Development and Validation of 3-Month Major Post-Stroke Depression Prediction Nomogram After Acute Ischemic Stroke Onset

Abstract

The early detection of major post-stroke depression (PSD) is essential to optimize patient care. A major PSD prediction tool needs to be developed and validated for early screening of major PSD patients. Patients and Methods: A total of 639 acute ischemic stroke (AIS) patients from three hospitals were consecutively recruited and completed a 3-month follow-up. Sociodemographic, clinical and laboratory test data were collected on admission. With major depression criteria being met in the DSM-V, 17-item Hamilton Rating Scale For Depression (HRSD) score ≥17 at 3 months after stroke onset was regarded as the primary endpoint. Multiple imputation was used to substitute the missing values and multivariable logistic regression model was fitted to determine associated factors with a bootstrap backward selection process. The nomogram was constructed based on the regression coefficients of the associated factors. Performance of the nomogram was assessed by discrimination (C-statistics) and calibration curve. Results: A total of 7.04% (45/639) of patients were diagnosed with major PSD at 3 months. The final logistic regression model included age, baseline NIHSS and mRS scores, educational level, calcium-phosphorus product, history of hypertension and atrial fibrillation. The model had acceptable discrimination, based on a C-statistic of 0.81 (95% CI, 0.791-0.829), with 71.1% sensitivity and 78.6% specificity. We also transformed the model to a nomogram, an easy-to-use clinical tool which could be used to facilitate the early screening of major PSD patients at 3 months. Conclusion:We identified several associated factors of major PSD at 3 months and constructed a convenient nomogram to guide follow-up and aid accurate prognostic assessment.

The etiologies of post-stroke depression: Different between lacunar stroke and non-lacunar stroke

“… 27 , 28 , 29 PSD after lacunar and non-lacunar stroke shared some common risk factors, such as female gender, degrees of neurological deficits and functional outcome, which had been studied and identified as the predictors of PSD for a series of studies. 29 , 30 , 31 , 32 , 33 , 34 Meanwhile, there were also some risk factors associated with PSD after lacunar stroke and non-lacunar stroke differently. For example, the unfavorable treatment effect was associated with PSD after lacunar stroke more strongly.…”

Predicting new-onset post-stroke depression from real-world data using machine learning algorithm

“…Post-stroke hypertension and higher diastolic blood pressure on post-stroke physical examination were associated with PSD in our predictive models. One prior study demonstrated that hypertension plays a role in predicting 3-month PSD ( 37 ). Another study reported that a longer duration of hypertension was also associated with new-onset depression after stroke ( 38 ).…”

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

“…Moreover, this study developed a prediction model for the individualized risk of behavioral problems in children with TD, and a nomogram was plotted for the prediction model. To date, the application of a nomogram to predict the risk of TD with behavioral problems is lacking, although nomograms have been widely used as a reliable clinical tool to create a simple intuitive graph to quantify the risk of a clinical event of interest in other diseases [ 19 , 20 ]. In the present study, the model based on age, abnormal birth history, parenting pattern, family history, TD type and tic severity had a significant predictive performance for behavioral problems in children with TD.…”