A study to evaluate the performance of software ‘AITRICS-VC’ for predicting sepsis, and major adverse events in a general ward inpatient using EMR data
- Conditions
- Certain infectious and parasitic diseases
- Registration Number
- KCT0008466
- Lead Sponsor
- Aitrics
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Completed
- Sex
- All
- Target Recruitment
- 10500
To be eligible for this clinical trial, participants must meet all of the following criteria:
1. Adult men and women aged 19 years or older
2. Patients admitted to six general wards of four selected departments within Keimyung University Dongsan Hospital within 8 months after IRB approval
3. Patients for whom age, systolic blood pressure, diastolic blood pressure, heart rate, respiratory rate, and body temperature information can be obtained from the electronic medical record during hospitalization.
If any of the following exclusion criteria apply, the participant cannot be enrolled in this clinical trial:
1. Patients who have already been diagnosed with sepsis and are receiving treatment
2. Participants deemed inappropriate for the study by the investigator due to factors that may affect the validity of the evaluation or other reasons.
Study & Design
- Study Type
- Observational Study
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method AUROC of the Sepsis Prediction Algorithm(SEPS) for Early Detection of Sepsis within 4 Hours;AUROC of the Major Adverse Events Prediction Algorithm(MAES) for Early Detection of Major Adverse Events within 6 Hours
- Secondary Outcome Measures
Name Time Method Sensitivity as evaluated by the SEPS algorithm for the prediction of sepsis;Specificity as evaluated by the SEPS algorithm for the prediction of sepsis;Comparison of the AUROC for the SEPS algorithm and qSOFA, SOFA score for predicting sepsis;Comparison of the sepsis detection time between the SEPS Algorithm and qSOFA, SOFA score for sepsis prediction;Sensitivity as evaluated by the MAES algorithm for the prediction of major adverse events;Specificity as evaluated by the MAES algorithm for the prediction of major adverse events;Comparison of the AUROC for the MAES algorithm and MEWS,NEWS score for predicting major adverse events;Comparison of the major adverse events detection time between the MAES Algorithm and MEWS, NEWS score for major adverse events prediction