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PubMed2026

The Progress of Gout Prediction Models Based on Multi-source Data.

INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.

Gout↗

PubMed1975

A discounted least squares quadratic growth prediction model for use in 2 year toxicity studies.

Weekly weighings of the laboratory rats are required to determine the correct dosage for mixing in the food. This creates problems in that the food mixing must be done immediately after the weighings and staff are often heavily taxed to perform the task. A discounted least squares growth prediction model allows for prediction of weights a week ahead of time, obviating the necessity for instantaneously processing the weight data. When dosages were prepared based on these predictions, for 10 treatment combinations 100% of the doses proved to be within 8-0% of the required dosage; 98-4% were within 5% of the required dosage; 78-7% were within 2% of the required dosage; and 51-6% were within 1% of the required dosage. The quadratic weight prediction model can also be incorporated into a model for predicting food consumption.

Animal Feed↗

PubMed1992

Development of a predictive model for postoperative pulmonary complications after cholecystectomy.

The purpose of this study was to develop a model to predict the occurrence of a postoperative pulmonary complication (PPC) following cholecystectomy. Seventeen potential risk factors were extracted from the literature by identifying and ranking those most frequently referenced. The study included only those risk factors available to the nurse in the preoperative, intraoperative, and immediate postoperative setting. Three institutions were used for data collection, and data were collected by a retrospective chart review of 300 randomly chosen subjects from a population of 720. Of the 300 subjects, 37 were omitted due to exclusion criteria. A PPC was present in 54 of the remaining 263 subjects (20.5%). Of the original 17 risk factors, 10 were included in model development. The 54 subjects with a PPC and 54 subjects without a PPC (randomly chosen from the remaining 209) were used to determine which combination of risk factors best predicted subject classification (PPC or no PPC). The direct entry discriminant function that provided the highest percentage of correct classification (PPC, no PPC) consisted of five variables: sex, age, smoking history, duration of anesthesia, and nasogastric tube. The resulting equation correctly classified 75% of the cases.

Adult↗

PubMed1988

The use of prediction models for eliminating effects due to regression-to-the-mean in road accident data.

In recent years, various methods have been proposed for estimating the true accident level, i.e. the expected number of accidents m when a total of x accidents have been observed at a junction, road section, etc., during a certain period of time. One such method has been named the Empirical Bayes Method (EB method). A description is given of a variant of the EB method utilizing prediction models for the number of accidents. Input data to the prediction models may consist, for example, of traffic flows in a junction. According to empirical comparisons of accidents in junctions, this variant of the EB method may be preferable in certain cases to the conventional EB method. However, it has not yet been determined how this variant of the EB method should generally take into account the precision of the prediction models. This means, for example, that in a nonexperimental before-and-after study of the effect of a particular action, varying results may be obtained according to the assumptions made concerning the precision of the prediction model.

Accident Prevention↗

PubMed1988

Predictive model of conjugative plasmid transfer in the rhizosphere and phyllosphere.

A computer simulation model was used to predict the dynamics of survival and conjugation of Pseudomonas cepacia (carrying the transmissible recombinant plasmid R388:Tn1721) with a nonrecombinant recipient strain in simple rhizosphere and phyllosphere microcosms. Plasmid transfer rates were derived for a mass action model, and donor and recipient survival were modeled as exponential growth and decay processes or both. Rate parameters were derived from laboratory studies in which donor and recipient strains were incubated in test tubes with a peat-vermiculite solution or on excised radish or bean leaves in petri dishes. The model predicted donor, recipient, and transconjugant populations in hourly time steps. It was tested in a microcosm planted with radish seeds and inoculated with donor and recipient strains and on leaf surfaces of radish and bean plants also growing in microcosms. Bacteria were periodically enumerated on selective media over 7 to 14 days. When donor and recipient populations were 10(6) to 10(8) CFU/g (wet weight) of plant or soil, transconjugant populations of about 10(1) to 10(4) were observed after 1 day. An initial rapid increase and a subsequent decline in numbers of transconjugants in the rhizosphere and on leaf surfaces were correctly predicted.

Computer Simulation↗

PubMed1991

Preliminary development of two predictive models for DNR patients in intensive care.

The purpose of this study was to identify which variables are the best predictors of a do-not-resuscitate (DNR) classification and develop a model to predict the nursing care required by DNR patients in the ICU. Data collected on DNR and non-DNR patients included nursing care requirements, severity of illness, resource allocation and sociodemographic characteristics. One model identified the best predictors of a DNR classification in intensive care as the origin of admission and the severity of illness score on the day of admission to intensive care. The second model identified the best predictors of nursing care requirements for DNR patients in intensive care as the number of days spent in intensive care prior to the DNR order, the average daily resource allocation points after the DNR order, and the severity of illness score on the day the DNR order was designated.

