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PubMed2006

The contribution of nursing data to the development of a predictive model for the detection of acute pancreatitis.

The increasing use of information system has resulted in the accumulation of a large volume of nursing data in electronic medical records. These data have great potential for supporting the various clinical decisions made by physicians, nurses, and managers. However, how to re-use of nursing data remains largely an issue of informatics. The aim of this study was to demonstrate how these nursing data can be used and how much they could contribute to developing a predictive model for an expert system for early detection of acute pancreatitis. We employed a probability-based model consisting of a Bayesian network and trained this model with the patient data retrospectively retrieved from the enterprise data warehouse of a tertiary hospital. The performance of the predictive model was measured based on the error rate and the area under receiver operating characteristics curve, which were 13.89 % and 0.93, respectively. The sensitivity of the acute pancreatitis to the findings from each nursing data was measured using a test of sensitivity. The results showed that the role of nursing data is as important as laboratory data in formulating a model for an expert system.

Acute Disease↗

PubMed1999

Introducing optimal experimental design in predictive modeling: a motivating example.

Predictive microbiology emerges more and more as a rational quantitative framework for predicting and understanding microbial evolution in food products. During the mathematical modeling of microbial growth and/or inactivation, great, but not always efficient, effort is spent on the determination of the model parameters from experimental data. In order to optimize experimental conditions with respect to parameter estimation, experimental design has been extensively studied since the 1980s in the field of bioreactor engineering. The so-called methodology of optimal experimental design established in this research area enabled the reliable estimation of model parameters from data collected in well-designed fed-batch reactor experiments. In this paper, we introduce the optimal experimental design methodology for parameter estimation in the field of predictive microbiology. This study points out that optimal design of dynamic input signals is necessary to maximize the information content contained within the resulting experimental data. It is shown that from few dynamic experiments, more pertinent information can be extracted than from the classical static experiments. By introducing optimal experimental design into the field of predictive microbiology, a new promising frame for maximization of the information content of experimental data with respect to parameter estimation is provided. As a case study, the design of an optimal temperature profile for estimation of the parameters D(ref) and z of an Arrhenius-type model for the maximum inactivation rate kmax as a function of the temperature, T, was considered. Microbial inactivation by heating is described using the model of Geeraerd et al. (1999). The need for dynamic temperature profiles in experiments aimed at the simultaneous estimation of the model parameters from measurements of the microbial population density is clearly illustrated by analytical elaboration of the mathematical expressions involved on the one hand, and by numerical simulations on the other.

Bacteria↗

PubMed1991

[Chronic lymphatic leukemia. II. Analysis of prognostic factors and development of survival predicting models. Study of 187 patients].

The univariate analysis of prognostic factors showed the value of clinical variables (age, symptoms, lymph node enlargement, splenomegaly, hepatomegaly, clinical stage), haematological variables (haemoglobin, platelet count, leucocyte count, lymphocyte count, bone marrow involvement and biopsy pattern, cleaved lymphocytes, prolymphocytes, LDT) and biochemical variables (BUN, creatinine, calcium, phosphorus, uric acid and albumin). The multivariate analysis chose the combination of Rai's stage, age, cervical lymph node involvement, phosphorus and BUN. Two predictive models capable of separating appropriately low, intermediate and high risk groups were developed and validated. The results were compared with others in the literature. The interest of predictive models arising from multivariate analysis is stressed.

Adult↗

PubMed1997

Driving predictive modelling on a risk assessment path for enhanced food safety.

How do we best protect our citizens to allow the highest quality of life? Where do we put our food safety resources so that we gain the greatest positive impact? Risk assessment provides the critical scientific basis for these types of important risk management decisions. Increasingly, risk assessment is used to guide legislated and voluntary changes intended to improve safety, yet its formal application for enhanced food safety is in its infancy. Risk assessment includes disease characterization. dose-response assessment, exposure assessment, and risk characterization. Quantitative data is critical for risk assessment to realize its full value, yet much of our knowledge about the incidence of pathogens or toxins in foods, dose-response knowledge, incidence of acute food-borne illness, incidence of chronic sequelae, and cost of food-borne illness is qualitative or estimates are controversial. Predictive modelling should help to improve estimates and thereby allow quantitation of food safety risks. Predictive modelling will also find application for assessing prevention strategies in risk management.

Food Contamination↗

PubMed1987

Development and application of a prediction model for dental caries.

The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk.

Child↗

PubMed1998

From twitch to tetanus for human muscle: experimental data and model predictions for m. triceps surae.

In models describing the excitation of muscle by the central nervous system, it is often assumed that excitation during a tetanic contraction can be obtained by the linear summation of responses to individual stimuli, from which the active state of the muscle is calculated. We investigate here the extent to which such a model describes the excitation of human muscle in vivo. For this purpose, experiments were performed on the calf muscles of four healthy subjects. Values of parameters in the model describing the behaviour of the contractile element (CE) and the series elastic element (SEE) of this muscle group were derived on the basis of a set of isokinetic release contractions performed on a special-purpose dynamometer as well as on the basis of morphological data. Parameter values describing the excitation of the calf muscles were optimized such that the model correctly predicted plantar flexion moment histories in an isometric twitch, elicited by stimulation of the tibial nerve. For all subjects, the model using these muscle parameters was able to make reasonable predictions of isometric moment histories at higher stimulation frequencies. These results suggest that the linear summation of responses to individual stimuli can indeed give an adequate description of the process of human muscle excitation in vivo.

Adult↗

PubMed2003

Comparison of predictive models for growth of parent and green fluorescent protein-producing strains of Salmonella.

The green fluorescent protein (GFP) from the jellyfish Aequorea victoria can be expressed in, and used to follow the fate of, Salmonella in microbiologically complex ecosystems such as food. As a first step in the evaluation of GFP as a tool for the development of predictive models for naturally contaminated food, the present study was undertaken to compare the growth kinetics of parent and GFP-producing strains of Salmonella. A previously established sterile chicken burger model system was used to compare the growth kinetics of stationary-phase cells of parent and GFP strains of Salmonella Enteritidis, Salmonella Typhimurium, and Salmonella Dublin. Growth curves for constant temperatures from 10 to 48 degrees C were fit to a two- or three-phase linear model to determine lag time, specific growth rate, and maximum population density. Secondary models for the growth parameters as a function of temperature were generated and compared between the parent and GFP strain pairs. The effects of GFP on the three growth parameters were significant and were affected by serotype and incubation temperature. The expression of GFP reduced specific growth rate and maximum population density while having only a small effect on the lag times of the three serotypes. The results of this study indicate that the growth kinetics of the GFP strains tested were different from those of the parent strains and thus would not be good marker strains for the development of predictive models for naturally contaminated food.

Animals↗

PubMed2005

Predictive modeling of protein adsorption along the bed height by taking into account the axial nonuniform liquid dispersion and particle classification in expanded beds.

Expanded bed adsorption (EBA) is a special chromatography technique with perfect classification of adsorbent particles in the column, thus the performance of protein adsorption in expanded beds is particular, obviously nonuniform and complex along the column. Detailed description of the complex adsorption kinetics of proteins in expanded bed is essential for better analyzing of adsorptive mechanisms, the design of chromatographic processes and the optimization of operation parameters of EBA processes. In this work, a theoretical model for the prediction of protein adsorption kinetics in expanded beds was developed by taking into account the classified distribution of adsorbent particles along the bed height, the nonuniform behaviors of axial liquid dispersion, the axial variation of local bed voidage as well as the axial changes of target component mass transfer. The model was solved using the implicit finite difference scheme combining with the orthogonal collocation method, and then applied to predict the breakthrough behaviors of bovine serum albumin (BSA) on Streamline DEAE and lysozyme on Streamline SP along the bed height in expanded beds under various conditions. In addition, the experiments of front adsorption of BSA on Streamline DEAE at different axial column positions were carried out to reveal the adsorption kinetics of BSA along the bed height in a 20 mm I.D. expanded bed, and the influences of liquid velocity and feed concentration on the breakthrough behaviors were also analyzed. The breakthrough behaviors predicted by the present model were compared with the experimental data obtained in this work and in the literature published. The agreement between the prediction and the experimental breakthrough curves is satisfied.

Adsorption↗

PubMed2000

A three-dimensional dynamic posture prediction model for simulating in-vehicle seated reaching movements: development and validation.

