| 1. |
- Ansar, Saema, et al.
(author)
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Subarachnoid hemorrhage induces upregulation of vascular receptors and reduction in rCBF via an ERKI/2 mechanism
- 2008
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In: Cerebral Vasospasm: New Strategies in Research and Treatment. - Vienna : Springer Vienna. - 0065-1419. - 9783211757178 ; 104, s. 65-67
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Conference paper (peer-reviewed)abstract
- Previous studies have shown that endothelin type B (ETB) and 5-hydroxytryptamine type IB (5-HTIB) receptors are upregulated following subarachnoid hemorrhage (SAH). The purpose of the present study was to test whether extracellular signal-regulated kinase (ERKI/2) inhibition could alter the degree of SAH induced receptor upregulation in addition to prevent the cerebral blood flow (CBF) reduction. The ERKI/2 inhibitor SB386023-b was injected intra cisternally in conjunction with and after the induced SAH in rats. Two days after SAH cerebral arteries were harvested and the contractile response to endothelin-1 (ET-I) and 5-carboxamidotryptamine (5-CT) were investigated with a myograph. The contractile responses to ET-I and 5-CT were increased after SAH compared to sham. Administration of SB-386023-b prevented the upregulated contraction elicited by application of ET-I and 5-CT in cerebral arteries. Regional CBF evaluated by an autoradiographic technique, revealed a reduced CBF by 50% after SAH this was prevented by treatment with SB-386023-b. The results indicate that an ERKI/2 mechanism is involved in cerebral vasospasm and ischemia associated with SAH.
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| 2. |
- Björk, Jonas, et al.
(author)
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A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department
- 2006
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In: BMC Medical Informatics and Decision Making. - : Springer Science and Business Media LLC. - 1472-6947. ; 6:28
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Journal article (peer-reviewed)abstract
- Background Several models for prediction of acute coronary syndrome (ACS) among chest pain patients in the emergency department (ED) have been presented, but many models predict only the likelihood of acute myocardial infarction, or include a large number of variables, which make them less than optimal for implementation at a busy ED. We report here a simple statistical model for ACS prediction that could be used in routine care at a busy ED. Methods Multivariable analysis and logistic regression were used on data from 634 ED visits for chest pain. Only data immediately available at patient presentation were used. To make ACS prediction stable and the model useful for personnel inexperienced in electrocardiogram (ECG) reading, simple ECG data suitable for computerized reading were included. Results Besides ECG, eight variables were found to be important for ACS prediction, and included in the model: age, chest discomfort at presentation, symptom duration and previous hypertension, angina pectoris, AMI, congestive heart failure or PCI/CABG. At an ACS prevalence of 21% and a set sensitivity of 95%, the negative predictive value of the model was 96%. Conclusions The present prediction model, combined with the clinical judgment of ED personnel, could be useful for the early discharge of chest pain patients in populations with a low prevalence of ACS.
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| 3. |
- Ekelund, Ulf, et al.
(author)
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Effektiviserad utredning möjlig vid misstänkt akut koronart syndrom. Nya undersökningsmetoder kan ge bättre vårdkvalitet och spara resurser
- 2005
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In: Läkartidningen. - 0023-7205. ; 102:7, s. 464-466
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Journal article (peer-reviewed)abstract
- The immediate evaluation of patients with suspected acute coronary syndrome (ACS) in the emergency department (ED) has remained almost unchanged for decades. At the same time, therapy for established ACS has undergone a remarkable and successful change towards early active intervention. Studies show that 7 out of 10 patients admitted with a suspicion of ACS do not have it, and that 2-5% of the patients with ACS are incorrectly sent home from the ED. With new diagnostic strategies, including e.g. risk prediction algorithms, new blood samples for plaque instability, special investigations like echocardiography, myocardial perfusion imaging and magnetic resonance imaging, as well as the Chest Pain Unit concept, improvements should definitely be possible. With the structured and evidence-based use of such strategies, it is our belief that more patients can be managed as outpatients, that length of stay can be shortened for those admitted, and that some patients with ACS can get an earlier adequate intervention.
