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Construction and Clinical Validation Study of a Prediction Model for Depression After Ischemic Stroke

NCT07294274 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 488

Last updated 2026-02-05

No results posted yet for this study

Summary

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.

Conditions

  • Post-stroke Depression

Interventions

DIAGNOSTIC_TEST

Group patients based on whether they have been diagnosed with PSD.

Group patients based on whether they have been diagnosed with PSD.

Sponsors & Collaborators

  • Chongqing Traditional Chinese Medicine Hospital

    collaborator OTHER

  • Min Su

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2025-11-01
Primary Completion
2026-07-20
Completion
2026-07-20

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07294274 on ClinicalTrials.gov