SEARCH · PubMed Health

Results for “predictive modelling”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,387 records · Page 77Linked to original sources

PubMed2001

Can costs be measured and predicted by modeling within a cooperative clinical trials group? Economic methodologic pilot studies of the radiation therapy oncology group (RTOG) studies 90-03 and 91-04.

PURPOSE: To (1) measure radiation therapy costs for patients in randomized controlled clinical trials, (2) compare measured costs to modeling predictions, (3) examine cost distributions, and (4) assess feasibility of collecting economic data within a cooperative group. METHODS: The Radiation Therapy Oncology Group conducted economic pilot studies for two Phase III studies that compared fractionation patterns. Expected quantities of Current Procedural Terminology (CPT) codes and relative value units (RVU) were modeled. Institutions retrospectively provided procedure codes, quantities, and components, which were converted to RVUs used for Medicare payments. Cases were included if the radiation therapy quality control review judged them to have been treated per protocol or with minor variation. Cases were excluded if economic quality review found incomplete economic data. RESULTS: The median and mean RVUs were within the range predicted by the model for all arms of one study and above the predicted range for the other study. CONCLUSION: The model predicted resource use well for patients who completed treatment per protocol. Actual economic data can be collected for critical cost items. Some institutions experienced difficulty collecting retrospective data, and prospective collection of data is likely to allow wider participation in future Radiation Therapy Oncology Group economic studies.

Brain Neoplasms↗

PubMed1999

Derivation and validation of a mathematical model for predicting the response to exogenous recombinant human growth hormone (GH) in prepubertal children with idiopathic GH deficiency. KIGS International Board. Kabi Pharmacia International Growth Study.

Postmarketing surveillance studies of recombinant human GH therapy, such as the Kabi Pharmacia International Growth Study (KIGS; Pharmacia & Upjohn, Inc., International Growth Database), have accumulated extensive data concerning the characteristics and growth outcomes of children with various causes of short stature. These data provide an opportunity to analyze the factors that determine responsiveness to GH and allow the development of disease-specific growth prediction models. We undertook a multiple regression analysis of height velocity (centimeter per yr) with various patient parameters of potential relevance using data from a cohort of 593 prepubertal children with idiopathic GH deficiency (GHD) from the KIGS database. Our aim was to produce models that would have practical utility for predicting prepubertal growth during each of the first 4 yr of GH replacement therapy. These models were validated by a prospective comparison of predicted and observed growth outcomes in an additional 3 cohorts of prepubertal children with idiopathic GHD: 237 additional KIGS patients, 29 patients from the Australian OZGROW study, and 33 patients from Tubingen, Germany. The most influential variable for first year growth response was the natural log (ln) of the maximum GH response during provocation testing, which was inversely correlated with height velocity. The first year growth response was also inversely correlated with chronological age and height SD score minus midparental height SD score. First year growth was positively correlated with body weight SD score, weekly GH dose (ln), and birth weight SD score. Two first year models were developed using these parameters, 1 including and 1 excluding the maximum GH response to provocative testing. The former model explained 61% of the response variability, with a SD of 1.46 cm; the latter model explained 45% of the variability, with a SD of 1.72 cm. The two models gave similar predictions, although the model excluding the maximum GH response to testing tended to underpredict the growth response in patients with very low GH secretory capacity. For the second, third, and fourth year growth responses, 4 predictors were identified: height velocity during the previous year (positively correlated), body weight SD score (positively correlated), chronological age (negatively correlated), and weekly GH dose (ln; positively correlated). The models for the second, third, and fourth year responses explained 40%, 37%, and 30% of the variability, respectively, with SDs of 1.19, 1.05, and 0.95 cm, respectively. When the models were applied prospectively to the other cohorts, there were no significant differences between observed and predicted responses in any of the cohorts in any year of treatment. The fourth year response model gave accurate prospective growth predictions for the fifth to the eighth prepubertal years of GH treatment in a subset of 48 KIGS patients. Analyses of Studentized residuals provided further validation of the models. The parameters used in our models do not explain all of the variability in growth response, but they have a high degree of precision (low error SDs). Moreover, the parameters used are robust and easily accessible. These properties give the models' practical utility as growth prediction tools. The availability of longitudinal, disease-specific models will be helpful in the future for enabling growth-promoting therapy to be planned at the outset, optimized for efficacy and economy, and individualized to meet treatment goals based on realistic expectations.

