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At least 1,297 records · Page 72Linked to original sources
PubMed2005

Assessing the skill of yes/no predictions.

Should healthy, middle-aged women receive precautionary mammograms? Should trauma surgeons use the popular TRISS score to predict the likelihood of patient survival? These are examples of questions confronting us when we decide whether to use a yes/no prediction. In order to trust a prediction we must show that it is more valuable than would be our best guess of the future in the absence of the prediction. Calculating value means identifying our loss should the prediction err and examining the past performance of the prediction with respect to that loss. A statistical test to do this is developed. Predictions that pass this test are said to have skill. Only skillful predictions should be used. Graphical and numerical methods to identify skill will be demonstrated. The usefulness of mammograms is explored.

Adult↗
PubMed1993

Predictive value of P300 event-related potentials compared with EEG and somatosensory evoked potentials in non-traumatic coma.

Developments in ethical decision making are increasing demand for more accurate predictions of outcome in coma. New neurophysiologic tests are needed to improve the ability to predict awakening as well as poor outcome. We have recently reported that the P300 event-related potential (P300) correlates with awakening and depth of nontraumatic coma. In this companion study, the predictive value of the P300 was compared with median nerve somatosensory evoked potentials (SEP) and EEG in 20 patients in non-traumatic coma. We also evaluated the predictive value of a simplified grading scale for both the EEG and SEP (the USC SEP scale and USC EEG scale). The presence of a P300 was significantly associated with higher Glasgow coma scores (GCS) and awakening. Severe abnormalities of the somatosensory evoked potentials significantly correlated with the absence of awakening and a low GCS. Moderate abnormalities of the SEP were significantly associated with awakening and higher GCS scores. The EEG was significantly associated with GCS score and severe abnormalities of the EEG were predictive of the absence of awakening and very low GCS scores. The data indicates that the P300 and SEP are more effective than the EEG in predicting awakening, and that the SEP and EEG are more effective than the P300 in predicting poor outcome. We conclude that, in addition to EEG and SEP, the P300 should be considered in the prognostic evaluation of patients in nontraumatic coma. Further, simplified scales for the EEG and SEP are predictive of depth of coma and outcome.

Adult↗
PubMedDate not supplied

Influence of calibration methodology on ground water flow predictions.

We constructed a numerical model of transient ground water flow and solute transport for a portion of the Biscayne Aquifer in Florida, and calibrated the model with three different combinations of data from a 193-day period: head (h) data alone, data on h and ground water discharge to a canal (q), and data on h, q, and ground water chloride concentration (C). We used each of the three calibrated models to predict h and q during a 182-day test period separate from the calibration period. All three calibrated models predicted h equally well during the test period (r = 0.95, where r = 1 indicates perfect agreement between measured and simulated values), though the model calibrated on h alone had significantly different parameter values than the other two models. Predictions of q during the test period depended on calibration methodology; models calibrated with multiple targets simulated q more accurately than the model calibrated on h alone (r = 0.79 compared to r = 0.49). Based on the results of these simulations, we conclude: (1) Post-calibration prediction is important in assessing the value of different data types in automated calibration; (2) inverse-solution uniqueness is not a requirement for accurate h predictions; (3) relatively simple models can predict with reasonable accuracy transient ground water flow in a complex aquifer, and parameters governing this prediction can be estimated by nonlinear regression methods that incorporate both h and q data; (4) addition of C data to the calibration did not improve model predictive capacity because the information in the C data was similar to that in the q data, from the perspective of model calibration (the subsurface chemical signal in question was controlled mainly by seepage of high-chloride canal water into the low-chloride ground water system).

Calibration↗
PubMed1992

Predicted thermophysiological responses of humans to MRI fields.

