Specific Risk Models Research Articles

An Apoptosis-Related Specific Risk Model for Breast Cancer: From Genomic Analysis to Precision Medicine.

Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients. Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes. A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib. This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.

TRAF2 promotes M2-polarized tumor-associated macrophage infiltration, angiogenesis and cancer progression by inhibiting autophagy in clear cell renal cell carcinoma

BackgroundThe management of advanced clear cell renal cell carcinoma (ccRCC) remains a major challenge in clinical practice, and the construction of more reliable prognostic prediction models and the further elucidation of key molecular mechanisms of tumor progression are topics in urgent need of in-depth investigation.MethodsWe used CIBERSORT to estimate the proportion of 22 tumor-infiltrating immune cell types in the TCGA-KIRC cohort. Weighted gene co-expression network analysis, least absolute shrinkage and selection operator regression analysis were used to build risk prediction models. Expression patterns and clinical significance of TRAF2 were determined through bioinformatics analysis, real-time qPCR, Western Blot, immunohistochemistry. GSEA analysis, transmission electron microscopy, 2D/3D colony formation assay, cell migration and invasion assay, and tube-formation assay were used to investigate the underlying function and mechanism of the TRAF2/M2 macrophage/autophagy axis.ResultsWe constructed a novel prognostic prediction model based on M2 macrophage-related genes, which was identified as an accurate, independent and specific prognostic risk model for ccRCC patients. A reliable nomogram was constructed to predict 1-, 3-, and 5-year overall survival for patients with ccRCC. As one of the constituent genes of the risk model, TRAF2 was determined to be upregulated in ccRCC and associated with poor clinical prognosis. We found that TRAF2 promotes malignant progression of ccRCC by regulating macrophage polarization, migration and angiogenesis. Mechanistically, we found that TRAF2 promotes the polarization of M2 macrophages, and this chemotaxis is achieved in an autophagy-dependent pathway. Orthotopic tumor growth assay results revealed that TRAF2 plays a key role as a promotor of ccRCC growth and metastasis.ConclusionsIn conclusion, this risk model is highly predictive of prognostic in ccRCC patients, which is expected to promote improved treatment evaluation and comprehensive management of ccRCC. Moreover, our findings reveal that the TRAF2/M2 macrophage/autophagy axis plays a key regulatory role in the malignant progression of ccRCC, and suggest that TRAF2 is a potential novel therapeutic target for advanced ccRCC.

Prediction models for cardiovascular disease risk among people living with HIV: A systematic review and meta-analysis.

HIV continues to be a major global health issue. The relative risk of cardiovascular disease (CVD) among people living with HIV (PLWH) was 2.16 compared to non-HIV-infections. The prediction of CVD is becoming an important issue in current HIV management. However, there is no consensus on optional CVD risk models for PLWH. Therefore, we aimed to systematically summarize and compare prediction models for CVD risk among PLWH. Longitudinal studies that developed or validated prediction models for CVD risk among PLWH were systematically searched. Five databases were searched up to January 2022. The quality of the included articles was evaluated by using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). We applied meta-analysis to pool the logit-transformed C-statistics for discrimination performance. Thirteen articles describing 17 models were included. All the included studies had a high risk of bias. In the meta-analysis, the pooled estimated C-statistic was 0.76 (95% CI: 0.72-0.81, I 2 = 84.8%) for the Data collection on Adverse Effects of Anti-HIV Drugs Study risk equation (D:A:D) (2010), 0.75 (95% CI: 0.70-0.79, I 2 = 82.4%) for the D:A:D (2010) 10-year risk version, 0.77 (95% CI: 0.74-0.80, I 2 = 82.2%) for the full D:A:D (2016) model, 0.74 (95% CI: 0.68-0.79, I 2 = 86.2%) for the reduced D:A:D (2016) model, 0.71 (95% CI: 0.61-0.79, I 2 = 87.9%) for the Framingham Risk Score (FRS) for coronary heart disease (CHD) (1998), 0.74 (95% CI: 0.70-0.78, I 2 = 87.8%) for the FRS CVD model (2008), 0.72 (95% CI: 0.67-0.76, I 2 = 75.0%) for the pooled cohort equations of the American Heart Society/ American score (PCE), and 0.67 (95% CI: 0.56-0.77, I 2 = 51.3%) for the Systematic COronary Risk Evaluation (SCORE). In the subgroup analysis, the discrimination of PCE was significantly better in the group aged ≤40 years than in the group aged 40-45 years (P = 0.024) and the group aged ≥45 years (P = 0.010). No models were developed or validated in Sub-Saharan Africa and the Asia region. The full D:A:D (2016) model performed the best in terms of discrimination, followed by the D:A:D (2010) and PCE. However, there were no significant differences between any of the model pairings. Specific CVD risk models for older PLWH and for PLWH in Sub-Saharan Africa and the Asia region should be established.Systematic Review Registration: PROSPERO CRD42022322024.

