Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation
After short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by th...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Stroke Research and Treatment |
| Online Access: | http://dx.doi.org/10.1155/2021/5546766 |
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| author | Jin Cho Krystal Place Rebecca Salstrand Monireh Rahmat Misagh Mansouri Nancy Fell Mina Sartipi |
| author_facet | Jin Cho Krystal Place Rebecca Salstrand Monireh Rahmat Misagh Mansouri Nancy Fell Mina Sartipi |
| author_sort | Jin Cho |
| collection | DOAJ |
| description | After short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by the healthcare team. Early prediction of discharge destination can optimize poststroke care and improve outcomes. Previous attempts to predict discharge disposition outcome after stroke have limited clinical validations. In this study, readmission status was used as a measure of the clinical significance and effectiveness of a discharge disposition prediction. Low readmission rates indicate proper and thorough care with appropriate discharge disposition. We used Medicare beneficiary data taken from a subset of base claims in the years of 2014 and 2015 in our analyses. A predictive tool was created to determine discharge disposition based on risk scores derived from the coefficients of multivariable logistic regression related to an adjusted odds ratio. The top five risk scores were admission from a skilled nursing facility, acute heart attack, intracerebral hemorrhage, admission from “other” source, and an age of 75 or older. Validation of the predictive tool was accomplished using the readmission rates. A 75% probability for facility discharge corresponded with a risk score of greater than 9. The prediction was then compared to actual discharge disposition. Each cohort was further analyzed to determine how many readmissions occurred in each group. Of the actual home discharges, 95.7% were predicted to be there. However, only 47.8% of predictions for home discharge were actually discharged home. Predicted discharge to facility had 15.9% match to the actual facility discharge. The scenario of actual discharge home and predicted discharge to facility showed that 186 patients were readmitted. Following the algorithm in this scenario would have recommended continued medical management of these patients, potentially preventing these readmissions. |
| format | Article |
| id | doaj-art-ee304f1841d644159b94e4bf474b5247 |
| institution | Kabale University |
| issn | 2090-8105 2042-0056 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stroke Research and Treatment |
| spelling | doaj-art-ee304f1841d644159b94e4bf474b52472025-02-03T07:23:31ZengWileyStroke Research and Treatment2090-81052042-00562021-01-01202110.1155/2021/55467665546766Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission ValidationJin Cho0Krystal Place1Rebecca Salstrand2Monireh Rahmat3Misagh Mansouri4Nancy Fell5Mina Sartipi6Department of Computer Science and Engineering, University of Tennessee at Chattanooga, USADepartment of Physical Therapy, University of Tennessee at Chattanooga, USADepartment of Physical Therapy, University of Tennessee at Chattanooga, USADepartment of Computer Science and Engineering, University of Tennessee at Chattanooga, USACenter for Urban Informatics and Progress, University of Tennessee at Chattanooga, USADepartment of Physical Therapy, University of Tennessee at Chattanooga, USADepartment of Computer Science and Engineering, University of Tennessee at Chattanooga, USAAfter short-term, acute-care hospitalization for stroke, patients may be discharged home or other facilities for continued medical or rehabilitative management. The site of postacute care affects overall mortality and functional outcomes. Determining discharge disposition is a complex decision by the healthcare team. Early prediction of discharge destination can optimize poststroke care and improve outcomes. Previous attempts to predict discharge disposition outcome after stroke have limited clinical validations. In this study, readmission status was used as a measure of the clinical significance and effectiveness of a discharge disposition prediction. Low readmission rates indicate proper and thorough care with appropriate discharge disposition. We used Medicare beneficiary data taken from a subset of base claims in the years of 2014 and 2015 in our analyses. A predictive tool was created to determine discharge disposition based on risk scores derived from the coefficients of multivariable logistic regression related to an adjusted odds ratio. The top five risk scores were admission from a skilled nursing facility, acute heart attack, intracerebral hemorrhage, admission from “other” source, and an age of 75 or older. Validation of the predictive tool was accomplished using the readmission rates. A 75% probability for facility discharge corresponded with a risk score of greater than 9. The prediction was then compared to actual discharge disposition. Each cohort was further analyzed to determine how many readmissions occurred in each group. Of the actual home discharges, 95.7% were predicted to be there. However, only 47.8% of predictions for home discharge were actually discharged home. Predicted discharge to facility had 15.9% match to the actual facility discharge. The scenario of actual discharge home and predicted discharge to facility showed that 186 patients were readmitted. Following the algorithm in this scenario would have recommended continued medical management of these patients, potentially preventing these readmissions.http://dx.doi.org/10.1155/2021/5546766 |
| spellingShingle | Jin Cho Krystal Place Rebecca Salstrand Monireh Rahmat Misagh Mansouri Nancy Fell Mina Sartipi Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation Stroke Research and Treatment |
| title | Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation |
| title_full | Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation |
| title_fullStr | Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation |
| title_full_unstemmed | Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation |
| title_short | Developing a Predictive Tool for Hospital Discharge Disposition of Patients Poststroke with 30-Day Readmission Validation |
| title_sort | developing a predictive tool for hospital discharge disposition of patients poststroke with 30 day readmission validation |
| url | http://dx.doi.org/10.1155/2021/5546766 |
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