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Results for “predictive modelling”

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PubMed1998

Can a rheological muscle model predict force depression/enhancement?

A new phenomenological model of activated muscle is presented. The model is based on a combination of a contractile element, an elastic element that engages upon activation, a linear dashpot and a linear spring. Analytical solutions for a few selected experiments are provided. This model is able to reproduce the response of cat soleus muscle to ramp shortening and stretching and, unlike standard Hill-type models, computations are stable on the descending limb of the force-length relation and force enhancement (depression) following stretching (shortening) is predicted correctly. In its linear version, the model is consistent with a linear force-velocity law, which in this model is a consequence rather than a fundamental characteristic of the material. Results show that the mechanical response of activated muscle can be mimicked by a viscoelastic system. Conceptual differences between this model and standard Hill-type models are analyzed and the advantages of the present model are discussed.

Algorithms↗

PubMed2003

Establishment of a predictive model of serum glucose changes under different exercise intensities and durations among patients with type 2 diabetes mellitus.

Regular exercise is regarded as one of necessary elements in treating diabetes mellitus (DM). The purpose of this study was to investigate the influence of different exercise intensities and durations on serum glucose changes after exercise in type 2 DM patients and to establish a predictive model of changes in serum glucose under different exercise intensities and durations. Thirty-seven type 2 DM patients were recruited from four teaching hospitals. A total of 12 exercise sessions were scheduled according to the results of a graded treadmill exercise test. The 12 exercise sessions were designed on the basis of different exercise intensities (40%, 60%, and 80% maximal workload) and exercise durations (10, 20, 30, and 40 min). Serum glucose level was measured before and after exercise. The findings indicate that the main effect of exercise intensity and duration was significant, but there was no interaction effect. All four variables, including exercise intensity, exercise duration, pre-exercise serum glucose levels, and gender, explained 37% of the variance in serum glucose changes after exercise. In conclusion, a dose-response relationship between exercise amount and serum glucose changes was demonstrated. This is helpful for health professionals to teach type 2 DM patients how to predict serum glucose response in different exercise situations.

Adult↗

PubMed2006

Anomalous oxygen isotope enrichment in CO2 produced from O+CO: estimates based on experimental results and model predictions.

The oxygen isotope fractionation associated with O+CO-->CO(2) reaction was investigated experimentally where the oxygen atom was derived from ozone or oxygen photolysis. The isotopic composition of the product CO(2) was analyzed by mass spectrometry. A kinetic model was used to calculate the expected CO(2) composition based on available reaction rates and their modifications for isotopic variants of the participating molecules. A comparison of the two (experimental data and model predictions) shows that the product CO(2) is endowed with an anomalous enrichment of heavy oxygen isotopes. The enrichment is similar to that observed earlier in case of O(3) produced by O+O(2) reaction and varies from 70 0/00 to 136 0/00 for (18)O and 41 0/00 to 83 0/00 for (17)O. Cross plot of delta (17)O and delta (18)O of CO(2) shows a linear relation with slope of approximately 0.90 for different experimental configurations. The enrichment observed in CO(2) does not depend on the isotopic composition of the O atom or the sources from which it is produced. A plot of Delta(delta (17)O) versus Delta(delta (18)O) (two enrichments) shows linear correlation with the best fit line having a slope of approximately 0.8. As in case of ozone, this anomalous enrichment can be explained by invoking the concept of differential randomization/stabilization time scale for two types of intermediate transition complex which forms symmetric ((16)O(12)C(16)O) molecule in one case and asymmetric ((16)O(12)C(18)O and (16)O(12)C(17)O) molecules in the other. The delta (13)C value of CO(2) is also found to be different from that of the initial CO due to the mass dependent fractionation processes that occur in the O+CO-->CO(2) reaction. Negative values of Delta(delta (13)C) ( approximately 12.1 0/00) occur due to the preference of (12)C in CO(2)* formation and stabilization. By contrast, at lower pressures (approximately 100 torr) surface induced deactivation makes Delta(delta (13)C) zero or slightly positive.

