Predicting Fear Extinction in Posttraumatic Stress Disorder.
Predicting Fear Extinction in Posttraumatic Stress Disorder.
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DOI：
10.3390/brainsci13081131
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发表时间：
2023-07-28
期刊：
影响因子：
3.3
通讯作者：
中科院分区：
文献类型：
--
作者：
恐惧消除是创伤后应激障碍（PTSD）暴露疗法的基础，但一半的患者没有改善。预测PTSD患者的恐惧消退可能会为个性化暴露疗法的发展提供信息。参与者是125名创伤暴露的成年人（96名女性），他们有一系列PTSD症状。在基线、黑暗增强惊吓、恐惧条件反射和消退期间记录肌电图、心电图和皮肤电导。使用交叉验证，保持样本预测方法，三个惩罚回归和传统的普通最小二乘法进行了训练，使用50个预测变量（5个临床，24个自我报告，21个生理）预测恐惧增强惊吓灭绝。通过惩罚回归算法选择的预测因子被纳入多变量回归分析，而单变量回归评估个体预测因子。所有惩罚回归在预测精度和泛化能力方面都优于OLS，这是由训练和保持子样本中的较低均方误差所指示的。在早期灭绝期间，所有建模方法的一致预测因子包括黑暗增强惊吓，分离体验量表的人格解体和现实解体子量表，以及PTSD过度觉醒症状评分。这些发现为建模方法和患者特征提供了新的见解，可以可靠地预测PTSD中的恐惧消退。惩罚回归显示了识别与疾病相关的变量以提高临床研究中预测建模准确性的希望。
Fear extinction is the basis of exposure therapies for posttraumatic stress disorder (PTSD), but half of patients do not improve. Predicting fear extinction in individuals with PTSD may inform personalized exposure therapy development. The participants were 125 trauma-exposed adults (96 female) with a range of PTSD symptoms. Electromyography, electrocardiogram, and skin conductance were recorded at baseline, during dark-enhanced startle, and during fear conditioning and extinction. Using a cross-validated, hold-out sample prediction approach, three penalized regressions and conventional ordinary least squares were trained to predict fear-potentiated startle during extinction using 50 predictor variables (5 clinical, 24 self-reported, and 21 physiological). The predictors, selected by penalized regression algorithms, were included in multivariable regression analyses, while univariate regressions assessed individual predictors. All the penalized regressions outperformed OLS in prediction accuracy and generalizability, as indexed by the lower mean squared error in the training and holdout subsamples. During early extinction, the consistent predictors across all the modeling approaches included dark-enhanced startle, the depersonalization and derealization subscale of the dissociative experiences scale, and the PTSD hyperarousal symptom score. These findings offer novel insights into the modeling approaches and patient characteristics that may reliably predict fear extinction in PTSD. Penalized regression shows promise for identifying symptom-related variables to enhance the predictive modeling accuracy in clinical research.
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影响因子：
3
作者：
Baldwin, Scott A.
通讯作者：
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影响因子：
3.3
作者：
Blevins, Christy A.;Weathers, Frank W.;Witte, Tracy K.
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影响因子：
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作者：
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通讯作者：
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DOI：
10.7717/peerj-cs.623
发表时间：
2021
期刊：
PeerJ. Computer science
影响因子：
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作者：
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通讯作者：
Jurman G
影响因子：
3.7
作者：
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通讯作者：
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