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PubMed2006

Operon prediction using both genome-specific and general genomic information.

We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively.

Bacillus subtilis↗
PubMed1998

Prediction of protein hydration sites from sequence by modular neural networks.

The hydration properties of a protein are important determinants of its structure and function. Here, modular neural networks are employed to predict ordered hydration sites using protein sequence information. First, secondary structure and solvent accessibility are predicted from sequence with two separate neural networks. These predictions are used as input together with protein sequences for networks predicting hydration of residues, backbone atoms and sidechains. These networks are trained with protein crystal structures. The prediction of hydration is improved by adding information on secondary structure and solvent accessibility and, using actual values of these properties, residue hydration can be predicted to 77% accuracy with a Matthews coefficient of 0.43. However, predicted property data with an accuracy of 60-70% result in less than half the improvement in predictive performance observed using the actual values. The inclusion of property information allows a smaller sequence window to be used in the networks to predict hydration. It has a greater impact on the accuracy of hydration site prediction for backbone atoms than for sidechains and for non-polar than polar residues. The networks provide insight into the mutual interdependencies between the location of ordered water sites and the structural and chemical characteristics of the protein residues.

Amino Acid Sequence↗
PubMed1988

Bayesian forecasting improves the prediction of intraoperative plasma concentrations of alfentanil.

To achieve therapeutic plasma concentrations of the opioid alfentanil, one must administer the drug as a variable rate continuous infusion. For most patients, using population pharmacokinetic parameters of alfentanil for dosing regimen allows accurate prediction of the plasma concentration of the drug over time. However, for some patients, using such parameters results in systematic over- or underprediction of the concentration. Retrospectively studying a data set (dosage history and measured concentrations) for 34 patients, the authors examined how Bayesian forecasting could improve the precision of prediction. For each patient, a Bayesian regression was performed to estimate "individualized" pharmacokinetic parameters, using population pharmacokinetic values for alfentanil and the measurement of alfentanil in one or more plasma samples from each patient. These individualized parameters were then used to predict the subsequent plasma concentrations of alfentanil over time. By comparing the value of each measured point with its corresponding predicted value, the authors calculated the prediction error as a percentage of the measured value. The precision of the prediction was assessed by the percent mean absolute prediction error. After Bayesian forecasting using a single point sampled at 80 min after start of anesthesia, the average precision of the prediction was 13.8 +/- 6.1% (SD). Using no Bayesian forecasting and only population values of the pharmacokinetic parameters for the prediction of the concentration, the precision was 24.3 +/- 16.9%. The improvement in precision brought by Bayesian forecasting was especially noticeable for those patients whose prediction of alfentanil was poor using population pharmacokinetic values (i.e., "outlier" patients).(ABSTRACT TRUNCATED AT 250 WORDS)

Alfentanil↗
PubMed1994

Testing for predictive validity in health care education research: a critical review.

BACKGROUND: The assessment of predictive validity is the essential core from which a sound model of prediction is built. METHOD: Three methods for assessing predictive validity in health care education research were reviewed: longitudinal profile development, cross-validation, and inspection of the adjusted R2. A total of 47 articles published between 1973 and 1993 in nine health care disciplines were critically reviewed to determine whether the studies tested for predictive validity by using these methods. RESULTS: Very few of the 47 studies used at least one of the three methods for assessing predictive validity. Furthermore, the proportion of variance explained that is reported in the articles is typically small even before assessment of predictive validity. It is sobering to note that these small values may be inflated, since shrinkage is likely to occur when assessing predictive validity on a second, or cross-validation, sample. CONCLUSION: The scarcity of testing of predictive validity in the studies reviewed highlights the necessity of future research to establish the degree of predictive validity, if improvements in predicting success in health care education research are to be realized.

Education↗
PubMed2002

Prospectively validated prediction of physiologic variables and organ failure in septic patients: The Systemic Mediator Associated Response Test (SMART).

OBJECTIVE: Conventional outcomes research provides only percentage risk of such end points as mortality rate, utilization of resources, and/or broad groupings of multiple organ system dysfunction. These prognostications generally are not applicable to individual patients. The purpose of the present study was to determine whether the Systemic Mediator Associated Response Test (SMART) methodology could identify interactions among demographics, physiologic variables, standard hospital laboratory tests, and circulating cytokine concentrations that predicted continuous and dichotomous dependent clinical variables, in advance, in individual patients with severe sepsis and septic shock, and whether these independent variables could be integrated into prospectively validated predictive models. DESIGN: Data review and multivariate stepwise logistic regression. SETTING: University research laboratory. PATIENTS: Three hundred three patients with severe sepsis or septic shock who comprised the placebo arm of a multiple-institution clinical trial, who were randomly separated into a model building training cohort (n = 200) and a predictive cohort (n = 103). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: From baseline data and baseline plus serial input, including patient demographics, hospital laboratory tests, and plasma concentrations of interleukin-6, interleukin-8, and granulocyte colony stimulating factor, multiple regression models were developed that predicted clinically important continuous dependent variables quantitatively, in individual patients. Multivariate stepwise logistic regression was used to develop models that prognosticated dichotomous dependent end points. Data from individual patients in the predictive cohort were inserted into each predictive model for each day, with prospective validation accomplished by simple linear regression of individual predicted vs. observed values for continuous dependent variables, and by establishing the receiver operator characteristics area under the curve for logistic regression models that predicted dichotomous end points. Of SMART models for continuous dependent variables, 100 of 143 (70%) were validated at r values >.7 through day 3, and 184 of 259 (71%) above r =.5 through day 5. SMART predictions of dichotomous end points achieved receiver operator characteristics areas under the curve >.7 for up to 84% of the equations in the first week. Many SMART models for both continuous and dichotomous dependent variables were validated at clinically useful levels of accuracy as far as 28 days after baseline. CONCLUSIONS: SMART integration of demographics, bedside physiology, hospital laboratory tests, and circulating cytokines predicts organ failure and physiologic function indicators in individual patients with severe sepsis and septic shock.

Clinical Laboratory Techniques↗
PubMed1998

The conundrum of the Glasgow Coma Scale in intubated patients: a linear regression prediction of the Glasgow verbal score from the Glasgow eye and motor scores.

BACKGROUND: The Glasgow Coma Scale (GCS), which is the foundation of the Trauma Score, Trauma and Injury Severity Score, and the Acute Physiology and Chronic Health Evaluation scoring systems, requires a verbal response. In some series, up to 50% of injured patients must be excluded from analysis because of lack of a verbal component for the GCS. The present study extends previous work evaluating derivation of the verbal score from the eye and motor components of the GCS. METHODS: Data were obtained from a state trauma registry for 24,565 unintubated patients. The eye and motor scores were used in a previously published regression model to predict the verbal score: Derived Verbal Score = -0.3756 + Motor Score * (0.5713) + Eye Score * (0.4233). The correlation of the actual and derived verbal and GCS scales were assessed. In addition the ability of the actual and derived GCS to predict patient survival in a logistic regression model were analyzed using the PC SAS system for statistical analysis. The predictive power of the actual and the predicted GCS were compared using the area under the receiver operator characteristic curve and Hosmer-Lemeshow goodness-of-fit testing. RESULTS: A total of 24,085 patients were available for analysis. The mean actual verbal score was 4.4 +/- 1.3 versus a predicted verbal score of 4.3 +/- 1.2 (r = 0.90, p = 0.0001). The actual GCS was 13.6 + 3.5 versus a predicted GCS of 13.7 +/- 3.4 (r = 0.97, p = 0.0001). The results of the comparison of the prediction of survival in patients based on the actual GCS and the derived GCS show that the mean actual GCS was 13.5 + 3.5 versus 13.7 + 3.4 in the regression predicted model. The area under the receiver operator characteristic curve for predicting survival of the two values was similar at 0.868 for the actual GCS compared with 0.850 for the predicted GCS. CONCLUSIONS: The previously derived method of calculating the verbal score from the eye and motor scores is an excellent predictor of the actual verbal score. Furthermore, the derived GCS performed better than the actual GCS by several measures. The present study confirms previous work that a very accurate GCS can be derived in the absence of the verbal component.

