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At least 145 records · Page 8Linked to original sources
PubMed2004

A predictive model for the release of slightly water-soluble drugs from HPMC matrices.

A model to predict the fraction of slightly water-soluble drug released as a function of release time (t, h), HPMC concentration (C(H), w/w), drug solubility in distilled water at 37 degrees C (C(s), g/100 mL), and volume of drug molecule (V, nm3) was derived when theophyline, tinidazole, and propylthiouracil were selected as model drugs. The model is log (M(t)/M(infinity)) = 0.8683 logt-0.1930C(s) logt + 0.5406V logt-1.227C(H) + 0.1594C(s) + 0.4423C(H)C(s) - 0.8655 (n = 130, r = 0.9969), where Mt is the amount of drug released at time t, Minfinity is the amount of drug released over a very long time, which corresponds in principle to the initial loading, n is the number of samples, and r is the correlation coefficient. The model was validated using sulfamethoxazole and satisfactory results were obtained. The model can be used to predict the release fraction of variousslightly water-soluble drugs from HPMC matrices having different polymer levels.

Delayed-Action Preparations↗
PubMed2003

Predictive modeling for the presence of prostate carcinoma using clinical, laboratory, and ultrasound parameters in patients with prostate specific antigen levels < or = 10 ng/mL.

BACKGROUND: The objective of the current study was to develop a model for predicting the presence of prostate carcinoma using clinical, laboratory, and transrectal ultrasound (TRUS) data. METHODS: Data were collected on 1237 referred men with serum prostate specific antigen (PSA) levels < or = 10 ng/mL who underwent an initial prostate biopsy. Variables analyzed included age, race, family history, referral indication(s), prior vasectomy, digital rectal examination (DRE), PSA level, PSA density (PSAD), and TRUS findings. Twenty percent of the data were reserved randomly for study validation. Logistic regression analysis was performed to estimate the relative risk, 95% confidence interval, and P values. RESULTS: Independent predictors of a positive biopsy result included elevated PSAD, abnormal DRE, hypoechoic TRUS finding, and age 75 years or older. Based on these variables, a predictive nomogram was developed. The sensitivity and specificity of the model were 92% and 24%, respectively, in the validation study for which the predictive probability > or = 10% was used to indicate the presence of prostate carcinoma. The area under the receiver operating characteristic curve (AUC) for the model was 73%, which was significantly higher compared with the prediction based on PSA alone (AUC, 62%). If it was validated externally, then application of this model to the biopsy decision could result in a 24% reduction in unnecessary biopsy procedures, with an overall reduction of 20%. CONCLUSIONS: Incorporation of clinical, laboratory, and TRUS data into a prebiopsy nomogram significantly improved the prediction of prostate carcinoma over the use of individual factors alone. Predictive nomograms may serve as an aid to patient counseling regarding prostate biopsy outcome and to reduce the number of unnecessary biopsy procedures.

Adenocarcinoma↗
PubMed2001

Comparison of outcome predictions made by physicians, by nurses, and by using the Mortality Prediction Model.

BACKGROUND: Critical care nurses must collaborate with physicians, patients, and patients' families when making decisions about aggressiveness of care. However, few studies address nurses' ability to predict outcomes. OBJECTIVES: To compare predictions of survival outcomes made by nurses, by physicians, and by using the Mortality Prediction Model. METHODS: Predictions of survival and function and attitudes toward aggressiveness of care based on the predictions were recorded on questionnaires in the emergency department by emergency and intensive care unit physicians and by intensive care unit nurses at the time of admission to the unit between February and September 1995 for 235 consecutive adult nontrauma patients. Scores on the Mortality Prediction Model were calculated on admission. Data on 85 of the 235 patients were analyzed by using descriptive, chi 2, and correlational statistics. Nurses' predictions of function were compared with patients' actual outcomes 6 months after admission. RESULTS: Nurses' predictions of survival were comparable to those of emergency physicians and superior to those obtained by using the objective tool. Years of nursing experience had no relationship to attitudes toward aggressiveness of care. Nurses accurately predicted functional outcomes in 52% of the followed-up cases. Intensive care physicians were more accurate than nurses and emergency physicians in predicting survival. All predictions made by clinicians were superior to those obtained by using the model. CONCLUSIONS: Nurses can predict survival outcomes as accurately as physicians do. Greater sensitivity and specificity are necessary before clinical judgment or predictive tools can be considered as screens for determining aggressiveness of care.

Adolescent↗
PubMed2005

Limits of predictive models using microarray data for breast cancer clinical treatment outcome.

Data from microarray studies have been used to develop predictive models for treatment outcome in breast cancer, such as a recently proposed predictive model for antiestrogen response after tamoxifen treatment that was based on the expression ratio of two genes. We attempted to validate this model on an independent cohort of 58 patients with resectable estrogen receptor-positive breast cancer. We measured expression of the genes HOXB13 and IL17BR with real time-quantitative polymerase chain reaction and assessed the association between their expression and outcome by use of univariate logistic regression, area under the receiver-operating-characteristic curve (AUC), a two-sample t test, and a Mann-Whitney test. We also applied standard supervised methods to the original microarray dataset and to another independent dataset from similar patients to estimate the classification accuracy obtainable by using more than two genes in a microarray-based predictive model. We could not validate the performance of the two-gene predictor on our cohort of samples (relation between outcome and the following genes estimated by logistic regression: for HOXB13, odds ratio [OR] = 1.04, 95% confidence interval [CI] = 0.92 to 1.16, P = .54; for IL17BR, OR = 0.69, 95% CI = 0.40 to 1.20, P = .18; and for HOXB13/IL17BR, OR = 1.30, 95% CI = 0.88 to 1.93, P = .18). Similar results were obtained with the AUC, a two-sample two-sided t test, and a Mann-Whitney test. In addition, estimates of classification accuracies applied to two independent microarray datasets highlighted the poor performance of treatment-response predictive models that can be achieved with the sample sizes of patients and informative genes to date.

Adult↗
PubMed1997

[Chronic metabolic syndrome: a predictive model of risk of macrovascular events?].

OBJECTIVES: To establish a predictive model of the risk of macrovascular complications in patients with Chronic Metabolic Syndrome by means of multiple logistic regression analysis. To identify Chronic Metabolic Syndrome as an independent health problem, given its frequency and importance in the genesis of macrovascular complications. DESIGN: A descriptive observational study. SETTING: An urban Health District in Malaga. PATIENTS: 47 patients with Chronic Metabolic Syndrome were chosen by systematic randomised sampling. MEASUREMENTS AND MAIN RESULTS: The best predictive model of macrovascular events was established as the one which included high values of the Waist-Hip index, blood pressure, Fibrinogenaemia and basal Glucaemia, and low values of HDL cholesterol. CONCLUSIONS: 1. A model to predict macrovascular events in patients with Chronic Metabolic Syndrome included high values of the Waist-Hip index, blood pressure, Fibrinogenaemia and basal Glucaemia, and low values of HDL cholesterol. 2. We believe that this association should be considered an independent health problem on the list of problems, with the name Syndrome X or Chronic Metabolic Syndrome.

Adult↗
PubMed2005

Predictive model for congenital muscular torticollis: analysis of 1021 infants with sonography.

OBJECTIVE: To construct a predictive model to foretell congenital muscular torticollis (CMT) on the basis of clinical correlates. DESIGN: Correlation study. SETTING: Regional hospital. PARTICIPANTS: A consecutive series of 1021 newborn infants. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE: Participants underwent portable ultrasonography to diagnose CMT. Significant clinical correlates were identified to construct a predictive model using the logistic regression model. RESULTS: Forty of 1021 infants were diagnosed with CMT using ultrasonography, yielding an overall incidence of 3.92%. Birth body length (odds ratio [OR]=1.38; 95% confidence interval [CI], 1.49-2.38), facial asymmetry (OR=21.75; 95% CI, 6.6-71.7), plagiocephaly (OR=22.3; 95% CI, 7.01-70.95), perineal trauma during delivery (OR=4.26; 95% CI, 1.25-14.52), and primiparity (OR=6.32; 95% CI, 2.34-17.04) were significant correlates. A predictive logistic regression model with the incorporation of these 4 correlates was developed. We used cross-validation with a receiver operating characteristic curve to validate the predictive model. CONCLUSIONS: Our study successfully developed a quantitative predictive model for estimating the risk of CMT on the basis of clinical correlates only. This model has good discriminative ability for classifying CMT and non-CMT by yielding acceptable values of false-negative and false-positive cases.

Female↗
PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline&#xff1a;A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗
PubMed2003

Screening test data analysis for liver disease prediction model using growth curve.

This study was done based on screening test data accumulated from 1994 to 2001 for studying of risk factor related with liver disease and prediction model of liver disease. In the existing study related with liver, the main current is studying on liver cancer, not on liver disease, previous step into liver cancer. As a result of estimating prediction model through the risk factors of liver disease and the growth curve on the basis of data, it is shown that most of the risk factors about liver disease are also those about known well as liver cancer. In addition, to investigate liver disease prevalence from the viewpoint of the future, this study presumed risk factor through the various growth curve analysis and examined logistic regression, decision tree and neural network from those estimators. In the case of neural network using growth curve estimator of Xi(5)=alphai+betaiT+epsiloniT, accuracy of liver disease was 72.55% and sensitivity was 78.62%. On the other hand, in the case of liver disease prediction model using recent screening test data estimator, accuracy was 72.09% and sensitivity was 71.72%. Those are lower than liver disease prediction model of growth curve analysis. In the various liver disease prediction models assumed by growth curve and many distinction models, when growth curve estimator was used, sensitivity value was improved.

Humans↗
PubMedDate not supplied

Predictive models of hepatotoxicity using gene expression data from primary rat hepatocytes.

With the aim of evaluating the usefulness of an in vitro system for assessing the potential hepatotoxicity of compounds, the paper describes several methods of obtaining mathematical models for the prediction of compound-induced toxicity in vivo. These models are based on data derived from treating rat primary hepatocytes with various compounds, and thereafter using microarrays to obtain gene expression 'profiles' for each compound. Predictive models were constructed so as to reduce the number of 'probesets' (genes) required, and subjected to rigorous cross-validation. Since there are a number of possible approaches to derive predictive models, several distinct modelling strategies were applied to the same data set, and the outcomes were compared and contrasted. While all the strategies tested showed significant predictive capability, it was interesting to note that the different approaches generated models based on widely disparate probesets. This implies that while these models may be useful in ascribing relative potential toxicity to compounds, they are unlikely to provide significant information on underlying toxicity mechanisms. Improved predictivity will be obtained through the generation of more comprehensive gene expression databases, covering more 'toxicity space', and by the development of models that maximize the observation, and combination, of individual differences between compounds.

Animals↗
PubMed2002

A predictive model for mortality of bloodstream infections. Bedside analysis with the Weibull function.

This paper describes the construction and validation of a prognostic model for predicting post-bloodstream infection survival up to Day 21. A Weibull multiple regression model was adopted in a prospective cohort study of all patients diagnosed with true bacteremia or fungemia in a teaching hospital between 1991 and 1994 (training set, 1,577 patients). The final model included six variables easily detected in any institution: source of infection, underlying neoplasm, septic shock, community-acquired, age over 65, and polymicrobial bacteremia. Using this model, it is possible to obtain a graphic representation of survival probability for any combination of these risk factors. The model was tested on a second set of patients diagnosed in the same hospital between 1996 and 1997 (validation set, 952 patients), confirming its reliability in predicting survival. In conclusion, the Weibull function, together with variables easily identified at bedside, enables a precise prediction of the short-term, post-bloodstream infection mortality of a given patient.

Aged↗
PubMed2006

A predictive model to identify women with injuries related to intimate partner violence.

PURPOSE: The diagnosis of intimate partner violence (IPV) is challenging. The authors conducted a cross-sectional study to develop a predictive model to identify IPV-related injuries and validate the model with an independent sample. MATERIALS AND METHODS: The authors enrolled women older than 18 years seeking treatment for injuries. They randomized the sample into index and validation datasets. They used the index dataset to develop a predictive model; the validation set served as an independent sample for assessing the predictive model's goodness of fit. Study variables included risk of self-report of an IPV-related injury and demographic and socioeconomic variables. The outcome variable was self-reported injury etiology (IPV or other). The authors used multiple logistic regression techniques to develop a predictive model that they then applied to the validation dataset, and they measured goodness of fit with the Hosmer-Lemeshow test. RESULTS: The sample was randomized into index (n = 201) and validation (n = 104) sets. For the index set, age, race and risk of IPV were associated with IPV-related injuries (P < .01). The accuracy of the model was 92 percent. Application of the model to the validation dataset resulted in excellent agreement between the observed and actual number of women with IPV-related injuries (accuracy: 93 percent). No statistically significant differences existed between the observed and predicted outcomes (P = .64). CONCLUSIONS: A predictive model composed of age, race and risk of experiencing IPV accurately characterizes women likely to report IPV-related injuries. CLINICAL IMPLICATIONS: Once the clinician diagnoses IPV-related injury, he or she can intervene to prevent future IPV-related injuries.

Adult↗
PubMed2005

A predictive model of therapeutic monoclonal antibody dynamics and regulation by the neonatal Fc receptor (FcRn).

