# Identification of Postoperative Pulmonary Complication Risk by Phenotyping Adult Surgical Patients who Underwent General Anesthesia with Mechanical Ventilation

> **NIH NIH F31** · DUKE UNIVERSITY · 2021 · $35,509

## Abstract

ABSTRACT
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 lead to increased admission to
intensive care units, longer 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, which
entails lower tidal volume, sufficient positive end expiratory pressure, optimal inspiratory time and an alveolar
recruitment maneuver, has been adopted 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. Personalized ventilator
parameters are a potential solution to solve this problem. A retrospective study leveraging electronic health
records (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) Examine the
incidence of PPCs in the overall study population and phenotype patients based on nonmodifiable patient,
surgical, and anesthesia characteristics; and examine the incidence of PPCs within each phenotypic subgroup;
(2) Determine the optimal modifiable intraoperative ventilatory parameters associated with the lowest severity
of PPCs within each phenotypic subgroup; and (3) Explore machine learning algorithms for predictive models
of the incidence of PPCs on patient, surgical, and anesthesia characteristics as well as intraoperative ventilator
parameters. The goal of this aim is to gain knowledge and training in machine learning to lay a foundation for
postdoctoral training.
Training: My 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 to, (2) gain knowledge in advanced analytic techniques for
conducting research using big data, and (3) gain an advanced understanding of pulmonary physiology and
pathophysiology that influence anesthesia and patient surgical outcomes. This fellowship will allow me
protected time to reach my training goals and to 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.

## Key facts

- **NIH application ID:** 10311613
- **Project number:** 1F31NR020128-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Hideyo Tsumura
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $35,509
- **Award type:** 1
- **Project period:** 2021-07-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10311613

## Citation

> US National Institutes of Health, RePORTER application 10311613, Identification of Postoperative Pulmonary Complication Risk by Phenotyping Adult Surgical Patients who Underwent General Anesthesia with Mechanical Ventilation (1F31NR020128-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10311613. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*