ArticleResearch and practice in thrombosis and haemostasis2022
Developing and optimizing a computable phenotype for incident venous thromboembolism in a longitudinal cohort of patients with cancer.
Article in Research and practice in thrombosis and haemostasis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Development and validation of venous thromboembolism-bidirectional encoder representations from transformers (VTE-BERT) natural language processing model.Journal of thrombosis and haemostasis : JTH · 2026Article
- The Impact of Anticoagulation in Patients With Isolated Cancer-Associated Splanchnic Vein Thrombosis: A Dual-Center Cohort Study.American journal of hematology · 2026Article
- A deep learning model to dynamically predict cancer-associated thromboembolism in large-scale healthcare systems.NPJ digital medicine · 2026Article
- Epidemiology of Cancer-Associated Venous Thromboembolism Across the United States.American journal of hematology · 2026Article
- Automated natural language processing to identify venous thromboembolism from diagnostic imaging reports.Blood vessels, thrombosis & hemostasis · 2026Article
- Automated Identification of Cancer-Associated Thrombosis Events via Natural Language Processing: A Systematic Review of the Literature.Bleeding, thrombosis and vascular biology · 2026Article
- Using Natural Language Processing to Identify Symptomatic Adverse Events in Pediatric Oncology: Tutorial for Clinician Researchers.JMIR bioinformatics and biotechnology · 2025Article
- Natural history of cancer-associated splanchnic vein thrombosis.Journal of thrombosis and haemostasis : JTH · 2024Article
- Machine learning in cancer-associated thrombosis: hype or hope in untangling the clot.Bleeding, thrombosis and vascular biology · 2024Article
- Risks factors and outcomes for isolated catheter-related deep venous thrombosis in patients undergoing allogeneic hematopoietic stem cell transplantation.Thrombosis research · 2023Article
- Venous thromboembolism risk in cancer patients receiving first-line immune checkpoint inhibitor versus chemotherapy.American journal of hematology · 2023Article
- Epidemiology of Cancer-Associated Venous Thromboembolism in Patients With Solid and Hematologic Neoplasms in the Veterans Affairs Health Care System.JAMA network open · 2023Article
- Derivation and Validation of a Clinical Risk Assessment Model for Cancer-Associated Thrombosis in Two Unique US Health Care Systems.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2023Article
- 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
- A Multi-Institutional Natural Language Processing Pipeline to Extract Performance Status From Electronic Health Records.Cancer control : journal of the Moffitt Cancer CenterArticle
- Serum Bilirubin Levels and Risk of Venous Thromboembolism among Influenza Patients: A Cohort Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
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13 authors.
Funding
Abstract
Background: Research on venous thromboembolism (VTE) that relies only on the International Classification of Diseases (ICD) can misclassify outcomes. Our study aims to discover and validate an improved VTE computable phenotype for people with cancer. Methods: We used a cancer registry electronic health record (EHR)-linked longitudinal database. We derived three algorithms that were ICD/medication based, natural language processing (NLP) based, or all combined. We then randomly sampled 400 patients from patients with VTE codes (n = 1111) and 400 from those without VTE codes (n = 7396). Weighted sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated on the entire sample using inverse probability weighting, followed by bootstrapped receiver operating curve analysis to calculate the concordance statistic (c statistic). Results: Among 800 patients sampled, 280 had a confirmed acute VTE during the first year after cancer diagnosis. The ICD/medication algorithm had a weighted PPV of 95% and a weighted sensitivity of 81%, with a c statistic of 0.90 (95% confidence interval [CI], 0.89-0.91). Adding Current Procedural Terminology codes for inferior vena cava filter removal or early death did not improve the performance. The NLP algorithm had a weighted PPV of 80% and a weighted sensitivity of 90%, with a c statistic of 0.93 (95% CI, 0.92-0.94). The combined algorithm had a weighted PPV of 98% at the higher cutoff and a weighted sensitivity of 96% at the lower cutoff, with a c statistic of 0.98 (95% CI, 0.97-0.98). Conclusions: Our ICD/medication-based algorithm can accurately identify VTE phenotype among patients with cancer with a high PPV of 95%. The combined algorithm should be considered in EHR databases that have access to such capabilities.
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