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PubMed2006

[Gastric cancer: probability assessment after lymph node-negative staging and its consequences].

OBJECTIVE: To provide an approach to calculating the probability of error after lymph node-negative staging in gastric cancer. PATIENTS AND METHOD: Retrospective data of 75 gastric resections for cancer were used to calculate the probability of error in general, according to T staging of the TNM classification (6th edition) and according to the type of lymphadenectomy performed. A modification of a procedure based on Bayes' theorem was used. RESULTS: For all tumors, at least 11 negative lymph nodes were required to ensure a true pN0. Two lymph nodes were required for T1 tumors, 11 for T2 tumors, and 14 for T3 tumors. A greater number of lymph nodes were required for a D2 lymphadenectomy than for a D1 lymphadenectomy. However, in D2 lymphadenectomy, pN0 stages were almost always reliable, while in D1 lymphadenectomy 24% of stagings were unreliable. CONCLUSIONS: The present study describes a simple and reproducible mathematical model that could help surgeons to determine the accuracy of lymph node-negative stages in a substantial group of patients with gastric cancer.

Bayes Theorem↗
PubMed2005

[The impact of socioeconomic status in the estimation of non-adult dental age].

The aim of this study is to observe the possible impact of socioeconomic level on stages of macroscopic dental development. The sample consists of 456 children from European origin, aged 3.5 to 16 years (242 girls and 214 boys). We apply a radiographic method of non-adult dental age estimation based on Bayes' theorem. Both socioeconomic level and geographic origin of grandparents are given in a questionnaire devised by the first author. First, results obtained with the Bayesian dependent method (BD method) are compared to those obtained with a more commonly used method of dental age estimation based on Correspondence Analysis and linear Regression (CAR method). Second, two approaches are proposed in order to test potential relationships between socioeconomic level and stages of macroscopic dental development. Both are based on observed shifts between dental age and chronological age with regard to socioeconomic level. Results obtained with the BD method are better, in terms of quality, than those obtained with the CAR method. We observed no influence of socioeconomic level on stages of macroscopic dental development.

Adolescent↗
PubMed2002

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data.

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.

Bacteria↗
PubMed2004

Inferring the most likely geographical origin of mtDNA sequence profiles.

In a number of practical cases it is important to determine the likely geographical origin of an individual or a biological sample. A dead body, old bones or a sample of semen may be available. Information on where the sample might come from can assist investigation or research. The first part of this paper is independent of specific data structure. We formulate the problem as a classification problem. Bayes' theorem allows different sources of information or data to be reconciled conveniently. The main part of the paper involves high dimensional data for which simple, standard methods are not likely to work properly. Mitochondrial DNA (mtDNA) data is a typical example of such data. We propose a procedure involving essentially two steps. First, principal component analysis is used to reduce the dimension of the data. Next, quadratic discriminant analysis performs the actual classification. A cross validation procedure is implemented to select the optimal number of principal components. The importance of using separate data sets for model fitting and testing is emphasized. This method distinguishes well between individuals with a self reported European (Icelandic or German) origin and SE Africans. In this case the error rate is 2.0%.

Africa↗
PubMed1995

The utility of three predictors of childhood myopia: a Bayesian analysis.

Any treatment to prevent the onset of juvenile myopia will require predictive tests in order to determine which children should receive treatment. Three risk factors for myopia were evaluated for their ability to predict myopia: (a) refraction at school entry; (b) refraction in infancy; and (c) parental history of myopia. Bayes' theorem was used to estimate these conditional probabilities. Refraction at school entry had twice the power to predict myopia (probability of juvenile myopia given the child is near emmetropia at school entry = 0.53) compared to either infant refraction (0.21-0.28) or parental myopia (0.20-0.25). While a history of any parent having myopia had the highest test sensitivity (probability of a positive family history of myopia given juvenile myopia in the child = 0.90) and refraction at school entry the highest test specificity (probability of more hyperopia than +0.50 D at school entry given no juvenile myopia = 0.91), none of these three factors had high values for both sensitivity and specificity. Further work is required to develop a battery of tests which could predict the onset of juvenile myopia with both adequate sensitivity and specificity.