Journal Article↗

PubMed2004

Testing a predictive model of the use of HIV/AIDS symptom self-care strategies.

Several types of self-care strategies have been reported by patients with HIV/AIDS to manage their HIV/AIDS related symptoms. However, little research has examined the factors influencing the use of different HIV symptom self-care strategies. This paper presents the results of testing a predictive model of the use of eight types of symptom self-care strategies: medications, complementary treatments, self-comforting, daily thoughts/activities, changing diet, help-seeking, exercise, and spiritual care. Logistic regression tests were used to examine the likelihood of using the eight types of symptom self-care strategies that were summarized and categorized from the questionnaires reported by patients with HIV/AIDS (n = 359). Sociodemographic variables (age, gender, race, education, injection drug use, insurance status, income status) and disease-related variables (taking antiretroviral medications, symptom intensity, symptom bothersomeness, impact of symptom on daily life) were selected as predictive variables. Logistic regression analysis demonstrated that race (white vs. non-white) was a significant predictor for the use of medications (odds ratio [OR] = 0.55, 95% confidence interval [CI] = 0.33-0.92), self-comforting (OR = 2.17, 95% CI = 1.24-3.79), help seeking (OR = 5.71, 95% CI = 2.57-12.70), and spiritual care (OR = 5.09, 95% CI = 1.81-14.30). In addition, symptom intensity significantly predicted the use of medications (OR = 1.22, 95% CI = 1.05-1.40) and gender significantly predicted the use of spiritual care (OR = 3.76, 95% CI = 1.71-8.25). Racial difference is the predominant predictor for the use of symptom self-care strategies. The cultural differences in the use of symptom self-care strategies should be considered in symptom management.

Adolescent↗

PubMed2006

Ecophysiology of ochratoxigenic Aspergillus ochraceus and Penicillium verrucosum isolates. Predictive models for fungal spoilage prevention - a review.

Ochratoxin A (OTA) is a secondary metabolite produced by several species of Aspergillus and Penicillium; among them Aspergillus ochraceus and Penicillium verrucosum are two ochratoxigenic species capable of growing in different climates and thus contamination of food crops with OTA can occur worldwide. OTA can be found in a wide range of foods such as cereals, coffee, cocoa, spices, beer, wine, dried vine fruit, grapes and meat products. OTA is toxic to animals, it presents neurotoxic, immunotoxic and nephrotoxic effects. It has been implicated in a human kidney disorder known as Balkan Endemic Nephropathy. This review focuses on the ecophysiology of ochratoxin-producing Aspergillus ochraceus and Penicillium verrucosum, the effect of environmental factors on their germination, mycelial growth, and OTA production. Knowledge of environmental conditions required for sucessive stages of fungal development represent the first step towards preventing mycotoxin formation. Predictive models for different stages of fungal development are presented, which allow prediction of the time before spoilage as a function of the abiotic factors. Finally, the implications of these studies in management of barley, coffee and grapes are described. This can help to identify the critical control points in their production, storage and distribution processes.

Aspergillus ochraceus↗

PubMed2005

Predictive model for reduction of Escherichia coli during acetic acid decontamination of chicken skin.

AIMS: The response surface methodology was used to evaluate the effect of operating variables (acetic acid concentration, spraying time and temperature) on the reduction of Escherichia coli populations on poultry breast skin in a laboratory showering process, as well as to identify the best conditions that are required to develop this operation. METHODS AND RESULTS: Skin samples were inoculated with a 24-h E. coli culture and afterwards treated according to experimental design under selected acetic acid concentration, spraying time, and solution temperature. The E. coli reduction model was significantly affected by the acetic acid concentration and spraying time (P < or = 0.05 and < or =0.01), while temperature did not show a significant effect (P > 0.05). CONCLUSION: The predictive model obtained was validated through additional confirmatory experiments and showed to be adequate, and it could be used as an approach to optimize the acetic acid spray washes during poultry carcasses processing. SIGNIFICANCE AND IMPACT OF THE STUDY: The use of acetic acid washes in the processing of poultry does not have the capability of eliminating E. coli populations from carcasses. However, significant reductions in the initial load could be achieved.

