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PubMed1984

Predicting the response of head and neck cancers to radiation therapy with a multivariate modelling system: an analysis of the RTOG head and neck registry.

Two thousand and sixty-six patients with primary head and neck cancers were entered in the RTOG Head and Neck Cancer Registry between 1977 and 1980. Nine hundred and ninety-seven (997) evaluable patients were treated initially with radiation therapy alone. Tumor site, T-stage, N-stage, histology, degree of infiltration, degree of differentiation, patient age, sex and Karnofsky performance score were all prospectively recorded. T-stage (p less than .001), N-stage (p = .007), primary site (p less than .001), and initial Karnofsky performance score (p less than .001) proved to be significant factors independently predictive of primary tumor clearance. Using these factors, a multivariate response model was constructed to predict primary tumor response. The predictive accuracy of the model proved to be highly reliable, and was tested by comparing the predicted vs. observed complete tumor clearance rates for each independent variable. For primary sites, the predicted number of complete responses vs. observed were: oral cavity, 139.8 predicted vs. 139 observed; nasopharynx, 51.8 predicted vs. 51 observed; oropharynx, 174.8 predicted vs. 176 observed, supraglottic larynx, 314.4 predicted vs. 318 observed; glottic larynx, 314.4 predicted vs. 318 observed; and hypopharynx, 49.4 predicted vs. 46 observed. For the entire group of patients predicted to have a 90% or better complete primary tumor response (including T3 and T4 tumors), 94% remained in initial complete remission at the primary site at one year, and 87% at two years. An accurate multivariate response model, such as the one presented in this paper, should prove to be a useful tool in selecting patients with head and neck cancers suitable for treatment with radiation therapy alone.

Adult↗
PubMedDate not supplied

Prediction of beta-turns in proteins using the first-order Markov models.

We present a method based on the first-order Markov models for predicting simple beta-turns and loops containing multiple turns in proteins. Sequences of 338 proteins in a database are divided using the published turn criteria into the following three regions, namely, the turn, the boundary, and the nonturn ones. A transition probability matrix is constructed for either the turn or the nonturn region using the weighted transition probabilities computed for dipeptides identified from each region. There are two such matrices constructed for the boundary region since the transition probabilities for dipeptides immediately preceding or following a turn are different. The window used for scanning a protein sequence from amino (N-) to carboxyl (C-) terminal is a hexapeptide since the transition probability computed for a turn tetrapeptide is capped at both the N- and C- termini with a boundary transition probability indexed respectively from the two boundary transition matrices. A sum of the averaged product of the transition probabilities of all the hexapeptides involving each residue is computed. This is then weighted with a probability computed from assuming that all the hexapeptides are from the nonturn region to give the final prediction quantity. Both simple beta-turns and loops containing multiple turns in a protein are then identified by the rising of the prediction quantity computed. The performance of the prediction scheme or the percentage (%) of correct prediction is evaluated through computation of Matthews correlation coefficients for each protein predicted. It is found that the prediction method is capable of giving prediction results with better correlation between the percent of correct prediction and the Matthews correlation coefficients for a group of test proteins as compared with those predicted using some secondary structural prediction methods. The prediction accuracy for about 40% of proteins in the database or 50% of proteins in the test set is better than 70%. Such a percentage for the test set is reduced to 30 if the structures of all the proteins in the set are treated as unknown.

Amino Acid Sequence↗
PubMed1995

Neural network predicted peak and trough gentamicin concentrations.

Predictions of steady state peak and trough serum gentamicin concentrations were compared between a traditional population kinetic method using the computer program NONMEM to an empirical approach using neural networks. Predictions were made in 111 patients with peak concentrations between 2.5 and 6.0 micrograms/ml using the patient factors age, height, weight, dose, dose interval, body surface area, serum creatinine, and creatinine clearance. Predictions were also made on 33 observations that were outside the 2.5 and 6.0 micrograms/ml range. Neural networks made peak serum concentration predictions within the 2.5-6.0 micrograms/ml range with statistically less bias and comparable precision with paired NONMEM predictions. Trough serum concentration predictions were similar using both neural networks and NONMEM. The prediction error for peak serum concentrations averaged 16.5% for the neural networks and 18.6% for NONMEM. Average prediction errors for serum trough concentrations were 48.3% for neural networks and 59.0% for NONMEM. NONMEM provided numerically more precise and less biased predictions when extrapolating outside the 2.5 and 6.0 micrograms/ml range. The observed peak serum concentration distribution was multimodal and the neural network reproduced this distribution with less difference between the actual distribution and the predicted distribution than NONMEM. It is concluded that neural networks can predict serum drug concentrations of gentamicin. Neural networks may be useful in predicting the clinical pharmacokinetics of drugs.