Aged↗

PubMed1991

Development of a predictive model for biodegradability based on BIODEG, the evaluated biodegradation data base.

A file of evaluated biodegradation data was used to develop a model for predicting aerobic biodegradability from chemical structure alone. Chemicals were initially divided into three groups: (i) chemicals that degrade rapidly under most environmental conditions without requiring acclimation; (ii) chemicals that degrade slowly or not at all; and (iii) chemicals that are biodegradable, but only after an acclimation period. Chemicals in the first two groups were then used to develop a model for classifying chemicals as rapidly or not rapidly biodegradable. The model is based on linear regression against 34 preselected substructures, and correctly classifies 92% (211 or 229) of the chemicals in the final training set.

Aerobiosis↗

PubMed1988

Predictive model for plasma concentration-versus-time profiles of investigational anticancer drugs in patients.

We report a model that provides a strong correlation between mouse toxicity data [mouse lethal dose 10% (LD10)] and human plasma concentration-versus-time (CXT) data for 22 commonly used anticancer agents. Mouse toxicity data (LD10) from two dosing schedules, daily times one and daily times seven, were evaluated for the two mouse strains BDF/1 and Swiss. Data from BDF/1 mice were selected for analysis because they were more abundant. Strong correlations were found between LD10 and human plasma CXT data for both daily times one and daily times seven dosing schedules--ln (CXT) = -1.6504 + [0.8408 X ln (LD10)], r = .84, P less than .0001, and ln (CXT) = -0.0754 + [0.8954 X ln (LD10)], r = .90, P less than .0001, respectively. These correlations may serve as useful models to predict the maximally tolerated dose of an investigational anticancer agent prior to entry into clinical trials and to assist in the selection of clinically relevant in vitro CXTs for new-agent screening against human tumors.

Animals↗

PubMed1990

Outcome prediction models on admission in a medical intensive care unit: do they predict individual outcome?

Prospectively acquired data from 941 patients staying greater than 24 h in a medical ICU were analyzed to determine the relevance of scoring on ICU admission by the following methods of outcome prediction: Acute Physiology and Chronic Health Evaluation (APACHE II), Simplified Acute Physiology Score (SAPS), and Mortality Prediction Model (MPM). Analysis was performed separately for all patients (group A) and for a subsample (group B), obtained by excluding coronary care patients. Calculation of risk and classification of patients were carried out as recommended in the literature for MPM, APACHE II, and SAPS. In group A, sensitivities (correct prediction of hospital mortality) were 44.7%, 51.1%, and 21.2% and specificities (correct prediction of survival) were 84.5%, 85.4%, and 96.8%, respectively; overall correct classification rates were 73.3%, 75.8%, and 75.6%. In group B, sensitivities were slightly higher, but total correct classification rates did not reach group A levels. Goodness-of-fit testing showed low levels of fit for all methods in both groups. Application of APACHE II to diagnostic subgroups, using disease-adapted risk calculations, revealed marked inconsistencies between the estimated risk and the observed mortality. We conclude that the estimation of risk on admission by the three methods investigated might be helpful for global comparisons of ICU populations, although the lack of disease specificity reduces their applicability for severity grading of a given illness. The inaccuracy of these methods makes them ineffective for predicting individual outcome; thus, they provide little advantage in clinical decision-making.

Female↗

PubMed1992

Combining logistic regression and neural networks to create predictive models.

Neural networks are being used widely in medicine and other areas to create predictive models from data. The statistical method that most closely parallels neural networks is logistic regression. This paper outlines some ways in which neural networks and logistic regression are similar, shows how a small modification of logistic regression can be used in the training of neural network models, and illustrates the use of this modification for variable selection and predictive model building with neural networks.

Algorithms↗

PubMed1991

Exploration of prediction models for caries risk assessment of the geriatric population.

The purpose of this study was to demonstrate a method for establishing a model designed to predict the caries risk of elderly individuals. Twenty-three patients over the age of 65 and living in a semi-independent retirement center were examined; several variables were collected and analyzed for their ability to predict the development of new carious lesions. The analysis was performed using logistic regression where the proportion of new decay was used as the dependent variable. The model for prediction of combined coronal and root caries included the variables flow rate, gender, and root caries index. The proposed method has the advantages of easily collected data, individualized criteria, and the ability to order patients as to the relative risk of developing decay.

