Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12911-017-0441-5
Title: FAM-FACE-SG: A score for risk stratification of frequent hospital admitters
Authors: Low, L.L 
Liu, N 
Lee, K.H 
Ong, M.E.H 
Wang, S
Jing, X
Thumboo, J 
Keywords: adult
aged
classification
electronic health record
female
hospital readmission
human
male
middle aged
retrospective study
risk assessment
Singapore
statistics and numerical data
tertiary care center
Adult
Aged
Electronic Health Records
Female
Humans
Male
Middle Aged
Patient Readmission
Retrospective Studies
Risk Assessment
Singapore
Tertiary Care Centers
Issue Date: 2017
Publisher: BioMed Central Ltd.
Citation: Low, L.L, Liu, N, Lee, K.H, Ong, M.E.H, Wang, S, Jing, X, Thumboo, J (2017). FAM-FACE-SG: A score for risk stratification of frequent hospital admitters. BMC Medical Informatics and Decision Making 17 (1) : 35. ScholarBank@NUS Repository. https://doi.org/10.1186/s12911-017-0441-5
Abstract: Background: An accurate risk stratification tool is critical in identifying patients who are at high risk of frequent hospital readmissions. While 30-day hospital readmissions have been widely studied, there is increasing interest in identifying potential high-cost users or frequent hospital admitters. In this study, we aimed to derive and validate a risk stratification tool to predict frequent hospital admitters. Methods: We conducted a retrospective cohort study using the readily available clinical and administrative data from the electronic health records of a tertiary hospital in Singapore. The primary outcome was chosen as three or more inpatient readmissions within 12 months of index discharge. We used univariable and multivariable logistic regression models to build a frequent hospital admission risk score (FAM-FACE-SG) by incorporating demographics, indicators of socioeconomic status, prior healthcare utilization, markers of acute illness burden and markers of chronic illness burden. We further validated the risk score on a separate dataset and compared its performance with the LACE index using the receiver operating characteristic analysis. Results: Our study included 25,244 patients, with 70% randomly selected patients for risk score derivation and the remaining 30% for validation. Overall, 4,322 patients (17.1%) met the outcome. The final FAM-FACE-SG score consisted of nine components: Furosemide (Intravenous 40 mg and above during index admission); Admissions in past one year; Medifund (Required financial assistance); Frequent emergency department (ED) use (?3 ED visits in 6 month before index admission); Anti-depressants in past one year; Charlson comorbidity index; End Stage Renal Failure on Dialysis; Subsidized ward stay; and Geriatric patient or not. In the experiments, the FAM-FACE-SG score had good discriminative ability with an area under the curve (AUC) of 0.839 (95% confidence interval [CI]: 0.825-0.853) for risk prediction of frequent hospital admission. In comparison, the LACE index only achieved an AUC of 0.761 (0.745-0.777). Conclusions: The FAM-FACE-SG score shows strong potential for implementation to provide near real-time prediction of frequent admissions. It may serve as the first step to identify high risk patients to receive resource intensive interventions. © 2017 The Author(s).
Source Title: BMC Medical Informatics and Decision Making
URI: https://scholarbank.nus.edu.sg/handle/10635/173850
ISSN: 14726947
DOI: 10.1186/s12911-017-0441-5
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