Adult↗
PubMed2003

Artificial neural networks in predicting optimum renal stone fragmentation by extracorporeal shock wave lithotripsy: a preliminary study.

OBJECTIVE: To assess the ability of artificial neural networks (ANNs) to predict optimum renal stone fragmentation in patients being managed by extracorporeal shock wave lithotripsy (ESWL). PATIENTS AND METHODS: The study included 82 patients with renal stones who were being treated by ESWL. Data (input and output values) from 60 patients in whom there was optimum fragmentation of stones by ESWL were used for training the ANN. These data mainly included the 24-h urinary variables, the radiological features of the stone disease and the ESWL settings used. The predictability of the trained ANN was tested on 22 subsequent patients, by supplying the input variables of the 22 patients into the trained ANN and recording the output values (predicted values). After subjecting these patients to ESWL, the actual results (observed values) were recorded. The predicted and the observed values were then compared. RESULTS: In the 22 patients in whom predictability was tested, the trained ANN predicted optimum fragmentation at < or = 13 000 shocks/stone (as per study protocol) in 17 and optimum fragmentation at> 13 000 shocks/stone in the other five. In the 17 patients (test set) where the trained ANN had predicted optimum fragmentation at < or = 13 000 shocks/stone, the optimum fragmentation was at that value, although the predicted and observed values were not identical. The overall correlation between the predicted and the observed values was 75.5% (correlation coefficient 0.7547) in these 17 patients. Of the other five patients, none had optimum fragmentation at < 13 000 shocks/stone, as predicted by the trained ANN, giving complete accuracy for this factor. CONCLUSION: This was a pilot study, i.e. an initial attempt to use an ANN in this regard, and although there were few patients, such that it is not possible to make final recommendations, the overall predictability was approximately 75%. An encouraging outcome of the study was that the trained ANN identified patients unlikely to benefit from ESWL. Using a larger dataset and identifying more significant variables, while eliminating inputs with a negative effect, the efficiency and utility of this ANN can probably be enhanced and in future it might be possible to predict stone fragmentation with reasonable accuracy.

Humans↗
PubMed2002

Fully automated ab initio protein structure prediction using I-SITES, HMMSTR and ROSETTA.

MOTIVATION: The Monte Carlo fragment insertion method for protein tertiary structure prediction (ROSETTA) of Baker and others, has been merged with the I-SITES library of sequence structure motifs and the HMMSTR model for local structure in proteins, to form a new public server for the ab initio prediction of protein structure. The server performs several tasks in addition to tertiary structure prediction, including a database search, amino acid profile generation, fragment structure prediction, and backbone angle and secondary structure prediction. Meeting reasonable service goals required improvements in the efficiency, in particular for the ROSETTA algorithm. RESULTS: The new server was used for blind predictions of 40 protein sequences as part of the CASP4 blind structure prediction experiment. The results for 31 of those predictions are presented here. 61% of the residues overall were found in topologically correct predictions, which are defined as fragments of 30 residues or more with a root-mean-square deviation in superimposed alpha carbons of less than 6A. HMMSTR 3-state secondary structure predictions were 73% correct overall. Tertiary structure predictions did not improve the accuracy of secondary structure prediction.

Algorithms↗
PubMed2004

Confirmation of data mining based predictions of protein function.

