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PubMedDate not supplied

Upper gastrointestinal tract bleeding: assessing the diagnostic contributions of the history and clinical findings.

Various strategies can be used in the diagnosis of upper gastrointestinal tract bleeding. This study investigates the relevance of anamnestic and clinical findings for the diagnosis of the bleeding source. The authors introduced a computer-aided diagnostic system using Bayes' theorem and compared it with clinicians' predictions using anamnestic and clinical findings only. There was no difference in the overall accuracy rates, but a difference was observed in the diagnostic behaviors of the two "systems." In addition, the discriminatory ability of the computer-aided system, the sharpness of the predictions obtained, and the reliability of the posterior probabilities were analyzed. It is concluded that the clinician and the computer-aided system are not able to discriminate well between the disease categories. Derived classification matrices and probability-based measures show the reasons for the inadequacy of diagnostic information obtainable from the clinical history and physical findings.

Bayes Theorem↗
PubMed2001

Retrospective exposure assessment using Bayesian methods.

This paper presents the application of a Bayesian framework for retrospective exposure assessment of workers in a nickel smelter. Using only sparsely available historical measurements will result in exposure estimates with large uncertainties. However, additional information, in the form of expert judgments informed by knowledge of historical plant conditions, can be brought to bear on this process. The experts are provided with an information packet that contains historical process information, process throughput levels for each year, the dimensions of the workplace, ventilation records, and task descriptions for each job category. Based on this information, the experts provide subjective prior probability distributions for input parameters to a general ventilation model that predicts building concentrations. These priors can be synthesized with the historical measurements using Bayes theorem. The prior distributions of exposures are updated using the average measured exposures (historical measurements) and their associated variances to obtain the posterior probability distributions for building concentrations as well as concentrations at specific locations in the building. Expert input was also obtained from a plant industrial hygienist, in the form of probability distributions, regarding the amounts of time spent by each job category in different locations in the building. Monte Carlo sampling, from the posterior probability distributions of concentrations in different micro-environments and the probability distributions of time spent by each job category in those micro-environments, was used to obtain worker exposures using a time-weighted averaging model.

Air Pollution↗
PubMed1984

Radionuclide exercise testing for coronary artery disease.

It is obvious that the indication and clinical applications of radionuclide stress testing have been expanded and that both techniques described in this article are useful for diagnostic and prognostic purposes. The sensitivity and specificity of noninvasive stress testing have been significantly enhanced by the introduction of these radionuclide approaches for detecting ischemia in patients with undiagnosed chest pain. As mentioned previously, these tests should be ordered with a sound understanding of the principle of Bayes' theorem and of probability analysis. High-risk patients with either stable CAD or recent myocardial infarction can be identified by the severity of the abnormal response elicited. Patients with multiple thallium defects, particularly of the redistribution type, appear to be at the highest risk for subsequent cardiac events. Similarly, patients with a greater than 10 per cent fall in ejection fraction with development of multiple wall motion abnormalities and an increase in end-systolic volume seem to be in a high risk subset. Further developments with single photon emission tomography and computer quantitation of thallium or ventriculographic images should make these tests even more reliable in obtaining useful information in patients with CAD.

Bayes Theorem↗
PubMed1998

Multivariate prediction of skeletal Class II growth.

Prediction of craniofacial growth is one of the keys to successful orthodontic treatment and stability. Despite numerous attempts at growth forecasting, our ability to accurately predict growth is limited. The present study outlines a possible new approach to prediction of craniofacial growth that differs from any previous attempt because of both the methods used and type of patients studied. The purpose of this study is to create and test prediction equations for forecasting favorable or unfavorable patterns of growth in skeletal Class II preadolescents. The subjects for this study include 19 females and 12 males from the Bolton growth center in Cleveland, Ohio. The patients were all untreated orthodontically, had lateral cephalometric headfilms taken biannually from the ages of 6 through 18 and had a Class II skeletal relationship at age 8. Twenty-six skeletal and dental landmarks were identified and digitized, and 48 linear, angular, and proportional measurements were calculated. The subjects were divided into two groups based on anterior-posterior changes in the relationship between the maxilla and mandible. Eleven patients were in the favorable growth group, with an average improvement of 4.13 degrees in the ANB angle; 20 patients were in the unfavorable growth group with an average increase of 0.16 degrees in the ANB angle. The following prediction formula was created with Bayes theorem and assuming a multivariate Gaussian distribution: P(Good¿Fn) = ke (-(0.5)) ¿Fn - mu(ng)¿sigma(g)(-1)¿Fn - mu(ng)¿T. The equation's sensitivity and specificity was calculated from serial cephalometric data from ages 6, 8, 10, and 12. The results obtained with this equation indicate 82.2% sensitivity, 95% specificity with a overall positive predictive value of 91%. This corresponds to 17.8% of patients being incorrectly identified as Poor Growers and only 5% of our patients were incorrectly identified as Good Growers. We conclude that this prediction formula improves the ability to predict favorable or unfavorable patterns of growth in this sample of skeletal Class II preadolescents.

