The differential diagnosis of children with joint hypermobility: a review of the literature

<p>Abstract</p> <p>Background</p> <p>In this study we aimed to identify and review publications relating to the diagnosis of joint hypermobility and instability and develop an evidence based approach to the diagnosis of children presenting with joint hypermobility and related symptoms.</p> <p>Methods</p> <p>We searched Medline for papers with an emphasis on the diagnosis of joint hypermobility, including Heritable Disorders of Connective Tissue (HDCT).</p> <p>Results</p> <p>3330 papers were identified: 1534 pertained to instability of a particular joint; 1666 related to the diagnosis of Ehlers Danlos syndromes and 330 related to joint hypermobility.</p> <p>There are inconsistencies in the literature on joint hypermobility and how it relates to and overlaps with milder forms of HDCT. There is no reliable method of differentiating between Joint Hypermobility Syndrome, familial articular hypermobility and Ehlers-Danlos syndrome (hypermobile type), suggesting these three disorders may be different manifestations of the same spectrum of disorders. We describe our approach to children presenting with joint hypermobility and the published evidence and expert opinion on which this is based.</p> <p>Conclusion</p> <p>There is value in identifying both the underlying genetic cause of joint hypermobility in an individual child and those hypermobile children who have symptoms such as pain and fatigue and might benefit from multidisciplinary rehabilitation management.</p> <p>Every effort should be made to diagnose the underlying disorder responsible for joint hypermobility which may only become apparent over time. We recommend that the term "Joint Hypermobility Syndrome" is used for children with symptomatic joint hypermobility resulting from any underlying HDCT and that these children are best described using <b>both </b>the term Joint Hypermobility Syndrome <b>and </b>their HDCT diagnosis.</p

Hypoglycemia-related electroencephalogram changes assessed by multiscale entropy

Background: Several clinical studies have shown that low blood glucose (BG) levels affect electroencephalogram (EEG) rhythms through the quantification of traditional indicators based on linear spectral analysis. Nonlinear measures used in the last decades to characterize the EEG in several physiopathological conditions have never been assessed in hypoglycemia. The present study investigates if properties of the EEG signal measured by nonlinear entropy-based algorithms are altered in a significant manner when a state of hypoglycemia is entered. Subjects and Methods: EEG was acquired from 19 patients with type 1 diabetes during a hyperinsulinemic– euglycemic–hypoglycemic clamp experiment. In parallel, BG was frequently monitored by the standard YSI glucose and lactate analyzer and used to identify two 1-h intervals corresponding to euglycemia and hypoglycemia, respectively. In each subject, the P3-C3 EEG derivation in the two glycemic intervals was assessed using the multiscale entropy (MSE) approach, obtaining measures of sample entropy (SampEn) at various temporal scales. The comparison of how signal irregularity measured by SampEn varies as the temporal scale increases in the two glycemic states provides information on how EEG complexity is affected by hypoglycemia. Results: For both glycemic states, the MSE analysis showed that SampEn increases at small time scales and then monotonically decreases as the time scale becomes larger. Comparing the two conditions, SampEn was higher in hypoglycemia only at medium time scales. Conclusions: A decrease in the complexity of EEG occurs when a state of hypoglycemia is entered, because of a degradation of the EEG long-range temporal correlations. Thanks to its ability to assess nonlinear dynamics of the EEG signal, the MSE approach seems to be a useful tool to complement information brought by standard linear indicators and provide new insights on how hypoglycemia affects brain functioning

Clinical effects of anticoagulant therapy in suspected acute myocardial infarction: systematic overview of randomised trials.

