The reliability of prehospital clinical evaluation for potential spinal injury is not affected by the mechanism of injury

Introduction. Traditional EMS teaching identifies mechanism of injury as an important predictor of spinal injury. Clinical criteria to select patients for immobilization are being studied in Michigan and have been implemented in Maine. Maine requires automatic immobilization of patients with “a positive mechanism” clearly capable of producing spinal injury. Objective. To determine whether mechanism of injury affects the ability of clinical criteria to identify patients with spinal injury. Methods. In this multicenter prospective cohort study, EMS personnel completed a check-off data sheet for prehospital spine-immobilized patients. Data included mechanism of injury and yes/no determinations of the clinical criteria: altered mental status, neurologic deficit, evidence of intoxication, spinal pain or tenderness, and suspected extremity fracture. Hospital outcome data included confirmation of spinal injury and treatment required. Mechanisms of injury were tabulated and rates of spinal injury for each mechanism were calculated. The patients were divided into three different high-risk and low-risk groups. Results. Data were collected for 6,500 patients. There were 209 (3.2%) patients with spinal injuries identified. There were 1,058 patients with 100 (9.4%) injuries in the first high-risk mechanism group, and 5,423 patients with 109 (2%) injuries in the first low-risk group. Criteria identified 97 of 100 (97%) injuries in the high-risk group and 102 of 109 (94%) in the low-risk group. Two additional data divisions yielded identical results. Conclusion. Mechanism of injury does not affect the ability of clinical criteria to predict spinal injury in this population.     

Safe surgery: how accurate are we at predicting intra‐operative blood loss?

Introduction Preoperative estimation of intra‐operative blood loss by both anaesthetist and operating surgeon is a criterion of the World Health Organization's surgical safety checklist. The checklist requires specific preoperative planning when anticipated blood loss is greater than 500 mL. The aim of this study was to assess the accuracy of surgeons and anaesthetists at predicting intra‐operative blood loss. Methods A 6‐week prospective study of intermediate and major operations in an academic medical centre was performed. An independent observer interviewed surgical and anaesthetic consultants and registrars, preoperatively asking each to predict expected blood loss in millilitre. Intra‐operative blood loss was measured and compared with these predictions. Parameters including the use of anticoagulation and anti‐platelet therapy as well as intra‐operative hypothermia and hypotension were recorded. Results One hundred sixty‐eight operations were included in the study, including 142 elective and 26 emergency operations. Blood loss was predicted to within 500 mL of measured blood loss in 89% of cases. Consultant surgeons tended to underestimate blood loss, doing so in 43% of all cases, while consultant anaesthetists were more likely to overestimate (60% of all operations). Twelve patients (7%) had underestimation of blood loss of more than 500 mL by both surgeon and anaesthetist. Thirty per cent (n = 6/20) of patients requiring transfusion of a blood product within 24 hours of surgery had blood loss underestimated by more than 500 mL by both surgeon and anaesthetist. There was no significant difference in prediction between patients on anti‐platelet or anticoagulation therapy preoperatively and those not on the said therapies. Conclusion Predicted intra‐operative blood loss was within 500 mL of measured blood loss in 89% of operations. In 30% of patients who ultimately receive a blood transfusion, both the surgeon and anaesthetist significantly underestimate the risk of blood loss by greater than 500 mL. Theatre staff must be aware that 1 in 14 patients undergoing intermediate or major surgery will have an unexpected blood loss exceeding 500 mL and so robust policies to identify and manage such circumstances should be in place to improve patient safety.     

产后出血预测护理评估表的设计与应用

目的探讨产后出血预测护理评估表对预防产后出血的效果。方法自行设计出血预测护理评估表,根据评估表内容进行全员培训,对产妇进行动态评估,并采取针对性护理措施。结果观察组产后出血发生率下降（P〈0．01）。结论产后出血预测护理评估表内容直观,评分方法简单,护理措施全面、有效、具针对性,能准确筛查产后出血高危人群,并能有效防范产后出血。     

Does simultaneous determination of LDL and HDL particle size improve prediction of coronary artery disease risk?

