Transition of Adolescents With Severe Asthma From Pediatric to Adult Care in Spain: The STAR Consensus.

OBJECTIVES: To assess the degree of consensus among a multidisciplinary expert panel on the transition of adolescents with severe asthma from pediatric to adult care. METHODS: A 61-item survey was developed based on guidelines for other chronic diseases, covering transition planning, preparation, effective transfer, and follow-up. A 2-round Delphi process assessed the degree of consensus among 98 experts (49 pediatricians, 24 allergists, and 25 pulmonologists). Consensus was established with ≥70% agreement. RESULTS: Consensus was reached for 42 items (70%). Panelists were unable to agree on an age range for initiation of transition. The main goal during the transition identified by the experts is for adolescents to gain autonomy in managing severe asthma and prescribed treatments. The panelists agreed on the importance of developing an individualized plan, promoting patient autonomy, and identifying factors associated with the home environment. They agreed that the adult health care team should have expertise in severe asthma, biologics, and management of adolescent patients. Pediatric and adult health care teams should share clinical information, agree on the criteria for maintaining biological therapy, and have an on-site joint visit with the patient before the effective transfer. Adult health care professionals should closely follow the patient after the effective transfer to ensure correct inhaler technique, adherence, and attendance at health care appointments. CONCLUSION: This consensus document provides the first roadmap for Spanish pediatric and adult teams to ensure that key aspects of the transition process in severe asthma are covered. The implementation of these recommendations will improve the quality of care offered to the patient.

Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study.

BACKGROUND: Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months. METHODS: PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT. RESULTS: The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients' clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes. CONCLUSION: This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA.

Transition of Adolescents With Severe Asthma From Pediatric to Adult Care in Spain: The STAR Consensus

Objective: To assess the degree of consensus among a multidisciplinary expert panel on the transition of adolescents with severe asthma from pediatric to adult care. Methods: A 61-item survey was developed based on guidelines for other chronic diseases, covering transition planning, preparation, effective transfer, and follow-up. A 2-round Delphi process assessed the degree of consensus among 98 experts (49 pediatricians, 24 allergists, and 25 pulmonologists). Consensus was established with ≥70% agreement. Results: Consensus was reached for 42 items (70%). Panelists were unable to agree on an age range for initiation of transition. The main goal during the transition identified by the experts is for adolescents to gain autonomy in managing severe asthma and prescribed treatments. The panelists agreed on the importance of developing an individualized plan, promoting patient autonomy, and identifying factors associated with the home environment. They agreed that the adult health care team should have expertise in severe asthma, biologics, and management of adolescent patients. Pediatric and adult health care teams should share clinical information, agree on the criteria for maintaining biological therapy, and have an on-site joint visit with the patient before the effective transfer. Adult health care professionals should closely follow the patient after the effective transfer to ensure correct inhaler technique, adherence, and attendance at health care appointments. Conclusions: This consensus document provides the first roadmap for Spanish pediatric and adult teams to ensure that key aspects of the transition process in severe asthma are covered. The implementation of these recommendations will improve the quality of care offered to the patient (AU)

Objetivo: Evaluar el grado de consenso con un panel multidisciplinar de expertos sobre la transición del adolescente con asma grave de los servicios de pediatría a atención de adultos. Métodos: Se elaboró un cuestionario de 61 ítems basado en recomendaciones de transición para otras patologías crónicas, abarcando la planificación de la transición, preparación, transferencia efectiva y seguimiento. Se evaluó el nivel de consenso entre 98 expertos (49 pediatras, 24 alergólogos y 25 neumólogos) mediante un proceso Delphi de dos rondas. El consenso se estableció con un acuerdo ≥70%. Resultados: Cuarenta y dos ítems (70%) alcanzaron consenso. Los panelistas no alcanzaron consenso en el rango de edad para iniciar la transición. El principal objetivo a conseguir durante la transición según los expertos fue que el adolescente gane autonomía en el manejodel asma grave y tratamientos prescritos. Asimismo, alcanzaron acuerdo en la importancia de desarrollar un plan individualizado, promover la autonomía del paciente e identificar los factores clave en el entorno familiar. Los especialistas de adultos deben tener experiencia en asma grave y tratamientos biológicos, así como en el manejo de pacientes adolescentes. Los equipos sanitarios de pediatría y de adultos deben compartir la información clínica, consensuar los criterios para mantener la terapia biológica y realizar una visita conjunta con el paciente antes de la transferencia. Los especialistas de adultos deben realizar un seguimiento estrecho del paciente tras la transferencia para asegurar una correcta técnica inhalatoria, el cumplimiento del tratamiento y la asistencia a las citas sanitarias. Conclusiones: Este documento de consenso proporciona la primera hoja de ruta en España para que los equipos especialistas de pediatría y adultos garanticen aspectos clave del proceso de transición en pacientes adolescentes con asma grave. La aplicación de estas (AU)

Understanding Severe Asthma Through Small and Big Data in Spanish Hospitals: The PAGE Study