A three-dimensional dynamic posture prediction model for simulating in-vehicle seated reaching movements is presented. The model employs a four-segment 7-degrees-of-freedom linkage structure to represent the torso, clavicle and right upper extremity. It relies on an optimization-based differential inverse kinematics approach to estimate a set of four weighting parameters that quantify a time-constant, inter-segment motion apportionment strategy. In the development phase, 100 seated reaching movements performed by 10 subjects towards five typical in-vehicle targets were modelled, resulting in 100 sets of weighting parameters. Statistical analysis was then conducted to relate these parameters to target and individual attributes. In the validation phase, the generalized model, with parameter values statistically synthesized, was applied to novel data sets containing 700 different reaching movements (towards different targets and/or by different subjects). The results demonstrated the model's ability to generate close representations in prediction: the overall mean time-averaged error in joint angle was 5.2 degrees, and the median was 4.7 degrees, excluding reaches towards two extreme targets (for which modelling errors were excessive). The model's general success in prediction and its unique characteristics led to implications with regard to the performance and underlying control strategies of human reaching movements.

Automobile Driving↗

PubMedDate not supplied

[A predictive model of mortality in the ICU of a Mexico City hospital].

OBJECTIVE: To explore the associations of mortality with routine logbook information as a preliminary to developing a quality control system in an ICU. METHODS: The ICU logbook contained seven variables of 2,745 consecutive cases hospitalized from Jan-01-1998 to Dec-31-2002. The univariate association of ICU mortality with five predictive and two non-predictive variables was explored. Gender was the only one unassociated. A logistic regression predictive model of mortality of three variables was generated (cause of hospitalization, age and type of patient). RESULTS: The global mortality was 27%. The highest risk was for non-surgery patients aged 80+ years with multiple organic failure, sepsis and/or pneumonia. They had a relative risk of 18.1 versus the reference group (surgical cases aged 20-39 with less severe illnesses). Three additional simple predictors were identified as potentially useful and are to be included in an updated logbook. CONCLUSIONS: 1. The logbook information appeared to be useful in monitoring ICU quality of service as six logbook variables were associated to mortality. 2. Our predictive model has the potential to operate as an index of severity of disease and may improve by the inclusion of additional information. 3. Its usefulness is to be compared prospectively with well-established predictors (Apache II and others).

Adult↗

PubMed1989

Usefulness of a single-dose prediction model for the determination of long-term maintenance therapy of valproic acid.

The single-dose prediction model (SDPM) of Slattery et al. has been shown accurately to predict short-term (3 to 7 days) steady-state valproic acid (VPA) concentrations in normal volunteers and in hospitalized patients receiving monotherapy. To assess the long-term usefulness of the SDPM in a real-life clinic setting, six ambulatory patients ranging in age from 2 to 8 years were studied. Blood samples were drawn at least 6 h after the initial dose but before the second dose of VPA, A steady-state trough concentration (Cminss) was measured 3 to 7 days after the initiation of therapy. Dosage adjustments and alterations in therapeutic regimen were allowed and another Cminss was measured after 1 to 12 months. Predicted Cminss values were calculated for both the short-term and long-term VPA regimens. Predictive performance analysis demonstrated that the SDPM was unbiased in predicting both short-term and long-term VPA Cminss values and precise in predicting only short-term VPA Cminss values. The SDPM is not a reliable predictor of long-term total VPA concentrations in seizure patients in an outpatient clinic setting.

Child↗

PubMed2006

Evaluation of comorbidity indices for inpatient mortality prediction models.

BACKGROUND AND OBJECTIVES: The objectives of the current study were: to compare the predictive capacity of the original Charlson comorbidity index (CCI), the CCI with new assigned diagnostic codes and estimated weights, and a new developed comorbidity index in a Brazilian population; and to study the effect of the number of comorbidity diseases recorded on the predictive capacity of the comorbidity indices. MATERIALS AND METHODS: The study was limited to the Ribeirão Preto region in the State of São Paulo, Brazil, from January 1996 to December 1998. We included only admissions in which the principal diagnoses were respiratory and circulatory diseases. RESULTS: Evaluation of the CCI indicates that revision of the clinical conditions studied by Charlson, as well as their weights, increased mortality model predictive capacity. The C statistic was 0.72 for the original CCI, and increased to 0.74 for the CCI with new weights and 0.76 for the new index. The C statistic increases in all the comorbidity indices with the utilization of more diagnostic information. This impact is greater when a second secondary diagnosis is added. CONCLUSIONS: The results of the validity analysis for comorbidity indices favor the utilization of empirically developed indices. However, the increase in predictive capacity was weak. In addition, age and principal diagnosis are the most important predictors of inpatient mortality.

