年龄相关性黄斑变性合并脉络膜新生血管患者自我效能与视觉生活质量的相关性

袁冰心; 谢瞻; 张海伟

doi:10.16766/j.cnki.issn.1674-4152.004325

年龄相关性黄斑变性合并脉络膜新生血管患者自我效能与视觉生活质量的相关性

doi: 10.16766/j.cnki.issn.1674-4152.004325

1.
南京医科大学第一附属医院麻醉与围手术期医学科，江苏南京 210000
2.
南京医科大学第一附属医院眼科

基金项目:

国家自然科学基金项目 82401281

详细信息

通讯作者:
张海伟，E-mail：13951807569@163.com

中图分类号: R774.5
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- 被引次数: 0
出版历程
- 收稿日期: 2025-08-28
- 网络出版日期: 2026-04-01

The correlation between self-efficacy and visual quality of life in patients with age-related macular degeneration combined with choroidal neovascularization

1.
Department of Anesthesiology and Perioperative Medicine, the First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu 210000, China

摘要

摘要: 目的分析年龄相关性黄斑变性(AMD)合并脉络膜新生血管(CNV)患者自我效能与视觉生活质量(VQOL)的相关性，探究影响患者视觉生活质量的因素。方法选取2022年1月—2023年6月南京医科大学第一附属医院收治的126例AMD合并CNV患者，入院24 h内采用自我效能量表和NEI VFQ-25中文版调研，采用Pearson法分析相关性，收集患者临床资料，按NEI VFQ-25评分中位数将患者分为VQOL良好组(69例)和不良组(57例)，采用二元logistic回归分析不良VQOL的影响因素，并采用ROC曲线评估其预测价值。结果自我效能评分与NEI VFQ-25呈正相关关系(r=0.461，P＜0.001)。AMD合并CNV患者VQOL不良的独立危险因素包括：优势眼logMAR值高、优势眼脉络膜厚度低、优势眼logCS值低以及合并糖尿病，高自我效能是其保护因素。联合预测的AUC为0.957，灵敏度为0.912、特异度为0.885，表明联合多指标预测的效能最佳，且灵敏度和特异度良好。结论 AMD合并CNV患者自我效能与VQOL呈正相关关系，高优势眼logMAR、低优势眼脉络膜厚度、低优势眼logCS及合并糖尿病为影响VQOL的独立危险因素，高自我效能是保护因素。多指标联合预测评估不良视觉生活质量灵敏度和特异度良好，故提高自我效能、控制上述危险因素对改善患者视觉生活质量有重要意义。
- 年龄相关性黄斑变性 /
- 脉络膜新生血管 /
- 自我效能 /
- 视觉生活质量 /
- 相关性分析 /
- 影响因素
Abstract: Objective To analyze the correlation between self-efficacy and visual quality of life (VQOL) in patients with age-related macular degeneration (AMD) combined with choroidal neovascularization (CNV), and to explore the influencing factors of patients ' visual quality of life. Methods A total of 126 patients with AMD and CNV admitted to the First Affiliated Hospital of Nanjing Medical University from January 2022 to June 2023 were selected. Within 24 hours of admission, the self-efficacy scale and NEI VFQ-25 Chinese version were used for research. The Pearson method was used to analyze the correlation, and clinical data of patients were collected. According to the median NEI VFQ-25 score, patients were divided into the good VQOL group (69 cases) and the poor VQOL group (57 cases). Binary logistic regression was used to analyze the influencing factors of poor VQOL, and the ROC curve was used to evaluate its predictive value. Results There was a positive correlation between self-efficacy score and NEI VFQ-25 (r=0.461, P < 0.001). The independent risk factors for poor VQOL in AMD patients with CNV include: high logMAR value in dominant eyes, low choroidal thickness in dominant eyes, low logCS value in dominant eyes, and diabetes. In addition, high self-efficacy was a protective factor. The AUC of the joint prediction was 0.957, with a sensitivity of 0.912 and a specificity of 0.885, indicating that the joint multi-index prediction has the best performance and good sensitivity and specificity. Conclusion The self-efficacy of AMD patients with CNV was positively correlated with VQOL. LogMAR of high dominance eyes, choroidal thickness of low dominance eyes, logCS of low dominance eyes, and diabetes were independent risk factors affecting VQOL, and high self-efficacy was a protective factor. The sensitivity and specificity of multi-index joint prediction and evaluation of poor visual quality of life are good, so improving self-efficacy and controlling the above risk factors are of great significance for improving patients ' visual quality of life.
- Age related macular degeneration /
- Choroidal neovascularization /
- Self-efficacy /
- Visual quality of life /
- Correlation analysis /
- Influencing factor

