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

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: ICD-10; frailty; geriatric assessment; health data; routinely collected; supervised machine learning.

Figure 1

Proportion of frail and non-frail phenotypes for each of the 18 components of the electronic Frailty Score* 1-Immune system; 2-Blood cells and hematopoietic system; 3-Endocrine system; 4-Metabolic system; 5-Nervous system; 6-Visual system; 7-Hearing system; 8-Heart; 9-Vascular system; 10-Respiratory system; 11-Naso-oro-pharyngo-laryngeal system; 12-Digestive system (excluding liver); 13-Liver; 14-Cutaneous system; 15-Musculoskeletal system; 16-Lymphatic system; 17-Urinary system (excluding kidneys); 18-Kidneys. * Proportions were calculated for the frail (n = 67) and non-frail (n = 402) study participants hospitalised at least once in the 12 months before Fried's frailty phenotype assessment. One study participant may have several deficient systems and organs.

Figure 2

Adjusted odds ratios/incidence rate ratios of frail phenotype and adverse health outcomes according to the electronic Frailty Score, Charlson comorbidity index, age, and sex eFS=electronic Frailty Score; CCI=2011 updated Charlson comorbidity index; FP=Fried's Frailty Phenotype; aOR=adjusted odds ratio; aIRR=adjusted incidence rate ratio; 95%CI=95% confidence interval. *reference = male. Markers in the graph represent aOR or aIRR estimates and vertical lines the 95%CI for these estimates. 95%CIs crossing the horizontal red line represent aORa or aIRRs that are not significantly different from one (i.e. no effect of the corresponding parameter).