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Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients
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| Zeitschriftentitel: | International Journal of Statistics in Medical Research |
|---|---|
| Personen und Körperschaften: | |
| In: | International Journal of Statistics in Medical Research, 2, 2013, 1, S. 55-66 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Lifescience Global
|
| Schlagwörter: |
| author_facet |
Xie, Bin Xie, Bin |
|---|---|
| author |
Xie, Bin |
| spellingShingle |
Xie, Bin International Journal of Statistics in Medical Research Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients Health Information Management Health Informatics Health Professions (miscellaneous) Statistics and Probability |
| author_sort |
xie, bin |
| spelling |
Xie, Bin 1929-6029 Lifescience Global Health Information Management Health Informatics Health Professions (miscellaneous) Statistics and Probability http://dx.doi.org/10.6000/1929-6029.2013.02.01.07 <jats:p>Background: Boarding, or patients waiting to be admitted to hospital, has been shown as a significant contributing factor at overcrowding in emergency departments (ED). Predicting hospital admission at triage has been proposed as having the potential to help alleviate ED overcrowding. The objective of this paper is to develop and validate a model to predict hospital admission at triage to help alleviate ED overcrowding. Methods: Administrative records between April 1, 2010 and November 31, 2010 in an adult ED were used to derive and validate two prediction models, one based on Coxian phase type distribution (the PH model), the other based on logistic regression. Separate data sets were used for model development (data between April 1, 2010 and July 31, 2010) and validation (data between August 1, 2010 and November 31, 2010). Results: There were a total of 14,542 ED visits and 2,602 (17.89%) hospital admissions in the derivation cohort. In both models, acuity levels, model of arrival, and main reason of the visit are strong predictors of hospital admission; number of patients at the ED, as well as gender, are also predictors, albeit with ORs closer to 1. Patient age and timing of visits are not strong predictors. The PH model has an AUC of 0.89 compared with AUC of 0.83 for logistic regression model; with a cut- off value of 0.50, the PH model correctly predicted 86.3% of visits, compared to 84.4% for the logistic regression model. Results of the validation cohort were similar: the PH model has an AUC of 0.88, compared to AUC of 0.83 for the logistic model. Conclusions: PH and logistic models can be used to provide reasonably accurate prediction of hospital admission for ED patients, with the PH model offering more accurate predictions</jats:p> Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients International Journal of Statistics in Medical Research |
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International Journal of Statistics in Medical Research |
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| title |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_unstemmed |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_full |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_fullStr |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_full_unstemmed |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_short |
Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_sort |
development and validation of models to predict hospital admission for emergency department patients |
| topic |
Health Information Management Health Informatics Health Professions (miscellaneous) Statistics and Probability |
| url |
http://dx.doi.org/10.6000/1929-6029.2013.02.01.07 |
| publishDate |
2013 |
| physical |
55-66 |
| description |
<jats:p>Background: Boarding, or patients waiting to be admitted to hospital, has been shown as a significant contributing factor at overcrowding in emergency departments (ED). Predicting hospital admission at triage has been proposed as having the potential to help alleviate ED overcrowding. The objective of this paper is to develop and validate a model to predict hospital admission at triage to help alleviate ED overcrowding.
Methods: Administrative records between April 1, 2010 and November 31, 2010 in an adult ED were used to derive and validate two prediction models, one based on Coxian phase type distribution (the PH model), the other based on logistic regression. Separate data sets were used for model development (data between April 1, 2010 and July 31, 2010) and validation (data between August 1, 2010 and November 31, 2010).
Results: There were a total of 14,542 ED visits and 2,602 (17.89%) hospital admissions in the derivation cohort. In both models, acuity levels, model of arrival, and main reason of the visit are strong predictors of hospital admission; number of patients at the ED, as well as gender, are also predictors, albeit with ORs closer to 1. Patient age and timing of visits are not strong predictors. The PH model has an AUC of 0.89 compared with AUC of 0.83 for logistic regression model; with a cut- off value of 0.50, the PH model correctly predicted 86.3% of visits, compared to 84.4% for the logistic regression model. Results of the validation cohort were similar: the PH model has an AUC of 0.88, compared to AUC of 0.83 for the logistic model.
