Reasons for hospitalisation and cumulative mortality in people, 75 years or older, at high risk of hospital admission: a prospective study.
Aged
Frailty
Hospital admission
Hospitalisation
Mortality
Older people
Prediction model
Journal
BMC geriatrics
ISSN: 1471-2318
Titre abrégé: BMC Geriatr
Pays: England
ID NLM: 100968548
Informations de publication
Date de publication:
20 Feb 2024
20 Feb 2024
Historique:
received:
01
06
2023
accepted:
02
02
2024
medline:
21
2
2024
pubmed:
21
2
2024
entrez:
20
2
2024
Statut:
epublish
Résumé
A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group. All residents, 75 years or older, living in Östergötland, Sweden, on January 1 Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95-100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95-100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85-100). Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised. clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.
Sections du résumé
BACKGROUND
BACKGROUND
A small proportion of the older population accounts for a high proportion of healthcare use. For effective use of limited healthcare resources, it is important to identify the group with greatest needs. The aim of this study was to explore frequency and reason for hospitalisation and cumulative mortality, in an older population at predicted high risk of hospital admission, and to assess if a prediction model can be used to identify individuals with the greatest healthcare needs. Furthermore, discharge diagnoses were explored to investigate if they can be used as basis for specific interventions in the high-risk group.
METHODS
METHODS
All residents, 75 years or older, living in Östergötland, Sweden, on January 1
RESULTS
RESULTS
Forty thousand six hundred eighteen individuals were identified (mean age 82 years, 57.8% women). The cumulative incidence of hospitalisation increased with increasing risk of hospital admission (24% for percentiles < 60 to 66% for percentiles 95-100). The cumulative mortality also increased with increasing risk (7% for percentiles < 60 to 43% for percentiles 95-100). The most frequent primary discharge diagnoses for the population were heart diseases, respiratory infections, and hip injuries. The incidence was significantly higher for heart diseases and respiratory infections and significantly lower for hip injuries, for the population with the highest risk of hospital admission (percentiles 85-100).
CONCLUSIONS
CONCLUSIONS
Individuals 75 years or older, with high risk of hospital admission, were demonstrated to have considerable higher cumulative mortality as well as incidence of hospitalisation. The results support the use of the prediction model to direct resources towards individuals with highest risk scores, and thus, likely the greatest care needs. There were only small differences in discharge diagnoses between the risk groups, indicating that interventions to reduce hospitalisations should be personalised.
TRIAL REGISTRATION
BACKGROUND
clinicaltrials.gov Identifier: NCT03180606, first posted 08/06/2017.
Identifiants
pubmed: 38378482
doi: 10.1186/s12877-024-04771-2
pii: 10.1186/s12877-024-04771-2
doi:
Banques de données
ClinicalTrials.gov
['NCT03180606']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
176Subventions
Organisme : County Council of Östergötland and Linköping University
ID : 2016186-14
Organisme : Swedish Research Council (ALF)
ID : RÖ-974820
Organisme : Medical Research Council of Southeastern Sweden
ID : FORSS-969444
Informations de copyright
© 2024. The Author(s).
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