Age Factors↗
PubMed1996

Is intertwin birth weight discordance predictable?

OBJECTIVE: To compare the efficacies of intertwin difference in abdominal circumferences (ACs) and estimated fetal weight (EFWs) to predict birth weight discordance in twin gestations. METHODS: Ninety pairs of twins with sonographic measurements of AC and femur length, performed within 2 weeks before delivery, were included in the study. The EFW was calculated (Hadlock's formula). We used Bayes' theorem and the Mantel-Haenszel chi 2 test to calculate and compare the rates of false-positive and false-negative prediction of the cutoff values of 15, 20, and 25% intertwin birth weight discordance by the intertwin EFW difference and an intertwin AC difference of > or = 18 mm. RESULTS: There were no significant difference between the two methods to predict birth weight discordance at 15-25% discordance (p = 0.254, OR = 0.33, 95% CI = 1.45-0.24) and at > 25% discordance (p = 0.112, OR 0.18, 95% CI = 0.03-1.34). CONCLUSIONS: Both the intertwin AC difference (> or = 18 mm) and intertwin EFW difference have similar negative predictive values and the same efficacy to exclude discordant growth. Because of the relatively low positive predictive values, birth weight discordance cannot be accurately predicted by either method.

Bayes Theorem↗
PubMed2004

Using Biowin, Bayes, and batteries to predict ready biodegradability.

Whether or not a given chemical substance is readily biodegradable is an important piece of information in risk screening for both new and existing chemicals. Despite the relatively low cost of Organization for Economic Cooperation and Development tests, data are often unavailable and biodegradability must be estimated. In this paper, we focus on the predictive value of selected Biowin models and model batteries using Bayesian analysis. Posterior probabilities, calculated based on performance with the model training sets using Bayes' theorem, were closely matched by actual performance with an expanded set of 374 premanufacture notice (PMN) substances. Further analysis suggested that a simple battery consisting of Biowin3 (survey ultimate biodegradation model) and Biowin5 (Ministry of International Trade and Industry [MITI] linear model) would have enhanced predictive power in comparison to individual models. Application of the battery to PMN substances showed that performance matched expectation. This approach significantly reduced both false positives for ready biodegradability and the overall misclassification rate. Similar results were obtained for a set of 63 pharmaceuticals using a battery consisting of Biowin3 and Biowin6 (MITI nonlinear model). Biodegradation data for PMNs tested in multiple ready tests or both inherent and ready biodegradation tests yielded additional insights that may be useful in risk screening.

Bayes Theorem↗
PubMed1994

Predictive performance of the Bayesian analysis: effects of blood sampling time, population parameters, and pharmacostatistical model.

The present paper reports theoretical equations for the predictive performance of the Bayesian forecasting method. The precision of parameter estimates and predicted concentrations for an individual was described by general equations with the aid of a variance-covariance matrix of parameter estimates that involved the Bayes theorem. The equations were applied to assess the predictive performance of the one-point Bayesian method in association with blood sampling time, the population parameters, and the pharmacostatistical model. The simulation study showed that the prediction error in parameter estimates essentially depended upon the sampling time but the magnitude of dependency was affected by the size of inter- and intraindividual variances. With a smaller value of interindividual variance, the dependency on sampling time was less apparent. Effects of sampling time were further examined using clinical data obtained from 20 patients taking theophylline, and the results were in good agreement with the theoretical consideration. The present general equations are useful to investigate the sampling strategy as well as structural and variance modeling on the predictive performance of the Bayesian method.

Adult↗
PubMed1992

The diagnostic value of imaging techniques for aortic dissection.

Previous studies conducted in high-risk populations have reported high predictive values and accuracies for diagnostic imaging techniques for aortic dissection. To see how these techniques perform in low-risk populations, we used Bayes' theorem to calculate predictive values and accuracies for angiography, CT, MRI, and TEE. In high-risk populations (disease prevalence = 50%), positive predictive values were all greater than 85%. In intermediate risk populations (disease prevalence = 10%), positive predictive values were greater than or equal to 90% for CT, MRI, and TEE, but were 65% for angiography. In low-risk populations (disease prevalence = 1%), positive predictive values were 100% for MRI and less than or equal to 50% for angiography, CT, and TEE. In all three populations, negative predictive values and accuracies were greater than or equal to 85%. From these results we conclude that diagnostic imaging techniques for aortic dissection do not perform as well in low-risk populations as they do in high-risk populations.

