Author
David B. Pryor
Other affiliations: Medical College of Wisconsin, Merck & Co., Memorial Hospital of South Bend ...read more
Bio: David B. Pryor is an academic researcher from Duke University. The author has contributed to research in topics: Coronary artery disease & Myocardial infarction. The author has an hindex of 62, co-authored 126 publications receiving 18269 citations. Previous affiliations of David B. Pryor include Medical College of Wisconsin & Merck & Co..
Papers published on a yearly basis
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TL;DR: The treadmill exercise test is shown to provide surprisingly little prognostic information beyond that obtained from basic clinical measurements.
Abstract: A method is presented for evaluating the amount of information a medical test provides about individual patients. Emphasis is placed on the role of a test in the evaluation of patients with a chronic disease. In this context, the yield of a test is best interpreted by analyzing the prognostic information it furnishes. Information from the history, physical examination, and routine procedures should be used in assessing the yield of a new test. As an example, the method is applied to the use of the treadmill exercise test in evaluating the prognosis of patients with suspected coronary artery disease. The treadmill test is shown to provide surprisingly little prognostic information beyond that obtained from basic clinical measurements.
2,735 citations
TL;DR: A general index of predictive discrimination is used to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease.
Abstract: Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size.
We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard ‘step-up’ variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.
1,657 citations
TL;DR: The Duke Activity Status Index is a valid measure of functional capacity that can be obtained by self-administered questionnaire and correlated well with peak oxygen uptake.
Abstract: To develop a brief, self-administered questionnaire that accurately measures functional capacity and assesses aspects of quality of life, 50 subjects undergoing exercise testing with measurement of peak oxygen uptake were studied. All subjects were questioned about their ability to perform a variety of common activities by an interviewer blinded to exercise test findings. A 12-item scale (the Duke Activity Status Index) was then developed that correlated well with peak oxygen uptake (Spearman correlation coefficient 0.80). To test this new index, an independent group of 50 subjects completed a self-administered questionnaire to determine functional capacity and underwent exercise testing with measurement of peak oxygen uptake. The Duke Activity Status Index correlated significantly (p less than 0.0001) with peak oxygen uptake (Spearman correlation coefficient 0.58) in this independent sample. The Duke Activity Status Index is a valid measure of functional capacity that can be obtained by self-administered questionnaire.
1,457 citations
TL;DR: The treadmill score is a useful and valid tool that can help clinicians determine prognosis and decide whether to refer outpatients with suspected coronary disease for cardiac catheterization, and was a better predictor of outcome than the clinical assessment.
Abstract: Background. The treadmill exercise test identifies patients with different degrees of risk of death from cardiovascular events. We devised a prognostic score, based on the results of treadmill exercise testing, that accurately predicts outcome among inpatients referred for cardiac catheterization. This study was designed to determine whether this score could also accurately predict prognosis in unselected outpatients. Methods. We prospectively studied 613 consecutive outpatients with suspected coronary disease who were referred for exercise testing between 1983 and 1985. Follow-up was 98 percent complete at four years. The treadmill score was calculated as follows: duration of exercise in minutes — (5 × the maximal ST-segment deviation during or after exercise, in millimeters) — (4 × the treadmill angina index). The numerical treadmill angina index was 0 for no angina, 1 for nonlimiting angina, and 2 for exercise-limiting angina. Treadmill scores ranged from —25 (indicating the highest risk) to +...
785 citations
TL;DR: This study examined the suitability of billing data compared with clinical data (prospectively collected for cardiology research and patient care) for use in clinical outcomes research and found that billing data is limited by the unstructured way in which they are collected.
Abstract: Objective: To determine the suitability of insurance claims information for use in clinical outcomes research in ischemic heart disease. Design: Concordance study of two databases. Setting: Tertiar...
639 citations
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TL;DR: In this article, an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, which are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
7,879 citations
TL;DR: Preamble and Transition to ACC/AHA Guidelines to Reduce Cardiovascular Risk S2 The goals of the …
Abstract: Preamble and Transition to ACC/AHA Guidelines to Reduce Cardiovascular Risk S2
The goals of the …
7,184 citations
TL;DR: The present guidelines supersede the 1994 guidelines and summarize both the evidence and expert opinion and provide final recommendations for both patient evaluation and therapy.
Abstract: The American College of Cardiology (ACC)/American Heart Association (AHA) Task Force on Practice Guidelines was formed to make recommendations regarding the diagnosis and treatment of patients with known or suspected cardiovascular disease. Coronary artery disease (CAD) is the leading cause of death in the United States. Unstable angina (UA) and the closely related condition non–ST-segment elevation myocardial infarction (NSTEMI) are very common manifestations of this disease. These life-threatening disorders are a major cause of emergency medical care and hospitalizations in the United States. In 1996, the National Center for Health Statistics reported 1 433 000 hospitalizations for UA or NSTEMI. In recognition of the importance of the management of this common entity and of the rapid advances in the management of this condition, the need to revise guidelines published by the Agency for Health Care Policy and Research (AHCPR) and the National Heart, Lung and Blood Institute in 1994 was evident. This Task Force therefore formed the current committee to develop guidelines for the management of UA and NSTEMI. The present guidelines supersede the 1994 guidelines.
The customary ACC/AHA classifications I, II, and III summarize both the evidence and expert opinion and provide final recommendations for both patient evaluation and therapy:
Class I: Conditions for which there is evidence and/or general agreement that a given procedure or treatment is useful and effective .
Class II: Conditions for which there is conflicting evidence and/or a divergence of opinion about the usefulness/efficacy of a procedure or treatment.
Class IIa: Weight of evidence/opinion is in favor of usefulness/efficacy.
Class IIb: Usefulness/efficacy is less well established by evidence/opinion.
Class III: Conditions for which there is evidence and/or general agreement that the procedure/treatment is not useful/effective and in some cases may be harmful.
The weight of the evidence was ranked highest (A) if the data …
5,020 citations
01 May 1981
TL;DR: This chapter discusses Detecting Influential Observations and Outliers, a method for assessing Collinearity, and its applications in medicine and science.
Abstract: 1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.
4,948 citations
24 Aug 2005
TL;DR: An easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities are discussed, applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes.
Abstract: Multivariable regression models are powerful tools that are used frequently in studies of clinical outcomes. These models can use a mixture of categorical and continuous variables and can handle partially observed (censored) responses. However, uncritical application of modelling techniques can result in models that poorly fit the dataset at hand, or, even more likely, inaccurately predict outcomes on new subjects. One must know how to measure qualities of a model's fit in order to avoid poorly fitted or overfitted models. Measurement of predictive accuracy can be difficult for survival time data in the presence of censoring. We discuss an easily interpretable index of predictive discrimination as well as methods for assessing calibration of predicted survival probabilities. Both types of predictive accuracy should be unbiasedly validated using bootstrapping or cross-validation, before using predictions in a new data series. We discuss some of the hazards of poorly fitted and overfitted regression models and present one modelling strategy that avoids many of the problems discussed. The methods described are applicable to all regression models, but are particularly needed for binary, ordinal, and time-to-event outcomes. Methods are illustrated with a survival analysis in prostate cancer using Cox regression.
4,905 citations