Progress on the CSE diagnostic study. Application of McNemar's test revisited

Proposal for a Standard Format for Neurophysiology Data Recording and Exchange

The lack of interoperability between information networks is a significant source of cost in health care. Standardized data formats decrease health care cost, improve quality of care, and facilitate biomedical research. There is no common standard digital format for storing clinical neurophysiologic data. This review proposes a new standard file format for neurophysiology data (the bulk of which is video-electroencephalographic data), entitled the Multiscale Electrophysiology Format, version 3 (MEF3), which is designed to address many of the shortcomings of existing formats. MEF3 provides functionality that addresses many of the limitations of current formats. The proposed improvements include (1) hierarchical file structure with improved organization; (2) greater extensibility for big data applications requiring a large number of channels, signal types, and parallel processing; (3) efficient and flexible lossy or lossless data compression; (4) industry standard multilayered data encryption and time obfuscation that permits sharing of human data without the need for deidentification procedures; (5) resistance to file corruption; (6) facilitation of online and offline review and analysis; and (7) provision of full open source documentation. At this time, there is no other neurophysiology format that supports all of these features. MEF3 is currently gaining industry and academic community support. The authors propose the use of the MEF3 as a standard format for neurophysiology recording and data exchange. Collaboration between industry, professional organizations, research communities, and independent standards organizations is needed to move the project forward.

Electrocardiographic findings in a middle-aged African population in the Seychelles islands

This study describes major electrocardiogram (ECG) measurements and diagnoses in a population of African individuals; most reference data have been collected in Caucasian populations and evidence exists for interethnic differences in ECG findings. This study was conducted in the Seychelles islands (Indian Ocean) and included 709 black individuals (343 men and 366 women) aged 25 to 64 years randomly selected from the general population. Resting ECG were recorded by using a validated ECG unit equipped with a measurement and interpretation software (Cardiovit AT-6, Schiller, Switzerland). The epidemiology of 14 basic ECG measurements, 6 composite criteria for left ventricular hypertrophy and 19 specific ECG diagnoses including abnormal rhythms, conduction abnormalities, repolarization abnormalities, and myocardial infarction were examined. Substantial gender and age differences were found for several ECG parameters. Moreover, tracings recorded in African individuals of the Seychelles differed from those collected similarly in Caucasian populations in many respects. For instance, heart rate was approximately 5 beats per minute lower in the African individuals than in selected Caucasian populations, prevalence of first degree atrio-ventricular block was especially high (4.8%), and the average Sokolow-Lyon voltage was markedly higher in African individuals of the Seychelles compared with black and white Americans. The integrated interpretation software detected "old myocardial infarction" in 3.8% of men and 0% of women and "old myocardial infarction possible" in 6.1% and 3%, respectively. Cardiac infarction injury scores are also provided. In conclusion, the study provides reference values for ECG findings in a specific population of people of African descent and stresses the need to systematically consider gender, age, and ethnicity when interpreting ECG tracings in individuals.

Assessment of diagnostic ECG results using information and decision theory. Results from the CSE diagnostic study

Information and decision theory were applied to assess the interpretation results of 15 computer programs (9 electrocardiogram and 6 vectorcardiogram) and 9 cardiologists; 8 of whom analyzed the electrocardiogram and 5 the vectorcardiogram, using a database of 1,220 clinically validated cases. The study demonstrates that information content and utility indices, by providing a comprehensive view of diagnostic performance, can enhance the insight given by the more classic statistical performance measures.

The diagnostic performance of computer programs for the interpretation of electrocardiograms

BACKGROUND

Computer programs for the interpretation of electrocardiograms (ECGs) are now widely used. However, a systematic assessment of various computer programs for the interpretation of ECGs has not been performed.

METHODS

We undertook a large international study to compare the performance of nine electrocardiographic computer programs with that of eight cardiologists in interpreting ECGs in 1220 clinically validated cases of various cardiac disorders. ECGs from the following groups were included in the sample: control patients (n = 382); patients with left ventricular hypertrophy (n = 183), right ventricular hypertrophy (n = 55), or biventricular hypertrophy (n = 53); patients with anterior myocardial infarction (n = 170), inferior myocardial infarction (n = 273), or combined myocardial infarction (n = 73); and patients with combined infarction and hypertrophy (n = 31). The interpretations of the computer programs and the cardiologists were compared with the clinical diagnoses made independently of the ECGs, and the computer interpretations were compared with those of the cardiologists.

RESULTS

The percentage of ECGs correctly classified by the computer programs (median, 91.3 percent) was lower than that of the cardiologists (median, 96.0 percent; P less than 0.01). The median sensitivity of the computer programs was also significantly lower than that of the cardiologists in diagnosing left ventricular hypertrophy (56.6 percent vs. 63.9 percent, P less than 0.02), right ventricular hypertrophy (31.8 percent vs. 46.6 percent, P less than 0.01), anterior myocardial infarction (77.1 percent vs. 84.9 percent, P less than 0.001), and inferior myocardial infarction (58.8 percent vs. 71.7 percent, P less than 0.0001). The median total accuracy level (the percentage of correct classifications) was 6.6 percent lower for the computer programs (69.7 percent) than for the cardiologists (76.3 percent; P less than 0.001). However, the performance of the best programs nearly matched that of the most accurate cardiologists.

CONCLUSIONS

Our study shows that some but not all computer programs for the interpretation of ECGs perform almost as well as cardiologists in identifying seven major cardiac disorders.