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Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients

BACKGROUND: Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrenc...

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Autores principales: Povalej Brzan, Petra, Obradovic, Zoran, Stiglic, Gregor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407280/
https://www.ncbi.nlm.nih.gov/pubmed/28462037
http://dx.doi.org/10.7717/peerj.3230
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author Povalej Brzan, Petra
Obradovic, Zoran
Stiglic, Gregor
author_facet Povalej Brzan, Petra
Obradovic, Zoran
Stiglic, Gregor
author_sort Povalej Brzan, Petra
collection PubMed
description BACKGROUND: Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. METHODS: The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. RESULTS: Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. DISCUSSION: The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations.
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spelling pubmed-54072802017-05-01 Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients Povalej Brzan, Petra Obradovic, Zoran Stiglic, Gregor PeerJ Diabetes and Endocrinology BACKGROUND: Reduction of readmissions after discharge represents an important challenge for many hospitals and has attracted the interest of many researchers in the past few years. Most of the studies in this field focus on building cross-sectional predictive models that aim to predict the occurrence of readmission within 30-days based on information from the current hospitalization. The aim of this study is demonstration of predictive performance gain obtained by inclusion of information from historical hospitalization records among morbidly obese patients. METHODS: The California Statewide inpatient database was used to build regularized logistic regression models for prediction of readmission in morbidly obese patients (n = 18,881). Temporal features were extracted from historical patient hospitalization records in a one-year timeframe. Five different datasets of patients were prepared based on the number of available hospitalizations per patient. Sample size of the five datasets ranged from 4,787 patients with more than five hospitalizations to 20,521 patients with at least two hospitalization records in one year. A 10-fold cross validation was repeted 100 times to assess the variability of the results. Additionally, random forest and extreme gradient boosting were used to confirm the results. RESULTS: Area under the ROC curve increased significantly when including information from up to three historical records on all datasets. The inclusion of more than three historical records was not efficient. Similar results can be observed for Brier score and PPV value. The number of selected predictors corresponded to the complexity of the dataset ranging from an average of 29.50 selected features on the smallest dataset to 184.96 on the largest dataset based on 100 repetitions of 10-fold cross-validation. DISCUSSION: The results show positive influence of adding information from historical hospitalization records on predictive performance using all predictive modeling techniques used in this study. We can conclude that it is advantageous to build separate readmission prediction models in subgroups of patients with more hospital admissions by aggregating information from up to three previous hospitalizations. PeerJ Inc. 2017-04-25 /pmc/articles/PMC5407280/ /pubmed/28462037 http://dx.doi.org/10.7717/peerj.3230 Text en ©2017 Povalej Brzan 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, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Diabetes and Endocrinology
Povalej Brzan, Petra
Obradovic, Zoran
Stiglic, Gregor
Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title_full Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title_fullStr Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title_full_unstemmed Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title_short Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
title_sort contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients
topic Diabetes and Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5407280/
https://www.ncbi.nlm.nih.gov/pubmed/28462037
http://dx.doi.org/10.7717/peerj.3230
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