Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Postoperative Complications

Intervention / Treatment
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* Other: Risk estimation


Participation Criteria
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Eligibility Criteria
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Inclusion criteria: Surgeons and anesthesiologists working in adult inpatient operative practices. Exclusion criteria: Surgeons and anesthesiologists working in obstetric and pediatric practices.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 100 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Physicians<br>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<br>* 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.<br>|
| Intelligent Perioperative System (IPS)<br>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<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area under the receiver operating curve (AUC) of risk estimates | 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. | At the end of six months study period. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Sensitivity and specificity of risk estimates | 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. | At the end of six months study period. | 
| Positive and negative predictive values | 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. | At the end of six months study period. | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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risk assessment, sepsis, acute kidney injury (AKI)