title: Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore.
abstract: The LACE index (length of stay, acuity of admission, Charlson comorbidity index, CCI, and number of emergency department visits in preceding 6 months) derived in Canada is simple and may have clinical utility in Singapore to predict readmission risk. We compared the performance of the LACE index with a derived model in identifying 30-day readmissions from a population of general medicine patients in Singapore. Additional variables include patient demographics, comorbidities, clinical and laboratory variables during the index admission, and prior healthcare utilization in the preceding year. 5,862 patients were analysed and 572 patients (9.8%) were readmitted in the 30 days following discharge. Age, CCI, count of surgical procedures during index admission, white cell count, serum albumin, and number of emergency department visits in previous 6 months were significantly associated with 30-day readmission risk. The final logistic regression model had fair discriminative ability c-statistic of 0.650 while the LACE index achieved c-statistic of 0.628 in predicting 30-day readmissions. Our derived model has the advantage of being available early in the admission to identify patients at high risk of readmission for interventions. Additional factors predicting readmission risk and machine learning techniques should be considered to improve model performance.
authors.affiliation: Department of Family Medicine & Continuing Care, Singapore General Hospital, Academia Level 4, 20 College Road, Singapore 169856.
authors.forename: Lian Leng
authors.initials: LL
authors.lastname: Low
authors.affiliation: Department of Family Medicine & Continuing Care, Singapore General Hospital, Academia Level 4, 20 College Road, Singapore 169856.
authors.forename: Kheng Hock
authors.initials: KH
authors.lastname: Lee
authors.affiliation: Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608 ; Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore.
authors.forename: Marcus Eng
authors.initials: ME
authors.lastname: Hock Ong
authors.affiliation: Integrated Health Information Systems, Singapore.
authors.forename: Sijia
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Family Medicine & Continuing Care, Singapore General Hospital, Academia Level 4, 20 College Road, Singapore 169856.
authors.forename: Shu Yun
authors.initials: SY
authors.lastname: Tan
authors.affiliation: Health Services and Systems Research, Duke-NUS Graduate Medical School, Singapore ; Department of Rheumatology & Immunology, Singapore General Hospital, Singapore.
authors.forename: Julian
authors.initials: J
authors.lastname: Thumboo
authors.affiliation: Department of Emergency Medicine, Singapore General Hospital, Outram Road, Singapore 169608 ; Centre for Quantitative Medicine, Duke-NUS Graduate Medical School, Singapore.
authors.forename: Nan
authors.initials: N
authors.lastname: Liu
mesh_terms: D002170:Canada; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012307:Risk Factors; D012846:Singapore; D013997:Time Factors