MOTIVATION: A central problem in bioinformatics is the assignment of function to sequenced open reading frames (ORFs). The most common approach is based on inferred homology using a statistically based sequence similarity (SIM) method, e.g. PSI-BLAST. Alternative non-SIM based bioinformatic methods are becoming popular. One such method is Data Mining Prediction (DMP). This is based on combining evidence from amino-acid attributes, predicted structure and phylogenic patterns; and uses a combination of Inductive Logic Programming data mining, and decision trees to produce prediction rules for functional class. DMP predictions are more general than is possible using homology. In 2000/1, DMP was used to make public predictions of the function of 1309 Escherichia coli ORFs. Since then biological knowledge has advanced allowing us to test our predictions. RESULTS: We examined the updated (20.02.02) Riley group genome annotation, and examined the scientific literature for direct experimental derivations of ORF function. Both tests confirmed the DMP predictions. Accuracy varied between rules, and with the detail of prediction, but they were generally significantly better than random. For voting rules, accuracies of 75-100% were obtained. Twenty-one of these DMP predictions have been confirmed by direct experimentation. The DMP rules also have interesting biological explanations. DMP is, to the best of our knowledge, the first non-SIM based prediction method to have been tested directly on new data. AVAILABILITY: We have designed the "Genepredictions" database for protein functional predictions. This is intended to act as an open repository for predictions for any organism and can be accessed at http://www.genepredictions.org

Abstracting and Indexing↗
PubMed2006

Hierarchical multi-label prediction of gene function.

MOTIVATION: Assigning functions for unknown genes based on diverse large-scale data is a key task in functional genomics. Previous work on gene function prediction has addressed this problem using independent classifiers for each function. However, such an approach ignores the structure of functional class taxonomies, such as the Gene Ontology (GO). Over a hierarchy of functional classes, a group of independent classifiers where each one predicts gene membership to a particular class can produce a hierarchically inconsistent set of predictions, where for a given gene a specific class may be predicted positive while its inclusive parent class is predicted negative. Taking the hierarchical structure into account resolves such inconsistencies and provides an opportunity for leveraging all classifiers in the hierarchy to achieve higher specificity of predictions. RESULTS: We developed a Bayesian framework for combining multiple classifiers based on the functional taxonomy constraints. Using a hierarchy of support vector machine (SVM) classifiers trained on multiple data types, we combined predictions in our Bayesian framework to obtain the most probable consistent set of predictions. Experiments show that over a 105-node subhierarchy of the GO, our Bayesian framework improves predictions for 93 nodes. As an additional benefit, our method also provides implicit calibration of SVM margin outputs to probabilities. Using this method, we make function predictions for multiple proteins, and experimentally confirm predictions for proteins involved in mitosis. SUPPLEMENTARY INFORMATION: Results for the 105 selected GO classes and predictions for 1059 unknown genes are available at: http://function.princeton.edu/genesite/ CONTACT: ogt@cs.princeton.edu.

Algorithms↗
PubMed2003

Deleterious mutation prediction in the secondary structure of RNAs.

Methods for computationally predicting deleterious mutations have recently been investigated for proteins, mainly by probabilistic estimations in the context of genomic research for identifying single nucleotide polymorphisms that can potentially affect protein function. It has been demonstrated that in cases where a few homologs are available, ab initio predicted structures modeled by the Rosetta method can become useful for including structural information to improve the deleterious mutation prediction methods for proteins. In the field of RNAs where very few homologs are available at present, this analogy can serve as a precursor to investigate a deleterious mutation prediction approach that is based on RNA secondary structure. When attempting to develop models for the prediction of deleterious mutations in RNAs, useful structural information is available from folding algorithms that predict the secondary structure of RNAs, based on energy minimization. Detecting mutations with desired structural effects among all possible point mutations may then be valuable for the prediction of deleterious mutations that can be tested experimentally. Here, a method is introduced for the prediction of deleterious mutations in the secondary structure of RNAs. The mutation prediction method, based on subdivision of the initial structure into smaller substructures and construction of eigenvalue tables, is independent of the folding algorithms but relies on their success to predict the folding of small RNA structures. Application of this method to predict mutations that may cause structural rearrangements, thereby disrupting stable motifs, is given for prokaryotic transcription termination in the thiamin pyrophosphate and S-adenosyl-methionine induced riboswitches. Ribo switches are mRNA structures that have recently been found to regulate transcription termination or translation initiation in bacteria by conformation rearrangement in response to direct metabolite binding. Predicting deleterious mutations on riboswitches may succeed to systematically intervene in bacterial genetic control.

Algorithms↗
PubMed1996

Predictive validity of the suicide probability scale among adolescents in group home treatment.