A relatively simple two-node model of human thermoregulation was developed to predict response changes during MRI procedures. Subsequent modifications of the model simulated impairments in cardiovascular function in terms of altered skin blood flow. In the present work, the model was programmed to predict the consequences of certain procedures used in the clinic, namely, precooling of the patient to the prevailing environment and covering the patient with a light blanket. Some of the fundamental predictions of the model during 20-min MRI scans at a low SAR were tested on two male subjects in the clinical setting. The following conclusions may be drawn: (1) Precooling of the patient for 20 min to the prevailing ambient conditions, whether inadvertent or deliberate, has little value in terms of preventing a rise in body temperatures. At the conclusion of a subsequent 20-min MRI scan, even at SARs as low as 2 W/kg, the modest effects of precooling are all but eliminated. Thus, inadvertent precooling should be no cause for concern; deliberate precooling carries little advantage for the patient and wastes valuable time. (2) Use of a blanket during an MRI scan should be discouraged in the normal clinical setting except when the SAR is 2 W/kg or less. At higher SARs, this added insulation impedes convective and radiative heat loss through evaporation of sweat. The result is an increase of heat storage in the body and a greater rise in core temperature than would occur otherwise. (3) Clinical tests on two normal male subjects have provided limited confirmation of the predictions of the two-node model. During 20-min MRI scans at a whole-body SAR of 1.2 W/kg, core and skin temperatures, sweat rate, and judgments of thermal sensation and discomfort were very similar to predicted values. Unexpected findings of an incremental increase in core temperature with successive scans and a sweating rebound following each scan may be important for future investigation. (4) Although pleased with the limited confirmation of our predictions, we are constantly aware of the limitations of the two-node model to accurately predict thermoregulatory responses of patients undergoing clinical MRI of various body parts. It is essential to keep in mind that the simulations are based on RF exposure of the whole body; thus, the predicted increase in core temperature will be proportionately higher than would be the case if only a portion of the body were exposed within the MRI device.(ABSTRACT TRUNCATED AT 400 WORDS)

Body Temperature↗
PubMed2005

In vitro-in vivo extrapolation of CYP2D6 inactivation by paroxetine: prediction of nonstationary pharmacokinetics and drug interaction magnitude.

Attempts at predicting drug-drug interactions perpetrated by paroxetine from in vitro data have utilized reversible enzyme inhibition models and have been unsuccessful to date, grossly underpredicting interaction magnitude. Recent data have provided evidence for mechanism-based inactivation of CYP2D6 by paroxetine. We have predicted the pharmacokinetic consequences of CYP2D6 inactivation by paroxetine from in vitro inactivation kinetics (kinact 0.17 min(-1), unbound KI 0.315 microM), in vivo inhibitor concentrations, and an estimated CYP2D6 degradation half-life of 51 h, using a mathematical model of mechanism-based inhibition. The model-predicted accumulation ratio of paroxetine was 5 times that expected from single-dose kinetics and in excellent agreement with the observed 5- to 6-fold greater accumulation. Magnitudes of interactions produced by paroxetine (20-30 mg/day) with desipramine, risperidone, perphenazine, atomoxetine, (S)-metoprolol, and (R)-metoprolol were predicted, considering the contribution of CYP2D6 to their oral clearance. Predicted fold-increases in victim drug AUC were 5-, 6-, 5-, 6-, 4-, and 6-fold, respectively, and are in reasonable agreement with observed values of 5-, 6-, >7-, 7-, 5-, and 8-fold, respectively. Failure to consider microsomal binding in vitro adversely affected predictive accuracy. Simulation of the sensitivities of these predictions to model inputs suggests a 2-fold underprediction of interaction magnitude when a CYP2D6 degradation half-life of 14 h (reported for rat CYP3A) is used. In summary, the scaling model for mechanism-based inactivation successfully predicted the pharmacokinetic consequences of CYP2D6 inactivation by paroxetine from in vitro data.

Cytochrome P-450 CYP2D6↗
PubMed2006

Stochastic prediction of CYP3A-mediated inhibition of midazolam clearance by ketoconazole.

