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PubMed1994

Mixed-effect models for predicting microbial interactions in the vaginal ecosystem.

Three statistical models that predict microbial interactions within the vaginal environment are presented. A large data set was assembled from in vivo studies describing the healthy vaginal environment, and the data set was analyzed to determine whether statistical models which would accurately predict the interactions of the microflora in this environment could be formulated. During assembly of the data set, two new variables were defined and were added to the data set, that is, cycle (sequence of menstrual cycle) and flow stage (subdivision of cycle determined by day of menstrual cycle). Concentrations of total aerobic (includes facultative) bacteria, total anaerobic bacteria, and a Corynebacterium sp. were identified by correlation analysis as variables with significant predictors. By using a regression method with a backward elimination procedure, significant predictors of these outcome variables were identified as the concentrations of Lactobacillus spp., anaerobic Streptococcus spp., and Staphylococcus spp., respectively. For all three outcome variables, pH and flow stage were also identified as significant independent variables. Because some of the data in the data set are repeated measurements for a subject, a mixed-effect model that accounts for the random effects of repeated-measurement data fit best the data set for predicting interactions between various members of the vaginal microflora. The predictive accuracies of the three models were tested by a comparison of model-predicted outcome-variable values with actual mean in vivo outcome-variable values. From these results, we concluded that it is possible to accurately predict vaginal microflora interactions by using a mixed-effect modeling system. The application of this type of modeling strategy and its future use are discussed.

Colony Count, Microbial↗

PubMed2002

Interval length and time-use by children with AD/HD: a comparison of four models.

Predictions made by 4 competing models of time use by children with AD/HD were tested using a computerized version of the Matching Familiar Figures Test in 2 studies. In Study 1 teacher-identified AD/HD children (N = 10) and non-AD/HD controls (N = 10) completed the task under 3 different trial duration conditions (5, 10, and 15 s). In Study 2 the same task was completed by a group of children with a diagnosis of Hyperkinetic Disorder (N = 12) and controls (N = 12). In both studies AD/HD children performed poorly on trials of both 5- and 15-s duration but as well as controls on the 10-s trials. This quadratic function provided support for the State Regulation Deficit model of time use in AD/HD. The value of tailoring the temporal features of learning contexts to the conceptual style of AD/HD children is discussed.

Analysis of Variance↗

PubMed1990

Bioimpedance assessment of antipyrine pharmacokinetics before and after enzyme induction.

Bioimpedance (BI) technology, a noninvasive method of measuring body composition, has been previously used to develop a predictive model for metabolic clearance under basal conditions. The present investigation was conducted to assess the ability of BI-derived models to predict drug disposition in a perturbed system. An antipyrine pharmacokinetic and impedance analysis of 15 healthy male subjects was performed before and on the 14th day of phenobarbital administration (i.e., after enzyme induction). The all subsets multiple regression technique was used to develop preinduction models for clearance (CI), (p = 0.021, R2 = 0.654) and volume of distribution at steady-state (Vdss) (p = 0.001, R2 = 0.867), using demographic and mean BI parameters. The preinduction Cl model significantly underestimated postinduction measurements and was not predictive of changes in Cl. The preinduction Vdss model was predictive of postinduction Vdss. It appears that the BI-derived model is unable to predict perturbations in metabolic clearance caused by enzyme induction.

Adult↗

PubMed2002

Reproductive intentions of the newlywed Bulgarian families--artificial neural network approach.

