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While echocardiographic parameters are used to quantify ventricular function in infants with single ventricle physiology, there are few data comparing these to invasive measurements. This study correlates echocardiographic measures of diastolic function with ventricular end-diastolic pressure in infants with single ventricle physiology prior to superior cavopulmonary anastomosis.
Data from 173 patients enrolled in the Pediatric Heart Network Infant Single Ventricle enalapril trial were analysed. Those with mixed ventricular types (n = 17) and one outlier (end-diastolic pressure = 32 mmHg) were excluded from the analysis, leaving a total sample size of 155 patients. Echocardiographic measurements were correlated to end-diastolic pressure using Spearman’s test.
Median age at echocardiogram was 4.6 (range 2.5–7.4) months. Median ventricular end-diastolic pressure was 7 (range 3–19) mmHg. Median time difference between the echocardiogram and catheterisation was 0 days (range −35 to 59 days). Examining the entire cohort of 155 patients, no echocardiographic diastolic function variable correlated with ventricular end-diastolic pressure. When the analysis was limited to the 86 patients who had similar sedation for both studies, the systolic:diastolic duration ratio had a significant but weak negative correlation with end-diastolic pressure (r = −0.3, p = 0.004). The remaining echocardiographic variables did not correlate with ventricular end-diastolic pressure.
In this cohort of infants with single ventricle physiology prior to superior cavopulmonary anastomosis, most conventional echocardiographic measures of diastolic function did not correlate with ventricular end-diastolic pressure at cardiac catheterisation. These limitations should be factored into the interpretation of quantitative echo data in this patient population.
Fatigue syndromes (FSs) affect large numbers of individuals, yet evidence from epidemiological studies on adverse outcomes, such as premature death, is limited.
Cohort study involving 385 general practices in England that contributed to the Clinical Practice Research Datalink (CPRD) with linked inpatient Hospital Episode Statistics (HES) and Office for National Statistics (ONS) cause of death information. A total of 10 477 patients aged 15 years and above, diagnosed with a FS during 2000–2014, were individually matched with up to 20 comparator patients without a history of having a FS. Prevalence ratios (PRs) were estimated to compare the FS and comparison cohorts on clinical characteristics. Adjusted hazard ratios (HRs) for subsequent adverse outcomes were estimated from stratified Cox regression models.
Among patients diagnosed with FSs, we found elevated baseline prevalence of: any psychiatric illness (PR 1.77; 95% CI 1.72–1.82), anxiety disorders (PR 1.92; 1.85–1.99), depression (PR 1.89; 1.83–1.96), psychotropic prescriptions (PR 1.68; 1.64–1.72) and comorbid physical illness (PR 1.28; 1.23–1.32). We found no significant differences in risks for: all-cause mortality (HR 0.99; 0.91–1.09), natural death (HR 0.99; 0.90–1.09), unnatural death (HR 1.00; 0.59–1.72) or suicide (HR 1.68; 0.78–3.63). We did, however, observe a significantly elevated non-fatal self-harm risk: HR 1.83; 1.56–2.15.
The absence of elevated premature mortality risk is reassuring. The raised prevalence of mental illness and increased non-fatal self-harm risk indicate a need for enhanced assessment and management of psychopathology associated with fatigue syndromes.
Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.
To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.
Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.
Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.
Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.
Conventional selected area diffraction patterns as obtained in the TEM present difficulties for identification of materials such as asbestifonn minerals, although diffraction data is considered to be one of the preferred methods for making this identification. The preferred orientation of the fibers in each field of measurement, and the spotty patterns that are obtained, do not readily lend themselves to measurement of the integrated intensity values for each dspacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. To overcome these problems, we have implemented an automatic method for diffraction pattern measurement. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring.