PDF(Pubmed)
Abstract：
UNASSIGNED: To determine the risk of postherpetic neuralgia (PHN) in patients with acute herpes zoster (HZ), this study developed and validated a novel clinical prediction model by incorporating a relevant peripheral blood inflammation indicator.
UNASSIGNED: Between January 2019 and June 2023, 209 patients with acute HZ were categorized into the PHN group (n = 62) and the non-PHN group (n = 147). Univariate and multivariate logistic regression analyses were conducted to identify risk factors serving as independent predictors of PHN development. Subsequently, a nomogram prediction model was established, and the discriminative ability and calibration were evaluated using the receiver operating characteristic curve, calibration plots, and decision curve analysis (DCA). The nomogram model was internally verified through the bootstrap test method.
UNASSIGNED: According to univariate logistic regression analyses, five variables, namely age, hypertension, acute phase Numeric Rating Scale (NRS-11) score, platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index, were significantly associated with PHN development. Multifactorial analysis further unveiled that age (odds ratio (OR) [95% confidence interval (CI)]: 2.309 [1.163-4.660]), acute phase NRS-11 score (OR [95% CI]: 2.837 [1.294-6.275]), and PLR (OR [95% CI]: 1.015 [1.010-1.022]) were independent risk factors for PHN. These three predictors were integrated to establish the prediction model and construct the nomogram. The area under the receiver operating characteristic curve (AUC) for predicting the PHN risk was 0.787, and the AUC of internal validation determined using the bootstrap method was 0.776. The DCA and calibration curve also indicated that the predictive performance of the nomogram model was commendable.
UNASSIGNED: In this study, a risk prediction model was developed and validated to accurately forecast the probability of PHN after HZ, thereby demonstrating favorable discrimination, calibration, and clinical applicability.
UNASSIGNED: Between January 2019 and June 2023, 209 patients with acute HZ were categorized into the PHN group (n = 62) and the non-PHN group (n = 147). Univariate and multivariate logistic regression analyses were conducted to identify risk factors serving as independent predictors of PHN development. Subsequently, a nomogram prediction model was established, and the discriminative ability and calibration were evaluated using the receiver operating characteristic curve, calibration plots, and decision curve analysis (DCA). The nomogram model was internally verified through the bootstrap test method.
UNASSIGNED: According to univariate logistic regression analyses, five variables, namely age, hypertension, acute phase Numeric Rating Scale (NRS-11) score, platelet-to-lymphocyte ratio (PLR), and systemic immune inflammation index, were significantly associated with PHN development. Multifactorial analysis further unveiled that age (odds ratio (OR) [95% confidence interval (CI)]: 2.309 [1.163-4.660]), acute phase NRS-11 score (OR [95% CI]: 2.837 [1.294-6.275]), and PLR (OR [95% CI]: 1.015 [1.010-1.022]) were independent risk factors for PHN. These three predictors were integrated to establish the prediction model and construct the nomogram. The area under the receiver operating characteristic curve (AUC) for predicting the PHN risk was 0.787, and the AUC of internal validation determined using the bootstrap method was 0.776. The DCA and calibration curve also indicated that the predictive performance of the nomogram model was commendable.
UNASSIGNED: In this study, a risk prediction model was developed and validated to accurately forecast the probability of PHN after HZ, thereby demonstrating favorable discrimination, calibration, and clinical applicability.
摘要：
■为了确定急性带状疱疹（HZ）患者的带状疱疹后遗神经痛（PHN）的风险，本研究通过纳入相关的外周血炎症指标,建立并验证了一种新型的临床预测模型.
■在2019年1月至2023年6月之间，将209例急性HZ患者分为PHN组（n=62）和非PHN组（n=147）。进行了单变量和多变量逻辑回归分析，以确定作为PHN发展独立预测因子的危险因素。随后，建立了列线图预测模型，并使用接收器工作特性曲线评估判别能力和校准，校准图,和决策曲线分析(DCA)。通过Bootstrap测试方法在内部验证了列线图模型。
■根据单变量逻辑回归分析，五个变量，即年龄,高血压,急性期数字评定量表（NRS-11）评分，血小板与淋巴细胞比率（PLR），全身免疫炎症指数，与PHN发育显著相关。多因素分析进一步揭示了年龄(优势比(OR)[95%置信区间(CI)]：2.309[1.163-4.660])，急性期NRS-11评分（OR[95%CI]：2.837[1.294-6.275]），和PLR（OR[95%CI]：1.015[1.010-1.022]）是PHN的独立危险因素。将这三个预测因子进行整合以建立预测模型并构造列线图。用于预测PHN风险的受试者工作特征曲线下面积（AUC）为0.787，使用Bootstrap方法确定的内部验证的AUC为0.776。DCA和校准曲线还表明，列线图模型的预测性能值得称赞。
■在这项研究中，建立并验证了风险预测模型，以准确预测HZ后PHN的概率，从而表现出有利的歧视，校准,和临床适用性。
■在2019年1月至2023年6月之间，将209例急性HZ患者分为PHN组（n=62）和非PHN组（n=147）。进行了单变量和多变量逻辑回归分析，以确定作为PHN发展独立预测因子的危险因素。随后，建立了列线图预测模型，并使用接收器工作特性曲线评估判别能力和校准，校准图,和决策曲线分析(DCA)。通过Bootstrap测试方法在内部验证了列线图模型。
■根据单变量逻辑回归分析，五个变量，即年龄,高血压,急性期数字评定量表（NRS-11）评分，血小板与淋巴细胞比率（PLR），全身免疫炎症指数，与PHN发育显著相关。多因素分析进一步揭示了年龄(优势比(OR)[95%置信区间(CI)]：2.309[1.163-4.660])，急性期NRS-11评分（OR[95%CI]：2.837[1.294-6.275]），和PLR（OR[95%CI]：1.015[1.010-1.022]）是PHN的独立危险因素。将这三个预测因子进行整合以建立预测模型并构造列线图。用于预测PHN风险的受试者工作特征曲线下面积（AUC）为0.787，使用Bootstrap方法确定的内部验证的AUC为0.776。DCA和校准曲线还表明，列线图模型的预测性能值得称赞。
■在这项研究中，建立并验证了风险预测模型，以准确预测HZ后PHN的概率，从而表现出有利的歧视，校准,和临床适用性。
