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At least 181 records · Page 10Linked to original sources
PubMed2026

Lymphangiogenesis-related gene signature-based risk model for prognostic assessment of cervical cancer: immune-metabolic characterization and molecular subtype analysis.

BACKGROUND: Lymphangiogenesis promotes tumor dissemination and may shape the immune contexture of cervical cancer, yet lymphangiogenesis-related prognostic stratification and its immunometabolic implications remain insufficiently defined in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). METHODS: TCGA-CESC transcriptomes and clinical data were obtained from UCSC Xena and integrated with normal cervix tissues from the Genotype-Tissue Expression Project after batch correction. Prognostic LYMRGs were first identified from the differentially expressed set using univariable Cox proportional hazards analysis. Candidate genes were then reduced using an L1-regularized Cox model (Least Absolute Shrinkage and Selection Operator), and the remaining markers were entered into a multivariable Cox regression to obtain the final coefficients and compute an individualized risk score. The model's prognostic value was further assessed in an independent Gene Expression Omnibus dataset. In addition, expression patterns of the signature genes were leveraged for molecular subtyping of TCGA samples via non-negative matrix factorization (NMF). Immune infiltration and immunotherapy-associated characteristics were interrogated through a multi-algorithm strategy (single-sample gene set enrichment analysis, CIBERSORT, ESTIMATE, Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore . Additional analyses included pathway enrichment (GSEA/GO/KEGG), drug sensitivity prediction (pRRophetic/CellMiner), and ceRNA network analysis. RESULTS: A six-gene LYMRG signature robustly stratified survival. High-risk patients had significantly worse overall survival in The Cancer Genome Atlas with AUCs of 0.819/0.801/0.801 at 1/3/5 years, and in GSE52903 (P = 0.001) with AUCs of 0.733/0.719/0.725. NMF identified two subtypes with distinct prognosis (P = 0.01) and divergent immune landscapes. Risk groups and subtypes exhibited consistent differences in immune infiltration, checkpoint expression, TIDE/IPS patterns, and pathway enrichment. Predicted chemosensitivity differed by risk group, and the ceRNA network suggested candidate upstream lncRNA regulators of the signature. CONCLUSION: A lymphangiogenesis-related six-gene model enables clinically meaningful prognostic stratification of CESC and links lymphangiogenesis programs to distinct tumor immune phenotypes and therapeutic vulnerabilities.

cancer↗
PubMed1996

Risk modeling in acute renal failure requiring dialysis: the introduction of a new model.

Predicting patient outcome in acute renal failure has become increasingly important as technology advances and ethical questions arise concerning life supporting therapies. We propose a new model which uses mortality as an endpoint and may be applied to the acute renal failure patient in the ICU setting who requires dialysis. This model is based on our ICU acute renal failure registry and has been prospectively validated for our institution. Our registry for the purposes of developing this model consists of data from 512 ICU patients requiring acute dialysis from 1988 until 1992. The model was developed by testing a variety of potential risk factors for mortality in a univariate analysis (Student's t-test and Chi square), and those factors found to be significant (p < 0.05) were subsequently tested in a multivariate fashion. The factors found significant included male gender, respiratory failure requiring intubation, hematologic dysfunction (platelet count < 50,000, leukocyte count < 2,500, or bleeding diathesis), bilirubin < 2.0 mg/dl, the absence of surgery, serum creatinine on the first dialysis treatment day, an increasing number of failed organ systems, and an increased BUN from the time of admission. Weights are assigned to each variable based on the odds ratio, and a score is generated with a range of 0 to 20. The initial data for the registry demonstrates good fit using the Hosmer and Lemeshow goodness-of-fit table. The model is next validated in 88 patients from 1993 through February 1994, then prospectively tested in 35 additional patients using a standard data collection form, and the model continues to demonstrate good fit. Although this model has been prospectively validated at our institution, this model or any such predictive model should be used with caution if not independently validated at any institution which proposes its use.

Acute Kidney Injury↗
PubMed1979

A note on Wong's competing risk model.