Background: Data on the prevalence of severe asthma (SA) are limited. Electronic health records (EHRs) offer a unique research opportunity to test machine learning (ML) tools in epidemiological studies. Our aim was to estimate the prevalence of SA among asthma patients seen in hospital asthma units, using both ML-based and traditional research methodologies. Our secondary objective was to describe patients with nonsevere asthma (NSA) and SA over a follow-up of 12 months. Methods: PAGE is a multicenter, controlled, observational study conducted in 36 Spanish hospitals and split into 2 phases: a cross-sectional phase for estimation of the prevalence of SA and a prospective phase (3 visits in 12 months) for the follow-up and characterization of SA and NSA patients. A substudy with ML was performed in 6 hospitals. Our ML tool uses EHRead technology, which extracts clinical concepts from EHRs and standardizes them to SNOMED CT. Results: The prevalence of SA among asthma patients in Spanish hospitals was 20.1%, compared with 9.7% using the ML tool. The proportion of SA phenotypes and the features of patients followed up were consistent with previous studies. The clinical predictions of patients’ clinical course were unreliable, and ML found only 2 predictive models with discriminatory power to predict outcomes. Conclusion: This study is the first to estimate the prevalence of SA in hospitalized asthma patients and to predict patient outcomes using both standard and ML-based research techniques. Our findings offer relevant insights for further epidemiological and clinical research in SA (AU)

Antecedentes: Los datos sobre la prevalencia del asma grave (SA) son limitados. La implantación de las historias clínicas electrónicas (EHR) ofrece una oportunidad única de investigación con tecnologías de aprendizaje máquina (ML) en los estudios epidemiológicos. El objetivo fue estimar la prevalencia del SA entre los pacientes atendidos en las unidades de asma hospitalarias, utilizando el ML como la metodología de investigación tradicional. Los objetivos secundarios fueron describir los pacientes con asma no grave (NSA) y con SA durante un período de seguimiento de 12 meses. Métodos: El estudio PAGE es un estudio multicéntrico, controlado y observacional realizado en 36 hospitales españoles y dividido en dos fases: una primera fase transversal para la estimación de la prevalencia de AS, y una segunda fase prospectiva (3 visitas en 12 meses) para el seguimiento y caracterización de los pacientes con SA y NSA. Se incluyó un subestudio con ML en 6 hospitales. Resultados: Se obtuvo una prevalencia de SA del 20,1% entre los pacientes asmáticos, frente al 9,7% de la herramienta ML. La proporción de fenotipos de SA y las características de los pacientes en seguimiento fueron consistentes con estudios anteriores. Las predicciones clínicas de la evolución de los pacientes fueron poco fiables, mientras que el ML sólo encontró dos modelos predictivos con potencial discriminatorio para predecir resultados. Conclusión: Este estudio es el primero en estimar la prevalencia del SA, en una población hospitalaria de pacientes con asma, y en predecir los resultados de los pacientes utilizando técnicas estándar y de ML (AU)

PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods.

BACKGROUND AND OBJECTIVES: Background: The proposal and the initiative for the Prevalence of Severe Asthma in Hospital Units in Spain (PAGE) study came about because of the widespread implementation of electronic medical records and the limited data available on the prevalence of severe asthma in hospitals in our setting. Objectives: The primary objective was to determine the prevalence of severe asthma in the outpatient clinics of allergy and pulmonology departments in Spain. The secondary objectives were to describe the most prevalent characteristics and phenotypes of severe asthma, to evaluate the selection criteria for receiving approved biological treatments for this disease, and to estimate consumption of resources. Furthermore, digital technology and new data collection sources made it possible to reuse information stored in electronic medical records (Big Data). The study was performed using one such tool, Savana. METHODS: The PAGE study was a multicenter, nonexperimental, observational, cross-sectional study in the first phase and a prospective study in the second phase. The study was controlled and population-based, with 2-stage selection of patients by random sampling. The research was carried out in 40 hospitals selected using convenience sampling in order to ensure geographical representativeness in Spain. RESULTS: This manuscript describes the study design and protocol. CONCLUSIONS: Our study design was sufficiently robust to avoid bias and to establish the prevalence of patients with severe asthma in Spanish hospitals. It was also the first to incorporate new tools that can help in routine clinical practice and research, such as big data analysis software, and to evaluate the reliability and efficiency of these tools.

PAGE Study: Summary of a Study Protocol to Estimate the Prevalence of Severe Asthma in Spain Using Big Data Methods

Background: The proposal and the initiative for the Prevalence of Severe Asthma in Hospital Units in Spain (PAGE) study came about because of the widespread implementation of electronic medical records and the limited data available on the prevalence of severe asthma in hospitals in our setting. Objectives: The primary objective was to determine the prevalence of severe asthma in the outpatient clinics of allergy and pulmonology departments in Spain. The secondary objectives were to describe the most prevalent characteristics and phenotypes of severe asthma, to evaluate the selection criteria for receiving approved biological treatments for this disease, and to estimate consumption of resources. Furthermore, digital technology and new data collection sources made it possible to reuse information stored in electronic medical records (Big Data). The study was performed using one such tool, Savana. Methods: The PAGE study was a multicenter, nonexperimental, observational, cross-sectional study in the first phase and a prospective study in the second phase. The study was controlled and population-based, with 2-stage selection of patients by random sampling. The research was carried out in 40 hospitals selected using convenience sampling in order to ensure geographical representativeness in Spain. Results: This manuscript describes the study design and protocol. Conclusions: Our study design was sufficiently robust to avoid bias and to establish the prevalence of patients with severe asthma in Spanish hospitals. It was also the first to incorporate new tools that can help in routine clinical practice and research, such as big data analysis software, and to evaluate the reliability and efficiency of these tools (AU)