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Clinical Predictors of Medication Compliance in Patients With Acute Herpetic Neuralgia.
Lyu, Hui; Wang, Ling-Yan; Wang, Rui-Xia; Sheng, Han; Xia, Jian-Mei; Cheng, Jun-Ya.
Afiliación
  • Lyu H; Department of Pain, First Hospital of Jiaxing, Jiaxing, China. Electronic address: lvhui1985@163.com.
  • Wang LY; Department of Intensive Care Unit, First Hospital of Jiaxing, Jiaxing, China. Electronic address: 349930938@qq.com.
  • Wang RX; Department of Nursing, Zhejiang Chinese Medical University, Hangzhou, China. Electronic address: whnona2021@163.com.
  • Sheng H; Department of Nursing, First Hospital of Jiaxing, Jiaxing, China. Electronic address: sh2013106129@163.com.
  • Xia JM; Department of Pain, First Hospital of Jiaxing, Jiaxing, China. Electronic address: 676912880@qq.com.
  • Cheng JY; Department of Nursing, First Hospital of Jiaxing, Jiaxing, China. Electronic address: 812762418@qq.com.
Pain Manag Nurs ; 2024 Aug 16.
Article en En | MEDLINE | ID: mdl-39153959
ABSTRACT

PURPOSE:

Pain is one of the most common and harmful symptoms experienced by individuals with acute herpetic neuralgia (AHN). In this population, studies to determine the causes that affect patients taking medications compliance are rare. This study aimed to construct a predictive model for medication compliance of patients with AHN and to verify its performance. DESIGN AND

METHODS:

In this prospective study of 398 patients with AHN who were discharged from a tertiary hospital with medications from July 2020 to October 2022, we used logistic regression analysis to explore the predictive factors of medication compliance of patients with AHN and to construct a nomogram. The area under the curve was used to evaluate the predictive effect of the model.

RESULTS:

A predictive model of drug compliance of patients with AHN was constructed based on the following four factors disease duration, pain severity before treatment, medication beliefs, and comorbidity of chronic diseases. The area under the curve of the model was 0.766 (95% confidence interval [0.713, 0.819]), with a maximum Youden's index of 0.431, sensitivity of 0.776, and specificity of 0.655. A linear calibration curve was found with a slope close to 1.

CONCLUSIONS:

The prediction model constructed in this study had good predictive performance and provided a reference for early clinical screening of independent factors that affected the medication compliance of patients with AHN.
Palabras clave

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Pain Manag Nurs Asunto de la revista: ENFERMAGEM / NEUROLOGIA / PSICOFISIOLOGIA Año: 2024 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Pain Manag Nurs Asunto de la revista: ENFERMAGEM / NEUROLOGIA / PSICOFISIOLOGIA Año: 2024 Tipo del documento: Article