Héctor Bueno1, José L Bernal2, Víctor Jiménez-Jiménez3, Francisco Javier Martín-Sánchez4, Xavier Rossello5, Guillermo Moreno6, Clara Goñi7, Víctor Gil8, Pere Llorens9, Nerea Naranjo10, Javier Jacob11, Pablo Herrero-Puente12, Sergio Garrote13, Juan Carlos Silla-Castro14, Stuart J Pocock15, Òscar Miró8. 1. Grupo de Investigación Cardiovascular Multidisciplinaria Traslacional, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Servicio de Cardiología, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Facultad de Medicina, Universidad Complutense, Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Spain. Electronic address: hbueno@cnic.es. 2. Servicio de Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain. 3. Laboratorio de Mecanoadaptación y Biología de Caveolas, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. 4. Grupo de Investigación Cardiovascular Multidisciplinaria Traslacional, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Facultad de Medicina, Universidad Complutense, Madrid, Spain; Servicio de Urgencias, Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos (IdISSC), Madrid, Spain. 5. Grupo de Investigación Cardiovascular Multidisciplinaria Traslacional, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Spain; Servei de Cardiologia, Institut d'Investigació Sanitària Illes Balears (IdISBa), Hospital Universitari Son Espases, Palma de Mallorca, Balearic Islands, Spain. 6. Servicio de Cardiología, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Facultad de Medicina, Universidad Complutense, Madrid, Spain. 7. Servicio de Cardiología, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain; Servicio de Control de Gestión, Hospital Universitario 12 de Octubre, Madrid, Spain. 8. Servei d'Urgències, Hospital Clínic, IDIBAPS, Universitat de Barcelona, Barcelona, Spain. 9. Servicio de Urgencias, Unidad de Corta Estancia y Hospitalización a Domicilio, Hospital General de Alicante, Alicante, Spain. 10. Facultad de Ingeniería Biomédica, Universidad Politécnica de Madrid, Madrid, Spain. 11. Servei d'Urgències, Hospital de Bellvitge, L'Hospitalet de Llobregat, Barcelona, Spain. 12. Servicio de Urgencias, Hospital Universitario Central de Asturias, Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Asturias, Spain. 13. Grupo de Investigación Cardiovascular Multidisciplinaria Traslacional, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Servicio de Cardiología, Hospital Universitario 12 de Octubre, Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), Madrid, Spain. 14. Unidad de Bioinformática, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain. 15. Grupo de Investigación Cardiovascular Multidisciplinaria Traslacional, Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain; Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, United Kingdom.
Abstract
INTRODUCTION AND OBJECTIVES: Composite endpoints are widely used but have several limitations. The Clinical outcomes, healthcare resource utilization and related costs (COHERENT) model is a new approach for visually displaying and comparing composite endpoints including all their components (incidence, timing, duration) and related costs. We aimed to assess the validity of the COHERENT model in a patient cohort. METHODS: A color graphic system displaying the percentage of patients in each clinical situation (vital status and location: at home, emergency department [ED] or hospital) and related costs at each time point during follow-up was created based on a list of mutually exclusive clinical situations coded in a hierarchical fashion. The system was tested in a cohort of 1126 patients with acute heart failure from 25 hospitals. The system calculated and displayed the time spent in each clinical situation and health care resource utilization-related costs over 30 days. RESULTS: The model illustrated the times spent over 30 days (2.12% in ED, 23.6% in index hospitalization, 2.7% in readmissions, 65.5% alive at home, and 6.02% dead), showing significant differences between patient groups, hospitals, and health care systems. The tool calculated and displayed the daily and cumulative health care-related costs over time (total, €4 895 070; mean, €144.91 per patient/d). CONCLUSIONS: The COHERENT model is a new, easy-to-interpret, visual display of composite endpoints, enabling comparisons between patient groups and cohorts, including related costs. The model may constitute a useful new approach for clinical trials or observational studies, and a tool for benchmarking, and value-based health care implementation.
INTRODUCTION AND OBJECTIVES: Composite endpoints are widely used but have several limitations. The Clinical outcomes, healthcare resource utilization and related costs (COHERENT) model is a new approach for visually displaying and comparing composite endpoints including all their components (incidence, timing, duration) and related costs. We aimed to assess the validity of the COHERENT model in a patient cohort. METHODS: A color graphic system displaying the percentage of patients in each clinical situation (vital status and location: at home, emergency department [ED] or hospital) and related costs at each time point during follow-up was created based on a list of mutually exclusive clinical situations coded in a hierarchical fashion. The system was tested in a cohort of 1126 patients with acute heart failure from 25 hospitals. The system calculated and displayed the time spent in each clinical situation and health care resource utilization-related costs over 30 days. RESULTS: The model illustrated the times spent over 30 days (2.12% in ED, 23.6% in index hospitalization, 2.7% in readmissions, 65.5% alive at home, and 6.02% dead), showing significant differences between patient groups, hospitals, and health care systems. The tool calculated and displayed the daily and cumulative health care-related costs over time (total, €4 895 070; mean, €144.91 per patient/d). CONCLUSIONS: The COHERENT model is a new, easy-to-interpret, visual display of composite endpoints, enabling comparisons between patient groups and cohorts, including related costs. The model may constitute a useful new approach for clinical trials or observational studies, and a tool for benchmarking, and value-based health care implementation.
Keywords:
Acute heart failure; COHERENT model; Composite outcomes; Cost; Costes; Days alive out of hospital; Días de vida fuera del hospital; Emergency department; Graphical representation; Hospitalización; Hospitalization; Insuficiencia cardiaca aguda; Modelo COHERENT; Readmission; Reingresos; Representación gráfica; Resultados combinados; Salud basada en valor; Urgencias; Value-based health care
Authors: Héctor Bueno; Clara Goñi; Rafael Salguero-Bodes; Beatriz Palacios; Lourdes Vicent; Guillermo Moreno; Nicolás Rosillo; Luis Varela; Margarita Capel; Juan Delgado; Fernando Arribas; Manuel Del Oro; Carmen Ortega; Jose L Bernal Journal: Front Cardiovasc Med Date: 2022-03-17
Authors: Carlos Escobar; Beatriz Palacios; Luis Varela; Martín Gutiérrez; Mai Duong; Hungta Chen; Nahila Justo; Javier Cid-Ruzafa; Ignacio Hernández; Phillip R Hunt; Juan F Delgado Journal: BMC Health Serv Res Date: 2022-10-08 Impact factor: 2.908