Risk factors for premature coronary artery disease (PCAD) in adults: a systematic review protocol.
Adeel KhojaPrabha H AndraweeraZohra S LassiMingyue ZhengMaleesa M PathiranaAnna AliEmily AldridgeMelanie R WittwerDebajyoti D ChaudhuriRosanna TavellaMargaret A ArstallPublished in: F1000Research (2021)
PCAD possesses a public health challenge resulting in years of productive life lost and an escalating burden on health systems. Objective of this review is to compare modifiable and non-modifiable risk factors for PCAD compared to those without PCAD. This review will include all comparative observational studies conducted in adults aged >18 years with confirmed diagnosis of PCAD (on angiography) compared to those without PCAD. Databases to be searched include; PubMed, CINAHL, Embase, Web of Science, and grey literature (Google Scholar). All identified studies will be screened for title and abstract and full-text against the inclusion criteria on Covidence software. Data relevant to exposures and outcomes will be extracted from all included studies. All studies selected for data extraction will be critically appraised for methodological quality. Meta-analysis using random-effects model will be performed using Review Manager 5.3. Effect sizes for categorical risk factors will be expressed as odds ratios with 95% confidence intervals. For risk factors measured in continuous form, mean difference (if units are consistent) otherwise standardized mean difference (if units are different across studies) will be reported. Heterogeneity between studies will be assessed using I 2 test statistics. GRADE will be used to assess the certainty of the findings. Systematic review registration number: PROSPERO Registration # CRD42020173216.
Keyphrases
- risk factors
- systematic review
- public health
- case control
- coronary artery disease
- big data
- computed tomography
- cardiovascular disease
- electronic health record
- air pollution
- optical coherence tomography
- randomized controlled trial
- multiple sclerosis
- artificial intelligence
- percutaneous coronary intervention
- skeletal muscle
- deep learning
- acute coronary syndrome
- quality improvement
- global health