Aortic Dissection↗
PubMed1994

Transvaginal sonography for diagnosing ectopic pregnancy: positivity criteria and performance characteristics.

Despite the widespread use of TVS for diagnosing EP and extensive literature on the subject, there is no consensus regarding the best positivity criterion for adnexal findings or the performance characteristics of TVS. We conducted a literature search to identify original studies presenting suitable data on the use of TVS for the diagnosis of EP. The data were combined to determine the sensitivity and specificity of four sonographic criteria for EP, listed in order from most to least stringent: Criterion A, living extrauterine pregnancy; criterion B, extrauterine gestational sac containing yolk sac or embryo; criterion C, empty "tubal ring" or extrauterine gestational sac containing yolk sac or embryo; and criterion D, any adnexal mass other than a simple cyst. Positive and negative predictive values were computed using Bayes' theorem. Ten studies involving a total of 2216 patients, 565 with EP and 1651 without EP, were included in our analysis. Based on the combined data from these studies, criteria A, B, and C all have high specificities (99.5-100%) and positive predictive values (97.8-100%) but low sensitivities (20.1-64.6%) and mediocre negative predictive values (78.5-89.1%). Criterion D, the most lax criterion, has the most uniformly excellent characteristics, with only slightly lower specificity (98.9%) and positive predictive value (96.3%) but considerably higher sensitivity (84.4%) and negative predictive value (94.8%). The performance characteristics of TVS criteria for EP, computed by pooling data from published studies, indicate that the appropriate TVS criterion to diagnose EP is any noncystic adnexal mass. These performance characteristics can be used as a basis for comparing TVS with other proposed diagnostic modalities for EP.

Bayes Theorem↗
PubMed2001

Solitary retinal capillary hemangioma: hereditary (von Hippel-Lindau disease) or nonhereditary?

OBJECTIVE: To estimate the probability of von Hippel-Lindau (VHL) disease in patients with solitary retinal capillary hemangioma. METHODS: Risk estimation was performed mathematically on the basis of the Bayes theorem using the published data on prevalence of VHL disease, prevalence of solitary retinal capillary hemangioma, and age-dependent penetrance of VHL disease. RESULTS: The probability of VHL disease in patients with solitary retinal capillary hemangioma was estimated to be 46%. When adjusted for age at the time of diagnosis, the risk varied from 45% in the young age group (</=10 years) to 0.5% in the older age group (61-70 years). CONCLUSIONS: Approximately half of the patients with solitary retinal capillary hemangioma are expected to have underlying VHL disease and the risk progressively diminishes with increasing age at diagnosis. Detailed clinical evaluation in such patients is recommended using standard screening protocols. Knowledge of the age-dependent risk for VHL disease can help the clinician modify recommendations regarding systemic and genetic testing.

Adolescent↗
PubMed1997

Implementation and evaluation of a stochastic control strategy for individualizing teicoplanin dosage regimen.

A stochastic control strategy for individualizing teicoplanin dosing schedule in neutropenic patients is proposed and compared to the usual Bayesian approach based on the mode of the posterior density of the model parameters. Teicoplanin disposition is described by a bicompartmental model. Age, body weight, serum creatinine, white blood cell count, and sex can be included as covariates. Posterior density of model parameters is obtained by Bayes theorem under a discrete form from which the posterior density of teicoplanin trough concentrations are computed for any dosing schedule. Optimal maintenance dose is determined by minimizing the cost associated, through a logarithmic risk function, to the concentrations being outside the therapeutic range. In Monte Carlo simulation studies on 300 individuals, stochastic control was more accurate than, and equally precise as the usual Bayesian approach. Two-sample based predictions were not better than one-sample based ones. Inclusion of covariates in the model improved dramatically the performances of both strategies. A small retrospective study based on real data (n = 16 patients) shows that reasonable accuracy (bias of 0.7 mg/L) and precision (3 mg/L) in teicoplanin trough concentration prediction is obtained with both strategies provided that covariates are taken into account.

