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PubMed2001

Use of neural network models to predict industrial bioreactor effluent quality.

Engineered bioreactors are useful tools for degrading wastes from crude oil refining facilities. One such bioreactor forms part of the wastewater remediation process used at a refinery in the San Francisco Bay Area. The flow rate and chemical concentrations of the waste vary, and it is necessary to be able to predict the efficiency of the reactor degradation process for this varied input. The complex biological, physical, and chemical processes of the reactor make deterministic modeling unsuitable. Therefore, predictive modeling for this system was performed using a neural network model. A predictive, time-series neural network model requires a complete data set. Often, in the case of a large industrial facility, data are missing. Various techniques can be used to reconstruct missing data, but comparisons of techniques have not been performed for large-scale remediation processes. In this manuscript, four techniques are used for reconstructing missing data to examine which ones provide superior predictive capabilities. It was found that the interpolated and moving average values methods provided the best predictions. The mean and median replacement methods, commonly used in neural network modeling, provided much poorer predictions. Another goal of this study is to determine which water quality parameters are more accurately predicted than others. In this study, pH was the most accurately predicted, while ammonia and total phenolics concentrations were the least accurately predicted.

Ammonia↗
PubMed1998

Prediction of the secondary structure contents of globular proteins based on three structural classes.

The prediction of the secondary structural contents (those of alpha-helix and beta-strand) of a globular protein is of great use in the prediction of protein structure. In this paper, a new prediction algorithm has been proposed based on Chou's database [Chou (1995), Proteins 21, 319-344]. The new algorithm is an improved multiple linear regression method, taking into account the nonlinear and coupling terms of the frequencies of different amino acids and the length of the protein. The prediction is also based on the structural classes of proteins, but instead of four classes, only three classes are considered, the alpha class, beta class, and the mixed alpha+beta and alpha/beta class or simply the alphabeta class. Thus the ambiguity that usually occurs between alpha+beta proteins and alpha/beta proteins is eliminated. A resubstitution examination for the algorithm shows that the average absolute errors are 0.040 and 0.035 for the prediction of alpha-helix content and beta-strand content, respectively. An examination of cross-validation, the jackknife analysis, shows that the average absolute errors are 0.051 and 0.045 for the prediction of alpha-helix content and beta-strand content, respectively. Both examinations indicate the self-consistency and the extrapolating effectiveness of the new algorithm. Compared with other methods, ours has the merits of simplicity and convenience for use, as well as high prediction accuracy. By incorporating the prediction of the structural classes, the only input of our method is the amino acid composition and the length of the protein to be predicted.

Algorithms↗
PubMed2000

Evaluation of three risk scores to predict postoperative nausea and vomiting.

BACKGROUND: So far there are three different scores to predict postoperative vomiting (PV: Apfel et al., 1998) or postoperative nausea and vomiting (PONV: Koivuranta et al., 1997; Palazzo and Evans, 1993). All three scores used logistic regression analysis to identify and create weights for the risk factors for PV or PONV. In short, these were sex, age, history of previous PONV, motion sickness, duration of anaesthesia, and use of postoperative opioids. However, an external evaluation and a comparison of these scores has not been performed so far. METHODS: Patients undergoing a variety of surgical procedures under general anaesthesia were studied prospectively. Preoperatively, they completed a questionnaire concerning potential risk factors for the occurrence of PV or PONV implemented in the three risk scores. Balanced anaesthesia (induction agent, nondepolarising neuromuscular blocker, opioid, and inhalation agent in nitrous oxide/oxygen) was performed. No intravenous anaesthesia or any antiemetic prophylaxis was applied. Postoperatively, the patients were observed in the recovery room for the occurrence of PV and PONV and were visited twice on the ward within the 24-h observation period. Both the patients and the nursing staff were asked whether PV or PONV was present. The severity of PONV was categorised using a standardised scoring algorithm. A total of 1,444 patients was finally included into the analysis. Using information of the predicted risk for the individual patients and the actual occurrence of PV or PONV, Receiver Operator Characteristics (ROC-curves) were drawn. The area under each ROC-curve was calculated as a means of the predictive properties of each score and was compared for statistical differences. RESULTS: For prediction of PONV (any severity) the AUC-values (AUC=area under the curve) and the corresponding 95%-confidence intervals were: Apfel: 0.70 (0.67-0.72); Koivuranta: 0.71 (0.69-0.73); Palazzo: 0.68 (0.65-0.70). For prediction of PV: Apfel: 0.73 (0.71-0.75); Koivuranta: 0.73 (0.70-0.75); Palazzo: 0.68 (0.65-0.70). Thus, all three scores appeared to have a moderate accuracy as measured by the AUC. The score of Koivuranta predicts PONV (P=0.007) and also PV (P=0.002) significantly better than Palazzo's score. Furthermore, for predicting of PV the score of Apfel was also superior to Palazzo's score (P=0.005). All three scores predict PV with the same accuracy as PONV. CONCLUSION: The occurrence of PV and PONV in patients undergoing surgery under balanced anaesthesia can be predicted with moderate but acceptable accuracy using one of the available risk scores, regardless of local surgical or anaesthesiological circumstances. For clinical practice, we recommend the score published by Koivuranta, since its calculation is very simple.