Adolescent↗
PubMed2000

Interpreting clinical evidence of malingering: a Bayesian perspective.

Customary ways of reporting on or testifying about malingering have shortcomings. Stating an opinion "with reasonable medical certainty" tells fact-finders little about how much confidence the opinion deserves; stating that an individual's behavior is similar to that of known malingerers does not convey the information that fact-finders really need to know, which is the likelihood that the evaluee in question is a malingerer, given the evaluator's findings. Mossman and Hart (Mossman D, Hart KJ: Presenting evidence of malingering to courts: insights from decision theory. Behav. Sci. Law 14:271-91, 1996) recommend that mental health professionals address this problem by using Bayes' theorem to interpret test data from evaluations. However, these authors do not discuss the use of evidence obtained during interviews and from other clinical contexts, nor do they describe a method for quantifying imprecision in Bayesian probabilities. This article provides examples of how forensic evaluators might use a Bayesian perspective to interpret clinical indicia of malingering observed during evaluations of adjudicatory competence. The article discusses sources of imprecision in Bayesian posterior probabilities, describes a method for characterizing that imprecision using confidence intervals, and then presents several sample calculations that illustrate how interview findings change the likelihood of malingering. The article also discusses the implications of the Bayesian approach for forensic evaluations and for future research on malingered incompetence.

Bayes Theorem↗
PubMed1984

Clinical inferences and decisions--II. Decision trees, receiver operator curves and subjective probability.

In patient management, clinical decisions follow a logical sequence which can be formally expressed as a decision tree in which the uncertainties associated with each alternative outcome may be made explicit using Bayes' theorem. Where test data is used in the formulation of a decision, the uncertainty associated with the information it conveys may be modified by changing the pass/fail criterion to alter the false positive and false negative error rate. Classical procedures based on information theory are described to illustrate how this may be achieved for any test. When hard data is not available to permit such an approach, the clinician must rely on his own past experience or that of a colleague. Several methods are available for quantifying such experience by estimating subjective probabilities associated with an action or test result. Two simple methods are described for deriving subjective probabilities for subsequent use within a Bayesian decision model.

Bayes Theorem↗
PubMed1981

Bayesian analysis of stress thallium-201 scintigraphy.

The variation of the diagnostic value of stress Tl-201 scintigraphy with prevalence of coronary heart disease (CHD) in the population has been investigated using Bayesian reasoning. From scintigraphic and arteriographic data obtained in 100 consecutive patients presenting with chest pain, the sensitivity of stress Tl-201 scintigraphy for the detection of significant CHD was 90% and the specificity was 88%. From Bayes' Theorem, the posterior probability of having CHD for a given test result was calculated for prevalences of CHD ranging from 1% to 99%. The discriminant value of stress Tl-201 scintigraphy was best when the prevalence of CHD lay between 30% and 70% and maximum for a prevalence of 52%. Thus, stress Tl-201 scintigraphy would be an unsuitable diagnostic test where the prior probability of CHD is low, e.g., population screening programmes, and would add little where the clinical probability of having CHD is already high. However, where the prior probability of having CHD is intermediate stress Tl-201 scintigraphy may provide valuable diagnostic information.

Bayes Theorem↗
PubMedDate not supplied

Evaluation of a fetal risk-scoring system.

