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Active, Not Recruiting

NCT Number: NCT02741986

Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment

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.

Active, Not Recruiting

This study is active but is not currently recruiting participants.

Key information

Conditions

Postoperative Complications Acute Kidney Injury Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Infections Inflammation Kidney Diseases Male Urogenital Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Renal Insufficiency Sepsis Systemic Inflammatory Response Syndrome Urogenital Diseases Urologic Diseases

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

UF Health, Gainesville, Florida, United States

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About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Surgeons and anesthesiologists working in adult inpatient operative practices.

Exclusion criteria

Surgeons and anesthesiologists working in obstetric and pediatric practices.

Treatment and study plan

Risk estimation

Other

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 outcomes

  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 outcomes

  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.

Sponsors and collaborators

Lead sponsor

University of Florida

Other

Collaborators

  • National Institute of General Medical Sciences (NIGMS)

Registry information

Official study title

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

Acronym: IDEALIST

Important dates

Study start
2016
Primary completion
2028
Study completion
2028
First posted
Apr 18, 2016
Registry last updated
Dec 5, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

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This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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