Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study
Objectives To identify risk factors for inpatient mortality after patients’ emergency admission and to create a novel model predicting inpatient mortality risk.Design This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split...
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| Format: | Article |
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BMJ Publishing Group
2019-09-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/9/e031382.full |
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| author | Marcus Eng Hock Ong Feng Xie Nan Liu Stella Xinzi Wu Yukai Ang Lian Leng Low Andrew Fu Wah Ho Sean Shao Wei Lam David Bruce Matchar Bibhas Chakraborty |
| author_facet | Marcus Eng Hock Ong Feng Xie Nan Liu Stella Xinzi Wu Yukai Ang Lian Leng Low Andrew Fu Wah Ho Sean Shao Wei Lam David Bruce Matchar Bibhas Chakraborty |
| author_sort | Marcus Eng Hock Ong |
| collection | DOAJ |
| description | Objectives To identify risk factors for inpatient mortality after patients’ emergency admission and to create a novel model predicting inpatient mortality risk.Design This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split into a derivation set and a validation set. The stepwise model selection was employed. We compared our model with one of the current clinical scores, Cardiac Arrest Risk Triage (CART) score.Setting A single tertiary hospital in Singapore.Participants All adult hospitalised patients, admitted via emergency department (ED) from 1 January 2008 to 31 October 2017 (n=433 187 by admission episodes).Main outcome measure The primary outcome of interest was inpatient mortality following this admission episode. The area under the curve (AUC) of the receiver operating characteristic curve of the predictive model with sensitivity and specificity for optimised cut-offs.Results 15 758 (3.64%) of the episodes were observed inpatient mortality. 19 variables were observed as significant predictors and were included in our final regression model. Our predictive model outperformed the CART score in terms of predictive power. The AUC of CART score and our final model was 0.705 (95% CI 0.697 to 0.714) and 0.817 (95% CI 0.810 to 0.824), respectively.Conclusion We developed and validated a model for inpatient mortality using EHR data collected in the ED. The performance of our model was more accurate than the CART score. Implementation of our model in the hospital can potentially predict imminent adverse events and institute appropriate clinical management. |
| format | Article |
| id | doaj-art-c2a4a4e1b50549f28aa7240b5ed330fb |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-c2a4a4e1b50549f28aa7240b5ed330fb2024-11-28T17:05:08ZengBMJ Publishing GroupBMJ Open2044-60552019-09-019910.1136/bmjopen-2019-031382Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational studyMarcus Eng Hock Ong0Feng Xie1Nan Liu2Stella Xinzi Wu3Yukai Ang4Lian Leng Low5Andrew Fu Wah Ho6Sean Shao Wei Lam7David Bruce Matchar8Bibhas Chakraborty9Health Services & Systems Research, Duke-NUS Medical School, Singapore4 Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Ontario, CanadaProgramme in Health Services and Systems Research, Duke-NUS Medical School, Singapore1 Duke-NUS Medical School, National University of Singapore, Singapore, Singapore1 Duke-NUS Medical School, National University of Singapore, Singapore, Singapore3 Department of Family Medicine and Continuing Care, Singapore General Hospital, Singapore, SingaporeDepartment of Emergency Medicine, Singapore General Hospital, Singapore1 Duke-NUS Medical School, National University of Singapore, Singapore, SingaporeHealth Services and Systems Research, Duke-NUS Medical School, Singapore2Duke-NUS Medical School, Singapore, SingaporeObjectives To identify risk factors for inpatient mortality after patients’ emergency admission and to create a novel model predicting inpatient mortality risk.Design This was a retrospective observational study using data extracted from electronic health records (EHRs). The data were randomly split into a derivation set and a validation set. The stepwise model selection was employed. We compared our model with one of the current clinical scores, Cardiac Arrest Risk Triage (CART) score.Setting A single tertiary hospital in Singapore.Participants All adult hospitalised patients, admitted via emergency department (ED) from 1 January 2008 to 31 October 2017 (n=433 187 by admission episodes).Main outcome measure The primary outcome of interest was inpatient mortality following this admission episode. The area under the curve (AUC) of the receiver operating characteristic curve of the predictive model with sensitivity and specificity for optimised cut-offs.Results 15 758 (3.64%) of the episodes were observed inpatient mortality. 19 variables were observed as significant predictors and were included in our final regression model. Our predictive model outperformed the CART score in terms of predictive power. The AUC of CART score and our final model was 0.705 (95% CI 0.697 to 0.714) and 0.817 (95% CI 0.810 to 0.824), respectively.Conclusion We developed and validated a model for inpatient mortality using EHR data collected in the ED. The performance of our model was more accurate than the CART score. Implementation of our model in the hospital can potentially predict imminent adverse events and institute appropriate clinical management.https://bmjopen.bmj.com/content/9/9/e031382.full |
| spellingShingle | Marcus Eng Hock Ong Feng Xie Nan Liu Stella Xinzi Wu Yukai Ang Lian Leng Low Andrew Fu Wah Ho Sean Shao Wei Lam David Bruce Matchar Bibhas Chakraborty Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study BMJ Open |
| title | Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study |
| title_full | Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study |
| title_fullStr | Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study |
| title_full_unstemmed | Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study |
| title_short | Novel model for predicting inpatient mortality after emergency admission to hospital in Singapore: retrospective observational study |
| title_sort | novel model for predicting inpatient mortality after emergency admission to hospital in singapore retrospective observational study |
| url | https://bmjopen.bmj.com/content/9/9/e031382.full |
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