Construction and validation of a prediction model for the risk of new-onset depression in stroke patients after admission

ObjectiveTo construct and validate a prediction model for the risk of new-onset depression in stroke patients after admission.

MethodsA total of 916 stroke patients who without depression before admission were selected.These patients were divided into the new-onset depression group and the non-depression group according to the occurrence of depression in the first week after admission.Single-factor and multi-factor logistic regression models were used to analyze the influencing factors of new-onset depression and establish the prediction model.Then the nomogram was draw.Meanwhile, the area under the ROC curve and Hosmer-Lemeshow test were used to evaluate the prediction effect of the model.A total of 298 hospitalized patients from April 2020 to April 2021 were selected as the model validation group.

ResultsThe incidence of new-onset depression in the model building group and validation group was 31.8% and 32.9%, respectively.Age, body mass index, education level, per capita annual household income, sleep quality, sleep duration, care after admission, Barthel index and NIHSS score were the influencing factors of new-onset depression in stroke patients after admission(P < 0.05).The validation results of the prediction model showed that the discrimination was good.The area under ROC curve of the risk of new-onset depression(P < 0.05) after admission was 0.868 in the model construction group and 0.813 in the validation group, respectively(P < 0.01).The calibration degree was also high, and the P values of Hosmer-Lemeshow test were 0.817 and 0.389, respectively.

ConclusionsThe prediction model constructed can effectively predict the occurrence of new-onset depression in stroke patients after admission, providing a reference for timely psychological intervention in clinical nursing work for stroke patients.