Cai Meng, Yin Jing, Zeng YongFen, Liu HongJun, Jin Yi
Department of Pain Medicine, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, People's Republic of China.
J Pain Res. 2024 Jul 1;17:2299-2309. doi: 10.2147/JPR.S466939. eCollection 2024.
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.
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.
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.
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.
为确定急性带状疱疹（HZ）患者发生带状疱疹后神经痛（PHN）的风险，本研究通过纳入一项相关外周血炎症指标，开发并验证了一种新型临床预测模型。
在2019年1月至2023年6月期间，将209例急性HZ患者分为PHN组（n = 62）和非PHN组（n = 147）。进行单因素和多因素逻辑回归分析，以确定作为PHN发生独立预测因素的风险因素。随后，建立列线图预测模型，并使用受试者工作特征曲线、校准图和决策曲线分析（DCA）评估其判别能力和校准情况。通过自助检验法对列线图模型进行内部验证。
根据单因素逻辑回归分析，年龄、高血压、急性期数字评分量表（NRS-11）评分、血小板与淋巴细胞比值（PLR）和全身免疫炎症指数这五个变量与PHN的发生显著相关。多因素分析进一步表明，年龄（比值比（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。将这三个预测因素整合以建立预测模型并构建列线图。预测PHN风险的受试者工作特征曲线下面积（AUC）为0.787，使用自助法确定的内部验证AUC为0.776。DCA和校准曲线也表明列线图模型的预测性能值得称赞。
在本研究中，开发并验证了一种风险预测模型，以准确预测HZ后发生PHN的概率，从而显示出良好的判别能力、校准情况和临床适用性。