Acetic Acid↗

PubMed2005

Canonical correlation analysis for data reduction in data mining applied to predictive models for breast cancer recurrence.

Data mining methods can be used for extracting specific medical knowledge such as important predictors for recurrence of breast cancer in pertinent data material. However, when there is a huge quantity of variables in the data material it is first necessary to identify and select important variables. In this study we present a preprocessing method for selecting important variables in a dataset prior to building a predictive model.In the dataset, data from 5787 female patients were analysed. To cover more predictors and obtain a better assessment of the outcomes, data were retrieved from three different registers: the regional breast cancer, tumour markers, and cause of death registers. After retrieving information about selected predictors and outcomes from the different registers, the raw data were cleaned by running different logical rules. Thereafter, domain experts selected predictors assumed to be important regarding recurrence of breast cancer. After that, Canonical Correlation Analysis (CCA) was applied as a dimension reduction technique to preserve the character of the original data.Artificial Neural Network (ANN) was applied to the resulting dataset for two different analyses with the same settings. Performance of the predictive models was confirmed by ten-fold cross validation. The results showed an increase in the accuracy of the prediction and reduction of the mean absolute error.

Breast Neoplasms↗

PubMed2000

Predictive modelling approach applied to spoilage fungi: growth of Penicillium brevicompactum on solid media.

Growth of Penicillium brevicompactum was examined on five solid media. Fungal growth was established by diameter measurements up to 50 days. Seventy experimental curves were fitted by Baranyi's primary predictive model. The growth rates were then analysed by non-parametric statistical methods. Penicillium brevicompactum could colonize the surface of solid media containing up to 700 g l-1 of sugar (50% glucose-50% fructose) with a growth rate of 0.9 mm day-1 (median values). Fitting curves by non-linear models followed by a non-parametric multiple comparison seems to be a convenient method for detecting differences in fungal growth on solid media. These two methods would be useful for studying fungal spoilage of bakery products with intermediate water activity.

Bread↗

PubMed2003

Time-frequency analyses of transient-evoked stimulus-frequency and distortion-product otoacoustic emissions: testing cochlear model predictions.

Time-frequency representations (TFRs) of otoacoustic emissions (OAEs) provide information simultaneously in time and frequency that may be obscured in waveform or spectral analyses. TFRs were applied to transient-evoked stimulus-frequency (SF) and distortion-product (DP) OAEs to test cochlear model predictions. SFOAEs and DPOAEs were elicited in 18 normal-hearing subjects using gated tones and tone pips. Synchronous spontaneous (SS) OAEs were measured to assess their contributions to SFOAEs and DPOAEs. A common form of TFR of measured OAEs was a collection of frequency-specific components often aligned with SSOAE sites, with each component characterized by one or more brief segments or a single long-duration segment. The spectral envelope of evoked OAEs differed from that of the evoking stimulus. Strong emission regions or cochlear "hot spots" were detected, and sometimes accounted for OAE energy observed outside the stimulus bandwidth. Contributions of hot spots and multiple internal reflections to the OAE, and differences between measured and predicted OAE spectra, increased as stimulus level decreased, consistent with level-dependent changes in the estimated cochlear reflectance. Suppression and frequency-pulling effects between components were observed. A recursive formulation was described for the linear coherent reflection emission theory [Zweig and Shera, J. Acoust. Soc. Am. 98, 2018-2047 (1995)] that is well suited for time-domain calculations.

Acoustic Stimulation↗

PubMed2005

Prospective validation of a prediction model for isolating inpatients with suspected pulmonary tuberculosis.

