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Abstract
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BACKGROUND: Chronic hepatitis B is the most widespread and most harmful infectious disease in China, often accompanied by depression. Patients with mild depression have a depressed mood, and those with severe symptoms have not only aggravated inherent diseases, but may even commit suicide. At present, the commonly used treatment measures have unsatisfactory effects, and active preventive nursing has become the focus among medical staff.
OBJECTIVE: To construct a Cox risk prediction model for depression in patients with chronic hepatitis B.
METHODS: A total of 245 patients with chronic hepatitis B admitted to the Affiliated Hospital of North Sichuan Medical College were selected as research subjects from June 2017 to May 2018, and their depression situation were followed up. After the data pre-processing was completed, all factors were conducted univariate analysis and multivariate COX risk factor analysis, and a risk prediction model was constructed. The nomogram was used to show the prediction model, and the receiver operating characteristic(ROC) curve was used to evaluate the model discrimination. The calibration curve was used to evaluate the accuracy of the model, and the clinical decision curve analysis was used to evaluate the validity of the model.
RESULTS: The Results of univariate analysis showed that there were significant differences in the incidence of depression among patients with different ages, occupations, education background, hepatitis B grades, infection time, diagnosis time, recurrence times, family status, marriage satisfaction, fear of difficult to cure the disease, fear of hospitalization environment, disease stage, complications, and confidence in treatment(P< 0.05). The Results of multivariate COX risk factor analysis showed that RR=-1.446 1×(the occupation is intellectual)-0.688 7×(education Background was high school or technical secondary school)-2.043 0×(frequent drinking)-0.783 5×(occasional smoking)-1.068 2 ×(frequent smoking)-0.894 0×(diagnosis time was 0.5-5 years)-1.092 4×(diagnosis time was less than 0.5 year) +1.335 2×(dissatisfaction with family status)+1.345 1×(dissatisfaction with marriage)-0.574 3×(not worried about unsuitable hospitalization environment). The area under the ROC curve(AUC) of the COX risk prediction model constructed in this study was 0.979 8. The specificity of the model was 0.972 5, the sensitivity was 0.940 7, and the accuracy was 0.954 9. The model had a positive likelihood ratio of 34.180 2, and the negative likelihood ratio was 0.060 9. The diagnostic value ratio was 560.916 7. The positive predictive value was 0.976 9 and the negative predictive value was 0.929 8. The model had great discrimination.In terms of model accuracy evaluation, when the event rate was below 16%, the model overestimated the risk; when the event rate was between 16% and 40%, the model underestimated the risk; when the event rate was between 40% and 80%, the model overestimated the risk; when the event rate was between 80% and 100%, the model underestimated the risk; when the event rate was 16%, 40% and 80%, the predicted value and observed value were exactly the same. On the whole, this model had good accuracy. The clinical decision curve showed that the net benefit value was high, which showed the effect of clinical decision-making based on the prediction Results of this model could bring great benefit to patient's condition.
CONCLUSION: The risk prediction model for depression in patients with chronic hepatitis B constructed in this paper can be used to predict the risk of depression in patients with chronic hepatitis B so as to guide the clinical medical staff to carry out targeted intervention measures, and ultimately avoid or reduce the possibility of depression, which is worthy of clinical application. Copyright © 2020 by the Chinese General Practice.
Language: zh |