Aged↗

PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans↗

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↗

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↗

PubMed2026

Genomic selection in timothy (Phleum pratense L.): a comprehensive evaluation of prediction models, multi-trait strategies, and forward validation across Norwegian environments.

This study presents a comprehensive evaluation of genomic selection (GS) in timothy (Phleum pratense L.), comparing nine prediction models across yield and quality traits at two Norwegian locations. Forward validation with independent full-sib (FS2) families revealed a substantial generalization gap, highlighting the need for realistic accuracy assessment in polyploid forage breeding. Timothy (Phleum pratense L.) is the most important forage grass in Northern Europe, yet genomic selection has not been systematically evaluated in this hexaploid species. We assessed 889 FS2-families originating from biparental crosses among 49 cultivars/populations. The FS2-families were genotyped with 30,698 SNP markers derived from genotyping-by-sequencing (GBS) and field tested for three harvest years at a highland and a lowland continental location in Southern Norway. Nine genomic prediction models were compared for six yield traits (dry matter yield per cut and total) and six quality traits (protein, digestibility, and fiber fractions) across three cuts/year. Within-training cross-validation accuracies were moderate to high (mean r = 0.62), with Random Forest and SVR consistently outperforming GBLUP. However, forward validation using 213 independent FS2-families revealed dramatically lower accuracies (mean r = 0.16), with only 16 of 30 trait-dataset combinations reaching statistical significance (p < 0.05). Genomic heritabilities (GREML), estimated across environments, ranged from near zero for the quality traits to 0.55 for the yield traits. Multi-trait models improved accuracy by 3-5% over single-trait approaches, while FS2 families-by-environment interaction models with Random Forest achieved the highest within-training accuracy (mean r = 0.71). Marker density analysis showed accuracy plateauing at approximately 15000 SNPs. Genetic correlations among the yield component traits were estimated by multi-trait REML; correlations among the quality traits could not be estimated reliably because their genomic heritabilities were low. A multi-trait selection index identified top-performing FS2-families for further crossing recommendations. These results provide a benchmark for GS implementation in hexaploid timothy and emphasize that cross-validation substantially overestimates prediction accuracy for truly independent material.

Norway↗

PubMed1986

Local recurrence and the EEA stapler--examination of a predictive model.

The overall local recurrence rate following resection of colorectal cancer with restoration of continuity with staples in Wellington was 24%. Nine of 11 patients with local recurrence following resection of rectal tumours had distant metastases at the time of diagnosis of their local recurrence. Using a predictive model to retrospectively estimate the probability of local recurrence it was found that nine of these 11 patients would have been expected to have had a lower local recurrence rate had they undergone abdominoperineal resection of the rectum initially. Since local recurrence is simply a local manifestation of systemic disease in 90% of patients, however, it is suggested that patients would prefer restoration of bowel continuity in preference to rectal excision and stoma formation, there being such little survival advantage for the latter procedure. The utility of the predictive model is therefore questioned.

Adult↗

PubMedDate not supplied

A predictive model for substrates of cytochrome P450-debrisoquine (2D6).

Molecular modeling techniques were used to derive a predictive model for substrates of cytochrome P450 2D6, an isozyme known to metabolize only compounds with one or more basic nitrogen atoms. Sixteen substrates, accounting for 23 metabolic reactions, with a distance of either 5 A ("5-A substrates", e.g., debrisoquine) or 7 A ("7-A substrates", e.g., dextromethorphan) between oxidation site and basic nitrogen atom were fitted into one model by postulating an interaction of the basic nitrogen atom with a negatively charged carboxylate group on the protein. This acidic residue anchors and neutralizes the positively charged basic nitrogen atom of the substrates. In case of "5-A substrates" this interaction probably occurs with the carboxylic oxygen atom nearest to the oxidation site, whereas in the case of "7-A substrates" this interaction takes place at the other oxygen atom. Furthermore, all substrates exhibit a coplanar conformation near the oxidation site and have negative molecular electrostatic potentials (MEPs) in a part of this planar domain approximately 3 A away from the oxidation site. No common features were found in the neighbourhood of the basic nitrogen atom of the substrates studied so that this region of the active site can accommodate a variety of N-substituents. Therefore, the substrate specificity of P450 2D6 most likely is determined by the distance between oxidation site and basic nitrogen atom, by steric constraints near the oxidation site, and by the degree of complementarity between the MEPs of substrate and protein in the planar region adjacent to the oxidation site.(ABSTRACT TRUNCATED AT 250 WORDS)

Astemizole↗