Conventional methods to forecast CYP3A-mediated drug-drug interactions have not employed stochastic approaches that integrate pharmacokinetic (PK) variability and relevant covariates to predict inhibition in terms of probability and uncertainty. Empirical approaches to predict the extent of inhibition may not account for nonlinear or non-steady-state conditions, such as first-pass effects or accumulation of inhibitor concentration with multiple dosing. A physiologically based PK model was developed to predict the inhibition of CYP3A by ketoconazole (KTZ), using midazolam (MDZ) as the substrate. The model integrated PK models of MDZ and KTZ, in vitro inhibition kinetics of KTZ, and the variability and uncertainty associated with these parameters. This model predicted the time- and dose-dependent inhibitory effect of KTZ on MDZ oral clearance. The predictive performance of the model was validated using the results of five published KTZ-MDZ studies. The model improves the accuracy of predicting the inhibitory effect of increasing KTZ dosing on MDZ PK by incorporating a saturable KTZ efflux from the site of enzyme inhibition in the liver. The results of simulations using the model supported the KTZ dose of 400 mg once daily as the optimal regimen to achieve maximum inhibition by KTZ. Sensitivity analyses revealed that the most influential variable on the prediction of inhibition was the fractional clearance of MDZ mediated by CYP3A. The model may be used prospectively to improve the quantitative prediction of CYP3A inhibition and aid the optimization of study designs for CYP3A-mediated drug-drug interaction studies in drug development.

Age Factors↗
PubMed1998

Levofloxacin population pharmacokinetics and creation of a demographic model for prediction of individual drug clearance in patients with serious community-acquired infection.

Population pharmacokinetic modeling is a useful approach to obtaining estimates of both population and individual pharmacokinetic parameter values. The potential for relating pharmacokinetic parameters to pharmacodynamic outcome variables, such as efficacy and toxicity, exists. A logistic regression relationship between the probability of a successful clinical and microbiological outcome and the peak concentration-to-MIC ratio (and also the area under the plasma concentration-time curve [AUC]/MIC ratio) has previously been developed for levofloxacin; however, levofloxacin assays for determination of the concentration in plasma are not readily available. We attempted to derive and validate demographic variable models to allow prediction of the peak concentration in plasma and clearance (CL) from plasma for levofloxacin. Two hundred seventy-two patients received levofloxacin intravenously for the treatment of community-acquired infection of the respiratory tract, skin or soft tissue, or urinary tract, and concentrations in plasma, guided by optimal sampling theory, were obtained. Patient data were analyzed by the Non-Parametric Expectation Maximization approach. Maximum a posteriori probability Bayesian estimation was used to generate individual parameter values, including CL. Peak concentrations were simulated from these estimates. The first 172 patients were used to produce demographic models for the prediction of CL and the peak concentration. The remaining 100 patients served as the validation group for the model. A median bias and median precision were calculated. A two-compartment model was used for the population pharmacokinetic analysis. The mean CL and the mean volume of distribution of the central compartment (V1) were 9.27 liters/h and 0.836 liter/kg, respectively. The mean values for the intercompartmental rate constants, the rate constant from the central compartment to the peripheral compartment (Kcp) and the rate constant from the peripheral compartment to the central compartment (Kpc), were 0.487 and 0.647 h(-1), respectively. The mean peak concentration and the mean AUC values normalized to a dosage of 500 mg every 24 h were 8.67 microg/ml and 72.53 microg x h/ml, respectively. The variables included in the final model for the prediction of CL were creatinine clearance (CLCR), race, and age. The median bias and median precision were 0.5 and 18.3%, respectively. Peak concentrations were predicted by using the demographic model-predicted parameters of CL, V1, Kcp and Kpc, in the simulation. The median bias and the median precision were 3.3 and 21.8%, respectively. A population model of the disposition of levofloxacin has been developed. Population demographic models for the prediction of peak concentration and CL from plasma have also been successfully developed. However, the performance of the model for the prediction of peak concentration was likely insufficient to be of adequate clinical utility. The model for the prediction of CL was relatively robust, with acceptable bias and precision, and explained a reasonable amount of the variance in the CL of levofloxacin from plasma in the population (r2 = 0.396). Estimated CLCR, age, and race were the final model covariates, with CLCR explaining most of the population variance in the CL of levofloxacin from plasma. This model can potentially optimize the benefit derived from the pharmacodynamic relationships previously developed for levofloxacin.

Adult↗
PubMed1992

Predictive equations for total lung capacity and residual volume calculated from radiographs in a random sample of the Michigan population.

