ArticleBMC medical informatics and decision making2015
Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment.
Article in BMC medical informatics and decision making, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advanced Machine Learning Models for Predicting Post-Thrombolysis Hemorrhagic Transformation in Acute Ischemic Stroke Patients: A Systematic Review and Meta-Analysis.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisPooled it
- Thrombolytic Refusal Over Telestroke.Neurology. Clinical practice · 2021Article
- Decision-Making Visual Aids for Late, Imaging-Guided Endovascular Thrombectomy for Acute Ischemic Stroke.Journal of stroke · 2020Article
- Executive Summary of the Early-Onset Breast Cancer Evidence Review Conference.Obstetrics and gynecology · 2020Review
- Paramedic Acute Stroke Treatment Assessment (PASTA): study protocol for a randomised controlled trial.Trials · 2019Article
- Article
- Racial disparities in refusal of stroke thrombolysis in Chicago.Neurology · 2018Article
- Shared decision-making in stroke: an evolving approach to improved patient care.Stroke and vascular neurology · 2017Review
- Development and Testing of Shared Decision Making Interventions for Use in Emergency Care: A Research Agenda.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2016Article
- Erratum to: Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment.BMC medical informatics and decision making · 2016Article
- Development of a computerised decision aid for thrombolysis in acute stroke care.BMC medical informatics and decision making · 2015Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
backgroundIndividualised prediction of outcomes can support clinical and shared decision making. This paper describes the building of such a model to predict outcomes with and without intravenous thrombolysis treatment following ischaemic stroke.
methodsA decision analytic model (DAM) was constructed to establish the likely balance of benefits and risks of treating acute ischaemic stroke with thrombolysis. Probability of independence, (modified Rankin score mRS ≤ 2), dependence (mRS 3 to 5) and death at three months post-stroke was based on a calibrated version of the Stroke-Thrombolytic Predictive Instrument using data from routinely treated stroke patients in the Safe Implementation of Treatments in Stroke (SITS-UK) registry. Predictions in untreated patients were validated using data from the Virtual International Stroke Trials Archive (VISTA). The probability of symptomatic intracerebral haemorrhage in treated patients was incorporated using a scoring model from Safe Implementation of Thrombolysis in Stroke-Monitoring Study (SITS-MOST) data.
resultsThe model predicts probabilities of haemorrhage, death, independence and dependence at 3-months, with and without thrombolysis, as a function of 13 patient characteristics. Calibration (and inclusion of additional predictors) of the Stroke-Thrombolytic Predictive Instrument (S-TPI) addressed issues of under and over prediction. Validation with VISTA data confirmed that assumptions about treatment effect were just. The C-statistics for independence and death in treated patients in the DAM were 0.793 and 0.771 respectively, and 0.776 for independence in untreated patients from VISTA.
conclusionsWe have produced a DAM that provides an estimation of the likely benefits and risks of thrombolysis for individual patients, which has subsequently been embedded in a computerised decision aid to support better decision-making and informed consent.
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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.