# Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery

> **NIH NIH K08** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $147,065

## Abstract

ABSTRACT
BACKGROUND: Postoperative pulmonary complications (PPCs) are common and major drivers of morbidity
and mortality after major inpatient surgery. Various risk prediction scores identify patients at high risk of
developing PPCs and observational research has connected peri-operative care practices with subsequent
risk. However, anesthesia providers do not have patient specific evidence based interventions to prevent
pulmonary complications.
RESEARCH: The proposed research will draw on a wealth of perioperative information available to identify the
interactions of patient, procedure and process of care factors which place patients at risk of PPCs. This will
incorporate advances in data science to the pre-operative prediction of PPCs (Aim 1). We will then revise and
improve this estimate in light of the high fidelity intraoperative data stream from ventilators, monitors and
patient response to real life decisions being made during the delivery of anesthesia care (Aim 2). This will allow
understanding of what features most contributed to patient specific risk (Aim 3). The proposed research and
training will provide Dr Colquhoun with the skills in data science to his transition to an independent researcher.
CANDIDATE: Dr Douglas A Colquhoun is a tenure track Assistant Professor of Anesthesiology at the
University of Michigan. He is board certified in Anesthesiology and Critical Care Medicine and maintains an
active clinical practice in the perioperative care of patients undergoing major surgery. During a T32 Research
Training Grant, Dr Colquhoun developed expertise in the derivation of outcomes and processes of care from
electronic medical record data. His long term career goal is to prevent postoperative pulmonary complications
by offering anesthesia providers data driven strategies for management delivered at the point of care.
ENVIRONMENT: The University of Michigan is the coordinating center for the Multicenter Perioperative
Outcomes Group (MPOG) an international consortium of 50 anesthesiology and surgical departments with
perioperative information systems. Sachin Kheterpal, MD, MBA is the primary mentor for Dr. Colquhoun, and is
the Director for MPOG. Dr Kheterpal and the Department of Anesthesiology have a rich history of developing
and deploying innovative software solutions to address problems in perioperative medicine and research. Dr
Colquhoun will additionally be advised from expert co-mentors drawn from across the institution and a scientific
advisory panel expert in the prevention and management of postoperative complications.

## Key facts

- **NIH application ID:** 10896014
- **Project number:** 5K08HL159327-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Douglas Alastair Colquhoun
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $147,065
- **Award type:** 5
- **Project period:** 2021-08-15 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896014

## Citation

> US National Institutes of Health, RePORTER application 10896014, Data Driven Approaches to Improving Risk Prediction of Pulmonary Complications After Major Inpatient Surgery (5K08HL159327-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10896014. Licensed CC0.

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