toplogo
Sign In

Predicting Central Line-Associated Bloodstream Infection Risk Using Static and Dynamic Random Forest Models Accounting for Competing Events


Core Concepts
Comparison of the predictive performance of random forest models using different outcome operationalizations (binary, multinomial, survival, competing risks) to predict the 7-day risk of central line-associated bloodstream infection (CLABSI) in the presence of competing events (discharge, death).
Abstract
The study compared the performance of random forest (RF) models using different outcome types (binary, multinomial, survival, competing risks) to predict the 7-day risk of central line-associated bloodstream infection (CLABSI) in the presence of competing events (discharge, death). The models were built using data from 27,478 hospital admissions with 30,862 catheter episodes (970 CLABSI, 1,466 deaths, 28,426 discharges). Key highlights: Binary, multinomial, and competing risks models had similar predictive performance, with AUROC up to 0.78 at day 5 of the catheter episode. Survival models that censored competing events at their occurrence time overestimated the CLABSI risk and had slightly lower discrimination. Keeping competing events in the risk set until the prediction horizon, as in Fine-Gray models, improved the survival model performance. Models using all outcome levels (multinomial, competing risks) had slightly higher AUPRC but were miscalibrated, likely due to optimizing the split statistic over multiple outcome classes. Binary and multinomial models had the lowest computation times. In the absence of censoring, complex modeling choices did not considerably improve predictive performance compared to a binary model for CLABSI prediction.
Stats
"Chemotherapy, antibiotics and CRP features are selected at first splits for models using all outcome levels, while binary and survival models favor TPN at early splits." "The models using multiple outcome levels choose at the first splits in the trees antibiotics, other infection than BSI and ICU, while the other models favor TPN at early splits."
Quotes
None.

Deeper Inquiries

What are the potential benefits of using survival or competing risks models in settings where multiple prediction horizons are of interest, beyond the 7-day horizon considered in this study

In settings where multiple prediction horizons are of interest, such as in healthcare or finance, the use of survival or competing risks models can offer several benefits beyond the 7-day horizon considered in this study. Flexible Time Horizons: Survival models allow for the prediction of events at various time points, accommodating the need for predictions at different horizons. This flexibility is crucial in scenarios where outcomes may occur at different intervals or where the timing of events is of particular interest. Accounting for Competing Risks: Competing risks models are essential when there are multiple possible outcomes that may prevent the occurrence of the event of interest. By considering all potential outcomes simultaneously, these models provide a more comprehensive understanding of the risks involved. Improved Risk Assessment: By incorporating information on competing events and their timing, survival and competing risks models can provide more accurate risk assessments. This can lead to better decision-making and resource allocation in various domains. Long-Term Planning: In situations where long-term planning is necessary, such as in healthcare interventions or financial investments, survival and competing risks models can offer insights into the likelihood of events occurring over extended periods. Enhanced Predictive Performance: These models can capture the complex relationships between predictors and outcomes over time, leading to improved predictive performance compared to simpler models like binary classification.

How would the feature selection process and model performance change if the dataset included stronger predictors for death and discharge outcomes, beyond the features focused on CLABSI prediction

If the dataset included stronger predictors for death and discharge outcomes, beyond the features focused on CLABSI prediction, the feature selection process and model performance would likely undergo significant changes: Feature Selection: The inclusion of stronger predictors for death and discharge outcomes would influence the feature selection process. These additional features would be considered during model training, potentially leading to different variable importance rankings and early splits in the decision trees. Model Performance: With more informative predictors for death and discharge, the model performance is expected to improve. The models would likely exhibit better discrimination, calibration, and overall predictive accuracy, as they would have access to more relevant information for making predictions. Complexity and Interpretability: The inclusion of additional predictors may increase the complexity of the models. While this could enhance performance, it might also make the models harder to interpret. Balancing model complexity with interpretability would be crucial in such scenarios. Generalizability: Stronger predictors for death and discharge outcomes could potentially enhance the generalizability of the models, allowing them to perform well on new data and in different settings.

How could the computational efficiency of survival and competing risks models be further improved, beyond the discretization and administrative censoring approaches used in this study

To further improve the computational efficiency of survival and competing risks models beyond the discretization and administrative censoring approaches used in this study, several strategies can be considered: Advanced Sampling Techniques: Implementing more sophisticated sampling techniques, such as stratified sampling or adaptive sampling, can help optimize the data used for model training and reduce computational burden. Parallel Processing: Utilizing parallel processing capabilities and distributed computing frameworks can speed up model training and prediction tasks by distributing the workload across multiple processors or machines. Feature Engineering: Conducting feature engineering to reduce the dimensionality of the dataset and focus on the most informative predictors can streamline model training and improve efficiency. Algorithm Optimization: Exploring algorithmic optimizations specific to survival and competing risks models, such as specialized tree-splitting criteria or parallelized implementations, can further enhance computational performance. Hardware Acceleration: Leveraging hardware acceleration techniques, such as GPU computing, can significantly speed up model training and prediction tasks for large datasets. By implementing these strategies in conjunction with the existing approaches, the computational efficiency of survival and competing risks models can be further enhanced, making them more practical for real-world applications.
0