Grants We Funded

Abstract
Project Summary: Incisional hernia (IH) is a frequent, but preventable, complication of abdominal surgery associated with high costs to patients and the healthcare system. These costs and morbidity could be greatly reduced through early, precise identification of at-risk patients prior to abdominal surgery. The formation and treatment of incisional hernia are well-defined yet major knowledge gaps exist with respect to understanding risk factors associated with hernia formation across surgical specialties and instruments to predict this long-term, challenging surgical outcome. Further, mechanisms and platforms to effectively communicate and use this risk in real-world clinical practice are limited. A precision, real-time, point-of-care risk prediction instrument that integrates broad perioperative data has the potential to transform surgical care. Our group has developed prediction models using longitudinal, structured patient- and procedure-specific health data. Our preliminary work has demonstrated the high predictive capacity of models, the importance of stakeholder input, and feasibility for integration into clinical practice. We will build upon prior exploratory research, discovering factors associated with IH to advance IH risk prediction. This work will provide a blueprint for integrating quantitative scientific approaches to innovate, discover and improve clinical care and surgical practice. Our approach is highlighted by collaboration and rigorous science that leverages quantitative approaches to address an age-old, challenging healthcare issue. We will define novel key perioperative risk factors associated with hernia across surgical specialties, delineate modifiable factors for optimal clinical consideration, leverage machine learning to improve model performance, and validate the instruments on diverse datasets to create a point-of-care, provider-adapted instrument for future use in practice. Impact Statement: Incisional hernia (IH) is a frequent, but preventable, complication of abdominal surgery associated with high costs to patients and the healthcare system, yet opportunities for effective risk reduction have been missed. A precision, real-time, point-of-care risk prediction instrument that integrates broad perioperative data to identify at-risk patients will advance this field and improve care. This work will define novel key perioperative risk factors associated with hernia across surgical specialties, delineate modifiable factors for optimal clinical consideration, leverage machine learning to improve model performance, and validate predictive instruments on diverse datasets to create a point-of-care, provider-adapted instrument for future use in practice.

Biography
Dr. John P. Fischer is an Assistant Professor and the Director of Clinical Research in the Department of Surgery at the Perelman School of Medicine at the University of Pennsylvania. Before joining the University of Pennsylvania Health System, Dr. Fischer completed his undergraduate studies at Hamilton College and medical school at SUNY Upstate Medical University followed by the completion of his plastic surgery training at the University of Pennsylvania. After becoming a faculty member, Dr. Fischer continued his education and received his MPH from the Bloomberg School at Johns Hopkins University. Dr. Fischer’s practice has a focus on aesthetic and reconstructive surgery of the trunk, specifically abdominal incisional hernia repair. Correspondingly, his research efforts focus on prevention, treatment and, clinical outcomes following abdominal hernia. He believes that risk prediction research will provide a pathway to prevention of incisional hernia as well as improve surgical outcomes. Dr. Fischer’s synergistic balance between active clinical practice and independent clinical research fosters a deeper appreciation for surgical and health problems which he proposes to use to maximize research impact and improve health of all patients.