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PubMed2006

Deficiencies of cardiovascular risk prediction models for type 1 diabetes.

OBJECTIVE: Cardiovascular risk prediction models are available for the general population (Framingham) and for type 2 diabetes (U.K. Prospective Diabetes Study [UKPDS] Risk Engine) but may not be appropriate in type 1 diabetes, as risk factors including younger age at diabetes onset and presence of diabetes complications are not considered. Therefore, our objective was to examine the accuracy of Framingham and UKPDS models for predicting coronary heart disease (CHD) in a type 1 diabetic cohort. RESEARCH DESIGN AND METHODS: Ten-year follow-up data from the Pittsburgh Epidemiology of Diabetes Complications (EDC) study, a prospective cohort study of 658 subjects with childhood-onset type 1 diabetes diagnosed between 1950 and 1980 first seen in 1986-1988, were analyzed. EDC study data were used to calculate the 10-year probability of CHD (fatal CHD, nonfatal myocardial infarction, or Q-waves) applying to the Framingham and UKPDS equations. RESULTS: Mean age at CHD onset was 39 years. When fatal/nonfatal myocardial infarction and CHD death were modeled, both the UKPDS and Framingham models showed significant lack of calibration (P < 0.0001) but moderate discrimination (0.76 UKPDS, 0.77 Framingham men, and 0.88 Framingham women). Both the UKPDS and Framingham models underestimated probability of events in highest risk deciles. CONCLUSIONS: Currently available CHD models poorly predict events in type 1 diabetes. Future research should focus on determining the risk factors accounting for the lack of fit and developing prediction models specific to this high-risk group.

Adult↗

PubMed2006

Validation of a prediction model for the follicle-stimulating hormone response dose in women with polycystic ovary syndrome.

OBJECTIVE: To validate a published model for the prediction of the individual FSH response dose for gonadotropin induction of ovulation in polycystic ovary syndrome (PCOS). DESIGN: Structured, complete, and carefully monitored patient-based data collection to test the external validity of the prediction model. SETTING: Twenty-nine hospitals in The Netherlands. PATIENT(S): Eighty-five clomiphene citrate (CC)-resistant women with PCOS. INTERVENTION(S): Ovulation induction in a chronic low-dose step-up FSH regimen. MAIN OUTCOME MEASURE(S): Predicted individual FSH response dose, defined as follicle growth >10 mm in diameter on ultrasound. RESULT(S): The model, using the women's body mass index, CC response, initial serum FSH level, and initial serum insulin-to-glucose ratio was studied in the validation sample. Overall, the FSH response dose predicted by the model was higher than the observed response dose. The predictive performance of the model was poor, with an R(2) of 0.11, and the average prediction error was 35 IU. CONCLUSION(S): The external validity of the model predicting the individual FSH response dose was inadequate in women with CC-resistant PCOS undergoing ovulation induction with recombinant FSH in a low-dose step-up regimen.

Adult↗

PubMed2002

A comparison of the Framingham and European Society of Cardiology coronary heart disease risk prediction models in the normative aging study.

BACKGROUND: A number of prediction models have been developed in an attempt to accurately identify patients at increased risk of a first coronary heart disease event. We sought to determine the ten-year incidence of coronary heart disease events in a healthy cohort with measurable risk factors, and to compare these results with the predicted number of events by use of both the Framingham and European Society of Cardiology risk prediction models. METHODS: We compared the predicted and observed number of events in 5 risk categories in 1393 subjects aged 30 to 74 years who were enrolled in the Normative Aging Study. RESULTS: The risk prediction models reliably stratify populations with regards to relative risk of coronary heart disease events and there is reasonable agreement between the 2 models (weighted kappa = 0.46, P <.01). The Framingham model underestimated the absolute risk of coronary heart disease events in the low-risk group, and both risk prediction models overestimated the absolute risk of events in the high- or very-high-risk groups (Framingham c-statistic = 0.60, European Society of Cardiology c-statistic = 0.58). CONCLUSIONS: Despite simplification, the accuracy of the European model was not significantly different from the Framingham model. But the accuracy of absolute risk prediction, particularly at the extremes of risk, is imperfect. Refinement and validation of these risk prediction models is important because they affect the management of individual patients and the allocation of community resources.