Adult↗
PubMed1990

The overconfidence effect in social prediction.

In five studies with overlapping designs and intents, subjects predicted a specific peer's responses to a variety of stimulus situations, each of which offered a pair of mutually exclusive and exhaustive response alternatives. Each prediction was accompanied by a subjective probability estimate reflecting the subjects' confidence in its accuracy--a measure validated in Study 5 by having subjects choose whether to "gamble" on the accuracy of their prediction or on the outcome of a simple aleatory event. Our primary finding was that in social prediction, as in other judgmental domains, subjects consistently proved to be highly overconfident. That is, regardless of the type of prediction item (e.g., responses to hypothetical dilemmas, responses to contrived laboratory situations, or self-reports of everyday behaviors) and regardless of the type of information available about the person whose responses they were predicting (e.g., predictions about roommates or predictions based on prior interviews), the levels of accuracy subjects achieved fell considerably below the levels required to justify their confidence levels. Further analysis revealed two specific sources of overconfidence. First, subjects generally were overconfident to the extent they were highly confident. Second, subjects were most likely to be overconfident when they knowingly or unknowingly made predictions that ran counter to the relevant response base rates and, as a consequence, achieved low accuracy rates that their confidence estimates failed to anticipate. Theoretical and normative implications are discussed and proposals for subsequent research offered.

Adult↗
PubMed2004

Child-specific thoracic gas volume prediction equations for air-displacement plethysmography.

OBJECTIVE: To develop child-specific thoracic gas volume (TGV) prediction equations for use in air-displacement plethysmography in 6- to 17-year-old children. RESEARCH METHODS AND PROCEDURES: Study 1 developed TGV prediction equations using anthropometric variables after completing a measured TGV and air-displacement plethysmography test in 224 healthy boys and girls (11.2 +/- 3.2 years, 45.3 +/- 18.7 kg, 149.9 +/- 18.5 cm). Study 2 cross-validated the prediction equations in a separate cohort of 62 healthy boys and girls (11.2 +/- 3.4 years, 44.2 +/- 15.3 kg, 149.4 +/- 19.3 cm). RESULTS: In Study 1 (development of TGV prediction equations), the quadratic relationship using height as the independent variable and the measured TGV as the dependent variable yielded the highest adjusted R(2) and the lowest SE of estimate in both genders, thus producing the following prediction equations: TGV = 0.00056 x H(2) - 0.12422 x H + 8.15194 (boys) and TGV = 0.00044 x H(2) - 0.09220 x H + 6.00305 (girls). In Study 2 (cross-validation), no significant difference between the predicted and measured TGVs (-0.018 +/- 0.377 liters) was observed. The regression between the measured TGV and the predicted TGV yielded a slope and intercept that did not significantly differ from the line of identity. Prediction accuracy was good as indicated by a high R(2) (0.862) and low SE of estimate (0.369 liters). DISCUSSION: The new child-specific TGV prediction equations accurately, precisely, and without bias estimated the actual TGV of 6- to 17-year-old children.

Adolescent↗
PubMed2006

Disarming the guarded prognosis: predicting survival in newly referred patients with incurable cancer.

