SEARCH · PubMed Health

Results for “Predictive modeling”

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 181 records · Page 10Linked to original sources

PubMed2003

Lateral facial soft-tissue prediction model: analysis using Fourier shape descriptors and traditional cephalometric methods.

This study was designed to investigate the relationship between traditional skeletal cephalometric measurement and Fourier analysis of the lateral soft-tissue profile. A random sample of 121 untreated subjects of European descent, with wide ranges of malocclusions and underlying facial patterns, was selected in the Orthodontic Unit at the University of Melbourne. Lateral cephalograms were available for all subjects. Both traditional lateral cephalometric analysis and Fourier soft-tissue profile analysis were carried out. Multivariate statistical analysis among 11 hard-tissue cephalometric measurements and the first 50 Fourier harmonics was then performed. This analysis formed the basis for a subsequently proposed soft-tissue prediction model. From this model, 50 predicted x- and y-harmonics were generated for each subject in the total sample. Calculation of Pearson's correlation coefficients between the actual and predicted harmonics revealed strong relationships for many of the lower-order harmonics. To further test the model, the prediction-coefficients derived from all 121 subjects were then used to make predictions for the first 50 x- and y-harmonics for a subgroup of 10 independent test subjects. Once again, Pearson's correlations between the actual and predicted harmonics of the test model in the lower-order harmonics revealed strong associations. Superimposition of the actual and predicted soft-tissue outlines, however, revealed that much actual detail in the region between the nose and the chin was still lost using the predicted Fourier harmonics. This suggests that soft-tissue prediction based on this Fourier test model, while already useful in Forensic facial reconstruction, may not yet be appropriate for useful diagnosis and planning in clinical disciplines.

Adolescent↗

PubMed1999

Shelf life of modified atmosphere packed cooked meat products: a predictive model.

The effect of temperature, concentration of dissolved CO2 and water activity on the growth of Lactobacillus sake was investigated by developing predictive models for the lag phase and the maximum specific growth rate of this specific spoilage organism for gas-packed cooked meat products. Two types of predictive model were compared: an extended Ratkowsky model and a response surface model. In general, response surface models showed a slightly better correlation, but the response surface model for the maximum specific growth rate showed illogical predictions at low water activities. The concentration of dissolved CO2 proved to be a significant independent variable for the maximum specific growth rate as well as for the lag phase of L. sake. Synergistic actions on the shelf life-extending effect were noticed between temperature and dissolved CO2, as well as between water activity and dissolved CO2. The developed models were validated by comparison with the existing model of Kant-Muermans et al. (1997) and by means of experiments in gas-packed cooked meat products. Both developed models proved to be useful in the prediction of the microbial shelf life of gas-packed cooked meat products.

Animals↗

PubMed2001

The potential of prediction models based on data from KIGS as tools to measure responsiveness to growth hormone. Pharmacia International Growth Database.

Various prediction models have been developed, based on data documented within KIGS (Pharmacia International Growth Database), for use in the growth hormone (GH) treatment of children with short stature resulting from GH deficiency (GHD) or other causes. In addition to the practical value of such models as part of a 'forward strategy' guiding GH treatment, we now propose that prediction models may also be useful for the identification of individual variance in responsiveness. In a comparison involving 1,800 children with idiopathic GHD (IGHD), 151 children who acquired GHD after treatment for medulloblastoma and 192 children with GHD accompanying craniopharyngioma, it was shown that the responsiveness to GH of patients with craniopharyngioma equalled that of IGHD patients, whereas patients with medulloblastoma were less responsive. These observations and the identification of 'good' and 'poor' responders to GH have practical clinical consequences (e.g. modification of treatment), and will, in the future, lead to the identification of those factors which determine the variability of sensitivity to GH. This will improve the efficacy and safety of GH treatment as well as reducing the costs involved.

Databases as Topic↗

PubMed1986

Oxygen concentration gradient across the ovarian follicular epithelium: model, predictions and implications.

A mathematical model has been devised for predicting the oxygen concentration gradient across the epithelium of ovarian follicles at pre-antral stages. Most dissolved oxygen entering the follicle by diffusion is evidently consumed in the outer layer of cells; little reaches the oocyte. Even when the rate of consumption or the diffusion coefficient of oxygen was adjusted by an order of magnitude to favour oxygen penetration, the concentration gradient into the follicle remained steep. On the basis of measurements of ovine granulosa cell respiration in vitro, the model predicts that a large pre-antral follicle with a radius of 0.15 mm consumes oxygen at the rate of 0.22 nmol min-1.

Animals↗

PubMed2026

Construction of a new predictive model in head and neck squamous cell carcinoma based on the investigation of extracellular matrix-associated genes.

A key aspect influencing immune cell infiltration is the composition of the extracellular matrix (ECM). Therefore, investigating the association between ECM-associated proteins and immune cell infiltration is key for the identification of new biomarkers to distinguish 'immune-hot' solid tumors and predict patient prognosis. A total of 513 head and neck squamous cell carcinoma (HNSCC) cases as training samples from The Cancer Genome Atlas and an additional 270 as testing samples from the Gene Expression Omnibus were obtained for use in the present study. Using a single-sample Gene Set Enrichment Analysis method, the 513 training samples were divided into Cluster 1 and Cluster 2. Subsequently, the present analysis uncovered 1,573 differentially expressed genes distinguishing the two clusters. After performing an intersection analysis with 751 ECM-associated genes, 103 differentially expressed ECM-associated genes were identified. Least absolute shrinkage and selection operator-Cox and multivariate Cox regression analyses were employed to identify candidate ECM risk genes (P<0.05) and to construct a predictive model. Finally, a nomogram and a three gene (cerebellin 2, galectin-10 and cathepsin G) predictive model were developed. Therefore, the present prognostic risk score model can evaluate the immune infiltration, predict the prognosis of HNSCC, and potentially guide more personalized immunotherapy interventions.

extracellular matrix↗

PubMed2002

Risk assessment in localized primary cutaneous melanoma: a Southwest Oncology Group study evaluating nine factors and a test of the Clark logistic regression prediction model.

We studied 9 clinical and pathologic factors in 259 patients using Cox model regression analysis to determine which factors have independent predictive value. Median follow-up time in all patients still alive was 12.3 years (range, 1.7 to 16.7 years). Tumor-infiltrating lymphocytes (P = .005), primary site (P = .006), and thickness (P = .02) had independent predictive value. Ulceration (P = .06) and age (P = .07) had marginal value. We used 6 of those factors to test the Clark logistic regression prediction model, which accurately predicted 8-year survival in 121 (72.9%) of 166 patients and accurately predicted melanoma-specific mortality in 32 (43%) of 74 patients. The combined or overall accuracy of the Clark model was only 64%.

Female↗

PubMed2002

Comparison of predictive models for postoperative nausea and vomiting.

BACKGROUND: In order to identify patients who would benefit from prophylactic amtiemetics, six predictive models have been described for the risk assessment of postoperative nausea and vomiting (PONV). This study compared the validity and practicability of these models in patients undergoing general anaesthesia. METHODS: Data were analysed from 1566 patients who underwent balanced anaesthesia without prophylactic antiemetic treatment for various types of surgery. A systematic literature search identified six predictive models for PONV. These models were compared with respect to validity (discriminating power and calibration characteristics) and practicability. Discriminating power was measured by the area under the receiver operating characteristic curve (AUC) and calibration was assessed by weighted linear regression analysis between predicted and actual incidences of PONV. Practicability was assessed according to the number of factors to be considered for the model (the fewer factors the better), and whether the score could be used in combination with a previously applied cost-effective concept. RESULTS: The incidence of PONV was 600/1566 (38.1%). The discriminating power (AUC) obtained by the models (named according to the first author) using the risk classes from the recommended prophylactic concept were as follows: Apfel, 0.68; Koivuranta, 0.66; Sinclair, 0.66; Palazzo, 0.63; Gan, 0.61; Scholz, 0.61. For four models, the following calibration curves (expressed as the slope and the offset) were plotted: Apfel, y=0.82x+0.01, r2=0.995; Koivuranta, y=1.13x-0.10, r2=0.999; Sinclair, y=0.49x+0.29, r2=0.789; Palazzo, y=0.30x+0.30, r2=0.763. The numbers of parameters to be considered were as follows: Apfel, 4; Koivuranta, 5; Palazzo, 5; Scholz, 9; Sinclair, 12; Gan, 14. CONCLUSION: The simplified risk scores provided better discrimination and calibration properties compared with the more complex risk scores. Therefore, simplified risk scores can be recommended for antiemetic strategies in clinical practice as well as for group comparisons in randomized controlled antiemetic trials.

