scholarly journals Temporal recalibration for improving prognostic model development and risk predictions in settings where survival is improving over time

2020 ◽  
Vol 49 (4) ◽  
pp. 1316-1325 ◽  
Author(s):  
Sarah Booth ◽  
Richard D Riley ◽  
Joie Ensor ◽  
Paul C Lambert ◽  
Mark J Rutherford
Keyword(s):  
Prognostic Model ◽  
Time Window ◽  
Cohort Analysis ◽  
Cox Proportional Hazards ◽  
Prognostic Models ◽  
Full Cohort ◽  
Simple Method ◽  
Term Survival ◽  
Period Analysis ◽  
Temporal Recalibration

Abstract Background Prognostic models are typically developed in studies covering long time periods. However, if more recent years have seen improvements in survival, then using the full dataset may lead to out-of-date survival predictions. Period analysis addresses this by developing the model in a subset of the data from a recent time window, but results in a reduction of sample size. Methods We propose a new approach, called temporal recalibration, to combine the advantages of period analysis and full cohort analysis. This approach develops a model in the entire dataset and then recalibrates the baseline survival using a period analysis sample. The approaches are demonstrated utilizing a prognostic model in colon cancer built using both Cox proportional hazards and flexible parametric survival models with data from 1996–2005 from the Surveillance, Epidemiology, and End Results (SEER) Program database. Comparison of model predictions with observed survival estimates were made for new patients subsequently diagnosed in 2006 and followed-up until 2015. Results Period analysis and temporal recalibration provided more up-to-date survival predictions that more closely matched observed survival in subsequent data than the standard full cohort models. In addition, temporal recalibration provided more precise estimates of predictor effects. Conclusion Prognostic models are typically developed using a full cohort analysis that can result in out-of-date long-term survival estimates when survival has improved in recent years. Temporal recalibration is a simple method to address this, which can be used when developing and updating prognostic models to ensure survival predictions are more closely calibrated with the observed survival of individuals diagnosed subsequently.

scholarly journals Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

2020 ◽  
Author(s):  
Chuxiang Lei ◽  
Wenlin Chen ◽  
Yuekun Wang ◽  
Binghao Zhao ◽  
Penghao Liu ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Validation Cohort ◽  
External Validation ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Normal Brain ◽  
Protective Effects ◽  
Lasso Regression ◽  
Metabolic Genes

Abstract Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001). Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients. Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001).Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

scholarly journals The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0251323
Author(s):  
Weijun Shi ◽  
Xincan Li ◽  
Xu Su ◽  
Hexin Wen ◽  
Tianwen Chen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Rate ◽  
Prognostic Model ◽  
Risk Group ◽  
Cohort Analysis ◽  
High Risk Group ◽  
Prognostic Models ◽  
Lower Survival Rate ◽  
Metabolic Genes ◽  
Chip Technology

The recent advances in gene chip technology have led to the identification of multiple metabolism-related genes that are closely associated with colorectal cancer (CRC). Nevertheless, none of these genes could accurately diagnose or predict CRC. The prognosis of CRC has been made by previous prognostic models constructed by using multiple genes, however, the predictive function of multi-gene prognostic models using metabolic genes for the CRC prognosis remains unexplored. In this study, we used the TCGA-CRC cohort as the test dataset and the GSE39582 cohort as the experimental dataset. Firstly, we constructed a prognostic model using metabolic genes from the TCGA-CRC cohort, which were also associated with CRC prognosis. We analyzed the advantages of the prognostic model in the prognosis of CRC and its regulatory mechanism of the genes associated with the model. Secondly, the outcome of the TCGA-CRC cohort analysis was validated using the GSE39582 cohort. We found that the prognostic model can be employed as an independent prognostic risk factor for estimating the CRC survival rate. Besides, compared with traditional clinical pathology, it can precisely predict CRC prognosis as well. The high-risk group of the prognostic model showed a substantially lower survival rate as compared to the low-risk group. In addition, gene enrichment analysis of metabolic genes showed that genes in the prognostic model are enriched in metabolism and cancer-related pathways, which may explain its underlying mechanism. Our study identified a novel metabolic profile containing 11 genes for prognostic prediction of CRC. The prognostic model may unravel the imbalanced metabolic microenvironment, and it might promote the development of biomarkers for predicting treatment response and streamlining metabolic therapy in CRC.