People affected by cancer want information about their prognosis but clinicians have trouble estimating and talking about it. We sought to determine the nature and accuracy of medical oncologists' estimates of life expectancy in newly referred patients with incurable cancer. With reference to each patient, medical oncologists estimated how long they thought 90, 50, and 10% of similar patients would live. These proportions were chosen to reflect worst case, predicted, and best case scenarios suitable for discussions. After a median follow-up of 35 months, 86 of the 102 patients had died with an observed median survival of 12 months. Oncologists' estimates of each patient's worst case, predicted and best case scenarios were well-calibrated: 10% of patients lived for fewer months than estimated for the worst 10% of similar patients; 50% lived for at least as long as estimated for 50% of similar patients (predicted survival), and 17% lived for more months than estimated for the best 10% of similar patients. Oncologists' estimates of each patient's predicted survival were imprecise: 29% were within 0.67-1.33 times the patient's actual survival, 35% were too optimistic (>1.33 times the actual survival), and 39% were too pessimistic (<0.67 times the actual survival). The proportions of patients with actual survival times bounded by simple multiples of their predicted survival were as follows: 61% between half to double their predicted, 6% at least three to four times their predicted, and 4% no more than 1/6 of their predicted; similar to the proportions in an exponential distribution (about 50%, 10% and 10% respectively). Ranges based on simple multiples of the predicted survival time appropriately convey prognosis and its uncertainty in newly referred people with incurable cancer.

Adolescent↗
PubMed2005

SNAP-II predicts mortality among infants with congenital diaphragmatic hernia.

OBJECTIVE: Outcomes analysis in congenital diaphragmatic hernia (CDH) requires a validated risk-adjustment tool. The purpose of this study was to use the Canadian Neonatal Network (CNN) database to validate the Score for Neonatal Acute Physiology, Version II (SNAP-II) for prediction of mortality among CDH infants admitted to a neonatal intensive care unit (NICU), and to compare this to the predictive equation recently developed by the Congenital Diaphragmatic Hernia Study Group (CDHSG). STUDY DESIGN: Infants with CDH in the CNN database were identified. Bivariate and multivariable logistic regression models were used to identify risk factors predictive of mortality. Model predictive performance and calibration were assessed using the area under the receiver operator characteristic curve and the technique of Hosmer-Lemeshow, respectively, and compared with the CDHSG predictive equation. RESULTS: There were 88 patients with CDH among 19,507 admissions to CNN hospitals. The mortality rate among CDH patients surviving to NICU admission was 17%, and 12.5% received extracorporeal membrane oxygenation therapy. Gestational age and admission SNAP-II score predicted mortality. Model predictive performance and calibration were optimized with these variables combined. The CDHSG equation was equally predictive of mortality, but was only marginally calibrated. CONCLUSIONS: SNAP-II is highly predictive of mortality among patients with CDH, and can be used to risk-adjust these patients.

Cause of Death↗
PubMed2003

Predicting normal lung function in patients with childhood spinal cord injury.

STUDY DESIGN: A prospective observational study. OBJECTIVES: To compare the height and arm span measurements in childhood spinal cord injured (SCI) people and examine the subsequent effect on calculating the predicted lung function using standard formulae and to discuss which of the two measurements is the most appropriate to use in these formulae. SETTING: National Spinal Injuries Centre, Stoke Mandeville Hospital, Aylesbury, UK. METHOD: A total of 12 children had lung function tests performed and at the same time had height and armspan measured. The predicted lung function was calculated twice; once using height and then using arm span and compared. The actual lung function test results were expressed as percentage of the two predicted values, respectively, and compared. RESULTS: The difference between the mean height (1499 mm) and arm span (1649 mm) measurements was significant (P<0.001). In all cases, the arm span measurement was greater than the height. The two predicted lung function values (one calculated using height and the other armspan) were significantly different (P<0.001). When lung function test results were expressed as percentage of the two predicted values they gave a very different interpretation of the results. The actual performance was much lower than the predicted values if arm span, rather than height, was used in prediction equations. CONCLUSION: In childhood SCI, the difference in height and arm span is significant. This affects the predicted lung function values significantly and thus changes the interpretation of the lung function test results. The most appropriate measurement to use in prediction equations (height or arm span) in these subjects is yet to be decided.