Glasgow Coma Scale↗
PubMed1998

"The demographics of trauma in 1995" revisited: an assessment of the accuracy and utility of trauma predictions.

OBJECTIVE: In 1987, the article "The Demographics of Trauma in 1995" (DT95) attempted to predict the future needs of trauma centers based on changing population distributions. This article foresaw a relative increase in the number of injuries to the elderly and a relative decrease in total injuries. Based on these predictions, the paper recommended increasing the capabilities of existing trauma centers rather than developing new facilities. We compared these predictions to actual experience to validate this use of demographic data in trauma system planning. METHODS: The predictions of DT95 were compared with the available population and injury data from the U.S. Census Bureau and the Centers for Disease Control and Prevention using age-related cohort analysis. RESULTS: As predicted, the highest-growing segment was the population older than 65 years, which increased 18% to 33.5 million. Also, the rate of injury-related deaths per 100,000 decreased from 61.20 in 1985 to 57.98 in 1995. The number of fatal motor vehicle crashes decreased from 45,958 in 1985 to 43,484 in 1995. Against predictions, the number of firearm deaths in 1994 increased from 31,566 to 35,957. Accurate predictions were thus made for most trauma demographic categories using a combination of census predictions and existing trauma demographic patterns. The increase in firearm deaths, however, was not anticipated using these sources and suggested the potential development of a more violent society. CONCLUSIONS: Demographic projections assist in predicting the number and type of future injuries. Sociologic and economic factors also need to be considered in any predictive determinations of the true demand for trauma centers.

Adolescent↗
PubMed1994

A time-insensitive predictive instrument for acute hospital mortality due to congestive heart failure: development, testing, and use for comparing hospitals: a multicenter study.

The purpose of this study was to develop a "time-insensitive" predictive instrument (TIPI) for acute hospital mortality due to congestive heart failure. In Phase 1, based on prospectively collected data on 401 congestive heart failure patients among 5,773 study patients who presented to six New England hospitals over a 2-year period whose chief complaints were chest pain, shortness of breath, or other cardiac symptoms, a multivariable logistic regression was used to develop the TIPI for acute mortality. Discrimination between patients who lived and those who died was reflected by receiver-operating characteristic (ROC) curve area of 0.90. Predicted mortality was found to not vary significantly from actual mortality rates across deciles of predicted probabilities from 0% to 100%. In Phase 2, the six hospitals' actual mortality rates for their congestive heart failure patients were compared to their respective rates predicted by the TIPI. Actual hospital mortality rates ranged from 3.6% to 11.3%, with no hospital having a statistically significantly higher rate. Predicted mortality rates ranged from 4% to 9%, with one hospital having a significantly lower predicted rate (P = .01), and one hospital having a borderline significantly higher predicted rate (P = .07). Individual hospitals' differences between actual and predicted mortality ranged from -3.8% to +4.7% (all NS). When grouped by hospital type, respectively for urban teaching, smaller city teaching, and rural non-teaching hospitals, the actual mortality rates were 5.1%, 10.5%, and 5.4%, (NS). The predicted mortality rates were 8.3%, 6.1%, and 5.4%, respectively, with the rate for urban major teaching centers being significantly higher (P = .03). No hospital type had significant differences between their actual and predicted mortality rates (NS). This congestive heart failure mortality TIPI (CHFM-TIPI) shows potential for risk-adjusted studies of hospitals, mortality for multi-hospital groups, hospital-to-hospital comparisons, and potentially for within-hospital assessment and if further validated, potentially also for real-time clinical use.

Acute Disease↗
PubMed1986

Prediction of steady-state trough serum procainamide concentrations after administration of a sustained-release procainamide preparation.

