The Use of the Perfusion Index to Predict Post-Induction Hypotension in Patients Undergoing General Anesthesia: A Systematic Review and Meta-Analysis.
Kuo-Chuan HungShu-Wei LiaoChia-Li KaoYen-Ta HuangJheng-Yen WuYao-Tsung LinChien-Ming LinChien-Hung LinI-Wen ChenPublished in: Diagnostics (Basel, Switzerland) (2024)
Post-induction hypotension (PIH) is a common and potentially serious complication of general anesthesia. This meta-analysis (Prospero registration number: CRD42024566321) aimed to evaluate the predictive efficacy of the perfusion index (PI) for PIH in patients undergoing general anesthesia. A comprehensive literature search was performed using multiple electronic databases (Google Scholar, EMBASE, Cochrane Library, and MEDLINE). Studies involving adult patients undergoing general anesthesia, with the PI measured before anesthesia induction and reporting PIH incidence, were included. The primary outcome was the diagnostic accuracy of the PI in predicting the probability of PIH. The secondary outcome was the pooled PIH incidence. Eight studies with 678 patients were included. The pooled incidence of PIH was 44.8% (95% confidence interval [CI]: 29.9%-60.8%). The combined sensitivity and specificity of the PI for predicting PIH were 0.84 (95% CI: 0.65-0.94) and 0.82 (95% CI: 0.70-0.90), respectively. The summary receiver operating characteristic (sROC) analysis revealed an area under curve of 0.89 (95% CI: 0.86-0.92). The Deek's funnel plot asymmetry test indicated no significant publication bias. The PI demonstrates high predictive efficacy for PIH in patients undergoing general anesthesia, indicating that it can be a valuable tool for identifying those at risk of PIH.
Keyphrases
- patients undergoing
- systematic review
- risk factors
- end stage renal disease
- case control
- chronic kidney disease
- randomized controlled trial
- ejection fraction
- newly diagnosed
- prognostic factors
- computed tomography
- machine learning
- clinical trial
- magnetic resonance imaging
- single cell
- young adults
- peritoneal dialysis
- big data
- double blind
- childhood cancer
- phase iii