BACKGROUND: Published predicted values for total lung capacity and residual volume are often based on a small number of subjects and derive from different populations from predicted spirometric values. Equations from the only two large studies gave smaller predicted values for total lung capacity than the smaller studies. A large number of subjects have been studied from a population which has already provided predicted values for spirometry and transfer factor for carbon monoxide. METHODS: Total lung capacity was measured from standard posteroanterior and lateral chest radiographs and forced vital capacity by spirometry in a population sample of 771 subjects. Prediction equations were developed for total lung capacity (TLC), residual volume (RV) and RV/TLC in two groups--normal and total. Subjects with signs or symptoms of cardiopulmonary disease were combined with the normal subjects and equations for all subjects were also modelled. RESULTS: Prediction equations for TLC and RV in non-smoking normal men and women were square root transformations which included height and weight but not age. They included a coefficient for duration of smoking in current smokers. The predictive equation for RV/TLC included weight, age, age and duration of smoking for current smokers and ex-smokers of both sexes. For the total population the equations took the same form but the height coefficients and constants were slightly different. CONCLUSION: These population based prediction equations for TLC, RV and RV/TLC provide reference standards in a population that has provided reference standards for spirometry and single breath transfer factor for carbon monoxide.

Adult↗
PubMed1995

Predicting conditional probability distributions: a connectionist approach.

Most traditional prediction techniques deliver a single point, usually the mean of a probability distribution. For multimodal processes, instead of predicting the mean, it is important to predict the full distribution. This article presents a new connectionist method to predict the conditional probability distribution in response to an input. The main idea is to transform the problem from a regression problem to a classification problem. The conditional probability distribution network can perform both direct predictions and iterated predictions, the latter task being specific for time series problems. We compare this new method to fuzzy logic and discuss important differences, and also demonstrate the architecture on two time series. The first is the benchmark laser series used in the Santa Fe competition, a deterministic chaotic system. The second is a time series from a Markov process which exhibits structure on two time scales. The network produces multimodal predictions for this series. We compare the predictions of the network with a nearest-neighbor predictor and find that the conditional probability network is more than twice as likely a model.

Neural Networks, Computer↗
PubMed2003

Spatial representation of predictive motor learning.

A key feature of skilled motor behavior is the ability of the CNS to predict the consequences of its actions. Such prediction occurs when one hand pulls on an object held in the other hand; the restraining hand generates an anticipatory increase in grip force, thereby preventing the object from slipping. When manipulating a novel object, the CNS adapts its predictive response to ensure that predictions are accurately tuned to the dynamics of the object. Here we examine whether learning to predict the consequences of an action on a novel object is restricted to the actions performed during manipulation or generalizes to novel actions. A bimanual task in which subjects held an object in each hand and the relationship between actions on one object and the motion of the other could be computer controlled from trial-to-trial was used. In four conditions we varied the spatial relationship between the direction of force subjects applied to the left-hand object and the consequent direction of motion of an object held in their right hand, which subjects were required to restrain. The results show that predictive learning was local to the direction of forces experienced during learning and that the magnitude of predictive responses was greatly reduced for novel directions of action of the left hand. The pattern of generalization shows that the representation of predictive learning is spatially local and can be approximated as having a spatially narrow Gaussian basis function.

Conditioning, Psychological↗
PubMed1997

Linear and nonlinear spectral integration in type IV neurons of the dorsal cochlear nucleus. II. Predicting responses with the use of nonlinear models.

Two nonlinear modeling methods were used to characterize the input/output relationships of type IV units, which are one principal cell type in the dorsal cochlear nucleus (DCN). In both cases, the goal was to derive predictive models, i.e., models that could predict the responses to other stimuli. In one method, frequency integration was estimated from response maps derived from single tones and simultaneous pairs of tones presented over a range of frequencies. This model combined linear integration of energy across frequency and nonlinear interactions of energy at different frequencies. The model was used to predict responses to noisebands with varying width and center frequency. In almost all cases, predictions using two-tone interactions were better than linear predictions based on single-tone responses only. In about half the cases, reasonable quantitative fits were achieved. The fits were best for noisebands with narrow bandwidth and low sound levels. In the second nonlinear method, the spectrotemporal receptive field (STRF) was derived from responses to broadband stimuli. The STRF could account for some qualitative features of the responses to broad noisebands and spectral notches embedded in broad noisebands. Quantitatively, however, the STRFs failed to predict the responses of type IV units even to simple broadband noise stimuli. For narrowband stimuli, the STRF failed to predict even qualitative features (such as excitatory and inhibitory frequency bands). The responses of DCN type IV units presumably result from interactions of two inhibitory sources, a strong one that is preferentially activated by narrowband stimuli and a weaker one that is preferentially activated by broadband stimuli. The results presented here suggest that the STRF measures effects related to the broadband inhibition, whereas two-tone interactions measure mostly effects related to narrowband inhibition. This explains why models based on two-tone interactions predict the responses to narrow noisebands much better then models based on STRFs. It is concluded that a minimal stimulus set for characterizing type IV units must contain both broadband and narrowband stimuli, because each stimulus class by itself activates only partially the integration mechanisms that shape the responses of type IV units. Similar conclusions are expected to hold in other parts of the auditory system: when characterizing a complex auditory unit, it is necessary to use a range of stimuli to ensure that all integration mechanisms are activated.

Acoustic Stimulation↗
PubMed1999

Effect of body build on the validity of predicted body fat from body mass index and bioelectrical impedance.

The objective of this study was to test whether differences between body fat percent (BF%) measured by densitometry and BF% predicted from body mass index (BMI) or bioimpedance (BIA) can be explained by differences in body build. Weight, height, sitting height (leg length), arm length, skeletal widths, BIA, bone mineral content from dual-energy X-ray absorptiometry, and BF% from densitometry were measured in 90 apparently healthy, young adult subjects (age range 18-31 years). The BMI was calculated and BF% predicted from BMI, age, and sex. BF% was also predicted from BIA. BF% measured by densitometry was 29.3 +/- 5.0% for women and 14.6 +/- 5.1% for men. BF% predicted from BIA (27.1 +/- 6.7% in women, 19.0 +/- 6.0% in men) was significantly different from measured values in both sexes. BF% predicted from BMI (25.9 +/- 2.2% for women, 15.6 +/- 2.4% for men) was only significantly different from BF% from densitometry in women, not in men. In both sexes skeletal widths, especially elbow width, were related to the prediction error of BF% from BMI, confirming the hypothesis that BF% predicted from BMI underestimates BF% in persons with a relatively slender body build or frame. The prediction error of BF% from BIA was found to be related to the length of the arms and legs, confirming the hypothesis that BIA overestimates BF% in persons with relatively long limbs.

Adipose Tissue↗
PubMed2004

Combination of APACHE-II score and an obesity score (APACHE-O) for the prediction of severe acute pancreatitis.

BACKGROUND: The advent of specific therapeutic or preventive treatments for severe acute pancreatitis creates a need to determine the risk of complications for each individual. Scoring systems used in acute pancreatitis identify groups of patients at risk of complications, but the pancreatitis-specific scores require 48 h of hospital admission to give full information. The APACHE-II score is useful within 24 h, but ignores simple clinical features, such as obesity, known to predict severity. The aim of this study was to evaluate a combination of the APACHE-II score with an obesity score in patients with acute pancreatitis, to predict severity using information available during the first 24 h of hospital admission. METHODS: Data were collected prospectively from 186 consecutive patients with acute pancreatitis, to allow calculation of the APACHE-II score and body mass index (BMI). BMI was categorised as normal (score = 0), overweight (BMI 26-30: score = 1), or obese (BMI >30: score = 2). A logistic regression model was used to identify factors significantly associated with complications (Atlanta criteria; 104 complications in 60 patients). RESULTS: Age, BMI and the acute physiology score independently predicted complications. Addition of the score for obesity to the APACHE-II score gave a composite score (APACHE-O) with greater predictive accuracy. At cut-off of >8, APACHE-O had sensitivity 82%, specificity 86%, positive predictive value 74%, negative predictive value 91% and overall accuracy 85%. CONCLUSIONS: This study confirms that age, obesity and APACHE-II measured in the first 24 h of hospital admission can predict complications in acute pancreatitis. Combination of the APACHE-II and obesity scores by simple addition improved categorical prediction of severity (mild or severe) in patients with acute pancreatitis.

APACHE↗
PubMed1992

Multivariate prediction of in-hospital mortality associated with coronary artery bypass graft surgery. Northern New England Cardiovascular Disease Study Group.

BACKGROUND: A prospective regional study was conducted to identify factors associated with in-hospital mortality among patients undergoing isolated coronary artery bypass graft surgery (CABG). A prediction rule was developed and validated based on the data collected. METHODS AND RESULTS: Data from 3,055 patients were collected from five clinical centers between July 1, 1987, and April 15, 1989. Logistic regression analysis was used to predict the risk of in-hospital mortality. A prediction rule was developed on a training set of data and validated on an independent test set. The metric used to assess the performance of the prediction rule was the area under the relative operating characteristic (ROC) curve. Variables used to construct the regression model of in-hospital mortality included age, sex, body surface area, presence of comorbid disease, history of CABG, left ventricular end-diastolic pressure, ejection fraction score, and priority of surgery. The model significantly predicted the occurrence of in-hospital mortality. The area under the ROC curve obtained from the training set of data was 0.74 (perfect, 1.0). The prediction rule performed well when used on a test set of data (area, 0.76). The correlation between observed and expected numbers of deaths was 0.99. CONCLUSIONS: The prediction rule described in this report was developed using regional data, uses only eight variables, has good performance characteristics, and is easily available to clinicians with access to a microcomputer or programmable calculator. This validated multivariate prediction rule would be useful both to calculate the risk of mortality for an individual patient and to contrast observed and expected mortality rates for an institution or a particular clinician.

Aged↗
PubMed1998

Prediction of coronary heart disease using risk factor categories.

BACKGROUND: The objective of this study was to examine the association of Joint National Committee (JNC-V) blood pressure and National Cholesterol Education Program (NCEP) cholesterol categories with coronary heart disease (CHD) risk, to incorporate them into coronary prediction algorithms, and to compare the discrimination properties of this approach with other noncategorical prediction functions. METHODS AND RESULTS: This work was designed as a prospective, single-center study in the setting of a community-based cohort. The patients were 2489 men and 2856 women 30 to 74 years old at baseline with 12 years of follow-up. During the 12 years of follow-up, a total of 383 men and 227 women developed CHD, which was significantly associated with categories of blood pressure, total cholesterol, LDL cholesterol, and HDL cholesterol (all P<.001). Sex-specific prediction equations were formulated to predict CHD risk according to age, diabetes, smoking, JNC-V blood pressure categories, and NCEP total cholesterol and LDL cholesterol categories. The accuracy of this categorical approach was found to be comparable to CHD prediction when the continuous variables themselves were used. After adjustment for other factors, approximately 28% of CHD events in men and 29% in women were attributable to blood pressure levels that exceeded high normal (> or =130/85). The corresponding multivariable-adjusted attributable risk percent associated with elevated total cholesterol (> or =200 mg/dL) was 27% in men and 34% in women. CONCLUSIONS: Recommended guidelines of blood pressure, total cholesterol, and LDL cholesterol effectively predict CHD risk in a middle-aged white population sample. A simple coronary disease prediction algorithm was developed using categorical variables, which allows physicians to predict multivariate CHD risk in patients without overt CHD.

Adult↗
PubMed2005

Prediction of type 2 diabetes mellitus with alternative definitions of the metabolic syndrome: the Insulin Resistance Atherosclerosis Study.

BACKGROUND: In addition to predicting cardiovascular disease (CVD) morbidity and mortality, the metabolic syndrome is strongly associated with the development of type 2 diabetes mellitus (DM), itself an important risk factor for CVD. Our objective was to compare the ability of various metabolic syndrome criteria (including those recently proposed by the International Diabetes Federation), markers of insulin resistance (IR) and inflammation, and impaired glucose tolerance (IGT) in the prediction of DM and to determine whether various proposed modifications to the National Cholesterol Education program (NCEP) metabolic syndrome definition improved predictive ability. METHODS AND RESULTS: We examined 822 subjects in the Insulin Resistance Atherosclerosis Study aged 40 to 69 years who were nondiabetic at baseline. After 5.2 years, 148 individuals had developed DM. IGT, metabolic syndrome definitions, and IR markers all significantly predicted DM, with odds ratios ranging from 3.4 to 5.4 (all P<0.001), although there were no significant differences in the areas under the receiver operator characteristic (AROC) curves between the definitions. Modifying or requiring obesity, glucose, or IR components in NCEP-defined metabolic syndrome did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). Similarly, although IR and inflammation variables were significantly associated with incident DM when included in multivariate models with NCEP-defined metabolic syndrome (all P<0.01), expanding the definition by adding these variables as components did not significantly alter the predictive ability of the definition under AROC curve criteria (all P>0.05). CONCLUSIONS: The International Diabetes Federation and NCEP metabolic syndrome definitions predicted DM at least as well as the World Health Organization definition, despite not requiring the use of oral glucose tolerance testing or measures of IR or microalbuminuria. Modifications or additions to the NCEP metabolic syndrome definition had limited impact on the prediction of DM.

Adult↗
PubMed2002

Long-term reward prediction in TD models of the dopamine system.

This article addresses the relationship between long-term reward predictions and slow-timescale neural activity in temporal difference (TD) models of the dopamine system. Such models attempt to explain how the activity of dopamine (DA) neurons relates to errors in the prediction of future rewards. Previous models have been mostly restricted to short-term predictions of rewards expected during a single, somewhat artificially defined trial. Also, the models focused exclusively on the phasic pause-and-burst activity of primate DA neurons; the neurons' slower, tonic background activity was assumed to be constant. This has led to difficulty in explaining the results of neurochemical experiments that measure indications of DA release on a slow timescale, results that seem at first glance inconsistent with a reward prediction model. In this article, we investigate a TD model of DA activity modified so as to enable it to make longer-term predictions about rewards expected far in the future. We show that these predictions manifest themselves as slow changes in the baseline error signal, which we associate with tonic DA activity. Using this model, we make new predictions about the behavior of the DA system in a number of experimental situations. Some of these predictions suggest new computational explanations for previously puzzling data, such as indications from microdialysis studies of elevated DA activity triggered by aversive events.

Animals↗
PubMed1994

Preoperative risk evaluation for lung cancer resection: predicted postoperative product as a predictor of surgical mortality.

We assessed the capacity to predict surgical mortality, complications, and functional loss by using the results of resting and exercise respiratory function. Measurements were made before and 4 mo after lung resection in 54 consecutive patients with bronchogenic carcinoma. Predicted postoperative (ppo) FEV1 and DLCO were derived using quantitative lung perfusion scans when baseline FEV1 was < 55% predicted, and by proportional loss of pulmonary segments (total = 19 segments) when FEV1 was > 55% predicted. The patients were aged 67 +/- 7 (mean +/- SD) yr, with an FEV1 of 76 +/- 23% predicted, FEV1/FVC of 55 +/- 13%. and DLCO of 85 +/- 22% predicted. Eleven of the patients had pneumonectomy, 29 had lobectomy, 12 had wedge resection, and two had no resection. Wilcoxon and stepwise logistic regression analyses were used to determine which indices best predicted outcome. Postoperative values were correlated (r = 0.87, p < 0.0001) with actual 4/12 postoperative values of FEV1% and of DLCO (r = 0.56, p < 0.0001). The best predictors (all p < 0.05) for each outcome, in order of usefulness, were as follows. For surgical mortality: (1) the predicted postoperative product (PPP) of ppo FEV1% x ppo DLCO%; (2) ppo DLCO%; (3) ppo FEV1%, and (4) RV, FRC, and SaO2 on the maximal step exercise test. For respiratory complications: body mass index (BMI) (for patients undergoing lobectomy or wedge resection only). For cardiac complications: (1) age; (2) SaO2 at baseline and on the maximal step exercise test; (3) PaO2; (4) PaCO2; and (5) minute ventilation at maximal exercise.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