In-Hospital Risk Prediction for Post-stroke Depression

Abstract

Background and Purpose— The timely detection of post-stroke depression is complicated by a decreasing length of hospital stay. Therefore, the Post-stroke Depression Prediction Scale was developed and validated. The Post-stroke Depression Prediction Scale is a clinical prediction model for the early identification of stroke patients at increased risk for post-stroke depression. Methods— The study included 410 consecutive stroke patients who were able to communicate adequately. Predictors were collected within the first week after stroke. Between 6 to 8 weeks after stroke, major depressive disorder was diagnosed using the Composite International Diagnostic Interview. Multivariable logistic regression models were fitted. A bootstrap-backward selection process resulted in a reduced model. Performance of the model was expressed by discrimination, calibration, and accuracy. Results— The model included a medical history of depression or other psychiatric disorders, hypertension, angina pectoris, and the Barthel Index item dressing. The model had acceptable discrimination, based on an area under the receiver operating characteristic curve of 0.78 (0.72–0.85), and calibration ( P value of the U- statistic, 0.96). Transforming the model to an easy-to-use risk-assessment table, the lowest risk category (sum score, <−10) showed a 2% risk of depression, which increased to 82% in the highest category (sum score, >21). Conclusions— The clinical prediction model enables clinicians to estimate the degree of the depression risk for an individual patient within the first week after stroke.

Risk Factors for Post-stroke Depression: A Meta-analysis

“…These 379 studies with their full text were retrieved and reviewed for eligibility, and 300 were excluded because of ineligible study design and outcome measures. The 79 studies met all the criteria, and these were selected for initial inclusion; after review, a total of 36 articles (Kotila et al, 1998; Pohjasvaara et al, 1998; Aben et al, 2002, 2006; Carota et al, 2005; Tang et al, 2005, 2011; Leentjens et al, 2006; Storor and Byrne, 2006; Brodaty et al, 2007; Lee et al, 2007; Lindén et al, 2007; van de Port et al, 2007; Fuentes et al, 2009; Jimenez et al, 2009; Schepers et al, 2009; Snaphaan et al, 2009; Farner et al, 2010; Ayerbe et al, 2011; Altieri et al, 2012; de Man-van Ginkel et al, 2013; Yang et al, 2013, 2015; Zhang et al, 2013; De Ryck et al, 2014a,b; Li et al, 2014; Ahn et al, 2015; Lewin-Richter et al, 2015; Shi et al, 2015; van Mierlo et al, 2015; Koh et al, 2016; Kootker et al, 2016; Malhotra et al, 2016; Metoki et al, 2016; Tsai et al, 2016) were included in the final analysis ( Figure 1 ).…”

“…In recent decades, overwhelming evidence has indicated that PSD is associated with well-known risk factors such as medical history (i.e., predisposing illness and smoking) (Shi et al, 2015), a history of mental disorders (de Man-van Ginkel et al, 2013), and stroke characteristics (Jiang et al, 2014). PSD is highly prevalent among both men and women; however, it appears that PSD is more common in women than men.…”

Towards a multi protein and mRNA expression of biological predictive and distinguish model for post stroke depression

“…Post stroke depression (PSD) is a frequent complication with approximately 30% prevalence that worsens rehabilitation outcomes and confers substantial risk for suicide [2–4]. Moreover, PSD in turn increased the risk of stroke recurrence [5].…”

Long-range temporal correlations of broadband EEG oscillations for depressed subjects following different hemispheric cerebral infarction

“…Cerebral infarction is a major cause of adult-onset disability and dependency worldwide, and neuropsychiatric disorders are often associated with it. Depressive disorder, which refers to emotional disorders with main symptoms of dropped interest and depression, is one of the most common emotional disorders afflicting stroke sufferers, which impedes the rehabilitation and recovery process, jeopardizes quality of life and severely increases mortality (Berg et al, 2003;Gaete and Bogousslavsky, 2008;De Man-Van Ginkel et al, 2013). Its pathophysiological mechanism has not yet been fully elucidated and is assumed to be a multifactorial process involving biological, behavioural and social factors (Chemerinski et al, 2001;de Coster et al, 2005;Singh et al, 2000;Pedroso et al, 2015;Zhang et al, 2015).…”