Child↗

PubMed1998

Value of animal models for predicting hypersensitivity reactions to medicinal products.

Although hypersensitivity reactions induced by medicinal products and chemicals are relatively common, few predictive models are available. A major difficulty is our currently limited understanding of the mechanisms involved, and efforts should be paid to better defining drug immunogenicity, hapten formation and immune effector mechanisms. A second difficulty is the multiplicity of clinical manifestations presumably due to varying mechanisms. Available models can only predict a few of these reactions. Anaphylaxis models in guinea-pigs can be only used for the safety assessment of macromolecules which are neither humanized or of human origin, whereas guinea-pig or mouse models can detect the majority of human contact sensitizers. In addition to the extensive validation of existing models, promising avenues of research are expected to be found in the use of novel animal models, particularly those using genetically modified animals, such as transgenic and knock-out mice.

Allergens↗

PubMed2000

Validated risk stratification model accurately predicts low risk in patients with unstable angina.

BACKGROUND: In the mid 1990s, two unstable angina risk prediction models were proposed but neither has been validated on separate population or compared. OBJECTIVES: The purpose of this study was to compare patient outcome among high, medium and low risk unstable angina patients defined by the Agency for Health Care Policy and Research (AHCPR) guideline to similar risk groups defined by a validated model from our institution (RUSH). METHODS: Four hundred sixteen patients consecutively admitted to the hospital with unstable angina between January 1, 1995, and December 31, 1997, were prospectively evaluated for risk factors. The presence of major adverse events such as myocardial infarction (MI), death and heart failure was assessed for each patient by chart review. RESULTS: The composite end point of heart failure, MI or death occurred in 3% and 5% of the RUSH and AHCPR low risk categories, respectively, and in 8% and 10% of AHCPR and RUSH high risk categories, respectively. Recurrent ischemic events were best predicted by the RUSH model (high: 24% vs. medium: 12% and low: 10%, p = 0.029), but not by the AHCPR model (high: 14% vs. medium: 13% and low: 9%, p = 0.876). The RUSH model identified five times more low risk patients than the AHCPR model. CONCLUSIONS: Both models identify patients with low and high event rates of MI, death or heart failure. However, the RUSH model allowed for five times more patients to be candidates for outpatient evaluation (low risk) with a similar observed event rate to the AHCPR model; also, the RUSH model more successfully predicted ischemic complications. We conclude that the RUSH model can be used clinically to identify patients for early noninvasive evaluation, thereby improving cost effectiveness of care.

Aged↗

PubMed2004

A new model for predicting infectious complications during fludarabine-based combination chemotherapy among patients with indolent lymphoid malignancies.

BACKGROUND: Fludarabine-containing combination chemotherapy regimens are increasingly used in the treatment of indolent lymphoid malignancies, with the associated risk of infection being the major toxicity. Predictors of infection during fludarabine-containing combination therapy are poorly defined and optimal strategies for infection prophylaxis are not known. The authors analyzed their experience with patients treated with the fludarabine-mitoxantrone (FM) or fludarabine-cyclophosphamide (FC) regimens to develop a predictive model for infections. METHODS: Ninety-two patients with indolent lymphoid malignancies were treated with FM (n = 29) or FC (n = 63). Baseline variables including age, gender, regimen, disease histology, previous therapy, time from diagnosis to current treatment, performance status, renal function, absolute neutrophil count (ANC), lymphocyte count, and immunoglobulin G levels were examined retrospectively for their association with risk of infectious complications during or within 4 weeks of therapy. RESULTS: Six risk factors were associated with infectious complications: age > 60 years, > or = 3 previous therapies, previous fludarabine exposure, time from diagnosis to current treatment of > 3 years, performance status > or = 2, and baseline ANC < 2.0 x 10(9)/L. Compared with patients with 0-2 risk factors, patients with > or = 3 risk factors had higher infection rates (26% vs. 7% per cycle, P < 0.0001), more Grade 4 neutropenia (41% vs. 8% per cycle, P < 0.0001), and more neutropenic sepsis (15% vs. 1% per cycle, P < 0.0001). CONCLUSIONS: Infection risk during fludarabine-containing combination chemotherapy was predicted with a model comprising six baseline risk factors. Patients predicted to be at high risk of infection were an appropriate group for consideration of prophylactic strategies.

Adult↗

PubMed2004

Performance of response surface and Davey model for prediction of Staphylococcus aureus growth parameters under different experimental conditions.

The combined effect of different temperatures (7 to 19 degrees C), pH levels (4.5 to 8.5), sodium chloride levels (0 to 8%), and sodium nitrite levels (0 to 200 ppm) on the predicted growth rate and lag time of Staphylococcus aureus under aerobic and anaerobic conditions was studied. The two predictive models developed, response surface (RS) and the Davey model, provided reliable estimates of the two kinetic parameters studied. The RS provided better predictions of maximum specific growth rate, with bias factors of 1.06 and 1.31 and accuracy factors of 1.17 and 1.37, respectively, in aerobic and anaerobic conditions. The Davey model performed more accurately for lag time, with a bias factor of 1.12 and an accuracy factor of 1.49, for both aerobic and anaerobic conditions. Predictive growth models are a valuable tool, enabling swift determination of Staphylococcus aureus growth rate and lag time. These data are essential for ensuring staphylococcus-related quality and safety of food products.

Consumer Product Safety↗

PubMed2006

Evaluation of models to predict BRCA germline mutations.

The selection of candidates for BRCA germline mutation testing is an important clinical issue yet it remains a significant challenge. A number of risk prediction models have been developed to assist in pretest counselling. We have evaluated the performance and the inter-rater reliability of four of these models (BRCAPRO, Manchester, Penn and the Myriad-Frank). The four risk assessment models were applied to 380 pedigrees of families who had undergone BRCA1/2 mutation analysis. Sensitivity, specificity, positive and negative predictive values, likelihood ratios and area under the receiver operator characteristic (ROC) curve were calculated for each model. Using a greater than 10% probability threshold, the likelihood that a BRCA test result was positive in a mutation carrier compared to the likelihood that the same result would be expected in an individual without a BRCA mutation was 2.10 (95% confidence interval (CI) 1.66-2.67) for Penn, 1.74 (95% CI 1.48-2.04) for Myriad, 1.35 (95% CI 1.19-1.53) for Manchester and 1.68 (95% CI 1.39-2.03) for BRCAPRO. Application of these models, therefore, did not rule in BRCA mutation carrier status. Similar trends were observed for separate BRCA1/2 performance measures except BRCA2 assessment in the Penn model where the positive likelihood ratio was 5.93. The area under the ROC curve for each model was close to 0.75. In conclusion, the four models had very little impact on the pre-test probability of disease; there were significant clinical barriers to using some models and risk estimates varied between experts. Use of models for predicting BRCA mutation status is not currently justified for populations such as that evaluated in the current study.

Area Under Curve↗

PubMed1996

A new model to predict final height in constitutionally tall children.

In order to develop new height prediction models for children with constitutionally tall stature, 55 such boys and 88 girls were recalled for measurement of adult final height (FH). Data on height (H), age (CA), and target height (TH) were collected from the hospital charts and radiographs of the left hand and wrist were retrieved and used for bone age (BA) determination [BA according to the methods of Greulich and Pyle (BAGP) and Tanner and Whitehouse (BARUS)]. Standard multiple regression techniques were used to develop prediction equations for FH. In addition to test the validity of the new equations, FH was measured in a second group of constitutionally tall children (n = 32) and compared with the predicted FH according to our models. In addition, a comparison was made with other prediction methods. Mean (SD) FH was 196.0 (4.9) cm in boys and 180.5 (3.8) cm in girls. The ultimate regression equation was FH (cm) = 216.07 + 0.75 x H + 0.25 x TH -11.09 BAGP + 0.74 x (CA x BAGP) for boys and FH = 161.42 + 0.73 x H + 0.15 x TH - 8.41 x CA -8.83 x BARUS -2.45 x M + 0.55 x (CA x BARUS) for girls. The models showed satisfying accuracy: the mean (SD) errors were -1.4(3.2) cm for boys and -0.5(3.1) cm for girls with corresponding mean (SD) absolute errors of 2.7 (2.2) cm and 2.0 (2.4) cm, respectively. Compared with the current prediction methods, the new models were quite promising. Their clinical validity has to be ascertained in larger groups of tall children.

Adult↗

PubMedDate not supplied

A fully computational model for predicting percutaneous drug absorption.

The prediction of transdermal absorption for arbitrary penetrant structures has several important applications in the pharmaceutical industry. We propose a new data-driven, predictive model for skin permeability coefficients k(p) based on an ensemble model using k-nearest-neighbor models and ridge regression. The model was trained and validated with a newly assembled data set containing experimental data and structures for 110 compounds. On the basis of three purely computational descriptors (molecular weight, calculated octanol/water partition coefficient, and solvation free energy), we have developed a model allowing for the reliable, purely computational prediction of skin permeability coefficients. The model is both accurate and robust, as we showed in an extensive validation (correlation coefficient for leave-one-out cross validation: Q = 0.948, mean standard error: 0.2 for log k(p)).

Animals↗

PubMed2002

Prospective evaluation of a model of prediction of invasive bacterial infection risk among children with cancer, fever, and neutropenia.

A risk prediction model for invasive bacterial infection (IBI) was prospectively evaluated among children presenting with cancer, fever, and neutropenia. The model incorporated assessment of 5 previously identified risk factors: serum level of C-reactive protein (CRP) >/=90 mg/L, hypotension, identification of relapse of leukemia as the cancer type, platelet count of </=50,000 platelets/mm(3), and recent receipt of chemotherapy [16]. Children were uniformly evaluated at enrollment and were classified as having high or low risk for IBI according to a model that considers the number and type of variables present. Of the 263 febrile episodes evaluated during a 17-month period, 140 (53%) were in IBI-positive children. The sensitivity, specificity, and positive and negative predictive values of the model were 92%, 76%, 82%, and 90%, respectively. Identification of these 5 risk factors during the first 24 h of hospitalization was helpful in discriminating between children with a high or low risk for IBI.

Adolescent↗

PubMedDate not supplied

Determinants of mortality in patients with severe sepsis.

OBJECTIVE: To evaluate the relative importance of predictors of in-hospital mortality in severe sepsis and compare the performance of generic and disease-specific mortality prediction models. METHODS: The author used data from all 826 patients receiving placebo in the Recombinant Human Activated Protein C Worldwide Evaluation in Severe Sepsis (PROWESS) trial. After a variety of clinical factors were examined for their univariate association with in-hospital mortality, logistic regression models incorporating successively more inclusive sets of predictors were created and compared. For each model, discrimination was assessed and the relative contribution of each model component to overall model explanatory power evaluated. The accuracy of using the Acute Physiology and Chronic Health Evaluation (APACHE) II score in isolation as an indicator of "high risk" was assessed by comparing model predictions from APACHE-only models to those of disease-specific models. RESULTS: Age, a number of laboratory values, and APACHE II score were significant univariate predictors of mortality. In multivariable models, age and laboratory values contributed the most information to model predictions; the contribution of the APACHE II score, in particular, the acute physiology component, was modest at best. A risk model including only the total APACHE II score had a c-statistic of 0.686, whereas the best performing disease-specific model had a c-statistic of 0.787. Use of the APACHE II score alone to establish high risk versus low risk resulted in misclassification of 26% of patients. CONCLUSIONS: Individual severe sepsis patient outcomes depend on an array of clinical predictors. Models incorporating sepsis disease-specific risk factors may predict mortality more accurately than generic ICU severity measures.

APACHE↗

PubMed2002

Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression.

BACKGROUND/PURPOSE: There is a paucity of outcome prediction models for injured children. Using the National Pediatric Trauma Registry (NPTR), the authors developed an artificial neural network (ANN) to predict pediatric trauma death and compared it with logistic regression (LR). METHODS: Patients in the NPTR from 1996 through 1999 were included. Models were generated using LR and ANN. A data search engine was used to generate the ANN with the best fit for the data. Input variables included anatomic and physiologic characteristics. There was a single output variable: probability of death. Assessment of the models was for both discrimination (ROC area under the curve) and calibration (Lemeshow-Hosmer C-Statistic). RESULTS: There were 35,385 patients. The average age was 8.1 +/- 5.1 years, and there were 1,047 deaths (3.0%). Both modeling systems gave excellent discrimination (ROC A(z): LR = 0.964, ANN = 0.961). However, LR had only fair calibration, whereas the ANN model had excellent calibration (L/H C stat: LR = 36, ANN = 10.5). CONCLUSIONS: The authors were able to develop an ANN model for the prediction of pediatric trauma death, which yielded excellent discrimination and calibration exceeding that of logistic regression. This model can be used by trauma centers to benchmark their performance in treating the pediatric trauma population.

Calibration↗

PubMed2006

Validation of predictive growth models describing superatmospheric oxygen effects on Pseudomonas fluorescens and Listeria innocua on fresh-cut lettuce.

Two microbial growth models predicting the growth of Pseudomonas fluorescens and Listeria innocua at superatmospheric oxygen and carbon dioxide concentrations at 7 degrees C were validated on fresh-cut butterhead lettuce. Cut lettuce was inoculated with the same strain of L. innocua as the in vitro experiments. The P. fluorescens strain was tagged with a gene encoding green fluorescent protein (GFP) in order to distinguish the inoculated strain from contaminating Pseudomonaceae. Also growth of aerobic mesophilic and lactic acid bacteria was monitored during the experiments. The suggested P. fluorescens model was appropriate to predict growth on cut lettuce. L. innocua on the other hand, grew considerably slower under in vivo circumstances than predicted. CO(2) had a growth promoting effect on L. innocua growing on cut lettuce, whereas in vitro an inhibiting effect was observed. Validation parameters are calculated and hypotheses to explain the discrepancy between predicted and observed growth of L. innocua are provided.

Carbon Dioxide↗

PubMed1997

Use of a clinical sepsis score for predicting bacteremia in neonatal dairy calves on a calf rearing farm.

In human, equine, and bovine neonates, early diagnosis of bacteremia remains a challenge for the internist. The objective of this study was to develop a predictive model for risk of bacteremia, based on a clinical evaluation system called the clinical sepsis score. Blood from 90 ill calves, 1- to 14-days-old from a calf-raising farm in the San Joaquin Valley of California was cultured. The calves were also scored according to a clinical score for hydration status, fecal appearance, general attitude, appearance of scleral vessels, and umbilical abnormality. Age, rectal temperature, heart rate, respiratory rate, and presence or absence of a focal site of infection were recorded. Prevalence of bacteremia was 31% (28/90). A logistic regression model indicated that high clinical score, presence of a focal infection, and increased age were associated with an increased risk of bacteremia in ill calves (P < 0.06). Calves for which the model predicted bacteremia with a probability > or = 40.8% were considered bacteremic, yielding acceptable sensitivity (75%) and specificity (71%) estimates. The predictive model was validated through a 2nd sampling of 100 calves (79 ill calves and 21 controls), of which 17 calves were bacteremic. The classification was 75% correct using the model, with an estimated sensitivity of 76% and specificity of 75%. Overall, results indicated that the model could be a useful tool for predicting bacteremia in ill calves in a clinical setting.

