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Use of real time leukaemia data to validate model predictions based on analyses and computer simulations.

Predictions arising out of a mathematical model that describes the expansion of leukaemia from a diffusion-orientated perspective are critiqued and validated by employing available real time data. Based on agreements found between model predictions and the data, but mindful of the limitations it presents, it is concluded that the model could be used to describe the dynamics of normal and abnormal cells in leukaemia. It is suggested that further studies of the behaviour of certain normal cell types in contrast to abnormal cells during leukaemic development could engender additional insights into leukaemia and its treatment.

Cell Death↗

Lactate after exercise in man: IV. Physiological observations and model predictions.

Following earlier papers that established the mathematical form of the time dependence of lactate concentrations during recovery from several types of exercise, and that set up a two-compartment model predicting the same time dependences, the present work applies the model to obtain parameters of specific physiological processes. Satisfactory agreement between predictions of the model and our experiment and literature data is obtained in the cases were comparisons can be made, as in the muscular lactate time evolution measured from biopsy samples, in blood flows through the active muscle at the end of exercise or at rest and their evolution during recovery, as well as in the volume of the active muscle compartment. The model prediction that lactate efflux from the muscles to the blood can reduce to zero during recovery is verified experimentally.

Arteries↗

Within-channel cues in comodulation masking release (CMR): experiments and model predictions using a modulation-filterbank model.

Experiments and model calculations were performed to study the influence of within-channel cues versus across-channel cues in comodulation masking release (CMR). A class of CMR experiments is considered that are characterized by a single (unmodulated or modulated) bandpass noise masker with variable bandwidth centered at the signal frequency. A modulation-filterbank model suggested by Dau et al. [J. Acoust. Soc. Am. 102, 2892-2905 (1997)] was employed to quantitatively predict the experimental data. Effects of varying masker bandwidth, center frequency, modulator bandwidth, modulator type, and signal duration on CMR were examined. In addition, the effect of band limiting the noise before or after modulation was shown to influence the CMR in the same way as a systematic variation of the modulation depth. It is demonstrated that a single-channel analysis, which analyzes only the information from one peripheral channel, quantitatively accounts for the CMR in most cases, indicating that an across-channel process is generally not necessary for simulating results from this class of CMR experiments. True across-channel processes may be found in another class of CMR experiments.

Adult↗

An efficient formulation of Krylov's prediction model for train induced vibrations based on the dynamic reciprocity theorem.

In Krylov's analytical prediction model, the free field vibration response during the passage of a train is written as the superposition of the effect of all sleeper forces, using Lamb's approximate solution for the Green's function of a halfspace. When this formulation is extended with the Green's functions of a layered soil, considerable computational effort is required if these Green's functions are needed in a wide range of source-receiver distances and frequencies. It is demonstrated in this paper how the free field response can alternatively be computed, using the dynamic reciprocity theorem, applied to moving loads. The formulation is based on the response of the soil due to the moving load distribution for a single axle load. The equations are written in the wave-number-frequency domain, accounting for the invariance of the geometry in the direction of the track. The approach allows for a very efficient calculation of the free field vibration response, distinguishing the quasistatic contribution from the effect of the sleeper passage frequency and its higher harmonics. The methodology is validated by means of in situ vibration measurements during the passage of a Thalys high-speed train on the track between Brussels and Paris. It is shown that the model has good predictive capabilities in the near field at low and high frequencies, but underestimates the response in the midfrequency band.

Journal Article↗

How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts.

BACKGROUND: Previous models used to predict individual risk of death from coronary heart disease (CHD) were developed from data of 3 decades ago from the Framingham Heart Study. CHD mortality rates have declined markedly since that period as a result of improvement in both risk factor status and medical interventions. Generalization of the results from this one study to the population at large remains a matter of concern. We compared predictive functions derived from the major risk factors for CHD from Framingham and 2 more recent national cohorts, the First and Second National Health and Nutrition Examination Survey (NHANES I and NHANES II). METHODS AND RESULTS: The participants included 1846 men and 2323 women 35 to 69 years of age and free of CHD at the fourth examination (1954 to 1958) from the Framingham Study; 2753 men and 3858 women from the NHANES I (1971 to 1975); and 2655 men and 3050 women from NHANES II (1976 to 1980). The 3 cohorts were monitored for 24, 20, and 15 years, respectively. Significant heterogeneity existed among studies in the magnitude of the Cox coefficients for the individual factors (ie, age, systolic blood pressure, serum total cholesterol, and smoking status), especially among men. When risk factors were considered collectively, however, functions derived from and applied to different cohorts had a similar ability to rank individual risk. The areas under the receiver operating characteristic curves were 0. 71 to 0.76 in men and 0.76 to 0.81 in women when different risk functions were applied to their own population or to a second population. The cumulative CHD survival observed in women in the 2 national cohorts was close to what was predicted from the Framingham equation. However, Framingham overestimated the cumulative CHD mortality rates in men in NHANES I and NHANES II. CONCLUSIONS: The Framingham risk model for the prediction of CHD mortality rates provides a reasonable rank ordering of risk for individuals in the US white population for the period 1975 to 1990. However, prediction of absolute risk is less accurate.

