Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score

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Version: Final published version
License: CC BY-NC-ND 4.0
Serval ID
serval:BIB_3CCA1BFA8716
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score
Journal
eClinicalMedicine
Author(s)
Pogam Marie-Annick Le, Seematter-Bagnoud Laurence, Niemi Tapio, Assouline Dan, Gross Nathan, Trächsel Bastien, Rousson Valentin, Peytremann-Bridevaux Isabelle, Burnand Bernard, Santos-Eggimann Brigitte
ISSN
2589-5370
Publication state
Published
Issued date
02/2022
Volume
44
Pages
101260
Language
english
Notes
L2016404402
2022-01-14
Abstract
Background: Most claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP). Methods: We used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score). Findings: In the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission. Interpretation: The eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults. Funding: The study received no external funding.
Keywords
General Medicine
Open Access
Yes
Create date
21/01/2022 15:16
Last modification date
09/08/2022 7:09
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