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Mapping of the Insomnia Severity Index and other sleep measures to EuroQol EQ-5D health state utilities

机译:将失眠严重度指数和其他睡眠指标映射到EuroQol EQ-5D健康状态实用程序

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Background This study sought to map the Insomnia Severity Index (ISI) and symptom variables onto the EQ-5D . Methods A cross-sectional survey was conducted among adult US residents with self-reported sleep problems. Respondents provided demographic, comorbidity, and sleep-related information and had completed the ISI and the EQ-5D profile. Respondents were classified into ISI categories indicating no, threshold, moderate, or severe insomnia. Generalized linear models (GLM) were used to map the ISI's 7 items (Model I), summary scores (Model II), clinical categories (Model III), and insomnia symptoms (Model IV), onto the EQ-5D . We used 50% of the sample for estimation and 50% for prediction. Prediction accuracy was assessed by mean squared errors (MSEs) and mean absolute errors (MAEs). Results Mean (standard deviation) sleep duration for respondents (N = 2,842) was 7.8 (1.9) hours, and mean ISI score was 14.1 (4.8). Mean predicted EQ-5D utility was 0.765 (0.08) from Models I-III, which overlapped with observed utilities 0.765 (0.18). Predicted utility using insomnia symptoms was higher (0.771(0.07)). Based on Model I, predicted utilities increased linearly with improving ISI (0.493 if ISI = 28 vs. 1.00 if ISI = 0, p EQ-5D ; thus, utilities diminished after an optimal amount of sleep. The MSEs/MAEs were substantially lower when predicting EQ-5D > 0.40, and results were comparable in all models. Conclusions Findings suggest that mapping relationships between the EQ-5D and insomnia measures could be established. These relationships may be used to estimate insomnia-related treatment effects on health state utilities.
机译:背景本研究试图将失眠严重程度指数(ISI)和症状变量映射到EQ-5D上。方法对有自我报告的睡眠问题的美国成年居民进行横断面调查。受访者提供了人口统计学,合并症和与睡眠有关的信息,并且已经完成了ISI和EQ-5D档案。受访者分为ISI类别,表示没有,阈值,中度或严重失眠。广义线性模型(GLM)用于将ISI的7个项目(模型I),汇总评分(模型II),临床类别(模型III)和失眠症状(模型IV)映射到EQ-5D上。我们将样本的50%用于估计,将50%用于预测。通过均方误差(MSE)和平均绝对误差(MAE)评估预测准确性。结果受访者(N = 2,842)的平均(标准差)睡眠时间为7.8(1.9)小时,平均ISI评分为14.1(4.8)。来自模型I-III的平均预测EQ-5D效用为0.765(0.08),与观察到的效用0.765(0.18)重叠。使用失眠症状的预测效用较高(0.771(0.07))。基于模型I,预测的效用随ISI的提高呈线性增加(如果ISI = 28,则为0.493;如果ISI = 0,p EQ-5D,则为1.00;因此,经过最佳睡眠时间后效用降低了。当MSI / MAE降低时)结论:发现EQ-5D与失眠量度之间的映射关系可以建立,这些关系可用于估计失眠相关治疗对健康状态效用的影响。

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