Pronóstico de los pacientes con cardiopatías congénitas del adulto intervenidos quirúrgicamente. Análisis de resultados y factores asociados a reingreso hospitalario y mortalidad

ObjectiveTo evaluate surgical results, mortality, readmission rates and identify risk factors. MethodsRetrospective study of all cases from 2012 to 2020. Risk factors were assessed fitting regression logistic models. Results137 patients were operated, mean age 37 (14-73), 39,4% female. Mean cardiopulmonary bypass time 119,28±67; aortic cross-clamp time 78±46,76. Mortality was 5.8% and readmission rate 9.95% mainly due to atrial fibrillation, congestive heart failure and infections. Indications were septal defects (20%), aortic and LVOT indications (21%), RVOT procedures (34%) and AV valves disease (12%). Mortality was associated to severe pulmonary hypertension, 2 or more previous cardiac surgery and RASCH-1 score 4. Readmission rate was associated to atrial fibrillation. ConclussionsAlthough these patients have high complexity, long cardiopulmonary bypass time and high rate of reinterventions, mortality is low. There is a number of readmissions and complications. Specific risk models should be validated. There is still a lack of information.

Site-specific Risk Stratification Models for Postoperative Recurrence and Survival Prediction in Patients with Upper Tract Urothelial Carcinoma Undergoing Radical Nephroureterectomy: Better Stratification for Adjuvant Therapy

BackgroundSite-specific postoperative risk models for localized upper tract urothelial carcinoma (UTUC) are unavailable. ObjectiveTo create specific risk models for renal pelvic urothelial carcinoma (RPUC) and ureteral urothelial carcinoma (UUC), and to compare the predictive accuracy with the overall UTUC risk model. Design, setting, and participantsA multi-institutional database retrospective study of 1917 UTUC patients who underwent radical nephroureterectomy (RNU) between 2000 and 2018 was conducted. Outcome measurements and statistical analysisA multivariate hazard model was used to identify the prognostic factors for extraurinary tract recurrence (EUTR), cancer-specific death (CSD), and intravesical recurrence (IVR) after RNU. Patients were stratified into low-, intermediate-, high-, and highest-risk groups. External validation was performed to estimate a concordance index of the created risk models. We investigated whether our risk models could aid decision-making regarding adjuvant chemotherapy (AC) after RNU. Results and limitationsThe UTUC risk models could stratify the risk of cumulative incidence of three endpoints. The RPUC- and UUC-specific risk models showed better stratification than the overall UTUC risk model for all the three endpoints, EUTR, CSD, and IVR (RPUC: concordance index, 0.719 vs 0.770, 0.714 vs 0.794, and 0.538 vs 0.569, respectively; UUC: 0.716 vs 0.767, 0.766 vs 0.809, and 0.553 vs 0.594, respectively). The UUC-specific risk model can identify the high- and highest-risk patients likely to benefit from AC after RNU. A major limitation was the potential selection bias owing to the retrospective nature of this study. ConclusionsWe recommend using site-specific risk models instead of the overall UTUC risk model for better risk stratification and decision-making for AC after RNU. Patient summaryUpper tract urothelial carcinoma comprises renal pelvic and ureteral carcinomas. We recommend using site-specific risk models instead of the overall upper tract urothelial carcinoma risk model in risk prediction and decision-making for adjuvant therapy after radical surgery.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