OBJECTIVE: To determine the predictive validity of the Suicide Probability Scale (SPS). METHOD: Prospective design, predicting subsequent suicide attempts, suicide verbalizations, and minor self-destructive behaviors from SPS scores at admission to a group home in a sample of 855 adolescents. RESULTS: SPS scores significantly predicted all three suicide-relevant measures. Only 48% of subsequent attempts could have been predicted from SPS criteria that would have put 21% of newly admitted adolescents in the at-risk group. Although the prediction is far from perfect, it can prevent some suicide attempts when combined with effective suicide precautions. CONCLUSIONS: This is the first known evidence of predictive validity of any measure of suicide risk in adolescents. Its predictive ability may have been reduced by interventions designed to reduce suicide risk. Despite the large number of false-positives and modest predictive validity, a large body of literature indicates that quantitative predictions perform better than clinical judgments in predicting psychological outcomes. However, the SPS measures only a subset of factors predictive of suicide, including suicidal ideation, hopelessness, and social isolation. Risk factors not assessed by the SPS include previous suicide attempts, conduct disorder, substance abuse, and reasons for living. These additional risk factors may be important to improve upon the predictive validity of the SPS.

Adolescent↗
PubMed2006

The predictive value of field versus arrival Glasgow Coma Scale score and TRISS calculations in moderate-to-severe traumatic brain injury.

BACKGROUND: Glasgow Coma Scale (GCS) scores are widely used to quantify level of consciousness in the prehospital environment. The predictive value of field versus arrival GCS is not well defined but has tremendous implications with regard to triage and therapeutic decisions as well as the use of various predictive scoring systems, such as Trauma Score and Injury Severity Score (TRISS). This study explores the predictive value of field GCS (fGCS) and arrival GCS (aGCS) as well as TRISS calculations using field (fTRISS) and arrival (aTRISS) data in patients with moderate-to-severe traumatic brain injury (TBI). METHODS: Major trauma victims with head Abbreviated Injury Scores of 3 or greater were identified from our county trauma registry over a 16-year period. The predictive ability of fGCS with regard to aGCS was explored using univariate statistics and linear regression modeling. The difference between aGCS and fGCS was also modeled against mortality and the composite endpoint using logistic regression, adjusting for fGCS. The predictive value of preadmission GCS (pGCS), defined as either fGCS or aGCS in nonintubated patients without a documented fGCS, with regard to mortality and a composite endpoint representing the need for neurosurgical care (death, craniotomy, invasive intracranial pressure monitoring, or intensive care unit care >48 hours) was determined using receiver-operator curve (ROC) analysis. Finally, fTRISS and aTRISS predicted survival values were compared with each other and to observed survival. RESULTS: A total of 12,882 patients were included. Mean values for fGCS and aGCS were similar (11.4 and 11.5, respectively, p = 0.336), and a strong correlation (r = 0.67, 95% CI 0.66-0.69, p < 0.0001) was observed between them. The difference between fGCS and aGCS was also predictive of outcome after adjusting for fGCS. Good predictive ability was observed for pGCS with regard to both mortality and neurosurgical intervention. Both fTRISS and aTRISS predicted survival values were nearly identical to observed survival. Observed and fTRISS predicted survival were nearly identical in patients undergoing prehospital intubation CONCLUSIONS: Values for fGCS are highly predictive of aGCS, and both are associated with outcome from TBI. A change in GCS from the field to arrival is highly predictive of outcome. The use of field data for TRISS calculations appears to be a valid methodological approach, even in severely injured TBI patients undergoing prehospital intubation.

Brain Injuries↗
PubMed1996

Comparison of two binding equations for prediction of the concentration of unbound valproic acid in the serum of adult epileptic polytherapy patients.

Because binding of valproic acid to plasma proteins affects the efficacy of the drug in the treatment of epilepsy (only the unbound fraction of the drug is effective) we have compared two methods which use different binding parameters to predict the in-vivo concentration of unbound valproic acid in serum. The study was performed on 46 serum samples from 29 polytherapy adult patients with epilepsy. Mean prediction error, mean absolute prediction error and root mean squared error were calculated for each method; these values served as a measure of prediction bias and precision. The mean absolute prediction errors and root mean squared errors for the two methods were similar in magnitude (Method 1, mean absolute prediction error = 10.0 microM, root mean squared error = 15.0 microM; Method 2, mean absolute prediction error = 10.3 microM, root mean squared error = 13.5 microM). Method 2 had a general tendency to over-predict unbound valproic acid; both methods had a tendency to over-prediction for total concentrations above 500 microM. Method 1 had a tendency to under-prediction at total concentrations below 250 microM. Within the total concentration range of valproic acid investigated, Method 1 was superior to Method 2 for prediction of unbound serum valproic acid. Our approach using Method 1 may be useful for prediction of unbound serum valproic acid concentration in patients with total valproic acid concentrations ranging from 250 to 500 microM; Method 2 may be useful for patients with total valproic acid below 500 microM. Our results suggest that there is wide and unpredictable variability in valproic acid binding to serum proteins among study populations.