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图 1 各指标预测不良VQOL的ROC曲线

Figure 1. ROC curve for each indicator in predicting poor VQOL

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表 1 VQOL不良组与VQOL良好组AMD合并CNV患者一般资料比较

Table 1. Comparison of general data between the VQOL poor group and the VQOL good group of AMD patients with CNV

项目	VQOL不良组(n=57)	VQOL良好组(n=69)	统计量	P值
年龄(x±s, 岁)	67.26±4.60	67.39±4.07	0.166^a	0.869
性别[例(%)]			0.646^b	0.422
男性	33(57.89)	35(50.72)
女性	24(42.11)	34(49.28)
BMI(x±s)	22.36±1.60	22.61±1.43	0.920^a	0.359
家族史[例(%)]			3.121^b	0.077
有	11(19.30)	23(33.33)
无	46(80.70)	46(66.67)
吸烟史[例(%)]			15.045^b	＜0.001
有	25(43.86)	9(13.04)
无	32(56.14)	60(86.96)
饮酒史[例(%)]			0.035^b	0.851
有	10(17.54)	13(18.84)
无	47(82.46)	56(81.16)
糖尿病[例(%)]			14.814^b	＜0.001
有	26(45.61)	10(14.49)
无	31(54.39)	59(85.51)
高血压[例(%)]			0.058^b	0.810
有	9(15.79)	12(17.39)
无	48(84.21)	57(82.61)
冠心病[例(%)]			0.001^b	0.982
有	10(17.54)	12(17.39)
无	47(82.46)	57(82.61)
AMD类型[例(%)]			0.044^b	0.834
干性	39(68.42)	46(66.67)
湿性	18(31.58)	23(33.33)
病变部位[例(%)]			0.094^b	0.759
左眼	28(49.12)	32(46.38)
右眼	29(50.88)	37(53.62)
优势眼logMAR(x±s)	1.41±0.47	1.08±0.45	4.046^a	＜0.001
非优势眼logMAR(x±s)	1.71±0.79	1.75±0.79	0.281^a	0.780
优势眼脉络膜厚度(x±s, μm)	64.16±10.04	72.12±10.13	4.413^a	＜0.001
非优势眼脉络膜厚度(x±s, μm)	64.85±20.23	65.69±19.98	0.234^a	0.816
优势眼logCS(x±s)	0.59±0.24	0.81±0.27	4.889^a	＜0.001
非优势眼logCS(x±s)	0.37±0.08	0.38±0.09	0.179^a	0.858
自我效能(x±s, 分)	21.33±4.51	28.38±4.36	8.884^a	＜0.001
注：^a为t值，^b为χ²值。

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表 2 变量赋值情况

Table 2. Variable assignment situation

变量	赋值方法
自我效能	连续变量，以实际值赋值
优势眼logMAR	连续变量，以实际值赋值
优势眼脉络膜厚度	连续变量，以实际值赋值
优势眼logCS	连续变量，以实际值赋值
吸烟史	有=1，无=0
糖尿病	有=1，无=0

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表 3 影响AMD合并CNV患者VQOL的二元logistic回归分析

Table 3. Binary logistic regression analysis of the factors influencing VQOL in patients with AMD combined with CNV

变量	B	SE	Waldχ²	P值	OR(95% CI)
自我效能	-0.358	0.080	20.117	＜0.001	0.699(0.598~0.818)
优势眼logMAR	2.018	0.775	6.885	0.009	7.602(1.665~34.700)
优势眼脉络膜厚度	-0.069	0.035	4.012	0.045	0.933(0.872~0.999)
优势眼logCS	-4.255	1.569	7.354	0.007	0.014(0.001~0.307)
吸烟史	0.799	0.692	1.333	0.248	2.224(0.573~8.641)
糖尿病	2.180	0.714	9.323	0.002	8.842(2.182~35.823)

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表 4 各指标预测不良VQOL的ROC曲线分析

Table 4. ROC curve analysis for each indicator in predicting poor VQOL

变量	AUC	95% CI	SE	P值	cut-off值	约登指数	灵敏度	特异度
自我效能	0.868	0.807~0.928	0.031	＜0.001	25.500	0.569	0.797	0.772
优势眼logMAR	0.687	0.595~0.779	0.047	＜0.001	1.370	0.318	0.579	0.739
优势眼脉络膜厚度	0.710	0.620~0.799	0.046	＜0.001	71.055	0.334	0.580	0.754
优势眼logCS	0.738	0.642~0.825	0.044	＜0.001	0.765	0.412	0.623	0.789
糖尿病	0.656	0.558~0.753	0.050	0.003	0.500	0.311	0.456	0.855
联合预测	0.957	0.928~0.987	0.015	＜0.001	0.360	0.767	0.912	0.885

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