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Can artificial intelligence using machine algorithm help in preventing fall in blood pressure in seriously ill patients

Comparison of hypotensive events in critically ill patients managed with or without machine learning algorithm

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CTRI
Registry ID
CTRI/2023/06/053973
Enrollment
54
Registered
2023-06-16
Start date
Unknown
Completion date
Unknown
Last updated
2023-06-26

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

Conditions

Health Condition 1: A00-B99- Certain infectious and parasitic diseases Health Condition 2: I00-I99- Diseases of the circulatory system Health Condition 3: K00-K95- Diseases of the digestive system Health Condition 4: H60-H95- Diseases of the ear and mastoid process Health Condition 5: H00-H59- Diseases of the eye and adnexa Health Condition 6: N00-N99- Diseases of the genitourinary system Health Condition 7: M00-M99- Diseases of the musculoskeletal system and connective tissue Health Condition 8

Interventions

Intervention1: Nil: Nil Control Intervention1: Patients being managed without machine learning algorithm (FloTrac): Patients connected to FloTrac sensor receive treatment once hypotension (MAP less th

Sponsors

Lady Hardinge Medical College and Associated Hospitals
Lead Sponsor

Eligibility

Inclusion criteria

Inclusion criteria: 1.More than 18years old 2.Critically ill patients admitted to intensive care with an arterial catheter in the radial artery for advanced hemodynamic monitoring

Exclusion criteria

Exclusion criteria: 1.Patients having sustained hypotension despite receiving maximum drug support 2.Patients admitted to intensive care with hypertensive emergencies

Design outcomes

Primary

MeasureTime frame
Comparison between median (IQR) number of hypotensive events in critically ill patients being managed with or without machine learning algorithmTimepoint: 48 hours

Secondary

MeasureTime frame
Comparison between groups of average duration (min) of each hypotensive eventTimepoint: 48 hours;Comparison between groups of percentage agreement between decision taken by the clinician to that suggested by the machine learning algorithmTimepoint: 48 hours;Comparison between groups of proportion of patients with morbidity within 3 days of first event of hypotension (number of patients with acute kidney injury, increased troponin levels)Timepoint: 72 hours;Mean/ median time to MAP less than 65 mmHg after HPI â?¥ 85Timepoint: 48 hours;Sensitivity, specificity, positive predictive value, negative predictive value of HPI & HPI threshold predicting hypotension using AUROCTimepoint: 48 hours

Countries

India

Contacts

Public ContactDr Maitree Pandey

Lady Hardinge Medical College

maitreepandey@gmail.com

Outcome results

None listed

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