Abstract
Objectives
Being able to predict which COVID-19 patients are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and don’t always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying ‘state of the art’ statistical methods in the development of a prognostic model to predict death in hospitalised COVID-19 patients.
Design
Data were analysed from COVID-19 admissions to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to five days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.
Setting
3 secondary and tertiary level centres in Greater Manchester, UK.
Participants
392 hospitalised patients with a diagnosis of COVID-19
Results
392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic (ROC) curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.
Conclusions
This study shows that clinical prediction models to predict death in hospitalised COVID-19 patients can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.
Being able to predict which COVID-19 patients are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and don’t always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying ‘state of the art’ statistical methods in the development of a prognostic model to predict death in hospitalised COVID-19 patients.
Design
Data were analysed from COVID-19 admissions to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to five days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework.
Setting
3 secondary and tertiary level centres in Greater Manchester, UK.
Participants
392 hospitalised patients with a diagnosis of COVID-19
Results
392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic (ROC) curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome.
Conclusions
This study shows that clinical prediction models to predict death in hospitalised COVID-19 patients can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.
| Original language | English |
|---|---|
| Journal | BMJ Open |
| Publication status | Accepted/In press - 2 Aug 2020 |