scholarly journals EMBR-03. PINEOBLASTOMA: A POOLED OUTCOME STUDY OF NORTH AMERICAN AND AUSTRALIAN THERAPEUTIC DATA

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i6-i6
Author(s):  
Jordan Hansford ◽  
Jie Huang ◽  
Andrew Dodgshun ◽  
Bryan Li ◽  
Eugene Hwang ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Radiation Therapy ◽  
Young Children ◽  
Clinical Features ◽  
North American ◽  
Proportional Hazards ◽  
Cohort Analysis ◽  
Cox Proportional Hazards ◽  
International Studies ◽  
Term Survival

Abstract Background Pineoblastoma (PB) is a rare embryonal brain tumour most often diagnosed in young children. To date, no clinical trials have been conducted specific to pediatric PB. Collaborative studies performed over the past 30 years have included PB in studies accruing for other embryonal tumours, primarily medulloblastoma (MB), but also including the entity formerly known as CNS-PNET and atypical teratoid rhabdoid tumors. Each of these studies have included only a small number of children with PB, making clinical features difficult to interpret and determinants of outcome difficult to ascertain. Patients and Methods Published centrally reviewed series with sufficient treatment and outcome data from North American and Australian cases were pooled. To investigate associations between variables, Fisher’s exact and Wilcoxon-Mann-Whitney tests, and Spearman correlations were used as appropriate. Kaplan-Meier plots, log-rank tests, and Cox proportional hazards models were used in survival analysis. Results We describe a 30-year review of the reported clinical features of PB and a pooled centrally reviewed, cohort analysis of cases (n=178) from the Children’s Oncology Group (COG) (n=82) groups and several published, centrally reviewed institutional series (n=96). We find young children &lt;3 years of age have a dramatically poorer outlook compared to older children (5-year OS 16.2% +/- 5.3% vs 67.3% +/- 5%) confirming new and novel approaches are needed in future clinical trials for this at risk group. Interestingly, male gender was predictive of worse outcome possibly suggestive of gender specific subgroup risks that needs validation in future studies. Assessment of radiation therapy is not possible as the vast majority of children under age three did not receive any form of radiation therapy. Conclusion Given the relative scarcity of this tumor and the emerging data on subgroups of pineoblastoma, prospective, collaborative international studies will be vital to improving the long-term survival of these patients.

Expected Long-Term Survival of Older Patients Diagnosed with Non-Hodgkin Lymphoma in 2007-2011.

Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 4525-4525
Author(s):  
Dianne Pulte ◽  
Adam Gondos ◽  
Hermann Brenner
Keyword(s):  
Survival Data ◽  
Older Patients ◽  
Cohort Analysis ◽  
Relative Survival ◽  
Term Survival ◽  
Period Analysis ◽  
Long Term Survival ◽  
Model Based ◽  
Survival Expectations

