Publicado

2025-08-20

Perfiles clínicos de pacientes con multimorbilidad manejados vía telemedicina en Medellín, Colombia

Clinical profile of patients with multimorbidity treated via telemedicine in Medellín, Colombia

Palabras clave:

Telemedicina, Enfermedad Crónica, Multimorbilidad, Aprendizaje Automático (es)
Telemedicine, Chronic Disease, Multimorbidity, Machine Learning, Cluster Analysis (en)

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Autores/as

  • Esteban Castrillón-Martínez Hospital Alma Máter de Antioquia - Area de Investigación - Medellín - Colombia. | Universidad de Antioquia - Facultad de Medicina - Semillero de Investigación en Salud (SEIS) - Medellín - Colombia. https://orcid.org/0000-0001-6875-5918
  • Viviana Ospina-Ospina Universidad de Antioquia - Facultad de Medicina - Programa de Medicina - Medellín - Colombia. https://orcid.org/0009-0007-5954-882X
  • Lorleiby Perea-Chaverra Hospital Alma Máter de Antioquia - Atención Domiciliaria - Medellín - Colombia. https://orcid.org/0009-0002-7635-7412
  • Alejandro Hernández-Arango Universidad de Antioquia - Facultad de Medicina - Programa de Medicina - Medellín - Colombia. | Hospital Alma Máter de Antioquia - Área de Informática Médica - Medellín - Colombia. https://orcid.org/0000-0002-7140-4358
  • Juan Pablo Zapata-Ospina Hospital Alma Máter de Antioquia - Área de Investigación - Medellín - Colombia. | Universidad de Antioquia - Facultad de Medicina - Instituto de Investigación Médicas - Medellín - Colombia. https://orcid.org/0000-0002-1815-5583

Introducción. La multimorbilidad es la coexistencia de dos o más enfermedades crónicas en un individuo y representa una carga significativa para la salud pública debido a su asociación con un mayor riesgo de muerte prematura, un alto grado de discapacidad y un uso elevado de servicios de atención en salud. Agrupar estos pacientes en perfiles clínicos permitiría optimizar su manejo.

Objetivo. Establecer los perfiles clínicos de los pacientes con multimorbilidad atendidos en el programa de atención domiciliaria por telemedicina del Hospital Alma Mater de Antioquia (HAMA) ubicado en Medellín (Colombia).

Materiales y métodos. Estudio transversal realizado con datos de 2 035 pacientes adultos con multimorbilidad inscritos en el programa de atención domiciliaria del HAMA y a los que se les brindaron servicios de atención en salud vía telemedicina al menos una vez entre enero de 2017 y diciembre de 2023. Los pacientes fueron agrupados en perfiles clínicos (clústeres) usando el algoritmo de agrupamiento k-medias y con base en las siguientes variables: edad, sexo, presencia de hipertensión arterial (HTA), diabetes mellitus tipo 2 (DM2), enfermedad renal crónica (ERC) y su estadio, enfermedad pulmonar obstructiva crónica, cáncer, enfermedad mental o neurodegenerativa, presencia de discapacidad, clase funcional, uso de anticoagulantes, tipo de telemedicina usada y si había tenido valoración por medicina interna a través de telexperticia. El número óptimo de clústeres (k) se determinó usando el método del codo.

Resultados. De acuerdo con el método del codo, el número óptimo de k en el algoritmo de agrupamiento K-Medias fue 4, por lo que los pacientes fueron agrupados en 4 clústeres o perfiles clínicos. El clúster 0 estuvo conformado por hombres y mujeres con ERC, HTA y DM2, sin discapacidad, pero con clase funcional 4; el clúster 1, por hombres con enfermedad mental o neurodegenerativa y ERC, con discapacidad y clase funcional 4; el clúster 2, por mujeres con enfermedad mental o neurodegenerativa y ERC, con discapacidad y clase funcional 4, y el clúster 3, por mujeres y hombres con ERC, HTA y DM2, con clase funcional 2.

Conclusión. Se encontraron 4 perfiles clínicos que se diferencian por el sexo, la combinación del tipo de enfermedades crónicas, la clase funcional y la presencia de discapacidad, y que, a su vez, pueden determinar el uso de servicios específicos del programa como la evaluación por especialistas en medicina interna mediante telexperticia.

Introduction: Multimorbidity is defined as the coexistence of two or more chronic diseases in an individual. It poses a significant burden on public health care due to its association with an increased risk of premature death, a high degree of disability, and significant use of medical services. Grouping these patients into clinical profiles could help to optimize their management.

Objective: To establish the clinical profiles of patients with multimorbidity treated via the telemedicine home care program of the Hospital Alma Máter de Antioquia (HAMA) located in Medellín (Colombia).

Materials and methods: Cross-sectional study conducted using data from 2 035 adult patients with multimorbidity enrolled in the HAMA home care program who had access to health care services via telemedicine at least once between January 2017 and December 2023. Patients were grouped into clinical profiles (clusters) using the k-means clustering algorithm and based on the following variables: age, sex, diagnosis of arterial hypertension (AHT), type 2 diabetes mellitus (DM2), chronic kidney disease (CKD) and stage, chronic obstructive pulmonary disease, cancer, mental or neurodegenerative disease, disability, functional class, use of anticoagulants, type of telemedicine service used, and prior assessment by the internal medicine service via telexpertise. The optimal number of clusters (k) was determined using the elbow method.

Results: According to the elbow method, the optimal value of k in the K-means clustering algorithm was 4, so patients were grouped into 4 clusters or clinical profiles. Cluster 0 corresponded to men and women with CKD, AHT, and DM2, without a disability but with functional class 4. Cluster 1 was formed by men with mental or neurodegenerative disease and CKD, with a disability, and functional class 4. Cluster 2 included women with mental or neurodegenerative disease and CKD, with a disability, and functional class 4. Finally, cluster 3 was comprised of women and men with CKD, AHT, and DM2, with functional class 2.

Conclusion: Four clinical profiles were identified, characterized by sex, combination of chronic diseases, functional class, and presence of a disability. This classification may contribute to the use of specific program services such as evaluation by internal medicine specialists via tele-expertise.

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