
Autor José Leonardo Mojica Peñaranda
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Documentos disponibles escritos por este autor (2)


Controlled asystole protocols: a key to more organ donors? / Juan Ignacio Marín Zuluaga ; José Leonardo Mojica Peñaranda ; Colina Vargas, Yerlin Andrés ; Paredes Zapata, David ; Vera Marín, Cristian ; Ramírez Paesano, Carlos
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Título : Controlled asystole protocols: a key to more organ donors? Tipo de documento : documento electrónico Autores : Juan Ignacio Marín Zuluaga, Autor ; José Leonardo Mojica Peñaranda, Autor ; Colina Vargas, Yerlin Andrés, Autor ; Paredes Zapata, David, Autor ; Vera Marín, Cristian, Autor ; Ramírez Paesano, Carlos, Autor Fecha de publicación : 2025 Títulos uniformes : Colombian Journal of Anesthesiology Idioma : Inglés (eng) Palabras clave : Transplantation Tissue and organ procurement Donor selection Brain death Induced cardiac arrest Resumen : Introduction: Donation after controlled circulatory death (DCD) is organ donation that takes place following diagnosis of death by cardio-respiratory criteria. This diagnosis is certified in patients admitted to the Intensive Care Unit (ICU) following the withdrawal of life-support treatment. Objectives: To describe the main causes of mortality that make the patient a potential donor under a controlled asystole protocol within the ICU of Hospital Pablo Tobón Uribe in Medellín, Colombia. Methods: This was an observational, descriptive, retrospective study conducted in adult patients who died during the first quarter of 2024 in any of the ICUs at Hospital Pablo Tobón Uribe in Medellín. Data obtained were analyzed using descriptive statistical methods, and all statistical analyses were performed using Jamovi software. Results: A total of 103 participants were included, 36 women (35%) and 67 men (65%), with a median age of 61 years (44.5–72). The leading cause of death among participants was septic shock. Seventeen potential donors (16.7%) were identified. Conclusions: There is significant potential to increase the organ donation rate through the implementation of appropriate controlled asystole protocols. Mención de responsabilidad : Yerlin Andrés Colina Vargas, David Paredes Zapata, Juan Ignacio Marín, Leonardo Mojica Peñaranda, Cristian Vera Marín, Carlos Ramírez Paesano Referencia : Colomb. J. Anesthesiol. [Internet]. 2025 May 21 [cited 2025 Jun. 19] DOI (Digital Object Identifier) : 10.5554/22562087.e1149 Derechos de uso : CC BY-NC-ND En línea : https://www.revcolanest.com.co/index.php/rca/article/view/1149 Enlace permanente : https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_dis Controlled asystole protocols: a key to more organ donors? [documento electrónico] / Juan Ignacio Marín Zuluaga, Autor ; José Leonardo Mojica Peñaranda, Autor ; Colina Vargas, Yerlin Andrés, Autor ; Paredes Zapata, David, Autor ; Vera Marín, Cristian, Autor ; Ramírez Paesano, Carlos, Autor . - 2025.
Obra : Colombian Journal of Anesthesiology
Idioma : Inglés (eng)
Palabras clave : Transplantation Tissue and organ procurement Donor selection Brain death Induced cardiac arrest Resumen : Introduction: Donation after controlled circulatory death (DCD) is organ donation that takes place following diagnosis of death by cardio-respiratory criteria. This diagnosis is certified in patients admitted to the Intensive Care Unit (ICU) following the withdrawal of life-support treatment. Objectives: To describe the main causes of mortality that make the patient a potential donor under a controlled asystole protocol within the ICU of Hospital Pablo Tobón Uribe in Medellín, Colombia. Methods: This was an observational, descriptive, retrospective study conducted in adult patients who died during the first quarter of 2024 in any of the ICUs at Hospital Pablo Tobón Uribe in Medellín. Data obtained were analyzed using descriptive statistical methods, and all statistical analyses were performed using Jamovi software. Results: A total of 103 participants were included, 36 women (35%) and 67 men (65%), with a median age of 61 years (44.5–72). The leading cause of death among participants was septic shock. Seventeen potential donors (16.7%) were identified. Conclusions: There is significant potential to increase the organ donation rate through the implementation of appropriate controlled asystole protocols. Mención de responsabilidad : Yerlin Andrés Colina Vargas, David Paredes Zapata, Juan Ignacio Marín, Leonardo Mojica Peñaranda, Cristian Vera Marín, Carlos Ramírez Paesano Referencia : Colomb. J. Anesthesiol. [Internet]. 2025 May 21 [cited 2025 Jun. 19] DOI (Digital Object Identifier) : 10.5554/22562087.e1149 Derechos de uso : CC BY-NC-ND En línea : https://www.revcolanest.com.co/index.php/rca/article/view/1149 Enlace permanente : https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_dis Reserva
