Dennis Lezotte

Limited Joint Mobility in Childhood Diabetes Mellitus Indicates Increased Risk for Microvascular Disease

We detected limited mobility of small and large joints in 92 (30 per cent) of 309 patients with diabetes who were one to 28 years old. Among patients who had had diabetes for more than 4.5 years (the shortest duration at which microvascular complications were noted), 82 of 169 had joint limitation. Forty-one of these 82 also had microvascular complications, but only 10 of the 87 patients without joint limitation had complications. Life-table analysis indicated an 83 per cent risk for microvascular complications after 16 years of diabetes if joint limitation was present, but only a 25 per cent risk if joint limitation was absent. Consequently, limited joint mobility identifies a population exceptionally at risk for the early development of microvascular complications, and intervention to forestall or prevent these complications can now be focused.

Thyroid autoimmunity in insulin-dependent diabetes mellitus: The case for routine screening

Of 771 young diabetic patients, thyroid microsomal autoantibodies occurred in 136 (17.6%) at a female/male ratio of nearly 2:1 and with a predominance of white patients (20.1%) over black patients (5.5%) (P less than 0.001). Thus, one in every four white female patients with insulin-dependent diabetes mellitus had TMA. Thyroglobulin autoantibodies were no more common in patients with IDDM than among controls. Of the 117 patients (out of the 136) with serologic evidence of chronic thyroiditis who could be studied, eight (7%) had hyperthyroidism and 45 (38%) were hypothyroid. Hyperthyroidism usually preceded or coincided with the appearance of IDDM, whereas hypothyroidism occurred with or following the onset of IDDM. Hypothyroidism appeared irreversible in most patients, but in three, periods of hypothyroidism were followed by euthyroidism, presumably explained by a compensatory hyperplasia of the thyroid gland. In the 136 patients with TMA, gastric and adrenocortical autoantibodies also occurred at relatively high frequencies (16.8% and 5.1%, respectively). On the basis of these studies, we urge that all patients with IDDM be screened for TMA and that those with positive results undergo annual thyroid function tests as well as determinations of gastric parietal and adrenocortical autoantibodies.

The use of immunocytochemical techniques for the detection of steroid hormones in breast cancer cells

Indirect immunofluorescence and immunoperoxidase assays were developed to detect estradiol and progesterone in breast cancer cells. Appropriate controls were used to confirm immunologic specificity. Studies of estradiol binding by human breast cancer cells identified three groups: no detectable binding (25%); all tumor cells exhibiting binding although to different degrees (4%); and tumors with varying numbers of positive and negative cells (71%). Similar observations were made with respect to progesterone binding. The percentage of cells with estradiol binding was correlated with the amount of estrogen receptors (ER) present in the tumor specimens. Post‐hormone binding events, e.g., nuclear binding of estradiol, were also evaluated. Some tumor cells showing cytoplasmic binding of estradiol did not show nuclear binding of estradiol; such tumors lacked detectable progesterone binding and progesterone receptors. Estradiol binding could not be competed with by diethylstilbestrol under routine assay conditions, and relatively high concentrations of estradiol were needed to observe estradiol‐specific staining. The results suggest that the immunocytochemical assays detect hormone‐specific binding, but that the binding is probably due to multiple classes of steroid‐binding sites.

Reference values for blood findings in relatively fit elderly persons.

In a retirement community group of 73 relatively fit elderly white persons, a cross‐sectional study of 53 different blood tests was conducted. The five test categories for blood values were hematology, chemistry, thyroid function, protein electrophoresis, and immunology. Fifteen percent of the blood findings were outside the range accepted as normal by the examining laboratories. Most of the subjects showed between 5 and 10 “abnormal” values within the five test categories. Since the findings were fairly predictable in view of the patho‐anatomic changes that accompany aging, and since the manifestations of disease were at most subclinical, only minor alterations in individual management were needed. Subsequent appropriate clinical re‐evaluation of these subjects during a six‐month follow‐up revealed no striking changes. It would appear that the ranges of “normal” reference values may need to be expanded. Although cross‐sectional laboratory studies are useful, longitudinal studies seem essential if clinicians are to attain a more valid perspective.

