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Advanced cardiovascular risk prediction in the emergency department: updating a clinical prediction model - a large
Charles Reynard1,2, Glen P Martin3, Evangelos Kontopantelis3
1Division of Cardiovascular Sciences, University of Manchester, Manchester, UK. Charlie.reynard@manchester.ac.uk.
Insights
Clinical prediction models (CPMs) like the Troponin-only Manchester Acute Coronary Syndromes (T-MACS) tool can drift over time. This study assesses T-MACS calibration drift and evaluates methods for updating the model using electronic health data to maintain diagnostic accuracy.
Area of Science:
- Cardiology
- Medical Informatics
- Clinical Epidemiology
Background:
- Chest pain is a common emergency department presentation, often requiring diagnosis of acute myocardial infarction (AMI).
- Clinical prediction models (CPMs) aid in early AMI diagnosis, with the Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid currently in use.
- CPMs are susceptible to calibration drift over time, potentially impacting their diagnostic accuracy.
Purpose of the Study:
- To assess calibration drift in the T-MACS clinical prediction model.
- To compare different methods for updating the T-MACS model to address calibration drift.
- To maintain or improve the predictive performance of CPMs over time.
Main Methods:
- Utilized routinely collected electronic health data from approximately 14,000 patient episodes across two NHS hospitals (June 2016 - October 2020).
- Assessed calibration drift of the existing T-MACS model and evaluated recalibration, model extension, and dynamic updating methods.
- Validated models using bootstrapping and prequential testing, evaluating predictive performance with calibration plots, c-statistics, and reclassification.
Main Results:
- The study will assess calibration drift and the benefit of updating the CPM through various methods.
- Predictive performance will be evaluated using calibration plots and c-statistics.
- Reclassification of predicted probability with updated TMACS models will be examined.
Conclusions:
- CPMs are valuable but vulnerable to calibration deterioration.
- Refining CPMs with routinely collected electronic data is more efficient than developing new models.
- Updating methods can protect initial investments and continually refine algorithms like T-MACS, maintaining or improving predictive performance.
Background:
Patients presenting with chest pain represent a large proportion of attendances to emergency departments. In these patients clinicians often consider the diagnosis of acute myocardial infarction (AMI), the timely recognition and treatment of which is clinically important. Clinical prediction models (CPMs) have been used to enhance early diagnosis of AMI. The Troponin-only Manchester Acute Coronary Syndromes (T-MACS) decision aid is currently in clinical use across Greater Manchester. CPMs have been shown to deteriorate over time through calibration drift. We aim to assess potential calibration drift with T-MACS and compare methods for updating the model.
Methods:
We will use routinely collected electronic data from patients who were treated using TMACS at two large NHS hospitals. This is estimated to include approximately 14,000 patient episodes spanning June 2016 to October 2020. The primary outcome of acute myocardial infarction will be sourced from NHS Digital's admitted patient care dataset. We will assess the calibration drift of the existing model and the benefit of updating the CPM by model recalibration, model extension and dynamic updating. These models will be validated by bootstrapping and one step ahead prequential testing. We will evaluate predictive performance using calibrations plots and c-statistics. We will also examine the reclassification of predicted probability with the updated TMACS model.
Discussion:
CPMs are widely used in modern medicine, but are vulnerable to deteriorating calibration over time. Ongoing refinement using routinely collected electronic data will inevitably be more efficient than deriving and validating new models. In this analysis we will seek to exemplify methods for updating CPMs to protect the initial investment of time and effort. If successful, the updating methods could be used to continually refine the algorithm used within TMACS, maintaining or even improving predictive performance over time.
Trial Registration:
ISRCTN number: ISRCTN41008456.
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