Four methods of predicting steady-state trough serum procainamide concentrations (SPC) were compared in 15 patients receiving sustained-release procainamide (Procan-SR) therapy. All methods were based on a one-compartment pharmacokinetic model. Method 1 utilized nine initial measured SPC and individualized pharmacokinetic parameters for prediction of the steady-state SPC. Method 2 utilized three SPC and individualized pharmacokinetic parameters. Method 3 utilized two SPC and an individualized apparent elimination rate constant plus other average pharmacokinetic parameters. Method 4 utilized all averaged pharmacokinetic parameters (required no initial SPC). The predicted and measured SPCs for each method were analyzed by linear regression. Regression equations and correlation coefficients (r) for Methods 1, 2, 3, and 4 were as follows: predicted SPC = 0.72 measured SPC + 1.60 (r = 0.86), predicted SPC = 0.67 measured SPC + 1.74 (r = 0.82), predicted SPC = 0.13 measured SPC + 2.57 (r = 0.58), and predicted SPC = 0.15 measured SPC + 2.47 (r = 0.52), respectively. The precision, as measured by the mean squared prediction error (95% confidence interval) for Methods 1, 2, 3, and 4 was 1.44 (0.61, 2.27), 1.75 (0.93, 2.57), 2.81 (1.3, 4.49), and 3.68 (1.12, 6.26), respectively. (Eighty-five percent of the predictions were within +/- 1.5 micrograms/ml of the measured SPC by Methods 1 and 2, as compared with 69% by Methods 3 and 4.) Bias, as measured by the mean prediction error (95% confidence interval) for Methods 1, 2, 3, and 4 were -0.44 (-0.90, 0.03), -0.34 (-0.87, 0.18), -0.43 (-1.09, 0.24), and -0.98 (-1.66, -0.31).(ABSTRACT TRUNCATED AT 250 WORDS)

Aged↗
PubMed1990

An evaluation of Bayesian microcomputer predictions of theophylline concentrations in newborn infants.

Determination of appropriate theophylline maintenance doses in preterm infants is confounded by interpatient variability. This study evaluated the performance of an IBM PC computer program applying Bayesian regression before and during steady state in 37 preterm infants. Prior population estimates of clearance and distribution volume in preterm infants and Bayesian estimates of clearance and distribution volume based on one to three theophylline plasma concentrations were used to predict subsequent concentrations (drawn 1-17 days later). We assessed the accuracy and precision of the predictive performance of the Bayesian program with the mean prediction error and the mean absolute prediction error. The absolute prediction error (mean absolute error +/- SEM) significantly decreased with increasing feedback concentrations from 3.54 +/- 0.45 micrograms/ml (population estimates) to 2.74 +/- 0.42 (one feedback) and 2.02 +/- 0.35 micrograms/ml (two feedback concentrations). Mean prediction errors (+/- SEM) based on one to three feedbacks (-1.5 +/- 0.40 micrograms/ml) were significant improvements over population predictions (-2.63 +/- 0.72 micrograms/ml, p less than 0.05), although a small but significant average overprediction remained. Absolute prediction error was correlated with postconceptional and postnatal age when zero or one but not two feedback concentrations were available. Computer program predictions based on one measured feedback concentration were more accurate and precise than population-based predictions. Refinement of population parameters or two feedback concentrations further improved performance.

Bayes Theorem↗
PubMed1996

Evaluation of unbound serum carbamazepine and carbamazepine-10,11-epoxide concentration prediction methods in polytherapy adult patients with epilepsy.

