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PubMed1988

The accuracy and stability of Bayesian theophylline predictions.

Pharmacokinetic parameters for theophylline were determined in 33 patients (3 women), mean age 61.2 years and weight 74.6 kg using the following three methods: (a) standard one-compartmental model calculations, assuming 100% bioavailability, after a single dose of theophylline syrup (mean dose 413 mg); (b) drug nomogram; and (c) Bayesian analysis. Patients entered a randomised study of three two-monthly dosage regimens using low, medium, and high theophylline twice daily doses. These doses produced mean (+/- SE) steady-state serum theophylline concentrations of 6.3 (+/- 0.4), 12.1 (+/- 0.3) and 18.3 (+/- 0.5) mg/L, respectively. A fourth period of placebo (2-month duration) was also included. At the end of each treatment period the measured serum theophylline concentration of each patient was compared with those predicted by each of the above three methods. The revised estimates derived from Bayesian analysis produced the least biased [mean prediction error (ME)] and most precise (mean squared prediction error) predictions for all three dosage periods. Statistical analysis of relative performance demonstrated that the difference in precision between the revised estimates and those of the other two methods was significant (p less than 0.05) with the magnitude of the difference increasing with dose. The revised estimates were also found to be less biased (p less than 0.05) than those of the nomogram. The ME (+/- SE) of the revised estimates for the low, medium, and high dosage periods was 0.34 (+/- 0.30), -0.02 (+/- 0.22) and -0.48 (+/- 0.31) mg/L, respectively.

Bayes Theorem↗
PubMed1991

An assessment of population-based and Bayesian methods to individualize digoxin doses shortly after the start of therapy for atrial fibrillation.

The accuracy of population-based methods and of Bayesian analysis to predict individual digoxin pharmacokinetic variables have been evaluated by their ability to predict a measured peak and trough serum digoxin concentration. We studied 13 digitalized patients (three women) whose mean (range) age and weight was 65.8 (60-78) years and 76.6 (68-101.6) kg and who had stable renal function. The population-based methods (using a clearance of 48.87 + 0.87 x creatinine clearance in ml/h/kg and volume of distribution, in litres, of either 7.3 x weight (kg) or 269 + 3.12 x creatinine clearance) were more than adequate for clinical purposes. The mean prediction errors of a measured steady-state peak concentration from these two population methods were -0.074 and 0.013 microgram/l respectively, whilst those of a measured trough concentration were -0.058 and 0.005 microgram/l. Bayesian analysis, using a sample drawn 11 h after the dose on day five of therapy, gave overall the least biased and most precise of the revised estimates. The mean prediction errors of peak and trough values using this sample were 0.069 and -0.005 microgram/l respectively. As expected, the closer the sample was drawn to the time of the trough concentration the more precise were the Bayesian-derived predictions. The value of the Bayesian technique to individualize digoxin doses could not be validated because it was not possible to distinguish between this and the population methods.(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗
PubMed1993

Computer dosing program for the initiation of vancomycin therapy.

The predictive performance of a computer dosing program used for initiating vancomycin therapy was studied. Initial serum vancomycin concentrations in 31 adult patients receiving vancomycin were estimated by using a computer program (T.D.M.S.) incorporating a two-compartment open model. Sixty-two serum vancomycin concentrations at steady state (Css) were obtained before and after one-hour infusions and compared with estimated Css values. Bias and precision were evaluated by calculating median error (ME) and median absolute error (MAE), respectively. Population-based estimates of volume of distribution (V) and clearance (CL) were compared with those obtained by fitting each patient's data set by using Bayesian analysis (BA) and non-linear least-squares regression (NLLS). Median (mean +/- S.D.) bias and precision for peak Css were 7.7 (10.2 +/- 10.8) and 7.7 (10.6 +/- 10.5) mg/L, and for trough Css were 7.4 (7.7 +/- 7.6) and 7.4 (8.8 +/- 6.2) mg/L. The medians were significantly different from zero. Estimated median (mean +/- S.D.) V, CL, and half-life were 0.72 L/kg, 0.60 (0.67 +/- 0.21) mL/min/kg, and 11.59 (12.87 +/- 3.91) hours. Median (mean +/- S.D.) CL values determined by BA and NLLS were 0.86 (0.89 +/- 0.32) and 0.85 (0.92 +/- 0.34) mL/min/kg, respectively. Both CL values were significantly greater than the population-based estimate. However, median V values determined by BA and NLLS did not differ from the population-based estimate. A revised clearance model derived from Bayesian analysis of data for the first 21 patients was tested in the 10 other patients and appeared to improve the predictive performance of the a priori model.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed1991

The prediction of steady-state plasma phenobarbitone concentrations (following low-dose phenobarbitone) to refine its use as an indicator of compliance.

