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PubMed2006

Frequency, management, and predictors of abnormal mood after stroke: the Auckland Regional Community Stroke (ARCOS) study, 2002 to 2003.

BACKGROUND AND PURPOSE: Mood disorders are an important consequence of stroke. We aimed to identify significant, clinically useful predictors of abnormal mood after stroke. METHODS: The Auckland Regional Community Stroke (ARCOS) study was a prospective population-based stroke incidence study conducted in Auckland, New Zealand, over a 12-month period from 2002 to 2003. All patients were followed up 6 months after stroke onset and abnormal mood was assessed using the 28-item General Health Questionnaire (GHQ-28) administered as part of a structured telephone interview. Multivariate stepwise logistic regression was used to develop a predictive model for "caseness" (score of > or = 5 on the GHQ-28) based on several premorbid patient and clinical variables assessed at baseline and 28 days of follow up. RESULTS: Of patients available at 6 months (n=1172), complete data on mood was available from 739 (60%) patients and 27% (95% confidence interval, 24 to 30%) were defined as cases. Key baseline predictors of abnormal mood were disability and history of depression after adjustment for sex, age, and comorbidity, but the model failed to predict a large amount of the variation in caseness (C statistic 0.587). CONCLUSIONS: This study emphasizes the complex nature of mood disturbance after stroke and that multiple factors are likely to contribute to mood disorders. A simple, clinically applicable, predictive model in stroke care appears difficult to develop.

Aged↗

PubMedDate not supplied

[The use of mathematical modelling and prediction for assessing the efficacy of the treatment of periodontitis].

The statistical prediction and mathematical modelling techniques were applied to the analysis of the results of treatment of periodontitis. The afflicted focus cross-sectional area (as measured by X-ray film) was taken as a variable. A total of 40 patients were divided into 2 groups. Computer analysis allowed developing a mathematical model of the afflicted focus ossification under different therapeutical approaches.

Administration, Topical↗

PubMed1995

Validation of a model to predict survival in elderly patients with acute myeloid leukaemia.

Intensive chemotherapy is the treatment of choice for selected elderly patients with acute myeloid leukaemia (AML). We recently developed a model to predict survival, thereby providing objective information upon which to select appropriate therapy for such patients. Such models, however, must be validated on a cohort of patients not used during the development of the model. We have tested the model using a series of 61 elderly patients consecutively treated with intensive chemotherapy. Using several statistical techniques, we have shown that the model is of value in predicting prognosis, though two patients did markedly better than the model prediction. This model may be useful for predicting survival in elderly patients with AML and warrants more extensive validation.

Acute Disease↗

PubMed2025

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.

BACKGROUND: Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. METHODS: Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model. RESULTS: A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost's optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959). CONCLUSION: This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide personalized reference for determining the necessity of primary prevention measures for MetS. Additionally, there may be interactions between traditional risk factors and GRS, and the integration of both in a comprehensive model is useful in the prediction of MetS occurrence.

Humans↗

PubMed2003

Patterns of reproductive skew in the polygynandrous acorn woodpecker.

We compared observed levels of reproductive skew in the cooperatively breeding acorn woodpecker (Melanerpes formicivorus) with those predicted by two alternative transactional models. "Concession" models predict the degree to which parentage is shared assuming that a single dominant is in complete control of reproduction. Alternatively, "restraint" models predict reproductive sharing assuming that the dominant controls only whether subordinates remain in the group but does not control its share of reproduction. Reproductive skew is high among males: on average, the most successful male sires more than three times as many offspring as the next most successful male. Females share parentage equally and have lower constraints on dispersal and lower survival rates compared with males, which is consistent with predictions from the concessions model. Also as predicted by the concessions model, yearly variation in opportunities for dispersal before the breeding season correlates positively with skew. However, in contrast to concessions but consistent with the restraint model, skew decreases with relatedness. Thus, neither model consistently predicts patterns of reproductive skew in this species. We suggest that models of reproductive skew will need to include competitive interactions among potential breeders and mate choice before they will adequately predict patterns of reproductive partitioning in most vertebrate societies.

Animals↗

PubMed2006

A general model for predicting the flash point of miscible mixtures.

A mathematical model is presented for predicting the flash point of miscible mixtures. This model is reducible and adequate for some specified systems as proposed previously. Except for multiple aqueous-organic solutions, the predictive capability of the reduced form for other miscible mixtures, including binary aqueous-organic solutions and flammables-only analogues, has been verified previously. The model was validated using the ternary aqueous-organic solutions, water+methanol+ethanol/isopropanol. The results of the study confirm that the model predicts the flash points of these solutions by utilizing the flash points of the individual components. Further, if the binary interaction parameters for a ternary aqueous-organic solution are not accessible, a model based upon the binary interaction parameters of the binary solutions may provide a very acceptable means of predicting the flash point for such a ternary solution through comparison of the predicted and experimental data, as demonstrated in this study.

2-Propanol↗

PubMed1996

Human population dynamics revisited with the logistic model: how much can be modeled and predicted?

"We revive the logistic model, which was tested and found wanting in early-20th-century studies of aggregate human populations, and apply it instead to life expectancy (death) and fertility (birth)....For death...the logistic portrays the situation crisply. Human life expectancy is reaching the culmination of a two-hundred year-process that forestalls death until about 80 for men and the mid 80s for women. No breakthroughs in longevity are in sight unless genetic engineering comes to help. For birth, the logistic covers quantitatively its actual morphology. However, because we have not been able to model this essential parameter in a predictive way over long periods, we cannot say whether the future of human population is runaway growth or slow implosion... From a niche point of view, resources are the limits to numbers, and access to resources depends on technologies. The logistic makes clear that for homo faber, the limits to numbers keep shifting. These moving edges may most confound forecasting the long-run size of humanity."

Conservation of Natural Resources↗

PubMed2004

Modelling and predicting the simultaneous growth of Listeria monocytogenes and spoilage micro-organisms in cold-smoked salmon.

AIMS: To evaluate and model the simultaneous growth of Listeria monocytogenes and spoilage micro-organisms in cold-smoked salmon. METHODS AND RESULTS: Growth kinetics of L. monocytogenes, lactic acid bacteria (LAB), Enterobacteriaceae, enterococci and Photobacterium phosphoreum were determined in two series of challenge tests with sliced and vacuum-packed cold-smoked salmon (SVP-CSS). The product contained a high level of smoke components and at 2 degrees C levels of L. monocytogenes increased <100-fold in 193 days. Without the addition of spoilage micro-organisms, L. monocytogenes reached ca 108 CFU g-1 at 5, 10, 17.5 and 25 degrees C. Inoculation with spoilage micro-organisms reduced this level to 102-104 CFU g-1. LAB dominated the spoilage microfora of SVP-CSS and competition between LAB and L. monocytogenes in SVP-CSS was appropriately described by a simple expansion of the Logistic model. This interaction model aided in predicting the growth of L. monocytogenes in naturally contaminated SVP-CSS when it was used in combination with expanded versions of existing secondary models for L. monocytogenes and LAB. CONCLUSIONS: Temperature, water activity/NaCl, simultaneous growth of LAB, smoke components and to a lesser extent lactate and pH control growth of L. monocytogenes in SVP-CSS. These factors must be included in mathematical models to predict growth of the pathogen in this product. SIGNIFICANCE AND IMPACT OF THE STUDY: The suggested predictive model can be used to support assessment and management of the human health risk due to L. monocytogenes in SVP-CSS.

Animals↗

PubMed2002

Inaccuracy of four coronary surgery risk-adjusted models to predict mortality in individual patients.

OBJECTIVES: This study was undertaken to evaluate the accuracy of four different risk-adjusted models in predicting mortality in individual patients who are undergoing coronary artery by-pass graft surgery. In the last decade several models to stratify patients before open heart surgery, according to factors affecting mortality, were developed with the aim of retrospectively comparing outcomes of open heart surgery, based on reliable stratification of case-mix, and of prospectively identifying high risk patients as a basis for a meaningful informed consent for patients counseling. METHODS: The pre-operative risk of death was calculated with four different models in 418 consecutive patients who underwent coronary artery by-pass surgery and then compared with the actual outcome. To discriminate patients with favorable and unfavorable outcome, the logistic regression analysis and the areas under the receiver-operating-characteristic curves were applied. The accuracy score was used to evaluate the reliability of each score to predict the individual outcome. RESULTS: Seven deaths (1.7%) were observed within 30 days from the operation, and the overall incidence was similar to that predicted by all models. Only the NBI score was not able to discriminate survivors from patients who will die, and the areas under the curves were 0.596 for the Parsonnet score, 0.861 for the Cleveland Clinic Foundation score, 0.823 for the French score, and 0.806 for the EuroSCORE. The four models were highly accurate (between 0.97 and 0.98) to predict the overall mortality. In seven patients who died the mean predictive scores were very low and ranged between 2.1 and 4.6, but were significantly higher than those of patients who survived (between 1.1 and 2.2). CONCLUSIONS: The four pre-surgical predictive models were similarly able to discriminate favorable vs. unfavorable outcomes and highly accurate to predict overall mortality, but very inaccurate to predict mortality in individual patients.

Adult↗

PubMed1992

Use of a multistage model to predict time trends in smoking induced lung cancer.

STUDY OBJECTIVE: The aims were to use a mathematical model to predict the time course of smoking induced lung cancer, and to investigate to what extent the most recent increases in lung cancer mortality are due to cigarette smoking. DESIGN: A mathematical model was developed and solved by simulation to construct detailed smoking histories of the US white male population given available prevalence data by age and cohort. A multistage carcinogenesis model was used to predict the time course of smoking induced lung cancer given the detailed smoking histories. SOURCE OF DATA AND MODEL PARAMETERS: The smoking prevalence figures were taken from work by Harris who calculated them using data collected in the Health Interview Survey. The parameters of the multistage model were taken from Whittemore who fitted the model to several sets of smoking and lung cancer data. MAIN RESULTS: The smoking model was used to construct detailed smoking histories of the US white male population from 1900 to 1985. In turn the multistage model was used to predict age and cohort specific smoking induced lung cancer mortality rates over this period. These results were compiled to predict the overall age adjusted trend in smoking induced lung cancer from 1970 to 1985. The model predicts a 12% decline in smoking induced lung cancer for this group over the 15 year period. CONCLUSIONS: The model calculations predict a 12% decline in smoking induced lung cancer for this group, during a period when the actual total rate of lung cancer increased by 26%. Taken together with the decline in average tar content in cigarettes over this period, and the relatively constant dose rate among smokers, these results strongly suggest that the recent increase in lung cancer among white males in the USA is due entirely or in large part to factors other than cigarette smoking.

Cohort Studies↗

PubMed2006

[Utility of the clinical models for predicting pulmonary thromboembolism].

OBJECTIVE: We considered to evaluate the efectivity of the clinical models for predicting pulmonary thromboembolism (PE). METHODS: Retrospective application of three published clinical models (Wells or Canadian model, Geneva model and Pisa model) to patients unequivocally diagnosed of acute PE. RESULTS: We evaluate 120 patients [Mean age 71+/-13 years, males 63 (52%)]: Nineteen (16%) diagnosed with pulmonary arteriography and 101 (84%) diagnosed with helical computed tomography. In the Canadian model 24% patients were of high clinical probability, 59% intermediate and 17% low clinical probability. In Geneva model 21% patients belonged to high clinical probability, 69% intermediate and 10% low clinical probability. In Pisa model 49% patients were of high clinical probability, 45% intermediate and 6% of low clinical probability. Sensitivity was 0.59, 0.67 and 0.89 respectively. Factors associated with low probability were in Canadian model the heart rate, the absence of signs of deep venous thrombosis, the presence of an alternative diagnosis and the low rate of cancer. In Geneva model, age, normal heart rate and PaO2 higher 70 mm Hg were associated with low probability, while in Pisa model normal chest X-Ray and radiological signs of pulmonary oedema were also associated with low clinical probability. CONCLUSIONS: Although all three clinical model showed deficiencies Pisa model was the most suitable clinical model for predicting PE. An intermediate clinical probability in the three models, should not serve to rule out PE, besides it is remarkable that patients with low clinical probability still could have PE, providing for clinical models with a limited value.

Aged↗

PubMed1989

The accuracy of some simple models for predicting particulate interception and retention in agricultural systems.

The accuracy of three radionuclide transfer models for predicting the interception and retention of airborne particles by agricultural crops was tested using Pu-bearing aerosols released to the atmosphere from nuclear fuel facilities on the U.S. Department of Energy's Savannah River Plant, near Aiken, SC. The models evaluated were: 1) NRC, the model defined in U.S. Nuclear Regulatory Guide 1.109; 2) FOOD, a model similar to the NRC model that also predicts concentrations in grains; and 3) AGNS, a model developed from the NRC model for the southeastern United States. Plutonium concentrations in vegetation and grain were predicted from measured deposition rates and compared to concentrations observed in the field. Crops included wheat, soybeans, corn and cabbage. Although predictions of the three models differed by less than a factor of 4, they showed different abilities to predict concentrations observed in the field. The NRC and FOOD models consistently underpredicted the observed Pu concentrations for vegetation. The AGNS model was a more accurate predictor of Pu concentrations for vegetation. Both the FOOD and AGNS models accurately predicted the Pu concentrations for grains.

Aerosols↗

PubMed1991

Experimental verification of a model for predicting transient temperature distributions by focused ultrasound.

A thermal model for predicting time-dependent temperature distributions during ultrasound heating was tested quantitatively. The relevant thermal processes incorporated in the model are heat conduction and ultrasound power absorption, and the required input parameters include the absolute ultrasound power, the shape and frequency of the ultrasound transducer, and the thermal and acoustical properties of the medium. Testing was done by heating an ultrasonically tissue-mimicking phantom with a 525 kHz, single element, focused source. The phantom has muscle-like acoustical properties and contains an array of copper-constantan thermocouples. Time-dependent temperature changes predicted with the thermal model were in very good agreement with those measured in the phantom, verifying the validity of the model for use in optimizing an ultrasound applicator design for a specific treatment situation.

Humans↗

PubMed2002

A dynamic model of N metabolism in the lactating dairy cow and an assessment of impact of N excretion on the environment.

Improving N utilization in dairy cows and especially reducing N output in excreta is desirable due to global concerns of agricultural contribution of N to environmental pollution, particularly as ammonia. Data from five N balance experiments were used to develop a dynamic model that was evaluated with independent data. Model predictions of feces, urine, and milk outputs were close to observed values. Statistical analysis showed that 96% of mean square prediction error for feces and urine N output predictions was due to random variation. However, the model tends to overpredict milk N output, especially at higher N intake levels. Evaluation of model predictions for independent experimental observations from Agricultural Development Advisory Service at Bridgets (U.K.) showed good agreement between predicted and observed urine N output (95% due to random variation). However, there was a slight underprediction for fecal N output (14% mean square prediction error due to bias) and overprediction of milk N output (22% of mean square prediction error due to bias). The model predictions of N outputs in excreta were sensitive to changes in energy concentration of the diet. Dietary protein degradability had only a small influence on predicted fecal N output. However, the model was sensitive in its predictions of urine N when protein degradability was varied. Application of the model to assess reduction in ammonia emissions from dairy cows showed that increasing the energy concentration could potentially reduce ammonia emissions by up to 25% per cow. Similarly, reducing CP concentration in the diet to about 16% could reduce ammonia production by 20% and lower degradability of CP to match microbial requirement by 19% per cow. The model is a first step toward a mechanistic approach of nutrient modeling, and it is a valuable method for predicting N excretions and estimating N emissions from dairy systems.

Ammonia↗

PubMed2000

Application of polynomial models to predict growth of mixed cultures of Pseudomonas spp. and Listeria in meat.

Three models for one rapid and one slow growing strain of Pseudomonas fragi and one slow growing strain of P. fluorescens were developed in a meat broth; they were designed to take account of variations in growth and to provide a growth response interval. These models, and another for Listeria monocytogenes (Lm14 model), were used to predict the growth of spoilage Pseudomonas spp. and pathogenic Listeria in meat products. The Pseudomonas and Listeria models provided satisfactory predictions concerning inoculated strains grown in decontaminated beef meat. It was also possible to use the Pseudomonas models to predict the growth of the natural flora (mainly Pseudomonas spp.) of refrigerated meat stored under aerobic conditions. In experiments with mixed populations, three situations were observed: (1) in decontaminated meat, L. monocytogenes inoculated alone grew well at 6 degrees C, and this result was correctly predicted by the model; (2) in decontaminated meat inoculated with Listeria and Pseudomonas strains, L. innocua grew well and was not affected by the presence of Pseudomonas, and the growth of both organisms was correctly predicted by the models; (3) in naturally contaminated meat inoculated with Listeria, the strain did not grow until Pseudomonas had reached the stationary phase. The models satisfactorily predicted the growth of Pseudomonas spp. but not that of Listeria. In conclusion, the Lm14 model cannot be used for refrigerated meat stored aerobically as the results suggest a 'fail-safe' level which may be too high: meat had already reached a spoilage state even though no increase in the level of Listeria was observed. The Pseudomonas models accurately predicted the growth of naturally occurring Pseudomonas spp.

