Methodology of ECG Interpretation in the Glasgow Program

This paper describes the methods currently used in Glasgow Royal Infirmary for computer analysis of electrocardiograms. The software is designed to analyse from 3 to 15 simultaneously recorded leads, with facilities for analysis of rhythm and serial changes. Options for Minnesota Code (with serial comparison) and XYZ lead interpretation are available.

Characterizing Pharmacogenomic-Guided Medication Use With a Clinical Data Repository

The extent to which pharmacogenomic-guided medication use has been adopted in various health systems is unclear. To assess the uptake of pharmacogenomic-guided medication use, we determined its frequency across our health system, which does not have a structured testing program. Using a multisite clinical data repository, we identified adult patients' first prescribed medications between January 2011 and December 2013 and investigated the frequency of germline and somatic pharmacogenomic testing, by the Pharmacogenomics Knowledgebase level of the US Food and Drug Administration label information. There were 268,262 medication orders for drugs with germline pharmacogenomic testing information in their drug labels. Pharmacogenomic testing was detected for 1.5% (129/8,718) of medication orders with recommended or required testing. Of the 3,817 medication orders associated with somatic pharmacogenomic testing information in their drug labels, 20% (372/1,819) of required tests were detected. The low rates of detectable pharmacogenomic testing suggest that structured testing programs are required to achieve the success of precision medicine.

Diagnostic accuracy of nucleic acid amplification tests in urine for pulmonary tuberculosis: a meta-analysis

OBJECTIVE

To determine the diagnostic accuracy of tuberculosis (TB) nucleic acid amplification tests (NAATs) in urine samples for individuals with active pulmonary tuberculosis (PTB).

DESIGN

Systematic review and meta-analysis. Electronic databases and reference lists were searched without age or setting restrictions up to May 2015. Eligible articles examined Mycobacterium tuberculosis NAATs in urine samples for PTB diagnosis in patients with sputum culture as the reference standard, and reported sufficient data to separately calculate sensitivity or specificity.

RESULTS

Eight studies, including 1212 participants from seven countries with a mean age ranging from 28 to 48 years, were included. Polymerase chain reaction (PCR) with insertion sequence (IS) 6110, rpoB or cfp32/hf6 as gene targets was used for NAATs. The pooled sensitivity and specificity was respectively 0.55 (95%CI 0.36-0.72) and 0.94 (95%CI 0.78-0.99), with slightly higher sensitivity in human immunodeficiency virus positive individuals, at 0.59 (95%CI 0.20-0.89). Sensitivity was higher in sputum microscopy-positive than -negative individuals. Storage temperatures below -70°C, centrifuge speed <5000 rpm and IS6110 increased sensitivity on meta-regression.

CONCLUSIONS

Urine M. tuberculosis PCR for active PTB diagnosis had high specificity but modest sensitivity (55%). Optimizing specimen handling, gene targets or PCR techniques may improve diagnostic accuracy. Reproducibility data are needed.

Microbial risk classifications for recreational waters and applications to the Swan and Canning Rivers in Western Australia

Protecting recreational water quality where 'whole-of-body contact' activities occur is important from a public health and economic perspective. Numerous studies have demonstrated that infectious illnesses occur when swimming in faecally polluted waters. With the release of the 2008 Australian recreational water guidelines, the Western Australian (WA) Department of Health conducted a formal evaluation to highlight the advantages of applying the microbial risk management framework to 27 swimming beaches in the Swan and Canning Rivers in Perth, WA. This involved a two-phase approach: (i) calculation of 95th percentiles using historical enterococci data; and (ii) undertaking sanitary inspections. The outcomes were combined to assign provisional risk classifications for each site. The classifications are used to promote informed choices as a risk management strategy. The study indicates that the majority of swimming beaches in the Swan-Canning Rivers are classified as 'very good' to 'good' and are considered safe for swimming. The remaining sites were classified as 'poor', which is likely to be attributed to environmental influences. Information from the study was communicated to the public via a series of press releases and the Healthy Swimming website. The guidelines provide a sound approach to managing recreational water quality issues, but some limitations were identified.

Screening health risk assessment of micropullutants for indirect potable reuse schemes: a three-tiered approach

Indirect potable reuse schemes are an important consideration in the sustainable management of scarce water resources. However, communities still hold real concerns about the potential health risks of micropullutants in recycled water entering their potable water supply. Microfiltration or ultrafiltration followed by reverse osmosis is currently the standard treatment technology for potable use of recycled water. Nevertheless, membranes are not 100% efficient in the removal of trace organic contaminants and the potential health risks of these micropullutants need to be assessed. The aim of this paper is to present a three-tiered approach for the preliminary assessment of micropullutants in recycled water. A risk quotient is calculated by comparing measured concentrations against benchmark values. Tier 1 corresponds to regulated chemicals; the maximum contaminant level in drinking water is used as benchmark value. Tier 2 corresponds to unregulated chemicals with toxicity information; slope factors or risk specific doses are used to calculate benchmark values. Tier 3 corresponds to unregulated chemicals without toxicity information. The "Threshold of Toxicological Concern" concept is used to calculate benchmark values. The characterization of chemicals of concern following reverse osmosis in a water reclamation plant and the application of the three-tiered approach for the evaluation of the potential health risks is presented.

Longitudinal versus traditional residencies: a study of continuity of care

BACKGROUND AND OBJECTIVES

Continuity of care is one of the presumed advantages of longitudinal residencies. However, it is not clear how well such residencies provide continuity of care, and, further, there is no recognized acceptable rate of good continuity. We compared traditional and longitudinal residencies to determine the extent to which the residents provided their patients with continuity of care.

METHODS

We conducted a systematic chart review at three longitudinal and three matched traditional block-rotation programs. In total, 628 charts were reviewed, and 6,256 visits were evaluated. Continuity with a primary resident was evaluated over a 2-year period, with continuity defined as the percentage of visits for which the patient saw the same resident.

RESULTS

There was no significant difference in overall rates of continuity between longitudinal and traditional programs (59.6% versus 57.8%). One longitudinal program, however, had a 74.8% rate of continuity, which was significantly higher than the rates in the otherfive programs.

CONCLUSIONS

There was no significant difference found in continuity of care provided by residents at longitudinal programs, compared with those at traditional programs. Our results do not support the hypothesis that longitudinal residency programs achieve superior rates of continuity of care. Further comparison studies of longitudinal and traditional programs would be useful.

Artificial neural networks for the diagnosis of atrial fibrillation

Different forms of artificial intelligence have been applied to pattern recognition in medicine. Recently, however, a relatively new technique involving software-based neural networks has become more readily available. Deterministic logic is currently applied to rhythm analysis in computer-assisted ECG interpretation methods developed in the University of Glasgow. The aim of the present study is to compare an artificial neural network with deterministic logic for separating sinus rhythm (SR) with supraventricular extrasystoles (SVEs) and/or ventricular extra-systoles (VEs) from atrial fibrillation (AF) at a particular point in the diagnostic logic of the Glasgow Program. A total of 2363 ECGs with 1495 AF and 868 SR + (SVEs and/or VEs) are used for training and testing a variety of neural networks, and the optimum design is selected. Methods for combining the results of the neural-network classification and the deterministic interpretation are also developed. A further 717 ECGs are used to test the selected network. The results show that the use of an artificial neural network can improve the sensitivity of reporting AF from 88.5% using the deterministic approach to 92%, without sacrificing specificity (92.3%).

Use of artificial neural networks within deterministic logic for the computer ECG diagnosis of inferior myocardial infarction

An investigation into the use of software-based artificial neural networks for the electrocardiographic (ECG) detection of inferior myocardial infarction was made. A total of 592 clinically validated subjects, including 208 with inferior myocardial infarction, 300 normal subjects, and 84 left ventricular hypertrophy cases, were used in this study. A total of 200 ECGs (100 from patients with inferior myocardial infarction and 100 from normal subjects) were fed to 66 supervised feedforward neural networks for training using a back-propagation algorithm. QRS and ST-T wave measurements were used as the input parameters for the neural networks. The best performing network using QRS measurements only and the best using QRS and ST-T data were selected by assessing a test set of 292 ECGs (108 from patients with inferior myocardial infarction, 84 from patients with left ventricular hypertrophy, and 100 from normal subjects). These two networks were then implanted separately into the deterministic Glasgow program for further study. After the implementation, it was found necessary to include a small inferior Q criterion to improve the specificity of reporting inferior myocardial infarction, thereby producing a small loss of sensitivity as compared with use of the network alone. The use of an artificial neural network within the deterministic logic performed better than either alone in the diagnosis of inferior myocardial infarction, producing a 20% gain in sensitivity with 2% loss in overall specificity compared with the original deterministic logic.

