Outcomes of sonographically-suspected fetal intra-abdominal cysts: Surgical intervention, conservative management and spontaneous regression.

Abstract

Objective:

The prenatal diagnosis of fetal intra-abdominal cysts is challenging. This study aimed to evaluate the diagnostic ability of prenatal ultrasound for fetal intra-abdominal cysts and to develop a predictive method for pre- and postnatal outcomes.

Methods:

We retrospectively reviewed fetuses with ultrasound-detected intra-abdominal cysts between January 2013 and January 2020. The maternal-fetal clinical characteristics and ultrasound parameters were integrated into a model of pre- or postnatal outcomes.

Results:

The study enrolled 190 eligible fetuses, including 94 cases of spontaneous regression, 33 cases of conservative management and 63 cases of surgical intervention. For the 63 cases of surgical intervention, prenatal ultrasound was found to identify fetal intra-abdominal cysts with 80.00% sensitivity (95% CI: 67.03%-89.57%), 37.50% specificity (95% CI: 8.52%-75.51%), 89.80% positive predictive value (95% CI: 83.51%-93.86%), 21.43% negative predictive value (95% CI: 8.80%-43.53%) and 74.60% accuracy (95% CI: 62.06%-84.73%). The predictive model of prenatal spontaneous regression was as follows: y = -3.291 + 0.083 × gestational age + 1.252 × initial diameter, with an area under the curve (AUC) of 0.819 (95% CI: 0.739-0.899) and an optimal cut-off value of 0.74. The large cyst diameter before delivery was an independent predictor of postnatal surgical intervention (p < 0.001), with an AUC of 0.710 (95% CI: 0.625-0.794) and an optimal cut-off value of 3.35 cm.

Conclusion:

Although ultrasound has a limited ability in the accurate diagnosis of fetal abdominal cysts, a simple method of measuring the diameter can predict fetal outcomes and identify the cases that may require surgical intervention or spontaneous regression.

• Authors: Shuangshuang Gai, Lixiu Wang, Weizeng Zheng, Bin Xu, Qiong Luo, Jiale Qin
• Journal: Frontiers in pediatrics (Front Pediatr) Vol. 10 Pg. 1015678 ( 2022) ISSN: 2296-2360 [Print] Switzerland
• PMID: 36741089 (Publication Type: Journal Article)
• Copyright: © 2022 Gai, Wang, Zheng, Xu, Luo and Qin.