2022 The Journal of craniofacial s…

Does Presurgical Nasoalveolar Molding Reduce the Need for Future Bone Grafting in Cleft Lip and Palate Patients? A Systematic Review and Meta-Analysis.

, , , ,

The Journal of craniofacial surgery Vol. 33 (7) : 2095-2099 • Oct 2022

OBJECTIVE: Nasoalveolar molding (NAM) is a technique that is utilized in patients with cleft lip/palate before performing lip surgery. This procedure has been shown to result in a more aesthetic nose with lesser columellar deviation and reduced scaring. The aim of our study was to evaluate the long-term results of NAM and gingivoperiosteoplasty in patients with cleft lip and palate. METHODS AND MATERIALS: An electronic search of databases (ie, PubMed, ISI Web of Science, EMBASE, Scopus, and Google Scholar) from inception to March 2021 was performed and after selecting the eligible studies, relevant data were collected using piloted extraction forms. The success rate of NAM and gingivoperiosteoplasty, and Bergland score were pooled using random-effects inverse variance meta-analysis. RESULTS: Seven studies were included in this meta-analysis and systematic review. The pooled mean success rate of NAM with gingivoperiosteoplasty (GPP) based on the continuity of alveolar bone structure was 71% (95% confidence interval [CI] = 54-85). This means that in 71% of cases NAM + GPP treatment eliminated the need for future bone grafts. Also, no significant difference between the success rate (risk ratio = 1.00, 95% CI = 0.64-1.58) and mean Bergland score (mean difference = 0.64, 95% CI = -1.04 to 2.31) of NAM + GPP and skeletal bone graft was found. CONCLUSIONS: Nasoalveolar molding and gingivoperiosteoplasty was successful in 71% of cases treating patients with cleft lip and palate. This treatment is similar with the secondary alveolar bone graft in both the success rate and the alveolar height that it generates while being less invasive and with lower morbidity.

No clinical trial protocols linked to this paper

Clinical trials are automatically linked when NCT numbers are found in the paper's title or abstract.
PICO Elements

No PICO elements extracted yet. Click "Extract PICO" to analyze this paper.

Paper Details
MeSH Terms
Associated Data

No associated datasets or code repositories found for this paper.

Related Papers

Related paper suggestions will be available in future updates.