WASPSS: A Clinical Decision Support System for Antimicrobial Stewardship

The increase of infections caused by resistant bacteria has become one of the major health-care problems worldwide. The creation of multidisciplinary teams dedicated to the implementation of antimicrobial stewardship programmes (ASPs) is encouraged by all clinical institutions to cope with this problem. In this chapter, we describe the Wise Antimicrobial Stewardship Program Support System (WASPSS), a CDSS focused on providing support for ASP teams. WASPSS gathers the required information from other hospital systems in order to provide decision support in antimicrobial stewardship from both patient-centered and global perspectives. To achieve this, it combines business intelligence techniques with a rule-based inference engine to integrate the data and knowledge required in this scenario. The system provides functions such as alerts, recommendations, antimicrobial prescription support and global surveillance. Furthermore, it includes experimental modules for improving the adoption of clinical guidelines and applying prediction models related with antimicrobial resistance. All these functionalities are provided through a multi-user web interface, personalized for each role of the ASP team

Medical informatics approaches for decision support in antimicrobial stewardship

Las organizaciones de salud pública están promoviendo el uso racional de antibióticos, o vigilancia antimicrobiana, con el objetivo de maximizar su efectividad y limitar el aumento de resistencias a los mismos. El Hospital Universitario de Getafe participó en el desarrollo del proyecto Wise Antimicrobial Stewardship Program (WASPSS): un sistema de ayuda a la decisión clínica (CDSS) con el objetivo de apoyar a los equipos multidisciplinares responsables de la vigilancia antimicrobiana en hospitales. El objetivo de esta tesis doctoral es demostrar que las reglas de producción son adecuadas para abordar los problemas de la vigilancia antimicrobiana desde la perspectiva de la informática médica. Nos enfrentamos a los problemas de: i) aumentar la efectividad de tratamientos antibióticos, ii) facilitar el uso de Guías de Práctica Clínica (CPGs) y iii) predecir infecciones causadas por microorganismos resistentes. Usaremos WASPSS como plataforma en la que implementar y probar nuestras estrategias. Primeramente, abordamos el mejorar los resultados de los tests de sensibilidad antimicrobiana (ASTs) para aumentar la efectividad de los tratamientos. Utilizamos reglas expertas para inferir nuevos patrones de resistencia a partir de los obtenidos en laboratorio. El mayor problema es modelar conocimiento basado en reglas y definido sobre taxonomías complejas. Utilizamos ontologías para traducir las taxonomías a jerarquías de conceptos y generamos reglas para relacionar cada término con sus definiciones. Probamos nuestro enfoque con los resultados de AST de un año, obteniendo un 26.4% de patrones nuevos de resistencia. Usando este enfoque, hemos implementado un nuevo módulo en WASPSS que amplía los resultados de AST disponibles y alerta sobre posibles clasificaciones erróneas de microorganismos o tratamientos inadecuados. A continuación, abordamos el problema de la integración de las CPG relacionadas con la administración de antibióticos en los CDSS para facilitar su uso diario. Usamos BPMN y DMN para modelar y visualizar los complejos procesos y decisiones incluidas en estas guías. Además, derivamos reglas de estos modelos para estimar la adherencia de una CPG a un paciente en concreto. Probamos nuestro enfoque modelando una CPG para la administración de vancomicina. Como resultado, hemos implementado un nuevo módulo para WASPSS que proporciona información contextualizada sobre la tarea actual. Esto también facilita la visualización de la CPG y la planificación de tareas. Finalmente, abordamos la predicción de infecciones causadas por enterococos resistentes a vancomicina (VRE). Esta clase de modelos de predicción, también conocidos como Reglas de Predicción Clínica (CPRs), deben abordar varios problemas, como los cambios del objetivo a predecir, los conjuntos de datos desbalanceados y el elevado número de predictores. Combinamos diferentes estrategias para resolver estos problemas y desarrollamos una CPR con la que predecir las infecciones por VRE. Obtuvimos un modelo final con AUC de 0.82, combinando una ventana deslizante de 30 meses, sobremuestreo, filtrado rápido basado en correlación y LASSO. Posteriormente, implementamos un nuevo módulo WASPSS que permite visualizar la predicción para un paciente concreto y alertar sobre pacientes con alto riesgo de infección por VRE. En conclusión, hemos probado nuestra hipótesis de que las reglas de producción son útiles en los CDSS para la vigilancia de antibióticos, sirviendo como base para incorporar diferentes tipos de conocimiento, así como comprobado sus limitaciones. Ontologías y reglas se pueden combinar para incorporar conocimientos basados en taxonomías complejas. BPMN y DMN junto con reglas pueden mejorar la tarea de modelar y visualizar procesos y decisiones complejas. Finalmente, las reglas de producción se pueden utilizar para incorporar modelos de predicción, mientras que es necesario combinar diferentes técnicas de minería para tratar los problemas intrínsecos de este escenario.Health-care organisations are promoting a rational use of antimicrobials, also known as antimicrobial stewardship, with the aim of maximizing their clinical outcomes while limiting the rise in antimicrobial resistance. The University Hospital of Getafe, Spain participated in the development of the Wise Antimicrobial Stewardship Programme Support System (WASPSS) project: A Clinical Decision Support System (CDSS) focused on assisting the multidisciplinary teams which are responsible for antimicrobial stewardship in hospitals. The aim of this PhD thesis is to prove that production rules are a suitable approach by which to address the key challenges of antimicrobial stewardship from a Medical Informatics perspective. We confront the problems of: i) increasing the effectiveness of antimicrobial treatments, ii) facilitating the use of Clinical Practice Guidelines (CPGs) and iii) predicting infections caused by resistant microorganisms. We use WASPSS as the platform on which to implement and test our approaches. We first focus on improving the results of antimicrobial susceptibility tests (AST) to increase the effectiveness of antimicrobial treatments. We decided to use expert rules to infer new resistance patterns from those obtained in a laboratory. The main challenge is to model knowledge based on rules and defined over complex taxonomies. We use ontologies to translate those taxonomies into a multi-hierarchical definition of concepts and generate rules to link each term with its definitions. We tested our approach with AST results obtained over a year, obtaining 26.4% of new resistance patterns. Using this approach, we have implemented a new WASPSS module that extends the available AST results and alerts clinicians to possible microorganism misclassification or improper treatments. We then deal with the problem of integrating CPGs related to antimicrobial administration into CDSSs to facilitate their use in daily hospital practice. We propose the use of BPMN and DMN to model and visualise the complex processes and decisions included in these guidelines. Moreover, we use production rules derived from these models to estimate the adherence of a CPG to a specific patient. We put our approach into practice by modelling a guideline for vancomycin administration. As a result, we have implemented a new module for WASPSS which provides contextualised information concerning the current task. This approach also facilitates both guideline visualisation and task scheduling. Finally, we confront the clinical problem of predicting infections caused by Vancomycin-Resistant Enterococci (VRE). This kind of prediction models, also known as Clinical Prediction Rules (CPRs), must deal with several challenging problems, such as concept drift, imbalanced datasets and the high number of predictors. We combine different strategies to deal with these problems and develop a CPR with which to predict VRE infections. We obtained a final model with an AUC of 0.82, by combining a 30-month sliding window, oversampling, Fast Correlation Based Filter and LASSO. We then implemented a new WASPSS module that provides decision support by visualising the predicted outcome for a patient and by alerting physicians to patients at high risk of VRE infection. In conclusion, we have proved our hypothesis that production rules can be used in CDSSs for antimicrobial stewardship, being a basis on which to incorporate different kinds of knowledge. In addition, we have confronted their limitations. Ontologies along with rules can be used to incorporate knowledge based on complex taxonomies. The use of BPMN and DMN along with rules can improve the task of modelling and visualising processes and complex decisions. Finally, production rules can be used to incorporate the results of prediction models into CDSSs, while it is necessary to combine different datamining techniques to deal with the intrinsic problems of this scenario