Abstract Abstract 4525 Background New therapeutic options have led to substantial increases in survival expectations of patients with non-Hodgkin lymphoma (NHL) in recent years. In contrast to many malignancies, survival in older patients has improved in NHL at a rate similar to that in younger patients. In the past, the impact of these innovations on long-term survival of NHL patients on the population level has only been disclosed with substantial delay. In order to reduce this delay, we employed a newly developed projection method to estimate survival of patients age 60 and older with NHL in 2007-11. Methods In order to demonstrate the validity of model based projection, we calculated survival for 4 prior periods for which observed 5-year survival data is available by three methods: cohort analysis, period analysis, and model based projection and compared the results to the actual observed survivals from the same time periods. We next calculated survival estimates for the most recent patient cohort for which 5- and 10-year survival data is available, period analysis for 2002-06, and projection estimates for 2007-11. Results A preliminary empirical evaluation of the method using historical data indicated good performance in projection of age specific and overall 5- and 10-year relative survival in older patients with model based projection giving a result closer to the observed survival than either cohort analysis or period analysis for all age groups in each time period. Five and 10-year survival estimates for 2007-11 for patients aged 60+ were 65.1% and 53.5%, respectively, 8.9 percentage units (% units) and 14.8% units, respectively, higher than survival estimated from the most recent cohort analysis available (see table). Age specific 5- and 10-year relative survival estimates using model based projection ranged from 76.9% and 66.6%, respectively, for age 60-64 to 50.7% and 37.5%, respectively, for patients age 80+. Survival estimates by model based projection were higher for all age groups including 80+, both high and low grade disease, nodal and extranodal disease, and both genders. Conclusions Patients over 60 diagnosed with NHL in 2007-2011 have much higher long-term survival expectations than suggested by previously available survival statistics. The use of model based projection is reliable in this patient population and demonstrates steady improvement in survival over time for older patients with NHL. This reinforces the findings from clinical trials which show that treatment of NHL is tolerable and effective in older patients. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Tumor immunogenomic signatures improve a prognostic model of melanoma survival

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Leah Morales ◽  
Danny Simpson ◽  
Robert Ferguson ◽  
John Cadley ◽  
Eduardo Esteva ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Proportional Hazards ◽  
Interferon Γ ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Tumor Mutation Burden ◽  
Model Combining ◽  
Mutation Burden ◽  
Cancer Genome Atlas

Abstract Background Tumor mutation burden (TMB) has been associated with melanoma immunotherapy (IT) outcomes, including survival. We explored whether combining TMB with immunogenomic signatures recently identified by The Cancer Genome Atlas (TCGA) can refine melanoma prognostic models of overall survival (OS) in patients not treated by IT. Methods Cox proportional-hazards (Cox PH) analysis was performed on 278 metastatic melanomas from TCGA not treated by IT. In a discovery and two validation cohorts Cox PH models assessed the interaction between TMB and 53 melanoma immunogenomic features to refine prediction of melanoma OS. Results Interferon-γ response (IFNγRes) and macrophage regulation gene signatures (MacReg) combined with TMB significantly associated with OS (p = 8.80E−14). We observed that patients with high TMB, high IFNγRes and high MacReg had significantly better OS compared to high TMB, low IFNγRes and low MacReg (HR = 2.8, p = 3.55E−08). This association was not observed in low TMB patients. Conclusions We report a model combining TMB and tumor immune features that significantly improves prediction of melanoma OS, independent of IT. Our analysis revealed that patients with high TMB, high levels of IFNγRes and MacReg had significantly more favorable OS compared to high TMB patients with low IFNγRes and low MacReg. These findings may substantially improve current melanoma prognostic models.

The replacement of generations in Serbia in the period 1950-2000

Stanovnistvo ◽  
2001 ◽  
Vol 39 (1-4) ◽  
pp. 45-71 ◽  
Author(s):  
Goran Penev
Keyword(s):  
Birth Cohort ◽  
20Th Century ◽  
Regional Differences ◽  
Cohort Analysis ◽  
Reproduction Rate ◽  
Demographic Analysis ◽  
Period Analysis ◽  
Net Reproduction

The article deals with the replacement of generations in Serbia, its dynamics in the second half of the 20th century, and the importance of direct determinants. It points to the major regional differences in the domain of the population reproduction among the large areas of Serbia (Central Serbia, Vojvodina, and Kosovo-Metohija). Two approaches of demographic analysis were applied: period and cohort analysis. Basic indicators, definitions, and shortcomings were presented. The results of the period analysis indicate that up until 1988 (with the exceptions of 1957 and 1981), the fertility in Serbia constantly reached a level of fertility necessary to ensure the replacement. Since 1989, the net reproduction rate has constantly been below unity. In Central Serbia and Vojvodina, the population has not been reproducing itself for more than 45 years (since 1956). The situation has been completely different in Kosovo-Metohija, where fertility has been above the level necessary to ensure reproduction during the entire second half of 20th century. The cohort analysis applied to six chosen generations (birth cohort of 1950, 1955, 1960, 1965, 1970, and 1975) indicates that in Serbia, only women born in 1960 ensured the replacement. In Central Serbia and Vojvodina, none of the studied generations succeeded in ensuring the replacement, while in Kosovo-Metohija all generations did.

Influence of age and shock severity on short-term survival in patients with cardiogenic shock

2021 ◽  
Author(s):  
Jacob C Jentzer ◽  
Benedikt Schrage ◽  
David R Holmes ◽  
Salim Dabboura ◽  
Nandan S Anavekar ◽  
...  
Keyword(s):  
Cardiogenic Shock ◽  
Mayo Clinic ◽  
Older Patients ◽  
Cox Proportional Hazards ◽  
Circulatory Support ◽  
Short Term ◽  
Term Survival ◽  
Risk Of Death ◽  
Coronary Syndrome ◽  
Short Term Survival

Abstract Aims Cardiogenic shock (CS) is associated with poor outcomes in older patients, but it remains unclear if this is due to higher shock severity. We sought to determine the associations between age and shock severity on mortality among patients with CS. Methods and results Patients with a diagnosis of CS from Mayo Clinic (2007–15) and University Clinic Hamburg (2009–17) were subdivided by age. Shock severity was graded using the Society for Cardiovascular Angiography and Intervention (SCAI) shock stages. Predictors of 30-day survival were determined using Cox proportional-hazards analysis. We included 1749 patients (934 from Mayo Clinic and 815 from University Clinic Hamburg), with a mean age of 67.6 ± 14.6 years, including 33.6% females. Acute coronary syndrome was the cause of CS in 54.0%. The distribution of SCAI shock stages was 24.1%; C, 28.0%; D, 33.2%; and E, 14.8%. Older patients had similar overall shock severity, more co-morbidities, worse kidney function, and decreased use of mechanical circulatory support compared to younger patients. Overall 30-day survival was 53.3% and progressively decreased as age or SCAI shock stage increased, with a clear gradient towards lower 30-day survival as a function of increasing age and SCAI shock stage. Progressively older age groups had incrementally lower adjusted 30-day survival than patients aged &lt;50 years. Conclusion Older patients with CS have lower short-term survival, despite similar shock severity, with a high risk of death in older patients with more severe shock. Further research is needed to determine the optimal treatment strategies for older CS patients.

scholarly journals The development and validation of prognostic models for overall survival in the presence of missing data in the training dataset: a strategy with a detailed example

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Kara-Louise Royle ◽  
David A. Cairns
Keyword(s):  
Overall Survival ◽  
Missing Data ◽  
Prognostic Model ◽  
Prognostic Index ◽  
Risk Groups ◽  
Prognostic Models ◽  
Training Dataset ◽  
Validation Process ◽  
Test Dataset ◽  
Development And Validation