Adolescent↗

Outcome prediction model for very elderly critically ill patients.

CONTEXT: Very elderly critically ill patients have three possible hospital outcomes: discharge to home, discharge to a skilled nursing or rehabilitation facility, or death. The factors associated with these outcomes are unknown. OBJECTIVE: To develop a three-outcome prediction model for very elderly critically ill patients. DESIGN: Retrospective chart abstraction with ordered logistic regression analysis. SETTING: Academic medical center. PATIENTS: Four hundred and fifty-five patients 85 yrs or older admitted to intensive care units (ICU) during 1996 and 1997. MEASUREMENTS AND MAIN RESULTS: A fitted ordinal logistic regression predictive model was developed using data from 243 patients hospitalized in 1996, and validated on data from 212 patients hospitalized in 1997. Model variables include age, gender, baseline support level, type of ICU, heart rate at ICU admission, use of mechanical ventilation, vasopressors or a pulmonary artery catheter during the ICU stay, and the development of respiratory, neurologic or hematologic failure or sepsis while in the ICU. When tested on the 1997 data, the model was well calibrated and had a high discriminant index. CONCLUSIONS: This mathematical model can be used to predict the risks of these three hospital outcomes for this population of patients. These predictions can provide a context when discussing goals and expectations with patients, families, and other healthcare providers and to aid in hospital discharge planning.

Academic Medical Centers↗

A predictive model for the behavior of radionuclides in lake systems.

This paper describes a predictive model for the behavior of 137Cs in lacustrine systems. The model was tested by comparing its predictions to contamination data collected in various lakes in Europe and North America. The migration of 137Cs from catchment basin and from bottom sediments to lake water was discussed in detail; these two factors influence the time behavior of contamination in lake water. The contributions to the levels of radionuclide concentrations in water, due to the above factors, generally increase in the long run. The uncertainty of the model, used as a generic tool for prediction of the levels of contamination in lake water, was evaluated. Data sets of water contamination analyzed in the present work suggest that the model uncertainty, at a 68% confidence level, is a factor 1.9.

Cesium Radioisotopes↗

Genomic selection in timothy (Phleum pratense L.): a comprehensive evaluation of prediction models, multi-trait strategies, and forward validation across Norwegian environments.

This study presents a comprehensive evaluation of genomic selection (GS) in timothy (Phleum pratense L.), comparing nine prediction models across yield and quality traits at two Norwegian locations. Forward validation with independent full-sib (FS2) families revealed a substantial generalization gap, highlighting the need for realistic accuracy assessment in polyploid forage breeding. Timothy (Phleum pratense L.) is the most important forage grass in Northern Europe, yet genomic selection has not been systematically evaluated in this hexaploid species. We assessed 889 FS2-families originating from biparental crosses among 49 cultivars/populations. The FS2-families were genotyped with 30,698 SNP markers derived from genotyping-by-sequencing (GBS) and field tested for three harvest years at a highland and a lowland continental location in Southern Norway. Nine genomic prediction models were compared for six yield traits (dry matter yield per cut and total) and six quality traits (protein, digestibility, and fiber fractions) across three cuts/year. Within-training cross-validation accuracies were moderate to high (mean r = 0.62), with Random Forest and SVR consistently outperforming GBLUP. However, forward validation using 213 independent FS2-families revealed dramatically lower accuracies (mean r = 0.16), with only 16 of 30 trait-dataset combinations reaching statistical significance (p < 0.05). Genomic heritabilities (GREML), estimated across environments, ranged from near zero for the quality traits to 0.55 for the yield traits. Multi-trait models improved accuracy by 3-5% over single-trait approaches, while FS2 families-by-environment interaction models with Random Forest achieved the highest within-training accuracy (mean r = 0.71). Marker density analysis showed accuracy plateauing at approximately 15000 SNPs. Genetic correlations among the yield component traits were estimated by multi-trait REML; correlations among the quality traits could not be estimated reliably because their genomic heritabilities were low. A multi-trait selection index identified top-performing FS2-families for further crossing recommendations. These results provide a benchmark for GS implementation in hexaploid timothy and emphasize that cross-validation substantially overestimates prediction accuracy for truly independent material.

