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Currently submitted to: Journal of Medical Internet Research

Date Submitted: Mar 23, 2020
Open Peer Review Period: Mar 23, 2020 - May 18, 2020
(currently open for review)

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Text processing for detection of fungal ocular involvement in critical care patients: A cross-sectional study

  • Sally Liu Baxter; 
  • Adam R Klie; 
  • Bharanidharan Radha Saseendrakumar; 
  • Gordon Y Ye; 
  • Michael Hogarth; 

ABSTRACT

Background:

Fungal ocular involvement can develop in patients with fungal bloodstream infections and can be vision-threatening. Ocular involvement has become less common in the current era of improved anti-fungal therapies, with multiple studies reporting only a few cases over several years. However, manual retrospective record review to detect cases is time-consuming.

Objective:

To determine the prevalence of fungal ocular involvement in a critical care database using both structured and unstructured electronic health record (EHR) data.

Methods:

We queried microbiology data from 46,467 critical care patients over a twelve-year period (2000-2012) from the Medical Information Mart for Intensive Care III (MIMIC-III) to identify 265 patients with culture-proven fungemia. For each fungemic patient, demographic data, fungal species present in blood culture, and risk factors for fungemia (presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status, etc.) were ascertained. All structured diagnosis codes and free-text narrative notes associated with each patient’s hospitalization were also extracted. Screening for fungal endophthalmitis was performed using two approaches: (1) by querying a wide array of eye- and vision-related diagnosis codes, and (2) by utilizing a custom regular expression pipeline to identify and collate relevant text matches pertaining to fungal ocular involvement. Both approaches were validated using manual record review. The main outcome measure was documentation of any fungal ocular involvement.

Results:

265 patients had culture-proven fungemia, with Candida albicans (43%) and Candida glabrata (28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 41%. Seven patients were identified as having eye- or vision-related diagnosis codes, none of whom had fungal endophthalmitis based on record review. There were 26,830 free-text narrative notes associated with these 265 patients. A regular expression pipeline based on relevant terms yielded possible matches in 683 notes from 108 patients. Subsequent manual record review again demonstrated that no patients had fungal ocular involvement. Therefore, the prevalence of fungal ocular involvement in this cohort was 0%.

Conclusions:

MIMIC-III contained no cases of ocular involvement among fungemic patients. This supports prior studies reporting low rates of ocular involvement in fungemia. Additionally, it demonstrates an application of natural language processing to expedite review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical exam findings that are documented within clinical notes.


 Citation

Please cite as:

Baxter SL, Klie AR, Radha Saseendrakumar B, Ye GY, Hogarth M

Text processing for detection of fungal ocular involvement in critical care patients: A cross-sectional study

JMIR Preprints. 23/03/2020:18855

DOI: 10.2196/preprints.18855

URL: https://preprints.jmir.org/preprint/18855

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