ArticlePloS one2013
Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 76 citations in OpenAlex.
- Pooled it
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- The Ability to Look Beyond: The Treatment of Peripheral Arterial Disease.Journal of clinical medicine · 2023Review
- Natural Language Processing: from Bedside to Everywhere.Yearbook of medical informatics · 2022Article
- Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes.Circulation research · 2021Review
- Noise-tolerant similarity search in temporal medical data.Journal of biomedical informatics · 2021Article
- Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification.Artificial intelligence in medicine · 2019Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.Circulation. Cardiovascular quality and outcomes · 2019Article
- Use of big data in drug development for precision medicine: an update.Expert review of precision medicine and drug development · 2019Article
- Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.Annual review of biomedical data science · 2018Article
- Safety of cilostazol in peripheral artery disease: a cohort from a primary healthcare electronic database.BMC cardiovascular disorders · 2018Observational
- Pharmacogenomics and big genomic data: from lab to clinic and back again.Human molecular genetics · 2018Review
- The Influence of Big (Clinical) Data and Genomics on Precision Medicine and Drug Development.Clinical pharmacology and therapeutics · 2018Article
- Review
- The use of machine learning for the identification of peripheral artery disease and future mortality risk.Journal of vascular surgery · 2016Article
- Predictive modeling of risk factors and complications of cataract surgery.European journal of ophthalmology · 2016Article
- Comparing high-dimensional confounder control methods for rapid cohort studies from electronic health records.Journal of comparative effectiveness research · 2016Article
- OrderRex: clinical order decision support and outcome predictions by data-mining electronic medical records.Journal of the American Medical Informatics Association : JAMIA · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPeripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF). METHODS AND
resultsWe analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients.
conclusionsThis proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.
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Registered trials
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.