Age Factors↗
PubMed2004

A mixture model application in disease mapping of malaria.

Disease mapping, a method for displaying the geographical distribution of disease occurrence, has received attention for more than 2 decades. Because traditional approaches to disease mapping have some deficiencies and disadvantages in presenting the geographical distribution of disease, the mixture model--as an alternative approach--overcomes some of these deficiencies and provides a clearer picture of the spatial risk structure. The purpose of this study was twofold: (1) to investigate the geographical distribution of malaria in Thailand during 1995, 1996, and 1997 by applying the mixture model to disease mapping, and (2) to investigate the dynamic nature of malaria in Thailand during the 3-year time frame by applying the space-time mixture model. Non-parametric maximum likelihood estimation was employed to estimate the parameters of both the mixture model and the space-time mixture model. Applying Bayes' theorem, the 76 provinces of Thailand were classified into component risk levels by the rate of malaria for each province. Malaria intensively occurred in 4 provinces on the Thai-Myanmar border and in 2 provinces on the Thai-Cambodian border. Of the 76 provinces studied, 10 showed an increasing trend over the 3-year period. A comparison of the map based on the mixture model with the map based on the traditional percentiles method indicates that the non-parametric mixture model removes random variability from the map and provides a clearer picture of the spatial risk structure. The advantage of the mixture model approach to disease mapping is the graphical visual presentation of the prevalence of disease. The space-time mixture model more adequately investigates the dynamic nature of disease than does the mixture model.

Bayes Theorem↗
PubMed1991

'Solubile': decision-making in the diagnosis of jaundice.

We have designed a computer program 'Solubile' to aid clinicians in the diagnosis of jaundice. Based on Bayes' theorem, 'Solubile' uses up to 47 items of information about the patient to produce the most probable diagnosis from 22 possible diseases. In a prospective analysis of 50 patients, 74% were correctly diagnosed in first place and 94% within the first three choices. The possibility of using 'Solubile' at differing locations was tested by prospectively diagnosing 100 cases at a second centre having a significantly different patient population. 75% of these patients were correctly diagnosed in first place and 89% within the first three choices. The diagnostic ability of 'Solubile' was compared with that of 20 clinicians of various grades. The clinicians correctly diagnosed 49.5% of cases (Solubile 74%) and placed 68.5% (Solubile 94%) within the first three choices. 'Solubile' will be of use to aid clinicians in aspects of the diagnosis and management of jaundice.

Bayes Theorem↗
PubMed1990

A computer model of antenatal care: relationship between the distribution of obstetric risk factors in simulated cases and in a real population.

The main purpose of antenatal care is to identify risk factors for the fetus. A number of systems have been described in which positive factors are summed to produce an overall risk score for the individual woman. In the present study a computer simulation of a population of pregnant women has been used to examine the use of Bayes theorem to calculate obstetric risk scores. The findings of the computer simulation have been compared with those in a real obstetric population (774 primigravidae; 930 multigravidae). A number of observations have been made which may be of considerable practical significance: (1) in the population as a whole the distribution of risk scores is skewed; most women have a low score while relatively few subjects have high scores; (2) a minimum number of risk factors must be present simultaneously for the risk in an individual to consistently exceed the overall population risk; this number is 4 in primigravidae and 5 in multigravidae; (3) the relationship between the number of risk factors and overall risk does not follow a straight line. The computer simulation provides a test bed in which various assumptions can be examined, such as the effect of removing or redefining given risk factors.

Bayes Theorem↗
PubMed2003

BREASTAID: Clinical results from early development of a clinical decision rule for palpable solid breast masses.

