Electrocardiographic prediction of lateral involvement in acute non-anterior wall myocardial infarction

AbstractPurposeRecent research has established that a tall R-wave in V1 indicates lateral wall involvement in non-anterior wall myocardial infarction (MI). The objective of this study was to assess the value of the admission electrocardiogram (ECG) to predict R-waves and consequently lateral wall damage in the late phase of non-anterior MI.MethodsECGs of 69 patients were analyzed. ST-segment changes in representative leads for lateral wall infarction such as V1, V2, V6 and I were correlated with the extent of QRS-wave changes in V1 and V6.ResultsST-segment elevation in V6 showed correlations with R/S ratio in V1 (r=0.802, B=0.440, P=<0.001) and with the depth of Q-waves in V6 (r=0.671, B=0.441, P=0.007). This correlation was higher in a small subgroup where the left circumflex branch (Cx) was the culprit vessel (r=0.888, B=1.469 and P=0.018). ST-segment depression in lead I correlated with the height of R and the surface of R in V1 (height times width of R) (r=0.542, B=−0.150, P=0.005 and r=0.538, B=−0.153, P=0.005 respectively), especially in the subgroup without proximal occlusions of RCA (r=0.711 and r=0.699). ST-segment depression in lead I also predicted Q-waves in V6 (r=0.538, B=0.114, P=0.006). ST-segment changes in V2 showed no significant correlation with either R- or Q-wave measurements.ConclusionsST-segment elevation in V6 in the acute phase of non-anterior MI predicts lateral involvement as expressed by the R/S ratio in V1 in the post reperfusion phase. A subgroup with Cx occlusion showed especially strong correlations, although the size of the group was small. In lead I ST-segment depression is correlated to height and surface of R in V1 and Q-waves in V6

The Normal College News, February 22, 1918

BACKGROUND: Knowledge graphs can represent the contents of biomedical literature and databases as subject-predicate-object triples, thereby enabling comprehensive analyses that identify e.g. relationships between diseases. Some diseases are often diagnosed in patients in specific temporal sequences, which are referred to as disease trajectories. Here, we determine whether a sequence of two diseases forms a trajectory by leveraging the predicate information from paths between (disease) proteins in a knowledge graph. Furthermore, we determine the added value of directional information of predicates for this task. To do so, we create four feature sets, based on two methods for representing indirect paths, and both with and without directional information of predicates (i.e., which protein is considered subject and which object). The added value of the directional information of predicates is quantified by comparing the classification performance of the feature sets that include or exclude it. RESULTS: Our method achieved a maximum area under the ROC curve of 89.8% and 74.5% when evaluated with two different reference sets. Use of directional information of predicates significantly improved performance by 6.5 and 2.0 percentage points respectively. CONCLUSIONS: Our work demonstrates that predicates between proteins can be used to identify disease trajectories. U

Discovering information from an integrated graph database

The information explosion in science has become a different problem, not the sheer amount per se, but the multiplicity and heterogeneity of massive sets of data sources. Relations mined from these heterogeneous sources, namely texts, database records, and ontologies have been mapped to Resource Description Framework (RDF) triples in an integrated database. The subject and object resources are expressed as references to concepts in a biomedical ontology consisting of the Unified Medical Language System (UMLS), UniProt and EntrezGene and for the predicate resource to a predicate thesaurus. All RDF triples have been stored in a graph database, including provenance. For evaluation we used an actual formal PRISMA literature study identifying 61 cerebral spinal fluid biomarkers and 200 blood biomarkers for migraine. These biomarkers sets could be retrieved with weighted mean average precision values of 0.32 and 0.59, respectively, and can be used as a first reference for further refinements

Thyroid cancer in a patient with a germline MSH2 mutation. Case report and review of the Lynch syndrome expanding tumour spectrum

Lynch syndrome (HNPCC) is a dominantly inherited disorder characterized by germline defects in DNA mismatch repair (MMR) genes and the development of a variety of cancers, predominantly colorectal and endometrial. We present a 44-year-old woman who was shown to carry the truncating MSH2 gene mutation that had previously been identified in her family. Recently, she had been diagnosed with an undifferentiated carcinoma of the thyroid and an adenoma of her coecum. Although the thyroid carcinoma was not MSI-high (1 out of 5 microsatellites instable), it did show complete loss of immunohistochemical expression for the MSH2 protein, suggesting that this tumour was not coincidental. Although the risks for some tumour types, including breast cancer, soft tissue sarcoma and prostate cancer, are not significantly increased in Lynch syndrome, MMR deficiency in the presence of a corresponding germline defect has been demonstrated in incidental cases of a growing range of tumour types, which is reviewed in this paper. Interestingly, the MSH2-associated tumour spectrum appears to be wider than that of MLH1 and generally the risk for most extra-colonic cancers appears to be higher for MSH2 than for MLH1 mutation carriers. Together with a previously reported case, our findings show that anaplastic thyroid carcinoma can develop in the setting of Lynch syndrome. Uncommon Lynch syndrome-associated tumour types might be useful in the genetic analysis of a Lynch syndrome suspected family if samples from typical Lynch syndrome tumours are unavailable

Disease Combinations Associated with Physical Activity Identified: The SMILE Cohort Study

In the search of predictors of inadequate physical activity, an investigation was conducted into the association between multimorbidity and physical activity (PA). So far the sum of diseases used as a measure of multimorbidity reveals an inverse association. How specific combinations of chronic diseases are associated with PA remains unclear. The objective of this study is to identify clusters of multimorbidity that are associated with PA. Cross-sectional data of 3,386 patients from the 2003 wave of the Dutch cohort study SMILE were used. Ward's agglomerative hierarchical clustering was executed to establish multimorbidity clusters. Chi-square statistics were used to assess the association between clusters of chronic diseases and PA, measured in compliance with the Dutch PA guideline. The highest rate of PA guideline compliance was found in patients the majority of whom suffer from liver disease, back problems, rheumatoid arthritis, osteoarthritis, and inflammatory joint disease (62.4%). The lowest rate of PA guideline compliance was reported in patients with heart disease, respiratory disease, and diabetes mellitus (55.8%). Within the group of people with multimorbidity, those suffering from heart disease, respiratory disease, and/or diabetes mellitus may constitute a priority population as PA has proven to be effective in the prevention and cure of all three disorders

Synergistic Effects of Six Chronic Disease Pairs on Decreased Physical Activity: The SMILE Cohort Study

Little is known about whether and how two chronic diseases interact with each other in modifying the risk of physical inactivity. The aim of the present study is to identify chronic disease pairs that are associated with compliance or noncompliance with the Dutch PA guideline recommendation and to study whether specific chronic disease pairs indicate an extra effect on top of the effects of the diseases individually. Cross-sectional data from 3,386 participants of cohort study SMILE were used and logistic regression analysis was performed to study the joint effect of the two diseases of each chronic disease pair for compliance with the Dutch PA guideline. For six chronic disease pairs, patients suffering from both diseases belonging to these disease pairs in question show a higher probability of noncompliance to the Dutch PA guideline, compared to what one would expect based on the effects of each of the two diseases alone. These six chronic disease pairs were chronic respiratory disease and severe back problems; migraine and inflammatory joint disease; chronic respiratory disease and severe kidney disease; chronic respiratory disease and inflammatory joint disease; inflammatory joint disease and rheumatoid arthritis; and rheumatoid arthritis and osteoarthritis of the knees, hips, and hands