ArticleJournal of clinical monitoring and computing2022
Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.
Article in Journal of clinical monitoring and computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of venous thromboembolism risk assessment models for hospital inpatients: the VTEAM evidence synthesis.Health technology assessment (Winchester, England) · 2024Pooled it
- Challenges in Venous Thromboembolism Risk Stratification After Pancreatic and Liver Cancer Surgery Using Claims Data.Journal of surgical oncology · 2026Article
- Differences in bleeding outcome capture between electronic health record review using natural language processing and ICD-10 coding in hospitalised children.Pediatric research · 2026Article
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Validating adverse events in administrative healthcare data in ireland: a retrospective chart review study.BMC health services research · 2025Article
- TriNetX and Real-World Evidence: A Critical Review of Its Strengths, Limitations, and Bias Considerations in Clinical Research.ASIDE internal medicine · 2025Article
- Observational
- Implementing a Quality Intervention to Improve Confidence in Outpatient Venous Thromboembolism Management.Cardiology and therapy · 2024Article
- A scoping review of the methodological approaches used in retrospective chart reviews to validate adverse event rates in administrative data.International journal for quality in health care : journal of the International Society for Quality in Health Care · 2024Article
- ClotCatcher: a novel natural language model to accurately adjudicate venous thromboembolism from radiology reports.BMC medical informatics and decision making · 2023Article
- Artificial intelligence for venous thromboembolism prophylaxis: Clinician perspectives.Research and practice in thrombosis and haemostasis · 2023Article
- Article
- Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients.Biomolecules & biomedicine · 2023Observational
- Observational
- Developing and optimizing a computable phenotype for incident venous thromboembolism in a longitudinal cohort of patients with cancer.Research and practice in thrombosis and haemostasis · 2022Article
- Real-world Health Data and Precision for the Diagnosis of Acute Kidney Injury, Acute-on-Chronic Kidney Disease, and Chronic Kidney Disease: Observational Study.JMIR medical informatics · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Big data analytics research using heterogeneous electronic health record (EHR) data requires accurate identification of disease phenotype cases and controls. Overreliance on ground truth determination based on administrative data can lead to biased and inaccurate findings. Hospital-acquired venous thromboembolism (HA-VTE) is challenging to identify due to its temporal evolution and variable EHR documentation. To establish ground truth for machine learning modeling, we compared accuracy of HA-VTE diagnoses made by administrative coding to manual review of gold standard diagnostic test results. We performed retrospective analysis of EHR data on 3680 adult stepdown unit patients identifying HA-VTE. International Classification of Diseases, Ninth Revision (ICD-9-CM) codes for VTE were identified. 4544 radiology reports associated with VTE diagnostic tests were screened using terminology extraction and then manually reviewed by a clinical expert to confirm diagnosis. Of 415 cases with ICD-9-CM codes for VTE, 219 were identified with acute onset type codes. Test report review identified 158 new-onset HA-VTE cases. Only 40% of ICD-9-CM coded cases (n = 87) were confirmed by a positive diagnostic test report, leaving the majority of administratively coded cases unsubstantiated by confirmatory diagnostic test. Additionally, 45% of diagnostic test confirmed HA-VTE cases lacked corresponding ICD codes. ICD-9-CM coding missed diagnostic test-confirmed HA-VTE cases and inaccurately assigned cases without confirmed VTE, suggesting dependence on administrative coding leads to inaccurate HA-VTE phenotyping. Alternative methods to develop more sensitive and specific VTE phenotype solutions portable across EHR vendor data are needed to support case-finding in big-data analytics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.