We retrospectively evaluated the ability of equations with in vivo population binding parameters of our previous study (Method 1) or an average unbound fraction of 0.25 of Pynnönen (Method 2) to predict the unbound serum carbamazepine (CBZ) concentration in 35 serum samples from 18 adult patients with epilepsy receiving polytherapy. In 9 serum samples from 6 patients, the ability of equations for unbound serum carbamazepine-10,11-epoxide (CBZ-E) concentration prediction was also determined in predictive performance with in vivo population binding parameters of our previous study (Method A) or an average unbound fraction of 0.5 of Pynnönen (Method B). Mean prediction error, mean absolute prediction error (MAE), and root mean squared error (RMSE) were calculated for each method, and these values served as a measure of prediction bias and precision. Method 1 shows a bias to underpredict unbound serum CBZ. The MAE and RMSE were smaller in Method 2 (MAE = 0.454 &mgr;m/L, RMSE = 0.597 &mgr;m/L) than Method 1 (MAE = 0.597 &mgr;m/L, RMSE = 0.721 &mgr;m/L). Method 2 is superior to Method 1 in accuracy and precision. The effects of antiepileptic comedications on predictive performance of Methods 1 and 2 were determined in each group of serum samples with (n = 18, Group 1) or without (n = 17, Group 2) valproic acid (VPA) comedication. The results obtained by Method 1 show a bias to underprediction in Group 1 and no bias to over- or underprediction in Group 2. Results obtained by Method 2 show no bias to over- or underprediction in Groups 1 and 2. The effects of VPA comedication on predictive performance of unbound serum CBZ are relatively larger in Method 1 than Method 2. There was a weak but significant positive relationship between age and unbound serum CBZ fraction by simple regression analysis (n = 35, r = 0.368, p = 0.0297). The determined coefficient indicated that only about 14% of variations in unbound serum CBZ fraction can be explained by age, however. In each of Groups 1 and 2, no significant relationship was observed between age and unbound serum CBZ fraction. The effects of age on predictive performance of unbound serum CBZ are relatively small in patients receiving polytherapy. For unbound CBZ-E prediction, each of Methods A and B has no bias to over- or underprediction. The MAE was larger in Method B (MAE = 0.311 &mgr;m/L) than Method A (MAE = 0.233 &mgr;m/L). The differences in RMSE were small between Methods A (RMSE = 0.349 &mgr;m/L) and B (RMSE = 0.333 &mgr;m/L), however. Each of Methods A and B may have similar accuracy and precision.

Journal Article↗
PubMed2001

Prediction of the archaeal exosome and its connections with the proteasome and the translation and transcription machineries by a comparative-genomic approach.

By comparing the gene order in the completely sequenced archaeal genomes complemented by sequence profile analysis, we predict the existence and protein composition of the archaeal counterpart of the eukaryotic exosome, a complex of RNAses, RNA-binding proteins, and helicases that mediates processing and 3'->5' degradation of a variety of RNA species. The majority of the predicted archaeal exosome subunits are encoded in what appears to be a previously undetected superoperon. In Methanobacterium thermoautotrophicum, this predicted superoperon consists of 15 genes; in the Crenarchaea, Sulfolobus solfataricus and Aeropyrum pernix, one and two of the genes from the superoperon, respectively, are relocated in the genome, whereas in other Euryarchaeota, the superoperon is split into a variable number of predicted operons and solitary genes. Methanococcus jannaschii partially retains the superoperon, but lacks the three core exosome subunits, and in Halobacterium sp., the superoperon is divided into two predicted operons, with the same three exosome subunits missing. This suggests concerted gene loss and an alteration of the structure and function of the predicted exosome in the Methanococcus and Halobacterium lineages. Additional potential components of the exosome are encoded by partially conserved predicted small operons. Along with the orthologs of eukaryotic exosome subunits, namely an RNase PH and two RNA-binding proteins, the predicted archaeal exosomal superoperon also encodes orthologs of two protein subunits of RNase P. This suggests a functional and possibly a physical interaction between RNase P and the postulated archaeal exosome, a connection that has not been reported in eukaryotes. In a pattern of apparent gene loss complementary to that seen in Methanococcus and Halobacterium, Thermoplasma acidophilum lacks the RNase P subunits. Unexpectedly, the identified exosomal superoperon, in addition to the predicted exosome components, encodes the catalytic subunits of the archaeal proteasome, two ribosomal proteins and a DNA-directed RNA polymerase subunit. These observations suggest that in archaea, a tight functional coupling exists between translation, RNA processing and degradation, (apparently mediated by the predicted exosome) and protein degradation (mediated by the proteasome), and may have implications for cross-talk between these processes in eukaryotes.

Adenosine Triphosphatases↗
PubMed2005

Closing in on the C. elegans ORFeome by cloning TWINSCAN predictions.