Adolescent↗
PubMed2005

Dosimetric impact of geometric errors due to respiratory motion prediction on dynamic multileaf collimator-based four-dimensional radiation delivery.

The synchronization of dynamic multileaf collimator (DMLC) response with respiratory motion is critical to ensure the accuracy of DMLC-based four dimensional (4D) radiation delivery. In practice, however, a finite time delay (response time) between the acquisition of tumor position and multileaf collimator response necessitates predictive models of respiratory tumor motion to synchronize radiation delivery. Predicting a complex process such as respiratory motion introduces geometric errors, which have been reported in several publications. However, the dosimetric effect of such errors on 4D radiation delivery has not yet been investigated. Thus, our aim in this work was to quantify the dosimetric effects of geometric error due to prediction under several different conditions. Conformal and intensity modulated radiation therapy (IMRT) plans for a lung patient were generated for anterior-posterior/posterior-anterior (AP/PA) beam arrangements at 6 and 18 MV energies to provide planned dose distributions. Respiratory motion data was obtained from 60 diaphragm-motion fluoroscopy recordings from five patients. A linear adaptive filter was employed to predict the tumor position. The geometric error of prediction was defined as the absolute difference between predicted and actual positions at each diaphragm position. Distributions of geometric error of prediction were obtained for all of the respiratory motion data. Planned dose distributions were then convolved with distributions for the geometric error of prediction to obtain convolved dose distributions. The dosimetric effect of such geometric errors was determined as a function of several variables: response time (0-0.6 s), beam energy (6/18 MV), treatment delivery (3D/4D), treatment type (conformal/IMRT), beam direction (AP/PA), and breathing training type (free breathing/audio instruction/visual feedback). Dose difference and distance-to-agreement analysis was employed to quantify results. Based on our data, the dosimetric impact of prediction (a) increased with response time, (b) was larger for 3D radiation therapy as compared with 4D radiation therapy, (c) was relatively insensitive to change in beam energy and beam direction, (d) was greater for IMRT distributions as compared with conformal distributions, (e) was smaller than the dosimetric impact of latency, and (f) was greatest for respiration motion with audio instructions, followed by visual feedback and free breathing. Geometric errors of prediction that occur during 4D radiation delivery introduce dosimetric errors that are dependent on several factors, such as response time, treatment-delivery type, and beam energy. Even for relatively small response times of 0.6 s into the future, dosimetric errors due to prediction could approach delivery errors when respiratory motion is not accounted for at all. To reduce the dosimetric impact, better predictive models and/or shorter response times are required.

Fluoroscopy↗
PubMed1998

Predictive reward signal of dopamine neurons.

The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in neurons in striatum, frontal cortex, and amygdala, which process specific reward information but do not emit a global reward prediction error signal. A cooperation between the different reward signals may assure the use of specific rewards for selectively reinforcing behaviors. Among the other projection systems, noradrenaline neurons predominantly serve attentional mechanisms and nucleus basalis neurons code rewards heterogeneously. Cerebellar climbing fibers signal errors in motor performance or errors in the prediction of aversive events to cerebellar Purkinje cells. Most deficits following dopamine-depleting lesions are not easily explained by a defective reward signal but may reflect the absence of a general enabling function of tonic levels of extracellular dopamine. Thus dopamine systems may have two functions, the phasic transmission of reward information and the tonic enabling of postsynaptic neurons.

Animals↗
PubMed2006

Predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays.

