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Selecting the best prediction model for readmission.

Lee EW - J Prev Med Public Health (2012)

Bottom Line: The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured.Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

View Article: PubMed Central - PubMed

Affiliation: College of Pharmacy, Gachon University, Incheon, Korea. ewlee@gachon.ac.kr

ABSTRACT

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model.

Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve.

Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.

Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

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Comparison of statistical prediction models by Lift chart. 1Decision tree.
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Figure 1: Comparison of statistical prediction models by Lift chart. 1Decision tree.

Mentions: From a comparison of the misclassification rate and root ASE to evaluate the predictive power of the three models (Table 4), the root ASE of the training data results was 0.385 for regression, 0.373 for the decision tree, and 0.384 for the neural network, while that of the validation data was 0.385 for the regression, 0.369 for the decision tree, and 0.383 for the neural network. Thus, the decision tree showed the highest predictive power for the root ASE. The misclassification rate with the model generation data was 0.214 for the regression, 0.180 for the decision tree, and 0.214 for the neural network while that obtained with the validation data was 0.217 for the regression analysis, 0.177 for the decision tree, and 0.211 for the neural network. Thus, the decision tree also showed the highest predictive power for the misclassification rate. The lift chart and ROC curve, which are widely used to evaluate a given model's predictive power, were also used, and from the results, both the lift chart (Figure 1) and ROC curve (Figure 2) found the decision tree to have stronger predictive power. From the model comparison, the decision tree was chosen in order to predict patients with readmission risk.


Selecting the best prediction model for readmission.

Lee EW - J Prev Med Public Health (2012)

Comparison of statistical prediction models by Lift chart. 1Decision tree.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3412989&req=5

Figure 1: Comparison of statistical prediction models by Lift chart. 1Decision tree.
Mentions: From a comparison of the misclassification rate and root ASE to evaluate the predictive power of the three models (Table 4), the root ASE of the training data results was 0.385 for regression, 0.373 for the decision tree, and 0.384 for the neural network, while that of the validation data was 0.385 for the regression, 0.369 for the decision tree, and 0.383 for the neural network. Thus, the decision tree showed the highest predictive power for the root ASE. The misclassification rate with the model generation data was 0.214 for the regression, 0.180 for the decision tree, and 0.214 for the neural network while that obtained with the validation data was 0.217 for the regression analysis, 0.177 for the decision tree, and 0.211 for the neural network. Thus, the decision tree also showed the highest predictive power for the misclassification rate. The lift chart and ROC curve, which are widely used to evaluate a given model's predictive power, were also used, and from the results, both the lift chart (Figure 1) and ROC curve (Figure 2) found the decision tree to have stronger predictive power. From the model comparison, the decision tree was chosen in order to predict patients with readmission risk.

Bottom Line: The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured.Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

View Article: PubMed Central - PubMed

Affiliation: College of Pharmacy, Gachon University, Incheon, Korea. ewlee@gachon.ac.kr

ABSTRACT

Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model.

Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve.

Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine, 10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater.

Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.

Show MeSH
Related in: MedlinePlus