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Biomedical subjects

A Hartz

Publications and source records attributed to A Hartz.

46 records · Page 3Linked to original sources
PubMed1979

Time trends in the association of a rural or urban background with physician location.

The association of rural or urban background with choice of practice location was investigated in two time periods, 1950--1955 and 1963--1968, using the alumni of Marquette School of Medicine (now the Medical College of Wisconsin) in Milwaukee, Wisconsin. Background included size of communities of birth, high school, college, and internship. Small communities were screened to distinguish suburban from rural settings. It was found that in the 1950s the percentage of physicians from rural schools who chose to practice in rural areas was 2.2 times as great as this percentage for physicians from nonrural high schools (26 percent versus 12 percent). In the 1960s the ratio increased to 3.5, (35 percent versus 10 percent). This time trend was reversed for physicians more than 28 years old at the time of graduation from medical school. Place of birth and place of internship also were significantly associated with practice location; place of college showed a significant association with practice location only in the 1950s; and class rank was not associated with place of practice.

Age Factors↗
PubMed1977

Relative importance of the effect of family environment and heredity on obesity.

In a study using data from 254 families with 546 non-biologically related children and 10 337 families with 25 554 biologically related children an analysis of variance technique was used to evaluate the effect of family environment and heredity for children ages 4-11. Family environment accounted for 32% of the variation in obesity if all children were used and more than 39% of the variation in obesity using subgroups of children similar with respect to sex and age. The estimate of heritability was quite low, only 11%. These results suggest that family environment, which consists of such things as parental example and child rearing techniques, has an important effect on childhood obesity.

Analysis of Variance↗
PubMedDate not supplied

Use of postoperative information to predict mortality rates for patients who have long stays in the intensive care unit after coronary artery bypass grafting.

OBJECTIVE: To derive and evaluate prediction models for patients who had undergone coronary artery bypass grafting (CABG) and had a difficult postoperative course. DESIGN: Observational. SETTING: Midwestern hospital specializing in high-risk CABG procedures. PATIENTS: One hundred eighty-three patients who stayed at least 10 consecutive days in the intensive care unit after a CABG procedure. OUTCOME MEASURE: Death within 60 days of surgery. INTERVENTION: None. RESULTS: The final logistic regression prediction models included the following findings: pulmonary capillary wedge pressure, cardiac index, heart rate, urine output, positive end-expiratory pressure, blood urea nitrogen levels, and the arterial pressure of carbon dioxide. The model was able to stratify patients into four risk groups with observed 60-day mortality rates of 0.0% (n = 107), 21% (n = 39), 55% (n = 20), and 88% (n = 17). Preoperative patient information was not associated with prognosis for these patients. CONCLUSIONS: The findings suggest that a risk model that is specific for patients who have undergone CABG and is based on postoperative findings may provide useful prognostic information for patients who are having a difficult postoperative course.

Aged↗
PubMedDate not supplied

Stochastic thresholds.

Thresholds have traditionally been represented by a single number; the optimal management of the patient depends on whether his probability of disease is above or below this number. The concept of a threshold as a single number, however, inadequately represents the treatment approach of a group of physicians who do not have all the same threshold or a single physician who is uncertain about the exact value of the threshold. An alternative to a single valued threshold is to consider the threshold as having a probability distribution: for every probability that the patient has the disease there is a probability that the threshold is exceeded. This "stochastic" threshold model contains information about the uncertainty of the threshold estimation. Stochastic thresholds can be useful for testing the sensitivity of a management decision to the patient's probability of disease. They can also be used for comparing the standards of practice of individual physicians or comparing the practice of an individual physician with that of a group.

Decision Making↗
PubMedDate not supplied

Measures of the value of a diagnostic test derived from stochastic thresholds.

Previous indices for measuring the potential impact of a diagnostic test on a physician's management of a given patient were derived based on a fixed threshold model. The authors adapted these indices to a stochastic threshold model. In the stochastic threshold model the physician's probability of treating the patient is a function of the patient's probability of disease. From this model the authors derived the management value index (the expected effect that the test has on the physician's probability for treating the patient) and the utility value index (the expected benefit to the patient if the diagnostic test is used). Graphs of the indices versus the patient's probability of disease may be useful in teaching appropriate use of diagnostic tests.

Diagnosis↗
PubMedDate not supplied

How physicians use the stress test for the management of angina.

The authors examined physicians' reasons for ordering an exercise tolerance test and the influence of the test results on management decisions. Subjects of this study included 265 family physicians in Pennsylvania who completed a questionnaire on the management of a patient with typical chronic stable angina. Eighty-one percent of the respondents reported they would order a noninvasive stress test as a first step in the management of the patient. Of these physicians, 40% would refer the patient to a cardiologist only if the exercise tolerance test were strongly positive, 8% would refer only if it were negative, and 53% would not change their referral decisions on the basis of test results. No more than 57% of the physicians rated as very important any given reason for ordering a noninvasive diagnostic test. The results suggest that a test may be ordered routinely but without a consensus as to why it is ordered and without an effect on clinical management decisions.

Angina Pectoris↗
PubMedDate not supplied

A measure of mortality risk for elderly patients with acute myocardial infarction.

The objective of this study was to derive and validate a simple scoring system that predicts risk of short-term mortality in elderly patients hospitalized with acute myocardial infarction (AMI) and to compare this derived score with the MedisGroups admission severity score. A myocardial infarction severity score (MISS) was derived from a database of clinical information abstracted using MedisGroups and follow-up information on 30-day mortality status. The MISS was validated and compared with the MedisGroups Admission Severity Groups (ASGs) in a separate database. The derivation set included 2,037 Medicare patients 65 years old or older with confirmed AMI who were randomly selected from patients discharged from hospitals in seven states during 1985. The validation set consisted of 6,323 patients from the 1988 MedisGroups comparative database who were at least 65 years of age and had confirmed AMI. Multivariate logistic regression analysis found a set of nine abnormal patient characteristics that independently predict 30-day mortality. There was good agreement between mortality rates predicted by the logistic model and observed mortality rates in the validation population. This regression model was then simplified to an additive score where eight of the characteristics were weighted as one point and one characteristic was weighted as two points. The MISS is the sum of the points for each patient. In the validation dataset, the 1,373 patients with the lowest MISS scores had a mortality rate of 4.6% and the 400 patients with the highest MISS scores had a mortality rate of 64%.(ABSTRACT TRUNCATED AT 250 WORDS)

Acute Disease↗