BACKGROUND: Genetic markers hold great promise for refining our ability to establish precise prognostic prediction for diseases. The development of comprehensive gene expression microarray technology has allowed the selection of relevant marker genes from a large pool of candidate genes in early-phased, developmental prognostic marker studies. The primary analytical task in such studies is to select a small fraction of relevant genes, typically from a list of significant genes, for further investigation in subsequent studies. RESULTS: We develop a methodology for predicting survival outcomes using subsets of significant genes in prognostic marker studies with microarrays. Key components in this methodology include building prediction models, assessing predictive performance of prediction models, and assessing significance of prediction results. As particular specifications, we assume Cox proportional hazard models with a compound covariate. For assessing predictive accuracy, we propose to use the cross-validated log partial likelihood. To assess significance of prediction results, we apply permutation procedures in cross-validated prediction. As an additional key component peculiar to prognostic prediction, we also consider incorporation of standard prognostic factors. The methodology is evaluated using both simulated and real data. CONCLUSION: The developed methodology for prognostic prediction using a subset of significant genes can provide new insights based on predictive capability, possibly incorporating standard prognostic factors, in selecting a fraction of relevant genes for subsequent studies.

Biomarkers↗
PubMed2003

Evaluation of methods for predicting drug-drug interactions by Monte Carlo simulation.

The ratio of the inhibitor concentration to the inhibition constant (K(i)) is used as the index for predicting drug-drug interactions involving metabolic inhibition. The maximum unbound concentration in the circulation (I(p, max, u)) and the maximum unbound concentration at the inlet to the liver (I(u, max)) have been used for the inhibitor concentration. In the present study, the methods for predicting drug-drug interactions using these concentrations were evaluated by Monte Carlo simulation. Information on the pharmacokinetic parameters of drugs and the K(i) values for cytochrome P450(CYP) were obtained from the literature. It was assumed that the pharmacokinetic parameters (intrinsic metabolic clearance, renal clearance and distribution volume for unbound fraction), serum protein binding and K(i) value for substrate and inhibitor are all log-normally distributed. Correlations among the parameters were assessed and were used for further simulations. A change in AUC of the substrate following co-administration of the inhibitor was simulated 1000 times using the physiologically based pharmacokinetic (PBPK) model. The percent of the drug combinations which exhibited a significant increase in the AUC (>125%) was 16.2% of the total combinations. The cases where the I/K(i) using I(u, max) and I(p, max, u) overestimated compared with the actual increased ratio of AUC (false positive prediction) were 41.2% and 16.7%, respectively. The cases where the predicted ratios of AUC from I/K(i) using I(u, max) and I(p, max, u) were comparable with the actual ratio were 3.2% and 8.7%, respectively. The prediction using I(p, max, u) was, thus, more reliable than that using I(u, max). However, in the case of I(u, max), there was no case where the actual increased ratio of AUC was greater than that predicted from I/K(i) (false negative prediction). On the other hand, for I(p, max, u), the rate of false negative prediction was 1.4%. The present study indicates that I(u, max) is better than I(p, max, u) for avoiding false negative predictions and I(p, max, u) is better than I(u, max) for increasing the probability of true positive and true negative predictions and avoiding false positive predictions.In conclusion, it is necessary to use both predictions involving I(u, max) and I(p, max, u) and to use them early on during the development stage of drug candidates. In order to finally choose which compound(s) to take forward to clinical trials, when predicting an interaction, the more quantitative and reliable method based on the PBPK model needs to be used.

Journal Article↗
PubMed2000

[The prediction of the endoscopic diagnosis in the dyspepsia patient: the value of the predominating presenting symptom and the initial clinical presumption].

AIM: To assess, in our patient population, whether upper gastrointestinal symptoms can be used to predict the presence of relevant pathology on endoscopy. PATIENTS AND METHODS: In the first retrospective phase, 311 gastroscopies, indicated for dyspeptic symptoms were selected. The gastroscopies were performed by the same endoscopist from April 1998 to March 1999. Patients were divided into four groups according to the main presenting symptom (epigastralgia, epigastralgia with pyrosis, pyrosis with nonspecific symptoms). The positive and negative predictive value, as well as the overall sensitivity and specificity of each group of symptoms for the presentation of a relevant pathology were calculated. In the second, prospective, phase an attempt was made, based on the clinical history taken in the initial consultation, to predict the results of the gastroscopies of 77 dyspeptic patients. There were four possibilities: normal or not relevant, esophagitis, ulcer and neoplasia. The positive predictive value, negative predictive value, sensitivity and specificity of the diagnostic hypothesis were calculated. RESULTS: a) Retrospective phase: the overall positive predictive value of dyspeptic symptoms to predict relevant pathology was 0.38. The highest positive predictive value corresponded to symptoms of pyrosis (0.48). Dividing the patients into groups according to age (older or younger than 45 years) did not improve the predictive value of symptoms. b) Prospective phase: the highest positive predictive value corresponded to "pathology without relevance" (0.7). The prediction of ulcer only gave a positive predictive value of 0.55. CONCLUSION: The main presenting symptom is not a good predictor of whether relevant pathology will be found on gastroscopy. The initial diagnostic hypothesis is not a sufficiently effective tool to accurately detect which patients will most benefit from gastroscopy.

