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Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment (IDEALIST)

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ClinicalTrials.gov Identifier: NCT02741986
Recruitment Status : Recruiting
First Posted : April 18, 2016
Last Update Posted : April 6, 2018
Sponsor:
Collaborator:
National Institute of General Medical Sciences (NIGMS)
Information provided by (Responsible Party):
University of Florida

Brief Summary:
Brief Summary: The goal of this study is to implement and test an intelligent perioperative system (IPS) that in real-time predicts risk for postoperative complications using routine clinical data collected in electronic health records. The accuracy of computer-generated risk scores will be compared to physician's risk scores for the same patients. Physicians will be also asked to provide the opinion regarding the computer-generated risk scores using interactive interface with the program. The information regarding the risk scores performance will be collected during the two 6-month periods. The accuracy of IPS and physicians will be compared at the end at those two time periods.

Condition or disease Intervention/treatment
Postoperative Complications Other: Risk estimation

Detailed Description:
Postoperative complications significantly increase morbidity, mortality and cost after surgery. In the current clinical practice the prediction of the risk for developing complications after surgery is manly based on physicians' clinical judgment. The predictive accuracy of that judgment is limited and poorly studied. The investigators will design an intelligent perioperative system (IPS) as the set of computer software and algorithms that in real-time predict risk for postoperative complications using routine clinical data in electronic health records. The system is designed as the self-learning system with the ability to interact with physicians and solicit their feedback. This study will compare the clinical judgment of physicians with computer generated risk scores for patients undergoing major surgery. All surgeons and anesthesiologists at large single-center tertiary academic center will be recruited to participate in this study. The IPS system will be implemented in real time and will generate risk scores for postoperative complications for patients planned to undergo surgery performed by the physicians enrolled in the study. Physicians will be asked to provide their risk scores (using visual analog risk scale from 0-100) for the same patients before and after interacting with the IPS. They will also have the opportunity to review computer-generated risk scores and provide their feedback. The information will be collected during two six-month periods. At the end of each 6-months period predicted risk estimates will be compared to the true occurrence of the complications. Predictive performance of physicians' risk scores will be compared to IPS generated risk scores using the comparison between area under the receiver-operating curve (AUC), sensitivity, specificity and positive and negative predicted values.

Study Type : Observational
Estimated Enrollment : 200 participants
Observational Model: Other
Time Perspective: Prospective
Official Title: Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment (IDEALIST)
Study Start Date : October 2016
Estimated Primary Completion Date : May 2021
Estimated Study Completion Date : May 2022

Resource links provided by the National Library of Medicine

MedlinePlus related topics: After Surgery

Group/Cohort Intervention/treatment
Physicians

All surgeons and anesthesiologists at large single-center tertiary academic center will be recruited to participate in this study.

Intervention: Risk estimation prior to surgery and immediately after the surgery.

Physicians will be asked to provide their risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for the same patients that IPS is producing the scores. Physicians will provide scores both before and after reviewing the risk scores produced by the IPS.

Other: Risk estimation

Physicians will be asked to provide their risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for the same patients that IPS is producing the scores. Physicians will provide scores both before and after reviewing the risk scores produced by the IPS.

The IPS system will generate risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for patients taken care by the physicians enrolled in the study.


Intelligent Perioperative System (IPS)

Intelligent perioperative system (IPS) is designed as the set of computer softwares and algorithms that in real-time predict risk for postoperative complications using routine clinical data in electronic health records. The system is designed as the self-learning system with the ability to interact with physicians and solicit their feedback.

Intervention: Risk estimation prior to surgery and immediately after the surgery.

The IPS system will generate risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for patients taken care by the physicians enrolled in the study.

Other: Risk estimation

Physicians will be asked to provide their risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for the same patients that IPS is producing the scores. Physicians will provide scores both before and after reviewing the risk scores produced by the IPS.

The IPS system will generate risk scores (ranging from 0 to 100) for postoperative complications prior to surgery and immediately after the surgery for patients taken care by the physicians enrolled in the study.





Primary Outcome Measures :
  1. Area under the receiver operating curve (AUC) of risk estimates [ Time Frame: At the end of six months study period. ]
    At the end of each 6-months period predicted risk estimates will be compared to the true occurrence of the complications. Predictive performance of physicians' will be compared to IPS by comparing the area under the receiver-operating curves (AUC) for risk estimates.


Secondary Outcome Measures :
  1. Sensitivity and specificity of risk estimates [ Time Frame: At the end of six months study period. ]
    At the end of each 6-months period predicted risk estimates will be compared to the true occurrence of the complications. Predictive performance of physicians' will be compared to IPS by comparing the sensitivity and specificity for risk estimates.

  2. Positive and negative predictive values [ Time Frame: At the end of six months study period. ]
    At the end of each 6-months period predicted risk estimates will be compared to the true occurrence of the complications. Predictive performance of physicians' will be compared to IPS by comparing the Positive and negative predictive values for risk estimates.



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Ages Eligible for Study:   18 Years to 100 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
All surgeons and anesthesiologists in the adult inpatient operative practices at the single-center academic tertiary center will be enrolled. Physicians in obstetric and pediatric practices will be excluded.
Criteria

Inclusion criteria:

Surgeons and anesthesiologists working in adult inpatient operative practices.

Exclusion criteria:

Surgeons and anesthesiologists working in obstetric and pediatric practices.


Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02741986


Contacts
Contact: Sandhya A Chheda, JD 352-273-8820 sandhya.chheda@medicine.ufl.edu
Contact: Azra Bihorac, MD 352-273-8821 Azra.Bihorac@medicine.ufl.edu

Locations
United States, Florida
UF Health Recruiting
Gainesville, Florida, United States, 32610-3003
Contact: Judith M Wishin, BSN    352-273-6789    jwishin@anest.ufl.edu   
Contact: Azra Bihorac, MD    352-273-9009    abihorac@anest.ufl.edu   
Sponsors and Collaborators
University of Florida
National Institute of General Medical Sciences (NIGMS)
Investigators
Principal Investigator: Azra Bihorac, MD University of Florida

Responsible Party: University of Florida
ClinicalTrials.gov Identifier: NCT02741986     History of Changes
Other Study ID Numbers: IRB201600262-N
R01GM110240 ( U.S. NIH Grant/Contract )
First Posted: April 18, 2016    Key Record Dates
Last Update Posted: April 6, 2018
Last Verified: April 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Yes
Plan Description: All deidentified data will be governed by University of Florida Integrated Data Repository. The investigators will comply with local, state, and federal laws, such as the Privacy Rule, a Federal regulation under the Health Insurance Portability and Accountability Act (HIPAA). The investigators will follow the general NIH data sharing guidance and provide a data sharing plan to be reviewed and approved by the relevant NIGMS Program Officer.

Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by University of Florida:
risk assessment
sepsis
acute kidney injury (AKI)

Additional relevant MeSH terms:
Postoperative Complications
Pathologic Processes