The genome of Caenorhabditis elegans was the first animal genome to be sequenced. Although considerable effort has been devoted to annotating it, the standard WormBase annotation contains thousands of predicted genes for which there is no cDNA or EST evidence. We hypothesized that a more complete experimental annotation could be obtained by creating a more accurate gene-prediction program and then amplifying and sequencing predicted genes. Our approach was to adapt the TWINSCAN gene prediction system to C. elegans and C. briggsae and to improve its splice site and intron-length models. The resulting system has 60% sensitivity and 58% specificity in exact prediction of open reading frames (ORFs), and hence, proteins-the best results we are aware of any multicellular organism. We then attempted to amplify, clone, and sequence 265 TWINSCAN-predicted ORFs that did not overlap WormBase gene annotations. The success rate was 55%, adding 146 genes that were completely absent from WormBase to the ORF clone collection (ORFeome). The same procedure had a 7% success rate on 90 Worm Base "predicted" genes that do not overlap TWINSCAN predictions. These results indicate that the accuracy of WormBase could be significantly increased by replacing its partially curated predicted genes with TWINSCAN predictions. The technology described in this study will continue to drive the C. elegans ORFeome toward completion and contribute to the annotation of the three Caenorhabditis species currently being sequenced. The results also suggest that this technology can significantly improve our knowledge of the "parts list" for even the best-studied model organisms.

Animals↗
PubMed1999

EMG and metabolite-based prediction of force in paralyzed quadriceps muscle under interrupted stimulation.

A major issue associated with functional electrical stimulation (FES) of a paralyzed limb is the decay with time of the muscle force as a result of fatigue. A possible means to reduce fatigue during FES is by using interrupted stimulation, in which fatigue and recovery occur in sequence. In this study, we present a model which enables us to evaluate the temporal force generation capacity within the electrically activated muscle during first stimulation fatigue, i.e., when the muscle is activated from unfatigued initial conditions, and during postrest stimulation, i.e., after different given rest durations. The force history of the muscle is determined by the activation as derived from actually measured electromyogram (EMG) data, and by the metabolic fatigue function expressing the temporal changes of muscle metabolites, from existing data acquired by in vivo 31P MR spectroscopy in terms of the inorganic phosphorus variables, Pi or H2PO4-, and by the intracellular pH. The model was solved for supra-maximal stimulation in isometric contractions separated by rest periods, and compared to experimentally obtained measurements. EMG data were fundamental for prediction of the ascending force during its posttetanic response. On the other hand, prediction of the decaying phase of the force was possible only by means of the metabolite-based fatigue function. The prediction capability of the model was assessed by means of the error between predicted and measured force profiles. The predicted force obtained from the model in first stimulation fatigue fits well with the experimental one. In postrest stimulation fatigue, the different metabolites provided different prediction capabilities of the force, depending on the duration of the rest period. Following rest duration of 1 min, Pi provided the best prediction of force; H2PO4- extended the prediction capacity of the model to up to 6 min and pH provided a reliable prediction for rest durations longer than 12 min. The results presented shed light on the roles of EMG and of metabolites in prediction of the force history of a paralyzed muscle under conditions where fatigue and recovery occur in sequence.

Algorithms↗
PubMed1999

Comparison of observed rheological properties of hard wheat flour dough with predictions of the Giesekus-Leonov, White-Metzner and Phan-Thien Tanner models.

The measured rheological behavior of hard wheat flour dough was predicted using three nonlinear differential viscoelastic models. The Phan-Thien Tanner model gave good zero shear viscosity prediction, but overpredicted the shear viscosity at higher shear rates and the transient and extensional properties. The Giesekus-Leonov model gave similar predictions to the Phan-Thien Tanner model, but the extensional viscosity prediction showed extension thickening. Using high values of the mobility factor, extension thinning behavior was observed but the predictions were not satisfactory. The White-Metzner model gave good predictions of the steady shear viscosity and the first normal stress coefficient but it was unable to predict the uniaxial extensional viscosity as it exhibited asymptotic behavior in the tested extensional rates. It also predicted the transient shear properties with moderate accuracy in the transient phase, but very well at higher times, compared to the Phan-Thien Tanner model and the Giesekus-Leonov model. None of the models predicted all observed data consistently well. Overall the White-Metzner model appeared to make the best predictions of all the observed data.

Elasticity↗
PubMed2000

A two-step algorithm for predicting portal dose images in arbitrary detectors.