BACKGROUND: Alterations in plasma lipoprotein subclass distribution affect the risk for coronary artery disease (CAD). However, it is unclear whether the determination of low-density lipoprotein (LDL) and high-density lipoprotein (HDL) phenotypes may or may not improve the ability to predict CAD development. METHODS: Polyacrylamide gradient (3-31%) gel electrophoresis was used to simultaneously determine size and distribution of lipoprotein subclasses in 181 CAD patients and 178 controls. RESULTS: Mean LDL and HDL subclass sizes were significantly smaller in patients than in controls (p < 0.001). Multivariate logistic regression analysis showed that small dense LDL particles were independent CAD risk predictors (OR = 2.867, p < 0.01), even when adjusted for other traditional risk factors, while small HDL particles lost their significance after adjustment (OR = 2.071, p = 0.054). The area under the ROC curve for LDL (0.671) and HDL (0.643) particle size measurement demonstrated low clinical accuracy when compared to the combination of traditional lipid risk factor measurements. CONCLUSIONS: CAD is associated with the predominance of smaller LDL and HDL particles. However, simultaneous determination of these two lipoprotein phenotypes provides no additional power in discriminating CAD and non-CAD subjects, beyond that obtained by the traditional risk factors.     

A novel classification system to predict the pathogenic effects of CHD7 missense variants in CHARGE syndrome

CHARGE syndrome is characterized by the variable occurrence of multisensory impairment, congenital anomalies, and developmental delay, and is caused by heterozygous mutations in the CHD7 gene. Correct interpretation of CHD7 variants is essential for genetic counseling. This is particularly difficult for missense variants because most variants in the CHD7 gene are private and a functional assay is not yet available. We have therefore developed a novel classification system to predict the pathogenic effects of CHD7 missense variants that can be used in a diagnostic setting. Our classification system combines the results from two computational algorithms (PolyPhen-2 and Align-GVGD) and the prediction of a newly developed structural model of the chromo- and helicase domains of CHD7 with segregation and phenotypic data. The combination of different variables will lead to a more confident prediction of pathogenicity than was previously possible. We have used our system to classify 145 CHD7 missense variants. Our data show that pathogenic missense mutations are mainly present in the middle of the CHD7 gene, whereas benign variants are mainly clustered in the 5' and 3' regions. Finally, we show that CHD7 missense mutations are, in general, associated with a milder phenotype than truncating mutations.     

Phenotype-optimized sequence ensembles substantially improve prediction of disease-causing mutation in cystic fibrosis

Cystic fibrosis transmembrane conductance regulator (CFTR) mutation is associated with a phenotypic spectrum that includes cystic fibrosis (CF). The disease liability of some common CFTR mutations is known, but rare mutations are seen in too few patients to categorize unequivocally, making genetic diagnosis difficult. Computational methods can predict the impact of mutation, but prediction specificity is often below that required for clinical utility. Here, we present a novel supervised learning approach for predicting CF from CFTR missense mutation. The algorithm begins by constructing custom multiple sequence alignments called phenotype-optimized sequence ensembles (POSEs). POSEs are constructed iteratively, by selecting sequences that optimize predictive performance on a training set of CFTR mutations of known clinical significance. Next, we predict CF disease liability from a different set of CFTR mutations (test-set mutations). This approach achieves improved prediction performance relative to popular methods recently assessed using the same test-set mutations. Of clinical significance, our method achieves 94% prediction specificity. Because databases such as HGMD and locus-specific mutation databases are growing rapidly, methods that automatically tailor their predictions for a specific phenotype may be of immediate utility. If the performance achieved here generalizes to other systems, the approach could be an excellent tool to help establish genetic diagnoses.     

Changes in microRNA expression induced by rabies virus infection in mouse brains

MicroRNAs (miRNAs) are small RNA (≈22 nt) molecules expressed endogenously in cells. They are involved in the regulation of gene expression. Recently, evidence has shown that cellular miRNAs have key regulatory roles in virus-host interactions. The rabies virus (RABV) causes a fatal infection of the central nervous systems (CNS) of warm-blooded animals, yet its pathogenesis remains poorly understood. To gain more insight into the pathogenesis of RABV, a miRNA microarray was performed as part of an investigation of changes in host miRNA expression in the brains of mice infected with RABV. The results showed that RABV infection induced modulation of the expression of sixteen miRNA molecules. These data were verified by real-time PCR. Functional analysis showed the differentially expressed miRNAs to be involved in many immune-related signaling pathways, such as the RIG-I-like receptor signaling pathway, JAK-STAT signaling pathway, chemokine signaling pathway, T-cell receptor signaling pathway, MAPK signaling pathway, leukocyte transendothelial migration, and natural killer cell mediated cytotoxicity. The predicted expression levels of the target genes of these modulated miRNAs correlated with measurements of gene expression measured by DNA microarray and qRT-PCR.