INTRODUCTION: The aim of this study was to describe the reproductive intentions of newlywed Bulgarian families, build predictive models of the desired number of children by artificial neural networks (ANN) and compare these models' overall prediction accuracy with conventional statistical analyses. MATERIAL AND METHODS: Data from a large cross-sectional study on family planning knowledge and habits of newlywed couples from the region of Plovdiv (Bulgaria) in the year 2000 were used. A subset of 17 variables was extracted from the database, in order to construct and train a back-propagation multilayer ANN. Desired number of children, separately for male and female spouses, was used as an output variable. The following statistical analyses were used: descriptive statistics, percentage distribution, nonparametric tests and logit regression analysis. RESULTS AND DISCUSSION: Factors that determine the opinion of male and female spouses on desired number of children have different relative importance in the phase space. The reproductive planning of the newly-married family can be better modeled and predicted in women than in men. The reproductive intentions of the modern newlywed Bulgarian family are decided on the basis of mutual agreement and understanding. There is no difference between desired number of children by both partners. The age at marriage of spouses is increasing with a tendency to reach the Western European patterns. Artificial neural networks are found to be a very useful tool in cross-sectional population studies for factor classification and outcome modeling, especially in cases where data are not entirely suitable for logistic regression analyses.

Bulgaria↗

PubMed2004

Comparison and cross-validation of simple and multiple logistic regression models to predict USMLE step 1 performance.

BACKGROUND: Early identification of students who may have difficulty with the United States Medical Licensing Examination-Step 1 examination is important for medical schools and students. Numerous models that predict Step 1 performance have been identified, but few of these models have been cross-validated. PURPOSE: To cross-validate different prediction models of Step 1 performance. METHODS: The development sample was 686 students from a Midwestern medical school. The cross-validation sample was 147 different students. Logistic regression was used to develop the multiple model and Year 1 grade point average (GPA) and Year 2 Fall GPA were used as the simple models. Receiver Operating Characteristic graphs were used to select optimal cutoffs for each model. Kappa coefficients were used to determine level of agreement, and sensitivity and specificity were used to assess classification accuracy. RESULTS: The Year 1 GPA model had relatively poor agreement with actual Step 1 performance, but the other models evidenced fair agreement. The multiple and Year 1 GPA models demonstrated statistically significant loss of classification accuracy on cross-validation, whereas the Year 2 Fall GPA model did not. CONCLUSIONS: Cross-validation is necessary to determine the generalizability and overall utility of prediction models.

Education, Medical↗

PubMed2000

Mixed-gas model for predicting decompression sickness in rats.

A mixed-gas model for rats was developed to further explore the role of different gases in decompression and to provide a global model for possible future evaluation of its usefulness for human prediction. A Hill-equation dose-response model was fitted to over 5,000 rat dives by using the technique of maximum likelihood. These dives used various mixtures of He, N(2), Ar, and O(2) and had times at depth up to 2 h and varied decompression profiles. Results supported past findings, including 1) differences among the gases in decompression risk (He < N(2) < Ar) and exchange rate (He > Ar approximately N(2)), 2) significant decompression risk of O(2), and 3) increased risk of decompression sickness with heavier animals. New findings included asymmetrical gas exchange with gas washout often unexpectedly faster than uptake. Model success was demonstrated by the relatively small errors (and their random scatter) between model predictions and actual incidences. This mixed-gas model for prediction of decompression sickness in rats is the first such model for any animal species that covers such a broad range of gas mixtures and dive profiles.

Animals↗

PubMed2006

Relationship of pulmonary vein flow to left ventricular short-axis epicardial displacement in diastole: model-based prediction with in vivo validation.

Previous studies in healthy humans have established that the (approximately 850 ml) volume enclosed by the pericardial sac is nearly constant over the cardiac cycle, exhibiting a transient approximately 5% decrease (approximately 40 ml) from end diastole to end systole. This volume decrease manifests as a "crescent" at the ventricular free wall level when short-axis MRI images of the epicardial surface acquired at end systole and end diastole are superimposed. On the basis of the (near) constant-volume property of the four-chambered heart, the volume decrease ("crescent effect") must be restored during subsequent early diastolic filling via the left atrial conduit volume. Therefore, volume conservation-based modeling predicts that pulmonary venous (PV) Doppler D-wave volume must be causally related to the radial displacement of the epicardium (Delta) (i.e., magnitude of "crescent effect" in the radial direction). We measured Delta from M-mode echocardiographic images and measured D-wave velocity-time integral (VTI) from Doppler PV flow of the right superior PV in 11 subjects with catheterization-determined normal physiology. In accordance with model prediction, high correlation was observed between Delta and D-wave VTI (r=0.86) and early D-wave VTI measured to peak D-wave velocity (r=0.84). Furthermore, selected subjects with various pathological conditions had values of Delta that differed significantly. These observations demonstrate the volume conservation-based causal relationship between radial pericardial displacement of the left ventricle and the PV D-wave-generated filling volume in healthy subjects as well as the potential role of the M-mode echo-derived radial epicardial displacement index Delta as a regional (radial) parameter of diastolic function.