It is suggested that a method proposed recently by Wong contains an unrealistic assumption. This can be avoided by making an alternative assumption which leads to a slight alteration to one of his equations.

Humans↗
PubMed2003

Predicting outcome in ischemic stroke: external validation of predictive risk models.

BACKGROUND: Six multivariable models predicting 3-month outcome of acute ischemic stroke have been developed and internally validated previously. The purpose of this study was to externally validate the previous models in an independent data set. SUMMARY OF REPORT: We predicted outcomes for 299 patients with ischemic stroke who received placebo in the National Institute of Neurological Disorders and Stroke rt-PA trial. The model equations used 6 acute clinical variables and head CT infarct volume at 1 week as independent variables and 3-month National Institutes of Health Stroke Scale, Barthel Index, and Glasgow Outcome Scale as dependent variables. Previously developed model equations were used to forecast excellent and devastating outcome for subjects in the placebo tissue plasminogen activator data set. Area under the receiver operator characteristic curve was used to measure discrimination, and calibration charts were used to measure calibration. The validation data set patients were more severely ill (National Institutes of Health Stroke Scale and infarct volume) than the model development subjects. Area under the receiver operator characteristic curves demonstrated remarkably little degradation in the validation data set and ranged from 0.75 to 0.89. Calibration curves showed fair to good calibration. CONCLUSIONS: Our models have demonstrated excellent discrimination and acceptable calibration in an external data set. Development and validation of improved models using variables that are all available acutely are necessary.

Aged↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗
PubMed2005

Asymmetrical dimethylarginine predicts progression to dialysis and death in patients with chronic kidney disease: a competing risks modeling approach.

High plasma asymmetrical dimethylarginine (ADMA) signals endothelial dysfunction and atherosclerosis in the general population and predicts mortality in ESRD. The relationship among plasma levels of ADMA, renal function, and the risk for progression to ESRD (halving GFR or dialysis start) and death in an incident cohort of 131 patients with chronic kidney disease was investigated. Cox's competing risk regression was used to model double-failure times (progression to ESRD and death) as a function of ADMA. Covariates that were considered for adjustment included clinical characteristics, baseline GFR (Modification of Diet in Renal Disease equation 7 formula), proteinuria, traditional cardiovascular risk factors, serum C-reactive protein, homocysteine, and concomitant therapies. Mean age at enrollment was 71 +/- 11 yr, and 24% of patients had diabetes. Baseline GFR ranged from 8 to 77 ml/min per 1.73 m2 (average 31 +/- 15 ml/min per 1.73 m2). ADMA was inversely related to GFR, ranking as the third predicting factor (partial r = -0.22, P = 0.01), after hemoglobin and urinary protein, in a general linear model that included multiple correlates of GFR. After a mean follow-up of 27 mo (range 3.4 to 36), 29 patients progressed to ESRD and 31 died. ADMA (hazard ratio per 0.1 muM/L 1.203; 95% confidence interval 1.071 to 1.350) predicted event occurrence independent of other potential confounders, including GFR, proteinuria, hemoglobin, and homocysteine. In patients with mild to advanced chronic kidney disease, plasma ADMA is inversely related to GFR and represents a strong and independent risk marker for progression to ESRD and mortality. These novel findings further expand the implications of previous observations in ESRD patients and generate hypotheses on the role of ADMA in progressive chronic nephropathies.

Aged↗
PubMed2001

A competing risk model of sudden infant death syndrome incidence in two US birth cohorts.

