Sammani, Arjan;
Jansen, Mark;
de Vries, Nynke M;
de Jonge, Nicolaas;
Baas, Annette F;
te Riele, Anneline SJM;
Asselbergs, Folkert W;
(2022)
Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening.
Frontiers in Cardiovascular Medicine
, 9
, Article 768847. 10.3389/fcvm.2022.768847.
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Abstract
Background: Unexplained Left Ventricular Hypertrophy (ULVH) may be caused by genetic and non-genetic etiologies (e.g., sarcomere variants, cardiac amyloid, or Anderson-Fabry's disease). Identification of ULVH patients allows for early targeted treatment and family screening. Aim: To automatically identify patients with ULVH in electronic health record (EHR) data using two computer methods: text-mining and machine learning (ML). Methods: Adults with echocardiographic measurement of interventricular septum thickness (IVSt) were included. A text-mining algorithm was developed to identify patients with ULVH. An ML algorithm including a variety of clinical, ECG and echocardiographic data was trained and tested in an 80/20% split. Clinical diagnosis of ULVH was considered the gold standard. Misclassifications were reviewed by an experienced cardiologist. Sensitivity, specificity, positive, and negative likelihood ratios (LHR+ and LHR-) of both text-mining and ML were reported. Results: In total, 26,954 subjects (median age 61 years, 55% male) were included. ULVH was diagnosed in 204/26,954 (0.8%) patients, of which 56 had amyloidosis and two Anderson-Fabry Disease. Text-mining flagged 8,192 patients with possible ULVH, of whom 159 were true positives (sensitivity, specificity, LHR+, and LHR- of 0.78, 0.67, 2.36, and 0.33). Machine learning resulted in a sensitivity, specificity, LHR+, and LHR- of 0.32, 0.99, 32, and 0.68, respectively. Pivotal variables included IVSt, systolic blood pressure, and age. Conclusions: Automatic identification of patients with ULVH is possible with both Text-mining and ML. Text-mining may be a comprehensive scaffold but can be less specific than machine learning. Deployment of either method depends on existing infrastructures and clinical applications.
| Type: | Article |
|---|---|
| Title: | Automatic Identification of Patients With Unexplained Left Ventricular Hypertrophy in Electronic Health Record Data to Improve Targeted Treatment and Family Screening |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fcvm.2022.768847 |
| Publisher version: | https://doi.org/10.3389/fcvm.2022.768847 |
| Language: | English |
| Additional information: | © 2022 Sammani, Jansen, de Vries, de Jonge, Baas, te Riele, Asselbergs and Oerlemans. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). |
| Keywords: | left ventricular hypertrophy (LVH), electronic health record, anderson-fabry disease, cardiac amyloidosis, text-mining |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10151293 |
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