Intensive Crit Care Nurs. 2023 Sep 30;80:103551. doi: 10.1016/j.iccn.2023.103551. Online ahead of print.
ABSTRACT
OBJECTIVE: This meta-analysis aimed to assess the predictive value of the rapid shallow breathing index for extubation outcomes.
METHODOLOGY: We conducted a systematic review of literature (inception to March 2023) and a meta-analysis. Statistical analysis was performed using Meta-Disc 1.4 software, RevMan 5.4 software and Stata 14.0 software to evaluate the predictive value of RSBI for extubation outcomes.
RESULTS: A total of 1,987 studies were retrieved, and after applying the inclusion criteria, 79 studies were included in the final analysis, involving 13,170 patients undergoing mechanical ventilation. The random-effects model was employed for statistical analysis. The summary receiver operating characteristic curves (SROC) area under the curve (AUC) was 0.8144. The pooled sensitivity was 0.60 (95% CI: 0.59, 0.61), the pooled specificity was 0.68 (95% CI: 0.66, 0.70).
CONCLUSIONS: The Rapid Shallow Breathing Index demonstrated moderate accuracy, poor pooled sensitivity and specificity in predicting successful extubation, however the study does not present adequate data to support or reject the use of this tool as a single parameter that predicts extubation outcome. Future studies should explore the combination of The Rapid Shallow Breathing Index with other indicators and clinical experience to improve the success rate of extubation and reduce the risk of extubation failure.
IMPLICATIONS FOR CLINICAL PRACTICE: Premature and delayed extubation in mechanically ventilated patients can have a negative impact on prognosis and prolong hospital stay. The Rapid Shallow Breathing Index is a simple, cost-effective, and easily monitored objective evaluation index, which can be used to predict the outcome of extubation, especially in primary hospitals. Our study comprehensively evaluated the value of this tool in predicting extubation outcomes, which can help clinicians combine subjective experience with objective indicators to improve the accuracy of extubation time decisions.
PMID:37783181 | DOI:10.1016/j.iccn.2023.103551
