Key result
17 preoperative factors including renal disease and ACS predict major in-hospital bleeding post-PCI.
Why the study?
Preoperative risk assessment of bleeding after PCI is vital for clinical quality registries, performance monitoring, and clinical decision-making.
What preoperative factors are associated with in-hospital major bleeding after percutaneous coronary intervention?
Systematic Review (n=32)
What preoperative factors are associated with in-hospital major bleeding after percutaneous coronary intervention?
This systematic review identified 17 key preoperative factors, such as renal disease and acute coronary syndrome, that are consistently associated with in-hospital major bleeding after PCI, which can aid in risk stratification and clinical decision-making.
Supports preoperative bleeding risk stratification before PCI; confirms 17 predictors including renal disease and ACS.
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.
No takes yet. Share an insight, caveat, or question.
Chowdhury et al. (2025) conducted a systematic review in In-hospital major bleeding after percutaneous coronary intervention (n=32). Preoperative risk factors was evaluated on In-hospital major bleeding post-PCI. Seventeen preoperative factors, including renal disease and acute coronary syndrome, were identified as key predictors of in-hospital major bleeding post-PCI (pooled prevalence 3.21%).
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: