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At least 865 records · Page 48Linked to original sources
PubMed2005

A predictive model of rectal tumor response to preoperative radiotherapy using classification and regression tree methods.

PURPOSE: The ability to predict rectal tumor response to preoperative radiotherapy before treatment would significantly impact patient selection. In this study, classification and regression tree (CART) methods were used to model tumor response to preoperative conformal high-dose rate brachytherapy by assessing the predictive value of vascular endothelial growth factor (VEGF), Bcl-2, p21, p53, and APAF-1. EXPERIMENTAL DESIGN: Immunohistochemistry was used to detect VEGF, Bcl-2, p21, p53, and APAF-1 from 62 pretreatment rectal tumor biopsies. Scores were assigned as percentages of positive tumor cell staining and were used in CART analysis to identify the proteins that best predicted response to radiotherapy. Ten-fold cross-validation was used to prevent overfitting and multiple cross-validation experiments were run to estimate the prediction error. RESULTS: Postoperative pathologic evaluation of the irradiated tumor bed revealed 43 responsive tumors [20 with complete response (T(0)) and 23 with partial response] and 19 nonresponsive tumors. The optimal tree resulting from CART analysis had five terminal nodes with a misclassification rate of 18%. Of the five proteins selected for their predictive value, VEGF and Bcl-2 contributed most to the classification of responsive and nonresponsive tumors. All 10 tumors with no VEGF were completely responsive (T(0)) to radiotherapy; 85% of those with VEGF and negative for Bcl-2 were responsive to therapy. CONCLUSIONS: VEGF and Bcl-2 status in pretreatment rectal tumor biopsies may be predictive of response to preoperative high-dose rate brachytherapy.

Apoptotic Protease-Activating Factor 1↗
PubMed1996

A statistical model predicting the seizure threshold for right unilateral ECT in 106 patients.

Titration of the electroconvulsive therapy (ECT) stimulus to the patient's convulsive threshold is the only way to directly assess the patient's seizure threshold. This technique is presently practiced by 39% of ECT providers, according to a recent survey. Because multiple variables influence the seizure threshold in patients, multivariate statistical methods may provide a useful strategy to determine which variables exert the most influence on convulsive threshold. A multivariate ordinal logistic model of seizure threshold was developed on an experimental group of 66 consecutive patients undergoing titrated right unilateral (RUL) ECT for major depression. The accuracy of the model was cross-validated on a second group of 40 patients undergoing similar RUL ECT procedures. The final multivariate ordinal logistic regression model for the seizure threshold level (STL) was significant (Likelihood ratio chi 2 = 54.115; p < 0.0001:R2 = 0.313). Increasing age, African-American race, and longer inion-nasion distances (p < 0.06) predicted higher STL. Female gender was associated with a lower STL. The ability of the final model to accurately predict STL for the validation group was fair (pairwise correlation was 0.576; p < 0.001). The model did well for predicting lower STL, but fared poorly for higher STL. In conclusion, modeling STL may help establish the relative contribution of variables thought to be important to seizure threshold. However, STL models remain impractical for clinical applications in estimating seizure threshold at this time, and empirical stimulus titration should be used.

Adult↗
PubMed1999

SEC proton prediction model: verification and analysis.

This paper describes a model that has been used at the NOAA Space Environment Center since the early 1970s as a guide for the prediction of solar energetic particle events. The algorithms for proton event probability, peak flux, and rise time are described. The predictions are compared with observations. The current model shows some ability to distinguish between proton event associated flares and flares that are not associated with proton events. The comparisons of predicted and observed peak flux show considerable scatter, with an rms error of almost an order of magnitude. Rise time comparisons also show scatter, with an rms error of approximately 28 h. The model algorithms are analyzed using historical data and improvements are suggested. Implementation of the algorithm modifications reduces the rms error in the log10 of the flux prediction by 21%, and the rise time rms error by 31%. Improvements are also realized in the probability prediction by deriving the conditional climatology for proton event occurrence given flare characteristics.

Algorithms↗
PubMed1996

Theoretical design of prodrug-enhancer combination based on a skin diffusion model: prediction of permeation of acyclovir prodrugs treated with 1-geranylazacycloheptan-2-one.

PURPOSE: A theoretical design of percutaneous penetration enhancement in which prodrug derivation and enhancer application are combined is proposed based on the skin diffusion model and it is experimentally verified. METHODS: Employing acyclovir as a model drug, the hypothesis was tested by synthesis of its prodrugs and evaluation of their in vitro permeation in the rat skin, with or without a penetration enhancer, 1-geranylazacycloheptan-2-one(GACH). RESULTS: Among five acyclovir prodrugs, those with higher lipophilicities (propionate, butyrate, valerate, and hexanoate prodrugs) showed greater skin penetration than those of hydrophilic prodrugs (acetate), when administered in combination with GACH. Furthermore, the observed enhancement ratios were in good agreement with those predicted by theoretical consideration. CONCLUSIONS: Thus, skin permeation of prodrugs applied with an enhancer can be predicted and optimized by model analysis.

Acyclovir↗
PubMed1998

Evaluation of the uniformity of fit of general outcome prediction models.

OBJECTIVE: To compare the performance of the New Simplified Acute Physiology Score (SAPS II) and the New Admission Mortality Probability Model (MPM II0) within relevant subgroups using formal statistical assessment (uniformity of fit). DESIGN: Analysis of the database of a multi-centre, multi-national and prospective cohort study, involving 89 ICUs from 12 European Countries. SETTING: Database of EURICUS-I. PATIENTS: Data of 16,060 patients consecutively admitted to the ICUs were collected during a period of 4 months. Following the original SAPS II and MPM II0 criteria, the following patients were excluded from the analysis: younger than 18 years of age; readmissions; acute myocardial infarction; burn cases; patients in the post-operative period after coronary artery bypass surgery and patients with a length of stay in the ICU shorter than 8 h, resulting in a total of 10,027 cases. INTERVENTIONS: Data necessary for the calculation of SAPS II and MPM II0, basic demographic statistics and vital status on hospital discharge were recorded. Formal evaluation of the performance of the models, comprising discrimination (area under ROC curve), calibration (Hosmer-Lemeshow goodness-of-fit H and C tests) and observed/expected mortality ratios within relevant subgroups. MAIN RESULTS: Better predictive accuracy was achieved in elective surgery patients admitted from the operative room/post-anaesthesia room with gastrointestinal, neurological or trauma diagnoses, and younger patients with non-operative neurological, septic or trauma diagnoses. All these characteristics appear to be linked to a lower severity of illness, with both models overestimating mortality in the more severely ill patients. CONCLUSIONS: Concerning the performance of the models, very large differences were apparent in relevant subgroups, varying from excellent to almost random predictive accuracy. These differences can explain some of the difficulties of the models to accurately predict mortality when applied to different populations with distinct patient baseline characteristics. This study stresses the importance of evaluating multiple diverse populations (to generate the design set) and of methods to improve the validation set before extrapolations can be made from the validation setting to new independent populations. It also underlines the necessity of a better definition of the patient baseline characteristics in the samples under analysis and the formal statistical evaluation of the application of the models to specific subgroups.

Europe↗
PubMed2001

HIV/AIDS knowledge, attitudes and beliefs based prediction models for practices in prison inmates, Sindh, Pakistan.

