Prediction Models for Postoperative Delirium of Cardiovascular Surgery (PODOCVS): Protocol for a Systematic Review
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Summary
This summary is machine-generated.This systematic review evaluates prediction models for postoperative delirium in cardiovascular surgery patients. It aims to improve secondary prevention strategies by assessing existing models and exploring machine learning for better risk stratification.
Area Of Science
- Medical research
- Clinical prediction modeling
- Cardiovascular surgery outcomes
Background
- Postoperative delirium in cardiovascular surgery (PODOCVS) is a serious complication with significant adverse outcomes.
- Current risk prediction models for PODOCVS are insufficient for effective secondary prevention.
- Early, personalized interventions are needed to mitigate PODOCVS impact.
Purpose Of The Study
- To systematically review and critically evaluate existing prediction models for PODOCVS.
- To assess the development, performance, and applicability of these models.
- To investigate the predictive accuracy of machine learning models compared to traditional statistical models for PODOCVS.
Main Methods
- Systematic search of Embase, PubMed, Web of Science, and reference lists.
- Inclusion of studies on multivariate predictive models for PODOCVS in adult cardiovascular surgery patients.
- Data extraction and quality assessment using CHARMS and PROBAST tools.
Main Results
- Data collection and analysis are ongoing, with results anticipated by August 2025.
- Preliminary findings will be published in a peer-reviewed journal.
- The study protocol is registered with PROSPERO (CRD42024578957).
Conclusions
- This systematic review protocol outlines a comprehensive approach to evaluating PODOCVS prediction models.
- The findings will provide an updated reference for clinicians, policymakers, and researchers.
- The study aims to identify superior prediction methods, including machine learning, for PODOCVS.
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