Validation of the Total Visual Acuity Extraction Algorithm (TOVA) for Automated Extraction of Visual Acuity Data From Free Text, Unstructured Clinical Records

Baughman, Douglas M.; Su, Grace L.; Tsui, Irena; Lee, Cecilia S.; Lee, Aaron

doi:10.1167/tvst.6.2.2

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…CF, hand motion (HM), light perception (LP) and no light perception (NLP) vision were converted to logMAR as follows: CF 1.9, HM: 2.3, LP: 2.7, NLP: 3.0. 14 , 15 …”

Section: Methodsmentioning

confidence: 99%

Retinal Detachment After Endophthalmitis: Risk Factors and Outcomes

Wang

Moinuddin

Abuzaitoun

et al. 2021

OPTH

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Purpose To analyze the risk factors, clinical course, and visual and anatomic outcomes of retinal detachment (RD) after endophthalmitis. Patients and Methods This retrospective study included 108 patients diagnosed with endophthalmitis between August 2014 and May 2019 at a single tertiary referral center. Sixteen patients developed RD after endophthalmitis. Retrospective analysis was performed to compare the cohort of endophthalmitis alone versus the cohort that developed RD after endophthalmitis, with analysis of potential risk factors for RD after endophthalmitis and treatment outcomes. Results The incidence of RD after endophthalmitis was 14.8% (N=16/108). The median time to develop RD after endophthalmitis was 27 days (range: 1–581 days, IQR: 25.3). Thirteen (81.3%) cases of RD occurred less than 2 months after the diagnosis of endophthalmitis. The incidence of aphakia (p=0.023) and posterior synechia (PS) (p=0.014) were significantly higher in the RD group. The mean initial and final visual acuity (VA) of the endophthalmitis alone group was 1.9±0.8 logMAR and 1.2±1.0 logMAR (p<0.0001), respectively, and 1.9±0.9 logMAR and 1.3±1.2 logMAR (p=0.07) in the RD group, respectively. Enucleation or evisceration occurred in 31.3% of cases with RD after endophthalmitis. The rate of final retinal re-apposition for the RD cohort was 56.3%. Conclusion The anatomic and functional outcomes for RD after endophthalmitis remain poor, with significant risk for permanent vision loss. Aphakia and posterior synechiae were seen more often in cases with RD after endophthalmitis.

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“…CF, hand motion (HM), light perception (LP) and no light perception (NLP) vision were converted to logMAR as follows: CF 1.9, HM: 2.3, LP: 2.7, NLP: 3.0. 14 , 15 …”

Section: Methodsmentioning

confidence: 99%

Retinal Detachment After Endophthalmitis: Risk Factors and Outcomes

Wang

Moinuddin

Abuzaitoun

et al. 2021

OPTH

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“…To address this, we developed a regular expression pipeline as an NLP-based approach to facilitate note review. 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 ].…”

Section: Discussionmentioning

confidence: 99%

“…One impactful application of NLP is information extraction from free-text clinical notes [ 17 - 19 ], which is especially relevant for ophthalmology, where many diagnoses are based on physical examination findings described in free-text notes rather than structured data such as laboratory values. 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%

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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“…Two studies have reported on EHR-derived VA data analysis using algorithms for harmonization of data. 5,6 The aim of both studies was to extract the best documented VA for data analysis from a given clinical encounter. Both noted problems where VA exists as free-text (instead of a structured data element) lacking formatting constraints J o u r n a l P r e -p r o o f 6 and encouraging errors.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast, Baughman et al, developed an algorithm applying natural language processing to inpatient ophthalmology consultation notes. 6 Regardless of the approach, both groups reported "success" compared to manually extracted data and noted limitations including data analysis from a single center, and inability to characterize VA by method of measurement (e.g., Snellen, ETDRS, HOTV, etc. ).…”

Section: Introductionmentioning

confidence: 99%