Proton pump inhibitors' use and risk of hip fracture: a systematic review and meta-analysis.
Salman HussainAli Nasir SiddiquiAnwar HabibMd Sarfaraj HussainAbul Kalam NajmiPublished in: Rheumatology international (2018)
In the last decade, epidemiological studies presented inconsistent findings concerning the proton pump inhibitors (PPI) use and the risk of hip fracture. So, this systematic review and meta-analysis were performed with the aim to quantify the risk of hip fracture associated with PPI use. PubMed® and Cochrane Central databases were searched from inception to January 2018. The quality of included studies in meta-analysis was assessed using Newcastle-Ottawa scale. Primary outcome of this study was to assess the risk of hip fracture among PPI user. Secondary outcomes include subgroup analysis based on study design, study quality, duration of PPI use, calcium intake, and geographical region. Sensitivity analysis was also performed. Review Manager (RevMan) was used to perform statistical analysis. This meta-analysis was based on seventeen studies. Pooled risk ratio showed a statistically significant association between PPI use and hip fracture risk (RR 1.26 [95% CI 1.17-1.35], p < 0.00001). Subgroup analysis, based on the study design, showed a highly significant association between PPI use and risk of hip fracture (p < 0.0001). The risk of hip fracture persisted even when stratified by calcium adjustment and the duration of PPI use (p < 0.0001). This meta-analysis suggests that PPI user have a 26% increased risk of hip fracture as compared to non-PPI user. Physicians should take caution in prescribing PPI to patients who are at increased risk of hip fracture.
Keyphrases
- hip fracture
- protein protein
- systematic review
- case control
- small molecule
- primary care
- meta analyses
- emergency department
- machine learning
- randomized controlled trial
- end stage renal disease
- type diabetes
- quality improvement
- metabolic syndrome
- body mass index
- artificial intelligence
- phase iii
- glycemic control
- open label
- big data
- adverse drug