1. A model for predicting the steady-state plasma concentration of phenobarbitone following low-dose phenobarbitone used as an indicator of compliance was derived using data for 10 healthy volunteers. 2. Each volunteer was given a single 30 mg oral dose of phenobarbitone and the pharmacokinetics were described. Subsequently, volunteers were given phenobarbitone 2 mg daily for 28 days and a further pharmacokinetic profile determined during and after this period. 3. An initial predicted estimate of steady-state plasma drug concentration was made using each volunteer's demographic details. This estimate was revised by Bayesian analysis using single timed samples (24, 48, 72 or 96 h) following the single dose. 4. The model was tested on a further 10 healthy volunteers given a single 8 mg dose and who were subsequently given 2 mg daily for 28 days. 5. The revised estimate of peak steady-state plasma phenobarbitone concentration utilising the 96 h post-single dose concentration (356 ng ml-1) was least biased (mean prediction error +/- 95% CI = 10.6 +/- 19.8 ng ml-1) and most precise (root mean square error +/- 95% CI = 28.3 +/- 19.0 ng ml-1). In all cases the peak or trough steady-state drug concentration was within 13% of the predicted value. 6. The model reflected compliance accurately in a further eight volunteers with simulated partial (two-thirds) compliance. 7. The use of a predictive model using Bayesian analysis to estimate expected steady-state plasma phenobarbitone concentrations could increase further the usefulness of low-dose phenobarbitone as an indicator of compliance.

Adult↗
PubMed1983

Electrocardiographic recognition of left atrial enlargement.

The ECG is widely used as a screening test for left atrial enlargement (LAE). Surprisingly, the most widely used criterion of LAE, the P-terminal force in lead V1 (PTF-V1) has not been systematically evaluated to determine the optimal level of PTF-V1 for detection of LAE in clinical populations. Accordingly, we examined the relationship between PTF-V1 and left atrial size by echocardiogram in 361 patients and performed a Bayesian analysis of test performance in populations with a varying prevalence of LAE. As PTF-V1 increased from greater than or equal to 0.03 to greater than or equal to 0.08, sensitivity in the 82 patients with LAE (LA dimension greater than 40 mm) fell from 51% to 23%, and specificity rose from 70% to 93%. In our study population (LAE prevalence = 23%), diagnostic performance of criteria was: PTF-V1 greater than or equal to 0.03 greater than or equal to 0.04 greater than or equal to 0.05 greater than or equal to 0.06 greater than or equal to 0.08 Positive Predictive Accuracy 33 46 52 58 50 Negative Predictive Accuracy 83 83 84 83 80 Per Cent Correct Diagnosis 66 76 78 80 77 Positive predictive accuracy and per cent correct diagnosis improved progressively as PTF-V1 rose from greater than or equal to 0.03 to greater than or equal to 0.06, but fell at greater than or equal to 0.08. Applying our sensitivity and specificity data to Bayesian analysis, PTF-V1 greater than or equal to 0.06 performed best in all populations with prevalence of LAE less than or equal to 50%. We conclude that use of PTF-V1 greater than or equal to 0.06 is superior to the standard criterion of PTF-V1 greater than or equal to 0.04 for all purposes ranging from screening of a general population to evaluation of diseased individuals whose likelihood of LAE ranges up to 50%.

Adolescent↗
PubMed1991

Bayesian subset analysis.

As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models. Exchange-ability among the interactions is a key assumption; thus, the results are of interest primarily in the context of examining a collection of subsets with no definite a priori distinction relative to treatment effect. Exchangeability leads to a shrinking of the posterior distributions of the interaction terms toward the natural origin of 0, offsetting the tendency of the estimated effects to disperse. The method is applied to parameter estimates from a proportional hazards regression analysis of survival data from a clinical trial, invoking the approximate multivariate normal distribution of the estimates. No subjective prior distributions are required. Vague priors are used for all of the regression coefficients except the treatment-by-covariate interactions, which are assumed to follow a normal distribution.

Clinical Trials as Topic↗
PubMed1983

Cross-validation of a patient classification procedure: an application of the U method.

The objective of this article is to present the methods used for the validation of a patient classification system that was based on the concept of types of care (PCTC system). The PCTC system was developed to improve placement decisions for long-term care patients and also to provide information required for planning in the field of long-term care. A sample of long-term care patients was selected from various institutions/programs and the patients in the sample were assessed and classified by the program practitioners (users) as well as an independent panel (criterion team) composed of a physician, a nurse, and a social worker, using prototype forms specially designed for the project. An objective and empiric classification model was developed by applying discriminant analysis, Bayesian classification procedure, and cluster analysis techniques. The classification validity was evaluated by the use of the R, H, and U methods.

Adult↗
PubMed1978

Effect of atypical antibiotic resistance on microorganism identification by pattern recognition.