Aging↗

PubMed2006

From descriptive to predictive distribution models: a working example with Iberian amphibians and reptiles.

BACKGROUND: Aim of the study was to identify the conditions under which spatial-environmental models can be used for the improved understanding of species distributions, under the explicit criterion of model predictive performance. I constructed distribution models for 17 amphibian and 21 reptile species in Portugal from atlas data and 13 selected ecological variables with stepwise logistic regression and a geographic information system. Models constructed for Portugal were extrapolated over Spain and tested against range maps and atlas data. RESULTS: Descriptive model precision ranged from 'fair' to 'very good' for 12 species showing a range border inside Portugal ('edge species', kappa (k) 0.35-0.89, average 0.57) and was at best 'moderate' for 26 species with a countrywide Portuguese distribution ('non-edge species', k = 0.03-0.54, average 0.29). The accuracy of the prediction for Spain was significantly related to the precision of the descriptive model for the group of edge species and not for the countrywide species. In the latter group data were consistently better captured with the single variable search-effort than by the panel of environmental data. CONCLUSION: Atlas data in presence-absence format are often inadequate to model the distribution of species if the considered area does not include part of the range border. Conversely, distribution models for edge-species, especially those displaying high precision, may help in the correct identification of parameters underlying the species range and assist with the informed choice of conservation measures.

Journal Article↗

PubMed2000

Outcome of nursing home-acquired pneumonia: derivation and application of a practical model to predict 30 day mortality.

OBJECTIVES: To derive a prediction model of 30 day mortality for nursing home-acquired pneumonia (NHAP) based on factors that can be readily identified by nursing home staff at the time of diagnosis and to apply the model to management issues related to NHAP including clarifying the importance of prepneumonia functional status as a predictor of outcome of NHAP. DESIGN: This was a retrospective chart review of 378 episodes of NHAP treated in the nursing home or hospital during two periods: November 1997 to April 1998 and November 1998 to April 1999. SETTING: Eleven nursing homes in the greater Buffalo, NY region. PARTICIPANTS: Nursing home residents with radiographically proven pneumonia who had at least one of the following signs/symptoms: cough, fever, purulent sputum, respiratory rate > or =25 breaths/minute, localized auscultatory findings, or pleuritic pain. MEASUREMENTS: Status (alive or dead) of each resident at 30 days (30 day mortality) after diagnosis of NHAP was the dependent variable. Factors predicting 30 day mortality were identified by logistic regression analysis. A scoring system was developed based on the results of the logistic model. Each episode of NHAP in the derivation cohort was scored using the model and the cohort was stratified by the model score into six categories or risk for mortality (0-5). The predictability of the model in the derivation cohort was measured using receiver operator characteristics curve analysis. RESULTS: Of 378 episodes of NHAP, 74% were treated initially in the nursing home and 26% were hospitalized initially for treatment. The overall 30 day mortality was 21.4%; however, the mortality rate was significantly higher for those treated initially in the hospital (29.6% vs 16.6%; P = .012). Logistic regression analysis identified four predictors of 30 day mortality: (1) respiratory rate >30 breaths/minute (2 points), (2) pulse > 125 beats/minute (1 point), (3) altered mental status (1 point), and (4) a history of dementia (1 point). Applying the scoring system to each episode in the derivation cohort demonstrated increasing mortality with increasing score. The c statistic for the model in the derivation cohort was .74. Based on the severity of NHAP, model episodes treated initially in the hospital were more acutely ill than those who were treated initially in the nursing home, and episodes treated with a parenteral antibiotic in the nursing home were more acutely ill than those who were treated with an oral agent. Functional status was not a predictor of 30 day mortality although there was a trend of higher mortality in the most dependent group (P = .065). The severity of NHAP model was able to define low and high risk mortality groups within a functional status category. CONCLUSIONS: A severity of NHAP model was derived from a large cohort of episodes in multiple facilities. The model had reasonable discriminatory power in the derivation cohort. The model may aid clinicians in making treatment decisions in the nursing home setting and in making hospitalization decisions. Although prepneumonia functional status provides a reasonable estimate of NHAP severity and prognosis, the severity of NHAP model permitted further refinement of these estimates. The severity of NHAP model requires validation before it can be recommended for general use.

