Journal of Shanghai Jiao Tong University (Medical Science) >
Construction of a decision-making model for primary headache based on Nomogram
Online published: 2021-09-23
Supported by
Program of Shanghai WU Meng-chao Medical Science Foundation(JJHXM-2019009)
·To establish a decision-making model for primary headache based on Nomogram.
·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.
·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.
·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.
Fang-fang LIU , Guan-shui BAO , Meng-xia YAN . Construction of a decision-making model for primary headache based on Nomogram[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2021 , 41(10) : 1323 -1329 . DOI: 10.3969/j.issn.1674-8115.2021.10.009
1 | Hagen K, ?sberg AN, Uhlig BL, et al. The epidemiology of headache disorders: a face-to-face interview of participants in HUNT4[J]. J Headache Pain, 2018, 19(1): 25. |
2 | Yu SY, Liu RZ, Zhao G, et al. The prevalence and burden of primary headaches in China: a population-based door-to-door survey[J]. Headache, 2012, 52(4): 582-591. |
3 | Yao CY, Wang Y, Wang LJ, et al. Burden of headache disorders in China, 1990—2017: findings from the Global Burden of Disease Study 2017[J]. J Headache Pain, 2019, 20(1): 102. |
4 | Saylor D, Steiner TJ. The global burden of headache[J]. Semin Neurol, 2018, 38(2): 182-190. |
5 | Takeshima T, Wan Q, Zhang Y, et al. Prevalence, burden, and clinical management of migraine in China, Japan, and South Korea: a comprehensive review of the literature[J]. J Headache Pain, 2019, 20(1): 111. |
6 | Leonardi M, Raggi A. A narrative review on the burden of migraine: when the burden is the impact on people's life[J]. J Headache Pain, 2019, 20(1): 41. |
7 | International Headache Society. Headache classification committee of the international headache society (IHS) the international classification of headache disorders, 3rd edition[J]. Cephalalgia, 2018, 38(1): 1-211. |
8 | 尹梓名, 董钊, 孔祥勇. 基于国际头痛诊断标准的原发性头痛辅助决策系统[J]. 计算机应用研究, 2019, 36(02): 461-465. |
9 | Krawczyk B, Simi? D, Simi? S, et al. Automatic diagnosis of primary headaches by machine learning methods[J]. Central Eur J Med, 2013, 8(2): 157-165. |
10 | Khayamnia M, Yazdchi M, Heidari A, et al. Diagnosis of common deadaches using hybrid expert-based systems[J]. J Med Signals Sens, 2019, 9(3): 174-180. |
11 | Simi? S, Bankovi? Z, Villar JR, et al. A hybrid fuzzy clustering approach for diagnosing primary headache disorder[J]. Log J IGPL, 2021, 29(2): 220-235. |
12 | Jun FEH. Regression modeling strategies with applications to linear models, logistic regression, and survival analysis[M]. New York: Springer Verlag, 2001. |
13 | GBD 2017 Disease and Injury Incidence and Prevalence Collaborators. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990—2017: a systematic analysis for the Global Burden of Disease Study 2017[J]. Lancet, 2018, 392(10159): 1789-1858. |
14 | GBD 2016 Neurology Collaborators. Global, regional, and national burden of neurological disorders, 1990—2016: a systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet Neurol, 2019, 18(5): 459-480. |
15 | Martin VT, Penzien DB, Houle TT, et al. The predictive value of abbreviated migraine diagnostic criteria[J]. Headache, 2005, 45(9): 1102-1112. |
16 | Lipton RB, Dodick D, Sadovsky R, et al. A self-administered screener for migraine in primary care: the ID migraine validation study[J]. Neurology, 2003, 61(3): 375-382. |
/
〈 |
|
〉 |