Conclusions: PH and logistic models can be used to provide reasonably accurate prediction of hospital admission for ED patients, with the PH model offering more accurate predictions</jats:p> |
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| description | <jats:p>Background: Boarding, or patients waiting to be admitted to hospital, has been shown as a significant contributing factor at overcrowding in emergency departments (ED). Predicting hospital admission at triage has been proposed as having the potential to help alleviate ED overcrowding. The objective of this paper is to develop and validate a model to predict hospital admission at triage to help alleviate ED overcrowding. Methods: Administrative records between April 1, 2010 and November 31, 2010 in an adult ED were used to derive and validate two prediction models, one based on Coxian phase type distribution (the PH model), the other based on logistic regression. Separate data sets were used for model development (data between April 1, 2010 and July 31, 2010) and validation (data between August 1, 2010 and November 31, 2010). Results: There were a total of 14,542 ED visits and 2,602 (17.89%) hospital admissions in the derivation cohort. In both models, acuity levels, model of arrival, and main reason of the visit are strong predictors of hospital admission; number of patients at the ED, as well as gender, are also predictors, albeit with ORs closer to 1. Patient age and timing of visits are not strong predictors. The PH model has an AUC of 0.89 compared with AUC of 0.83 for logistic regression model; with a cut- off value of 0.50, the PH model correctly predicted 86.3% of visits, compared to 84.4% for the logistic regression model. Results of the validation cohort were similar: the PH model has an AUC of 0.88, compared to AUC of 0.83 for the logistic model. Conclusions: PH and logistic models can be used to provide reasonably accurate prediction of hospital admission for ED patients, with the PH model offering more accurate predictions</jats:p> |
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| spelling | Xie, Bin 1929-6029 Lifescience Global Health Information Management Health Informatics Health Professions (miscellaneous) Statistics and Probability http://dx.doi.org/10.6000/1929-6029.2013.02.01.07 <jats:p>Background: Boarding, or patients waiting to be admitted to hospital, has been shown as a significant contributing factor at overcrowding in emergency departments (ED). Predicting hospital admission at triage has been proposed as having the potential to help alleviate ED overcrowding. The objective of this paper is to develop and validate a model to predict hospital admission at triage to help alleviate ED overcrowding. Methods: Administrative records between April 1, 2010 and November 31, 2010 in an adult ED were used to derive and validate two prediction models, one based on Coxian phase type distribution (the PH model), the other based on logistic regression. Separate data sets were used for model development (data between April 1, 2010 and July 31, 2010) and validation (data between August 1, 2010 and November 31, 2010). Results: There were a total of 14,542 ED visits and 2,602 (17.89%) hospital admissions in the derivation cohort. In both models, acuity levels, model of arrival, and main reason of the visit are strong predictors of hospital admission; number of patients at the ED, as well as gender, are also predictors, albeit with ORs closer to 1. Patient age and timing of visits are not strong predictors. The PH model has an AUC of 0.89 compared with AUC of 0.83 for logistic regression model; with a cut- off value of 0.50, the PH model correctly predicted 86.3% of visits, compared to 84.4% for the logistic regression model. Results of the validation cohort were similar: the PH model has an AUC of 0.88, compared to AUC of 0.83 for the logistic model. Conclusions: PH and logistic models can be used to provide reasonably accurate prediction of hospital admission for ED patients, with the PH model offering more accurate predictions</jats:p> Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients International Journal of Statistics in Medical Research |
| spellingShingle | Xie, Bin, International Journal of Statistics in Medical Research, Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients, Health Information Management, Health Informatics, Health Professions (miscellaneous), Statistics and Probability |
| title | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_full | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_fullStr | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_full_unstemmed | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_short | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| title_sort | development and validation of models to predict hospital admission for emergency department patients |
| title_unstemmed | Development and Validation of Models to Predict Hospital Admission for Emergency Department Patients |
| topic | Health Information Management, Health Informatics, Health Professions (miscellaneous), Statistics and Probability |
| url | http://dx.doi.org/10.6000/1929-6029.2013.02.01.07 |