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Artificial Intelligence Predicts Refractory Septic Shock in Sepsis Patients: A Validated Model for Early prediction

Development and validation of AI-model to predict refractory septic shock - NIL

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2024/06/069604
Enrollment
2200
Registered
2024-06-27
Start date
Unknown
Completion date
Unknown
Last updated
2024-07-22

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

Conditions

Health Condition 1: B948- Sequelae of other specified infectious and parasitic diseases

Interventions

Intervention1: NIL: Not Applicable Control Intervention1: NIL: Not Applicable

Sponsors

Mukkelli Vinay Gandhi
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: Derivation Cohort: Inclusion: We will include all adult patients with a diagnosis of sepsis at the time of ICU admission and who developed Refractory septic shock from the Medical and surgical ICU. Validation Cohort: Inclusion 1. Patients consenting to the study. 2. Patients with a diagnosis of sepsis at the time of ICU admission and developed Refractory septic shock (for validation), within 7 days.

Exclusion criteria

Exclusion criteria: Exclusion criteria for derivation cohort: 1.Patients with age 2.Patients with Non-Septic Shock. 3.Patients with septic and cardiogenic shock Validation Cohort Exclusion criteria: 1.Patients with age 2.Patients with Non-Septic Shock 3. Patients with septic and cardiogenic shock

Design outcomes

Primary

MeasureTime frame
Early Prediction of Refractory Septic ShockTimepoint: Within seven days of ICU admission—

Secondary

MeasureTime frame
NOT APPLICABLETimepoint: NOT APPLICABLE—

Countries

India

Contacts

Public ContactMukkelli Vinay Gandhi

AIIMS, New Delhi

k.punit@yahoo.com09873106516

Outcome results

None listed

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