Cargando…

Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore

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 wit...

Descripción completa

Detalles Bibliográficos
Autores principales: Low, Lian Leng, Lee, Kheng Hock, Hock Ong, Marcus Eng, Wang, Sijia, Tan, Shu Yun, Thumboo, Julian, Liu, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4670852/
https://www.ncbi.nlm.nih.gov/pubmed/26682212
http://dx.doi.org/10.1155/2015/169870
_version_ 1782404312278761472
author Low, Lian Leng
Lee, Kheng Hock
Hock Ong, Marcus Eng
Wang, Sijia
Tan, Shu Yun
Thumboo, Julian
Liu, Nan
author_facet Low, Lian Leng
Lee, Kheng Hock
Hock Ong, Marcus Eng
Wang, Sijia
Tan, Shu Yun
Thumboo, Julian
Liu, Nan
author_sort Low, Lian Leng
collection PubMed
description 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.
format Online
Article
Text
id pubmed-4670852
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher Hindawi Publishing Corporation
record_format MEDLINE/PubMed
spelling pubmed-46708522015-12-17 Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore Low, Lian Leng Lee, Kheng Hock Hock Ong, Marcus Eng Wang, Sijia Tan, Shu Yun Thumboo, Julian Liu, Nan Biomed Res Int Research Article 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. Hindawi Publishing Corporation 2015 2015-11-23 /pmc/articles/PMC4670852/ /pubmed/26682212 http://dx.doi.org/10.1155/2015/169870 Text en Copyright © 2015 Lian Leng Low et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Low, Lian Leng
Lee, Kheng Hock
Hock Ong, Marcus Eng
Wang, Sijia
Tan, Shu Yun
Thumboo, Julian
Liu, Nan
Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title_full Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title_fullStr Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title_full_unstemmed Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title_short Predicting 30-Day Readmissions: Performance of the LACE Index Compared with a Regression Model among General Medicine Patients in Singapore
title_sort predicting 30-day readmissions: performance of the lace index compared with a regression model among general medicine patients in singapore
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4670852/
https://www.ncbi.nlm.nih.gov/pubmed/26682212
http://dx.doi.org/10.1155/2015/169870
work_keys_str_mv AT lowlianleng predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT leekhenghock predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT hockongmarcuseng predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT wangsijia predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT tanshuyun predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT thumboojulian predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore
AT liunan predicting30dayreadmissionsperformanceofthelaceindexcomparedwitharegressionmodelamonggeneralmedicinepatientsinsingapore