Description of multimorbidity clusters of admitted patients in medical departments of a general hospital.

OBJECTIVE: We aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group. METHODS: We used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis. RESULTS: We identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70-80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60-80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases). CONCLUSIONS: We identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.

El hospital en los tiempos del Covid-19: capacidad de reacción y adaptación / Hospital in times of Covid-19: reaction and adaptation

Objetivo: Describir las repercusiones sobre la hospitalización y las características de los pacientes atendidos en las primeras semanas tras la declaración del estado de alarma durante la pandemia por COVID-19 en un hospital general. Métodos: Estudio observacional de todos los ingresos, en todos servicios hospitalarios, entre los días 1 de marzo y 30 de abril de los años 2017, 2018, 2019 y 2020 en un hospital general. La fuente de información fue el conjunto mínimo básico de datos del centro. Consideramos las 00.00 horas del día 14 de marzo como el inicio del estado de alarma y punto de corte entre dos periodos: previo al estado de alarma (días 1 a 13 de marzo) y estado de alarma (días 14 a 30 de abril). Resultados: Tras la declaración del estado de alarma disminuyó el número de hospitalizaciones (p<0.0001), en un rango entre el 3,5% y el 55,9% con respecto al promedio de los 3 años previos en los diez principales servicios médicos y quirúrgicos de adultos y por todas las modalidades de ingreso (p<0.001). En paralelo se redujo la estancia media ( p<0.001) y se incrementó el porcentaje de ingresados de procedencia urbana (p< 0.01). Si bien la mortalidad global no mostró cambios, si aumentaron los fallecidos en las primeras 24 horas de ingreso hospitalario ( p<0.008). Conclusiones: Este estudio describe los mecanismos de reacción y adaptación de un hospital durante el estado de alarma por la pandemia por COVID-19. Nuestros resultados podrían ayudar a otros centros a diseñar y dimensionar sus preparativos. (AU)

Aim: Describe the patient’s features and the hospital changes during the first weeks of the COVID-19 pandemic alarm in a General Hospital.Method: Observational study that asses all the admissions in the hospital departments between March 1st and April 30th of 2017, 2018, 2019 and 2020 in a General Hospital. The information was obtained from the basic data set of the Center. We consider 00.00 on March 14th of 2020 as the beginning of the alarm state and as cut-off point between two periods: before the state of alert (March 1st-13th) and the state of alert (March 14th- April 30th) Results: After the state of alarm the number of admissions decreased (p< 0,0001) in all kind of admissions (p<0,001) and in the ten medical and surgical services of adults between 3,5% and 55,9% comparing with the main of 3 previous years. At the same time main stay decreased ( p<0,001) and rate of admitted from urban areas increased (p<0,01). Although total mortality did not change, deaths during the first 24 hours after admissions were increased (p<0,008). Conclusions: This study describes surge and adaptation mechanisms of a hospital during state of alert by COVID-19 pandemic. Our results could help other Centers with designing and measuring their preparations. (AU)

Characteristics and temporal pattern of the readmissions of patients with multiple hospital admissions in the medical departments of a general hospital.

INTRODUCTION: Patients with multiple hospital admissions represent a small percentage of total hospitalizations but result in a considerable proportion of the healthcare expenditure. There are no studies that have analyzed their long-term clinical evolution. OBJECTIVES: To study the characteristics, temporal patterns of readmissions and clinical evolution of patients with multiple hospital admission in the long term. METHODS: A retrospective study was conducted of all hospital admissions in the medical area of the Hospital of Lugo (Spain) between January 1, 2000 and December 31, 2012, based on data from the center's minimum basic data set. RESULTS: A total of 139,249 hospital admissions for 62,515 patients were studied. Six hospital admissions were recorded for 6.4% of the patients. The overall mortality rate was 16% (9982 patients). The readmissions rate steadily increased with each new admission, from 48% after the first event to 74.6% after the fifth. The rate of hospital readmission before 30days increased from 18.3% in the second admission to 36.3% in the sixth. The number of chronic diseases increased from 3.1 (SD, 2) in the first hospital admission up to 4.9 (2.8) in the sixth. The Department of Internal Medicine treated a third of all hospital admissions. In the sixth hospitalization, conditions associated with admission in Internal Medicine were CIRS score, age, heart failure, COPD, dementia, diabetes, atrial fibrillation and anemia. CONCLUSIONS: Patients with multiple hospital admissions are complex patients whose temporal pattern of readmissions changes with time, such that each hospital admission constitutes a factor facilitating the next.