Cargando…
Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity
BACKGROUND: To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity,...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147878/ https://www.ncbi.nlm.nih.gov/pubmed/27936053 http://dx.doi.org/10.1371/journal.pone.0167413 |
_version_ | 1782473751153082368 |
---|---|
author | Low, Lian Leng Liu, Nan Wang, Sijia Thumboo, Julian Ong, Marcus Eng Hock Lee, Kheng Hock |
author_facet | Low, Lian Leng Liu, Nan Wang, Sijia Thumboo, Julian Ong, Marcus Eng Hock Lee, Kheng Hock |
author_sort | Low, Lian Leng |
collection | PubMed |
description | BACKGROUND: To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool. METHOD: This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital’s electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index. RESULTS: 74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as ‘requiring inpatient dialysis during index admission, and ‘treatment with intravenous furosemide 40 milligrams or more’ during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77–0.79) versus 0.70 (95% CI: 0.69–0.71). CONCLUSION: Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity. |
format | Online Article Text |
id | pubmed-5147878 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-51478782016-12-28 Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity Low, Lian Leng Liu, Nan Wang, Sijia Thumboo, Julian Ong, Marcus Eng Hock Lee, Kheng Hock PLoS One Research Article BACKGROUND: To reduce readmissions, it may be cost-effective to consider risk stratification, with targeting intervention programs to patients at high risk of readmissions. In this study, we aimed to derive and validate a prediction model including several novel markers of hospitalization severity, and compare the model with the LACE index (Length of stay, Acuity of admission, Charlson comorbidity index, Emergency department visits in past 6 months), an established risk stratification tool. METHOD: This was a retrospective cohort study of all patients ≥ 21 years of age, who were admitted to a tertiary hospital in Singapore from January 1, 2013 through May 31, 2015. Data were extracted from the hospital’s electronic health records. The outcome was defined as unplanned readmissions within 30 days of discharge from the index hospitalization. Candidate predictive variables were broadly grouped into five categories: Patient demographics, social determinants of health, past healthcare utilization, medical comorbidities, and markers of hospitalization severity. Multivariable logistic regression was used to predict the outcome, and receiver operating characteristic analysis was performed to compare our model with the LACE index. RESULTS: 74,102 cases were enrolled for analysis. Of these, 11,492 patient cases (15.5%) were readmitted within 30 days of discharge. A total of fifteen predictive variables were strongly associated with the risk of 30-day readmissions, including number of emergency department visits in the past 6 months, Charlson Comorbidity Index, markers of hospitalization severity such as ‘requiring inpatient dialysis during index admission, and ‘treatment with intravenous furosemide 40 milligrams or more’ during index admission. Our predictive model outperformed the LACE index by achieving larger area under the curve values: 0.78 (95% confidence interval [CI]: 0.77–0.79) versus 0.70 (95% CI: 0.69–0.71). CONCLUSION: Several factors are important for the risk of 30-day readmissions, including proxy markers of hospitalization severity. Public Library of Science 2016-12-09 /pmc/articles/PMC5147878/ /pubmed/27936053 http://dx.doi.org/10.1371/journal.pone.0167413 Text en © 2016 Low et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Low, Lian Leng Liu, Nan Wang, Sijia Thumboo, Julian Ong, Marcus Eng Hock Lee, Kheng Hock Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title | Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title_full | Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title_fullStr | Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title_full_unstemmed | Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title_short | Predicting 30-Day Readmissions in an Asian Population: Building a Predictive Model by Incorporating Markers of Hospitalization Severity |
title_sort | predicting 30-day readmissions in an asian population: building a predictive model by incorporating markers of hospitalization severity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5147878/ https://www.ncbi.nlm.nih.gov/pubmed/27936053 http://dx.doi.org/10.1371/journal.pone.0167413 |
work_keys_str_mv | AT lowlianleng predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity AT liunan predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity AT wangsijia predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity AT thumboojulian predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity AT ongmarcusenghock predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity AT leekhenghock predicting30dayreadmissionsinanasianpopulationbuildingapredictivemodelbyincorporatingmarkersofhospitalizationseverity |