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

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

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.

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

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.

medExtractR: A targeted, customizable approach to medication extraction from electronic health records

OBJECTIVE

We developed medExtractR, a natural language processing system to extract medication information from clinical notes. Using a targeted approach, medExtractR focuses on individual drugs to facilitate creation of medication-specific research datasets from electronic health records.

MATERIALS AND METHODS

Written using the R programming language, medExtractR combines lexicon dictionaries and regular expressions to identify relevant medication entities (eg, drug name, strength, frequency). MedExtractR was developed on notes from Vanderbilt University Medical Center, using medications prescribed with varying complexity. We evaluated medExtractR and compared it with 3 existing systems: MedEx, MedXN, and CLAMP (Clinical Language Annotation, Modeling, and Processing). We also demonstrated how medExtractR can be easily tuned for better performance on an outside dataset using the MIMIC-III (Medical Information Mart for Intensive Care III) database.

RESULTS

On 50 test notes per development drug and 110 test notes for an additional drug, medExtractR achieved high overall performance (F-measures >0.95), exceeding performance of the 3 existing systems across all drugs. MedExtractR achieved the highest F-measure for each individual entity, except drug name and dose amount for allopurinol. With tuning and customization, medExtractR achieved F-measures >0.90 in the MIMIC-III dataset.

DISCUSSION

The medExtractR system successfully extracted entities for medications of interest. High performance in entity-level extraction provides a strong foundation for developing robust research datasets for pharmacological research. When working with new datasets, medExtractR should be tuned on a small sample of notes before being broadly applied.

CONCLUSIONS

The medExtractR system achieved high performance extracting specific medications from clinical text, leading to higher-quality research datasets for drug-related studies than some existing general-purpose medication extraction tools.

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

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.

Identifying surgical site infections in electronic health data using predictive models

Objective

The objective was to prospectively derive and validate a prediction rule for detecting cases warranting investigation for surgical site infections (SSI) after ambulatory surgery.

Methods

We analysed electronic health record (EHR) data for children who underwent ambulatory surgery at one of 4 ambulatory surgical facilities. Using regularized logistic regression and random forests, we derived SSI prediction rules using 30 months of data (derivation set) and evaluated performance with data from the subsequent 10 months (validation set). Models were developed both with and without data extracted from free text. We also evaluated the presence of an antibiotic prescription within 60 days after surgery as an independent indicator of SSI evidence. Our goal was to exceed 80% sensitivity and 10% positive predictive value (PPV).

Results

We identified 234 surgeries with evidence of SSI among the 7910 surgeries available for analysis. We derived and validated an optimal prediction rule that included free text data using a random forest model (sensitivity = 0.9, PPV = 0.28). Presence of an antibiotic prescription had poor sensitivity (0.65) when applied to the derivation data but performed better when applied to the validation data (sensitivity = 0.84, PPV = 0.28).

Conclusions

EHR data can facilitate SSI surveillance with adequate sensitivity and PPV.