We classified microorganisms from the clinical laboratory by using information provided by the Gram stain and antibiotic sensitivity profiles obtained with the Bauer-Kirby technique. Approximately 4,000 microorganisms, routinely identified and tested for antibiotic sensitivities in a large hospital microbiology laboratory, were used as a data set for several pattern recognition classification methods: K--nearest-neighbor analysis, statistical isolinear multicomponent analysis, Bayesian inference, and linear discriminant analysis. K--nearest-neighbor analysis yielded the highest prospective classification accuracy for gram-negative organisms, 90%. When those organisms displaying an atypical antibiotic resistance pattern were excluded from the data, the gram-negative classification accuracy improved to 95%. These results are inferior to currently accepted biochemical identification methods. Microorganisms with atypical antibiotic resistance patterns are likely to be misidentified and are common enough (17% of our isolates) to limit the feasibility of routine identification of microorganisms from their antibiotic sensitivities.

Anti-Bacterial Agents↗
PubMed1987

Expert systems in psychiatry. A review.

Existing computer-based decision aids in the areas of psychiatric diagnosis and consultation are reviewed, and the prospects for expert system development within the mental health field are discussed. Emphasis is placed upon the decision-making models used in these systems rather than on their particular application area. The decision-making paradigms discussed are (1) data bank analysis, (2) statistical pattern recognition, (3) Bayesian analysis, (4) logical flow chart method, and (5) knowledge-based (expert system) approaches. For each paradigm, its essential features, its strengths and weaknesses, and some example applications are presented.

Diagnosis, Computer-Assisted↗
PubMed1990

Bayesian versus Fourier spectral analysis of ion cyclotron resonance time-domain signals.

The frequency-domain spectrum obtained by Fourier transformation (FT) of a time-domain signal is accurate only for a continuous noiseless time-domain signal of infinite duration. For discrete noisy truncated time-domain signals, non-FT (e.g., Bayesian analysis) methods may provide more accurate spectral estimates of time-domain signal frequencies, relaxation time(s), and relative abundances. In this paper, we show that Bayesian analysis of simulated and experimental ion cyclotron resonance (ICR) time-domain noisy signals can produce a spectrum with mass accuracy improved by a factor of 10 or more over that obtained from a magnitude-mode discrete fast Fourier transform (FFT) spectrum. Moreover, Bayesian analysis offers the useful advantage that it automatically estimates the precision of its iteratively determined spectral parameters. The main disadvantage of Bayesian analysis is its lengthy computation time compared to that of FFT (hours vs seconds on the same hardware for approximately 4K time-domain data points); the Bayesian computation time increases rapidly with the number of spectral peaks and (less rapidly) with the number of time-domain data points. Bayesian analysis should thus prove useful for those FT/ICR applications involving relatively few data points and/or requiring high mass accuracy.

Bayes Theorem↗
PubMed1991

Multivariate analysis of cardiovascular reflexes applied to the diagnosis of autonomic neuropathy.

A battery of cardiovascular reflex tests is usually performed for the diagnosis of autonomic neuropathy. The tests discriminate well between normal and definitely abnormal autonomic function. However, in some patients the results are borderline and their autonomic status cannot be better defined. We performed multivariate statistical analysis of six cardiovascular autonomic tests with the aim of increasing their diagnostic efficiency. Eighty-five healthy subjects and 95 patients at risk for autonomic neuropathy were studied. Principal component analysis and two pattern recognition methods, the Bayesian technique and the SIMCA method, were applied. It was found that: (1) normal models obtained by Bayesian analysis showed very high specificity and sensitivity; (2) a battery of two tests for parasympathetic function (R-R interval variation test, deep breathing) and two tests for sympathetic function (blood pressure responses to standing and to sustained handgrip) provide an appropriate diagnostic approach, if multivariate analysis is used; (3) multivariate analysis allows a more precisely defined assessment of autonomic nervous system function in so-called borderline patients.

Adult↗
PubMed1991

[Bayesian and RFLP linkage analysis on a DMD family].

Three methods were applied to estimate the carrier risk of the daughter of an obligate carrier of the Duchenne gene in a pedigree of Duchenne muscular dystrophy (DMD). According to Mendel's law of segregation, the daughter (III-3) of the obligate carrier (II-2) has a 50% chance of being a carrier. III-3 is also found to have a normal value in a carrier test, creatine phosphate kinase (CPK), and so the Bayesian analysis in risk estimation shows that the posterior probability that she is a carrier (25%). The restriction fragment length polymorphism (RFLP) linkage analysis indicates that the probability that III-3 is a carrier less than 5%. This result can be included in the Bayesian analysis to give an even lower risk of less than 2%. Our study demonstrates that the best result of carrier risk estimation can be obtained by a simultaneous application of Mendel's law, RFLP linkage analysis and the Bayesian analysis.