OBJECTIVES: To compare changing incidence and changing risk factors associated with sudden infant death syndrome (SIDS) in the 1989 and 1996 US birth cohorts. STUDY DESIGN: All available singleton births over 500 g from the 1989 linked birth-infant death file and the 1996 and 1997 Perinatal Mortality files were examined. A log-logistic survival model was used to explicitly account for declining competing risks among low birth weight infants. RESULTS: Controlling for maternal prenatal smoking and other confounders, SIDS incidence declined by >33% between the 2 survey years (adjusted odds ratio = 0.628 with 95% CI [0.598, 0.660]). Self-reported declines in maternal prenatal smoking were also associated with significant declines in SIDS incidence. African American infants and infants born weighing <1000 g experienced increased relative risk compared with non-Hispanic white infants born weighing >2500 g. Hispanic/Latino infants had significantly lower SIDS risk than non-Hispanic white infants in both years. Accounting for declining competing risks and other factors, relative SIDS risks among infants born between 500 and 1000 g increased over the study period. CONCLUSIONS: SIDS incidence sharply declined between 1989 and 1996. High incidence of SIDS in African Americans and increased relative SIDS risk for infants born weighing <1000 g require increased attention from clinicians and public health policy makers.

Birth Weight↗
PubMedDate not supplied

Testing a life-style risk model in an Afro-American population.

This article discusses the results of a research study investigating the possible relationship between life-style risk factors, e.g., weight, dietary patterns, stress, activity and smoking in the development of diseases. The author concludes that there is a need for individuals to improve weight, dietary patterns, reduce stress and increase recreational activity to reduce life-style risk.

Adult↗
PubMed2006

Modelling risks in disease mapping.

In this article, we propose a strategy of analysis of mortality data with the aim of providing a guideline for epidemiologists and public health researchers to choose a reasonable model for estimating mortality (or incidence) risks. Maps displaying the crude mortality rates or ratios are usually misleading because of the instability of the estimators in low populated areas. As an alternative, many smoothing methods have been presented in the literature based on Poisson inference. They account for the extra-Poisson variation (overdispersion), frequently present in the homogeneous Poisson model, by incorporating random effects. Here, we recommend to test for the potential sources of extra-Poisson variation because, depending on them, the models which fit better the data may be different. Overdispersion can be mainly due to spatial autocorrelation, unstructured heterogeneity or to a combination of these two, and also, when studying very rare diseases, it can be due to an excess of zeros in the data. In this article, different situations the analyst may encounter are detailed and appropriate procedures for each case are presented. The alternative models are illustrated using mortality data provided by the Statistical Institute of Navarra, Spain.

Cardiovascular Diseases↗
PubMed1986

A multivariate risk model for childhood behavior problems.

In a study of 306 children whose parents have a mental illness, the relationship between coping skills and environmental assets and deficits determined the degree of behavior problems among these high-risk children. Two environmental stressors (proportion of mentally ill family members and mother-child discord), two coping skills (activity competence and school competence), and an interaction (between proportion of mentally ill family members and activity competence) were found to explain 40% of the variance in child behavior problems.

Adaptation, Psychological↗
PubMed2003

Do traditional Gail model risk factors account for increased breast cancer in women with lupus?

OBJECTIVE: To determine to what extent the observed experience of breast cancer in a combined cohort of patients with systemic lupus erythematosus (SLE) could be explained by the profile of breast cancer risk factors. METHODS: Data were pooled from 2 centers, the Montreal General Hospital and the Feinberg School of Medicine at Northwestern University in Chicago. For each female cohort member, the probability of developing breast cancer during followup was estimated based on factors (including the individual's age, parity, age at first live birth, age of menarche, personal history of benign breast disease, and family history) using the Gail model, an established model for predicting breast cancer risk. The actual occurrence of cancer cases was determined by linkage with regional cancer registries. RESULTS: Of the 583 women in the combined cohort, 5 had been diagnosed with breast cancer prior to cohort entry, and 14 declined participation. In those remaining, 12 cases of breast cancer occurred compared to 5.6 predicted by the Gail model (standardized incidence ratio 2.1, 95% confidence interval: 1.1, 3.7). Thus, after controlling for risk factors, the incidence of breast cancer was elevated. CONCLUSION: Our data suggest that the risk of breast cancer in our SLE cohort is not completely explained by traditional factors found in the Gail model. Other factors, such as carcinogenic exposures (i.e., alkylating agents and immunosuppressive drugs) or the immunologic dysregulation of SLE itself, may be contributory.