Coronary Circulation↗

PubMed2003

Modeling beta-adrenergic control of cardiac myocyte contractility in silico.

The beta-adrenergic signaling pathway regulates cardiac myocyte contractility through a combination of feedforward and feedback mechanisms. We used systems analysis to investigate how the components and topology of this signaling network permit neurohormonal control of excitation-contraction coupling in the rat ventricular myocyte. A kinetic model integrating beta-adrenergic signaling with excitation-contraction coupling was formulated, and each subsystem was validated with independent biochemical and physiological measurements. Model analysis was used to investigate quantitatively the effects of specific molecular perturbations. 3-Fold overexpression of adenylyl cyclase in the model allowed an 85% higher rate of cyclic AMP synthesis than an equivalent overexpression of beta 1-adrenergic receptor, and manipulating the affinity of Gs alpha for adenylyl cyclase was a more potent regulator of cyclic AMP production. The model predicted that less than 40% of adenylyl cyclase molecules may be stimulated under maximal receptor activation, and an experimental protocol is suggested for validating this prediction. The model also predicted that the endogenous heat-stable protein kinase inhibitor may enhance basal cyclic AMP buffering by 68% and increasing the apparent Hill coefficient of protein kinase A activation from 1.0 to 2.0. Finally, phosphorylation of the L-type calcium channel and phospholamban were found sufficient to predict the dominant changes in myocyte contractility, including a 2.6x increase in systolic calcium (inotropy) and a 28% decrease in calcium half-relaxation time (lusitropy). By performing systems analysis, the consequences of molecular perturbations in the beta-adrenergic signaling network may be understood within the context of integrative cellular physiology.

Adenylyl Cyclases↗

PubMed1992

Multiple-model adaptive predictive control of mean arterial pressure and cardiac output.

A multiple-model adaptive predictive controller has been designed to simultaneously regulate mean arterial pressure and cardiac output in congestive heart failure subjects by adjusting the infusion rates of nitroprusside and dopamine. The algorithm is based on the multiple-model adaptive controller and utilizes model predictive controllers to provide reliable control in each model subspace. A total of 36 linear small-signal models were needed to span the entire space of anticipated responses. To reduce computation time, only the six models with the highest probabilities were used in the control calculations. The controller was evaluated on laboratory animals that were either surgically or pharmacologically altered to exhibit symptoms of congestive heart failure. During trials, the controller performance was robust with respect to excessive switching between models and nonconvergence to a single dominant model. A comparison is also made with a previous multiple-drug controller design.

Animals↗

PubMed1996

A comprehensive methodology for the fitting of predictive accident models.

Recent years have seen considerable progress in techniques for establishing relationships between accidents, flows and road or junction geometry. It is becoming increasingly recognized that the technique of generalized linear models (GLMs) offers the most appropriate and soundly-based approach for the analysis of these data. These models have been successfully used in the series of major junction accident studies carried out over the last decade by the U.K. Transport Research Laboratory (TRL). This paper describes the form of the TRL studies and the model-fitting procedures used, and gives examples of the models which have been developed. The paper also describes various technical problems which needed to be addressed in order to ensure that the application of GLMs would produce robust and reliable results. These issues included: the low mean value problem, overdispersion, the disaggregation of data over time, allowing for the presence of a trend over time in accident risk, random errors in the flow estimates, the estimation of prediction uncertainty, correlations between predictions for different accident types, and the combination of model predictions with site observations. Each of these problems has been tackled by extending or modifying the basic GLM methodology. The material described in the paper, then, constitutes a comprehensive methodology for the development of predictive accident models.

Accidents, Traffic↗

PubMed1992

Prediction of the tertiary structure of parathyroid-hormone-related protein (residues 1-34) by the island model.

Prediction of the tertiary structure of a 34 residue N-terminal fragment of parathyroid-hormone-related protein was carried out by the island model. This peptide is known as a major causative agent of humoral hypercalcemia of malignancy, but structural information studied by X-ray diffraction has not been reported. We adopted the secondary structure determined by NMR and packed on the basis of island model of protein folding developed by us. Predicted structure is discussed in connection with the interaction of active sites.

Amino Acid Sequence↗

PubMed2005

Prediction of flap necrosis with laser induced indocyanine green fluorescence in a rat model.

Prediction of necrosis has a clinical relevance in all fields of plastic surgery. The new application of indocyanine green (ICG) fluoroscopy in plastic surgery allows an objective quantification of skin perfusion and a high topographical resolution. The aim of the present study is to determine threshold values for flap perfusion under well-defined experimental conditions. Twenty random pattern flaps with a length to width ratio of 4:1 (8 x 2 cm(2)) were dissected on the anterior abdominal wall of 20 male Sprague-Dawley rats. ICG fluoroscopy was performed at the end of the operation. The animals were sacrificed at the seventh postoperative day with a reliable necrosis of the distal part of the flaps. Postoperative ICG fluoroscopy then was analysed both in regions that will survive and undergo necrosis. At day 7 a mean area of 5.5 cm(2) (57% of the total flap area) survived and a mean of 3.8 cm(2) (43%) became necrotic. The surviving part of the flap had a mean perfusion index of 62% compared to reference skin. The distal parts of the flap that necrotised showed an average perfusion index of only 19% postoperatively. Differences were statistically highly significant (p<0.001). Indocyanine green fluoroscopy is a useful tool to evaluate perfusion topographically and predict necrosis. From a statistical point of view a perfusion index of less than 25% of the reference skin can be considered as a sign of developing flap necrosis.

Animals↗

PubMed2005

Applicability and relevance of models that predict short term outcome after intracerebral haemorrhage.

OBJECTIVES: Several models for prediction of short term outcome after intracerebral haemorrhage (ICH) have been published, however, these are rarely used in clinical practice for treatment decisions. This study was conducted to identify current models for prediction of short term outcome after ICH and to evaluate their clinical applicability and relevance in treatment decisions. METHODS: MEDLINE was searched from 1966 to June 2003 and studies were included if they met predefined criteria. Regression coefficients of multivariate models were extracted. Two neurologists independently evaluated the models for applicability in clinical practice. To assess clinical relevance and accuracy of each model, in a validation series of 122 patients the proportion with a >or=95% probability of death or poor outcome and the actual 30 day case fatality in these patients were calculated. Receiver operator characteristic (ROC) curves were computed for assessment of discriminatory power. RESULTS: A total of 18 prognostic models were identified, of which 14 appeared easy to apply. In the validation series, the proportion of patients with a >or=95% probability of death or poor outcome ranged from 0% to 43% (median 23%). The 30 day case fatality in these patients ranged from 75% to 100% (median 93%). The area under the ROC curves ranged from 0.81 to 0.90. CONCLUSIONS: Most models are easy to apply and can generate a high probability of death or poor outcome. However, only a small proportion of patients have such a high probability, and 30 day case fatality is not always correctly predicted. Therefore, current models have limited relevance in triage, but can be used to estimate the chances of survival of individual patients.

Adolescent↗

PubMed2001

A self-paced step test to predict aerobic fitness in older adults in the primary care clinic.

