ArticleBMC medical informatics and decision making2017
FAM-FACE-SG: a score for risk stratification of frequent hospital admitters.
Article in BMC medical informatics and decision making, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Utilisation of Electronic Health Records for Public Health in Asia: A Review of Success Factors and Potential Challenges.BioMed research international · 2019Pooled it
- A Qualitative Study Examining the Unintended Consequences from Implementing a Case Management Team to Reduce Avoidable Hospital Readmission in Singapore.Health services insights · 2025Article
- Evaluation of a patient-centered integrated care program for individuals with frequent hospital readmissions and multimorbidity.Internal and emergency medicine · 2022Article
- Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old.Einstein (Sao Paulo, Brazil) · 2022Article
- AutoScore: A Machine Learning-Based Automatic Clinical Score Generator and Its Application to Mortality Prediction Using Electronic Health Records.JMIR medical informatics · 2020Article
- Epidemiologic Characteristics of Multimorbidity and Sociodemographic Factors Associated With Multimorbidity in a Rapidly Aging Asian Country.JAMA network open · 2019Article
- Retrospective evaluation of healthcare utilisation and mortality of two post-discharge care programmes in Singapore.BMJ open · 2019Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAn 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.
methodsWe 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.
resultsOur 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).
conclusionsThe 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.
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