Norway↗

Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.

Aged↗

Predictive model to identify trauma patients with blood alcohol concentrations > or = 50 mg/dl.

OBJECTIVE: To develop a simple model for identification of trauma patients who are likely to have a blood alcohol concentration > or = 50 mg/dL (BAC + 50). METHODS: Demographic, clinical, and BAC data were collected from the clinical trauma registry and toxicology data base at a Level I trauma center. Logistic regression was used to analyze data from 11,206 patients to develop a predictive model, which was validated using a subsequent cohort of 3,523 patients. RESULTS: In the model development cohort, alcohol was detected in the blood of 3,180 BAC-tested patients (28.7%), of whom 91.2% had a BAC + 50 status. Preliminary analysis revealed associations between a BAC + 50 status and sex, age, race, injury type (intentional vs. unintentional), and time of injury (night vs. day and weekend vs. weekday). A predictive model using four attributes (sex and injury type) identified patients at low, medium, and high risk for being BAC + 50. The model was validated using the second group of patients. CONCLUSION: Injured patients with a high probability of being alcohol positive can be identified using a simple scoring system based on readily available demographic and clinical information.

Adolescent↗

Predictive modeling care management program produces dramatic bottom line results.

Predictive modeling program identifies high-risk patients before complications lead to skyrocketing costs. It's a new-generation effort, but it has already proven its worth in a large population, producing savings of $1 million to $2 million per month. The effort begins with the development of a constantly evolving registry of high-risk patients. Specialized case management efforts are then focused on stabilizing individuals within this group. See how traditional interventions have been combined with new technology to deliver results.

Algorithms↗

Evaluation of the white finger risk prediction model in ISO 5349 suggests need for prospective studies.

The risk prediction model for white fingers in Annex A of ISO 5349 is not likely to offer protection from all tools and all work processes. It is also probable that some work place changes it has initiated are either redundant or lack the intended effect. The main reasons for these shortcomings are the following. The often demonstrated disagreement between predicted and observed white fingers occurrence may be related to the fact that the model is based on latency data. This leads to an overestimation, to an unknown extent, of true group risks. A possible healthy worker effect, resulting in underestimation, has not been considered, and uncertainty because of recall bias is connected with using latency as effect variable in a slowly developing disorder like white fingers. The diagnostic criteria for white fingers have varied over the years, causing a possible inclusion of circulatory disturbances other than those induced by vibration. Among insufficiently clarified matters unrelated to vibration are variations in individual susceptibility and other host factors that modify vibration effects, uncertainty concerning daily or total effective exposure, and the fact that variation in work methods and processes as well as ergonomic factors other than vibration tend to make different groups incomparable form the viewpoint of risk of injury. Lack of sufficient data on vibration measurements and employment durations add to the uncertainty, as do variations in tool conditions (grinder wheels, etc) and inherent difficulties in measurement. Finally, the ISO 5349 frequency-weighting curve only relates to acute sensory effects rather than chronic effects on vascular functions like white fingers, and directional difference in sensitivity has not been incorporated in the curve. Data on exposure-response relationships are needed from prospective studies that monitor the dose of exposure to special vibration types and all relevant environmental agents, employ diagnostics with good sensitivity, specificity and predictive value, and pay attention to environmental or individual confounding factors and effect modifiers. Before such data are available, the ISO 5349 model should not be used for risk prediction. It can serve, however, as an incentive for manufacturers to produce tools that vibrate less, and for employers to implement practical measures in order to reduce the total and dose of effective exposure.

Fingers↗

Analyzing predictive models following definitive radiotherapy for prostate carcinoma.

