Factors affecting hospital readmission rates following an acute coronary syndrome: A systematic review.
Amineh Rashidi, Lisa Whitehead, Courtney Glass
PMID 34811845WHAT IT FOUND
Readmission rates after acute coronary syndrome varied widely, from 4.2% to 81%.
Older age, female gender, smoking, and comorbidities like diabetes and renal disease were linked to higher risk, though findings on age and gender were inconsistent across studies.
Key findings
01The 30-day readmission rate for patients with acute coronary syndrome ranged from 4.2% to 81% across the included studies.
02Recurrent ischaemic events, such as re-infarction and unstable angina, were the leading cause of readmission in 12 of the studies.
03Comorbidities including pulmonary disease, diabetes, renal disease, anaemia, and hypertension were consistently associated with increased readmission risk.
STILL TO COME
How it was doneWhat they foundWhat it means for RNs
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What it does not show
The review could not perform a meta-analysis due to significant heterogeneity in study designs and data collection methods. Most included studies were observational (cohort), which limits the ability to establish cause-and-effect relationships between factors and readmission. The search was restricted to English-language studies, which may have missed relevant international data. Readmission rates varied widely (4.2% to 81%), suggesting differences in patient populations, healthcare systems, or definitions of readmission that were not fully accounted for. The review did not account for the number of people readmitted against the number who survived, which may skew rate calculations.
Declared interests
No conflicts of interest or funding sources were explicitly stated in the provided text.
The easy way to misread this
Do not assume that these factors definitively cause readmission. The evidence is largely observational and heterogeneous, with inconsistent findings for key variables like age and gender. These factors should inform risk assessment and monitoring, not deterministic predictions of individual patient outcomes.