Limb-shaking transient ischemic attacks: case report and review of literature

BACKGROUND: Limb shaking Transient Ischemic Attack is a rare manifestation of carotid-occlusive disease. The symptoms usually point towards a seizure like activity and misdiagnosed as focal seizures. On careful history the rhythmic seizure like activity reveals no Jacksonian march mainly precipitated by maneuvers which lead to carotid compression. We here present a case of an elderly gentleman who was initially worked up as suffering from epileptic discharge and then later on found to have carotid occlusion. CASE PRESENTATION: Elderly gentleman presented with symptoms of rhythmic jerky movements of the left arm and both the lower limbs. Clinical suspicion of focal epilepsy was made and EEG, MRI-Brain with MRA were done. EEG and MRI-Brain revealed normal findings but the MRA revealed complete occlusion of right internal carotid artery. On a follow-up visit jerky movements of the left arm were precipitated by hyperextension and a tremor of 3–4 Hz was revealed. Based on this the diagnosis of low flow TIA was made the patient was treated conservatively with adjustment of his anti-hypertensive and anti-platelet medications. CONCLUSION: Diagnosis of limb-shaking TIA is important and should be differentiated from other disorders presenting as tremors. Timely diagnosis is important as these patients are shown to benefit from reperfusion procedures either surgical or radiological reducing their risk of stroke

Machine learning and decision support in critical care

Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply reusing the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability, and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding “secondary use of medical records” and “Big Data” analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of “precision medicine.” This review article covers many of the issues involved in the collection and pre-processing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data

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After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not? Intrusive memories are key to cognitive behavioural treatment for post-traumatic stress disorder, and an aetiological understanding is warranted. We present here analyses using multivariate pattern analysis (MVPA) and a machine learning classifier to investigate whether peri-traumatic brain activation was able to predict later intrusive memories (i.e. before they had happened). To provide a methodological basis for understanding the context of the current results, we first show how functional magnetic resonance imaging (fMRI) during an experimental analogue of trauma (a trauma film) via a prospective event-related design was able to capture an individual's later intrusive memories. Results showed widespread increases in brain activation at encoding when viewing a scene in the scanner that would later return as an intrusive memory in the real world. These fMRI results were replicated in a second study. While traditional mass univariate regression analysis highlighted an association between brain processing and symptomatology, this is not the same as prediction. Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory. We also report here brain networks key in intrusive memory prediction. MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms