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[The prognostic model--an aid for decision-making in the use of adjuvant cytostatic therapy in breast cancer].

The prognostic model as a help in deciding for the application of the adjuvant cytostatic therapy (ACT) in breast cancer is proposed. Risk factors including size of tumour, status of axial, value of hormonal receptors, grade of differentiation of tumour and age are included in the prognostic model. The clinical study comprises patients with breast cancer in premenopausal age with (30 patients) and without ACT (36). By using a multiple regression method and correlation analysis (multivariate analysis), the "weight"--the values of each risk factor have been determined in relation to the disease free interval and survival are obtained by employing the proposed prognostic model. By simple mathematical methods type of therapy is determined by the prognosis. This is the proposed model: Y = beta 0 + beta 1 x x1 + beta 2 x x2 + ... + beta k x xk. Coefficient values for each risk factor have been obtained by this clinical study. Full attention is not paid to the results of the ACT application in this presentation.

Adult↗

PubMed2000

Prognostic models based on literature and individual patient data in logistic regression analysis.

Prognostic models can be developed with multiple regression analysis of a data set containing individual patient data. Often this data set is relatively small, while previously published studies present results for larger numbers of patients. We describe a method to combine univariable regression results from the medical literature with univariable and multivariable results from the data set containing individual patient data. This 'adaptation method' exploits the generally strong correlation between univariable and multivariable regression coefficients. The method is illustrated with several logistic regression models to predict 30-day mortality in patients with acute myocardial infarction. The regression coefficients showed considerably less variability when estimated with the adaptation method, compared to standard maximum likelihood estimates. Also, model performance, as distinguished in calibration and discrimination, improved clearly when compared to models including shrunk or penalized estimates. We conclude that prognostic models may benefit substantially from explicit incorporation of literature data.

Age Factors↗

PubMed2025

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans↗

PubMed2005

A biochemical prognostic model of outcome in paracetamol-induced acute liver injury.

BACKGROUND: The aim of this study was to develop a prognostic model of outcome for patients with paracetamol induced acute liver injury based on admission parameters METHODS: We used a cohort of 97 patients admitted to the Scottish Liver Transplant Unit between 1997 and 1998 to identify biochemical prognostic markers of outcome and thus create a prognostic model. Blood samples were taken on admission for analysis. The model was subsequently validated by testing it on a second cohort of 86 patients admitted between 1999 and 2000. RESULTS: The following were identified as independent variables of poor prognosis (death/ transplant); phenylalanine, pyruvate, alanine, acetate, calcium, haemoglobin and lactate. A prognostic model was then constructed by stepwise forward logistic regression analysis: (400xPyruvate mmols/L)+(50xPhenylalanine (mmols/L)-(4 x Hemoglobin (g/dL). A value of <16 had an accuracy of 93% in predicting death correctly. When applied to the validation cohort this model had a positive predictive value of 91%, a negative predictive value of 94%, a sensitivity of 91%, and a specificity of 94%. On the same population overall, the positive and negative predictive value of the King's criteria were 94% and 93% respectively, whereas their sensitivity and specificity were 88% and 96% respectively. CONCLUSIONS: Using admission characteristics our model is able to identify patients who die from paracetamol overdose fulminant hepatic failure as accurately as King's College criteria, but at a much earlier stage in their condition.

Acetaminophen↗

PubMed2000

Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets.

Logistic regression analysis may well be used to develop a prognostic model for a dichotomous outcome. Especially when limited data are available, it is difficult to determine an appropriate selection of covariables for inclusion in such models. Also, predictions may be improved by applying some sort of shrinkage in the estimation of regression coefficients. In this study we compare the performance of several selection and shrinkage methods in small data sets of patients with acute myocardial infarction, where we aim to predict 30-day mortality. Selection methods included backward stepwise selection with significance levels alpha of 0.01, 0.05, 0. 157 (the AIC criterion) or 0.50, and the use of qualitative external information on the sign of regression coefficients in the model. Estimation methods included standard maximum likelihood, the use of a linear shrinkage factor, penalized maximum likelihood, the Lasso, or quantitative external information on univariable regression coefficients. We found that stepwise selection with a low alpha (for example, 0.05) led to a relatively poor model performance, when evaluated on independent data. Substantially better performance was obtained with full models with a limited number of important predictors, where regression coefficients were reduced with any of the shrinkage methods. Incorporation of external information for selection and estimation improved the stability and quality of the prognostic models. We therefore recommend shrinkage methods in full models including prespecified predictors and incorporation of external information, when prognostic models are constructed in small data sets.

Age Factors↗

PubMed2026

Machine learning prognostic model and drug survival analysis for lung adenocarcinoma in the context of radiotherapy.

BACKGROUND: Patients with lung adenocarcinoma (LUAD) receiving radiotherapy represent an important but underexplored clinical subgroup. These patients often undergo concomitant pharmacologic treatments, yet the prognostic impact and underlying determinants of such combined regimens remain poorly understood. OBJECTIVE: This retrospective observational study aimed to develop and validate a radiotherapy-specific machine learning prognostic model for LUAD and to compare survival across concomitant pharmacologic regimens. METHODS: In this retrospective observational study, using genomic and clinical data from TCGA, a radiotherapy-specific prognostic model for LUAD was developed and validated through ten machine learning algorithms. Survival analyses were conducted across distinct concomitant pharmacologic strategies, followed by functional enrichment to elucidate molecular mechanisms underlying differential outcomes. RESULTS: Demonstrating robust prognostic abilities, the model efficiently sorted patients into high- and low-risk categories. Both treatment type and risk score independently predicted overall survival, with significant interaction effects. Low-risk patients receiving targeted or combination therapy-mainly erlotinib, gefitinib, or bevacizumab-exhibited substantially improved survival compared with those receiving conventional chemotherapy. Enrichment of "Exogenous peptide presentation," "MHC class II assembly," "Peptide-MHC II assembly," and "Symbiotic interaction" pathways indicated immune modulation and host-tumor crosstalk as key mediators of treatment efficacy. CONCLUSION: This study establishes a radiotherapy-specific prognostic model for lung adenocarcinoma, demonstrating distinct molecular and therapeutic heterogeneity and highlighting the superior survival benefit of targeted combination therapy in low-risk patients.

Humans↗

PubMed2003

Developing a prognostic model in the presence of missing data: an ovarian cancer case study.

When developing prognostic models in medicine, covariate data are often missing and the standard response is to exclude those individuals whose data are incomplete from the analyses. This practice leads to a reduction in the statistical power, and may lead to biased results. We wished to develop a prognostic model for overall survival from 1,189 primary cases (842 deaths) of epithelial ovarian cancer. A complete case analysis restricted the sample size to 518 (380 deaths). After applying a multiple imputation (MI) framework we included three real values for each one imputed, and constructed a model composed of more statistically significant prognostic factors and with increased predictive ability. Missing values can be imputed in cases where the reason for the data being missing is known, particularly where it can be explained by available data. This will increase the power of an analysis and may produce models that are more statistically reliable and applicable within clinical practice.

Adolescent↗

PubMed2006

[Prognostic models for Hodgkin's disease].

A prognostic model for Hodgkin's disease was worked out using the data on disease-free survival among patients receiving 4-8 courses of COOP(MOPP)/ABVD plus (sub)total irradiation. Patients with stage I-II Hodgkin's disease (less then 4 lesions) without large involved mediastinal masses, intoxication symptoms and focal splenic involvement were referred to the favorable prognosis group. The poor prognosis group featured stage III(2)-IV tumor as well as large masses of involved mediastinal tissue, focal splenic involvement at any stage plus 7 or more lesions. An assessment of tumor advancement across lesions is more significant for radiotherapy planning rather than that of organ involvement. It is reasonable to distinguish two substages--III(1) and III(2). Our model was compared with GHSG and it was suggested that ways be found to use both of them in prognosing of disease outcome.

Hodgkin Disease↗

PubMed1995

Construction, validation and updating of a prognostic model for kidney graft survival.

The construction, validation and updating of a prognostic model for kidney graft survival is reported using data from the Eurotransplant database. First, a model is constructed for data from transplantations in the period 1984 to 1987. The model is later updated for the 1988 1990 data. The first data set was randomly split into a training set (two-thirds of the data) and a validation set (one-third). To prevent overfitting empirical Bayes estimation of the transplantation centre effect was employed. After that, the validation set was used for fine-tuning by shrinkage. For updating with the 1988 1990 data parametric models were used after suitable transformation of the time axis; it appeared that survival had slightly improved. This necessitated a correction of the parameters in the exponential model. Correctness of the model was checked by extension to a Weibull model. The lack of fit was statistically significant, but practically ignorable. Recommendations are made to place less emphasis on the selection of variables and cut-off points, and more emphasis on the fine-tuning of the prognostic model by means of low-dimensional parametric models in independent data sets.

Female↗

PubMed2026

Targeting RELA and STAT3 regulates TNFRSF10A-mediated apoptosis in a novel apoptosis-based prognostic model for clear cell renal cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal malignancy and remains a major cause of cancer-related mortality worldwide. Although advances in surgery, targeted therapy, and immunotherapy have improved outcomes for patients, reliable biomarkers for predicting prognosis remain limited. Therefore, robust gene-based prognostic models are urgently needed to improve risk stratification and guide individualized treatment strategies. METHODS: We developed a novel prognostic model integrating apoptosis and immune - related genes (AIRGs) to predict overall survival (OS) in patients with ccRCC. RESULT: Using Gene Set Enrichment Analysis (GSEA) combined with least absolute shrinkage and selection operator (LASSO) Cox regression, we identified 7 key prognostic genes, namely, CCR4, TNFRSF10A, TEK, TGFA, CD14, IFITM1, and SEMA3G, that collectively demonstrated strong predictive performance in TCGA cohort with c-index = 0.711. Functional enrichment analyses revealed that apoptosis, immune regulation, and multiple oncogenic signaling pathways were significantly associated with the risk score, highlighting the critical role of the tumor microenvironment in ccRCC progression. Transcription factor binding analysis based on the JASPAR database suggested that RELA and STAT3 with scores of 0.829 and 0.951, respectively are potential upstream regulators within the prognostic network, particularly influencing TNFRSF10A expression. External validation using the International Cancer Genome Consortium (ICGC) dataset confirmed the robustness of the prognostic model with c-index = 0.612 Furthermore, in vitro experiments demonstrated that RELA and STAT3 regulate TNFRSF10A-mediated apoptotic signaling in ccRCC cells, providing mechanistic support for the bioinformatic findings. CONCLUSION: This study establishes a biologically informed and clinically relevant prognostic framework for ccRCC. Our findings highlight the therapeutic potential of targeting the RELA/STAT3-TNFRSF10A axis and contribute to the advancement of precision medicine in ccRCC.

Humans↗

PubMed1997

Prognosis of patients with advanced Hodgkin's disease: evaluation of four prognostic models using 344 patients included in the Group d'Etudes des Lymphomes de l'Adulte Study.

BACKGROUND: To determine whether a high risk group could be identified within a group of patients with advanced stage Hodgkin's disease (HD), the authors applied several prognostic models to patients treated according to the H89 protocol. METHODS: This study included 344 patients with Stage IIIB-IV HD who were treated with chemotherapy alone (8 cycles) or chemotherapy (6 cycles) plus radiation therapy. Four prognostic models were selected for this study: the numeric prognostic index of the Scotland and Newcastle Lymphoma Group, the Christie Hospital (Manchester)-St. Bartholomew's Hospital (London) model, the Memorial Sloan-Kettering Cancer Center (MSKCC) model, and the criteria used in the European Bone Marrow Transplant (EBMT)/Intergroup Trial. RESULTS: Univariate analysis of H89 protocol patients showed that 5 variables included in the models had prognostic significance: age > 45 years (P = 0.0001), anemia (hemoglobin < 12 g/dL for males and < 10 g/dL for females) (P = 0.0001), number of extranodal sites > or = 2 (P = 0.0013), serum lactic acid dehydrogenase greater than the normal value (P = 0.0018), and lymphocyte count < 0.75 x 10(9) L(-1) (P = 0.0063). All four models divided patients into prognostic subgroups. Significant differences among the subgroups were found by log rank analysis (chi-square test = 11-48; P = 0.01-0.0001). The worst prognostic group defined by the MSKCC model (> or = 3 adverse factors) had an overall survival rate of 59% at 3 years and an estimated 3-year event free survival rate of 43%. CONCLUSIONS: Patients with at least three adverse factors according to the MSKCC model or the EBMT criteria had a higher risk of failure with conventional treatment; however, based on survival rate, no very high risk group could be identified. Nonetheless, these prognostic models may be useful to recognize patients with good prognosis who can be cured with conventional therapy and for whom treatment morbidity and mortality can be minimized.

Adult↗

PubMed2003

[Predicted functional outcome 100 days after ischemic strokes: design of a prospective study about the external validation of a prognostic model].

The purpose of this study is to externally validate previously developed prognostic models predicting functional outcome 100 days after acute ischaemic stroke. Data prospectively collected from 1,754 patients were used to develop two models predicting functional dependence (Barthel Index < 95) and mortality. Both models were internally validated and calibrated. A prospective multicentre study is being performed to validate the developed models with an independent data set. On admission to one of 15 participating hospitals, all patients with acute ischaemic stroke will be registered prospectively in the co-ordinating centre. Within 72 hours, potential predictive variables will be assessed. 1,975 patients will have to be recruited to achieve a power of 95% at the 5% significance level. The resulting prognostic models will be useful for adequately stratifying treatment groups in clinical trials and to accurately predicting the outcome variable.

Brain Ischemia↗

PubMed2002

Simplifying a prognostic model: a simulation study based on clinical data.

Prognostic models are designed to predict a clinical outcome in individuals or groups of individuals with a particular disease or condition. To avoid bias many researchers advocate the use of full models developed by prespecifying predictors. Variable selection is not employed and the resulting models may be large and complicated. In practice more parsimonious models that retain most of the prognostic information may be preferred. We investigate the effect on various performance measures, including mean square error and prognostic classification, of three methods for estimating full models (including penalized estimation and Tibshirani's lasso) and consider two methods (backwards elimination and a new proposal called stepdown) for simplifying full models. Simulation studies based on two medical data sets suggest that simplified models can be found that perform nearly as well as, or sometimes even better than, full models. Optimizing the Akaike information criterion appears to be appropriate for choosing the degree of simplification.

Aortic Aneurysm, Abdominal↗

PubMed2026

Benchmark of biomarker identification and prognostic modeling methods on diverse censored data.

The practices of identifying biomarkers and developing prognostic models using genomic data has become increasingly prevalent. Such data often features characteristics that make these practices difficult, namely high dimensionality, correlations between predictors, and sparsity. Many modern methods have been developed to address these problematic characteristics while performing feature selection and prognostic modeling, but a large-scale comparison of their performances in these tasks on diverse right-censored time to event data (aka survival time data) is much needed. We have compiled many existing methods, including some machine learning methods, several which have performed well in previous benchmarks, primarily for comparison in regards to variable selection capability, and secondarily for survival time prediction on many synthetic datasets with varying levels of sparsity, correlation between predictors, and signal strength of informative predictors. For illustration, we have also performed multiple analyses on a publicly available and widely used cancer cohort from The Cancer Genome Atlas using these methods. We evaluated the methods through extensive simulation studies in terms of the false discovery rate, F1-score, concordance index, Brier score, root mean square error, and computation time. Of the methods compared, CoxBoost and the Adaptive LASSO performed well in all metrics, and the LASSO and elastic net excelled when evaluating concordance index and F1-score. The Benjamini-Hoschberg and q-value procedures showed volatile performances in controlling the false discovery rate. Some methods' performances were greatly affected by differences in the data characteristics. With our extensive numerical study, we have identified the best performing methods for a plethora of data characteristics using informative metrics. This will help cancer researchers in choosing the best approach for their needs when working with genomic data.

Humans↗

PubMed2002

Development and validation of a prognostic model to predict the length of survival in patients with carcinomas of an unknown primary site.

PURPOSE: To identify clinical and biologic variables with significant impact on survival in patients with carcinomas of an unknown primary site and to develop a simple prognostic model for the selection of patients in prospective clinical trials. PATIENTS AND METHODS: Univariate and multivariate prognostic factor analyses were conducted in a population of 150 unselected patients and led to the construction of two successive classification schemes. An external data set of 116 patients enrolled onto two prospective trials was used for validation. RESULTS: When studying clinical variables only, poor performance status (2 or 3) and presence of liver metastases were retained in the multivariate analysis. The first classification scheme consisted of three subgroups of patients with median survivals of 10.8, 6.0, and 2.4 months, according to the number of adverse prognostic factors. With the introduction of serum lactate dehydrogenase (LDH) levels in a further step, liver metastases were no longer significant. The second classification scheme therefore included poor performance status (relative risk [RR], 2.1) and elevated serum LDH level (RR, 2.1). Good-risk and poor-risk patients were identified, with median survivals of 11.7 months and 3.9 months, respectively (P <.0001). The 1-year survival rates were 45% and 11%, respectively. This second classification scheme was validated in an external data set: the median survival rates of patients assigned to the good-risk group and the poor-risk group were 12 months and 7 months, respectively (P =.0089). The 1-year survival rates were 53% and 23%, respectively. CONCLUSION: A simple prognostic model using performance status and serum LDH levels was developed and validated. It allows the assignment of patients into two subgroups with divergent outcome. Further prospective trials will be designed using this prognostic model.

Analysis of Variance↗

PubMed2003

Age and National Institutes of Health Stroke Scale Score within 6 hours after onset are accurate predictors of outcome after cerebral ischemia: development and external validation of prognostic models.

BACKGROUND AND PURPOSE: To date, no validated, comprehensive, and practicable model exists to predict functional recovery within the first hours of cerebral ischemic symptoms. The purpose of this study was to externally validate 2 prognostic models predicting functional outcome and survival at 100 days within the first 6 hours after onset of acute cerebral ischemia. METHODS: On admission to a participating hospital, patients were registered prospectively and included according to defined criteria. Follow-up was performed 100 days after the event. With the use of prospectively collected data, 2 prognostic models were developed and internally calibrated in 1079 patients and externally validated in 1307 patients. By means of age and National Institutes of Health Stroke Scale (NIHSS) score as independent variables, model I predicts incomplete functional recovery (Barthel Index <95) versus complete functional recovery, and model II predicts mortality versus survival. RESULTS: In the validation data set, model I correctly predicted 62.9% of the patients who were incompletely restituted or had died and 83.2% of the completely restituted patients, and model II correctly predicted 57.9% of the patients who had died and 91.5% of the surviving patients. Both models performed better than the treating physicians' predictions made within 6 hours after admission. CONCLUSIONS: The resulting prognostic models are useful to correctly stratify treatment groups in clinical trials and should guide inclusion criteria in clinical trials, which in turn increases the power to detect clinically relevant differences.

Age Factors↗

PubMed2006

[Construction and utilization of the prognostic model of serous ovarian adenocarcinoma].

OBJECTIVE: To analyze the related factors with prognosis in patients with serous ovarian adenocarcinoma and to set up a prognostic model of serous ovarian adenocarcinoma. METHODS: The clinical, pathological and follow-up data of 104 cases with serous ovarian adenocarcinoma were retrospectively analyzed. Kaplan-meier univariate analysis was used to screen the prognostic factors; COX univariate and multivariate analyses were used to determine the risk coefficient of each factors and different layers in each factor. Pearson rank correlation was used to reject the influence of different factors with each other. And the prognostic model of serous ovarian adenocarcinoma was set up based on the result of the above study, which could be used to deduce the survival probability of patients with serous ovarian adenocarcinoma. RESULTS: International Federation of Gynecology and Obstetrics (FIGO) stage (P = 0.0029), histological grade (P = 0.0054), residual disease (P = 0.0000), metastasis of lymph nodes (P = 0.0000) and chemotherapy (P = 0.0000) were the related factors of prognosis in patients with serous ovarian adenocarcinoma, of which FIGO stage was the most important one, followed sequentially by histological grade, metastasis of lymph node, residual disease and chemotherapy (the independent risk coefficient of each factor was 1.3392, 0.9206, 0.7071, 0.6004, 0.4985 in sequence). We set up a prognosis model according to the prognostic index of each factors. The effect of chemotherapy and residual disease on prognosis could be quantified by this model, and the higher the score, the lower the survival probability of patients. CONCLUSIONS: FIGO stage, histological grade, residual disease, metastasis of lymph nodes and chemotherapy are important prognostic factors of serous ovarian adenocarcinoma. This model can be used to estimate the prognosis of patients with serous ovarian adenocarcinoma, and the effect of both chemotherapy and residual disease on the prognosis could be quantified by the model.

Adult↗

PubMed1996

A prognostic model for predicting 10-year survival in patients with primary melanoma. The Pigmented Lesion Group.

OBJECTIVE: To develop a prognostic model, based on clinical and pathologic data that are routinely available to the clinician, that would estimate the chance for survival of a patient with primary cutaneous melanoma after definitive surgical therapy. DESIGN: Cohort analytical study. SETTING: University medical center. PATIENTS: 488 patients with primary cutaneous melanoma who had no apparent metastatic disease. Patients were followed prospectively for at least 10 years. An independent validation sample of 142 patients was used to assess the stability of the model. MEASUREMENTS: Six clinical and pathologic variables that predict survival and are readily available to the clinician were used to develop a prediction model. The variables were tested for their association with death by using a univariate logistic regression model. Point estimates were generated for the probability of surviving melanoma at 10 years. Variables that were statistically significantly associated with survival were retained for testing in a logistic regression model. RESULTS: 488 patients were followed prospectively for a median of 13.5 years (minimum, 10.0 years; maximum, 20.5 years). The overall 10-year survival of the study group was 78%. Four variables were found to be independent predictors of survival. Presented as adjusted odds ratios, from strongest to weakest relative predictive strength, these variables were tumor thickness (odds ratio, 50.8), site of primary melanoma (odds ratio, 4.4), age of the patient (odds ratio, 3.0), and sex of the patient (odds ratio, 2.0). The four-variable model was significantly more accurate than tumor thickness alone, particularly for predicting death. Overall, use of the model reduced the error rate of the prediction of death by 50%. CONCLUSIONS: A prognostic model that uses four readily accessible variables more accurately predicts outcome in patients with primary melanoma than does tumor thickness alone. This four-variable model can identify patients at high risk for the recurrence of disease, an identification that becomes increasingly important as adjuvant therapies are developed for treatment of melanoma.

Adult↗