Evidence map›Paper›PMID 28390405›Full record

ArticleBMC medical informatics and decision making2017

FAM-FACE-SG: a score for risk stratification of frequent hospital admitters.

Lian Leng Low, Nan Liu, Kheng Hock Lee, Marcus Eng Hock Ong, Sijia Wang, Xuan Jing, Julian Thumboo

Abstract read
In one paragraph

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.

0numbers the graph read from it
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7citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Lian Leng LowDepartment of Family Medicine & Continuing Care, Singapore General Hospital, Singapore, Singapore. low.lian.leng@singhealth.com.sg.
Nan LiuHealth Services Research Centre, Singapore Health Services, Singapore, Singapore. liu.nan@singhealth.com.sg.
Kheng Hock LeeDepartment of Family Medicine & Continuing Care, Singapore General Hospital, Singapore, Singapore.
Marcus Eng Hock OngDepartment of Emergency Medicine, Singapore General Hospital, Singapore, Singapore.
Sijia WangIntegrated Health Information Systems, Singapore, Singapore.
Xuan JingHealth Services Research Centre, Singapore Health Services, Singapore, Singapore.
Julian ThumbooHealth Services Research Centre, Singapore Health Services, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

AdultAgedElectronic Health RecordsFemaleHumansMaleMiddle AgedPatient ReadmissionRetrospective StudiesRisk AssessmentSingaporeTertiary Care CentersElectronic health recordFrequent hospital admittersLACE indexRisk scoreStratification

Identifiers

PMID28390405
PMCPMC5385059

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.