2,032 research outputs found
Development of a decision analytic model to support decision making and risk communication about thrombolytic treatment
Background
Individualised 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.
Methods
A 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 mRSââ€â2), 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.
Results
The 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.
Conclusions
We 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
A novel design process for selection of attributes for inclusion in discrete choice experiments:Case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke
On-line structured prioritisation exercise (SPE). Full survey used to collect data. (DOCX 42ĂÂ kb
A stroke service model developed in the private sector
This dissertation seeks primarily to enlighten the medical fraternity about the development of a stroke service at Constantiaberg Medi-Clinic and, additionally, how this has been implemented. The objective is to try to improve the outcome of patients presenting with various types and levels of neurological deficits as a consequence of cerebrovascular accidents
A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke
A discrete choice experiment (DCE) is a method used to elicit participantsâ preferences and the relative importance of different attributes and levels within a decision-making process. DCEs have become popular in healthcare; however, approaches to identify the attributes/levels influencing a decision of interest and to selection methods for their inclusion in a DCE are under-reported. Our objectives were: to explore the development process used to select/present attributes/levels from the identified range that may be influential; to describe a systematic and rigorous development process for design of a DCE in the context of thrombolytic therapy for acute stroke; and, to discuss the advantages of our five-stage approach to enhance current guidance for developing DCEs. A five-stage DCE development process was undertaken. Methods employed included literature review, qualitative analysis of interview and ethnographic data, expert panel discussions, a quantitative structured prioritisation (ranking) exercise and pilot testing of the DCE using a âthink aloudâ approach. The five-stage process reported helped to reduce the list of 22 initial patient-related factors to a final set of nine variable factors and six fixed factors for inclusion in a testable DCE using a vignette model of presentation. In order for the data and conclusions generated by DCEs to be deemed valid, it is crucial that the methods of design and development are documented and reported. This paper has detailed a rigorous and systematic approach to DCE development which may be useful to researchers seeking to establish methods for reducing and prioritising attributes for inclusion in future DCEs.Financial support for this study was provided entirely by a grant from the
National Institute for Health Research (NIHR) Health Services and Delivery
Research Programme (project number: 12/5001/45)
Exploring openEHR-based clinical guidelines in acute stroke care and research
Largely speaking, health information systems today are not able to exchange data between each other and understand the dataâs meaning automatically by means of their information technology components. This lack of âinteroperabilityâ also leads to patients experiencing an undesired discontinuity in their care. This thesis is a part of a health informatics field which tackles interoperability barriers by offering standardised information models for electronic health records. More specifically, this work explores possibilities of combining standardised information models offered by the openEHR interoperability approach with knowledge from evidence-based clinical practice guidelines. The applied methodology includes openEHR archetypes, the openEHR reference information model, standard medical terminologies such as SNOMED CT, the international stroke treatment registry SITS, a newly developed model for representing guideline knowledge (the âCare Entry-Network Modelâ), and rules authored in the Guideline Definition Language, a formalism recently endorsed by openEHR as a part of its specifications. The study design used is based on evaluating the work done by means of retrospectively checking the compliance of completed patient cases with guidelines from the domain of acute stroke management in Europe, both experimentally and using thousands of real patient cases from SITS. Our overall findings are that i) the Care Entry-Network Model facilitates an intermediate step between narrative guideline text and computer-interpretable guidelines to be deployed in openEHR systems, ii) the Guideline Definition Language is practicable for creating and automatically running openEHR-based computer-interpretable guidelines, where we also provide detailed accounts of our employed GDL technologies, and iii) the Guideline Definition Language combined with real patient data from patient data registries can generate new clinical knowledge, which in our case has benefited stroke carers and researchers working with acute stroke thrombolysis. In conclusion, using our methodology, health care stakeholders would get evidence-based knowledge components in their electronic health records based on shareable, well maintainable information and knowledge models in the form of archetypes and GDL rules respectively. However, our approach still needs to be tested at the point of clinical decision making and compared to other approaches for providing exchangeable computer-interpretable guidelines
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Implementing clinical guidelines.