A population of 2029 pregnant women (929 primiparas; 1100 multiparas) has been used to examine aspects of the calculation of obstetric risk scores from the presence of individual risk factors. The findings have been related to fetal outcome in these pregnancies and the following conclusions were reached: (1) the use of Bayes theorem for the calculation of a risk score is superior to the simple addition of weighted risk factors; (2) the diagnostic efficiency of a risk score is somewhat reduced when the data base used for calculation of the score is derived from a population different from that of the current pregnancy; (3) there is almost total overlap of risk scores in women with satisfactory and unsatisfactory fetal outcome. It is concluded that risk scores can be used to identify a small group of women at particularly high risk; in the remainder of the population scores are unhelpful except perhaps to indicate the women who do not require an intensive program of antenatal care.

Bayes Theorem↗
PubMedDate not supplied

A new logistic regression approach for the evaluation of diagnostic test results.

The value of a dichotomous diagnostic test is often described in terms of sensitivity, specificity, and likelihood ratios (LRs). Although it is known that these test characteristics vary between subgroups of patients, they are generally interpreted, on average, without considering information on patient characteristics, such as clinical signs and symptoms, or on previous test results. This article presents a reformulation of the logistic regression model that allows to calculate the LRs of diagnostic test results conditional on these covariates. The proposed method starts with estimating logistic regression models for the prior and posterior odds of disease. The regression model for the prior odds is based on patient characteristics, whereas the regression model for the posterior odds also includes the diagnostic test of interest. Following the Bayes theorem, the authors demontsrate that the regression model for the LR can be derived from taking the differences between the regression coefficients of the 2 models. In a clinical example, they demonstrate that the LRs of positive and negative test results and the sensitivity and specificity of the diagnostic test varied considerably between patients with different risk profiles, even when a constant odds ratio was assumed. The proposed logistic regression approach proves an efficient method to determine the performance of tests at the level of the individual patient risk profile and to examine the effect of patient characteristics on diagnostic test characteristics.

Angiography↗
PubMed1995

A rough sets approach to patient classification in medical records.

This study used the original model of rough sets for data analysis of objective clinical findings from pneumonia patients. Rough sets data analysis was used to draw data dependencies, data reductions, approximate set classifications, and rule inductions from records in a clinical database. This study utilized Pawlak's rough classification algorithm [1] to examine the relationship between a constellation of findings and the ICD-9-CM classification scheme used for disease coding in order to generate a reduct. The reduct is a logical construct of the most information-preserving findings from a decision table. The Iliad expert system, which is based on Bayes Theorem, was used to validate the results we obtained using rough sets analysis to discriminate between the different types of pneumonia in the patient population. From a set of 25 objective clinical findings, chosen because they had a positive or negative predictive value for probability of death for pneumonia patients, the rough sets analysis constructed a logical classifier of six attributes that was as discriminatory as the statistical classifier in Iliad which utilized 11 of those 25 attributes. The rough sets methods are capable of identifying a minimal set of attribute values that are associated with a disease label. These methods could be applied to develop more consistent labeling methods, and, potentially flag activities which do not contribute to diagnostic labeling or health results.

Bayes Theorem↗
PubMed2001

Consanguinity and the transmission/disequilibrium test for allelic association.

Consanguineous marriages, usually between first cousins or between uncle and niece, are common in certain societies. The transmission/disequilibrium test (TDT) compares the transmission from parents to an affected child of alleles at a marker locus, and differential transmission indicates linkage and allelic association between the marker locus and a disease locus. We investigate the consequences for the TDT, as a test for allelic association, of consanguineous marriages. For each parental marker mating type, we calculate the frequency of each disease mating type, and the associated probability that an offspring is affected. We use Bayes' Theorem to estimate the probability that an affected child inherits the given allele from a heterozygous parent, then combine our results across marker mating types. The effect of consanguineous marriage is usually small. For candidate genes, the effects were greater for uncle-niece marriages, for rare disease alleles, and for high genotype relative risk. For markers, the effects were generally negligible. The Type I error probability of the TDT is essentially unchanged by intermarriage, except for a purely recessive disease allele. The power of the TDT is increased for a recessive allele and decreased for a dominant allele. However, consideration of levels of consanguinity that arise in practice indicates that standard power calculations for the TDT will usually need only minor modification.

Bayes Theorem↗
PubMed2007

Structured incorporation of prior information in relationship identification problems.