Adult↗

PubMed1995

Using prediction models and cost-effectiveness analysis to improve clinical decisions: emergency department patients with acute chest pain.

Prediction models and cost-effectiveness analysis are two of the methodologies included in the broad definition of outcomes research. These methodologies are designed to improve physicians' abilities to identify clinical risks and to choose appropriate management strategies based on these risks. For the evaluation and management of patients with acute chest pain, prediction models have markedly improved our ability to estimate risk, and cost-effectiveness analyses have helped guide the development of new paradigms and the incorporation of new technologies. In the past decade, the management of patients who come to emergency departments with acute chest pain has fundamentally changed, with far fewer patients being admitted to coronary intensive care units and an increasing majority being admitted to nonintensive, observation units for shorter and shorter periods of time. These changes in management approaches actually allow more patients to be admitted, hence reducing the risk of inappropriate discharge, while still reducing the utilization of resources.

Chest Pain↗

PubMed1996

A predictive model to estimate the risk of serious bacterial infections in febrile infants.

UNLABELLED: Low risk criteria have been defined to identify febrile infants unlikely to have serious bacterial infection (SBI). Using these criteria approximately 40% of all febrile infants can be defined as being at low risk. Of the remaining infants (60%) only 10%-20% have an SBI. No adequate criteria exist to identify these infants. All infants aged 2 weeks-1 year, presenting during a 1-year-period with rectal temperature > or = 38.0 degrees C to the Sophia Children's Hospital were included in a prospective study. Infants with a history of prematurity, perinatal complications, known underlying disease, antibiotic treatment or vaccination during the preceding 48 h were excluded. Clinical and laboratory variables at presentation were evaluated by a multivariate logistic regression model using SBI as the dependent variable. By using likelihood ratios a predictive model was derived, providing a post test probability of SBI for every individual patient. Of the 138 infants included in the study, 33 (24%) had SBI. Logistic regression analysis defined C-reactive protein (CRP), duration of fever, standardized clinical impression score, a history of diarrhoea and focal signs of infection as independent predictors of SBI. CONCLUSION: CRP, duration of fever, the "standardized clinical impression score", a history of diarrhoea and focal signs of infection were the independent, most powerful predictors of SBI in febrile infants, identified by logistic regression analysis. Although the predictive model is not validated for direct clinical use, it illustrates the clinical potential of the used technique. This technique offers the advantage of assess the probability of SBI in every individual infant. This probability will form the best basis for well-founded decisions in the management of the individual febrile infant.

Bacterial Infections↗

PubMed2025

Proteomic signatures and predictive modeling of cadmium-associated anxiety in middle-aged and elderly populations: an environmental exposure association study.

