Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital
Abstract Background The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney...
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| Format: | Article |
| Language: | English |
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BMC
2018-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | http://link.springer.com/article/10.1186/s12911-017-0580-8 |
| _version_ | 1818200647010353152 |
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| author | Yashar Maali Oscar Perez-Concha Enrico Coiera David Roffe Richard O. Day Blanca Gallego |
| author_facet | Yashar Maali Oscar Perez-Concha Enrico Coiera David Roffe Richard O. Day Blanca Gallego |
| author_sort | Yashar Maali |
| collection | DOAJ |
| description | Abstract Background The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. Methods A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. Results The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. Conclusions This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice. |
| first_indexed | 2024-12-12T02:40:59Z |
| format | Article |
| id | doaj.art-433ccd33a257486799bd034e4f9c02e4 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-12T02:40:59Z |
| publishDate | 2018-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-433ccd33a257486799bd034e4f9c02e42022-12-22T00:41:10ZengBMCBMC Medical Informatics and Decision Making1472-69472018-01-0118111110.1186/s12911-017-0580-8Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospitalYashar Maali0Oscar Perez-Concha1Enrico Coiera2David Roffe3Richard O. Day4Blanca Gallego5Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie UniversityCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie UniversityCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie UniversityChief Information Officer, St Vincent’s Health AustraliaClinical Pharmacology and Toxicology, St Vincent’s HospitalCentre for Health Informatics, Australian Institute of Health Innovation, Macquarie UniversityAbstract Background The identification of patients at high risk of unplanned readmission is an important component of discharge planning strategies aimed at preventing unwanted returns to hospital. The aim of this study was to investigate the factors associated with unplanned readmission in a Sydney hospital. We developed and compared validated readmission risk scores using routinely collected hospital data to predict 7-day, 30-day and 60-day all-cause unplanned readmission. Methods A combination of gradient boosted tree algorithms for variable selection and logistic regression models was used to build and validate readmission risk scores using medical records from 62,235 live discharges from a metropolitan hospital in Sydney, Australia. Results The scores had good calibration and fair discriminative performance with c-statistic of 0.71 for 7-day and for 30-day readmission, and 0.74 for 60-day. Previous history of healthcare utilization, urgency of the index admission, old age, comorbidities related to cancer, psychosis, and drug-abuse, abnormal pathology results at discharge, and being unmarried and a public patient were found to be important predictors in all models. Unplanned readmissions beyond 7 days were more strongly associated with longer hospital stays and older patients with higher number of comorbidities and higher use of acute care in the past year. Conclusions This study demonstrates similar predictors and performance to previous risk scores of 30-day unplanned readmission. Shorter-term readmissions may have different causal pathways than 30-day readmission, and may, therefore, require different screening tools and interventions. This study also re-iterates the need to include more informative data elements to ensure the appropriateness of these risk scores in clinical practice.http://link.springer.com/article/10.1186/s12911-017-0580-8Hospital readmissionReadmission risk scores |
| spellingShingle | Yashar Maali Oscar Perez-Concha Enrico Coiera David Roffe Richard O. Day Blanca Gallego Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital BMC Medical Informatics and Decision Making Hospital readmission Readmission risk scores |
| title | Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital |
| title_full | Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital |
| title_fullStr | Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital |
| title_full_unstemmed | Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital |
| title_short | Predicting 7-day, 30-day and 60-day all-cause unplanned readmission: a case study of a Sydney hospital |
| title_sort | predicting 7 day 30 day and 60 day all cause unplanned readmission a case study of a sydney hospital |
| topic | Hospital readmission Readmission risk scores |
| url | http://link.springer.com/article/10.1186/s12911-017-0580-8 |
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