Anthropometry↗
PubMed2002

Accuracy of eosinophils and eosinophil cationic protein to predict steroid improvement in asthma.

BACKGROUND: There is a large variability in clinical response to corticosteroid treatment in patients with asthma. Several markers of inflammation like eosinophils and eosinophil cationic protein (ECP), as well as exhaled nitric oxide (NO), are good candidates to predict clinical response. AIM: We wanted to determine whether we could actually predict a favourable response to inhaled corticosteroids in individual patients. METHODS: One hundred and twenty patients with unstable asthma were treated with either prednisolone 30 mg/day, fluticasone propionate 1000 microg/day b.i.d. or fluticasone propionate 250 microg/day b.i.d., both via Diskhaler. They were treated during 2 weeks, in a double-blind, parallel group, double dummy design. We measured eosinophils and ECP in blood and sputum, and exhaled nitric oxide as inflammatory parameters before and after 2 weeks in order to predict the changes in forced expiratory volume in 1 s (FEV1), provocative concentration of methacholine causing a 20% fall in FEV1 (PC20 Mch), and asthma quality of life (QOL). Secondly, to test whether these results were applicable in clinical practice we determined the individual prediction of corticosteroid response. RESULTS: We found that changes in FEV1, PC20 Mch and QOL with corticosteroids were predominantly predicted by their respective baseline value and to a smaller extent by eosinophils in blood or sputum. ECP, measured in blood or sputum, was certainly not better than eosinophils in predicting clinical response to corticosteroids. Smoking status was an additional predictor for change in FEV1, but not for change in PC20 Mch or QOL. Prediction of a good clinical response was poor. For instance, high sputum eosinophils (> or = 3%) correctly predicted an improvement in PC20 Mch in only 65% of the patients. CONCLUSION: Our findings show that baseline values of the clinical parameters used as outcome parameters are the major predictors of clinical response to corticosteroids. Eosinophil percentage in blood or sputum adds to this, whereas ECP provides no additional information. Correct prediction of clinical response in an individual patient, however, remains poor with our currently used clinical and inflammatory parameters.

Adolescent↗
PubMed1999

Prediction of protein (domain) structural classes based on amino-acid index.

A protein (domain) is usually classified into one of the following four structural classes: all-alpha, all-beta, alpha/beta and alpha + beta. In this paper, a new formulation is proposed to predict the structural class of a protein (domain) from its primary sequence. Instead of the amino-acid composition used widely in the previous structural class prediction work, the auto-correlation functions based on the profile of amino-acid index along the primary sequence of the query protein (domain) are used for the structural class prediction. Consequently, the overall predictive accuracy is remarkably improved. For the same training database consisting of 359 proteins (domains) and the same component-coupled algorithm [Chou, K.C. & Maggiora, G.M. (1998) Protein Eng. 11, 523-538], the overall predictive accuracy of the new method for the jackknife test is 5-7% higher than the accuracy based only on the amino-acid composition. The overall predictive accuracy finally obtained for the jackknife test is as high as 90.5%, implying that a significant improvement has been achieved by making full use of the information contained in the primary sequence for the class prediction. This improvement depends on the size of the training database, the auto-correlation functions selected and the amino-acid index used. We have found that the amino-acid index proposed by Oobatake and Ooi, i.e. the average nonbonded energy per residue, leads to the optimal predictive result in the case for the database sets studied in this paper. This study may be considered as an alternative step towards making the structural class prediction more practical.

Algorithms↗
PubMed2000

Pharmacokinetic model-driven infusion of sufentanil and midazolam during cardiac surgery: assessment of the prospective predictive accuracy and the quality of anesthesia.