Adult↗

PubMed1995

Validation and adjustment of the mathematical prediction model for human rectal temperature responses to outdoor environmental conditions.

Models to predict rectal temperature (Tre) have been based on indoor laboratory studies. The present study was conducted to validate and adjust a previously suggested model for outdoor environmental conditions. Four groups of young male volunteers were exposed to three different climatic conditions (30 degrees C, 65% rh; 31 degrees C, 41% rh; 40 degrees C, 20% rh). They were tested both in shaded and open field areas (radiation: 80 and 900 W.m-2, respectively) at different work loads (100, 300 and 450 watt). Exercise consisted of two bouts of 10 minutes rest and 50 minutes walking on a treadmill, at a constant speed (1.4 m.s-1) and different grades. The subjects were tested wearing cotton fatigues and protective garments. Their Tre and heart rate were monitored every 5 min and skin temperature every 15 min, oxygen uptake was measured towards the end of each bout of exercise; concomitantly, ambient temperature, relative humidity and solar load were monitored. We concluded that: (a) the corrected model to predict rectal temperature overestimates the actual measurements when applied outdoors; (b) radiative and convective heat exchanges should be considered separately when using the model outdoors; (c) radiative heat exchange should also be considered separately for short-wave radiation (solar radiation) and long-wave emission from the body to the atmosphere. Finally, an adjusted model to be used outdoors was suggested.

Adolescent↗

PubMed2000

A predictive model of fatigue in human skeletal muscles.

Fatigue is a major limitation to the clinical application of functional electrical stimulation. The activation pattern used during electrical stimulation affects force and fatigue. Identifying the activation pattern that produces the greatest force and least fatigue for each patient is, therefore, of great importance. Mathematical models that predict muscle forces and fatigue produced by a wide range of stimulation patterns would facilitate the search for optimal patterns. Previously, we developed a mathematical isometric force model that successfully identified the stimulation patterns that produced the greatest forces from healthy subjects under nonfatigue and fatigue conditions. The present study introduces a four-parameter fatigue model, coupled with the force model that predicts the fatigue induced by different stimulation patterns on different days during isometric contractions. This fatigue model accounted for 90% of the variability in forces produced by different fatigue tests. The predicted forces at the end of fatigue testing differed from those observed by only 9%. This model demonstrates the potential for predicting muscle fatigue in response to a wide range of stimulation patterns.

Electric Stimulation↗

PubMed1995

Application of multivariate cluster, discriminate function, and stepwise regression analyses to variable selection and predictive modeling of sperm cryosurvival.

OBJECTIVE: To develop a mathematical model that predicts sperm cryodamage based on the kinematic characteristics of seminal sperm as detected by computer-aided sperm analysis (CASA). DESIGN: Computer-aided sperm analysis was performed on donor semen before and after freezing. An iterative multivariate statistical analysis technique was developed to identify sperm subpopulations and to select the best variables for modeling. Stepwise, multivariate regression was performed on the selected subpopulations to predict the post-thaw percentage of motile sperm from prefreeze kinematic values. SETTING: Andrology laboratories, IVF laboratories, and sperm cryobanks. PARTICIPANTS: Semen donors in an academic research environment. MAIN OUTCOME MEASURES: Identification of predictive kinematic variables; number of sperm subpopulations per sample; number of kinematic variables per subpopulation; prediction error for subpopulation membership; and an equation for prediction of post-thaw percentage of motile sperm from prefreeze CASA variables. RESULTS: The number of subpopulations for each specimen was predicted by 3 to 5 kinematic variables. Straight-line velocity (VSL) and linearity were the most commonly predictive primary variables, whereas curvilinear velocity and amplitude of lateral head displacement were the most commonly predictive secondary variables. The best linear model predicted the post-thaw percentage of motile sperm from the difference in VSL between the subpopulation with the highest value and the subpopulation with the lowest value in each prefreeze specimen. CONCLUSIONS: A small number of consistent kinematic variables accurately described physiologic subpopulations of sperm in prefreeze and post-thaw specimens from different men. An equation based on the characteristics of these subpopulations predicts the post-thaw percentage of motile sperm (i.e., sperm recovery) from simple prefreeze kinematic variables. This equation could improve specimen screening by eliminating the requirements for freezing and thawing in order to identify a specimen's vulnerability to cryodamage.