Clinical guidelines that support practice and improve care are essential in this era of evidence-based medicine. However, implementing this guidance often falls short in practice. Sharing knowledge and auditing practice are important, but not sufficient to implement change. This article brings together evidence from the study of behaviour, education and clinical practice and offers practical tips on how practising neurologists might bring about change in the healthcare environment. Common themes include the importance of team working, multidisciplinary engagement, taking time to identify who and what needs changing, and selecting the most appropriate tool(s) for the job. Engaging with the challenge is generally more rewarding than resisting and is important for the effective provision of care
Risk of intracerebral haemorrhage with alteplase after acute ischaemic stroke : a secondary analysis of an individual patient data meta-analysis
Background Randomised trials have shown that alteplase improves the odds of a good outcome when delivered within 4.5 h of acute ischaemic stroke. However, alteplase also increases the risk of intracerebral haemorrhage; we aimed to determine the proportional and absolute effects of alteplase on the risks of intracerebral haemorrhage, mortality, and functional impairment in different types of patients. Methods We used individual patient data from the Stroke Thrombolysis Trialists' (STT) meta-analysis of randomised trials of alteplase versus placebo (or untreated control) in patients with acute ischaemic stroke. We prespecified assessment of three classifications of intracerebral haemorrhage: type 2 parenchymal haemorrhage within 7 days; Safe Implementation of Thrombolysis in Stroke Monitoring Study's (SITS-MOST) haemorrhage within 24-36 h (type 2 parenchymal haemorrhage with a deterioration of at least 4 points on National Institutes of Health Stroke Scale [NIHSS]); and fatal intracerebral haemorrhage within 7 days. We used logistic regression, stratified by trial, to model the log odds of intracerebral haemorrhage on allocation to alteplase, treatment delay, age, and stroke severity. We did exploratory analyses to assess mortality after intracerebral haemorrhage and examine the absolute risks of intracerebral haemorrhage in the context of functional outcome at 90-180 days. Findings Data were available from 6756 participants in the nine trials of intravenous alteplase versus control. Alteplase increased the odds of type 2 parenchymal haemorrhage (occurring in 231 [6.8%] of 3391 patients allocated alteplase vs 44 [1.3%] of 3365 patients allocated control; odds ratio [OR] 5.55 [95% CI 4.01-7.70]; absolute excess 5.5% [4.6-6.4]); of SITS-MOST haemorrhage (124 [3.7%] of 3391 vs 19 [0.6%] of 3365; OR 6.67 [4.11-10.84]; absolute excess 3.1% [2.4-3.8]); and of fatal intracerebral haemorrhage (91 [2.7%] of 3391 vs 13 [0.4%] of 3365; OR 7.14 [3.98-12.79]; absolute excess 2.3% [1.7-2.9]). However defined, the proportional increase in intracerebral haemorrhage was similar irrespective of treatment delay, age, or baseline stroke severity, but the absolute excess risk of intracerebral haemorrhage increased with increasing stroke severity: for SITS-MOST intracerebral haemorrhage the absolute excess risk ranged from 1.5% (0.8-2.6%) for strokes with NIHSS 0-4 to 3.7% (2.1-6.3%) for NIHSS 22 or more (p=0.0101). For patients treated within 4.5 h, the absolute increase in the proportion (6.8% [4.0% to 9.5%]) achieving a modified Rankin Scale of 0 or 1 (excellent outcome) exceeded the absolute increase in risk of fatal intracerebral haemorrhage (2.2% [1.5% to 3.0%]) and the increased risk of any death within 90 days (0.9% [-1.4% to 3.2%]). Interpretation Among patients given alteplase, the net outcome is predicted both by time to treatment (with faster time increasing the proportion achieving an excellent outcome) and stroke severity (with a more severe stroke increasing the absolute risk of intracerebral haemorrhage). Although, within 4.5 h of stroke, the probability of achieving an excellent outcome with alteplase treatment exceeds the risk of death, early treatment is especially important for patients with severe stroke.Peer reviewe
Paramedic identification of stroke mimic presentations : development and preliminary evaluation of a pre-hospital clinical assessment tool
PhD ThesisBackground
Stroke mimic (SM) conditions produce stroke-like symptoms through diverse mechanisms.
Up to 43% of pre-hospital suspected stroke patients are SM because identification tools
prioritise sensitivity over specificity, leading to inefficient use of ambulances and stroke
services. No existing pre-hospital SM identification tools could be identified. A pragmatic SM
identification tool using easily available information from suspected stroke patients was
developed.
Methods
A systematic literature review and a national paramedic survey generated possible tool
content. Independent predictors were isolated by regression analysis of selected variables
documented in ambulance records of suspected stroke patients linked to primary hospital
diagnoses (derivation dataset, n=1,650, 40% SM). The tool was refined using an expanded
dataset (n=3,797, 41% SM), usability testing and professional focus groups. The potential
clinical impact was evaluated through basic service efficiency modelling and focus groups.
Results
The âSTEAM toolâ combines six variables:
1 point for Systolic blood pressure<90mmHg
1 point for Temperature>38.5oC with heart rate>90bpm
1 point for seizures or 2 points for seizures with known diagnosis of Epilepsy
1 point for Age<40 years or 2 points for age<30 years
1 point for headache with known diagnosis of Migraine
1 point for FAST-ve suspected stroke
A score of â„2 on STEAM predicted SM diagnosis in the refinement dataset with 5.5%
sensitivity, 99.6% specificity and positive predictive value (PPV) of 91.4%. External validation
(n=1,848, 33% SM) showed 5.6% sensitivity, 99.5% specificity and a PPV of 85.0%.
Focus groups with paramedics and hospital clinicians identified benefits and risks to patients
ii
and clinical services from using STEAM.
Conclusions
A multi-method approach developed and validated a tool using common clinical
characteristics to identify a small proportion of SM patients with a high degree of certainty.
The tool appears feasible for pre-hospital use but its impact will depend upon local models
of stroke care.The Stroke Associatio
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