BACKGROUND: Emerging evidence implicates environmental contaminants such as cadmium (Cd) as modifiable risk factors for anxiety. Despite growing recognition of heavy metal toxicity in neuropsychiatric disorders, the molecular mechanisms linking environmental exposure to anxiety pathogenesis remain poorly understood. METHODS: Based on the established cohort of individuals with cognitive impairment in cadmium-contaminated areas, this cross-sectional association study enrolled 50 middle-aged and elderly hospitalized patients from these regions, adhering to the STROBE guidelines. Blood concentrations of cadmium (Cd), lead (Pb), and mercury (Hg) were analyzed in relation to anxiety severity assessed via the Hamilton Anxiety Rating Scale (HAMA). Plasma proteomic profiling was performed using data-independent acquisition (DIA) quantitative technology with an LC-MS/MS platform (timsTOF Pro, Bruker Daltonics), systematically characterizing 2,531 proteins across all samples. Machine learning techniques, specifically XGBoost and LASSO, were employed to identify biomarkers that were subsequently validated through mediation analysis and animal experiments, allowing for the screening of key protein signatures. Finally, clinical variables were integrated to construct a comprehensive model, which was then thoroughly evaluated. RESULTS: Anxious individuals exhibited significantly higher blood Cd levels than controls (β = 0.50, 95% CI: 0.07-0.93, p < 0.01), with anxiety positively correlating with depression (r = 0.62, p = 0.003) and inversely with ApoE3 genotype prevalence. Proteomics identified 120 differentially expressed proteins in anxious patients, enriched in oxidative phosphorylation and neurodegenerative pathways. CCDC126 emerged as a cadmium-associated biomarker, validated in rat models exposed to Cd. Combining CCDC126, blood Cd, Pb, and hypertension, a clinical prediction model achieved robust discrimination (AUC = 0.80, validation cohort). CONCLUSIONS: This first integrative environmental-proteomic study highlights cadmium's synergistic role in anxiety pathophysiology and psychiatric comorbidity. The predictive model offers translatable potential for early risk stratification, while CCDC126 provides mechanistic insights for targeted interventions in populations exposed to environmental pollutants.

Cadmium↗

PubMed1995

Predictive Models of Lumbar Loadings When Handling Boxes.

Back problems resulting from the compression forces on the intervertebral disks during manual material-handling tasks are an important problem affecting workers in various industries. The quantification of these forces using intradiscal pressure or biomechanical modeling is complex, time consuming, and costly, and these methods cannot be readily used in the workplace to estimate loadings on the lower back. The objective of this study was to develop a predictive model that would allow the estimation of lumbar loadings for lifting and lowering boxes using easily measured anthropometric variables and variables related to the task. A dynamic and planar segmental model and a model of internal forces at L5/S1 were used to determine the compression forces on the lower back. Two predictive models, a field model and a laboratory model, were developed to estimate the compression forces when lifting or lowering 3.3 kg to 22.0 kg boxes between heights of 15 cm and 185 cm. Both models were validated by an examination of the residuals. Their predictive performance was also compared, with the laboratory model offering a slightly better prediction than the field model. Thus, these equations represent a practical tool for a better planning of handling tasks in the working environment with the purpose of reducing the back injuries of workers.

biomechanics↗

PubMed2000

A predictive model for neutropenia associated with cancer chemotherapy.

Studies of primary prophylaxis of febrile neutropenia (FN) with recombinant human granulocyte colony-stimulating factor (rHu-G-CSF, filgrastim) administered to all patients starting their initial course of chemotherapy have demonstrated clinical effectiveness and an economic advantage in a wide range of settings. A recent meta-analysis confirmed the ability of filgrastim to reduce the risk of FN and documented infection in a variety of malignancies in both adults and children. The threshold risk for FN at which a cost saving is achieved by using filgrastim is inversely related to the daily cost of the drug and duration of hospitalization. Clinical practice guidelines for the use of filgrastim were developed based on these observations. Recent studies incorporating indirect institutional costs demonstrated that a cost saving can be achieved at substantially lower FN risk thresholds than previously estimated. Despite the demonstrated efficacy of filgrastim in primary prophylaxis, its value may be further increased by appropriately selecting patients and better understanding the importance of sustaining dose intensity in specific malignancies. Clinical prediction models capable of identifying individuals at high risk for neutropenic complications yield further reductions in FN risk thresholds and treatment costs in patients receiving cancer chemotherapy. These models also may be used to evaluate the cost-effectiveness or cost-efficiency of filgrastim. A clinical prediction model recently was presented and validated incorporating both baseline clinical characteristics as well as the results of the first cycle of chemotherapy in patients with early-stage breast cancer. A cost-effectiveness ratio of $34,297/year of life saved was estimated based on dose-response assumptions derived from a previously reported adjuvant breast cancer trial studying the impact of dose reduction on disease-free survival. The cost-effectiveness of filgrastim was evident over a wide range of clinical and cost assumptions. Clinical prediction models permit the rational and cost-effective identification of patients for filgrastim support. Existing clinical practice guidelines should be reevaluated in light of new information available on both the total costs associated with FN as well as the cost-effectiveness of these agents in patients receiving chemotherapy for sensitive and potentially curable malignancies.