BackgroundOvarian cancer (OC) is the deadliest tumor in the female reproductive tract. And increased resistance to platinum-based chemotherapy represents the major obstacle in the treatment of OC currently. Robust and accurate gene expression models are crucial tools in distinguishing platinum therapy response and evaluating the prognosis of OC patients.MethodsIn this study, 230 samples from The Cancer Genome Atlas (TCGA) OV dataset were subjected to mRNA expression profiling, single nucleotide polymorphism (SNP), and copy number variation (CNV) analysis comprehensively to screen out the differentially expressed genes (DEGs). An SVM classifier and a prognostic model were constructed using the Random Forest algorithm and LASSO Cox regression model respectively via R. The Gene Expression Omnibus (GEO) database was applied as the validation set.ResultsForty-eight differentially expressed genes (DEGs) were figured out through integrated analysis of gene expression, single nucleotide polymorphism (SNP), and copy number variation (CNV) data. A 10-gene classifier was constructed which could discriminate platinum-sensitive samples precisely with an AUC of 0.971 in the training set and of 0.926 in the GEO dataset (GSE638855). In addition, 8 optimal genes were further selected to construct the prognostic risk model whose predictions were consistent with the actual survival outcomes in the training cohort (p = 9.613e-05) and validated in GSE638855 (p = 0.04862). PNLDC1, SLC5A1, and SYNM were then identified as hub genes that were associated with both platinum response status and prognosis, which was further validated by the Fudan University Shanghai cancer center (FUSCC) cohort.ConclusionThese findings reveal a specific risk model that could serve as effective biomarkers to identify patients’ platinum response status and predict survival outcomes for OC patients. PNLDC1, SLC5A1, and SYNM are the hub genes that may serve as potential biomarkers in OC treatment.

Development and validation of gender specific risk models in prediction of mortality for hospitalized heart failure patients in a multiethnic asian population

Abstract Funding Acknowledgements Type of funding sources: None. Background/Introduction Heart failure carries substantial morbidity and mortality. Female has different characteristics compared to male which may affect prognosis and are not represented well in many trials. Multiple risk scores for in-hospital mortality have been created and validated such as ADHERE (Acute Decompensated Heart Failure National Registry) and GWTG (Get With The Guidelines). However, these risk scores were in Western population and not gender specific. Purpose To develop and validate a gender specific risk model in predicting mortality amongst Asian patients admitted for acute decompensated heart failure (ADHF). Methods We analyzed data from our national centre’s ADHF registry. Epidemiological, clinical, laboratory and imaging variables were ascertained. Univariate and multivariate analysis using forward stepwise logistic regression were performed to identify predictors for all-cause in hospital mortality with emphasis on disparities between male and female. The accuracy of risk score was assessed using the concordance statistics while calibration was done using the Hosmer-Lemeshow method. The validity of the risk score was determined using separate gender validation cohorts. Results A total of 10148 patients admitted for ADHF were analyzed. 8262 patients admitted for ADHF from January 2009 to December 2018 (6008 males and 2254 females) were in the derivation cohort where as 1886 patients (1304 males and 582 females) were selected randomly from January 2019 to July 2021 for our validation cohort. Female had better survival (p value =0.04). There were 8 predictive variables for female cohort and 10 predictive variables for male cohort. Similar variables were low systolic blood pressure, loop diuretic use, dialysis, mechanical ventilation and CPR (cardiopulmonary resuscitation). Interestingly, there were significant differences. For female, other variables that had impact on mortality were hypertension (AOR, 0.23; 95% CI,0.09–0.58), high urea (AOR, 3.32; 95% CI, 1.36–8.09) and hyponatremia (AOR, 2.87, 95% CI, 1.21–6.78). However for male, the variables were renal insufficiency (AOR, 1.749; 95% CI, 1.13–2.72), history of stroke (AOR, 2.386, 95% CI, 1.22–4.65), LVEF less than 40% (AOR, 2.81; 95% CI, 1.40–5.63), absence of ARB/ACEi (AOR, 2.31; 95% CI, 1.48–3.60) and absence of beta blockers (AOR, 2.65; 95% CI, 1.73–4.05). The AUC for the female derivation cohort was 0.97 (95% CI, 0.95–0.99) and 0.94 (95% CI, 0.92–0.96) for male derivation cohort. AUC in the female validation cohort was 0.91 (95% CI, 0.83-0.98) and male was 0.90 (95% CI, 0.84–0.97). The risk scores showed good calibration (female; x²=8.88, p = 0.18 and male; x²=13.11, p = 0.07). Conclusion A gender specific risk score for ADHF was developed and validated successfully in our Asian population. This will change our practice in providing valuable prognostic information in both female and male thereby guiding the need for more intensive treatment. Abstract Figure. ROC Curves (Female and Male) Abstract Figure. Key Variables (Female and Male)

