内鼻阀静态参数间的相关性研究

Correlation analysis between static parameters of internal nasal valve

摘要:

目的 探究内鼻阀(INV)的重要参数包括单侧INV面积(A_INV-R、A_INV-L)、INV总面积(A_INV)、单侧INV高度(H_INV-R、H_INV-L)、单侧INV底线长度(W_INV-R、W_INV-L)、单侧INV角(α_INV-R、α_INV-L)和INV角之和(α_INV)之间的相关关系。方法 选取40例无鼻部疾病及症状汉族成年人的颌面部三维CT的数据资料，其中男20例，女20例；年龄20~80岁，平均年龄(47.05±17.82)岁。以重建鼻腔气流三维模型,定位INV并测量其重要参数。根据INV的形态特点，构建预测模型：单侧A_INV=W_INV×H_INV/2=tan[radians(α_INV)]×(H_INV)²/2，比较计算值与实测值并分析二者的相关性，并进一步作INV静态参数之间以及其与左右侧别、年龄和性别的相关性分析。结果 A_INV-L的实测值与计算值无统计学差异且相关性强(Z=-1.075，P＞0.05；r=0.766 2，P＜0.01)，A_INV-R的实测值与计算值无统计学差异且相关性极强(t=-1.238，P＞0.05；r=0.818 2，P＜0.01)。A_INV的实测值与计算值无统计学差异且相关性极强(Z=-1.156，P＞0.05；r=0.799 3，P＜0.01)。比较左右两侧的INV参数可知，A_INV-R较A_INV-L偏大，差异具有统计学意义(t=-3.081、-2.029，P＜0.05)。比较男性及女性的INV参数可知，男性的A_INV-L、A_INV-R及A_INV较女性偏大，差异具有统计学意义(t=-2.405、-3.336、-3.297、-4.223及-4.041，P＜0.05)，A_INV-L与H_INV-L、A_INV-R与H_INV-R间呈正相关(r=0.619 4、0.7101,P＜0.01)。比较不同年龄组间的INV参数可知，＜50岁人群的A_INV-L及A_INV较≥50岁人群的偏大(t=3.694、3.131，P＜0.01)，A_INV-L、A_INV与年龄呈负相关(r_s=-0.519 8、-0.433 1,P＜0.01)。这种负相关可能与年龄增长过程中α_INV-L的减小有关，相关分析提示α_INV-L与年龄呈负相关(r=-0.374 7，P＜0.05)。进一步比较不同年龄组内的左右两侧INV参数，发现：＜50岁人群的α_INV-L及α_INV-R、A_INV-L及A_INV-R无统计学差异，然而≥50岁人群的α_INV-R较α_INV-L偏大、A_INV-R较A_INV-L偏大(Z=-2.726、-2.987，P＜0.01)。相关分析提示:α_{INV R-L}与年龄呈正相关(r=0.342 8，P＜0.05),A_{INV R-L}与α_{INV R-L}呈正相关(r=0.710 9，P＜0.01),A_{INV R-L}实测值与A_{INV R-L}计算值呈正相关(r=0.779 5，P＜0.01)。结论 本研究提出预测模型单侧A_INV=W_INV×H_INV/2=tan[radians(α_INV)]×H_INV²/2并进行验证，计算值与实测值无统计学差异且相关性强。该模型还可用来解释不同侧别、性别及年龄分组间的INV静态参数差异,并补充A_INV-L、A_INV、α_INV-L与年龄呈负相关。

Abstract:

Objective To investigate the correlations among critical parameters of the internal nasal valve (INV) including unilateral cross-sectional area of the INV(A_INV-R,A_INV-L), total cross-sectional area of the INV(A_INV), unilateral height of the INV (H_INV-R, H_INV-L), unilateral angle of the INV(α_INV-R、α_INV-L) and the sum of INV angles(α_INV).Methods Maxillofacial computed tomographic images from 40 Han adults (20 males, 20 females; age ranged from 20 to 80 years old and 50% with age < 50 years old) without nasal diseases were applied to reconstruct the three-dimensional nasal airway models. After identifying the INV plane, the following parameters were measured: A_INV-R, A_INV-L, A_INV, H_INV-R, H_INV-L, α_INV-R and α_INV-L. Based on the morphological characteristics of INV, the prediction model was proposed as unilateral A_INV=W_INV×H_INV/2=tan[radians(α_INV)]×(H_INV)²/2, and the calculated A_INV based on the model was compared with the measured A_INV. Correlation analyses among the INV parameters, ages and genders were performed.Results There were no statistic differences but good correlations between the calculated and the measured values: A_INV-L(Z=-1.075，P＞0.05；r=0.766 2，P＜0.01), A_INV-R(t=-1.238，P＞0.05；r =0.818 2，P＜0.01)and A_INV(Z=-1.156，P＞0.05；r=0.799 3，P＜0.01). Comparison of the INV parameters between different sides showed that A_INV-R was larger than A_INV-L, which could be explained by the larger α_INV-R(t=-3.081,-2.029，P<0.05). Comparison of the INV parameters between different genders revealed that A_INV-L, A_INV-R and A_INV of the male group were larger than those of the female group, which could be explained by the larger H_INV-L and H_INV-R of the male group (t=-2.405, -3.336, -3.297, -4.223,-4.041，P<0.05). Comparison of the INV parameters between different age groups disclosed that A_INV-L and A INV of the young group (＜50 years old)were larger than those of the old group (≥50 years old), which could be explained by the negative correlation between A_INV-L, A_INV and age (r_s=-0.519 8,-0.433 1, P<0.01). Further, the comparison of the left and right INV parameters from the same age group showed significant differences between α_INV-R and α_INV-L, A_INV-R and A_INV-L in the old group(Z=-2.726, -2.987, P<0.01), this negative correlation might be related to the decrease of α_INV-L in the process of aging. Correlation analyses demonstrated that α_{INV R-L} was positively correlated with age (r=0.3428，P<0.05), A_{INV R-L} was positively correlated with α_{INV R-L} (r=0.710 9，P<0.01), and the measured A INV R-L was positively correlated with the calculated (r=0.779 5，P<0.01).Conclusion In this study, the proposed prediction model of unilateral A_INV can not only assess parameters of INV with good correlations between the calculated and the measured values, but also interpret the differences between sides, genders and age groups, as well as the negative correlations among α_INV-L, A_INV-L, A_INV and age.