Antineoplastic Agents↗

PubMed1993

Predicting survival from in-hospital CPR: meta-analysis and validation of a prediction model.

OBJECTIVE: To better clarify patient factors that predict survival from in-hospital cardiopulmonary resuscitation (CPR), using two methods: 1) meta-analysis and 2) validation of a prediction model, the pre-arrest morbidity (PAM) index. DESIGN: Meta-analysis of previously published studies by standard techniques. Retrospective chart review of validation sample. SETTING: University-affiliated teaching hospital. PATIENTS/PARTICIPANTS: Meta-analytic sample of 21 previous studies from 1965-1989. The validation sample consisted of all patients surviving resuscitation from the authors' hospital during the period September 1986 to January 1991. A matched sample of patients who did not survive from the same time period was used as the comparison group. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The strongest negative predictors of survival, by meta-analysis, were renal failure (r = 0.088, p < 0.0002), cancer (r = 0.08, p < 0.0002), and age more than 60 years (r = 0.063, p < 0.006). Sepsis (r = 0.046, p < 0.02), recent cerebrovascular accident (CVA) (r = 0.038, p < 0.04), and congestive heart failure (CHF) class III/IV (r = 0.036, p < 0.05) were weaker negative predictors. Presence of acute myocardial infarction (AMI) was a significant positive predictor of survival (r = 0.15, p < 0.0001). The PAM score was highly predictive of survival in a logistic regression model (p < 0.0003, R2 = 9.6%). No patient who survived to discharge had a PAM score higher than 8. CONCLUSION: Meta-analysis reveals that the most significant negative predictors of survival from CPR are renal failure, cancer, and age more than 60 years, while AMI is a significant positive predictor. The PAM index is a useful method of stratifying probability of survival from CPR, especially for those patients with high PAM scores, who have essentially no chance of survival.

Age Factors↗

PubMed1996

Prognostic factors and a predictive model of follicular lymphoma: a 25-year study at a single institution in Japan.

The incidence of follicular lymphoma in Japan is far lower than that in western countries, and no large-scale clinicopathologic studies on this neoplasm have been conducted in Japan. We reviewed histopathological specimens from 118 of 135 patients who had been diagnosed as having follicular lymphoma between 1968 and 1993. Prognostic factors influencing survival were analyzed using univariate and multivariate analyses. Factors that were independently significant upon multivariate analysis were incorporated into a predictive model. Ninety-three patients (78.8%) had a confirmed diagnosis of follicular lymphoma. Twenty-one of the remaining 25 patients were categorized as having other lymphoma subtypes, and four patients showed indefinite findings or those suggesting diseases other than lymphoma. Major characteristics of the 93 patients with follicular lymphoma were a median age of 53 years (20-85); 59 males (63%) and 34 females (37%); small cleaved cell type in 33 (35%), mixed cell type in 41 (44%) and large cell type in 19 (20%); stage I/II in 41 (44%) and stage III/IV in 50 (54%). Overall survival was 71% at 5 years, 58% at 10 years, and 43% at 15 years with a median survival of 13.3 years. Multivariate analysis revealed that two variables, age (>60) (P=0.001) and the serum LDH level (>1 x normal value) (P=0.026), were unfavorably significant prognostic factors influencing survival. The predictive model using these two variables identified three risk groups with estimated five-year survival rates of 88.5%, 56.8%, and 31.5%. Age and serum LDH were significant predictors of survival in Japanese patients with follicular lymphoma. Our predictive model may provide a basis for future therapeutic trials against follicular lymphoma in Japan.

Adult↗

PubMed1993

Development of predictive models of laboratory animal growth using artificial neural networks.

Traditional regression analysis of body weight growth curves encounters problems when the data are extremely variable. While transformations are often employed to meet the criteria of the analysis, some transformations are inadequate for normalizing the data. Regression analysis also requires presuppositions regarding the model to be fit and the techniques to be used in the analysis. An alternative approach using artificial neural networks is presented which may be suitable for developing predictive models of growth. Neural networks are simulators of the processes that occur in the biological brain during the learning process. They are trained on the data, developing the necessary algorithms within their internal architecture, and produce a predictive model based on the learned facts. A dataset of Sprague-Dawley rat (Rattus norvegicus) weights is analyzed by both traditional regression analysis and neural network training. Predictions of body weight are made from both models. While both methods produce models that adequately predict the body weights, the neural network model is superior in that it combines accuracy and precision, being less influenced by longitudinal variability in the data. Thus, the neural network provides another tool for researchers to analyze growth curve data.

