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Pressure injury surveillance in the intensive care unit: Development, validation, and clinical application of a
Julia K Pilowsky1, Jae-Won Choi2, Nhi Nguyen3
1Intensive Care NSW, NSW Agency for Clinical Innovation, Australia; School of Public Health, Faculty of Medicine and Health, University of Sydney, Australia.
Insights
An algorithm using natural language processing can effectively surveil for pressure injuries (PIs) in intensive care units (ICUs). Most PIs acquired in the ICU occur within the first five days of admission.
Area of Science:
- Medical Informatics
- Critical Care Medicine
- Clinical Surveillance
Background:
- Pressure injuries (PIs) are a frequent hospital-acquired complication in intensive care units (ICUs).
- Existing surveillance systems for PIs suffer from limitations, notably poor data quality.
- Previous research on PIs in critically ill patients often involved small sample sizes and did not pinpoint the timing of acquisition during ICU stays.
Purpose of the Study:
- To develop and validate a natural language processing (NLP) algorithm for pressure injury surveillance in adult ICUs.
- To quantify the incidence of PIs within the study cohort.
- To identify clinical characteristics associated with PI development in the ICU setting.
Main Methods:
- A retrospective, multicenter cohort study involving five Australian ICUs (June 2017-June 2023).
- Development of an NLP algorithm to detect PIs documented in clinical progress notes.
- External validation of the algorithm using clinical codes and manual chart review, followed by linkage to the ANZICS Adult Patient Database for clinical characteristic retrieval.
Main Results:
- The study analyzed data from 40,033 patients, encompassing over 120 million free-text fields.
- The NLP surveillance algorithm achieved satisfactory performance on external validation (F1 score: 0.743-0.749).
- PIs were identified in 8.35% of patients, with 63.19% acquired during ICU admission, predominantly within the first 5 days (70.3%). Patients with PIs exhibited higher illness severity and increased rates of invasive ventilation.
Conclusions:
- Natural language processing is a viable tool for effective pressure injury surveillance in ICUs, supporting observational research.
- The early occurrence of ICU-acquired PIs highlights the need to integrate this finding into quality improvement initiatives to reduce incidence.
Introduction:
Pressure injuries (PIs) are one of the most common hospital-acquired complications in patients admitted to an intensive care unit (ICU). Existing PI surveillance systems are known to have multiple limitations, including poor data quality. Previous studies investigating PI acquisition in critically ill populations have had relatively small sample sizes and have not considered when PIs occur during the ICU admission.
Objectives:
The objective of this study was to develop and validate an algorithm capable of performing PI surveillance in the adult ICU population and to use this algorithm to describe the number of patients with PIs identified in the cohort and clinical characteristics associated with PI development.
Methods:
A multicentre, retrospective cohort study was conducted across five Australian ICUs from June 2017 to June 2023. Natural language processing techniques were used to develop the surveillance algorithm, which detected PIs documented in the progress notes. The surveillance algorithm was externally validated using a combination of clinical codes and manual chart review. Data from the algorithm were then linked to the Australian and New Zealand Intensive Care Society Adult Patient Database to obtain the clinical characteristics for the cohort.
Results:
Data from 40,033 patients were included in the study, including over 120 million free-text fields. The surveillance algorithm demonstrated satisfactory overall performance (F1 score: 0.743-0.749) on external validation. PIs were identified in 8.35% (n = 3344) of the cohort, with 63.19% (n = 2113) of these acquired during the ICU admission. PIs acquired in the ICU tended to occur early in the ICU admission, with 70.3% (n = 1486) identified within the first 5 days. Patients identified as having a PI had a higher severity of illness (median Acute Physiology and Chronic Health Evaluation III score: 67 vs 49) and were more likely to have received invasive ventilation (57.1% vs 32.5%).
Conclusions:
Natural language processing can be used to perform PI surveillance in the ICU and facilitate the conduct of observational research. Future studies should consider integrating the finding that ICU-acquired PIs occur early in the ICU stay into quality improvement programs aimed at reducing PI incidence.
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