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Preoperative Factors Associated With In-Hospital Major Bleeding After Percutaneous Coronary Intervention: A
Mohammad Rocky Khan Chowdhury1, Dion Stub2, Diem Dinh1
1Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Vic, Australia.
Insights
This systematic review identified 17 key preoperative factors predicting major bleeding after percutaneous coronary intervention (PCI). These factors aid clinicians in balancing ischemic and bleeding risks for better patient outcomes.
Area of Science:
- Cardiology
- Clinical Epidemiology
- Medical Informatics
Background:
- Preoperative risk assessment for bleeding after percutaneous coronary intervention (PCI) is crucial for clinical decision-making and quality monitoring.
- Identifying preoperative factors associated with post-PCI bleeding is essential for patient safety and effective management.
Purpose of the Study:
- To systematically review and summarize preoperative factors linked to in-hospital major bleeding following PCI.
- To identify repeatedly reported factors that can inform risk stratification models.
Main Methods:
- A comprehensive systematic search of multiple databases (MEDLINE, EMBASE, CINAHL, Web of Science, Scopus) was conducted until December 2023.
- Included studies were critically appraised, and data on preoperative factors associated with major bleeding post-PCI were extracted and summarized descriptively.
- Analysis focused on identifying factors consistently reported across studies.
Main Results:
- Out of 17,997 studies, 32 were included, revealing a 3.21% prevalence of in-hospital major bleeding post-PCI.
- 100 independent preoperative factors were associated with bleeding; 17 were repeatedly identified, including renal disease, acute coronary syndrome, age, and gender.
- Commonly used statistical methods included logistic regression (81.2%), with some studies employing machine learning (9.4%) and reporting model discrimination via ROC scores.
Conclusions:
- The 17 identified preoperative factors can assist clinicians in managing ischemic versus bleeding risks.
- Risk adjustment models require enhancement through the incorporation of these factors, improved handling of missing data, robust validation, and potential use of machine learning.
Background:
Preoperative risk assessment of bleeding after percutaneous coronary intervention (PCI) is vital for clinical quality registries, performance monitoring, and, most importantly, for clinical decision-making. This systematic review aims to summarise preoperative factors associated with post-PCI in-hospital major bleeding.
Method:
The MEDLINE, EMBASE, CINAHL, Web of Science, and Scopus databases until December 2023 without any language restriction were systematically searched to identify preoperative factors related to in-hospital major bleeding post-PCI. Data were systematically appraised and summarised in a descriptive manner, following the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies.
Results:
The search yielded 17,997 studies, of which, 32 articles were included for the final assessment. The pool prevalence of in-hospital major bleeding post-PCI was 3.21%. One hundred independent preoperative factors were significantly associated with in-hospital major bleeding and, of them, 17 factors appeared repeatedly in various studies were identified as potential factors. Factors that repeatedly used in various models were but not limited to renal disease (n=25, 78.1%), acute coronary syndrome (n=21, 65.6%), age (n=17, 53.1%), gender (n=17, 53.1%), cardiac events (n=15, 46.9%), anticoagulant therapy/drugs (n=12, 40.6%), anaemia/haemoglobin/haematocrit (n=11, 34.4%), percutaneous access site (n=10, 31.3%), glycoprotein inhibitors (n=10, 31.3%), body surface area (n=9, 28.1%), hypertension (n=9, 28.1%), and heart failure or disease (n=9, 28.1%). Eight (25.0%) articles used the imputation method to treat missing values. Logistic regression was used by 26 (81.2%) articles, and three (9.4%) articles used machine learning method. Eleven articles (34.4%) reported the model's discrimination ability using internal validation with receiver operating characteristics score ranging from 0.620 (95% confidence interval 0.575-0.665) to 0.837 (95% confidence interval 0.772-0.903).
Conclusions:
The 17 preoperative factors identified in this study can help clinicians balance ischaemic and bleeding risks and implement strategies to reduce bleeding. Risk adjustment models need further improvement in their quality through the inclusion of these factors, appropriately handling missing values and model validation, and using machine learning methods.
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