Algorithms↗

PubMed2002

Comparison of prediction models for adverse outcome in pediatric meningococcal disease using artificial neural network and logistic regression analyses.

The objective of this study was to compare artificial neural network (ANN) and multivariable logistic regression analyses for prediction modeling of adverse outcome in pediatric meningococcal disease. We analyzed a previously constructed database of children younger than 20 years of age with meningococcal disease at four pediatric referral hospitals from 1985-1996. Patients were randomly divided into derivation and validation datasets. Adverse outcome was defined as death or limb amputation. ANN and multivariable logistic regression models were developed using the derivation set, and were tested on the validation set. Eight variables associated with adverse outcome in previous studies of meningococcal disease were considered in both the ANN and logistic regression analyses. Accuracies of these models were then compared. There were 381 patients with meningococcal disease in the database, of whom 50 had adverse outcomes. When applied to the validation data set, the sensitivities for both the ANN and logistic regressions models were 75% and the specificities were both 91%. There were no significant differences in any of the performance parameters between the two models. ANN analysis is an effective tool for developing prediction models for adverse outcome of meningococcal disease in children, and has similar accuracy as logistic regression modeling. With larger, more complete databases, and with advanced ANN algorithms, this technology may become increasingly useful for real-time prediction of patient outcome.

Adolescent↗

PubMed2001

Z score prediction model for assessment of bone mineral content in pediatric diseases.

The objective of this study was to develop an anthropometry-based prediction model for the assessment of bone mineral content (BMC) in children. Dual-energy X-ray absorptiometry (DXA) was used to measure whole-body BMC in a heterogeneous cohort of 982 healthy children, aged 5-18 years, from three ethnic groups (407 European- American [EA], 285 black, and 290 Mexican-American [MA]). The best model was based on log transformations of BMC and height, adjusted for age, gender, and ethnicity. The mean +/- SD for the measured/predicted in ratio was 1.000 +/- 0.017 for the calibration population. The model was verified in a second independent group of 588 healthy children (measured/predicted In ratio = 1.000 +/- 0.018). For clinical use, the ratio values were converted to a standardized Z score scale. The whole-body BMC status of 106 children with various diseases (42 cystic fibrosis [CF], 29 juvenile dermatomyositis [JDM], 15 liver disease [LD], 6 Rett syndrome [RS], and 14 human immunodeficiency virus [HIV]) was evaluated. Thirty-nine patients had Z scores less than -1.5, which suggest low bone mineral mass. Furthermore, 22 of these patients had severe abnormalities as indicated by Z scores less than -2.5. These preliminary findings indicate that the prediction model should prove useful in determining potential bone mineral deficits in individual pediatric patients.

Absorptiometry, Photon↗

PubMed1998

Delirium: comparison of four predictive models in hospitalized critically ill elderly patients.

Delirium, a cognitive and behavioral disorder affecting more than one third of all hospitalized elderly patients, is often misdiagnosed or unrecognized by caregivers, leading to higher patient morbidity and mortality rates. Prediction of the disorder, based on known predisposing and precipitating risk factors, can be used to target susceptible patients for prevention and early intervention. Predictive models need to be evaluated for clinical application and predictive value. Therefore, in this study, four predictive models were applied on a case-by-case basis to an elderly sample of 10 delirious patients and 10 nondelirious patients to determine sensitivity, specificity, and predictive values in a critical care setting. Results indicated six individual significant variables in these models: age, infection, dementia, blood urea nitrogen-to-creatinine ratio, severe illness, and comorbidity. A final multivariate model, derived from all variables, exhibited a sensitivity of 100% and specificity of 90% in predicting delirium in this study. Further studies are needed to substantiate these results. Then, identified risk factors can be incorporated into delirium prevention protocols for use by nurses at the bedside.

Aged↗