Identification of people with acquired hemophilia in a large electronic health record database

Wang, Michael; Cyhaniuk, Anissa; Cooper, David L.; Iyer, N.C.

doi:10.2147/jbm.s136060

Cited by 7 publications

(5 citation statements)

References 24 publications

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“…erefore, developing big data-driven intelligent algorithms that automatically learn massive information from medical record pathological sections and image data to provide guidance for diagnosis and disease treatment and establish different disease models have become more crucial in the era of intelligent healthcare than in the past [28][29][30]. By extracting and structuring ICD-11-coded data and utilizing expert knowledge, such as ICD-11 and SNOMED CT, the algorithm with the use of ICD-11 could hold potential value for solving critical healthcare problems that cannot be solved by traditional ICD-10.…”

Section: Discussionmentioning

confidence: 99%

A Bibliometric Analysis of the Development of ICD-11 in Medical Informatics

Chen

Zhang

Zhao

et al. 2019

Journal of Healthcare Engineering

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The International Classification of Diseases (ICD), which is used to group and report health conditions and factors, provides a basis for healthcare statistics. The 11th revision of the ICD (ICD-11) released by the World Health Organization provides stakeholders with novel perspectives on solving the complexity of critical problems in medical informatics. This study conducts a bibliometric analysis of research published over the period of 1989–2018 to examine the development of ICD-related research and its trends. First, over 4000 ICD-related papers spanning the 30-year period are retrieved from the Web of Science database. Then, based on the meta data of the selected papers, time trend analysis is performed to examine the development of different ICD revisions. Finally, the keywords and topics of these papers are analyzed and visualized using VOSViewer and CiteSpace. Our findings indicate that ICD-11-related research has grown rapidly in recent years compared with studies on ICD-9 and ICD-10. Moreover, the most popular research directions of ICD-11 include the topics psychiatry, psychology, information science, library science, and behavioral science. In terms of perspectives, information system-related research is more common than big data- and knowledge discovery-related work. However, the popularity of big data- and knowledge discovery-related developments has grown in recent years. The use of ICD-11 facilitates the development of medical informatics from the perspectives of information systems, big data, and knowledge discovery.

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Section: Discussionmentioning

confidence: 99%

A Bibliometric Analysis of the Development of ICD-11 in Medical Informatics

Chen

Zhang

Zhao

et al. 2019

Journal of Healthcare Engineering

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“…Validation study is useful for assessing these biases, but MDV data is anonymized and difficult to trace back to the original data. To maximize the validity of definitions in this study, definitions validated in the previous report [ 27 ] and constructed in consultation with clinicians were used.…”

Section: Methodsmentioning

confidence: 99%

ORIHIME study: real-world treatment patterns and clinical outcomes of 338 patients with acquired hemophilia A from a Japanese administrative database

et al. 2022

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Background Acquired hemophilia A (AHA) is a rare disorder, and clinical practices for treating AHA have not been fully clarified in Japan. Objectives This study aims to investigate the epidemiology of AHA and real-world treatment practices in Japan. Patients/methods This observational study was based on a health administrative database of hospitalized patients diagnosed with AHA who were treated with immunosuppressants. Results The study included 214 males and 124 females (mean age 75.7 years). The most frequently used bypassing agent was recombinant activated factor VII. The predominant choice of immunosuppressant for first-line treatment was steroid monotherapy. Median days from the index date to the start of rehabilitation was 65.0 for cardiovascular, 35.5 for respiratory and 23.0 for locomotor. The proportion of patients with an activities of daily living (ADL) score < 70 points was high at both first admission and final discharge (47.4% and 38.8%). The percentage of deaths during hospitalization was 18.6%. Conclusions This study clarified the treatment patterns and clinical outcomes of AHA in a large population in Japan. This was the first study showing ADL score distribution and time to rehabilitation. Further investigation is needed to develop better clinical practices for treatment of AHA.

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“…Earlier diagnosis of patients with chronic diseases such as multiple sclerosis and celiac disease was facilitated in the absence of diagnostic code data [ 30 , 31 ], and patients with asthma experiencing allergic bronchopulmonary aspergillosis as a disease exacerbation were accurately identified despite the lack of a specific code [ 32 ]. Additionally, 2 studies that aimed to identify patients with either congenital or acquired hemophilia found potentially high numbers of false-positive identifications when using diagnostic codes alone [ 33 , 34 ]. The current study further adds to a body of evidence illustrating the value of using unstructured data, and it is the first to demonstrate utility in HAE, a rare and debilitating disease for which more efficient diagnosis and effective management are needed.…”

Section: Discussionmentioning

confidence: 99%

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records

Brouwer

Bratton

Near

et al. 2021

Allergy Asthma Clin Immunol

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Background The epidemiologic impact of hereditary angioedema (HAE) is difficult to quantify, due to misclassification in retrospective studies resulting from non-specific diagnostic coding. The aim of this study was to identify cohorts of patients with HAE-1/2 by evaluating structured and unstructured data in a US ambulatory electronic medical record (EMR) database. Methods A retrospective feasibility study was performed using the GE Centricity EMR Database (2006–2017). Patients with ≥ 1 diagnosis code for HAE-1/2 (International Classification of Diseases, Ninth Revision, Clinical Modification 277.6 or International Classification of Diseases, Tenth Revision, Clinical Modification D84.1) and/or ≥ 1 physician note regarding HAE-1/2 and ≥ 6 months’ data before and after the earliest code or note (index date) were included. Two mutually exclusive cohorts were created: probable HAE (≥ 2 codes or ≥ 2 notes on separate days) and suspected HAE (only 1 code or note). The impact of manually reviewing physician notes on cohort formation was assessed, and demographic and clinical characteristics of the 2 final cohorts were described. Results Initially, 1691 patients were identified: 190 and 1501 in the probable and suspected HAE cohorts, respectively. After physician note review, the confirmed HAE cohort comprised 254 patients and the suspected HAE cohort decreased to 1299 patients; 138 patients were determined not to have HAE and were excluded. The overall false-positive rate for the initial algorithms was 8.2%. Across final cohorts, the median age was 50 years and > 60% of patients were female. HAE-specific prescriptions were identified for 31% and 2% of the confirmed and suspected HAE cohorts, respectively. Conclusions Unstructured EMR data can provide valuable information for identifying patients with HAE-1/2. Further research is needed to develop algorithms for more representative HAE cohorts in retrospective studies.

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Identification of people with acquired hemophilia in a large electronic health record database

Cited by 7 publications

References 24 publications

A Bibliometric Analysis of the Development of ICD-11 in Medical Informatics

A Bibliometric Analysis of the Development of ICD-11 in Medical Informatics

ORIHIME study: real-world treatment patterns and clinical outcomes of 338 patients with acquired hemophilia A from a Japanese administrative database

Leveraging unstructured data to identify hereditary angioedema patients in electronic medical records

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