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A bootstrap approach for assessing the uncertainty of outcome probabilities when using a scoring system
Gabriele Cevenini1, Paolo Barbini
1Department of Surgery and Bioengineering, University of Siena, Siena, Italy.
BMC Medical Informatics and Decision Making
|August 28, 2010
Summary
This study introduces a bootstrap method to improve the reliability of scoring systems for predicting patient morbidity after heart surgery. This approach enhances the accuracy of outcome probability estimates, aiding clinical decision-making.
Area of Science:
- Clinical predictive modeling
- Cardiothoracic surgery outcomes research
- Biostatistics and statistical modeling
Background:
- Scoring systems are valuable clinical predictive models due to their ease of use.
- A key limitation is the difficulty in reliably associating prognostic probabilities with scores.
- This study addresses the need for improved prognostic probability estimation in post-cardiac surgery morbidity scoring.
Purpose of the Study:
- To describe and apply a bootstrap approach for estimating confidence intervals of outcome probabilities.
- To design and optimize a scoring system for intensive care unit (ICU) morbidity after heart surgery.
- To enhance the trustworthiness of prognostic probabilities associated with clinical scoring systems.
Main Methods:
- Utilized the bias-corrected and accelerated bootstrap method to estimate 95% confidence intervals for outcome probabilities.
- Calculated confidence intervals for each score and design step using one thousand bootstrapped samples.
- Compared 78 potential predictors in 1090 adult coronary artery bypass graft patients, randomly assigned to training and testing sets.
Main Results:
- Evaluated multiple scoring systems based on discrimination, generalization, and prognostic probability uncertainty.
- Observed overlapping confidence intervals between score classes, suggesting the benefit of merging classes.
- A refined six-score-group model demonstrated a favorable balance between discrimination and generalization, with well-separated confidence intervals.
Conclusions:
- Traditional scoring systems often prioritize discrimination and generalization over trustworthy probability prediction.
- Bootstrap estimation of outcome probability confidence intervals offers valuable insights into score-class statistics.
- This method guides clinicians in selecting optimal models for predicting morbidity in specific clinical contexts.
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