Bayes Theorem↗
PubMed1996

Uremic autonomic neuropathy: recovery following bicarbonate hemodialysis.

Autonomic function was followed in 8 chronic uremic patients on periodic hemodialysis over a period of almost eight years. The cardiovascular autonomic testing included R-R interval variation test, deep breathing, Valsalva manoeuvre, heart rate and blood pressure responses to standing, sustained handgrip. The patients were investigated on entry into the study and after 18, 56, and 92 months. Six months after the study at time 56 months, they switched from acetate to bicarbonate dialysis. The response to deep breathing test was significantly reduced at time 18 months versus baseline (P = 0.014), but significantly increased at time 92 months versus 56 months (P = 0.042). A significant decrease was found in the systolic blood pressure response to standing between baseline and 18 months (P = 0.014) and in the response to handgrip test between 18 and 56 months (P = 0.014). Multivariate analysis of the autonomic tests by a pattern recognition method (Bayesian analysis) showed that, at the time of entry into the study, two out of eight patients had autonomic damage. At 18 and 56 months, 6/8 patients had autonomic dysfunction. At the last time of investigation, 30 months after the onset of bicarbonate dialysis, all the patients showed a reversal of autonomic damage. Age and duration of dialysis on entry did not affect autonomic function. The present study is the first demonstration that autonomic neuropathy can recover after long-term dialysis. Since chronic hypoxemia is a cause of polyneuropathy, we postulate that: 1) hypoxemia in dialysis patients may have a role in the pathogenesis of uremic polyneuropathy, and particularly of autonomic dysfunction; 2) in patients on bicarbonate dialysis, a greater hemodynamic stability with less hypoxemia may lead to a recovery of autonomic function.

Acetates↗
PubMed1986

Evaluation of a Bayesian regression-analysis computer program for predicting phenytoin concentration.

A microcomputer program using Bayesian regression analysis to predict serum phenytoin concentrations was evaluated. Phenytoin concentration-time data from nine healthy male volunteers and one male patient were obtained from published studies. For two different dosage regimens that each subject received, the last available predose concentration on the sixth day of the regimen was predicted using observed predose concentrations on both the morning of the third day and on each of the first three days of phenytoin administration. In nine subjects who received at least 10 days of phenytoin therapy, observed concentrations after more than 10 days of therapy were predicted using both one and three observed serum concentrations. Also, in six subjects, the observed predose concentrations for the first three days of an initial phenytoin regimen were used to predict the last predose concentration observed during each subject's second regimen. Predictive performance of the program was evaluated using mean error (m.e.) as a measure of bias, mean absolute error (m.a.e.) as a measure of precision, and root mean square error (r.m.s.e.) as a composite measure of bias and precision. The majority of the predicted serum concentrations were accurate. Predictions of serum concentrations after six days and after more than 10 days of phenytoin therapy were somewhat more accurate when three serum concentrations were used than when only one concentration was used. In the six subjects for whom concentrations from an initial regimen were used to predict those in a second regimen, the largest prediction error was 5 mg/L (m.e. 0.88, m.a.e. 1.9, and r.m.s.e. 2.4).(ABSTRACT TRUNCATED AT 250 WORDS)

Bayes Theorem↗
PubMed1991

Nuclear grading of breast carcinoma by image analysis. Classification by multivariate and neural network analysis.

The use of nuclear grade as a prognostic indicator for breast carcinoma has been limited by interobserver variability. Advances in image analysis and automated cell classification offer one approach to this problem. The authors used the CAS-100 (Cell Analysis System. Elmhurst, IL) system to measure and analyze nuclear morphometric and texture features of cytologic preparations from 35 breast carcinomas (well, moderate, and poorly differentiated) as well as benign lesions. Morphometric and Markovian texture feature data from breast cancer nuclei of various grades comprised a training set, which was then used to establish classification criteria by multivariate (Bayesian) analysis and to train a neural network system. Both systems were tested for the ability to classify the nuclear grade of individual nuclei. There was good agreement between computer classification and the grade assigned by human observer to individual nuclei using either Bayesian or neural network analysis. Thirty-one unknown cases, which were assigned an overall grade by an observer, were then analyzed by computer, and an overall grade assigned based on the grade of nucleus most frequently present. Using this method, both classification systems were able to assign a "correct" grade to low-grade lesions (approximately 70% correct) more often than to high-grade tumors (approximately 20%). Difficulty in computer assignment of high-grade tumors was explained by nuclear heterogeneity in these tumors (i.e., although the percentage of high-grade nuclei was increased compared with that of low-grade tumors, high-grade nuclei frequently did not predominate). The authors present this study to demonstrate the feasibility of using image analysis as an objective means of nuclear grading. Further studies will be needed to establish criteria for assigning overall nuclear grade based on computer analysis of imaging data.

Artificial Intelligence↗