Unlimited volumes of laboratory data: a confusing and diagnostically deceptive product of modern technology.

The large volumes of laboratory data currently available in clinical practice can lead to erroneous conclusions. Our current statistical interpretation of these data is univariate (one variable at a time) and often not age-and sex-corrected. Using an optimal technique of multivariate analysis, a SMAC® profile of 19 tests performed on normal subjects resulted in over a 500% improvement in defining the reference range. Using physiologic subsets of the SMAC profile for patients, improvements in interpretation of between 100% and 300% are possible. Results indicate a serious clinical problem that will require modification of laboratory reports using modern technology as an adjunct for diagnostic medicine.

Establishing a multivariate clinical laboratory data base.

A regional or hospital-based “reference” value study is well within the range of every clinical laboratory. A program is described that samples one to two “health” subjects each working day under tightly controlled conditions. Sixty-seven variables are tested simultaneously to provide univariate age and sex ranges, and variance/covariance matrices from which associated correlation coefficients are obtained. This ongoing “reference” value program offers essential information for both univariate and multivariate interpretations along with validation and quality control for certain methods within the clinical laboratory.

The role of modeling methods in medical diagnosis

Modeling methods in medical diagnosis are concerned with medical information processing as it pertains to utilizing biological modeling methods to facilitate patient care. Major considerations in this particular area are (1) the classification problem related to the establishment of disease entities—the taxonomy problem, and (2) the diagnosis of diseases. Available are properties, criteria, signs, symptoms, and manifestations of diseases that have been cumulated and categorized by clinicians and researchers. The problem is to optimally utilize the information content of a sign or set of signs in the practice of patient care as pertaining to the medical diagnosis problem. Some mathematical approaches implemented to facilitate such analyses include cluster analysis, discriminant analysis, Bayesian methods, computer approaches, game theory, information theory, stochastic representations, stepwise procedures, decision analysis, and pattern recognition techniques. Each of these has been studied in depth by numerous researchers advocating computer applications in medicine. Here we discuss the scope and limitations of utilizing modeling methods as a viable approach to interpreting vast amounts of biological data collected on a single patient during an encounter. We consider the following: (1) limitations associated with modeling methodologies: (2) levels of responsibilities, ranging over logging, summarizing, reporting, monitoring, and therapy selection; and (3) operational strategies and considerations as they affect hardware logistics, the actual algorithm utilized, and implementation of these sophisticated analysis systems.

The evolution of a functional real-time laboratory records retrieval and archival system

The vast flood of information resulting from medical record keeping in a clinical laboratory must be cataloged and archived. To deal with this problem we designed an approach employing automated report generation by a laboratory computer, data base management of patient laboratory results, and automated microform generation via computer output microform (COM). This paper documents the steps that led to the system we are currently data, creates a microform archive and an on-line index, and serves as the data base for research inquiries. In addition, we have experienced a cost savings over manual procedures and now possess the capability of expansion without the costly addition of personnel.

A multivariate laboratory data analysis system: Introduction

In an era severely affected by the advanced stages of technocracy, it should not astound anyone that highly sophisticated technologies have metastasized throughout our hospital system. While simplifying many complex problems, the advantages of modern technology also create many interesting conflicts. One such dilemma surfaces as a consequence of clinical laboratories being able to produce a large number of test results in a relatively short time with a high degree of accuracy. Optimization of laboratory information must precede successful utilization of this extensive and expensive wealth of data.

Determining clinical significance in repeated laboratory measurements. The "Clinical Delta Range".

A study of healthy subjects was conducted at the University of Florida wherein 52 routine clinical laboratory procedures were measured during normal operation of the clinical laboratories. The goal of the study was to quantify nonsignificant personal variation for a number of laboratory procedures referred to as a “Clinical Delta Range.” which results from taking several samples per day, several days during the week. Results of the statistical test of hypotheses are given together with summary statistics for the 52 different laboratory procedures. A discussion of the model and testing methods are provided. Further, examples are given to illustrate the methodology and certain limitations associated with the concept of a Clinical Delta Range.