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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Blythe, Robin Parsons, Rex Barnett, Adrian G. Cook, David McPhail, Steven M. White, Nicole M. |
| Abstract | Background Binary classification models are frequently used to predict clinical deterioration, however they ignore information on the timing of events. An alternative is to apply time-to-event models, augmenting clinical workflows by ranking patients by predicted risks. This study examines how and why time-to-event modelling of vital signs data can help prioritise deterioration assessments using lift curves, and develops a prediction model to stratify acute care inpatients by risk of clinical deterioration. Methods We developed and validated a Cox regression for time to in-hospital mortality. The model used time-varying covariates to estimate the risk of clinical deterioration. Adult inpatient medical records from 5 Australian hospitals between 1 January 2019 and 31 December 2020 were used for model development and validation. Model discrimination and calibration were assessed using internal–external cross validation. A discrete-time logistic regression model predicting death within 24 h with the same covariates was used as a comparator to the Cox regression model to estimate differences in predictive performance between the binary and time-to-event outcome modelling approaches. Results Our data contained 150,342 admissions and 1016 deaths. Model discrimination was higher for Cox regression than for discrete-time logistic regression, with cross-validated AUCs of 0.96 and 0.93, respectively, for mortality predictions within 24 h, declining to 0.93 and 0.88, respectively, for mortality predictions within 1 week. Calibration plots showed that calibration varied by hospital, but this can be mitigated by ranking patients by predicted risks. Conclusion Time-varying covariate Cox models can be powerful tools for triaging patients, which may lead to more efficient and effective care in time-poor environments when the times between observations are highly variable. |
| Related Links | https://ccforum.biomedcentral.com/counter/pdf/10.1186/s13054-024-05021-y.pdf |
| Ending Page | 13 |
| Page Count | 13 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 13648535 |
| DOI | 10.1186/s13054-024-05021-y |
| Journal | Critical Care |
| Issue Number | 1 |
| Volume Number | 28 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2024-07-17 |
| Access Restriction | Open |
| Subject Keyword | Intensive Critical Care Medicine Emergency Medicine Survival analysis Logistic regression Prediction model Clinical deterioration Early warning score Area under curve |
| Content Type | Text |
| Resource Type | Article |
| Subject | Critical Care and Intensive Care Medicine |
| Journal Impact Factor | 8.8/2023 |
| 5-Year Journal Impact Factor | 10.4/2023 |
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