BACKGROUND: Current guidelines for the control of nosocomial transmission of tuberculosis (TB) recommend respiratory isolation for all patients with suspected TB. Application of these guidelines has resulted in many patients without TB being isolated on admission to the hospital, significantly increasing hospital costs. This study was conducted to prospectively validate a clinical decision rule to predict the need for respiratory isolation in inpatients with suspected TB. METHODS: A cohort of 516 individuals, who presented to 2 New York City hospitals between January 16, 2001, and September 29, 2002, and who were isolated on admission for clinically suspected TB, were enrolled in the study. Face-to-face interviews were conducted to determine the presence of clinical variables associated with TB in the prediction model, including TB risk factors, clinical symptoms, and findings from physical examination and chest radiography. RESULTS: Of the 516 patients, 19 were found to have TB (prevalence, 3.7%; 95% confidence interval [CI], 2.2%-5.7%). The prediction rule had a sensitivity of 95% (95% CI, 74%-100%) and a specificity of 35% (95% CI, 31%-40%). Using a prevalence of TB of 3.7%, the positive predictive value was 9.6% and the negative predictive value was 99.7%. CONCLUSIONS: Among inpatients with suspected active pulmonary TB who are isolated on admission to the hospital, a prediction rule based on clinical and chest radiographic findings accurately identified patients at low risk for TB. Approximately one third of the unnecessary episodes of respiratory isolation could have been avoided had the prediction rule been applied. Future studies should assess the feasibility of implementing the rule in clinical practice.

Adult↗

PubMed2002

Non-professional paint stripping, model prediction and experimental validation of indoor dichloromethane levels.

We have experimentally quantified exposure to dichloromethane during non-professional paint stripping and validated the mathematical paint exposure model of van Veen et al. (1999). The model innovates the prediction of the dichloromethane evaporation rate and room concentration by accounting for transport in the paint stripper matrix. The experiments show that peak concentrations range from 600 to 1600 mg/m3, increasing to 2000 mg/m3 when direct sun radiation increases evaporation. A naive model prediction, using a priori parameter values from the experimental set-up and a previous experiment with alkanes, accurately predicts the upper range of the experimental values, but overpredicted four out of six experiments. Statistical fit of the two paint stripper layer parameters to the experimental data resulted in a good coincidence of predicted and experimental data. Model and experiment indicate that 10-30% of dichloromethane is immediately available for evaporation.

Air Pollution, Indoor↗

PubMed2003

Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling.

Bone remodeling occurs asynchronously at multiple sites in the adult skeleton and involves resorption by osteoclasts, followed by formation of new bone by osteoblasts. Disruptions in bone remodeling contribute to the pathogenesis of disorders such as osteoporosis, osteoarthritis, and Paget's disease. Interactions among cells of osteoblast and osteoclast lineages are critical in the regulation of bone remodeling. We constructed a mathematical model of autocrine and paracrine interactions among osteoblasts and osteoclasts that allowed us to calculate cell population dynamics and changes in bone mass at a discrete site of bone remodeling. The model predicted different modes of dynamic behavior: a single remodeling cycle in response to an external stimulus, a series of internally regulated cycles of bone remodeling, or unstable behavior similar to pathological bone remodeling in Paget's disease. Parametric analysis demonstrated that the mode of dynamic behavior in the system depends strongly on the regulation of osteoclasts by autocrine factors, such as transforming growth factor beta. Moreover, simulations demonstrated that nonlinear dynamics of the system may explain the differing effects of immunosuppressants on bone remodeling in vitro and in vivo. In conclusion, the mathematical model revealed that interactions among osteoblasts and osteoclasts result in complex, nonlinear system behavior, which cannot be deduced from studies of each cell type alone. The model will be useful in future studies assessing the impact of cytokines, growth factors, and potential therapies on the overall process of remodeling in normal bone and in pathological conditions such as osteoporosis and Paget's disease.

Autocrine Communication↗

PubMed1990

Return to work after stroke: development of a predictive model.