We constructed a novel physiologically-based pharmacokinetic (PBPK) model for predicting interactions between the neonatal Fc receptor (FcRn) and anti-carcinoembryonic antigen (CEA) monoclonal antibodies (mAbs) with varying affinity for FcRn. Our new model, an integration and extension of several previously published models, includes aspects of mAb-FcRn dynamics within intracellular compartments not represented in previous PBPK models. We added mechanistic structure that details internalization of class G immunoglobulins by endothelial cells, subsequent FcRn binding, recycling into plasma of FcRn-bound IgG and degradation of free endosomal IgG. Degradation in liver is explicitly represented along with the FcRn submodel in skin and muscle. A variable tumor mass submodel is also included, used to estimate the growth of an avascular, necrotic tumor core, providing a more realistic picture of mAb uptake by tumor. We fitted the new multiscale model to published anti-CEA mAb biodistribution data, i.e. concentration-time profiles in tumor and various healthy tissues in mice, providing new estimates of mAb-FcRn related kinetic parameters. The model was further validated by successful prediction of F(ab')2 mAb fragment biodistribution, providing additional evidence of its potential value in optimizing intact mAb and mAb fragment dosing for clinical imaging and immunotherapy applications.

Animals↗
PubMed2006

Validated prediction model for the development of primary open-angle glaucoma in individuals with ocular hypertension.

OBJECTIVE: To test the validity and generalizability of the Ocular Hypertension Treatment Study (OHTS) prediction model for the development of primary open-angle glaucoma (POAG) in a large independent sample of untreated ocular hypertensive individuals and to develop a quantitative calculator to estimate the 5-year risk that an individual with ocular hypertension will develop POAG. DESIGN: A prediction model was developed from the observation group of the OHTS and then tested on the placebo group of the European Glaucoma Prevention Study (EGPS) using a z statistic to compare hazard ratios, a c statistic for discrimination, and a calibration chi2 for systematic overestimation/underestimation of predicted risk. The 2 study samples were pooled to increase precision and generalizability of a 5-year predictive model for developing POAG. PARTICIPANTS: The OHTS observation group (n = 819; 6.6 years' median follow-up) and EGPS placebo group (n = 500; 4.8 years' median follow-up). TESTING: Data were collected on demographic characteristics, medical history, ocular examination visual fields (VFs), and optic disc photographs. MAIN OUTCOME MEASURE: Development of reproducible VF abnormality or optic disc progression as determined by masked readers and attributed to POAG by a masked end point committee. RESULTS: The same predictors for the development of POAG were identified independently in both the OHTS observation group and the EGPS placebo group-baseline age, intraocular pressure, central corneal thickness, vertical cup-to-disc ratio, and Humphrey VF pattern standard deviation. The pooled multivariate model for the development of POAG had good discrimination (c statistic, 0.74) and accurate estimation of POAG risk (calibration chi2, 7.05). CONCLUSIONS: The OHTS prediction model was validated in the EGPS placebo group. A calculator to estimate the 5-year risk of developing POAG, based on the pooled OHTS-EGPS predictive model, has high precision and will be useful for clinicians and patients in deciding the frequency of tests and examinations during follow-up and advisability of initiating preventive treatment.

Cornea↗
PubMed2005

Building prediction models for coronary heart disease by synthesizing multiple longitudinal research findings.

BACKGROUND: No methodology is currently available to allow the combining of individual risk factor information derived from different longitudinal studies for a chronic disease in a multivariate fashion. This paper introduces such a methodology, named Synthesis Analysis, which is essentially a multivariate meta-analytic technique. DESIGN: The construction and validation of statistical models using available data sets. METHODS AND RESULTS: Two analyses are presented. (1) With the same data, Synthesis Analysis produced a similar prediction model to the conventional regression approach when using the same risk variables. Synthesis Analysis produced better prediction models when additional risk variables were added. (2) A four-variable empirical logistic model for death from coronary heart disease was developed with data from the Framingham Heart Study. A synthesized prediction model with five new variables added to this empirical model was developed using Synthesis Analysis and literature information. This model was then compared with the four-variable empirical model using the first National Health and Nutrition Examination Survey (NHANES I) Epidemiologic Follow-up Study data set. The synthesized model had significantly improved predictive power (chi = 43.8, P<0.00001). CONCLUSIONS: Synthesis Analysis provides a new means of developing complex disease predictive models from the medical literature.

Coronary Disease↗
PubMedDate not supplied

Implementing a predictive modeling program, part 1.

Effective predictive modeling is a powerful, statistically valid tool, which identifies individuals at increased future risk of an untoward health event. This article looks at one company's experience in developing a successful program; the issues related to an effective implementation strategy; and lessons learned during the process. The goal is to implement the right intervention, at the right time, for the right patient.