This study was conducted on prison inmates in Sindh to determine whether HIV/AIDS related knowledge, attitudes and beliefs can predict their practices which risk HIV infection. A pre-designed questionnaire was administered in this cross-sectional study to collect the data on HIV/AIDS related knowledge, attitudes, beliefs, practices and demographic variables in a systematic sample of 3,395 prison inmates during July 1994. The data on responses of inmates to HIV/AIDS related knowledge, attitudes, and beliefs were analyzed and a clear interpretable factor structure emerged for each set of questions labeled as knowledge, attitude and beliefs. Similarly based on responses of inmates to practice questions, three factors emerged and were labeled as heterosexuality, homosexuality and drugs. The standardized factor scores of inmates for each of these six factors were computed and used in further analyses. Multiple linear regression analyses were carried out separately using heterosexuality, homosexuality and drugs factors score as dependent variables to identify if any of the independent variables (demographic variables, knowledge beliefs and attitude) predict these practice factors. The model for heterosexuality explained 23% of the variance and included HIV/AIDS related knowledge, beliefs, age, ethnicity and marital status and duration of imprisonment (F = 84.33, p < 0.001; R2= 23.0). The predictors in the model for homosexuality together explained 10% of the variance and included significant contribution by belief, martial status, ethnicity, education, age and duration of imprisonment (F = 24.76, p < 0.001; R2= 0.10). The model for drugs had significant contributions from HIV/AIDS related beliefs, marital status and ethnicity (F = 20.10, p < 0.001; R2= 0.03). Implications of prevention program based on these results are considered.

Adult↗
PubMed2005

Epileptic transitions: model predictions and experimental validation.

The essence of epilepsy is that a patient displays (long) periods of normal EEG activity (i.e., nonepileptiform) intermingled occasionally with epileptiform paroxysmal activity. The mechanisms of transition between these two types of activity are not well understood. To provide more insight into the dynamics of the neuronal networks leading to seizure generation, the authors developed a computational model of thalamocortical circuits based on relevant patho(physiologic) data. The model exhibits bistability, i.e., it features two operational states, ictal and interictal, that coexist. The transitions between these two states occur according to a Poisson process. An alternative scenario for transitions can be a random walk of network parameters that ultimately leads to a paroxysmal discharge. Predictions of bistable computational model with experimental results from different types of epilepsy are compared.

Adolescent↗
PubMed1998

Predictive model for tensile true stress-strain behavior of chemically and mechanically degraded ultrahigh molecular weight polyethylene.

The gamma radiation sterilization of ultrahigh molecular weight polyethylene (UHMWPE) components in air generates long-lived free radicals that oxidize slowly over time during shelf storage and after implantation. To investigate the combined effects of chemical and mechanical degradation on the mechanical behavior of UHMWPE, sterilized tensile specimens were immersed in 0.5% hydrogen peroxide solution at 37 degrees C for up to 9 months and concurrently subjected to cyclic stress levels of 0 (control), 0 to 5, and 0 to 10 MPa. After chemical and mechanical preconditioning, specimen density was measured using the density gradient column technique. The true stress-strain behavior was measured up to 0.12 true strain and characterized using a multilinear material model, the parameters of which were found to vary linearly with density and cyclic stress history. The mechanical behavior of as-irradiated and degraded UHMWPE was accurately predicted by an analytical composite beam model of the tensile specimens. The results of this study support the hypothesis that chemical and mechanical degradation affect the true stress-strain behavior of UHMWPE. In the future, the material model data presented in this study will enable more accurate prediction of the stresses and strains in UHMWPE components following gamma sterilization in air and subsequent in vivo degradation.

Biocompatible Materials↗
PubMed2002

Response of Ceriodaphnia dubia to ionic silver: discrepancies among model predictions, measured concentrations and mortality.

Silver thiosulfate, often a waste product of photoprocessing, is less bioavailable or toxic to aquatic organisms than is ionic silver. We conducted duplicate 48-h Ceriodaphnia dubia tests in reconstituted laboratory water using treatments of 92.7 nM Ag+ with various concentrations of thiosulfate. Expected Ag+ concentrations were generated for thiosulfate treatment levels using MINEQL + chemical equilibrium modeling. Ag+ concentrations in treatments were determined using a novel silicon-based sensor. Based on predicted Ag+ and published 48-h LC50 values for C. dubia, we did not expect to observe adverse effects. Yet, 100% mortality was observed at low thiosulfate treatments, whereas > 85% and > 95% survival was observed at higher thiosulfate treatment levels, respectively. Our results indicate that biotic responses match the sensor-based Ag+ concentrations. However, there is a discrepancy between these empirical results and responses expected to occur with Ag+ concentrations as predicted by MINEQL + chemical modeling. By correlating silicon sensor data with toxicity results obtained from our laboratory, our work clearly relates a specific chemical form (Ag+) to toxicity results.