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review.

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.

Development of a Risk Score to Predict Peripherally Inserted Central Catheter Thrombosis in Active Cancer

BackgroundPeripherally inserted central catheters (PICCs) are commonly used in cancer patients. PICC-associated upper limb venous thromboembolism (PaULVTE, non-deep and deep involving veins proximal to the brachial vein) and distant VTE (i.e. pulmonary embolism [PE], lower limb deep vein thrombosis [DVT]) prevention is important due to the bleeding risk from subsequent anticoagulation.The Khorana risk score (KRS) is used to predict VTE in malignancies, has been externally validated and prospectively utilized in VTE prophylaxis studies in cancer patients. It utilizes routinely available variables: cancer type, prechemotherapy platelet count, hemoglobin, leukocyte count and body mass index (BMI) to stratify patients into three risk groups. However, the KRS was not designed to predict PaULVTE so its utility in this context is unknown. A specific PaULVTE risk model is the Michigan Risk Score (MRS) which assesses concurrent central catheter at time of PICC insertion, leukocyte count at insertion, PICC lumen number, antecedent VTE (DVT or PE) and active cancer (the latter confers a score of 3). The MRS has not been externally validated and is not specifically designed for cancer patients which comprised 6.2% of their derivation cohort.ObjectivesTo measure the discriminative ability for PaULVTE of the KRS and MRS and the corresponding prevalence of PaULVTE in patients with active cancer in various KRS and MRS strata. To modify the KRS and MRS, to improve discriminative accuracy for PaULVTE.MethodsWe retrospectively reviewed consecutive patients with malignancies, who received chemotherapy through a PICC between April 2017 to July 2018. Exclusion criteria for cancer cases included PICC insertion for other reasons such as supportive care. We did not exclude patients on prophylactic or therapeutic anticoagulation or those with a history of PaULVTE.The outcome measures included objectively confirmed PaULVTE incidence whilst the PICC remained in place or within 30 days post removal, treatment and to assess the prevalence of PaULVTE according to KRS and MRS categories in patients with active cancer. For cancer patients, KRS and MRS were calculated based on clinical and laboratory values at PICC insertion. We also assessed whether combining components of both scores would yield improved discriminative ability. Furthermore, we recorded symptomatic, radiologically confirmed distant VTE (i.e. PE, DVT in the proximal or distal lower limb) if this occurred whilst the PICC was in situ or up to 30 days post removal.ResultsAmong 147 cancer patients, median age 64 years, PICC duration 70 days (range, 2-452), 7% developed PaULVTE (95%CI 3.6-12.2) and 4%(95%CI 2-9) distant VTE. Of KRS<1, 22% had no PaULVTE (95%CI 0-11) compared to 9% (95%CI 5-16) in KRS≥1(n=111), p=0.12. Patients with MRS<5 and MRS≥5 developed (95%CI 2-11) and 21% (95%CI 7-48) PaULVTE respectively, p=0.002 and accounted for 83% and 17% of patients respectively. The c-statistic for KRS was 0.6 (95%CI 0.44-0.75) whilst MRS was 0.63 (95%CI 0.44-0.82). The addition of thrombocytosis, a variable from KRS, to MRS (modified MRS, mMRS) improved discriminative value (mMRS 0.72 [95%CI 0.58-0.85]) more than BMI≥35kg/m 2 (0.67 [95%CI 0.49-0.85)] (Figure 1). mMRS3 (lowest score) occurred in 47% with 1.4% (95%CI 0-8.3) PaULVTE compared to 11.8% (95%CI 6.1-21.2) in mMRS>3, p=0.01 (Figure 2). The addition of lumen number from MRS to KRS did not improve the area under the curve (0.6 [95%CI 0.44-0.82]).ConclusionPaULVTE risk is high in active cancer (7%). The MRS dichotomized at a score of above or less than 5 showed moderate discriminative ability for PaULVTE. With simple modification of the MRS with the addition of thrombocytosis (mMRS), a score of 3 had a low rate of PaULVTE (1.4%) and accounted for 47% of patients. mMRS>3 comprised 53% of the cohort and had a statically higher rate of PaULVTE 11.8%.More patients were accurately classified as low PaULVTE risk using mMRS3 compared to KRS<1 (47% versus 22%) but both have low rates of PaULVTE. Prospective validation of mMRS would be invaluable to determine which patients are at high risk of PaULVTE and may benefit from prophylaxis. [Display omitted] DisclosuresNo relevant conflicts of interest to declare.

