METHODS: A retrospective cohort study was performed in the Wilhelmina Children\'s Hospital in Utrecht and child records from 380 individuals with CL/P registered from 2008 to 2017 were retrospectively reviewed. Inclusion criteria comprised the diagnosis of CL/P, primary palatoplasty according to Sommerlad\'s technique, and speech assessment at five years or older. Association between cleft type and width, presence of additional genetic disorders and postoperative complications (palatal dehiscence, fistula) were assessed using odds ratios and chi squared tests.
RESULTS: A total of 239 patients were included. The VPI rate was 52.7% (n = 126) and in 119 patients (49.8%) a speech correcting surgery was performed. Severe cleft type, as indicated by a higher Veau classification, was associated with a significant higher rate of speech correcting surgeries (p = 0.033). Significantly more speech correcting surgeries were performed in patients with a cleft width >10 mm, compared to patients with a cleft width ≤10 mm (p < 0.001). Patients with oronasal fistula underwent significantly more speech correcting surgeries than those without fistula (p = 0.004). No statistically significant difference was found in the incidence of speech correcting surgery between patients with and without genetic disorders (p = 0.890).
CONCLUSIONS: Variations in cleft morphology, cleft width and complications like oronasal fistula are associated with different speech outcomes. Future research should focus on creating a multivariable prediction model for speech correcting surgery in CL/P patients.
方法:在乌得勒支的Wilhelmina儿童医院进行了一项回顾性队列研究,对2008年至2017年登记的380名CL/P患者的儿童记录进行了回顾性分析。纳入标准包括CL/P的诊断,根据Sommerlad的技术进行原发性腭成形术,和5岁以上的语言评估。裂缝类型和宽度之间的关联,存在其他遗传性疾病和术后并发症(腭裂开,瘘管)使用比值比和卡方检验进行评估。
结果:共纳入239例患者。VPI率为52.7%(n=126),在119例患者(49.8%)中进行了言语矫正手术。严重裂隙型,如更高的Veau分类所示,与明显较高的言语矫正手术率相关(p=0.033)。在裂隙宽度>10毫米的患者中进行了更多的言语矫正手术,与裂隙宽度≤10mm的患者相比(p<0.001)。与没有口瘘的患者相比,口鼻瘘的患者接受了更多的言语矫正手术(p=0.004)。在有和没有遗传疾病的患者之间,言语矫正手术的发生率没有统计学上的显着差异(p=0.890)。
结论:裂隙形态的变化,裂隙宽度和口鼻瘘等并发症与不同的言语结局相关。未来的研究应该集中在为CL/P患者的言语矫正手术创建多变量预测模型。