Abstract
Aim: Early Warning Scores are support tools intended to help clinicians prevent adverse patient outcomes. Although it has been shown that trends in a patient's medical condition are associated with patient-outcome, the incorporation of this knowledge within early warning score development has been slow. Our goal is to find the minimal-best-performing set of predictors for logistic regression models that includes trends in a patient's medical state.
Materials and methods: We used a large data set obtained from a single large hospital in the south of England and logistic regression modelling to search for the smallest possible set of predictors that simultaneously has a high predictive performance. Efficiency curves were used to estimate the trade-off between clinical workload and the sensitivity of the models and to compare performance with the National Early Warning Score (NEWS), the Laboratory-Decision Tree Early Warning Score (LDTEWS) and LDTEWS:NEWS.
Results: Comparing the efficiency curves of the different models showed, that the number of consecutive observations (2 to 5 observations) had little impact on model performance. Even in the simplest scenario, using 2 consecutive observations, the best model identified between 17 and 293 more deteriorating patients per 1000 patients compared to established non-trend early warning systems, at a comparable clinical workload. This best model uses linear regression coefficients obtained from consecutive NEWS values, the current LDTEWS value as well as the mean of the respiratory rates.
Conclusions: The results of this study confirm that, not only can the performance of models predicting clinical deterioration be increased by including trends, but that a logistic regression-based model with very few predictors can predict the risk of deterioration better than current non-trend models. Thus, models incorporating trends have the potential to prevent deterioration in more patients than contemporary early warning scores, however further validation is necessary.
Materials and methods: We used a large data set obtained from a single large hospital in the south of England and logistic regression modelling to search for the smallest possible set of predictors that simultaneously has a high predictive performance. Efficiency curves were used to estimate the trade-off between clinical workload and the sensitivity of the models and to compare performance with the National Early Warning Score (NEWS), the Laboratory-Decision Tree Early Warning Score (LDTEWS) and LDTEWS:NEWS.
Results: Comparing the efficiency curves of the different models showed, that the number of consecutive observations (2 to 5 observations) had little impact on model performance. Even in the simplest scenario, using 2 consecutive observations, the best model identified between 17 and 293 more deteriorating patients per 1000 patients compared to established non-trend early warning systems, at a comparable clinical workload. This best model uses linear regression coefficients obtained from consecutive NEWS values, the current LDTEWS value as well as the mean of the respiratory rates.
Conclusions: The results of this study confirm that, not only can the performance of models predicting clinical deterioration be increased by including trends, but that a logistic regression-based model with very few predictors can predict the risk of deterioration better than current non-trend models. Thus, models incorporating trends have the potential to prevent deterioration in more patients than contemporary early warning scores, however further validation is necessary.
| Original language | English |
|---|---|
| Journal | Resuscitation |
| Early online date | 4 Oct 2025 |
| DOIs | |
| Publication status | Early online - 4 Oct 2025 |
Keywords
- Clinical deterioration
- Decision Support
- Vital Sign Trends
- Risk Prediction