Brazil↗

PubMed1990

Validity of knee flexion and extension peak torque prediction models.

The primary purpose of this study was to test the validity of predictive models relating isokinetic knee torque production to anthropometric and demographic variables. Subjects were 23 healthy female and 15 healthy male volunteers between the ages of 10 and 77 years. We measured subjects' peak knee flexion and extension torque production at two angular velocities. For each torque dependent variable, we calculated a Pearson product-moment correlation coefficient between the measured torque values and the values obtained with prediction equations. The difference between the squared value of the correlation coefficients and the regression multiple R2 values obtained for an original group of 134 subjects ranged between .05 and .10 for the torque dependent variables. The results indicate the validity of the regression models at the level specified by the multiple regression R2 values. Clinicians can use the prediction equations presented in this article to establish rehabilitation goals for patients and can estimate the error involved in applying each prediction equation.

Adolescent↗

PubMed2000

New paradigms for growth hormone treatment in the 21st century: prediction models.

Inconsistent and sometimes disappointing final height outcomes in studies in which exogenous growth hormone (GH) was given to children with short stature resulting from various causes have led to attempts to determine the factors that influence responsiveness to GH. Many such factors have been identified, including the genetically determined height potential of the child, the height deficit, current and perinatal auxological and biological factors, and, importantly, GH treatment modalities. These factors vary depending on the cause of the growth failure and during the course of childhood and treatment with GH. Data from well-defined large cohorts can be entered into multiple regression analyses to derive algorithms describing the variation in growth response during a defined period and the influence on this of various factors. Pharmacoepidemiologic surveys have been particularly useful in this regard. Algorithms with low-error SD values, which have included the dose of GH as a variable, can be used to predict the response to a putative GH dose in a similar cohort or in an individual over an equivalent period. A sequential series of such algorithms can be integrated to form a predictive model. Such a model can be used for planning a course of treatment, but the patient data required for entry into the model should be easily obtainable if the model is to have widespread utility. The development of such models will allow GH treatment to be better individualized and optimized for growth and cost outcomes. Growth prediction models will facilitate realistic expectations and will permit stepwise goals to be set and monitored.

Adolescent↗

PubMed2004

Development and field validation of a biotic ligand model predicting chronic copper toxicity to Daphnia magna.

In this study, we developed a toxicity model predicting the long-term effects of copper on the reproduction of the cladoceran Daphnia magna that is based on previously reported toxicity tests in 35 exposure media with different water chemistries. First, it was demonstrated that the acute copper biotic ligand model (BLM) for D. magna could not serve as a reliable basis for predicting chronic copper toxicity. Consequently, BLM constants for chronic exposures were derived by multiple regression analysis of 21-d median effective concentrations (EC50s; expressed as Cu2+ activity) versus physicochemistry from a large toxicity dataset and the results of an additional experiment in which the individual effect of sodium on copper toxicity was investigated. The effect of sodium on chronic toxicity (log K NaBL = 2.91) seemed to be similar to its effect on acute toxicity (log K NaBL = 3.19). However, in contrast to the acute BLM, no significant calcium, magnesium, or combined competition effect was observed, and an increase in proton competition and bioavailability of CuOH+ and CuCO3 complexes was noted. Some indirect evidence was also found for some limited toxicity of complexes of copper with two of three tested types of dissolved organic matter. Because the latter was only a minor effect, this factor was not included in the chronic Cu BLM. The newly developed model performed well in predicting 21-d EC50s and no-observed-effect concentrations in natural water samples: 79% of the toxicity threshold values were predicted within a factor of two of the observed values. It is clear, however, that more research is needed to provide information on the exact mechanisms that have resulted in different BLM constants for chronic exposures (as opposed to acute exposures). It is suggested that the developed model can contribute to the improvement of risk assessment procedures of copper by incorporating bioavailability of copper in these regulatory exercises.

Animals↗

PubMed2004

Concise prediction models of anticancer efficacy of 8 drugs using expression data from 12 selected genes.