OBJECTIVE: To develop a clinical decision rule (entitled BREASTAID) that will predict the probability of malignancy in women with palpable solid breast masses. SUMMARY BACKGROUND DATA: Currently, 80% of open breast biopsies are benign, resulting in excessive economic, psychologic, and physical morbidity. METHODS: A total of 452 solid breast masses were evaluated in a surgical breast clinic between November 1994 and February 1998. Breast cancer status was defined histologically as ductal carcinoma in situ or invasive cancer. Noncancer status included benign histology, mass resolution, or stability at 12-month follow-up. Data were collected on risk factors, clinical breast examination, mammography, and cytology results. Three multiple logistic regression models were used to generate the probability of cancer at 3 logical steps in the workup; Bayes' theorem was applied in a stepwise fashion to generate a final probability of cancer. RESULTS: A model incorporating only clinical breast examination and mammography resulted in an excessive number of either missed cases or biopsies compared with one that included cytology. Using a cut-point of 4%, this latter BREASTAID model had 97.6% sensitivity and 85.1% specificity. Compared with triple diagnosis, BREASTAID would have reduced the open biopsy rate from 39.8% (180 of 452) to 22.3% (101 of 452), improving the diagnostic yield from 22.7% to 40.6%. CONCLUSIONS: This study convincingly demonstrates that at minimum, clinical, radiologic, and cytologic evaluations are required to accurately evaluate a solid breast mass. BREASTAID has the potential to minimize the number of open biopsies performed while allowing safe triage to follow-up. Before widespread application, further validation studies are required.

Bayes Theorem↗
PubMed2004

A prospective, blinded evaluation of indexes proposed to predict weaning from mechanical ventilation.

OBJECTIVE: To conduct a blinded evaluation of the predictors of weaning from mechanical ventilation. DESIGN: A prospective clinical study. SETTING: A 23-bed general intensive care unit. PATIENTS: Ninety-three non-selected patients, ventilated for more than 48 h. METHODS: The study had two steps: at first, patients' data were used to select the cut-off value for weaning predictors (the minimal false classification). The cut-off value for each index was prospectively assessed in a group of 52 patients. The predictive performance of these indexes was evaluated by calculating the area under the receiver operating characteristic curve. In the prospective-validation set we used Bayes' theorem to assess the probability of each test in predicting weaning. The physicians making decisions about the weaning process were always unaware of the predictive values. Weaning was considered successful if spontaneous breathing was sustained for more than 48 h after extubation. MEASUREMENTS AND RESULTS: During the first 2 min after discontinuation of mechanical ventilation the following tests were performed: vital capacity, tidal volume, airway occlusion pressure (P(0.1)), minute ventilation, respiratory rate, maximal inspiratory pressure (MIP), respiratory frequency to tidal volume (f/V(T)), P(0.1)/MIP and P(0.1) x f/V(T). The areas under the curve showed that the tests had not the ability to distinguish between successful and unsuccessful weaning. CONCLUSION: Our results show that all the evaluated indexes are poor predictors of weaning outcome in a general intensive care unit population.

Aged↗
PubMed2003

Predicting end-stage renal disease: Bayesian perspective of information transfer in the clinical decision-making process at the individual level.

BACKGROUND: Predicting outcomes such as end-stage renal disease (ESRD) by integration and better utilization at individual level of epidemiologic data may facilitate clinical decision-making processes. METHODS: To predict individual ESRD risk in an average patient in the United States, ESRD prevalence and levels of uncertainty and conditional risk factors independence were considered by population data (1998) and pooled analysis of 11 randomized trials. Data integration and input were by decision-tree simulation approach (simple, parallel, and sequential scenarios) and Bayes' theorem. Sensitivity analysis and risk profiles were employed to address uncertainty and assess different risk factor combinations. A health state values, associated with ESRD outcome levels, were taken from the literature. RESULTS: In this theoretical study, we provided a scholarly example about the use of two known risk factors (urinary protein >/=3 g/day and systolic blood pressure >/=140 mm Hg) to predict individual ESRD risk in an average patient in the United States. The highest posterior (decisional) probability of ESRD occurrence (risk of 3.61% to 5.07%) in the individual patient was associated with the worst health state, as assessed by multidimensional scenarios when both risk factors were present. CONCLUSION: Decision tree models through an empirical Bayesian approach may serve to predict the individual ESRD risk on the basis of simple epidemiologic, demographic, and clinical information that is easily available already at the first patient evaluation.

Algorithms↗