Di Carlo Gabriele, Gili Tommaso, Caldarelli Guido, Polimeni Antonella, Cattaneo Paolo M
Department of Oral and Maxillo-Facial Sciences, Sapienza University of Rome, Rome, Italy.
Networks Unit, IMT School for Advanced Studies Lucca, Lucca, Italy.
Orthod Craniofac Res. 2021 Dec;24 Suppl 2:172-180. doi: 10.1111/ocr.12490. Epub 2021 Jun 14.
The interaction between skeletal class and upper airway has been extensively studied. Nevertheless, this relationship has not been clearly elucidated, with the heterogeneity of results suggesting the existence of different patterns for patients' classification, which has been elusive so far, probably due to oversimplified approaches. Hence, a network analysis was applied to test whether different patterns in patients' grouping exist.
Ninety young adult patients with no obvious signs of respiratory diseases and no previous adeno-tonsillectomy procedures, with thirty patients characterized as Class I (0 < ANB < 4); 30 Class II (ANB > 4); and 30 as Class III (ANB < 0).
A community detection approach was applied on a graph obtained from a previously analysed sample: thirty-two measurements (nineteen cephalometric and thirteen upper airways data) were considered.
An airway-orthodontic complex network has been obtained by cross-correlating patients. Before entering the correlation, data were controlled for age and gender using linear regression and standardized. By including or not the upper airway measurements as independent variables, two different community structures were obtained. Each contained five modules, though with different patients' assignments.
The community detection algorithm found the existence of more than the three classical skeletal classifications. These results support the development of alternative tools to classify subjects according to their craniofacial morphology. This approach could offer a powerful tool for implementing novel strategies for clinical and research in orthodontics.
骨骼错颌分类与上气道之间的相互作用已得到广泛研究。然而，这种关系尚未得到明确阐明，结果的异质性表明存在不同的患者分类模式，而这种模式至今仍难以捉摸，可能是由于方法过于简单。因此，应用网络分析来检验患者分组中是否存在不同模式。
90名无明显呼吸系统疾病迹象且既往未行腺样体扁桃体切除术的年轻成年患者，其中30名患者为I类错颌（0 < ANB < 4）；30名患者为II类错颌（ANB > 4）；30名患者为III类错颌（ANB < 0）。
对从先前分析的样本中获得的图形应用社区检测方法：考虑32项测量指标（19项头影测量指标和13项上气道数据）。
通过对患者进行交叉关联获得了一个气道 - 正畸复合网络。在进行关联之前，使用线性回归对年龄和性别数据进行控制并标准化。通过将上气道测量指标作为或不作为自变量纳入，获得了两种不同的社区结构。每种结构都包含五个模块，不过患者的分配不同。
社区检测算法发现存在超过三种经典的骨骼分类。这些结果支持开发替代工具，以便根据颅面形态对受试者进行分类。这种方法可为正畸临床和研究实施新策略提供强大工具。
