Major postoperative complications (PC) are common and lead to increase in mortality and healthcare cost. Some cost-effective strategies, implemented in a timely fashion, can ameliorate the risk for PC but the ability to use them depends on the timely and accurate identification of those patients at greatest risk. Assessment of that risk requires timely, accurate and dynamic synthesis of the large amount of clinical information obtained throughout the perioperative period. Today it is not possible to predict and quantify, for a given patient, a personal and real-time risk for PC that integrates preoperative risk with the risk incurred by the physiologic response to events during surgery. The interventions that could prevent PC are applied without consideration of a patient's personal risk profile or often not applied at all because the risk is underestimated. There is an abundance of physiologic, laboratory and other clinical data in the perioperative electronic health records (EHR), but their magnitude and complexity often overwhelms a physicians' ability to comprehend and use the information in an optimal and timely way. The objective is to develop an intelligent system, composed of high- performance computers, algorithms and physicians interacting in real time, which can generate usable medical knowledge with both increased speed and accuracy using complex clinical data. Our multidisciplinary team of scientific experts in medicine and engineering will address methodological challenges related to implementation of real-time data integration and processing, data analytics and knowledge exchange between computers and physicians in the clinical environment. There are three specific aims: 1. Refine and validate predictive risk models for major complications using EHR integrated with intraoperative physiologic time series using a temporal database for 10,000 surgical patients. 2. Implement and validate two-way knowledge exchange between predictive risk models and physicians. We will design an interactive knowledge exchange application that presents the knowledge behind predictive models to physicians, while allowing them to input their own assessment into the models. 3. Implement and evaluate an intelligent perioperative system for automated risk analysis using real-time EHR data. In a prospective clinical study of 60 physicians we will validate the diagnostic performance of predictive risk models, compare them with the physicians' risk assessment and measure change in physicians' risk perception after knowledge exchange with the system. This methodology will provide a significant step towards personalized perioperative medicine by modeling and quantifying the body's responses to surgery while using clinical data acquired during routine medical care.
Each year complications after surgery affect more than 1.5 million of patients imposing an acute burden of death and suffering. In alignment with NIH's mission to advance disease diagnosis through medical applications of new tools and technologies this proposal applies computational methods to timely identify patients at the greatest risk of complications using readily available medical data in electronic health records. Preventive interventions to improve outcomes could then be tailored to each patient's personal risk profile.
|Stortz, Julie A; Murphy, Tyler J; Raymond, Steven L et al. (2017) Evidence for Persistent Immune Suppression in Patients WHO Develop Chronic Critical Illness After Sepsis. Shock :|
|Askenazi, D J; Heung, Michael; Connor Jr, Michael J et al. (2017) Optimal Role of the Nephrologist in the Intensive Care Unit. Blood Purif 43:68-77|
|Loftus, Tyler J; Mira, Juan C; Ozrazgat-Baslanti, Tezcan et al. (2017) Sepsis and Critical Illness Research Center investigators: protocols and standard operating procedures for a prospective cohort study of sepsis in critically ill surgical patients. BMJ Open 7:e015136|
|Lysak, Nicholas; Bihorac, Azra; Hobson, Charles (2017) Mortality and cost of acute and chronic kidney disease after cardiac surgery. Curr Opin Anaesthesiol 30:113-117|
|Laksshman, Sundaram; Bhat, Rajendra Rana; Viswanath, Vivek et al. (2017) DeepBipolar: Identifying genomic mutations for bipolar disorder via deep learning. Hum Mutat 38:1217-1224|
|Hobson, Charles E; Bihorac, Azra; Hadian, Mehrnaz et al. (2017) The Impact of Changes in Medicare's Physician Payment System on Critical Care. Crit Connect 15:18-19|
|Hobson, Charles; Ruchi, Rupam; Bihorac, Azra (2017) Perioperative Acute Kidney Injury: Risk Factors and Predictive Strategies. Crit Care Clin 33:379-396|
|Bihorac, Azra; Hobson, Charles (2017) Kidney and Brain, an Unbroken Chain. Am J Respir Crit Care Med 195:1546-1548|
|Ozrazgat-Baslanti, Tezcan; Thottakkara, Paul; Huber, Matthew et al. (2016) Acute and Chronic Kidney Disease and Cardiovascular Mortality After Major Surgery. Ann Surg 264:987-996|
|Thottakkara, Paul; Ozrazgat-Baslanti, Tezcan; Hupf, Bradley B et al. (2016) Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications. PLoS One 11:e0155705|
Showing the most recent 10 out of 15 publications