Prevalencia nacional de Eventos Adversos Medicamentosos en pacientes hospitalizados

Objetivo: El propósito de este trabajo es estudiar la prevalencia de eventos adversos medicamentosos (EAM) en pacientes hospitalizados durante el período 2019-2020 en Chile. Además, como parte de la investigación, se realizó una validación del método que etiqueta la ocurrencia de EAM en base a los diagnósticos de egreso de los casos analizados. Diseño: La prevalencia de EAM fue estudiada para cerca de 1,7 millones de pacientes, para los cuales se analizó, además de los diagnósticos CIE10 de egreso, información sociodemográfica e indicadores de resultado sanitario de la atención, tales como el peso GRD, largo de estadía y mortalidad. Para la validación del método de identificación de EAM, se seleccionó una muestra aleatoria representativa estratificada por sexo y especialidad médica del año 2019 en un hospital público de Chile, cuyos resúmenes de egreso fueron analizados por un grupo de expertos de forma retrospectiva. Resultados: Los resultados muestran una prevalencia de EAM u otras sustancias de 2,7% y 3,1% en los egresos hospitalarios de los años 2019 y 2020 a nivel nacional y una precisión del instrumento de al menos un 83,3% (IC 90%). Conclusiones: Este estudio permite describir un fenómeno por medio de la estimación basada en datos reales, el cual es esencial para el diseño de políticas públicas en salud y estudios que apunten a enriquecer la calidad y seguridad del paciente.

Objective: To study the prevalence of adverse drug events (ADE) in hospitalized patients in Chile. As part of our research, we also assessed the validity of the method used to identify the occurrence of an ADE based on the discharge diagnoses of the patient. Design: The study included 1,7 million patients hospitalized during 2019-2020. We analyzed the following variables for each patient: ICD-10 discharge diagnoses, sociodemographic information, and clinical outcome indicators, i.e., diagnosis-related group (DRG) weight, length of stay, and mortality. To validate the method for the identification of ADEs, first, we generated a random representative sample of patients, stratified by sex and medical specialty, hospitalized in a Chilean public hospital in 2019, and then we compared the outcome of the method with the opinion of a group of clinical experts that reviewed each patient's discharge summary retrospectively. Results: The prevalence of ADEs in hospitalized patients in Chile during 2019 and 2020 was 2,7% and 3,1%, respectively. The precision of the method used to identify ADEs was 83,3% or higher (CI 90%). Conclusions: This paper uses nationwide data to describe the prevalence of ADEs and their correlation with different factors associated with the patient, the patient's disease, and the health service. These studies are essential to designing public health policies that effectively address healthcare quality and patient safety.

[Adverse Drug Events in Hospitalized Patients in Chile: Prevalence & Implications]. / Prevalencia nacional de Eventos Adversos Medicamentosos en pacientes hospitalizados.

OBJECTIVE: To study the prevalence of adverse drug events (ADE) in hospitalized patients in Chile. As part of our research, we also assessed the validity of the method used to identify the occurrence of an ADE based on the discharge diagnoses of the patient. DESIGN: The study included 1,7 million patients hospitalized during 2019-2020. We analyzed the following variables for each patient: ICD-10 discharge diagnoses, sociodemographic information, and clinical outcome indicators, i.e., diagnosis-related group (DRG) weight, length of stay, and mortality. To validate the method for the identification of ADEs, first, we generated a random representative sample of patients, stratified by sex and medical specialty, hospitalized in a Chilean public hospital in 2019, and then we compared the outcome of the method with the opinion of a group of clinical experts that reviewed each patient's discharge summary retrospectively. RESULTS: The prevalence of ADEs in hospitalized patients in Chile during 2019 and 2020 was 2,7% and 3,1%, respectively. The precision of the method used to identify ADEs was 83,3% or higher (CI 90%). CONCLUSIONS: This paper uses nationwide data to describe the prevalence of ADEs and their correlation with different factors associated with the patient, the patient's disease, and the health service. These studies are essential to designing public health policies that effectively address healthcare quality and patient safety.