Adolescent↗
PubMed2000

Predicting binocular visual field sensitivity from monocular visual field results.

PURPOSE: To compare methods of predicting binocular visual field sensitivity of patients with glaucoma from monocular visual field data. METHODS: Monocular and binocular visual fields were obtained for 111 patients with varying degrees of glaucomatous damage in one or both eyes, using the Humphrey 30-2 full-threshold procedure. Four binocular sensitivity prediction models were evaluated: BEST EYE, predictions based on individual values for the most sensitive eye, defined by mean deviation (MD); AVERAGE EYE, predictions based on the average sensitivity between eyes at each visual field location; BEST LOCATION, predictions based on the highest sensitivity between eyes at each visual field location; and BINOCUIAR SUMMATION, predictions based on binocular summation of sensitivity between eyes at each location. Differences between actual and predicted binocular sensitivities were calculated for each model. RESULTS: The average difference between predicted and actual binocular sensitivities was close to zero for the BINOCULAR SUMMATION and BEST LOCATION models, with 95% of all predictions being within +/-3 dB of actual binocular sensitivities. The best eye (MD) prediction had an average error of 1.5 dB (95% confidence limits [CL], +/-3.7 dB). The average eye prediction was the poorest, with an average error of 3.7 dB (95% CL, +/-4.6 dB). CONCLUSIONS: The BINOCULAR SUMMATION and BEST LOCATION models provided better predictions of binocular visual field sensitivity than the other two models, with a statistically significant difference in performance. The small difference in performance between the BINOCULAR SUMMATION and BEST LOCATION models was not statistically significant. For evaluations of functional visual field influences on task performance, daily activities, and related quality-of-life issues, either the BINOCULAR SUMMATION or BEST LOCATION model provides good estimates of binocular visual field sensitivity.

Glaucoma↗
PubMedDate not supplied

Comparison of two methods of space prediction in the mixed dentition.

PURPOSE: This study compares the accuracy of space prediction for the unerupted permanent canines and premolars by a recognized method of mixed dentition space analysis (Moyers technique) vs estimation by simple visual observation (SVO). METHODS: Twenty clinicians with varying levels of dental experience and training blindly assessed study models of 4 intact arches (2 maxillary and 2 mandibular) from 3 patients in the mixed dentition using both Moyers and SVO space prediction methods. Corresponding full-mouth panoramic radiographs were available for each case. Follow-up records of the eventual outcome in the permanent dentition for each case available (ie, study models prior to any form of orthodontic intervention) served as the standard for further comparison of the space predictions made. Predictions by both methods were compared with each other as well as with the eventual space situation in the permanent dentition. RESULTS: The differences in overall mean space prediction between the Moyers technique (excluding molar shift) and SVO ranged between 3.67 mm to 6.9 mm (lower arches) and 4.3 mm to 4.8 mm (upper arches). Diagnostic consistency between both methods' predictions was highly variable, with correlation ranging from moderate (r = 0.53, P = .01) to very weak (r = -0.1). Generally, more crowding was estimated with the SVO method's predictions. However, the inclusion of molar shift in the Moyers analysis resulted in the prediction of more crowding in the mandible compared to SVO and eventual outcome in the permanent dentition. The range and variability in predictions were always smaller with the Moyers technique compared to SVO. Neither technique's mean space prediction more closely resembled the eventual space situation in the permanent dentition. CONCLUSIONS: This study demonstrated that although the Moyers technique demonstrated less variation and more reproducibility than SVO in its space predictions, neither of the techniques was any more accurate in predicting the final space outcome in the permanent dentition.

Analysis of Variance↗