OBJECTIVES: To study the potential usefulness of a submaximal self-paced step test as a prediction of maximal aerobic capacity (VO2max) in older adults in the primary care setting. DESIGN: Data were collected during a prospective randomized study of an exercise program. SETTING: Four university family medical clinics in London, Ontario, Canada. PARTICIPANTS: A random sample of 240 healthy older (> or =65) men (n = 118) and women (n = 122) from four family medical clinics underwent self-paced step testing in the clinic with a family physician (n = 16), and step testing and a maximal exercise treadmill test with measurement of respired gases in an exercise laboratory. Testing was done in random order (clinic/laboratory) separated by 2 weeks and then repeated at 52 weeks, following introduction of an exercise program. Relationships between outcome variables were examined by Pearson correlation coefficients while prediction of VO2max was examined using multivariate regression analysis. Cross-validation with 30 age-matched hypertensive and 40 age-matched post-hip arthroplasty patients was used to test the accuracy of the predictive models. MEASUREMENTS: Measured VO2max, predicted VO2max, step test time, step test heart rate, body mass index (BMI), and O2 pulse. RESULTS: Two hundred women (n = 108) and men (n = 92) completed both the initial and 52-week assessments. Stepping time, heart rate, age, BMI, and O2 pulse were strongly associated with VO2max for both a normal and a fast step pace and were chosen to develop the predictive model. Normal step-pace correlation with VO2max (ml/kg/min) was no different (female 0.93: male 0.91) from fast pace (0.95:0.90) with no difference between clinic and laboratory measurement at baseline or 52 weeks. Cross-validation showed no significant difference from the main group using the predictive model. CONCLUSIONS: The self-paced step test is a safe and simple clinical instrument that strongly and reliably predicts VO2max, is sensitive to change, and is generalizable in the family practice setting among community-dwelling older adults differing in fitness and health status.

Aged↗

PubMed2000

Neural network model for predicting peak photochemical pollutant levels.

In this paper, an attempt is made for the 24-hr prediction of photochemical pollutant levels using a neural network model. For this purpose, a model is developed that relates peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows a good agreement. In addition, a series of sensitivity tests is performed in order to evaluate the sensitivity of the model to the uncertainty in meteorological variables. Model forecasts are generally rather insensitive to small perturbations in most of the input meteorological data, while they are relatively more sensitive in changes in wind speed and direction.

Air Pollutants↗

PubMed2002

Heterogeneity in older people: examining physiologic failure, age, and comorbidity.

OBJECTIVES: To derive a clinically relevant age-independent physiologic failure scoring system and to use this system to examine aspects of the association of physiologic failure, age, and comorbidity with inpatient mortality. DESIGN: Retrospective, secondary analysis of a derivation and validation cohort selected from the Cleveland Health Quality Choice Coalition data set. SETTING: Thirty hospitals in greater Cleveland. PARTICIPANTS: Thirty-one thousand nine hundred seventy-six inpatients aged 50 and older discharged in 1993 with a diagnosis of congestive heart failure, pneumonia, or stroke. MEASUREMENTS: The Inpatient Physiologic Failure Score (IPFS) was developed and used to calculate physiologic failure. Forty-four candidate variables were examined for their association with inpatient mortality, and 12 were selected. A point value (2, 3, 4, or 6) based on adjusted odds ratio was assigned for an abnormal result for each of the 12 common physiologic variables. Each patient's abnormal physiology points were summed to produce a physiologic failure score (range 0-39). Comorbidity was quantified using the Patient Management Category Severity Scale. The association between mortality and increasing physiologic failure, increasing age and comorbidity, and distribution of physiologic failure with increasing age and comorbidity were examined. A threshold age was sought. Models for predicting inpatient mortality were developed. RESULTS: Twelve physiologic variables constitute the IPFS. Increasing physiologic failure, age, and comorbidity were associated with increasing mortality. Increasing physiologic failure was not associated with increasing age or comorbidity. We did not find a threshold age. The area under the receiver operating characteristic (ROC) curve for predicting inpatient mortality for IPFS was 0.730, and for comorbidity was 0.741 (not significant). The area under the ROC curve for a mortality prediction model based on age was significantly less (0.603). Accounting for patient age did not significantly improve the predictive ability of the IPFS model (area = 0.752, P <.05). The complete model best predicted mortality (0.829). CONCLUSIONS: The IPFS represents a clinically relevant method for scoring physiologic failure. Physiologic failure, age, and comorbidity are independently and differently associated with inpatient mortality. Physiology fails independent of age and comorbidity.