Animals↗
PubMed1993

Penetration of industrial chemicals across the skin: a predictive model.

The recently reported dermal absorption and toxicity potential of industrial chemicals is reconsidered using an alternative physicochemically based model of skin penetration. In this model, the outermost, and least permeable, component of the skin [namely, the stratum corneum (SC)] is considered to provide only a lipoidal transport pathway into the body for chemicals that come into contact with the skin. The predictive algorithm of the model is biophysically compatible with known SC properties, and is based on experimental determinations of permeability coefficients through human skin in vitro for nearly 100 compounds of widely divergent physicochemical properties. This simpler prediction results in significantly lower estimates of maximum percutaneous penetration fluxes.

Diffusion↗
PubMed1993

Do physicians locate as spatial competition models predict? Evidence from Alberta.

This article analyses how physicians choose locations of practice in response to spatial competition forces and considers the implications of such choices for public policy to alleviate shortages of practitioners in rural areas. The predicted geographic distribution of physicians, as determined through spatial competition modelling, was compared with the actual distribution of physicians in 1990 among Alberta's 19 census divisions. Physicians were found to respond to spatial competition forces in choosing where to practise, with the qualification that 1 urban patient had a demand weight equal to 2.32 rural patients. A policy to attract more physicians to rural areas by means of income subsidies is technically feasible but expensive. The high cost means that alternative policies such as a bigger and more effective ambulance network to transport patients to medical centres should become the focus of public policies to improve health care in rural areas.

Alberta↗
PubMed2003

Can predictive models for prostate cancer patients derived in the United States of America be utilized in European patients? A validation study of the Partin tables.

OBJECTIVES: Prostate cancer patients in the US and Europe differ due to selection and treatment differences. Accuracy of predictive tools derived in the US might therefore suffer when applied to European patients. We tested the validity of the widely accepted Partin tables for their ability to predict pathologic stage in German patients. METHODS: Clinical and pathological characteristics were obtained from 1,298 consecutive men with clinically localized prostate cancer undergoing radical prostatectomy at the University Hospital Hamburg between January 1992 and February 2000. Receiver operating characteristic (ROC) curve analysis was performed to compare observed and predicted Partin rates for each pathologic stage. RESULTS: The rate for organ confinement was 56% in Hamburg patients compared to 48% in the Partin study. The rates of Hamburg patients for extracapsular extension without seminal vesicle or lymph node involvement were 25%, for seminal vesicle without lymph node involvement 14% and for lymph node metastases 5%. The corresponding rates of the Partin study were 40, 7 and 5%, respectively. The accuracy of Partin table derived probability was high with an area under the ROC curve of 0.817 (95% CI, 0.757-0.876) for organ confinement and 0.807 (95% CI, 0.781-0.833) for lymph node involvement. CONCLUSION: Our study demonstrated that predictive tools for prostate cancer developed in the US could be applied to European patients with comparable accuracy to that reported for validation studies performed with US patients.

Europe↗
PubMed1993

Growth of Listeria monocytogenes, Aeromonas hydrophila and Yersinia enterocolitica in pâté and a comparison with predictive models.

A reference or type strain and a food derived-strain of the cold-tolerant pathogens Listeria monocytogenes, Aeromonas hydrophila and Yersinia enterocolitica were individually inoculated into samples of commercial pâté and incubated at 4 and 10 degrees C. The organisms were periodically estimated by presumptive counts, then values for the lag and generation times were calculated. Both strains of L. monocytogenes grew at both temperatures. The food strain of A. hydrophila grew only at 10 degrees C, and the type strain did not grow at either temperature. Similarly, the type strain of Y. enterocolitica did not grow at either temperature, whereas the food strain grew at both 4 and 10 degrees C. In some cases growth of non-test organisms may have inhibited the growth of these latter two species. The measured values of lag and generation times did not, in general, correlate well with those predicted by response surface models, taken from the literature and produced in this laboratory. It may be that the pâté contained an inhibitor that affected the growth of the organisms. The two strains of A. hydrophila and Y. enterocolitica showed significantly different growth characteristics, reinforcing the value of using a 'cocktail' of strains in growth experiments. Differences in predicted kinetic values from the models indicate that a model for any particular strain may not reflect the growth of naturally occurring contaminants of the same species.

