Congratulations to Hideyo Tsumura, PhD student, and her faculty sponsors Wei Pan and Debra Brandon, associate professors, and Xiao Hu, Ann Henshaw Gardiner Distinguished Professor of Nursing, who have received an award for her NINR NRSA Fellowship proposal entitled: "Identification of Postoperative Pulmonary Complication Risk by Phenotyping Adult Surgical Patients who Underwent General Anesthesia with Mechanical Ventilation." This award is for a twenty-six month period, awarded June 7, 2021.

Science: One in five patients who develop a postoperative pulmonary complication (PPC) dies within 30 days of surgery. PPCs are the second most frequent surgical complications and leads to increased intensive care unit admissions, hospital length of stay, and high resource utilization. Ventilator induced lung injury (VILI) secondary to intraoperative mechanical ventilation is a risk for PPCs. Lung protective ventilation (LPV), which entails lower tidal volume, sufficient positive end expiratory pressure (PEEP) as well as optimal inspiratory time and alveolar recruitment maneuver, has been adapted for intraoperative use to protect pulmonary parenchyma against VILI and ultimately reduce PPC incidence. However, we still do not know the optimal ventilator parameters to yield the lowest incidence of PPCs, because what is best varies from patient to patient and surgery to surgery. Personalized ventilator parameters are a potential solution to solve this problem. A retrospective study leveraging EHRs is proposed to identify PPC risks by phenotyping adult surgical patients who underwent general anesthesia with mechanical ventilation.

The specific aims of this project are to: (1) determine the incidence of PPCs in the overall study population; and to phenotype patients based on non- modifiable patient, surgical, and anesthesia characteristics and examine the incidence of PPCs within each phenotypic subgroup; (2) identify the optimal intraoperative ventilatory parameters as modifiable characteristics that are associated with the lowest incidence of PPCs within each phenotypic subgroup; and (3) explore machine learning algorithms for predictive models of interaction effects between phenotypic subgroups and intraoperative ventilator parameters on the incidence of PPCs and compare the findings from this aim with those from Aims 1 & 2. Knowledge gained would inform interventions to reduce PPCs.

Training: The long-term training goal is to become a leading nurse scientist in precision health using data science to improve patient outcomes following surgery, such as reducing PPCs. To achieve this goal, I have three short-term goals during my fellowship training: (1) gain knowledge and skills in research design to enhance precision health in anesthesiology, (2) gain knowledge in advanced analytic techniques for conducting research using big data, and (3) gain an advanced understanding of pulmonary physiology and pathophysiology influencing anesthesia and patient surgical outcomes. This fellowship will allow me protected time to reach my training goals and build a foundation for my long-term career goals. During the next twenty- six months as a trainee, I will obtain additional training in (1) research methods and design, (2) advanced statistical methods, (3) precision health, and (4) advanced pulmonary physiology and pathophysiology.