Activities of Daily Living↗

PubMed2006

Bayesian random-effect model for predicting outcome fraught with heterogeneity--an illustration with episodes of 44 patients with intractable epilepsy.

OBJECTIVE: The study aimed to develop a predictive model to deal with data fraught with heterogeneity that cannot be explained by sampling variation or measured covariates. METHODS: The random-effect Poisson regression model was first proposed to deal with over-dispersion for data fraught with heterogeneity after making allowance for measured covariates. Bayesian acyclic graphic model in conjunction with Markov Chain Monte Carlo (MCMC) technique was then applied to estimate the parameters of both relevant covariates and random effect. Predictive distribution was then generated to compare the predicted with the observed for the Bayesian model with and without random effect. Data from repeated measurement of episodes among 44 patients with intractable epilepsy were used as an illustration. RESULTS: The application of Poisson regression without taking heterogeneity into account to epilepsy data yielded a large value of heterogeneity (heterogeneity factor = 17.90, deviance = 1485, degree of freedom (df) = 83). After taking the random effect into account, the value of heterogeneity factor was greatly reduced (heterogeneity factor = 0.52, deviance = 42.5, df = 81). The Pearson chi2 for the comparison between the expected seizure frequencies and the observed ones at two and three months of the model with and without random effect were 34.27 (p = 1.00) and 1799.90 (p < 0.0001), respectively. CONCLUSION: The Bayesian acyclic model using the MCMC method was demonstrated to have great potential for disease prediction while data show over-dispersion attributed either to correlated property or to subject-to-subject variability.

Adolescent↗

PubMed2005

A cellular-level approach to predicting resting energy expenditure across the adult years.

BACKGROUND: We previously derived a whole-body resting energy expenditure (REE) prediction model by using organ and tissue mass measured by magnetic resonance imaging combined with assumed stable, specific resting metabolic rates of individual organs and tissues. Although the model predicted REE well in young persons, it overpredicted REE by approximately 11% in elderly adults. This overprediction may occur because of a decline in the fraction of organs and tissues as cell mass with aging. OBJECTIVE: The aim of the present study was to develop a cellular-level REE prediction model that would be applicable across the adult age span. Specifically, we tested the hypothesis that REE can be predicted from a combination of organ and tissue mass, the specific resting metabolic rates of individual organs and tissues, and the cellular fraction of fat-free mass. DESIGN: Fifty-four healthy subjects aged 23-88 y had REE, organ and tissue mass, body cell mass, and fat-free mass measured by indirect calorimetry, magnetic resonance imaging, whole-body (40)K counting, and dual-energy X-ray absoptiometry, respectively. RESULTS: REE predicted by the cellular-level model was highly correlated with measured REE (r = 0.92, P < 0.001). The mean difference between measured REE (x+/- SD: 1487 +/- 294 kcal/d) and predicted REE (1501 +/- 300 kcal/d) for the whole group was not significant, and the difference between predicted and measured REE was not associated with age (r = 0.009, NS). CONCLUSION: The present approach establishes an REE-body composition link with the use of a model at the cellular level. The combination of 2 aging-related factors (ie, decline in both the mass and the cellular fraction of organs and tissues) may account for the lower REE observed in elderly adults.

Absorptiometry, Photon↗