# Combined Use of Statistical Process Control and Whole Genome Sequencing to Detect and Investigate Nontuberculous Mycobacterial Clusters and Outbreaks

> **NIH NIH K08** · DUKE UNIVERSITY · 2024 · $188,377

## Abstract

Project Summary/Abstract
Nontuberculous mycobacteria (NTM) are emerging pathogens that cause substantial morbidity and mortality,
especially among immunosuppressed patients. While NTM are increasingly implicated in healthcare facility-
associated (HCFA) infections and outbreaks, no systematic method for NTM clinical surveillance exists. As a
result, current infection control practices inconsistently detect clinically important increases in NTM rates, or NTM
clusters, leading to delayed outbreak detection and mitigation. Furthermore, the presence of NTM in many
healthcare environments increases the difficulty of determining whether a cluster of positive cultures for a given
NTM represents polyclonal contamination from environmental sources or a true monoclonal outbreak. Therefore,
U.S. hospitals need better approaches for early detection and characterization of HCFA NTM outbreaks.
The combination of 1) systematic analytic techniques for early detection of HCFA NTM clusters and 2) molecular
epidemiology to characterize the relevant NTM represents an innovative and powerful approach to mitigating
and preventing NTM outbreaks in vulnerable populations. The overall objective of this proposal is to combine
optimized statistical process control (SPC) methods with whole genome sequencing (WGS) as an integrated
platform to improve detection and investigation of HCFA NTM clusters and outbreaks. We will use these two
techniques within a hospital network to test the central hypothesis that optimized SPC methods combined with
molecular epidemiology can detect and mitigate HCFA NTM clusters more quickly and effectively than standard
infection control techniques and ultimately reduce morbidity from NTM outbreaks. We plan to test this hypothesis
by pursuing the following three Specific Aims: 1) Develop an optimized SPC strategy for identification of clinically
important increases in rates of HCFA NTM; 2) Compare the effectiveness of optimized SPC surveillance for
HCFA NTM to traditional (non-SPC) surveillance methods; and 3) Utilize WGS to evaluate clonal relatedness of
NTM clinical isolates associated with clinically important NTM clusters. Completion of these Aims will develop a
novel strategy to identify important NTM clusters, improve understanding of NTM acquisition, and ultimately
prevent HCFA NTM infection. This work has potential to change the way healthcare facilities perform NTM
surveillance and prevent NTM infection, thereby reducing the risk of harm to hospitalized patients.
The candidate’s short-term goals include enhancing his skillsets in time series analyses, genomic sequencing,
and multicenter collaborative studies. His long-term goal is to become a successful, independent physician
scientist, well suited to lead a team dedicated to the design and implementation of novel interventions aimed at
preventing healthcare-associated infections in immunosuppressed patients. Several key factors will assist the
candidate in achieving these goals, includin...

## Key facts

- **NIH application ID:** 10867337
- **Project number:** 5K08AI163462-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Arthur W Baker
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $188,377
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10867337

## Citation

> US National Institutes of Health, RePORTER application 10867337, Combined Use of Statistical Process Control and Whole Genome Sequencing to Detect and Investigate Nontuberculous Mycobacterial Clusters and Outbreaks (5K08AI163462-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10867337. Licensed CC0.

---

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