Cell Survival↗

PubMed2005

Predictive model for assessing cognitive impairment by quantitative electroencephalography.

OBJECTIVE: To assess the utility of quantitative electroencephalographic analysis as an indicator of cognitive impairment, we examined the correlation between Mini-Mental State Examination (MMSE) scores and quantitative electroencephalographic (QEEG) power values in elderly patients and constructed a regression model to predict MMSE scores. BACKGROUND: Because of the growing number of elderly individuals with cognitive deficits, there is an increasing need for simple and objective methods with which to evaluate cognitive function. Although QEEG is reportedly a useful method for this purpose, few researchers have constructed a QEEG-based model for predicting the degree of cognitive impairment in clinical settings. METHOD: We evaluated brain function using QEEG in 44 elderly patients with memory complaints and compared the results with their MMSE scores. RESULTS: In the correlation analysis, no significant correlation was found between MMSE scores and QEEG power values. However, a regression model created using relative QEEG and gender for predicting MMSE scores had an adjusted R2 of 0.471. CONCLUSIONS: This finding suggests that QEEG analysis may be a useful indicator of cognitive decline in patients with memory complaints.

Aged↗

PubMedDate not supplied

Evaluating operative risk in colorectal cancer surgery: ASA and POSSUM-based predictive models.

OBJECTIVE: To review two predictive models, based on the American Society of Anaesthesiologists (ASA) and the Physiological and Operative Severity Score for the enumeration of Mortality and morbidity (POSSUM)-used for estimating postoperative mortality in patients, undergoing surgery for colorectal disease, in the UK. METHODS: Data was derived from three multicentre, UK-based studies involving a total of 16,006 patients with malignant or non-malignant bowel pathologies. Data sources were: The Colorectal-POSSUM (CR-POSSUM) Study population, comprising 6883 patients undergoing colorectal surgery in 15 UK hospitals between 1993 and 2001; The Association of Coloproctology of Great Britain and Ireland (ACPGBI) Colorectal Cancer (CRC) Database, encompassing 8077 newly diagnosed CRC patients, undergoing surgical resections in 79 hospitals, between April 2000 and March 2002; The ACPGBI Malignant Bowel Obstruction (MBO) Study, encompassing 1046 patients with MBO in 148 hospitals, treated between April 1998 and March 1999. Multifactorial logistic regression analyses were used to adjust for case-mix, identify risk factors for in-hospital/30-day operative mortality and to accommodate the variability of outcomes between hospitals. RESULTS: In the ACPGBI CRC study, 7374 patients had surgery, 6622(89.8%) a major bowel resection and 1465(19.9%) emergency surgery. Nine hundred and eighty-nine (94.6%) patients with MBO had surgery and 854(86.3%) underwent bowel resection. In the CR-POSSUM study, of the 6790(98.6%) patients undergoing surgery, 3451(50.8%) had a major colorectal resection, including 2107(31.0%) as an emergency. The operative mortality was 7.5% for the ACPGBI CRC study, 15.7% for patients with MBO and 5.7% for patients in the CR-POSSUM study. When tested, the predictive models showed good discrimination, with an area under the receiver-operator characteristic curve of 77.5% for the ACPGBI CRC, 80.1% for the MBO and 89.8% for the CR-POSSUM. CONCLUSIONS: Prediction of postoperative death can be made by the clinician using simple, numerical, tables derived from the ACPGBI CRC, MBO and CR-POSSUM models. The models can be used in everyday practice for pre-operative counselling of patients and their carers, as a part of the process of informed consent. They may also be used to compare the outcomes between multidisciplinary CRC teams.

Adult↗

PubMed2006

A predictive model to correlate fuel specifications with on-road vehicles emissions in Mexico.

