Using natural language processing for identification of herpes zoster ophthalmicus cases to support population‐based study

Zheng, Chengyi; Luo, Yi; Mercado, Cheryl; Sy, Lina S.; Jacobsen, Steven J.; Ackerson, Bradley; Lewin, Bruno; Tseng, Hung Fu

doi:10.1111/ceo.13340

Cited by 18 publications

(19 citation statements)

References 38 publications

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“…Population-based data on the epidemiology of HZ in adults, especially those who are immunocompetent and who have not received zoster vaccination, are needed in order to better understand the present burden of disease, as well as to critically evaluate the potential impact of an RZV vaccination program [ 6 ]. We have previously reported on the incidence of postherpetic neuralgia (PHN) and herpes zoster ophthalmicus (HZO) in an integrated health care organization [ 7–9 ]. Therefore, the objectives of this study were to estimate the incidence of HZ, the proportion of HZ-related complications, the proportion of HZ patients that require hospitalization, HZ case-fatality rate, and HZ recurrence rate, among an immunocompetent and ZVL-unvaccinated population aged ≥50 years.…”

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confidence: 99%

The Epidemiology of Herpes Zoster in Immunocompetent, Unvaccinated Adults ≥50 Years Old: Incidence, Complications, Hospitalization, Mortality, and Recurrence

Tseng

Bruxvoort

Ackerson

et al. 2019

The Journal of Infectious Diseases

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Background Data on the epidemiology of herpes zoster (HZ), particularly in the unvaccinated immunocompetent population, are needed to assess disease burden and the potential impact of vaccination. Methods The study at a large health care organization comprised: (1) incidence estimated from immunocompetent adults aged ≥50 years unvaccinated with zoster vaccine live who had incident HZ in 2011–2015; (2) proportion of HZ-related nonpain complications assessed by double abstraction of electronic health records (EHRs) of 600 incident patients 2011–2015; (3) HZ-related hospitalizations among HZ patients diagnosed in 2015; (4) HZ-related death determined from automated data and EHRs; and (5) recurrent HZ identified from a cohort initially diagnosed with HZ in 2007–2008 and followed through 2016. Results HZ incidence rate was 9.92/1000 person-years (95% confidence interval [CI], 9.82–10.01). Proportions of cutaneous, neurologic, and other complications were 6.40% (95% CI,1.73%–11.07%), 0.77% (95% CI, .00%–2.36%), and 1.01% (95% CI, .00%–2.93%), respectively. Only 0.86% of patients had an HZ-related hospitalization. The case-fatality rate was 0.04%. Recurrence rate was 10.96/1000 person-years (95% CI, 10.18–11.79) with 10-year recurrence risk of 10.26% (95% CI, 9.36%–11.23%). Conclusions These recent HZ epidemiology data among an immunocompetent, unvaccinated population measure real-world disease burden.

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confidence: 99%

The Epidemiology of Herpes Zoster in Immunocompetent, Unvaccinated Adults ≥50 Years Old: Incidence, Complications, Hospitalization, Mortality, and Recurrence

Tseng

Bruxvoort

Ackerson

et al. 2019

The Journal of Infectious Diseases

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“…NLP has been used to abstract findings from radiology and pathology reports [ 40 - 44 ], for identifying phenotypes from narrative notes [ 45 - 48 ], and specifically for ophthalmic data extraction such as visual acuity and surgical complications [ 20 - 22 ]. NLP has been shown to enhance case detection compared to structured diagnosis codes alone, for example, for identifying cases of pseudoexfoliation syndrome [ 26 ] and herpes zoster ophthalmicus [ 25 ]. Structured diagnosis codes have known limitations such as incomplete or inaccurate coding, insufficient granularity, and the fact that clinicians may not code for every condition at every encounter [ 29 ].…”

Section: Discussionmentioning

confidence: 99%

“…At first glance, the structured diagnosis code query may seem to have been “better” than the unstructured NLP approach since it identified fewer patients and therefore appeared more specific and more efficient. However, in several prior studies, using diagnosis codes alone yielded fewer positive cases, resulting in under-detection and decreased sensitivity [ 25 , 26 ]. In this study, we could not assess the relative sensitivity and specificity of each approach, given that there were zero cases of fungal ocular involvement.…”

