Predictors of prolonged mechanical ventilation in patients admitted to intensive care units: A systematic review

OBJECTIVE: Although intensive care medicine has evidenced a significant growth in recent decades, the number of patients requiring prolonged mechanical ventilation (PMV) still represents a considerable burden on health-care expenditure. The prediction of the need for PMV seems to provide a plausible cost-effective intervention. The objective of this study is to systematically review the predictors of the need for PMV of adult patients admitted to intensive care units (ICUs) due to medical and surgical needs. METHODS: We conducted a systematic search on three online databases (PubMed, Embase, and MEDLINE) till February 20, 2019. The search process employed several combinations of specific keywords and Boolean operators. RESULTS: A total of 15 articles were included in the study. Based on pooling the outcomes of odds ratios (ORs) and their respective 95% confidence intervals (CIs) as reported from logistic regression analyses, the pooled PMV incidence in 8220 patients (69.59% males) was 17.67 cases per 100 ICU admissions (95% CI 13.69–21.65). We could not conduct a meta-analysis of ORs and 95% CIs due to the significant heterogeneity observed between the included studies (P < 0.001, I2 = 97%). Pre-operative/preadmission kidney dysfunction and chronic obstructive pulmonary disease were the most significant independent predictors of the need for PMV. Following cardiac surgeries, repeated or emergency surgery, prolonged cardiopulmonary bypass time, and the need for blood transfusion were predictors of the need for PMV. CONCLUSION: Within the study limitations, several predictors were identified, which could be further investigated using a unified PMV definition. Successful prediction of the need for PMV would assist clinicians in identifying and adjusting a “weaning strategy” as well as improving patient care to reduce morbidity. Furthermore, establishing specialized weaning units could be warranted based on PMV incidence and prediction in the local settings.