Is quantitative interpretation likely to increase sensitivity of dobutamine stress echocardiography? A study of false-negative results

Background: False-negative interpretations of do-butamine stress echocardiography (DSE) may be associated with reduced wall stress. using measurements of contraction, we sought whether these segments were actually ischemic but unrecognized or showed normal contraction. Methods. We studied 48 patients (29 men; mean age 60 +/- 10 years) with normal regional function on the basis of standard qualitative interpretation of DSE. At coronary angiography within. 6 months of DSE, 32 were identified as having true-negative and 16 as having false-negative results of DSE. Three apical views were used to measure regional function with color Doppler tissue, integrated backscatter, and strain rate imaging. Cyclic variation of integrated backscatter was measured in 16 segments, and strain rate and peak systolic strain was calculated in 6 walls at rest and peak stress. Results. Segments with false-negative results of DSE were divided into 2 groups with and without low wall stress according to previously published cut-off values. Age, sex, left ventricular mass, left ventricular geometric pattern, and peak workload were not significantly different between patients with true and false-negative results of DSE. Importantly, no significant differences in cyclic variation and strain parameters at rest and peak stress were found among segments with true-and false-negative results of DSE with and without low wall stress. Stenosis severity had no influence on cyclic variation and strain parameters at peak stress. Conclusions: False-negative results of DSE reflect lack of ischemia rather than underinterpretation of regional left ventricular function. Quantitative markers are unlikely to increase the sensitivity of DSE.

Improving precision and reducing bias in biological surveys: estimating false-negative error rates

The use of presence/absence data in wildlife management and biological surveys is widespread. There is a growing interest in quantifying the sources of error associated with these data. We show that false-negative errors (failure to record a species when in fact it is present) can have a significant impact on statistical estimation of habitat models using simulated data. Then we introduce an extension of logistic modeling, the zero-inflated binomial (ZIB) model that permits the estimation of the rate of false-negative errors and the correction of estimates of the probability of occurrence for false-negative errors by using repeated. visits to the same site. Our simulations show that even relatively low rates of false negatives bias statistical estimates of habitat effects. The method with three repeated visits eliminates the bias, but estimates are relatively imprecise. Six repeated visits improve precision of estimates to levels comparable to that achieved with conventional statistics in the absence of false-negative errors In general, when error rates are less than or equal to50% greater efficiency is gained by adding more sites, whereas when error rates are >50% it is better to increase the number of repeated visits. We highlight the flexibility of the method with three case studies, clearly demonstrating the effect of false-negative errors for a range of commonly used survey methods.

Maximum Margin Classifiers with Specified False Positive and False Negative Error Rates

This paper addresses the problem of maximum margin classification given the moments of class conditional densities and the false positive and false negative error rates. Using Chebyshev inequalities, the problem can be posed as a second order cone programming problem. The dual of the formulation leads to a geometric optimization problem, that of computing the distance between two ellipsoids, which is solved by an iterative algorithm. The formulation is extended to non-linear classifiers using kernel methods. The resultant classifiers are applied to the case of classification of unbalanced datasets with asymmetric costs for misclassification. Experimental results on benchmark datasets show the efficacy of the proposed method.

Optimizing the balance between false positive and false negative error probabilities of confirmatory methods for the detection of veterinary drug residues

GC-MS data on veterinary drug residues in bovine urine are used for controlling the illegal practice of fattening cattle. According to current detection criteria, peak patterns of preferably four ions should agree within 10 or 20% from a corresponding standard pattern. These criteria are rigid, rather arbitrary and do not match daily practice. A new model, based on multivariate modeling of log peak abundance ratios, provides a theoretical basis for the identification of analytes and optimizes the balance between the avoidance of false positives and false negatives. The performance of the model is demonstrated on data provided by five laboratories, each supplying GC-MS measurements on the detection of clenbuterol, dienestrol and 19 beta-nortestosterone in urine. The proposed model shows a better performance than confirmation by using the current criteria and provides a statistical basis for inspection criteria in terms of error probabilities.

Clostridium difficile Ribotype 023 Lacks the Ability To Hydrolyze Esculin, Leading to False-Negative Results on Chromogenic Agar

41.Connor, M.C., Fairley, D.J. Marks, N.J. McGrath, J.W. (2016) Clostridium difficile Ribotype 023 lacks the ability to hydrolyse esculin, leading to false negative results on chromogenic agar. Letters in Applied Microbiology

Dengue-4 false negative results by Panbio (R) Dengue Early ELISA assay in Brazil

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Screening for carriage of methicillin-resistant Staphylococcus aureus shortly after exposure may lead to false-negative results

We evaluated a double screening strategy for carriage of methicillin-resistant Staphylococcus aureus (MRSA) in patients exposed to a newly detected MRSA carrier. If the first screening of the exposed patient yielded negative results, screening was repeated 4 days later. This strategy detected 12 (28%) of the 43 new MRSA carriers identified during the study period. The results suggest that there is an incubation period before MRSA carriage is detectable.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Treatment plan complexity metrics for predicting IMRT pre-treatment quality assurance results

The planning of IMRT treatments requires a compromise between dose conformity (complexity) and deliverability. This study investigates established and novel treatment complexity metrics for 122 IMRT beams from prostate treatment plans. The Treatment and Dose Assessor software was used to extract the necessary data from exported treatment plan files and calculate the metrics. For most of the metrics, there was strong overlap between the calculated values for plans that passed and failed their quality assurance (QA) tests. However, statistically significant variation between plans that passed and failed QA measurements was found for the established modulation index and for a novel metric describing the proportion of small apertures in each beam. The ‘small aperture score’ provided threshold values which successfully distinguished deliverable treatment plans from plans that did not pass QA, with a low false negative rate.