Systematic Review and Critical Appraisal of Prediction Models for Readmission in Coronary Artery Disease Patients: Assessing Current Efficacy and Future Directions
Bibliographic record
Abstract
Yunhao Zhang,1 Xuejiao Zhu,1 Fuer Gao,1 Shulan Yang2 1College of Nursing, Hangzhou Normal University, Hangzhou, People’s Republic of China; 2Department of Nursing, Zhejiang Hospital, Hangzhou, People’s Republic of ChinaCorrespondence: Xuejiao Zhu, College of Nursing, Hangzhou Normal University, Hangzhou, 311121, People’s Republic of China, Email jj_ice@163.comPurpose: Coronary artery disease (CAD) patients frequently face readmissions due to suboptimal disease management. Prediction models are pivotal for detecting early unplanned readmissions. This review offers a unified assessment, aiming to lay the groundwork for enhancing prediction models and informing prevention strategies.Methods: A search through five databases (PubMed, Web of Science, EBSCOhost, Embase, China National Knowledge Infrastructure) up to September 2023 identified studies on prediction models for coronary artery disease patient readmissions for this review. Two independent reviewers used the CHARMS checklist for data extraction and the PROBAST tool for bias assessment.Results: From 12,457 records, 15 studies were selected, contributing 30 models targeting various CAD patient groups (AMI, CABG, ACS) from primarily China, the USA, and Canada. Models utilized varied methods such as logistic regression and machine learning, with performance predominantly measured by the c-index. Key predictors included age, gender, and hospital stay duration. Readmission rates in the studies varied from 4.8% to 45.1%. Despite high bias risk across models, several showed notable accuracy and calibration.Conclusion: The study highlights the need for thorough external validation and the use of the PROBAST tool to reduce bias in models predicting readmission for CAD patients.Keywords: coronary artery disease, readmission, prediction model, systematic review
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.132 | 0.432 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.018 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".