Mexico is currently in the process of implementing its third air management program, which includes control measures targeting emissions reductions from mobile, point, and area sources. Achieving the program goals will require changes in the composition and in physical properties of gasoline and implementing an emissions reduction schedule. For that purpose a study was undertaken to support understanding of the effect of gasoline fuel parameters on exhaust emissions. Specifically, the relative impacts of Reid vapor pressure, distillation parameters, oxygen, sulfur, olefins, and aromatic contents on the exhaust emissions of in-use vehicles of the metropolitan area of Mexico City were investigated. The results were used to develop a model to predict CO, nitrogen oxides, total hydrocarbons, and toxic emissions such as benzene, 1,3-butadiene, formaldehyde, and acetaldehyde. Also a statistical model that predicts evaporative emissions was built. Results of the present model are compared with those obtained using the complex model of the United States Environmental Protection Agency.

Air Pollutants↗

PubMed2000

Predictive modelling of the growth and survival of Listeria in fishery products.

Predictive microbiology provides a powerful tool to aid the exposure assessment phase of 'quantitative microbial risk assessment'. Using predictive models changes in microbial populations on foods between the point of production/harvest and the point of eating can be estimated from changes in product parameters (temperature, storage atmosphere, pH, salt/water activity, etc.). Thus, it is possible to infer exposure to Listeria monocytogenes at the time of consumption from the initial microbiological condition of the food and its history from production to consumption. Predictive microbiology models have immediate practical application to improve microbial food safety and quality, and are leading to development of a quantitative understanding of the microbial ecology of foods. While models are very useful decision-support tools it must be remembered that models are, at best, only a simplified representation of reality. As such, application of model predictions should be tempered by previous experience, and used with cognisance of other microbial ecology principles that may not be included in the model. Nonetheless, it is concluded that predictive models, successfully validated in agreement with defined performance criteria, will be an essential element of exposure assessment within formal quantitative risk assessment. Sources of data and models relevant to assessment of the human health risk of L. monocytogenes in seafoods are identified. Limitations of the current generation of predictive microbiology models are also discussed. These limitations, and their consequences, must be recognised and overtly considered so that the risk assessment process remains transparent. Furthermore, there is a need to characterise and incorporate into models the extent of variability in microbial responses. The integration of models for microbial growth, growth limits or inactivation into models that can predict both increases and decreases in microbial populations over time will also improve the utility of predictive models for exposure assessment. All of these issues are the subject of ongoing research.

Animals↗

PubMed2006

Is this "my" patient? Development and validation of a predictive model to link patients to primary care providers.

BACKGROUND: Evaluating the quality of care provided by individual primary care physicians (PCPs) may be limited by failing to know which patients the PCP feels personally responsible for. OBJECTIVE: To develop and validate a model for linking patients to specific PCPs. DESIGN: Retrospective convenience sample. PARTICIPANTS: Eighteen PCPs from 10 practice sites within an academic adult primary care network. MEASUREMENTS: Each PCP reviewed the records for all outpatients seen over the preceding 3 years (16,435 patients reviewed) and designated each patient as "My Patient" or "Not My Patient." Using this reference standard, we developed an algorithm with logistic regression modeling to predict "My Patient" using development and validation subsets drawn from the same patient set. Quality of care was then assessed by "My Patient" or "Not My Patient" designation by analyzing cancer screening test rates. RESULTS: Overall, PCPs designated 11,226 patients (68.3%, range per provider 15% to 93%) to be "My Patient." The model accurately categorized patients in development and validation subsets (combined sensitivity 80.4%, specificity 93.7%, and positive predictive value 96.5%). To achieve positive predictive values of > 90% for individual PCPs, the model excluded 19.6% of PCP "My Patients" (range 5.5% to 75.3%). Cancer screening rates were higher among model-predicted "My Patients." CONCLUSIONS: Nearly one-third of patients seen were considered "Not My Patient" by the PCP, although this proportion varied widely. We developed and validated a simple model to link specific patients and PCPs. Such efforts may help effectively target interventions to improve primary care quality.

Adult↗

PubMed2001

Models of respiratory rhythm generation in the pre-Bötzinger complex. III. Experimental tests of model predictions.