BACKGROUND: As we approach the 21st century, clinically useful predictive models for prostate carcinoma are urgently needed to stratify patients reliably for future treatment strategies. Recently, many investigators have developed models that employ prostate specific antigen (PSA)-based constructs or groupings in an attempt to predict outcome accurately following definitive radiotherapy. This investigation was conducted to determine which of these models provides the closest "fit" to independent clinical outcome data measuring biochemical freedom from failure (bNED control), thereby warranting further exploration. METHODS: Six models were analyzed in a definitive radiotherapy series of 421 patients with localized prostate carcinoma treated with a median dose of 74 Gray (Gy) between March 1988 and November 1994. A stepwise Cox proportional hazards multivariate analysis (MVA) was performed to predict for bNED control using the following covariates: PSA, Gleason's score, stage, dose, PSA density, and perineural invasion. Subsequent MVAs were performed for each model incorporating the new construct or prognostic groupings. The adequacy of the models was confirmed using plots of score residuals against time to bNED failure and comparisons were made used Akaike's Information Criteria (AIC) in which a smaller value corresponds to a statistically improved model based on explained variation and the number of predictors. Because PSA was distributed in a log-normal fashion in the current study population, the model-building process was duplicated using a logarithmic transformation analysis. Biochemical failure was defined as 2 consecutive elevations in the PSA > or = 1.5 ng/mL. The median follow-up time was 34 months (range, 2-87 months). RESULTS: Initially, the model developed by Pisansky et al. appeared the most predictive due to the parsimony in their risk estimate, which is the sole predictor of outcome, as well as its associated lowest AIC value. However, after the logarithmic transformation analysis, all the models appeared to be equally predictive of bNED outcome. CONCLUSIONS: A plethora of accurate models for predicting outcome following definitive radiotherapy for prostate carcinoma recently have been engineered, all of which are essentially equally predictive in this data base (via a logarithmic conversion process). This analysis should be corroborated in other large radiotherapy series.

Follow-Up Studies↗

Development of a predictive model for optimal zona pellucida binding using insemination volume and sperm concentration.

OBJECTIVE: To develop a predictive model under hemizona assay (HZA) conditions for human spermatozoa concentrations and insemination volume for optimum zona pellucida (ZP) binding. DESIGN: Analysis of 20 different insemination volumes for zona binding and sperm morphology under HZA conditions. SETTING: Reproductive biology unit, tertiary medical center. PATIENTS: Four proven fertile sperm donors. MAIN OUTCOME MEASURES: 5-, 20-, 50-, 80-, and 100-microL droplets were analyzed with four different concentrations of 0.5 x 10(6), 1.0 x 10(6), 2.0 x 10(6), and 4.0 x 10(6) cells/mL to determine the number of sperm bound to each hemizona. Fifteen hemizonae were used for each insemination volume or microdroplet. Response surface regression model with volume and concentration as the regressor variables has been used. RESULTS: The response surface of binding for the factors concentration and volume showed nonlinear association. A formula, indicating the optimal sperm insemination volume for maximum sperm binding to the ZP, Vmax = -(b1 + b5c)/2b6c, is described. The transformed data indicated 60 microL containing 4 x 10(6) sperm/mL to be optimal. Although morphology of zona spermatozoa is superior compared with seminal and postswim-up samples, no difference among the percentage of the normal morphology in different microdroplets could be demonstrated. CONCLUSION: Optimal volume for the obtained concentration of spermatozoa from a patient can be calculated and therapeutically used for cases of severe oligozoospermic patients by microvolume inseminations in IVF practice.

Female↗

Comparison of two predictive models for prognosis in critically ill patients in a Veteran's Affairs Medical Center coronary care unit.

STUDY OBJECTIVE: The acute physiologic and chronic health evaluation score has been developed to assess prognosis in critically ill patients. Knaus et al initially determined and validated diagnosis-specific coefficients for prediction of outcome in a group of multidisciplinary ICUs. Teskey et al found different coefficients for cardiac diagnoses in a retrospective analysis of coronary care unit (CCU) only patients. This study compares the actual mortality in a Veteran's Affairs Medical Center (VAMC) CCU with the mortality predicted by the two equations. DESIGN AND SETTING: Data were prospectively collected for patients admitted to the medical CCU at a university-affiliated, tertiary care VAMC. PATIENTS: Patients (n = 338) admitted to the CCU with the diagnoses of coronary artery disease (CAD), myocardial infarction (MI), congestive heart failure (CHF), arrhythmia (Arr), and other cardiac-related diagnoses. RESULTS: The entire CCU population showed no significant differences from either the predictions by Knaus et al and Teskey et al in 1991. However, when specific disease states were analyzed as a whole, significant differences from both prediction models for CAD and MI actual mortality were found. Teskey et al was a better predictor for the Arr and CHF population, while both were equally reliable for other. CONCLUSIONS: Either prediction model is reliable for a CCU population in general. However, diagnosis-specific coefficients of Teskey et al appear to correlate better with actual mortality for this VAMC. Significant outcome differences compared with those predicted may reflect the patient population specific to a VAMC.