We developed concise, accurate prediction models of the in vitro activity for 8 anticancer drugs (5-FU, CDDP, MMC, DOX, CPT-11, SN-38, TXL and TXT), along with individual clinical responses to 5-FU using expression data of 12 genes. We first performed cDNA microarray analysis and MTT assay of 19 human cancer cell lines to sort out genes which were correlative in expression levels with cytotoxicities of the 8 drugs; we selected 13 genes with proven functional significance to drug sensitivity from a huge number of potent prediction marker genes. The correlation significance of each was confirmed using expression data quantified by real-time RT-PCR, and finally 12 genes (ABCB1, ABCG2, CYP2C8, CYP3A4, DPYD, GSTP1, MGMT, NQO1, POR, TOP2A, TUBB and TYMS) were selected as more reliable predictors of drug response. Using multiple regression analysis, we fixed 8 prediction formulae which embraced the variable expressions of the 12 genes and arranged them in order, to predict the efficacy of the drugs by referring to the value of Akaike's information criterion for each sample. These formulae appeared to accurately predict the in vitro efficacy of the drugs. For the first clinical application model, we fixed prediction formulae for individual clinical response to 5-FU in the same way using 41 clinical samples obtained from 30 gastric cancer patients and found to be of predictive value in terms of survival, time to treatment failure and tumor growth. None of the 12 selected genes alone could predict such clinical responses.

Antimetabolites, Antineoplastic↗

PubMed2003

A predictive model for length of Barrett's esophagus with hiatal hernia length and duration of esophageal acid exposure.

BACKGROUND: A significant correlation between the duration and height of esophageal acid exposure and the length of Barrett's mucosa has been demonstrated. The aims of this study were to determine if there is a correlation between hiatal hernia length and Barrett's esophagus length, and to develop a predictive model for Barrett's esophagus length by using hiatal hernia length and duration of esophageal acid exposure. METHODS: Consecutive patients with Barrett's esophagus diagnosed endoscopically were enrolled in the study. Barrett's esophagus was defined by the presence of intestinal metaplasia in biopsy specimens obtained from salmon-colored mucosa extending into the esophagus. Barrett's mucosa 3 cm or greater in length was considered long-segment Barrett's esophagus; and less than 3 cm long was considered short-segment Barrett's esophagus. Hiatal hernia was considered present if the esophagogastric junction was displaced 1 cm or more proximal to the diaphragmatic hiatus. RESULTS: Twenty-four men (mean age 66.1 +/-2.4 [SE]) with Barrett's esophagus were included in this study. Mean Barrett's length was 4.1 +/-0.7 cm. The Pearson correlation coefficient between hiatal hernia length and Barrett's esophagus length was 0.62 (p < 0.01). Similarly, there was a significant correlation between esophageal acid exposure and Barrett's length (r = 0.62; p < 0.01). Multiple linear regression analysis revealed that hiatal hernia length and duration of esophageal acid exposure were associated significantly with length of Barrett's mucosa (R(2) = 0.54; p < 0.001). A regression equation was developed expressing mean Barrett's length (cm) = 0.79 + (0.68) hernia length (cm) + (0.075) duration of esophageal acid exposure (% time pH < 4). CONCLUSIONS: The length of Barrett's mucosa correlated with the length of hiatal hernia. A predictive model for Barrett's length by using hiatal hernia length and duration of esophageal acid exposure was developed. This suggested that these two pathophysiologic factors are good predictors of the length of Barrett's mucosa.

Aged↗

PubMed1993

Wave V latency shifts with age and sex in normals and patients with cochlear hearing loss: development of a predictive model.

In order to study the combined effect of age, sex and cochlear hearing loss on the auditory brainstem response in an adult population, absolute wave V latency measurements were made in 117 ears of 64 patients with cochlear hearing loss and 105 ears of 58 otologically normal control subjects. Among normals, wave V latency was found to increase with age. Latencies in female subjects were significantly shorter than in male subjects. The best predictive model for wave V latency in normals was: wave V latency (ms) = 4.982 + 0.007 x age + 0.091 x sex, where age is measured in years and sex is given a value of 1 for females and 2 for males. In patients with hearing loss, wave V latency increase was determined largely by the degree of hearing loss and the effect of age was moderate. The influence of sex was minimal. Wave V latency in patients with hearing loss was predicted by the formula: wave V latency (ms) = 4.911 + 0.007 x hearing loss + 0.004 x age + 0.081 x sex. These predictive models can be utilized to accurately estimate expected wave V latency in patients with cochlear hearing loss.

Adult↗