We used the testable predictions of mathematical models proposed by Butera et al. to evaluate cellular, synaptic, and population-level components of the hypothesis that respiratory rhythm in mammals is generated in vitro in the pre-Bötzinger complex (pre-BötC) by a heterogeneous population of pacemaker neurons coupled by fast excitatory synapses. We prepared thin brain stem slices from neonatal rats that capture the pre-BötC and maintain inspiratory-related motor activity in vitro. We recorded pacemaker neurons extracellularly and found: intrinsic bursting behavior that did not depend on Ca(2+) currents and persisted after blocking synaptic transmission; multistate behavior with transitions from quiescence to bursting and tonic spiking states as cellular excitability was increased via extracellular K(+) concentration ([K(+)](o)); a monotonic increase in burst frequency and decrease in burst duration with increasing [K(+)](o); heterogeneity among different cells sampled; and an increase in inspiratory burst duration and decrease in burst frequency by excitatory synaptic coupling in the respiratory network. These data affirm the basis for the network model, which is composed of heterogeneous pacemaker cells having a voltage-dependent burst-generating mechanism dominated by persistent Na(+) current (I(NaP)) and excitatory synaptic coupling that synchronizes cell activity. We investigated population-level activity in the pre-BötC using local "macropatch" recordings and confirmed these model predictions: pre-BötC activity preceded respiratory-related motor output by 100-400 ms, consistent with a heterogeneous pacemaker-cell population generating inspiratory rhythm in the pre-BötC; pre-BötC population burst amplitude decreased monotonically with increasing [K(+)](o) (while frequency increased), which can be attributed to pacemaker cell properties; and burst amplitude fluctuated from cycle to cycle after decreasing bilateral synaptic coupling surgically as predicted from stability analyses of the model. We conclude that the pacemaker cell and network models explain features of inspiratory rhythm generation in vitro.

Animals↗

PubMed1999

Solitary pulmonary nodules: clinical prediction model versus physicians.

OBJECTIVE: To determine whether a clinical prediction model developed to identify malignant lung nodules based on clinical data and radiologic lung nodule characteristics could predict a malignant lung nodule diagnosis with higher accuracy than physicians. MATERIAL AND METHODS: One hundred cases were obtained by using a stratified random sample from a retrospective cohort of 629 patients with newly discovered 4- to 30-mm radiologically indeterminate solitary pulmonary nodules (SPNs) on chest radiography. A chest radiologist, pulmonologist, thoracic surgeon, and general internist made predictions of a malignant lesion and recommendations for management (thoracotomy, transthoracic needle aspiration biopsy, or observation) on the basis of radiologic and clinical data used to develop the clinical prediction rule. The predictions of a malignant lung nodule were compared with the probability of malignant involvement from a previously validated clinical prediction model to identify malignant nodules on the basis of three clinical characteristics (age, smoking status, and history of cancer greater than or equal to 5 years previously) and three radiologic characteristics (nodule diameter, spiculation, and upper lobe location). RESULTS: Receiver operating characteristic analysis showed no significant difference between the logistic model and the physicians' predictions. Calibration curves revealed that physicians overestimated the probability of a malignant lesion in patients with low risk of malignant disease by the prediction rule; this finding suggests a potential for the decision rule to improve the management of patients with SPNs that are likely to be benign. CONCLUSION: The prediction model was not better than physicians' predictions of malignant SPNs. The prediction rule may have potential to improve the management of patients with SPNs that are likely to be benign.

Diagnosis, Differential↗

PubMed1998

Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure.

Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.

Animals↗

PubMed1999

Validating predictive models of food spoilage organisms.

The accuracy and bias of a predictive model for the maximum specific growth rate of Pseudomonas spp. were studied by means of percentage discrepancy and bias indicators. These were calculated for observations obtained both in laboratory media and in food. When independent pseudomonad data generated in broth were compared with model predictions, the error was smaller than in the case of food. The extent to which the food structure and composition of the microflora contribute to the overall error of the model was quantified.

Animals↗

PubMed2006

Validation of a calibrated prediction model for response to growth hormone treatment in an independent cohort.

