research highlights changes over several months and waves and the importance of factors such as vaccination, the predominant variant of the virus, and public health interventions in the rise and fall of the outbreaks.
Hospitalization Burden Related to Herpes Zoster Infection in Spain (2016–2019)
(Springer Science and Business Media LLC, 2022-11-08) Corcuera-Munguia, Marta; Gil-Prieto, Ruth; Garcia-Carretero, Rafael; Gil-de-Miguel, Angel
Introduction: Herpes zoster (HZ) and its complications still represent a significant burden for patients and health care systems. In Spain, vaccination is progressively being introduced and recommended for patients between 65 and 80 years old and patients [ 18 years of age suffering from certain immunosuppression conditions. The aim of this study is to estimate the number of hospital admissions related to HZ from 2016 to 2019 in Spain. Methods: Data were collected from the Minimum Basic DataSet (MBDS) and codified according to the Spanish version of the 10th International Classification of Disease (ICD-10- CM codes B02—B02.9). Among others, variables such as sex, age and presence of complications were included. Results: A total of 27,642 hospitalizations were identified (90% in patients [ 50 and 45.8% in patients [80). Women represented 51.2% of the patients, and 59.9% of patients presented complications related to HZ. The hospitalization rate was 17.74, the mortality rate was 1.2, and the case fatality rate was 6.75%. All rates were significantly higher with age, among men and in complicated HZ. Immunosuppression status for which vaccination had been recommended represented 22.7% of the total cases, affecting mostly individuals [ 65 and causing more deaths in those[ 80 years. The estimated annual cost of hospitalization for herpes zoster was €35,738,285, and the mean cost per patient was €5172. Conclusion: The hospitalization burden for HZ is still important in Spain. Data on the current epidemiology are important to evaluate future vaccination strategies.
Identification of clinically relevant features in hypertensive patients using penalized regression: a case study of cardiovascular events
(Springer Science and Business Media LLC, 2019-07-25) Garcia-Carretero, Rafael; Barquero-Perez, Oscar; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Goya-Esteban, Rebeca; Ramos-Lopez, Javier
Insulin resistance is a cardiovascular risk factor in hypertensive adults without type 2 diabetes mellitus
(Springer Science and Business Media LLC, 2023-10-09) Garcia-Carretero, Rafael; Vazquez-Gomez, Oscar; Gil-Prieto, Ruth; Gil-de-Miguel, Angel
Predictive modeling of hypophosphatasia based on a case series of adult patients with persistent hypophosphatasemia
(Springer Science and Business Media LLC, 2021-02-22) Garcia-Carretero, Rafael; Olid-Velilla, Monica; Perez-Torrella, Diana; Darnaude-Ortiz, Maria-Teresa; Diaz-Bustamante-Zuloeta, Aranzazu; Torres-Pacho, Nemesio; Jair-Antonio, Tenorio
Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic Populations
(Springer Science and Business Media LLC, 2019-12) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Barquero-Perez, Oscar; Ramos-Lopez, Javier
Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population
(Springer Science and Business Media LLC, 2020-05) Garcia-Carretero, Rafael; Vigil-Medina, Luis; Mora-Jimenez, Inmaculada; Soguero-Ruiz, Cristina; Barquero-Perez, Oscar; Ramos-Lopez, Javier
Prediabetes is a type of hyperglycemia in which patients have blood glucose levels above normal but below the threshold for type 2 diabetes mellitus (T2DM). Prediabetic patients are considered to be at high risk for developing T2DM, but not all will eventually do so. Because it is difficult to identify which patients have an increased risk of developing T2DM, we developed a model of several clinical and laboratory features to predict the development of T2DM within a 2-year period. We used a supervised machine learning algorithm to identify at-risk patients from among 1647 obese, hypertensive patients. The study period began in 2005 and ended in 2018. We constrained data up to 2 years before the development of T2DM. Then, using a time series analysis with the features of every patient, we calculated one linear regression line and one slope per feature. Features were then included in a K-nearest neighbors classification model. Feature importance was assessed using the random forest algorithm. The K-nearest neighbors model accurately classified patients in 96% of cases, with a sensitivity of 99%, specificity of 78%, positive predictive value of 96%, and negative predictive value of 94%. The random forest algorithm selected the homeostatic model assessment–estimated insulin resistance, insulin levels, and body mass index as the most important factors, which in combination with KNN had an accuracy of 99% with a sensitivity of 99% and specificity of 97%. We built a prognostic model that accurately identified obese, hypertensive patients at risk for developing T2DM within a 2-year period. Clinicians may use machine learning approaches to better assess risk for T2DM and better manage hypertensive patients. Machine learning algorithms may help health care providers make more informed decisions.