A lightweight acquisition of expert rules for interoperable clinical decision support system

© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted Manuscript version of a Published Work that appeared in final form in [Knowledge-Based Systems]. To access the final edited and published work see [10.1007/978-3-030-45385-5_9]The process of adding new knowledge in the form of rules to already running Clinical Decision Support Systems (CDSSs) in hospitals is extremely costly and time consuming. There are two principal limitations: (1) the lack of a broad consensus regarding a uniform representation of clinical rules; and (2) the integration of new rule-based knowledge into hospital information systems. Objective: To provide a guideline with which to support knowledge acquisition for rule-based CDSSs and to facilitate the integration of that knowledge into hospital datasets using standard clinical terminologies and ontologies as reference elements. Materials and Methods: We have designed a straightforward 4-step methodology with which to incorporate the external knowledge sources and data integration required to run CDSSs in hospitals. This lightweight methodology is based on a reference ontology that integrates standard clinical terminologies and its objective is to effectively acquire procedural knowledge in the form of rules. Results: We have applied the methodology in the context of antimicrobial stewardship at a hospital. Recommendations from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) were added to WASPSS, a CDSS running at the hospital. The reference ontology combines a subset of ATC terminologies for antibiotics and those of NCBI for microorganisms, including 584 and 1714 concepts, respectively. A total of 94 new rules were added to the CDSS so as to represent EUCAST knowledge. We also evaluated different implementations in order to study their scalability, during which time we analysed Drools 7.5 as a production rule engine, HermiT as an ontology reasoner and RuQAR as an integration tool. Our experiments show that the combination of a production rule engine and an ontology reasoner in runtime is more efficient than using a single rule engine with a knowledge base derived from the reference ontology (1.9 times faster than the next approach when executing 1000 expert rules on an ontology of 1000 concepts). Discussion: The methodology proposed helped to implement the knowledge acquisition process of EUCAST rules in a running CDSS. This methodology is applicable to other clinical domains when knowledge can be modelled with rules. Since it is a lightweight methodology, different implementation strategies are possible. The use of clinical standards also facilitates the future interoperation between CDSSs, particularly when using SNOMED as a reference ontology and employing future rule-sharing standards