Section: Discussionmentioning

confidence: 99%

“…NLP has been applied to extract data on visual acuity [ 20 ] and intracameral antibiotic injections and posterior capsular rupture [ 21 ]. It has also been used to extract surgical laterality and intraocular lens implant power and model information [ 22 ], glaucoma-related characteristics [ 23 ], measurements of epithelial defects and stromal infiltrates in microbial keratitis [ 24 ], as well as identification of herpes zoster ophthalmicus [ 25 ] and pseudoexfoliation [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

Baxter¹,

Klie²,

Saseendrakumar³

et al. 2020

J Med Internet Res

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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 antifungal therapies. Retrospectively determining the prevalence of fungal ocular involvement is important for informing clinical guidelines, such as the need for routine ophthalmologic consultations. However, manual retrospective record review to detect cases is time-consuming. Objective This study aimed 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 12 years (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 (eg, presence of indwelling catheters, recent major surgery, diabetes, immunosuppressed status) 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 the documentation of any fungal ocular involvement. Results In total, 265 patients had culture-proven fungemia, with Candida albicans (n=114, 43%) and Candida glabrata (n=74, 28%) being the most common fungal species in blood culture. The in-hospital mortality rate was 121 (46%). In total, 7 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, consistent with prior studies reporting low rates of ocular involvement in fungemia. This study demonstrates an application of natural language processing to expedite the review of narrative notes. This approach is highly relevant for ophthalmology, where diagnoses are often based on physical examination findings that are documented within clinical notes.

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“…Building on our prior work using natural language processing (NLP) to identify HZO, we refined the algorithm and expanded it to differentiate between ocular HZO and nonocular HZO cases [ 11 ]. We then applied the algorithm to a large cohort of HZ patients to examine the characteristics of patients with ocular HZO.…”

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confidence: 99%

Text-Based Identification of Herpes Zoster Ophthalmicus With Ocular Involvement in the Electronic Health Record: A Population-Based Study

Zheng

Tanenbaum

et al. 2021

Open Forum Infectious Diseases

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Background Diagnosis codes are inadequate for accurately identifying herpes zoster ophthalmicus (HZO). Manual review of medical records is expensive and time-consuming, resulting in a lack of population-based data on HZO. Methods We conducted a retrospective cohort study, including 87 673 patients aged ≥50 years who had a new HZ diagnosis and associated antiviral prescription between 2010 and 2018. We developed and validated an automated natural language processing (NLP) algorithm to identify HZO with ocular involvement (ocular HZO). We compared the characteristics of NLP-identified ocular HZO, nonocular HZO, and non-HZO cases among HZ patients and identified the factors associated with ocular HZO among HZ patients. Results The NLP algorithm achieved 94.9% sensitivity and 94.2% specificity in identifying ocular HZO cases. Among 87 673 incident HZ cases, the proportion identified as ocular HZO was 9.0% (n = 7853) by NLP and 2.3% (n = 1988) by International Classification of Diseases codes. In adjusted analyses, older age and male sex were associated with an increased risk of ocular HZO; Hispanic and black race/ethnicity each were associated with a lower risk of ocular HZO compared with non-Hispanic white. Conclusions The NLP algorithm achieved high accuracy and can be used in large population-based studies to identify ocular HZO, avoiding labor-intensive chart review. Age, sex, and race were strongly associated with ocular HZO among HZ patients. We should consider these risk factors when planning for zoster vaccination.

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Using natural language processing for identification of herpes zoster ophthalmicus cases to support population‐based study

Cited by 18 publications

References 38 publications

The Epidemiology of Herpes Zoster in Immunocompetent, Unvaccinated Adults ≥50 Years Old: Incidence, Complications, Hospitalization, Mortality, and Recurrence

The Epidemiology of Herpes Zoster in Immunocompetent, Unvaccinated Adults ≥50 Years Old: Incidence, Complications, Hospitalization, Mortality, and Recurrence

Text Processing for Detection of Fungal Ocular Involvement in Critical Care Patients: Cross-Sectional Study

Text-Based Identification of Herpes Zoster Ophthalmicus With Ocular Involvement in the Electronic Health Record: A Population-Based Study

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