A Comparison Of The Effectiveness Of The Mallampati Score And Other Methods In Predicting Intubation Difficulty: A Systematic Literature Review

Abstract

Intubation difficulties are a major challenge in airway management for patients undergoing general anesthesia, as they can lead to hypoxia, aspiration, airway trauma, and even death. The Mallampati Score is the most commonly used predictive method in preoperative assessment; however, its accuracy as a single predictor remains limited. Various other methods, such as thyromental distance, sternomental distance, Cormack-Lehane grading, the upper lip bite test, and multivariate models, are also used to improve predictive accuracy. This study aims to compare the effectiveness of the Mallampati Score and other methods in predicting intubation difficulty. This study employed a Systematic Literature Review (SLR) following the PRISMA guidelines. A literature search was conducted in the PubMed, ScienceDirect, and Google Scholar databases covering the years 2016–2026. Included studies comprised randomized controlled trials, cohort studies, prospective studies, and retrospective comparative studies evaluating the Mallampati Score and other methods in predicting intubation difficulty in patients undergoing general anesthesia. Quality assessment was performed using the Joanna Briggs Institute (JBI) instrument. A total of 1,104 articles were identified in the initial stage. After removing duplicates, 1,061 articles were selected for screening. A total of 24 articles passed the selection based on title, year, method, and abstract. Thirteen articles underwent full-text review, and 8 articles met the inclusion criteria for analysis. The results indicate that the Mallampati Score remains effective as an initial screening tool because it is simple, quick, non-invasive, and has reasonably good sensitivity. However, the Mallampati Score is not sufficiently robust when used as a single predictor. Other methods, such as thyromental distance, have higher specificity, while combinations of multiple parameters or machine learning approaches demonstrate better predictive accuracy. The Mallampati Score remains relevant as an initial screening tool for predicting intubation difficulty, but its effectiveness is optimized when combined with other methods. No single method is the most accurate; therefore, a multimodal approach is the most rational strategy for evaluating difficult airways.