Seventy-nine stroke patients who underwent a vocationally oriented, comprehensive, inpatient stroke rehabilitation program were followed up to evaluate their return to work. At follow-up, 49% had returned to work a mean of 3.1 months after rehabilitation discharge. Factors associated with success and with failure of vocational rehabilitation were then identified, and a predictive model was developed. There were positive associations between return to work and Barthel Index on admission (p = 0.0002) and discharge (p = 0.0015). Negative associations were found between return to work and aphasia (p = 0.0009), rehabilitation length of stay (p less than 0.0001), and prior alcohol consumption (p = 0.03). A step-wise multiple regression model explained 42% of the variance in return to work. Those most likely to return to work were not aphasic; they had shorter rehabilitation lengths of stay and higher Barthel Index scores on discharge; and they were lighter consumers of alcoholic beverages before their strokes. In conclusion, a set of factors predictive of return to work in younger stroke patients was identified, including, most notably, a strong negative association with aphasia and an intriguing negative association with prior alcohol consumption.

Adult↗

PubMed2005

Jet-induced skin puncture and its impact on needle-free jet injections: experimental studies and a predictive model.

Needle-free jet injections constitute an important method of drug delivery, especially for insulin and vaccines. This report addresses the mechanisms of interactions of liquid jets with skin. Liquid jets first puncture the skin to form a hole through which the fluid is deposited into skin. Experimental studies showed that the depth of the hole significantly affects drug delivery by jet injections. At a constant jet exit velocity and nozzle diameter, the hole depth increased with increasing jet volume up to an asymptotic value and decreased with increasing values of skin's uniaxial Young's modulus. A theoretical model was developed to predict the hole depth as a function of jet and skin properties. A simplified model was first verified with polyacrylamide gels, a soft material in which the fluid mechanics during hole formation is well understood. Prediction of the hole depth in the skin is a first step in quantitatively predicting drug delivery by jet injection.

Biomechanical Phenomena↗

PubMed2002

Doppler-derived left ventricular end-diastolic pressure prediction model using the combined analysis of mitral and pulmonary A waves in patients with coronary artery disease and preserved left ventricular systolic function.

The aim of this study was to analyze the components of mitral and pulmonary A waves and to construct a Doppler-derived left ventricular (LV) end-diastolic pressure (EDP) prediction model based on the combined analysis of transmitral and pulmonary venous flow velocity curves. Combined analysis of transmitral and pulmonary venous flow velocity curves at atrial contraction is a reliable predictor of increased LV filling pressure. The duration of pulmonary and mitral A waves is determined by the sum of respective acceleration and deceleration time. Mitral flow and left upper pulmonary vein flow velocity curves were recorded simultaneously with LVEDP in 40 consecutive patients (aged 59 +/- 8 years) with coronary artery disease and preserved LV systolic function. Differences in all parameters represent values of pulmonary minus those of mitral A wave curve. The difference in deceleration time was the strongest candidate, being included in all models. After redundancy evaluation, we reached the following model: LVEDP = 20.61 + 0.229 x difference in deceleration time (r(2) = 0.80, p <0.001). In the entire study group, the difference in duration and in deceleration time of the A wave was highly correlated with LVEDP (r = 0.79, p <0.001, and r = 0.88, p <0.001, respectively). The entire study group was further divided according to whether LVEDP was above (group I, 20 patients) or below (group II, 20 patients) the median value (15.5 mm Hg). In group I, the difference in duration and in deceleration time correlated well (r = 0.62, p = 0.01, and r = 0.75, p = 0.001, respectively) with LVEDP, whereas in group II only the difference in deceleration time correlated well (r = 0.68, p = 0.005). In patients with coronary artery disease and preserved LV systolic function, the combined analysis of mitral and pulmonary A waves can predict LVEDP. The difference in deceleration time between pulmonary and mitral A waves can reliably evaluate high and normal LVEDP.

Blood Flow Velocity↗

PubMed2005

[Perform analyse on the predictive model of ischemic cardiovascular diseases in Qingdao].