OBJECTIVE: To evaluate the prospective predictive accuracy and the quality of anesthesia of pharmacokinetic model-driven infusion of sufentanil and midazolam designed to establish and maintain a plasma level of drug during cardiac surgery. DESIGN: Prospective analysis. SETTING: Operating room at a university hospital. PARTICIPANTS: Twenty adult patients younger than 75 years old scheduled for valvular or coronary artery bypass graft surgery. INTERVENTIONS: Patients were anesthetized using a variable predicted concentration of sufentanil (1 to 10 ng/mL) combined with a stable predicted concentration of midazolam (100 ng/mL). MEASUREMENTS AND MAIN RESULTS: For each patient, arterial samples were taken before (6 samples), during (2 samples), and after (2 samples) cardiopulmonary bypass (CPB). Plasma sufentanil and midazolam concentrations were measured by specific radioimmunoassay and high-performance liquid chromatography techniques. Predicted sufentanil and midazolam concentrations were derived using the data sets of Gepts et al and Maitre et al. The predictive performance, the percentage prediction error (PE), and the absolute percentage error were calculated for each sample. The bias, inaccuracy, and dispersion were assessed by determining the median of the individual medians of the prediction errors (MDPE), the median of the individual median of the absolute prediction errors (MDAPE), and the 10th and 90th percentiles of PE. For midazolam, the inaccuracy was low (MDAPE < 21%), but CPB was associated with a dilution of the measured concentration associated with a negative bias. For sufentanil, the inaccuracy was also low before CPB (MDAPE = 18%) but increased during and after CPB (MDAPE > 40%). During the whole procedure, the hemodynamic control necessitated only a few interventions. CONCLUSIONS: Pharmacokinetic model-driven infusion of sufentanil and midazolam using the pharmacokinetic sets of Gepts et al and Maitre et al is a safe and accurate anesthetic technique before CPB in adult patients undergoing cardiac surgery when high sufentanil (1 to 10 ng/mL) and low midazolam (100 ng/mL) predicted plasma concentrations are targeted.

Adult↗
PubMed1986

Reversibility of cardiac wall-motion abnormalities predicted by positron tomography.

Positron emission tomography (PET) can be used with nitrogen-13-ammonia (13NH3) to estimate regional myocardial blood flow, and with fluorine-18-deoxyglucose (18FDG) to measure exogenous glucose uptake by the myocardium. We used PET to predict whether preoperative abnormalities in left ventricular wall motion in 17 patients who underwent coronary-artery bypass surgery were reversible. The abnormalities were quantified by radionuclide or contrast angiography or both, before and after grafting. PET images were obtained preoperatively. Abnormal wall motion in regions in which PET images showed preserved glucose uptake was predicted to be reversible, whereas abnormal motion in regions with depressed glucose uptake was predicted to be irreversible. According to these criteria, abnormal contraction in 35 of 41 segments was correctly predicted to be reversible (85 percent predictive accuracy), and abnormal contraction in 4 of 26 regions was correctly predicted to be irreversible (92 percent predictive accuracy). In contrast, electrocardiograms showing pathological Q waves in the region of asynergy predicted irreversibility in only 43 percent of regions. We conclude that PET imaging with 13NH3 to assess blood flow and 18FDG to assess the metabolic viability of the myocardium is an accurate method of predicting potential reversibility of wall-motion abnormalities after surgical revascularization.

Ammonia↗
PubMed2001

Predicting outcomes of trials of labor in women attempting vaginal birth after cesarean delivery: a comparison of multivariate methods with neural networks.

OBJECTIVE: Our aim was to assess the utility and effectiveness of a neural network for predicting the likelihood of success of a trial of labor, relative to standard multivariate predictive models. STUDY DESIGN: We identified 100 failed trials of labor and 300 successful trials of labor in women with a prior cesarean delivery performed at our institution. Information was collected on >70 potential predictors of labor outcomes from the medical records, including demographic, historical, and past obstetric information, as well as information from the index pregnancy. Bivariate analyses comparing women in whom a trial of labor failed with those whose trial succeeded were performed. These initial analyses were used to select variables for inclusion into our muitivariate predictive model. From the same data we trained and tested a neural network, using a back-propagation algorithm. The test characteristics of the multivariate predictive model and the neural network were compared. RESULTS: From the bivariate analysis a history of substance abuse (adjusted odds ratio, 0.27; 95% confidence interval, 0.09-0.80), a successful prior vaginal birth after cesarean delivery (adjusted odds ratio, 0.13; 95% confidence interval, 0.05-0.31), cervical dilatation at admission (adjusted odds ratio, 0.53; 95% confidence interval, 0.31-0.88), and the need for labor augmentation (adjusted odds ratio, 2.15; 95% confidence interval, 1.14-4.06) were ultimately discovered to be important in predicting the likelihood of the success or failure of a trial of labor. With these variables in the predictive model the sensitivity of the derived rule for predicting failure was 77%, the specificity was 65%, and the overall accuracy was 69%. We also built a network using the 4 variables that were included in the final multivariate model. We were unable to achieve the same degree of sensitivity and specificity that we observed with the regression-based predictive model (sensitivity and specificity, 59% and 44%). CONCLUSION: In this study a standard multivariate model was better able to predict outcome in women ttempting a trial of labor.

