FAM-FACE-SG: a score for risk stratification of frequent hospital admitters
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...
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| Format: | Article |
| Language: | English |
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BMC
2017-04-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-017-0441-5 |
| _version_ | 1819177862559694848 |
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| author | Lian Leng Low Nan Liu Kheng Hock Lee Marcus Eng Hock Ong Sijia Wang Xuan Jing Julian Thumboo |
| author_facet | Lian Leng Low Nan Liu Kheng Hock Lee Marcus Eng Hock Ong Sijia Wang Xuan Jing Julian Thumboo |
| author_sort | Lian Leng Low |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T21:33:24Z |
| format | Article |
| id | doaj.art-92946bf7ee51492ea4213bc45a5b0857 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-22T21:33:24Z |
| publishDate | 2017-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-92946bf7ee51492ea4213bc45a5b08572022-12-21T18:11:51ZengBMCBMC Medical Informatics and Decision Making1472-69472017-04-0117111110.1186/s12911-017-0441-5FAM-FACE-SG: a score for risk stratification of frequent hospital admittersLian Leng Low0Nan Liu1Kheng Hock Lee2Marcus Eng Hock Ong3Sijia Wang4Xuan Jing5Julian Thumboo6Department of Family Medicine & Continuing Care, Singapore General HospitalHealth Services Research Centre, Singapore Health ServicesDepartment of Family Medicine & Continuing Care, Singapore General HospitalDepartment of Emergency Medicine, Singapore General HospitalIntegrated Health Information SystemsHealth Services Research Centre, Singapore Health ServicesHealth Services Research Centre, Singapore Health ServicesAbstract 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.http://link.springer.com/article/10.1186/s12911-017-0441-5Risk scoreStratificationFrequent hospital admittersElectronic health recordLACE index |
| spellingShingle | Lian Leng Low Nan Liu Kheng Hock Lee Marcus Eng Hock Ong Sijia Wang Xuan Jing Julian Thumboo FAM-FACE-SG: a score for risk stratification of frequent hospital admitters BMC Medical Informatics and Decision Making Risk score Stratification Frequent hospital admitters Electronic health record LACE index |
| title | FAM-FACE-SG: a score for risk stratification of frequent hospital admitters |
| title_full | FAM-FACE-SG: a score for risk stratification of frequent hospital admitters |
| title_fullStr | FAM-FACE-SG: a score for risk stratification of frequent hospital admitters |
| title_full_unstemmed | FAM-FACE-SG: a score for risk stratification of frequent hospital admitters |
| title_short | FAM-FACE-SG: a score for risk stratification of frequent hospital admitters |
| title_sort | fam face sg a score for risk stratification of frequent hospital admitters |
| topic | Risk score Stratification Frequent hospital admitters Electronic health record LACE index |
| url | http://link.springer.com/article/10.1186/s12911-017-0441-5 |
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