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Código de barras Número de Ubicación Tipo de medio Ubicación Sección Estado DD002378 AC-2025-061 Archivo digital Producción Científica Artículos científicos Disponible Early Prediction of ICU Readmissions Using Machine Learning in a Colombian University Hospital / José Leonardo Mojica Peñaranda ; Camacho Cocollo, J.E ; Lerma Pazos, Joel Andrés
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Título : Early Prediction of ICU Readmissions Using Machine Learning in a Colombian University Hospital Tipo de documento : documento electrónico Autores : José Leonardo Mojica Peñaranda, Autor ; Camacho Cocollo, J.E, Autor ; Lerma Pazos, Joel Andrés, Autor Fecha de publicación : 2025 Títulos uniformes : Springer Idioma : Inglés (eng) Resumen : Hospital readmissions are common, unplanned, and potentially avoidable occurrences linked to elevated morbidity and mortality rates. In Colombia, the primary concerns regarding ICU readmissions involve the significant expenses associated with treating patients readmitted due to deteriorating circumstances or heightened illness severity, alongside inadequate discharge decisions made by certain physicians or experts, which decrease the efficacy of healthcare facilities. This study aims to develop a predictive model that provides an early warning for the readmission of adult patients to the intensive care unit. In order to accomplish this, a variety of database administration and data analysis technologies were employed, alongside with machine learning models referenced in the literature. These models were trained and validated using data provided by the Hospital Pablo Tobón Uribe (HPTU) in Medellín. As a result of this project, a predictive model was developed with an accuracy score of 0.74 and an AUC value of 0.74, providing valuable information to physicians and specialists when making ICU discharge decisions. This model generates alerts for patients at risk of readmission if discharged, improving decision-making in intensive care settings. Mención de responsabilidad : Joel Andrés Lerma Pazos, J. E. Camacho Cogollo, Jose Leonardo Mojica Peñaranda DOI (Digital Object Identifier) : 10.1007/978-3-031-96538-8_11 Derechos de uso : CC BY-NC-ND En línea : https://link.springer.com/chapter/10.1007/978-3-031-96538-8_11 Enlace permanente : https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_dis Early Prediction of ICU Readmissions Using Machine Learning in a Colombian University Hospital [documento electrónico] / José Leonardo Mojica Peñaranda, Autor ; Camacho Cocollo, J.E, Autor ; Lerma Pazos, Joel Andrés, Autor . - 2025.
Obra : Springer
Idioma : Inglés (eng)
Resumen : Hospital readmissions are common, unplanned, and potentially avoidable occurrences linked to elevated morbidity and mortality rates. In Colombia, the primary concerns regarding ICU readmissions involve the significant expenses associated with treating patients readmitted due to deteriorating circumstances or heightened illness severity, alongside inadequate discharge decisions made by certain physicians or experts, which decrease the efficacy of healthcare facilities. This study aims to develop a predictive model that provides an early warning for the readmission of adult patients to the intensive care unit. In order to accomplish this, a variety of database administration and data analysis technologies were employed, alongside with machine learning models referenced in the literature. These models were trained and validated using data provided by the Hospital Pablo Tobón Uribe (HPTU) in Medellín. As a result of this project, a predictive model was developed with an accuracy score of 0.74 and an AUC value of 0.74, providing valuable information to physicians and specialists when making ICU discharge decisions. This model generates alerts for patients at risk of readmission if discharged, improving decision-making in intensive care settings. Mención de responsabilidad : Joel Andrés Lerma Pazos, J. E. Camacho Cogollo, Jose Leonardo Mojica Peñaranda DOI (Digital Object Identifier) : 10.1007/978-3-031-96538-8_11 Derechos de uso : CC BY-NC-ND En línea : https://link.springer.com/chapter/10.1007/978-3-031-96538-8_11 Enlace permanente : https://hospitalpablotobon.cloudbiteca.com/pmb/opac_css/index.php?lvl=notice_dis Reserva
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Código de barras Número de Ubicación Tipo de medio Ubicación Sección Estado DD002379 AC-2025-062 Archivo digital Producción Científica Artículos científicos Disponible