OBJECTIVE: To explore the clinical usage of the methods and tools of the 10-year's risk estimation of ischemic cardiovascular disease (ICVD) in Chinese. METHODS: The risk of ICVD in 2287 middle-aged Qingdao people was evaluated by the methods and tools of the 10-year's risk estimation of ICVD in Chinese, which was developed by Cardiovascular Institute, Chinese Academy of Medical Sciences. RESULTS: (1) 98.16% of male and 99.39% of female had a 10-year absolute risk of ICVD less than 10%. 0.19% of male and 0.15% of female had a 10-year absolute risk of ICVD higher than 20%. (2) In the low risk group (absolute risk < 10%) detection rate of abnormal SBP, FBG, TC, BMI was 100%, 20.8%, 75%, 87.5% respectively. While in the high risk group (absolute risk > or = 20%) detection rate of abnormal SBP, FBG, TC, BMI was 7.31%, 3.4%, 37.74%, 59.26% respectively. CONCLUSIONS: The prediction models and simplified tools for estimating 10-year-risk of ICVD in Chinese can predict satisfactorily the occurrence of cardiovascular disease in Qingdao area.

Adult↗

PubMed2006

Oral mucositis in myeloma patients undergoing melphalan-based autologous stem cell transplantation: incidence, risk factors and a severity predictive model.

Melphalan-based autologous stem cell transplant (Mel-ASCT) is a standard therapy for multiple myeloma, but is associated with severe oral mucositis (OM). To identify predictors for severe OM, we studied 381 consecutive newly diagnosed myeloma patients who received Mel-ASCT. Melphalan was given at 200 mg/m2 body surface area (BSA), reduced to 140 mg/m2 for serum creatinine >3 mg/dl. Potential covariates included demographics, pre-transplant serum albumin and renal and liver function tests, and mg/kg melphalan dose received. The BSA dosing resulted in a wide range of melphalan doses given (2.4-6.2 mg/kg). OM developed in 75% of patients and was severe in 21%. Predictors of severe OM in multiple logistic regression analyses were high serum creatinine (odds ratio (OR)=1.581; 95% confidence interval (CI): 1.080-2.313; P=0.018) and high mg/kg melphalan (OR=1.595; 95% CI: 1.065-2.389; P=0.023). An OM prediction model was developed based on these variables. We concluded that BSA dosing of melphalan results in wide variations in the mg/kg dose, and that patients with renal dysfunction who are scheduled to receive a high mg/kg melphalan dose have the greatest risk for severe OM following Mel-ASCT. Pharmacogenomic and pharmacokinetic studies are needed to better understand interpatient variability of melphalan exposure and toxicity.

Adult↗

PubMed2005

Toward a predictive model of patient satisfaction with nurse practitioner care.

PURPOSE: (a) To determine if caring behaviors of nurse practitioners (NPs), gender of NPs, setting (urban or rural), and age, gender, ethnicity, education, and income of patients were predictors of patient satisfaction; (b) to determine which of these characteristics was the best predictor(s) of patient satisfaction; and (c) begin to develop a conceptual model for explaining patient satisfaction with NP care. DATA SOURCES: Responses to the Caring Behaviors Inventory (CBI) and a demographic inquiry by 348 NPs in Louisiana and completion of the Di'Tomasso-Willard Patient Satisfaction Questionnaire (DWPSQ) and a demographic inquiry from 817 patients in Louisiana served as data sources. A predictive modeling design explored which variable(s) is the best predictor of patient satisfaction, and multiple regression was used to determine the equation for the best-fitting line and the optimal model for the best predictor(s) of patient satisfaction. CONCLUSIONS: CBI mean scores were high for all NPs. No statistically significant difference was found between male NPs' and female NPs' total mean CBI scores and between urban or rural total mean CBI scores. DWPSQ mean scores and subscale scores indicated high satisfaction with NP care. No statistically significant relationships were found between the NPs' CBI mean scores and the patients' DWPSQ mean scores. There were significant relationships between the DWPSQ subscales, including Wait Time and Patient Management. Stepwise linear regression revealed that patients' age group was a predictor of DWPSQ total mean scores. IMPLICATIONS FOR PRACTICE: NPs need to be aware of developmental differences in all age groups and the differences in perceptions of care. There are many variables to consider when determining patient satisfaction with care, including the patients' sociodemographic and health variables, the healthcare system, and characteristics of the healthcare providers. Awareness of these variables may affect how NPs deliver care and ensure quality care with which the patients are satisfied.

Adolescent↗