BACKGROUND: Prediction models, e.g. for prediction of response to growth hormone treatment, need validation in appropriate independent cohorts, comparing predicted and observed outcomes. In a previous validation of a model for predicting the first-year response to growth hormone treatment in children with idiopathic growth hormone deficiency, overfitting was observed. We modified the prediction formula and now report validation of this modified model. PATIENTS AND METHODS: The modified and original prediction models were applied to a group of patients selected from Lilly's GeNeSIS database using the same inclusion and exclusion criteria as for the original model. For both prediction methods, observed first-year height velocity was plotted vs. predicted height velocity in a calibration plot. For a valid prediction, the regression line should correspond to the line of identity (observed outcome is equal to predicted outcome); the regression lines for each prediction model were tested for significant differences from this line of identity. RESULTS: The number of patients fulfilling the criteria was 226. The regression line in the calibration plot of the modified model was not significantly different from the line of identity (p = 0.43), in contrast to the original model (p < 0.001). For the modified model the mean (SD) prediction error was -0.11 (2.05) cm/year and for the original model 0.28 (2.11) cm/year. CONCLUSION: The modified prediction method, obtained after calibration of the original model, performs well in an independent patient sample and gives more accurate predictions than the original model.

Body Height↗

PubMed2002

A multivariate prediction model of schizophrenia.

Univariate prediction models of schizophrenia may be adequate for hypothesis testing but are narrowly focused and limited in predictive efficacy. Therefore, we used a multivariate design to maximize the prediction of schizophrenia from premorbid measures and to evaluate the relative importance of various predictors. Two hundred twelve Danish subjects with at least one parent diagnosed in the schizophrenia spectrum (high risk) and 99 matched subjects with no such parent (low risk) were assessed on 25 premorbid variables in seven domains (genetic risk, birth factors, autonomic responsiveness, cognitive functioning, rearing environment, personality, and school behavior) when the subjects averaged 15 years of age. Twenty-five years later, 33 subjects had received lifetime diagnoses of schizophrenia. Discriminant function analyses were used to discriminate schizophrenia outcomes from no mental illness and nonschizophrenia outcomes on the basis of premorbid measures. Regardless of the comparison group used, schizophrenia was predicted by the interaction of genetic risk with rearing environment, and disruptive school behavior. Within the high-risk group, two-thirds of schizophrenia outcomes were correctly predicted by these premorbid measures; three-quarters of those with no mental illness were also correctly predicted. Prediction was enhanced among those with two schizophrenia spectrum parents, lending support to a multiplicative gene x environment model. Implications for early identification/primary prevention efforts are discussed.

Adolescent↗

PubMed1997

Paediatric index of mortality (PIM): a mortality prediction model for children in intensive care.

OBJECTIVE: To develop a logistic regression model that predicts the risk of death for children less than 16 years of age in intensive care, using information collected at the time of admission to the unit. DESIGN: Three prospective cohort studies, from 1988 to 1995, were used to determine the variables for the final model. A fourth cohort study, from 1994 to 1996, collected information from consecutive admissions to all seven dedicated paediatric intensive care units in Australia and one in Britain. RESULTS: 2904 patients were included in the first three parts of the study, which identified ten variables for further evaluation. 5695 children were in the fourth part of the study (including 1412 from the third part); a model that used eight variables was developed on data from four of the units and tested on data from the other four units. The model fitted the test data well (deciles of risk goodness-of-fit test p = 0.40) and discriminated well between death and survival (area under the receiver operating characteristic plot 0.90). The final PIM model used the data from all 5695 children and also fitted well (p = 0.37) and discriminated well (area 0.90). CONCLUSIONS: Scores that use the worst value of their predictor variables in the first 12-24 h should not be used to compare different units: patients mismanaged in a bad unit will have higher scores than similar patients managed in a good unit, and the bad unit's high mortality rate will be incorrectly attributed to its having sicker patients. PIM is a simple model that is based on only eight explanatory variables collected at the time of admission to intensive care. It is accurate enough to be used to describe the risk of mortality in groups of children.

Adolescent↗