Algorithms↗
PubMed1997

Derivation and validation of a pulmonary tuberculosis prediction model.

OBJECTIVE: To describe the derivation and validation of a pulmonary tuberculosis (TB) prediction model that would enable early discontinuation of unnecessary respiratory isolation. DESIGN: Patients placed in isolation for suspected pulmonary TB were studied retrospectively (derivation cohort) and prospectively (validation cohort). Independent predictors of pulmonary TB in the derivation cohort (January 1992-March 1994) were identified by retrospective analysis. Predictors in the model were assigned weights on the basis of the results of the multivariate analysis in order to quantitate the risk of TB in an individual patient. The prospective validation consisted of application of the model to patients placed in isolation during the period April 1994 to June 1995. The predictability of the model in the derivation and validation cohorts was evaluated using receiver operating characteristics (ROC), curve analysis, and calculation of the area under the ROC curve (AUC). SETTING: A university-affiliated, urban, public hospital with a large population of prison inmates and patients with human immunodeficiency virus infection. INTERVENTIONS: Prospective application of the prediction model to patients placed in isolation during the validation period. RESULTS: Four factors were found to be independent predictors of pulmonary TB among 296 isolation episodes in the derivation cohort; positive acid-fast sputum smear (odds ratio [OR], 5.8; 95% confidence interval [CI95], 3.0-11.0; weight = 3 points), localized chest radiograph findings (OR, 2.5; CI95, 1.3-4.9; weight = 2 points), residence in a correctional facility (OR, 2.3; CI95, 1.2-4.4; weight = 2 points), and history of weight loss (OR, 1.8; CI95, 1.0-3.2; weight = 1 points). Infection control practitioners applied the model prospectively to 220 isolation episodes. The mean (+/-SE) AUCs of the ROC curve for the derivation and validation cohorts were not significantly different (.86 +/- .04 vs .86 +/- .07; P = .90). There was a significant decline in the mean duration of isolation from the onset of an automatic TB isolation policy in August 1992 to the end of the study (P = .045 by analysis of variance). CONCLUSIONS: A pulmonary TB prediction model was derived and validated prospectively in a hospital with a moderately high prevalence of TB. The model quantitated the risk of TB in an individual patient and aided infection control practitioners and primary-care physicians in their decisions to discontinue isolation during the validation period. Utilization of the model was responsible, in part, for a decrease in the mean duration of isolation during the study period. Although the model may not have general applicability due to the uniqueness of the patient population studied, this study illustrates how prediction models can be developed and used effectively to deal with a clinical problem.

Adult↗
PubMed1996

A predictive model for the treatment approach to community-acquired pneumonia in patients needing ICU admission.

OBJECTIVE: To create a predictive model for the treatment approach to community-acquired pneumonia (CAP) in patients needing Intensive Care Unit (ICU) admission. DESIGN: Multicenter prospective study. SETTING: Twenty-six Spanish ICUs. PATIENTS: One hundred seven patients with CAP, all of them with accurate etiological diagnosis, divided in three groups according to their etiology in typical (bacterial pneumonia), Legionella and other atypical (Mycoplasma, Chlamydia spp. and virus). For the multivariate analysis we grouped Legionella and other atypical etiologies in the same category. METHODS: We recorded 34 variables including clinical characteristics, risk factors and radiographic pattern. We used a multivariate logistic regression analysis to find out a predictive model. RESULTS: We have the complete data in 70 patients. Four variables: APACHE II, (categorized as a dummy variable) serum sodium and phosphorus and "length of symptoms" gave an accurate predictive model (c = 0.856). From the model we created a score that predicts typical pneumonia with a sensitivity of 90.2% and specificity 72.4%. CONCLUSION: Our model is an attempt to help in the treatment approach to CAP in ICU patients based on a predictive model of basic clinical and laboratory information. Further studies, including larger numbers of patients, should validate and investigate the utility of this model in different clinical settings.

APACHE↗
PubMed1987

An evaluation of the U.S. Army fallout prediction model.

The U.S. Army fallout prediction method was evaluated against an advanced fallout prediction model--SIMFIC (Simplified Fallout Interpretive Code). The danger zone areas of the U.S. Army method were found to be significantly greater (up to a factor of 8) than the areas of corresponding radiation hazard as predicted by SIMFIC. Nonetheless, because the U.S. Army's method predicts danger zone lengths that are commonly shorter than the corresponding "hot line distances" of SIMFIC, the U.S. Army's method is not reliably conservative.

Military Medicine↗