Innovative Methods for Real-time Risk Modeling of Postoperative Complications

术后并发症实时风险建模的创新方法

• 批准号: 9904738
• 负责人: GYORGY SIMON
• 金额: $ 36.11万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9904738
• 关键词: American College of Surgeons; Anesthesiology; Area; Awareness; Caring; Clinic; Clinical; Clinical Decision Support Systems; Clinical Informatics; Collection; Computer Models; Consensus; Data; Data Collection; Data Mart; Decision Support Systems; Electronic Health Record; Environment; Foundations; Future; Goals; Health Services; Heterogeneity; Hospital Nursing; Human; Infrastructure; Intensive Care; Intervention; Knowledge; Methods; Minnesota; Modeling; Nosocomial Infections; Operating Rooms; Operative Surgical Procedures; Outcome; Patient risk; Patients; Performance; Perioperative; Perioperative Care; Pneumonia; Population; Postoperative Complications; Preparation; Registries; Research; Research Support; Resolution; Risk; Risk Estimate; Risk Factors; Risk stratification; Running; Sepsis; Service delivery model; Site; Stream; Surgeon; Surgical Wound Infection; System; Techniques; Time; Universities; Urinary tract infection; Validation; Work; base; clinical care; clinical decision support; clinical research site; data modeling; data registry; data standards; electronic data; experience; health disparity; high risk; hospital readmission; improved; innovation; interest; model development; mortality; multi-task learning; novel; patient subsets; portability; precision medicine; predictive modeling; prevent; prospective; surgery outcome

PROJECT SUMMARY Surgical procedures carry the risk of post-operative complications, which can be severe, expensive and put patients' lives at risk. Risk stratification in the context of perioperative decision support can help plan for and mitigate these complications. Research aimed at understanding the risk factors and developing risk models for these complications is supported by high-quality registry data, such as the National Surgical Quality Improvement Project (NSQIP) registry. A growing body of research indicates that intraoperative risk factors influence the risk of complications, but they are poorly captured even in the NSQIP. In this work, we propose developing and implementing advanced risk models based on preoperative and real-time streaming high-resolution intraoperative data. This system will have the ability to establish a preoperative baseline state for a patient, track his condition as the surgery progresses and provide an up-to-date estimate of the patient's risk of different complications at any time before, during and after surgery automatically (without human intervention). It will help us understand the value of intraoperative data in predicting postoperative complications. We carry out our project at two sites: at the University of Minnesota affiliated Fairview Health Services and Mayo Clinic. We will develop modeling techniques that can take patient heterogeneity (e.g. health disparities) into account, yet produce models that are portable across the two sites. We construct models at the two sites independently, validate the models cross- institutionally and implement the validated models in the clinical decision support systems of the respective sites. The implemented system forms the foundation of a future interactive real-time perioperative decision support system.

项目总结

项目成果

The Value of Aggregated High-Resolution Intraoperative Data for Predicting Post-Surgical Infectious Complications at Two Independent Sites.

汇总高分辨率术中数据对于预测两个独立部位的术后感染并发症的价值。

• DOI: 10.3233/shti190251
• 发表时间: 2019
• 期刊: Studies in health technology and informatics
• 作者: Tourani,Roshan;Murphree,DennisH;Melton-Meaux,Genevieve;Wick,Elizabeth;Kor,DarylJ;Simon,GyorgyJ
• 通讯作者: Simon,GyorgyJ

Patient Heterogeneity and the J-Curve Relationship Between Time-to-Antibiotics and the Outcomes of Patients Admitted With Bacterial Infection.

患者异质性以及抗生素使用时间与细菌感染患者转归之间的 J 曲线关系。

• DOI: 10.1097/ccm.0000000000005429
• 发表时间: 2022
• 期刊: Critical care medicine
• 影响因子: 8.8
• 作者: Usher,MichaelG;Tourani,Roshan;Webber,Ben;Tignanelli,ChristopherJ;Ma,Sisi;Pruinelli,Lisiane;Rhodes,Michael;Sahni,Nishant;Olson,AndrewPJ;Melton,GenevieveB;Simon,Gyorgy
• 通讯作者: Simon,Gyorgy

Frequent Causal Pattern Mining: A Computationally Efficient Framework For Estimating Bias-Corrected Effects.

• DOI: 10.1109/bigdata47090.2019.9005977
• 发表时间: 2019-12
• 期刊: Proceedings : ... IEEE International Conference on Big Data. IEEE International Conference on Big Data
• 作者: Yadav P;Caraballo PJ;Steinbach M;Kumar V;Castro MR;Simon G
• 通讯作者: Simon G

A Holistic Clustering Methodology for Liver Transplantation Survival.

肝移植存活率的整体聚类方法。

• DOI: 10.1097/nnr.0000000000000289
• 发表时间: 2018
• 期刊: Nursing research
• 影响因子: 2.5
• 作者: Pruinelli,Lisiane;Simon,GyörgyJ;Monsen,KarenA;Pruett,Timothy;Gross,CynthiaR;Radosevich,DavidM;Westra,BonnieL
• 通讯作者: Westra,BonnieL

Characterizing Functional Health Status of Surgical Patients in Clinical Notes.

在临床记录中描述手术患者的功能健康状况。

• 发表时间: 2018
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Skube,StevenJ;Lindemann,ElizabethA;Arsoniadis,ElliotG;Akre,Mari;Wick,ElizabethC;Melton,GenevieveB
• 通讯作者: Melton,GenevieveB