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MDPI, International Journal of Environmental Research and Public Health, 15(19), p. 9084, 2022

DOI: 10.3390/ijerph19159084

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Endoscopic Endonasal Repair of Congenital Choanal Atresia: Predictive Factors of Surgical Stability and Healing Outcomes

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Background: To assess the long-term outcomes and independent predictors of surgical success of a one-stage minimally invasive surgical procedure for congenital choanal atresia (C.C.A.). Methods: a retrospective multicentric study was conducted between 2010 and 2022. An endonasal endoscopic approach was performed in 38 unilateral or bilateral C.C.A. children. All the patients were clinically and radiologically assessed and followed for at least 2 years. Seven outcome measures were applied. Consequently, surgical success was correlated with all the independent variables reported. Results: 18/38 (47.36%) patients presented normal postoperative healing, 8/38 (21.05) had moderate restenosis (<50%), while 12/38 (31.57%) cases were severe (>50%), requiring a surgical revision. No statistical significance was found for average hospital stay between stenosis >50% and <50% patients (p = 0.802) and postoperative pain (p = 0.075); instead, the severe restenosis group demonstrated a higher delay of breast suction (p < 0.001). Among the independent variables predictors of surgical success, the presence of Charge syndrome and rhinopharyngeal stenosis demonstrated higher risks for surgical revision (OR: 4.00, 95% CI: 0.57–28.01, and OR: 2.75, 95% CI: 0.55–13.69, respectively). On the contrary, the hypoplastic inferior turbinate and bilateral C.C.A. showed a lower risk for severe restenosis by a higher endoscopic surgical space and creating a single larger opening (OR: 0.88, 95% CI: 0.22–3.52, and OR: 0.45, 95% CI: 0.10–2.08). Conclusion: Several independent variables could influence the surgical success after C.C.A. endoscopic repair; however, more high-quality evidence is needed to generate an effective predictive model.