Aeromonas hydrophila↗
PubMed2003

Validation of a transfusion prediction model in head and neck cancer surgery.

BACKGROUND: Allogeneic transfusions are necessary in 14% to 80% of patients undergoing major head and neck cancer surgery. Defining the risk for receiving allogeneic transfusion allows for informed decisions regarding appropriateness of type and crossmatch, preoperative autologous blood donation, and priming with erythropoietin. Based on logistic regression analysis of transfusion risk factors in 438 patients, we developed a transfusion prediction risk assessment (TPRA) model to determine the need for transfusion based on the preoperative hemoglobin value, tumor stage, and need for flap reconstruction. OBJECTIVE: To examine the utility of this TPRA model in clinical practice by assessing the performance of the model in a validation set of patients. METHODS: Between 1996 and 1999, 125 consecutive patients entered into a clinical care pathway underwent major surgical procedures. The ability of the model to discriminate between patients requiring and those not requiring transfusion was assessed using the area under the receiver operating characteristic curve. The agreement between actual and predicted risks was tested using the chi2 goodness-of-fit statistic. RESULTS: The overall transfusion rate was 25%. A 1-U transfusion was required in 7 patients, and multiple units were necessary for 24 patients. Flap reconstruction was required in 63 patients, 44 patients had preoperative anemia by normative values, and 64 had T3/T4 tumors. Among the low-risk non-T3/T4 patients whose preoperative hemoglobin level was normal, the actual/predicted transfusion rate without flap reconstruction was 10%/2%. For high-risk patients with T3/T4 tumors, anemia, and flap reconstruction, the actual/predicted transfusion rate was 43%/65%. The area under the receiver operating characteristic curve was 0.72. The goodness-of-fit statistic indicated lack of fit of the original model, but a recalibrated model fit the observed data well. CONCLUSIONS: In general, the TPRA model identifies patients at low or high risk for allogeneic transfusion and provides guidelines for preoperative counseling regarding the risk of receiving a transfusion. Knowledge of a patient's risk can help direct cost-effective utilization of type and crossmatch, preoperative autologous blood donation, and preoperative priming with erythropoietin.

Adult↗
PubMed2002

A mortality prediction model in diabetic ketoacidosis.

AIM: To assess the value of clinical and laboratory parameters in predicting mortality in patients presenting with diabetic ketoacidosis (DKA). METHODS: The records of all DKA admissions within 10 years were reviewed. Eighteen variables were evaluated at initial presentation and 20 variables at 4, 12 and 24 h from admission. A scoring system derived from these variables was compared to the APACHE III scoring system. RESULTS: Among 154 patients (52 males, mean age 58 +/- 12 years), 20 (13%) died in hospital. Multivariate analysis yielded six variables as significant independent predictors (P < 0.05) of mortality: severe coexisting diseases (SCD) and pH < 7.0, at presentation; units of regular insulin required in the first 12 h > 50 and serum glucose > 16.7 mmo/l, after 12 h; depressed mental state and fever, after 24 h. An integer-based scoring system was derived, as follows: number of points = 6 (SCD at presentation) + 4 (pH < 7.0 at presentation) + 4 (regular insulin required > 50 IU after 12 h) + 4 (serum glucose > 16.7 mmo/l after 12 h) + 4 (depressed mental state after 24 h) + 3 (fever after 24 h). Patients with 0-14 points had 0.86% risk of death, whereas for those with 19-25 points the risk was 93.3%. Median APACHE III scores differed significantly (P < 0.001) among groups of patients stratified according to the above scoring system. CONCLUSIONS: Risk stratification of patients with diabetic ketoacidosis is possible from simple clinical and laboratory variables available during the first day of hospitalization.

APACHE↗