Age Factors↗

PubMed1998

Appendicular skeletal muscle mass: prediction from multiple frequency segmental bioimpedance analysis.

OBJECTIVES: Bioimpedance analysis (BIA) methods have potential to predict appendicular skeletal muscle mass (SM), although available 50 kHz prediction models include, in addition to impedance (Z), an independent age term. An age term in models is undesirable as it reflects incomplete understanding of underlying conduction physiology. This study tested the hypothesis, based on fluid distribution models related to aging, that appendicular SM bioimpedance analysis (BIA) prediction models would no longer include an independent age term, after first controlling for stature-adjusted appendicular impedance (height2/Z), at injected frequencies greater than 50 kHz. DESIGN: Cross-sectional evaluation of adults who had segmental Z and phase angle (phi) measured with multiple frequency BIA, and arm and leg SM with dual-energy X-ray absorptiometry (DXA). Skeletal muscle prediction models were developed with appendicular SM as dependent variable and height2/Z, gender, age and phi as potential independent variables. RESULTS: Examination of hypothesis in 49 subjects indicated: both arm and leg SM were highly correlated with height2/segmental Z at frequencies ranging from 1-300 kHz; gender was significant covariate in prediction models only at 1 kHz; age remained a significant covariate after controlling for height2/segmental Z at all frequencies; phi did not add significantly to models; and SM prediction models gave maximum R2 at 50 kHz for arm but R2 continued to rise up to 300 kHz for leg. CONCLUSION: Although multifrequency BIA did not eliminate SM prediction model age term, our findings suggest injected frequencies up to 300 kHz may have advantages for evaluating leg SM over conventional 50 kHz method.

Adult↗

PubMed2003

A structured clinical model for predicting the probability of pulmonary embolism.

PURPOSE: To develop a structured model to predict the clinical probability of pulmonary embolism. METHODS: We studied 1,100 consecutive patients with suspected pulmonary embolism in whom a definite diagnosis had been established. We used logistic regression analysis to estimate the probability of pulmonary embolism based on patients' clinical characteristics; the probability was categorized as low (< or = 10%), intermediate (>10%, < or = 50%), moderately high (>50%, < or = 90%), or high (>90%). RESULTS: The overall prevalence of pulmonary embolism was 40% (n = 440). Ten characteristics were associated with an increased risk of pulmonary embolism (male sex, older age, history of thrombophlebitis, sudden-onset dyspnea, chest pain, hemoptysis, electrocardiographic signs of acute right ventricular overload, radiographic signs of oligemia, amputation of the hilar artery, and pulmonary consolidation suggestive of infarction), and five were associated with a decreased risk (prior cardiovascular or pulmonary disease, high fever, pulmonary consolidation other than infarction, and pulmonary edema on the chest radiograph). With this model, 432 patients (39%) were rated a low probability, of whom 19 (4%) had pulmonary embolism; 283 (26%) were rated an intermediate probability, of whom 62 (22%) had pulmonary embolism; 72 (7%) were rated a moderately high probability, of whom 53 (74%) had pulmonary embolism; and 313 (28%) were rated a high probability, of whom 306 (98%) had pulmonary embolism. CONCLUSION: This prediction model may be useful for estimating the probability of pulmonary embolism before obtaining definitive test results.

Adolescent↗