JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE) ›› 2021, Vol. 41 ›› Issue (10): 1323-1329.doi: 10.3969/j.issn.1674-8115.2021.10.009

• Clinical research • Previous Articles     Next Articles

Construction of a decision-making model for primary headache based on Nomogram

Fang-fang LIU1(), Guan-shui BAO1,2(), Meng-xia YAN1   

  1. 1.Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China
    2.Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
  • Online:2021-10-28 Published:2021-09-23
  • Contact: Guan-shui BAO E-mail:583468089@qq.com;baogs@163.com
  • Supported by:
    Program of Shanghai WU Meng-chao Medical Science Foundation(JJHXM-2019009)

Abstract: Objective

·To establish a decision-making model for primary headache based on Nomogram.

Methods

·Two hundred and ten patients with migraine or tension-type headache who visited the Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine between October 2019 to December 2020 were studied retrospectively. Among them, 152 patients who visited the hospital from October 2019 to August 2020 were served as the modeling group. Fifty eight patients who visited the hospital from September 2020 to December 2020 were served as the validation group. Univariate and multivariate Logistic regression were used to analyze the independent predictive factors to distinguish migraine and tension-type headache. According to the regression coefficient of independent variables, R software was used to construct a nomogram model of migraine or tension-type headache. The internal verification of the model was carried out through Bootstrap, and the external verification was carried out according to the data of the validation group. Receiver operator characteristic curve (ROC curve), area under the curve (AUC) and calibration curve were used respectively to estimate the discrimination and calibration of the prediction model.

Results

·There were 80 patients with migraines and 72 patients with tension-type headaches in the modeling group. There were 35 patients with migraines and 23 patients with tension-type headaches in the validation group. There was no statistical difference in characteristics between the modeling group and the validation group. According to the results of univariate Logistic analysis, 9 characteristic variables were extracted and included in the multivariate analysis. Multivariate Logistic regression analysis showed the independent predictive factors that were used to distinguish migraine and tension-type headache, including course, whether the headache was located in the occipital, severity intensity of the headache, whether the headache was accompanied by nausea/vomiting, whether the headache was accompanied by photophobia/phonophobia and the change of headache after activities. The decision Nomogram model was constructed based on this result. The internal and external verification of the model found that AUC of the modeling group and the validation group were 0.896 [95% confidence interval (CI) 0.842?0.950] and 0.884 (95%CI 0.793?0.976) respectively, suggesting that the prediction model has a good discrimination capacity.The calibration curve of the modeling group and the validation group was very close to the standard curve, and had a good calibration degree, which showed that the model was consistent in the two groups.

Conclusion

·This study has constructed a decision-making model for distinguishing migraine and tension-type headache based on Nomogram. The model has a good discrimination and calibration in the modeling group and the validation group, which is beneficial to improve clinicians' early identification and diagnosis capabilities for migraine and tension-type headache.

Key words: primary headache, migraine, tension-type headache, decision-making model, model validation, Nomogram

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