# Automated Surveillance of Postoperative Infections (ASPIN)

> **NIH AHRQ R01** · UNIVERSITY OF COLORADO DENVER · 2020 · $392,599

## Abstract

PROJECT SUMMARY/ABSTRACT
 Our long term goal is to reduce postoperative infections. We will start by developing a system to accurately
and completely identify their occurrence by applying machine learning algorithms to electronic health record
(EHR) data. We will utilize a comprehensive audit and feedback system to create reports of risk-adjusted rates
and specific details of postoperative infectious complications that are shared with surgeons and other
healthcare providers to facilitate their awareness. We call this system the Automated Surveillance of
Postoperative Infections (ASPIN). ASPIN will be piloted in the four major hospitals of the University of
Colorado Health system (UCHealth) with a combined surgical volume of approximately 80,000 patients per
year. We expect this will supersede the costly and laborious manual partial sampling of postoperative
infectious complications which is current utilized by many hospitals.
Specific Aim 1. Expand and enhance models for preoperative risk prediction and postoperative identification
of surgical infections using EHR and ACS NSQIP data from patients who underwent operations at four
UCHealth hospitals.
 Specific Aim 1a) Enhance previously-developed models for identification of postoperative infections by
controlling Type-I errors via “knockoffs,” a recent statistical innovation for high dimensional model selection
using false discovery rate correction.
Specific Aim 1b) Deploy natural language processing methods using EHR text reports of these patients to
identify additional indicators of postoperative infections and further refine the models.
Specific Aim 1c) Create preoperative risk models for infection using EHR data - similar to the models
 implemented in the AHRQ-funded Surgical Risk Preoperative Assessment System - but that do not require
 additional data entry by the health care providers.
Specific Aim 2. From the beginning of the study, develop ASPIN with input from an Advisory Committee
composed of administrators and surgeons from all four UCHealth hospitals. Additional feedback from surgeons
will be obtained through focus groups and semi-structured interviews at several steps of ASPIN development
and implementation planning.
Specific Aim 3. A pilot implementation of ASPIN will utilize the RE-AIM framework to guide and examine the
preliminary effectiveness and feasibility of ASPIN at UCHealth. We will recruit 30 surgeon participants from all
four UCHealth hospitals to use ASPIN, and we will evaluate the reach, effectiveness, adoption, and
implementation of ASPIN.
 This research responds to AHRQ priorities by utilizing existing data to develop a learning health system
with a distinct focus on improving surveillance and reporting of postoperative healthcare-associated infections.

## Key facts

- **NIH application ID:** 10117905
- **Project number:** 1R01HS027417-01A1
- **Recipient organization:** UNIVERSITY OF COLORADO DENVER
- **Principal Investigator:** Kathryn Louise Colborn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2020
- **Award amount:** $392,599
- **Award type:** 1
- **Project period:** 2020-09-30 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10117905

## Citation

> US National Institutes of Health, RePORTER application 10117905, Automated Surveillance of Postoperative Infections (ASPIN) (1R01HS027417-01A1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10117905. Licensed CC0.

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