Methodologically grounded semantic analysis of large volume of chilean medical literature data applied to the analysis of medical research funding efficiency in Chile.

BACKGROUND: Medical knowledge is accumulated in scientific research papers along time. In order to exploit this knowledge by automated systems, there is a growing interest in developing text mining methodologies to extract, structure, and analyze in the shortest time possible the knowledge encoded in the large volume of medical literature. In this paper, we use the Latent Dirichlet Allocation approach to analyze the correlation between funding efforts and actually published research results in order to provide the policy makers with a systematic and rigorous tool to assess the efficiency of funding programs in the medical area. RESULTS: We have tested our methodology in the Revista Médica de Chile, years 2012-2015. 50 relevant semantic topics were identified within 643 medical scientific research papers. Relationships between the identified semantic topics were uncovered using visualization methods. We have also been able to analyze the funding patterns of scientific research underlying these publications. We found that only 29% of the publications declare funding sources, and we identified five topic clusters that concentrate 86% of the declared funds. CONCLUSIONS: Our methodology allows analyzing and interpreting the current state of medical research at a national level. The funding source analysis may be useful at the policy making level in order to assess the impact of actual funding policies, and to design new policies.

Machine Learning Readmission Risk Modeling: A Pediatric Case Study.

BACKGROUND: Hospital readmission prediction in pediatric hospitals has received little attention. Studies have focused on the readmission frequency analysis stratified by disease and demographic/geographic characteristics but there are no predictive modeling approaches, which may be useful to identify preventable readmissions that constitute a major portion of the cost attributed to readmissions. OBJECTIVE: To assess the all-cause readmission predictive performance achieved by machine learning techniques in the emergency department of a pediatric hospital in Santiago, Chile. MATERIALS: An all-cause admissions dataset has been collected along six consecutive years in a pediatric hospital in Santiago, Chile. The variables collected are the same used for the determination of the child's treatment administrative cost. METHODS: Retrospective predictive analysis of 30-day readmission was formulated as a binary classification problem. We report classification results achieved with various model building approaches after data curation and preprocessing for correction of class imbalance. We compute repeated cross-validation (RCV) with decreasing number of folders to assess performance and sensitivity to effect of imbalance in the test set and training set size. RESULTS: Increase in recall due to SMOTE class imbalance correction is large and statistically significant. The Naive Bayes (NB) approach achieves the best AUC (0.65); however the shallow multilayer perceptron has the best PPV and f-score (5.6 and 10.2, resp.). The NB and support vector machines (SVM) give comparable results if we consider AUC, PPV, and f-score ranking for all RCV experiments. High recall of deep multilayer perceptron is due to high false positive ratio. There is no detectable effect of the number of folds in the RCV on the predictive performance of the algorithms. CONCLUSIONS: We recommend the use of Naive Bayes (NB) with Gaussian distribution model as the most robust modeling approach for pediatric readmission prediction, achieving the best results across all training dataset sizes. The results show that the approach could be applied to detect preventable readmissions.

[Waiting lists management model based on timeliness and justice]. / Modelo de gestión de listas de espera centrado en oportunidad y justicia.

Waiting lists for elective surgery are considered a major health policy concern in most countries of the world. The most common reason to explain this phenomenon is that demand exceeds supply. Traditionally, the management of waiting lists has been focused on timeliness of medical attention. The objective of this paper is to present a waiting lists management model that includes the concepts of timeliness and justice simultaneously. We designed a prioritization method based solely on medical criteria. We developed a computer software to register patients, to prioritize and monitor the waiting lists. The system was implemented in 2013 and is currently used in all surgical specialties at a public hospital. The results show that timeliness does not suffice to manage the waiting lists for elective surgery, and therefore it should be complemented with an indicator of justice. Under this management model, hospitals should attempt to balance justice with timeliness of care and prioritize initiatives that improve both indicators at the same time. In addition, we propose using this model to manage the waiting lists of other hospital processes.