Can artificial intelligence using machine algorithm help in preventing fall in blood pressure in seriously ill patients
Not Applicable
- Registration Number
- CTRI/2023/06/053973
- Lead Sponsor
- ady Hardinge Medical College and Associated Hospitals
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- ot Yet Recruiting
- Sex
- Not specified
- Target Recruitment
- 0
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
1.Patients having sustained hypotension despite receiving maximum drug support
2.Patients admitted to intensive care with hypertensive emergencies
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Comparison between median (IQR) number of hypotensive events in critically ill patients being managed with or without machine learning algorithmTimepoint: 48 hours
- Secondary Outcome Measures
Name Time Method 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