OBJECTIVES: Most randomised trials of anticoagulant therapy for suspected acute myocardial infarction have been small and, in some, aspirin and fibrinolytic therapy were not used routinely. A systematic overview (meta-analysis) of their results is needed, in particular to assess the clinical effects of adding heparin to aspirin. DESIGN: Computer aided searches, scrutiny of reference lists, and inquiry of investigators and companies were used to identify potentially eligible studies. On central review, 26 studies were found to involve unconfounded randomised comparisons of anticoagulant therapy versus control in suspected acute myocardial infarction. Additional information on study design and outcome was sought by correspondence with study investigators. SUBJECTS: Patients with suspected acute myocardial infarction. INTERVENTIONS: No routine aspirin was used among about 5000 patients in 21 trials (including half of one small trial) that assessed heparin alone or heparin plus oral anticoagulants, and aspirin was used routinely among 68,000 patients in six trials (including the other half of one small trial) that assessed the addition of intravenous or high dose subcutaneous heparin. MAIN OUTCOME MEASUREMENTS: Death, reinfarction, stroke, pulmonary embolism, and major bleeds (average follow up of about 10 days). RESULTS: In the absence of aspirin, anticoagulant therapy reduced mortality by 25% (SD 8%; 95% confidence interval 10% to 38%; 2P = 0.002), representing 35 (11) fewer deaths per 1000. There were also 10 (4) fewer strokes per 1000 (2P = 0.01), 19 (5) fewer pulmonary emboli per 1000 (2P < 0.001), and non-significantly fewer reinfarctions, with about 13 (5) extra major bleeds per 1000 (2P = 0.01). Similar sized effects were seen with the different anticoagulant regimens studied. In the presence of aspirin, however, heparin reduced mortality by only 6% (SD 3%; 0% to 10%; 2P = 0.03), representing just 5 (2) fewer deaths per 1000. There were 3 (1.3) fewer reinfarctions per 1000 (2P = 0.04) and 1 (0.5) fewer pulmonary emboli per 1000 (2P = 0.01), but there was a small non-significant excess of stroke and a definite excess of 3 (1) major bleeds per 1000 (2P < 0.0001). CONCLUSIONS: The clinical evidence from randomised trials dose not justify the routine addition of either intravenous or subcutaneous heparin to aspirin in the treatment of acute myocardial infarction (irrespective of whether any type of fibrinolytic therapy is used)

Variability of EEG Theta Power Modulation in Type 1 Diabetics Increases during Hypo-glycaemia

EEG spectral content has been widely investigated to illuminate cognitive processes and assess clinical conditions of patients. In particular, increase of powers in EEG low frequency bands were proved to reflect low levels of glucose concentration in the blood, i.e., hypo-glycaemia states. In the present work we investigate if and how levels of glucose concentrations affect the time course of EEG power modulations in the conventional theta, alpha and beta bands. To this aim, the reactivity index \u3c1, recently introduced for characterizing individual modulations of alpha rhythms, was utilized to quantify, for each band, EEG power modulations at the P3-C3 channel during induced hypo-glycaemia experiments performed with 10 type-1 diabetic volunteers. Results show that, in any glycemic state, i.e., hyper/eu/hypo-glycaemia, \u3c1 continuously vary in any band, alternating increases and decreases of powers with respect to preceding intervals. In particular, in the theta band, the variability of EEG power modulations during hypo-glycaemia (measured by the \u3c1 sample standard deviation) is significantly higher than in hyper- and eu- glycaemia. This suggests that the variability of \u3c1 in the theta band can be a useful indicator to quantitatively investigate glucoserelated EEG changes

Hypoglycaemia-Related EEG Changes Assessed by Approximate Entropy

Several studies performed in human beings demonstrated that glucose concentration in blood can affect EEG rhythms, typically evaluated by standard spectral analysis techniques. In the present work, we investigate if EEG complexity assessed by a nonlinear algorithm, Approximate Entropy (ApEn), reflects changes of glucose concentration levels during an induced hypoglycaemia experiment. In particular, in 10 type-1 diabetic volunteers, ApEn was computed from the P3-C3 EEG channel at different temporal scales and then correlated to the three classes of glycaemic states, i.e. hyper/eu/hypo-glycaemia. Results show that, for all considered temporal scales, EEG complexity in hypoglycaemia is lower, with statistical significance, than in eu- and in hyperglycaemia. No statistically significant difference can be evidenced between ApEn values in hyper- and in eu-glycaemic states. In conclusion, in addition to power indexes in the four traditional EEG bands, other indicators, and ApEn in particular, can be used to quantitatively investigate glucose-related EEG changes