Max A. Woodbury

The normal Q-T interval

Abstract 1. 1. The correlation between the Q-T and the R-R intervals was determined in 649 healthy men and 311 healthy women from 20 to 59 years of age, with consideration of constitutional and physiologic variables, and ten additional electrocardiographic items using an electronic computer. 2. 2. Of the numerous factors investigated, only age made a small, but statistically significant, contribution to the relationship between the Q-T and the R-R intervals. 3. 3. As best fit, logarithmic and linear regression equations were obtained. In actual application the difference between these two regression equations was so small that the simple linear regression equation is preferred. 4. 4. In comparison with six other formulas for the relationship between Q-T and R-R intervals, most commonly used in clinical application, the logarithmic CWS(1) and the linear CWS(2) regression equations gave the least discrepancies to the actual values in 9 subjects near the extremes of the wide scatter of Q-T versus R-R intervals. 5. 5. There was excellent agreement in the range of heart rate from 56 to 115 per minute between upper (97.5 per cent) and lower (2.5 per cent) normal limits determined from the percentile distribution and predicted from the linear regression equation. 6. 6. The normal limits of the Q-T interval are presented in five ranges of heart rate for convenient clinical application, and are believed to be more reliable than those previously suggested. The limitations of prediction of the Q-T interval from the heart rate are discussed.

A Method for Electrocardiogram Wave-Pattern Estimation Example: Left Ventricular Hypertrophy

Canine antisera to rabbit and hog renin were coupled to several fluorescein dyes. The antiserum was immunologically adsorbed once each with rabbit-liver and with bone-marrow powder, thereby removing excess dye and eliminating nonspecific staining. Mounted frozen-dried sections of kidneys from sodium- deficient rabbits (in which hyperplasia and hypergranulation of juxtaglomerular [JG] cells were present), from control rabbits, from a sodium-deficient dog, and from sodium-deficient rats were treated with the adsorbed labeled antiserum. Ultraviolet microscopy (direct technique) revealed an intense yel low-green fluorescence sharply limited to the granules of the juxtagloinerular cells in all sections studied from kidneys of rabbit and dog. JG granules in rats did not fluoresce, an observation in accord with the species spe cificity of renin. The indirect (“sandwich”) method was also employed (with adsorbed, labeled, rabbit antiserum to canine globulin), and although slight staining of gloinerular epithelium resulted, that in the JG granules was far more intense. In our hands, the faint glornerular staining was not blocked by prior treatment with unlabeled renin. Staining of JG granules in any kidney paralleled the intensity of JG granulation by light microscopy and the amount of extractable renin in the same kidney. JG-granule staining (direct and indirect techniques) was blocked by neutralization of the antirenin with renin. Heterogenous anti sera (to insulin; to human gamma globulin) failed to stain. Other rabbit tissues (heart, lung, liver) similarly treated with labeled antirenin never stained. If this work can be repeated with immunologically pure renin, the evidence presented here, together with previously published studies from this and other laboratories, will establish beyond any reasonable doubt that the source of renin in the kidney is the juxtaglomerular cell, as postulated first by Goormagh tigh nearly a quarter of a century ago.

FACTOR ANALYSIS WITH MISSING DATA

Factor analysis has been used primarily in application to problems of multiple correlation. It has been most frequently applied to matrices of correlation coefficients. However, while the classical model for factor analysis is based on this data framework, the underlying assumptions on the nature of the data do not restrict it to this purpose and it may be used fruitfully on a much broader scale in the analysis of data which may be arranged in tabular form. Since even data which are inherently continuous must be sampled a t discrete intervals for digital computer analysis, continuous as well as discrete data may find factor methods useful in their analysis. In particular, we deal with measurements which may be placed in the form of a rectangular two-dimensional table or matrix of a function of two variables. All the entries in the matrix are measurements of the same dependent variable under different conditions. The row in which the measurement is entered is determined by one independent variable, and the column by another. An example of such a table is shown in TABLE 1, in which the entries are values of the ratio of sulfanilamide concentration in tissue and plasma; the column number is determined by the location in the body at which the concentration is measured, and the row number by the type of sulfanilamide used. Such tables are, of course, extremely common in biomedical work. Factor analysis may be of assistance in the analysis of such data when the dependent variable is equal to a function of one independent variable times a function of another, or to a sum of such functions. The terms “function” and “independent variable” must here be viewed in a very broad sense. They may be continuous or discrete, or a combination of these. For example, one independent variable might be the concentration of the drug, taking on many values from zero (control) to some maximum; this would be a sampled continuous variable. Or, an independent variable might be, as in TABLE 1, the type of drug administered; this is a nominal discrete variable. For two continuous independent variables, the data are fitted by:

Coding of medical case history data for computer analysis

computer diagnosis of the electrocardiogram by using pattern recognition techniques. Within the past few years, digital and analog computer methods have been used to study medical case history data. The studies reflect an interest in several experimental areas. In one, the case history data were combined with various computer analyses to simulate differential diagnosis [1, 2]. In another, information derived from medical case histories was itemized and coded to enable its transfer to various types of computers, for analyses to be performed at a future date [3, 4]. In this report, items of data conventionally recorded in a medical examination-including history, physical exanfi-nation and laboratory tests-were tabulated. The information was then coded to enable automatic transfer to standard punched cards and then to paper or magnetic tape for use in all standard computers. Copies of the form are available to investigators. The major categories of the Information Code include data of history, physical examination and laboratory tests. Each category was divided into various subsections. Standard text sources [5, 6] aided the compilation of data included in the code. The complete code includes over 1200 items of information. Since each item m a y be answered in multiple ways, over 45,000 bits of data are included. Initial assignment of the data was made on 58 punched cards of 80 columns and 10 rows each. The History Code is divided into the following subsections: identifying data, chief complaint, present illness , family history, social history, past history and systemic review.