Adult↗
PubMed1991

Prediction of the thermodynamics of protein unfolding: the helix-coil transition of poly(L-alanine).

The method given earlier for predicting the thermodynamics of protein unfolding from the x-ray structure of a protein is applied here to the poly(L-alanine) helix. First, the fitting parameters derived earlier from a data base of 10 proteins were used to predict the unfolding thermodynamics of 4 other proteins. The agreement between the observed and predicted values is comparable to that found for the 10 proteins studied initially. Next, the temperature dependences of the Gibbs energy and enthalpy changes for unfolding of bacteriophage T4 lysozyme were predicted and compared with data in the literature. The predicted and observed temperature dependences are similar and the predicted results indicate that cold denaturation should be observed at low temperatures, as observed recently for a T4 lysozyme mutant. The fitting parameters derived from thermodynamic data for protein unfolding and for hydration of model compounds were used to predict the unfolding thermodynamics of the poly(L-alanine) helix. The results predict that helix formation is enthalpy-driven, and the predicted enthalpy change for unfolding (0.86 kcal per mol per residue) is close to the value found in a recent calorimetric study of a 50-residue alanine-rich helix.

Kinetics↗
PubMed1994

Using computer-based models for predicting human thermal responses to hot and cold environments.

Four influential models, capable of predicting human responses to hot and cold environments and potentially suitable for use in practical applications, were evaluated by comparing their predictions with human data published previously. The models were versions of the Pierce Lab 2-node and Stolwijk and Hardy 25-node models of human thermoregulation, the Givoni and Goldman model of rectal temperature response, and ISO/DIS 7933. Experimental data were available for a wide range of environmental conditions, with air temperatures ranging from -10 to 50 degrees C, and with different levels of air movement, humidity, clothing and work. The experimental data were grouped into environment categories to allow examination of the effects of variables, such as wind or clothing, on the accuracy of the models' predictions. This categorization also enables advice to be given regarding which model is likely to provide the most accurate predictions for a particular combination of environmental conditions. Usually at least one of the models was able to give predictions with an accuracy comparable with the degree of variation that occurred within the data from the human subjects. The evaluation suggests that it is possible to make useful predictions of deep-body and mean skin temperature responses to cool, neutral, warm and hot environmental conditions. The models' predictions of deep-body temperature in the cold were poor. Overall, the 25-node model provided the most consistently accurate predictions. The 2-node model was often accurate but could be poor for exercise conditions. The rectal-temperature model usually overestimated deep-body temperature, although its predictions for very hot or heavy exercise conditions could be useful. The ISO model's allowable exposure times would not have protected subjects for some exercise conditions.

Body Temperature Regulation↗
PubMed1997

Prediction of the pharmacokinetic parameters of reduced-dolasetron in man using in vitro-in vivo and interspecies allometric scaling.

