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Using AI to Predict Bedsores in Stroke Survivors

Machine Learning Algorithm for Screening Risk of Pressure Ulcer in Post Stroke Patients: A Predictive Tool - nil

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2025/05/087677
Enrollment
700
Registered
2025-05-26
Start date
Unknown
Completion date
Unknown
Last updated
2025-06-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Health Condition 1: I633- Cerebral infarction due to thrombosis of cerebral arteries Health Condition 2: I634- Cerebral infarction due to embolism of cerebral arteries Health Condition 3: L899- Pressure ulcer of unspecified site Health Condition 4: I679- Cerebrovascular disease, unspecified

Interventions

Intervention1: NIL: NIL Intervention2: Nil: Nil Control Intervention1: NIL : NIL

Sponsors

Nitte Institute of Physiotherapy
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Patients admitted with stroke episode in Justice K. S. Hegde Charitable Hospital Mangalore Admission as well as discharged patients to the hospital from (01/02/2025) to (01/02/2026) will be considered Age group (35 years to 80 years) Gender (both, male and female) All types of strokes

Exclusion criteria

Exclusion criteria: Who is not willing to give consent to participate Bedridden patients prevailing with conditions other than stroke

Design outcomes

Primary

MeasureTime frame
Braden ScaleTimepoint: on admission (basline) and on discharge ( approximately 2 weeks)—

Secondary

MeasureTime frame
NILTimepoint:—

Countries

India

Contacts

Public ContactDr Purusotham Chippala

Nitte Institute of Physiotherapy

chippala_puru@nitte.edu.in9916460185

Outcome results

None listed

Source: CTRI (via WHO ICTRP) · Data processed: Feb 4, 2026