Abstract Background The United Kingdom Myeloma Research Alliance (UK-MRA) Myeloma Risk Profile is a prognostic model for overall survival. It was trained and tested on clinical trial data, aiming to improve the stratification of transplant ineligible (TNE) patients with newly diagnosed multiple myeloma. Missing data is a common problem which affects the development and validation of prognostic models, where decisions on how to address missingness have implications on the choice of methodology. Methods Model building The training and test datasets were the TNE pathways from two large randomised multicentre, phase III clinical trials. Potential prognostic factors were identified by expert opinion. Missing data in the training dataset was imputed using multiple imputation by chained equations. Univariate analysis fitted Cox proportional hazards models in each imputed dataset with the estimates combined by Rubin’s rules. Multivariable analysis applied penalised Cox regression models, with a fixed penalty term across the imputed datasets. The estimates from each imputed dataset and bootstrap standard errors were combined by Rubin’s rules to define the prognostic model. Model assessment Calibration was assessed by visualising the observed and predicted probabilities across the imputed datasets. Discrimination was assessed by combining the prognostic separation D-statistic from each imputed dataset by Rubin’s rules. Model validation The D-statistic was applied in a bootstrap internal validation process in the training dataset and an external validation process in the test dataset, where acceptable performance was pre-specified. Development of risk groups Risk groups were defined using the tertiles of the combined prognostic index, obtained by combining the prognostic index from each imputed dataset by Rubin’s rules. Results The training dataset included 1852 patients, 1268 (68.47%) with complete case data. Ten imputed datasets were generated. Five hundred twenty patients were included in the test dataset. The D-statistic for the prognostic model was 0.840 (95% CI 0.716–0.964) in the training dataset and 0.654 (95% CI 0.497–0.811) in the test dataset and the corrected D-Statistic was 0.801. Conclusion The decision to impute missing covariate data in the training dataset influenced the methods implemented to train and test the model. To extend current literature and aid future researchers, we have presented a detailed example of one approach. Whilst our example is not without limitations, a benefit is that all of the patient information available in the training dataset was utilised to develop the model. Trial registration Both trials were registered; Myeloma IX-ISRCTN68454111, registered 21 September 2000. Myeloma XI-ISRCTN49407852, registered 24 June 2009.

scholarly journals Cigarette smoking may accelerate the progression of IgA nephropathy

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siqing Wang ◽  
Aiya Qin ◽  
Gaiqin Pei ◽  
Zheng Jiang ◽  
Lingqiu Dong ◽  
...  
Keyword(s):  
Risk Factor ◽  
Propensity Score ◽  
Cigarette Smoking ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Cohort Analysis ◽  
Cox Proportional Hazards ◽  
Study Endpoint ◽  
Cox Regression Analysis ◽  
Ckd Stage

Abstract Background Whether cigarette smoking is associated with the progression of immunoglobulin A nephropathy (IgAN) remains uncertain; therefore, we aimed to evaluate the effect of cigarette smoking on the prognosis of IgAN. Methods We divided 1239 IgAN patients from West China Hospital of Sichuan University who met the inclusion criteria into smoker (current or former) and non-smoker groups. The endpoint was end-stage renal disease (ESRD: eGFR < 15 mL/min/1.73 m2 or undergoing renal replacement treatment) and/or eGFR decreased by > 50%. Kaplan–Meier, correlation, logistic regression and Cox proportional hazards analyses were performed. The association between cigarette smoking and IgAN was further verified by propensity-score-matched cohort analysis. Results During the mean follow-up period of 61 months, 19% (40/209) of the smoker group and 11% (110/1030) of the non-smoker group reached the study endpoint (p < 0.001). Multivariate Cox regression analysis revealed that cigarette smoking (hazard ratio (HR) = 1.58; p = 0.043) was an independent risk factor predicting poor renal progression in IgAN, and that IgAN patients with chronic kidney disease (CKD) stage 3–4 were more susceptible to cigarette smoking (p < 0.001). After propensity score matching (PSM), a significant correlation between cigarette smoking and renal outcomes in IgAN patients was seen. Furthermore, Spearman’s correlation test revealed that smoking dose was negatively correlated with eGFR (r = 0.141; p < 0.001) and positively related with proteinuria (r = 0.096; p = 0.001). Conclusions Cigarette smoking is an independent risk factor for IgAN progression, especially for advanced patients.

Sign in / Sign up

Export Citation Format

Share Document