1. Dolasetron (Anzemet) is a potent and selective 5-HT3 receptor antagonist which is rapidly and extensively reduced to yield its major pharmacologically active metabolite, reduced dolasetron (RD). RD is further metabolized by CYP450 enzymes as well as undergoing renal excretion. As both in vitro and in vivo data on RD were available from animals and man, two approaches to predict the human pharmacokinetic parameters of RD were assessed. 2. First, in vitro studies, using liver microsomes from animal species and man, were undertaken to measure Vmax and K(m) and to assess the intrinsic clearance (CLint). With appropriate liver weight and liver blood flow scaling factors the predicted in vivo metabolic clearance (CLm-pred) was calculated. Human CLm-pred was underestimated by a factor of 5 when it was calculated using the above scaling factors. As, in a prospective study, the observed human in vivo metabolic clearance (CLm-obs) is unknown, CLm-pred was substituted into the least-squares correlation equation obtained from a plot of CLm-pred against CLm-obs' using animal data. The estimate of human CLm-obs was improved as it was only underestimated by a factor of 1.5. 3. Second, allometric scaling of in vivo animal pharmacokinetic data, using body weight, was performed to predict pharmacokinetic parameters in man. Good predictions of human pharmacokinetic parameters of RD were obtained for plasma clearance (1.7 l/min predicted versus 1.61/min observed), half-life (6.0 h predicted versus 5.6 h observed), and volume of distribution (860.91 predicted versus 770.41 observed). 4. The integration of in vitro metabolic data from microsomes gave similar results to conventional allometric scaling, whereas the normalization of clearance by brain weight resulted in an approximately three-fold underestimation of human clearance. 5. For RD, a drug that is eliminated by both renal and metabolic clearance, retrospective conventional allometric scaling allowed accurate prediction of pharmacokinetic parameters in man, whereas in vitro-in vivo scaling resulted in an underestimation of in vivo CLm. Although these results are somewhat at variance, ideally both scaling methods should be applied to improve the prediction of human pharmacokinetic parameters.

Animals↗
PubMed2004

The utility of structure-activity relationship (SAR) models for prediction and covariate selection in developmental toxicity: comparative analysis of logistic regression and decision tree models.

Structure-activity relationship (SAR) models can be used to predict the biological activity of potential developmental toxicants whose adverse effects include death, structural abnormalities, altered growth and functional deficiencies in the developing organism. Physico-chemical descriptors of spatial, electronic and lipophilic properties were used to derive SAR models by two modeling approaches, logistic regression and Classification and Regression Tree (CART), using a new developmental database of 293 chemicals (FDA/TERIS). Both single models and ensembles of models (termed bagging) were derived to predict toxicity. Assessment of the empirical distributions of the prediction measures was performed by repeated random partitioning of the data set. Results showed that both the decision tree and logistic regression derived developmental SAR models exhibited modest prediction accuracy. Bagging tended to enhance the prediction accuracy and reduced the variability of prediction measures compared to the single model for CART-based models but not consistently for logistic-based models. Prediction accuracy of single logistic-based models was higher than single CART-based models but bagged CART-based models were more predictive. Descriptor selection in SAR for the understanding of the developmental mechanism was highly dependent on the modeling approach. Although prediction accuracy was similar in the two modeling approaches, there was inconsistency in the model descriptors.

Animals↗
PubMed2001

A comparison of the individual best versus the predicted peak expiratory flow in patients with chronic asthma.

In the management of patients with asthma, peak expiratory flow (PEF) monitoring is used and based on the individual best PEF or the predicted PEE Recent international guidelines have recommended the use of the best PEF rather than the predicted PEF as an index, although there is little evidence to support which index is more appropriate. Therefore, we investigated the relationship between the best PEF and the predicted PEF in 166 consecutive asthmatic patients to see which value would be the better basis for their PEF monitoring. All eligible patients had undergone treatment for their asthma for over 6 months and were asked to measure their PEF four times a day. The best PEF was defined as the maximal PEF achieved at any time from all previous measurements. The predicted PEF was calculated based on a report on the standard PEF in normal Japanese subjects. The mean best PEF was significantly higher than the mean predicted PEF (p < 0.001). There was a strong correlation between the best PEF and the predicted PEF (r = 0.77, p < 0.001). However, in 72 patients (43%) the ratio of the best PEF to the predicted PEF was over 110%, and in 20 patients (12%) the ratio was lower than 90%. The best PEF was higher than the predicted PEF in 76 patients (46%) and lower in 22 patients (13%) by more than 50 L/min. These results suggest that when the predicted PEF was used as the index, pulmonary function was either underestimated or overestimated in over half of these patients. Therefore, the best PEF may be the better index for the management of patients with asthma.

Adult↗