The objective of this paper is to show how various sources of information can be modelled and integrated to address relationship identification problems. Applications come from areas as diverse as evolution and conservation research, genealogical research in human, plant and animal populations, and forensic problems including paternity cases, identification following disasters, family reunions and immigration issues. We propose assigning a prior probability distribution to the sample space of pedigrees, calculating the likelihood based on DNA data using available software and posterior probabilities using Bayes' Theorem. Our emphasis here is on the modelling of this prior information in a formal and consistent manner. We introduce the distinction between local and global prior information, whereby local information usually applies to particular components of the pedigree and global prior information refers to more general features. When it is difficult to decide on a prior distribution, robustness to various choices should be studied. When suitable prior information is not available, a flat prior can be used which will then correspond to a strict likelihood approach. In practice, prior information is often considered for these problems, but in a generally ad hoc manner. This paper offers a consistent alternative. We emphasise that many practical problems can be addressed using freely available software.

Bayes Theorem↗
PubMed1975

[Praxis of the blood group opinion of parentage: systems with low exclusions potency; evidence of serological exclusion; biostatistical problems. Comments to a paper of H. Ritter in FamRZ 1973, 121 (author's transl)].

Blood group systems with a rare second allel are in praxis ineffective and therefore uneconomical because of their low exclusion potency as well as their low serostatistical value. The evidence of exclusion in particular blood group systems is fixed by the German "Richtlinien". With the new German law, however, one has to differentiate between the requirements in respect of the "offenbar unmöglich" ("fatherhood evidently impossible") in cases of extramatriomonial children, and those in respect of "schwerwiegende Zweifel" ("serious doubts in fatherhood") in cases of illegitimate children. The serostatistical results can give an indication on non-fatherhood, but in no case a real exclusion of paternity. Further evidence, as an anthropological or an HL-A expert opinion, is necessary to decide if the negative hint is reliable or not. To evaluate blood group (and HL-A) findings biostatistically, one uses the BAYES' Theorem with ESSEN-MOLLER's frequencies X and Y (in two-hypothesis cases). As prior plausibility one sets ESSEN-MOLLER's normative 0.5 or a realistic value, calculated from a concrete material of forensic cases. Statistical values on the basis of the exclusion chance as A, WA, Z, P and x do not give the whole information, because the phenotype of the man is not (or not fully) regarded. Therefore these values are of no use in serostatistical evaluation. With appropriate frequencies it is possible to calculate the probability of paternity also in cases in which foreign people is involved. If foreign frequencies are not available, the German "Richtlinien" allow to use european frequencies, if the involved people shares the caucasoid (europid) race.

Alleles↗
PubMedDate not supplied

[Bayesian analysis of myocardial scintigraphy using 99mTc-Sn(II)-methylenediphosphonate and using 99mTc-Sn(II)-pyrophosphate].

Myocardial scintigraphy with 99mTc-Sn (II)-Methylenediphosphonate (MDP) and with 99mTc-Sn (II) pyrophosphate (PPi) was performed in 185 patients with proven acute myocardial infarction (90 with MDP; 95 with PPi), and in 65 subjects with acute chest pain of a different etiology (37 with MDP; 28 with PPi), during the first five days after the onset of illness. Sensitivity, specificity and accuracy of the procedure were higher with PPi (0.895, 0.893, 0.894) than with MDP (0.822, 0.865, 0.835) in the diagnosis of AMI. Based on the usual clinical electrocardiographic and biochemical criteria, the likelihood of AMI on the population studied with MDP was of 70.9% and with PPi of 77.2%. Application of Bayes' theorem showed an increase in the likelihood of AMI, when myocardial scintigraphy was positive, from 70.9% to 93.7% when MDP was used, and from 77.2% to 96.6 when PPi was the tracer, with incremental ruling in gaings of 22.8% and 19.4%) respectively. A normal scintigraphy, on the other hand, reduced the likelihood of AMI from 70.9% to 33.4% when MDP was used, and from 77.2% to 29.2% when PPi was the tracer, with respective incremental ruling-out gains of 37.5% and 48.0%. Although homogeinity between the populations studied with each tracer was not proven, existing practical, clinical and biochemical evidence allows the conclusion that PPi is a better agent than MDP in the study of patients with acute chest pain.

Adolescent↗
PubMed2002

Remark on utility and error rates of the allopurinol test in detecting mild ornithine transcarbamylase deficiency.