Animals↗

PubMed1983

Logistic models for prediction of enteric morbidity in the treatment of ovarian and cervical cancers.

To identify patients at high risk for the development of enteric injury, an analysis was made of 212 women who received extensive pelvic and abdominal radiotherapy at the University of Minnesota from 1970 through 1981. One hundred one patients with cervical carcinomas received 8,000 to 8,500 rads to point A, 6,000 rads to point B, and 4,500 to 5,075 rads to the periaortic lymph nodes. One hundred eleven women with ovarian cancers received 2,000 rads to the entire abdomen, followed by an additional 2,975 to 3,000 rads to the pelvis. The overall complication rate was 6.6% (14/212). The only patients who sustained chronic radiation morbidity had thin physiques. The maximum likelihood multiple logistic model was utilized to predict the probability of enteric injury as a function of thin physique, previous operations, and hypertension for individual patients. The usefulness of predictive models is discussed, and possible reasons for the susceptibility of thin women to ionizing radiation are explored.

Adolescent↗

PubMed1994

An example of the stages in the development of a predictive mathematical model for microbial growth: the effects of NaCl, pH and temperature on the growth of Aeromonas hydrophila.

The stages involved in developing a predictive model are illustrated using data describing the effects of temperature (3-20 degrees C), NaCl concentration (0.5-4.5% w/v) and pH (4.6-7.0) on the aerobic growth of Aeromonas hydrophila (cocktail of 6 strains). Optical density measurements using micro-titre plates were used as an initial screen, to determine the appropriate sampling times for viable counts to be made and to determine the approximate boundaries for growth. Growth curves were generated from viable counts and fitted using a modified Gompertz equation. Quadratic response surface equations were fitted to the log of lag and generation times, in response to the variables of temperature, NaCl and pH (in terms of hydrogen ion concentration). The effects of various combinations of these controlling factors are described. Comparisons between predicted growth rates and lag times from our response surface equations and other models for growth of A. hydrophila, developed with viable count data and optical density measurements, are made, together with comparisons with data from the literature on the growth of this bacterium in foods.

Aeromonas hydrophila↗

PubMed2004

Cigarette smoking in a student sample: neurocognitive and clinical correlates.

Why do adolescents begin to smoke in the face of profound health risks and aggressive antismoking campaigns? The present study tested predictions based on two theoretical models of tobacco use in young adults: (1) the self-medication model; and (2) the orbitofrontal/disinhibition model. Investigators speculated that a significant number of smokers were self-medicating since nicotine possesses mood-elevating and hedonic properties. The self-medication model predicts that smokers will demonstrate increased rates of psychopathology relative to nonsmokers. Similarly, researchers have suggested that individuals with attention-deficit/hyperactivity disorder (ADHD) employ nicotine to enhance cognitive function. The ADHD/self-medication model predicts that smokers will perform poorly on tests of executive function and report a greater number of ADHD symptoms. A considerable body of research indicates that tobacco use is associated with several related personality traits including extraversion, impulsivity, risk taking, sensation seeking, novelty seeking, and antisocial personality features. Antisocial behavior and related personality traits as well as tobacco use may reflect, in part, a failure to effectively employ reward and punishment cues to guide behavior. This failure may reflect orbitofrontal dysfunction. The orbitofrontal/disinhibition model predicts that smokers will perform poorly on neurocognitive tasks considered sensitive to orbitofrontal dysfunction and will obtain significantly higher scores on measures of behavioral disinhibition and antisocial personality relative to nonsmokers. To test these predictions, we administered a battery of neuropsychological tests, clinical scales, and personality questionnaires to university student smokers and nonsmokers. Results did not support the self-medication model or the ADHD/self-medication model; however, findings were consistent with the orbitofrontal/disinhibition model.

Adolescent↗