Building a tool to predict depression after an ischemic stroke
Develops a risk prediction model for depression in people after ischemic stroke.
Plain-language title generated from the registry record
Registry title: Construction and Clinical Validation Study of a Prediction Model for Depression After Ischemic Stroke
Key facts
- Where
- Chongqing, ChinaSee the site
Where in China
- The First Affiliated Hospital of Chongqing Medical UniversityChongqing, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Min Su
- Trial code
- NCT07294274
- Record updated
- 5 February 2026
- Original description
Post-stroke depression (PSD) is the most common neuropsychiatric disorder after a stroke, with an incidence rate of 20% to 60%. PSD is not only associated with higher mortality rates, poorer recovery, more obvious cognitive impairments, greater economic burdens, and lower quality of life, but also brings additional medical expenses and care pressure to families. Society also needs to bear higher medical costs. Currently, the early diagnosis of PSD is difficult, which may lead to poor prognosis after stroke. This study aims to utilize machine learning technology to integrate multi-dimensional indicators of patients with ischemic stroke, establish a risk prediction model for PSD, and assist in early, accurate, and individualized assessment of PSD risk in clinical practice.Read full original descriptionShow less
Construction and Clinical Validation Study of a Prediction Model for Depression After Ischemic Stroke
Post-stroke depression (PSD) is the most common neuropsychiatric disorder after a stroke, with an incidence rate of 20% to 60%. PSD is not only associated with higher mortality rates, poorer recovery, more obvious cognitive impairments, greater economic burdens, and lower quality of life, but also brings additional medical expenses and care pressure to families. Society also needs to bear higher medical costs. Currently, the early diagnosis of PSD is difficult, which may lead to poor prognosis after stroke. This study aims to utilize machine learning technology to integrate multi-dimensional indicators of patients with ischemic stroke, establish a risk prediction model for PSD, and assist in early, accurate, and individualized assessment of PSD risk in clinical practice.
Inclusion: Patients with acute ischemic stroke; Admission within 7 days of symptom onset; The patient and/or the family members sign a written informed consent form.
Exclusion: Confusion of consciousness,severe cognitive impairment, etc; Individuals with a history of depression, schizophrenia, bipolar disorder, etc; Individuals unable to participate in neuropsychological examinations due to hearing im pairments, lack of coordination, or neurological deficits, including se vere aphasia.