Development and validation of multivariable mortality risk-prediction models in older people undergoing an interRAI home-care assessment (RiskOP).
Mortality
Older people
Risk prediction
Journal
EClinicalMedicine
ISSN: 2589-5370
Titre abrégé: EClinicalMedicine
Pays: England
ID NLM: 101733727
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
09
06
2020
revised:
08
10
2020
accepted:
13
10
2020
entrez:
13
1
2021
pubmed:
14
1
2021
medline:
14
1
2021
Statut:
epublish
Résumé
Currently, one-year survival of older people with complex co-morbidities is unpredictable. Identifying older adults with a reduced life expectancy will lead to more targeted care and better healthcare resource allocation. Development and validation of one-year and three-month mortality risks in people aged ≥65 years who had completed an International Resident Assessment Instrument-Home Care (interRAI-HC) assessment between July 2012 and March 2018. Data was split into development (90%) and validation data sets (10%). A multivariable logistic regression model using data from 108 interRAI questions across multiple domains was developed and validated using discrimination metrics and calibration curves. Variables each explaining at least 1% of the model were then used to develop and validate a parsimonious model. Subgroups by sex, age, ethnicity, and comorbidities were evaluated. There were 104,436 persons (60.2% female; mean age 82.1 years) in the study cohort of whom 20,972 (20.1%) died within one year. The full multivariable model had area under the curves (AUCs) of 0.778 to 0.795 in the 5 validation datasets and was well calibrated. After variable reduction a parsimonious model consisted of 16 variables and was well calibrated and the AUC remained high: 0.773 (0.769 to 0.777). The three-month parsimonious model comprised 22 variables and was well calibrated with an AUC of 0.843 (95%CI: 0.839 to 0.848). These community-based risk prediction models accurately predict mortality in older people with complex co-morbidities. They may contribute to both forecasting for policy making and clinical decision making regarding an individual's needs. The New Zealand Health Research Council.
Sections du résumé
BACKGROUND
BACKGROUND
Currently, one-year survival of older people with complex co-morbidities is unpredictable. Identifying older adults with a reduced life expectancy will lead to more targeted care and better healthcare resource allocation.
METHODS
METHODS
Development and validation of one-year and three-month mortality risks in people aged ≥65 years who had completed an International Resident Assessment Instrument-Home Care (interRAI-HC) assessment between July 2012 and March 2018. Data was split into development (90%) and validation data sets (10%). A multivariable logistic regression model using data from 108 interRAI questions across multiple domains was developed and validated using discrimination metrics and calibration curves. Variables each explaining at least 1% of the model were then used to develop and validate a parsimonious model. Subgroups by sex, age, ethnicity, and comorbidities were evaluated.
FINDINGS
RESULTS
There were 104,436 persons (60.2% female; mean age 82.1 years) in the study cohort of whom 20,972 (20.1%) died within one year. The full multivariable model had area under the curves (AUCs) of 0.778 to 0.795 in the 5 validation datasets and was well calibrated. After variable reduction a parsimonious model consisted of 16 variables and was well calibrated and the AUC remained high: 0.773 (0.769 to 0.777). The three-month parsimonious model comprised 22 variables and was well calibrated with an AUC of 0.843 (95%CI: 0.839 to 0.848).
INTERPRETATION
CONCLUSIONS
These community-based risk prediction models accurately predict mortality in older people with complex co-morbidities. They may contribute to both forecasting for policy making and clinical decision making regarding an individual's needs.
FUNDING
BACKGROUND
The New Zealand Health Research Council.
Identifiants
pubmed: 33437945
doi: 10.1016/j.eclinm.2020.100614
pii: S2589-5370(20)30358-8
pmc: PMC7788437
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100614Informations de copyright
© 2020 The Author(s).
Déclaration de conflit d'intérêts
All authors declare no conflicts of interest.
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