Age Factors↗
PubMed2003

Risk modeling, assessment, and management of lahar flow threat.

The 1991 eruption of Mount Pinatubo in the Philippines is considered one of the most violent and destructive volcanic activities in the 20th century. Lahar is the Indonesian term for volcanic ash, and lahar flows resulting from the massive amount of volcanic materials deposited on the mountain's slope posed continued post-eruption threats to the surrounding areas, destroying lives, homes, agricultural products, and infrastructures. Risks of lahar flows were identified immediately after the eruption, with scientific data provided by the Philippine Institute of Volcanology, the U.S. Geological Survey, and other research institutions. However, competing political, economic, and social agendas subordinated the importance of scientific information to policy making. Using systemic risk analysis and management, this article addresses the issues of multiple objectives and the effective integration of scientific techniques into the decision-making process. It provides a modeling framework for identifying, prioritizing, and evaluating policies for managing risk. The major considerations are: (1) applying a holistic approach to risk analysis through hierarchical holographic modeling, (2) applying statistical methods to gain insight into the problem of uncertainty in risk assessment, (3) using multiobjective trade-off analysis to address the issue of multiple decisionmakers and stakeholders in the decision-making process, (4) using the conditional expected value of extreme events to complement and supplement the expected value in quantifying risk, and (5) assessing the impacts of multistage decisions. Numerical examples based on ex post data are formulated to illustrate applications to various problems. The resulting framework from this study can serve as a general baseline model for assessing and managing risks of natural disasters, which the Philippines' lead agency-the National Disaster Coordinating Council (NDCC)-and other related organizations can use for their decision-making processes.

Journal Article↗
PubMedDate not supplied

Risk modeling in breast cancer.

Women with BRCA-1 or BRCA-2 mutations have a significantly increased risk for breast cancer. While genetic testing can provide valuable information concerning this increased risk and the proposed benefit of risk-reducing interventions, the absolute breast cancer risk conferred by BRCA-1 and BRCA-2 mutations remains to be fully determined. The American Society of Clinical Oncology (ASCO) guidelines indicate that testing should be considered in women whose mutation probability is greater than 10%. None of the currently available approaches for determining BRCA gene mutation probability are 100% accurate. The computer program BRCAPRO is a useful adjunct for estimating these probabilities, but limitations inherent in mathematical models make it essential that genetic counselors are involved in decisions to perform genetic testing.

Adult↗
PubMed2005

Racial and ethnic disparities in perinatal mortality: applying the perinatal periods of risk model to identify areas for intervention.

OBJECTIVES: To determine the feto-infant mortality rate for New York City, assess racial/ethnic variations and identify areas for intervention using the Perinatal Periods of Risk (PPOR) approach. METHODS: The PPOR model examines fetal and infant deaths by age at death (fetal, neonatal, postneonatal) and birthweight (500-1499, > or =1500 g). It groups age at death and birthweight into four categories to identify problems hypothesized to lead to the death: factors related to Maternal Health and Prematurity, Maternal Care, Newborn Care and Infant Health. The model was applied to fetal and infant deaths occurring in New York City using Vital Records data from 1996-2000. Analysis was completed for the entire city and by race/ethnicity (white non-Hispanic, black non-Hispanic, Hispanic, Asians/Pacific Islander). RESULTS: The overall feto-infant mortality rate was 11.5/1,000 live births plus fetal deaths. This rate varied by race/ethnicity; black non-Hispanics had a higher rate than other racial/ethnic groups. Conditions related to maternal health and prematurity were the largest contributing factors to feto-infant mortality (5.9/1000) in New York City. Among blacks and Hispanics, problems related to maternal health and prematurity contributed a larger share than among whites and Asians/Pacific Islanders. CONCLUSION: The use of the PPOR approach shows that the racial/ethnic disparities in feto-infant mortality that exist in New York City are largely related to maternal health and prematurity. Interventions to reduce the feto-infant mortality rate should include preconception care and improvements in women's health.

Ethnicity↗