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Using natural language processing and machine learning to identify patients at high risk of potentially preventable carbapenem-resistant Enterobacterales (CRE) infection

Award Number: K01HS028363
ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY
OPDIV: AHRQ
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 09/30/2021
PERIOD OF PERFORMANCE END DATE: 09/29/2026

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Using natural language processing and machine learning to identify patients at high risk of potentially preventable carbapenem-resistant Enterobacterales (CRE) infection - Project Summary Carbapenem-resistant Enterobacterales (CRE) cause more than 13,000 infections in U.S. inpatients annually, with mortality rates that can exceed 50%. In hospitals, asymptomatic colonization is emerging as a critical target for CRE infection prevention: epidemiologically, early identification of colonized patients can reduce intra-hospital spread. And clinically, colonized inpatients face significantly higher — but potentially modifiable — risks of CRE infection. Due to diagnostic limitations, however, widescale CRE colonization screening remains impractical for most U.S. hospitals. Prediction models offer alternative strategies for identifying patients at high risk of colonization and of subsequent infection. However, models face two methodological obstacles, limiting their wider utility: (1) strong colonization risk factors are “locked” in electronic health record (EHR) free-text that is unavailable for model-building unless records are reviewed manually; and (2) due to low CRE prevalence, most statistical models can only evaluate limited numbers of candidate variables. We propose to exploit state-of-the-art machine learning and natural language processing (NLP) techniques to improve identification of CRE-colonized and infected patients. We will apply these methods to EHRs from >21,000 patients screened for CRE at The University of Maryland and The Johns Hopkins hospitals. In Aim 1, we will build and validate NLP algorithms on admission histories to detect pre-admission exposures that are strong colonization risk factors but poorly captured in structured EHR data fields. NLP is a cutting-edge computational technique for “unlocking” these types of unstructured data. We will also use text-mining approaches to identify potential new or local CRE risk factors. In Aims 2 and 3, we will build and validate models from NLP-derived variables and other EHR data to predict colonization at admission (Aim 2) and progression to infection (Aim 3) using machine learning algorithms that excel on high-dimensional data. Taken together, this work will help hospitals identify patients at high risk of CRE colonization and infection early, when deleterious patient outcomes are still preventable. Because NLP is automated, successful models could be exported to other hospitals and integrated into EHRs; all algorithms resulting from this work will be made freely available. This will be the first study to deploy NLP for bacterial carriage screening and the largest U.S. study to follow CRE-colonized inpatients for infection. As a PhD-trained epidemiologist with a CRE and machine learning background, and who previously practiced FDA law, I am drawn to interdisciplinary, rigorous approaches and policies for reducing the toll of antibiotic resistance in hospitalized patients. In the short-term, Career Development Award support would allow me to build experience using sophisticated computational approaches for EHR-based information extraction and predictive modeling. In the long-term, the skills I acquire would position me as a leader at leveraging novel health information technology tools to design and test new strategies for responding to the threat of antibiotic resistance and other emerging pathogens in U.S. hospitals.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $114,977 )
2024	2024	UNIVERSITY OF MARYLAND, BALTIMORE	220 ARCH ST OFC LEVEL2	BALTIMORE	MD	21201	BALTIMORE CITY	USA	Research on Healthcare Costs, Quality and Outcomes	000	4	8/5/2024	NON-COMPETING CONTINUATION	$114,977
														Subtotal = $114,977

Issue Date FY: 2023 ( Subtotal = $123,617 )
2023	2023	UNIVERSITY OF MARYLAND, BALTIMORE	220 ARCH ST OFC LEVEL2	BALTIMORE	MD	21201	BALTIMORE CITY	USA	Research on Healthcare Costs, Quality and Outcomes	000	3	8/11/2023	NON-COMPETING CONTINUATION	$123,617
														Subtotal = $123,617

Issue Date FY: 2022 ( Subtotal = $126,029 )
2022	2022	UNIVERSITY OF MARYLAND	220 ARCH ST RM 02148	BALTIMORE	MD	21201	BALTIMORE CITY	USA	Research on Healthcare Costs, Quality and Outcomes	001	2	9/16/2022	NON-COMPETING CONTINUATION	$126,029
2022	2021	UNIVERSITY OF MARYLAND	220 ARCH ST RM 02148	BALTIMORE	MD	21201	BALTIMORE CITY	USA	Research on Healthcare Costs, Quality and Outcomes	000	1	12/3/2021	NEW	$0
														Subtotal = $126,029

Issue Date FY: 2021 ( Subtotal = $130,277 )
2021	2021	UNIVERSITY OF MARYLAND	220 ARCH ST RM 02148	BALTIMORE	MD	21201	BALTIMORE CITY	USA	Research on Healthcare Costs, Quality and Outcomes	000	1	9/9/2021	NEW	$130,277
														Subtotal = $130,277

Grand Total All Awards = $494,900

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Using natural language processing and machine learning to identify patients at high risk of potentially preventable carbapenem-resistant Enterobacterales (CRE) infection

Award Number: K01HS028363

ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY

OPDIV: AHRQ

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 09/30/2021

PERIOD OF PERFORMANCE END DATE: 09/29/2026

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