Carriers of X-linked ornithine transcarbamylase deficiency (OTCD) are themselves mildly affected. The allopurinol test is quite sensitive (92.7%) and very specific in detecting these carriers. Consequently, it has also been recommended for the diagnosis of mild OTCD in the general population. However, there is a controversy on its utility since OTCD could not be demonstrated in several patients with positive test results but negative family histories. We show that this controversy is due to an improper use of statistical concepts, i.e., to the postulate of a specificity of "100%," and to the confusion of specificity with type I error rate. Spontaneous orotic aciduria implies a positive allopurinol test and limits the specificity of the test to a maximum of 99.7%. Therefore, according to Bayes' theorem, almost all positive test results in the general population must turn out to be type I errors, due to the minute prevalence (1/32,000) of mild OTCD (i.e., asymptomatic carriers and male patients with inapparent disease). Family history seems to be the only preselective parameter that can sufficiently raise the prevalence in the group to be tested. Bayesian analysis also yields the rate of type II errors (OTCD inspite of a negative test) which is high in closely related at-risk females (22.6% in mothers of male patients) but minimal in the general population. Conclusion. The allopurinol test is useful for the exclusion but not for the diagnosis of inapparent OTCD in sporadic individuals. Test results in possible carriers should be interpreted with caution.

Allopurinol↗
PubMed2006

[Stepwise diagnostic workup in general practice as a consequence of the Bayesian reasoning].

The dependency of the predictive values of tests on the prevalence of diseases is an escrow issue of clinical diagnostics. The relation between pre-test probability and post-test probability is well explained by Bayes' theorem, and the relation between prevalence and false diagnoses can be described well by modifying this theorem. In cases of low prevalence the positive predictive value (PPV) is lower and the false-positive predictive value (FPPV) higher. These aspects mainly depend on the test specificity. But basically, in cases of low prevalence there is a higher negative predictive value (NPV) and a lower false negative predictive value (FNVP). Depending on the sensitivity and specificity, NPV and FNPV vary only slightly in low prevalence ranges. These statistical relations are able to explain the typical mode of operation of general practitioners with their unselected patients. In order to increase PPV and decrease FPPV in the diagnostic workup, the GP must use his clinical experience, time and stepwise diagnostic procedures. More diagnostic studies are necessary to improve the diagnostic workup in patient care.

Bayes Theorem↗
PubMedDate not supplied

Sequential decision making with continuous disease states and measurements: I. Theory.

The question of whether to perform a continuous valued test to assess a continuous valued health state such as blood pressure or serum cholesterol is explored by decision analysis. Principal assumptions are that the underlying health state and measurement variability are both normally distributed, and that the impact of treatment on the utility of outcomes varies linearly with the underlying health state. Using Bayes' theorem, an expression for the expected utility of performing the test is derived and compared with immediate treatment or decision to withhold treatment. The calculations can be carried out with a pocket calculator and a table of the normal distribution. Iterating the analysis, a sequential decision making process is developed, leading to a series of no treat/test again and test again/treat thresholds with which a running average of independently obtained measurements can be compared to produce stepwise optimal results. The thresholds are readily calculated on a microcomputer. Finally, the conjugate-normal-linear model is extended to encompass the correlated observations that may be made on a single visit. This paper concentrates on the mathematics of decision making with continuous variables. The companion paper illustrates its application to diastolic blood pressure.

Bayes Theorem↗
PubMed2004

Prior convictions: Bayesian approaches to the analysis and interpretation of clinical megatrials.

Large, randomized clinical trials ("megatrials") are key drivers of modern cardiovascular practice, since they are cited frequently as the authoritative foundation for evidence-based management policies. Nevertheless, fundamental limitations in the conventional approach to statistical hypothesis testing undermine the scientific basis of the conclusions drawn from these trials. This review describes the conventional approach to statistical inference, highlights its limitations, and proposes an alternative approach based on Bayes' theorem. Despite its inherent subjectivity, the Bayesian approach possesses a number of practical advantages over the conventional approach: 1). it allows the explicit integration of previous knowledge with new empirical data; 2). it avoids the inevitable misinterpretations of p values derived from megatrial populations; and 3). it replaces the misleading p value with a summary statistic having a natural, clinically relevant interpretation-the probability that the study hypothesis is true given the observations. This posterior probability thereby quantifies the likelihood of various magnitudes of therapeutic benefit rather than the single null magnitude to which the p value refers, and it lends itself to graphical sensitivity analyses with respect to its underlying assumptions. Accordingly, the Bayesian approach should be employed more widely in the design, analysis, and interpretation of clinical megatrials.

Bayes Theorem↗