Effects of age, sex, and race on ECG interval measurements

The effects of age, sex, and race on the electrocardiogram (ECG) were studied using three separate populations: a pediatric group of 1,782 neonates, infants, and children, and adult white group of 1,555 individuals, and an adult Chinese cohort of 503 individuals. All ECGs were processed using the same computer program, and various interval measurements were derived, including QRS duration, heart rate, QT dispersion, and selected Q-wave durations. Also, a small subgroup of 195 white subjects had a signal-averaged ECG recorded. In the pediatric group, there was a clear link between age and QRS duration, which increased linearly from about 1 year of age to adolescence. In the adults, the principal differences were an increased QRS duration in men compared with women both in the standard and signal-averaged ECG. Upper limits of normal heart rate also tended to be higher in women than in men in the two adult populations. Small racial differences could be seen in some measurements, but were not thought to be of clinical significance.

Detection of electrocardiographic 'left ventricular strain' using neural nets

The use of artificial neural networks for classification of ST-T abnormalities of the electrocardiogram (ECG) was investigated. A training set of 356 lateral leads selected from 105 ECGs was visually classified as exhibiting one particular ST-T morphology (left ventricular (LV) strain) or not. Selected measurements, together with the classification, were fed as input to a three-layer software-based network during the learning process. The performance of the network was evaluated by comparing the results obtained from the network with conventional criteria, using two test sets. Set 1 comprised 63 lateral leads from 32 ECGs with ST-T changes showing atypical forms of LV strain. Set 2 consisted of 80 lateral leads from 20 ECGs containing normal and abnormal T-waves. For set 1, the network outperformed conventional criteria, having a higher sensitivity (96 per cent against 85 per cent) and specificity (67 per cent against 50 per cent). With test set 2, both network and conventional criteria were 100 per cent sensitive and 100 per cent specific. For sets 1 and 2 combined, the network had a higher overall sensitivity (97 per cent against 89 per cent) and specificity (88 per cent against 82 per cent). The results suggest that neural networks may be useful in selected areas of electrocardiography, but care is required when selecting patterns for use in the training process.

Classification of electrocardiographic ST-T segments--human expert vs artificial neural network

Artificial neural networks, which can be used for pattern recognition, have recently become more readily available for application in different research fields. In the present study, the use of neural networks was assessed for a selected aspect of electrocardiographic (ECG) waveform classification. Two experienced electrocardiographers classified 1000 ECG complexes singly on the basis of the configuration of the ST-T segments into eight different classes. ECG data from 500 of these ST-T segments together with the corresponding classifications were used for training a variety of neural networks. After this training process, the optimum network correctly classified 399/500 (79.8%) ST-T segments in the separate test set. This compared with a repeatability of 428/500 (85.6%) for one electrocardiographer. Conventional criteria for the classification of one type of ST-T abnormality had a much worse performance than the neural network. It is concluded that neural networks, if carefully incorporated into selected areas of ECG interpretation programs, could be of value in the near future.

Neural networks for classification of ECG ST-T segments

The usefulness of neural networks for pattern recognition in electrocardiographic (ECG) ST-T segments was assessed. Two thousand ST-T segments from the 12-lead ECG were visually classified singly into 7 different groups. The material was divided into a training set and a test set. Computer-measured ST-T data for each element in the training set, paired with the corresponding classification, was input to various configurations of software-based neural networks during a learning process. Thereafter, the networks correctly classified 90-95% of the individual ST-T segments in the test set. The importance of the size and composition of the training set in determining the performance of a network was clearly demonstrated. In conclusion, neural networks can be used for classification of ST-T segments. If carefully incorporated into a conventional ECG interpretation program, neural networks may well be of value for automated ECG interpretation in the near future.

Methodology of ECG interpretation in the Glasgow program

This paper describes the methods currently used in Glasgow Royal Infirmary for computer analysis of electrocardiograms. The software is designed to analyse from 3 to 15 simultaneously recorded leads, with facilities for analysis of rhythm and serial changes. Options for Minnesota Code (with serial comparison) and XYZ lead interpretation are available.