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| 4. |
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| 5. |
- Green, Michael, et al.
(author)
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Explaining artificial neural network ensembles: A case study with electrocardiograms from chest pain patients
- 2008
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In: Proceedings of the ICML/UAI/COLT 2008 Workshop on Machine Learning for Health-Care Applications.
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Conference paper (peer-reviewed)abstract
- Artificial neural networks is one of the most commonly used machine learning algorithms in medical applications. However, they are still not used in practice in the clinics partly due to their lack of explanatory capacity. We compare two case-based explanation methods to two trained physicians on analysis of electrocardiogram (ECG) data from patients with a suspected acute coronary syndrome (ACS). The median overlaps of the top 5 selected features between the two physicians, and a given physician and a method, were initially low. Using a correlation analysis of the features the median overlap increased to values typically in the range 3-4. In conclusion, both our case-based methods generate explanations similar to those of trained expert physicians on the problem of diagnosing ACS from ECG data.
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| 6. |
- Green, Michael, et al.
(author)
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Exploring new possibilities for case based explanation of artificial neural network ensembles
- 2009
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In: Neural Networks. - : Elsevier BV. - 1879-2782 .- 0893-6080. ; 22:1, s. 75-81
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Journal article (peer-reviewed)abstract
- Artificial neural network (ANN) ensembles have long suffered from a lack of interpretability. This has severely limited the practical usability of ANNs in settings where an erroneous decision can be disastrous. Several attempts have been made to alleviate this problem. Many of them are based on decomposing the decision boundary of the ANN into a set of rules. We explore and compare a set of new methods for this explanation process on two artificial data sets (Monks 1 and 3), and one acute coronary syndrome data set consisting of 861 electrocardiograms (ECG) collected retrospectively at the emergency department at Lund University Hospital. The algorithms managed to extract good explanations in more than 84% of the cases. More to the point, the best method provided 99% and 91% good explanations in Monks data 1 and 3 respectively. Also there was a significant overlap between the algorithms. Furthermore, when explaining a given ECG, the overlap between this method and one of the physicians was the same as the one between the two physicians in this study. Still the physicians were significantly, p-value <0.001, more similar to each other than to any of the methods. The algorithms have the potential to be used as an explanatory aid when using ANN ensembles in clinical decision support systems.
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| 7. |
- Lundager Hansen, Jakob
(author)
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Improving early diagnosis of acute coronary syndrome and resource utilisation in acute chest pain patients
- 2013
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Doctoral thesis (other academic/artistic)abstract
- A high volume of acute chest pain patients, poor early diagnosis and high admission rates result in high resource utilization. In 1000 consecutively chest pain patients the majority of the direct cost was found due to admission time. The difference between mean cost of an “ACS-rule-out” admission and a discharge from the ED was 6.2 kSEK (9.7 kSEK in 2011). Early diagnosis can be improved by 1) using the information already available better, 2) adding new diagnostic information, or 3) re-engineering the diagnostic approach. The thesis includes examples of all these strategies A logistic regression model and an artificial neural network (ANN) model significantly predicted ACS better than experienced physicians applied retrospectively on 643 consecutive chest pain patients. In a prospective study including 560 patients, our ANN models detected STEMI and the need of acute PCI on the ambulance ECG with higher sensitivity than the CCU physician. The ANN could potentially reduce the amount of ECGs transmitted to the CCU physician by 2/3. A simple prediction model including data immediately available at presentation to the ED did not perform better than the more complex models using only the ECG. In a convenience sample of 40 low risk patients needing admission due to the suspension of ACS, acute MPI showed a 100 % negative predictive value for ACS and was estimated to reduce overall cost. This thesis has shown examples of strategies to improve early diagnosis of ACS. The Predictions models should be externally validated before clinical use. Further studies are needed. Such studies should include newer cardiac biomarkers and include both the diagnostic and prognostic performance and the associated resource utilisation.
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