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R F Nease

Publications and source records attributed to R F Nease.

5 recordsLinked to original sources
PubMed1992

Occupational exposure to human immunodeficiency virus and hepatitis B virus: a comparative analysis of risk.

OBJECTIVE: To estimate the occupational risk from infection with the human immunodeficiency virus (HIV) in terms of loss of (quality-adjusted) life expectancy, and to compare that risk to those posed by other hazards faced by health care workers. DESIGN: Decision-analytic model. RESULTS: For a 30-year-old female health care worker (unvaccinated for hepatitis B virus [HBV]), the loss of life expectancy from a needlestick from a symptomatic HIV-positive (HIV+) patient is 39 days (range, 17 to 93 days), as compared with a loss of 17 days from a needlestick from a patient who is hepatitis-B-surface-antigen-positive (HBsAg+), and 38 days from a needlestick from a patient who is hepatitis-B-e-antigen-positive (HBeAg+). When morbidity is included in the analysis of risk (through calculation of the quality-adjusted loss of life expectancy), the risk from both HBV and HIV increases. The quality-adjusted loss of life expectancy due to a needlestick exposure from a symptomatic HIV+ patient is 45 days (range, 20 to 108 days), as compared with a quality-adjusted loss of life expectancy of 48 days from a needlestick from an HBsAg+ patient, and 109 days from a needlestick from a patient who is known to be HBeAg+. By comparison, a cross-country automobile trip is associated with a loss of life expectancy of approximately 1 day. The 45- to 50-day loss of quality-adjusted life expectancy from percutaneous exposures to HIV and HBV is approximately the same magnitude as the gain in life expectancy from 10 years of annual screening for breast cancer with mammography and physical examination. CONCLUSIONS: The risk associated with percutaneous exposures to symptomatic HIV+ patients is comparable to other risks that health care workers have faced knowingly and have accepted in the recent past. However, the loss of quality-adjusted life expectancy associated with a needlestick exposure is significant. Identification of cost-effective methods that increase the safety of medical personnel but also ensure full access to high-quality care for HIV+ patients should be a high priority.

Acquired Immunodeficiency Syndrome↗
PubMedDate not supplied

Solid recommendations from soft numbers: the test/treatment decision.

The authors review the probability threshold approach to test/treatment decisions developed by Pauker and Kassirer, emphasizing that certain aspects of the nature of medical decisions call for a new approach. The utility threshold approach, while maintaining all the advantages of threshold methods in general, brings improvements. It diminishes the need to accurately assess one of the decision's parameters: the patient's utility for the outcome states. For a simple case of one disease with three outcome states (cured, diseased, dead) and one test, three utility thresholds are derived. The treat/no treat threshold, denoted by u, separates the utility space in two. If the patient's value for the diseased state is greater than u, the analyst can feel confident in recommending the patient forego treatment. Similar interpretations are developed for u1, the no treatment/test utility threshold (the value u must take, given a positive test result, for the patient to be indifferent between foregoing and receiving treatment), and u2, the test/treatment utility threshold (the value u must take, given a negative test result, for the patient to be indifferent between foregoing and receiving treatment.

Decision Trees↗
PubMedDate not supplied

Threshold analysis using diagnostic tests with multiple results.

Clinical problems represented by decision trees can be analyzed in terms of the probability threshold model, which provides management recommendations based on the prior probability of disease, the test threshold, and the test-treatment threshold. As originally proposed, the threshold model assumes that diagnostic tests provide information about a single event that is relevant to the decision. For some problems, however, a diagnostic test may provide information about more than one such event (e.g., a computed tomography [CT] scan gives information about both mediastinal and hilar metastases in lung cancer). The authors extend the probability threshold model to cases in which a single test provides information about two events that are relevant to the decision. They derive four thresholds that determine the best strategy for any combination of test results. The approach is illustrated for the decision to use a CT scan to stage lung cancer. The analysis reveals that: 1) the range of prior probabilities for which testing is optimal increases; 2) for some prior probabilities only test results about one event are important; 3) for some prior probabilities test results about both events are important; and 4) failure to account fully for information provided by a test can lead to erroneous test and treatment recommendations.

Bayes Theorem↗