APACHE↗

[Development of a predictive model for respiratory isolation of patients suspected of having pulmonary tuberculosis].

INTRODUCTION: As numerous nosocomial outbreaks of pulmonary tuberculosis have been reported during the last two decades, prompt identification and effective isolation of contagious patients should be made a priority in tuberculosis control policies. There is a need to develop a predictive model which would allow prompt recognition and isolation of smear-positive patients. CURRENT KNOWLEDGE AND KEY POINTS: Various authors have attempted to improve the respiratory isolation policies for patients suspected of having pulmonary tuberculosis. A French multicenter prospective study of 211 patients suspected of having pulmonary tuberculosis established that: 1) the current respiratory isolation policy of suspected pulmonary tuberculosis needs improvement (sensitivity = 71.4%; i.e., 28.6% of smear-positive patients are admitted without isolation) and 2) better interpretation of clinical and radiological data available on patient admission could improve the adequacy of respiratory isolation. Univariate analysis showed that predictive factors of pulmonary tuberculosis were chest X-rays (P < 0.00001), symptoms (P = 0.0004), age (mean: 40.8 years for TB vs. 47.5 for non-TB, P = 0.04), HIV infection (10.6% vs. 28.7%, P = 0.01), immigrant (72% vs. 55%, P = 0.03) and BCG status (P = 0.025), while multivariate analysis demonstrated that chest X-ray pattern (P < 0.00001), HIV infection (P = 0.002) and symptoms (P = 0.009) were independent predictive factors. FUTURE PROSPECTS AND PROJECTS: From these data, a model was proposed and evaluated in the derivation cohort using the receiver operating characteristics (ROC) curve. We retrospectively studied the predictive model in two populations different from the one from which it was derived. The model would have improved sensitivity of the respiratory isolation policy from 71.4% (current respiratory isolation policy) to 82.4% and 91.1%, respectively. Prospective, multicenter studies are requested to establish the value of such a predictive model in improving the respiratory isolation policy for patients suspected of having pulmonary tuberculosis.

Cohort Studies↗

Posterior predictive model checks for disease mapping models.

Disease incidence or disease mortality rates for small areas are often displayed on maps. Maps of raw rates, disease counts divided by the total population at risk, have been criticized as unreliable due to non-constant variance associated with heterogeneity in base population size. This has led to the use of model-based Bayes or empirical Bayes point estimates for map creation. Because the maps have important epidemiological and political consequences, for example, they are often used to identify small areas with unusually high or low unexplained risk, it is important that the assumptions of the underlying models be scrutinized. We review the use of posterior predictive model checks, which compare features of the observed data to the same features of replicate data generated under the model, for assessing model fitness. One crucial issue is whether extrema are potentially important epidemiological findings or merely evidence of poor model fit. We propose the use of the cross-validation posterior predictive distribution, obtained by reanalyzing the data without a suspect small area, as a method for assessing whether the observed count in the area is consistent with the model. Because it may not be feasible to actually reanalyze the data for each suspect small area in large data sets, two methods for approximating the cross-validation posterior predictive distribution are described.

Algorithms↗

Comparison of two prognostic models predicting survival in patients with malignant melanoma.

Accurate predictions of prognosis are important in the clinical management of patients with malignant melanoma. Primary lesions from 55 patients with cutaneous melanoma, having 10 or more years of clinical follow-up, were evaluated by two models predicting patient survival. One model was simple and relied solely on tumor thickness. The other model was complex and considered stage of tumor progression, and six clinical and histological variables. Accuracy of the two models was determined by retrospective review of the medical record, and the predictive power of each model was compared by receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (a measure of the "goodness" of the model) for the single variable model was 0.70 +/- 0.0775 standard error (SE), and the area under the ROC curve for the multiple variable model was 0.77 +/- 0.0779 SE. Although a modest improvement in predictive power is suggested for the multiple variable model, the SEs for the two models overlap, and the difference is not statistically significant. Further study using a larger database may be required to determine definitively if the multiple variable model significantly increases the ability to predict patient survival or death, and make better clinical decisions.

Adult↗