Recently, portal imaging systems have been successfully demonstrated in dosimetric treatment verification applications, where measured and predicted images are quantitatively compared. To advance this approach to dosimetric verification, a two-step model which predicts dose deposition in arbitrary portal image detectors is presented. The algorithm requires patient CT data, source-detector distance, and knowledge of the incident beam fluence. The first step predicts the fluence entering a portal imaging detector located behind the patient. Primary fluence is obtained through ray-tracing techniques, while scatter fluence prediction requires a library of Monte Carlo-generated scatter fluence kernels. These kernels allow prediction of basic radiation transport parameters characterizing the scattered photons, including fluence and mean energy. The second step of the algorithm involves a superposition of Monte Carlo-generated pencil beam kernels, describing dose deposition in a specific detector, with the predicted incident fluence. This process is performed separately for primary and scatter fluence, and yields a predicted dose image. A small but noticeable improvement in prediction is obtained by explicitly modeling the off-axis energy spectrum softening due to the flattening filter. The algorithm is tested on a slab phantom and a simple lung phantom (6 MV). Furthermore, an anthropomorphic phantom is utilized for a simulated lung treatment (6 MV), and simulated pelvis treatment (23 MV). Data were collected over a range of air gaps (10-80 cm). Detectors incorporating both low and high atomic number buildup are used to measure portal image profiles. Agreement between predicted and measured portal dose is better than 3% in areas of low dose gradient (<30%/cm) for all phantoms, air gaps, beam energies, and detector configurations tested here. It is concluded that this portal dose prediction algorithm is fast, accurate, allows separation of primary and scatter dose, and can model arbitrary detectors.

Air↗
PubMed2001

Predicting speech metrics in a simulated classroom with varied sound absorption.

By systematically varying the amount of sound absorption, and the location of the sound-absorbing material in a simulated classroom, it was possible to assess the accuracy of the prediction of speech metrics in quite simple acoustical environments. Predictions of speech level, early-to-late sound ratios (C50) and speech transmission index (STI) values were obtained analytically and with two hybrid ray-based computer programs, RAYNOISE 3.0 and ODEON 4.1. The RAYNOISE predictions were accomplished with a purely specular reflection model and also with a calibrated diffuse reflection model. ODEON uses a parameter called transition order, TO, to change the reflection procedure from purely specular to diffuse for reflections that have orders higher than TO. A parametric study was conducted to determine the best transition order for the ODEON prediction of speech metrics. It was found that the analytical predictions of speech level and C50 were on average accurate to about 1 just-noticeable difference (jnd), whereas the analytical predictions of STI were on average within 2 jnd's. ODEON predictions of speech level, C50 and STI were on average within 2 jnd's. RAYNOISE predictions of C50 and STI with the specular model were on average within 2 jnd's. However, the RAYNOISE predictions of speech level, with both types of reflection models, and the RAYNOISE predictions of C50 and STI with the diffuse model had average errors greater than 2 jnd's. The effects of the sound-absorption treatments on the measured speech metric values are also discussed.

Humans↗
PubMed2004

Certainty and mortality prediction in critically ill children.

OBJECTIVES: The objective of this study is to investigate the relationship between a physician's subjective mortality prediction and the level of confidence with which that mortality prediction is made. DESIGN AND PARTICIPANTS: The study is a prospective cohort of patients less than 18 years of age admitted to a tertiary Paediatric Intensive Care Unit (ICU) at a University Children's Hospital with a minimum length of ICU stay of 10 h. Paediatric ICU attending physicians and fellows provided mortality risk predictions and the level of confidence associated with these predictions on consecutive patients at the time of multidisciplinary rounds within 24 hours of admission to the paediatric ICU. Median confidence levels were compared across different ranges of mortality risk predictions. RESULTS: Data were collected on 642 of 713 eligible patients (36 deaths, 5.6%). Mortality predictions greater than 5% and less than 95% were made with significantly less confidence than those predictions <5% and >95%. Experience was associated with greater confidence in prognostication. CONCLUSIONS: We conclude that a physician's subjective mortality prediction may be dependent on the level of confidence in the prognosis; that is, a physician less confident in his or her prognosis is more likely to state an intermediate survival prediction. Measuring the level of confidence associated with mortality risk predictions (or any prognostic assessment) may therefore be important because different levels of confidence may translate into differences in a physician's therapeutic plans and their assessment of the patient's future.

Child↗