Construction of Prognostic Risk Model of 5-Methylcytosine-Related Long Non-Coding RNAs and Evaluation of the Characteristics of Tumor-Infiltrating Immune Cells in Breast Cancer.

Purpose: The role of 5-methylcytosine-related long non-coding RNAs (m5C-lncRNAs) in breast cancer (BC) remains unclear. Here, we aimed to investigate the prognostic value, gene expression characteristics, and correlation between m5C-lncRNA risk model and tumor immune cell infiltration in BC. Methods: The expression matrix of m5C-lncRNAs in BC was obtained from The Cancer Genome Atlas database, and the lncRNAs were analyzed using differential expression analysis as well as univariate and multivariate Cox regression analysis to eventually obtain BC-specific m5C-lncRNAs. A risk model was developed based on three lncRNAs using multivariate Cox regression and the prognostic value, accuracy, as well as reliability were verified. Gene set enrichment analysis (GSEA) was used to analyze the Kyoto Encyclopedia of Genes and Genomes signaling pathway enrichment of the risk model. CIBERSORT algorithm and correlation analysis were used to explore the characteristics of the BC tumor-infiltrating immune cells. Finally, reverse transcription-quantitative polymerase chain reaction was performed to detect the expression level of three lncRNA in clinical samples. Results: A total of 334 differential m5C-lncRNAs were identified, and three BC-specific m5C-lncRNAs were selected, namely AP005131.2, AL121832.2, and LINC01152. Based on these three lncRNAs, a highly reliable and specific risk model was constructed, which was proven to be closely related to the prognosis of patients with BC. Therefore, a nomogram based on the risk score was built to assist clinical decisions. GSEA revealed that the risk model was significantly enriched in metabolism-related pathways and was associated with tumor immune cell infiltration based on the analysis with the CIBERSORT algorithm. Conclusion: The efficient risk model based on m5C-lncRNAs associated with cancer metabolism and tumor immune cell infiltration could predict the survival prognosis of patients, and AP005131.2, AL121832.2, and LINC01152 could be novel biomarkers and therapeutic targets for BC.

Individualised decision making: interpretation of risk for extremely preterm infants—a survey of UK neonatal professionals

BackgroundThe British Association of Perinatal Medicine (BAPM) published a revised framework for perinatal management of extremely preterm infants (EPIs) in 2019. We aimed to assess UK neonatal professionals’ interpretation of...

Estimating long-term health risks after breast cancer radiotherapy: merging evidence from low and high doses

In breast cancer radiotherapy, substantial radiation exposure of organs other than the treated breast cannot be avoided, potentially inducing second primary cancer or heart disease. While distant organs and large parts of nearby ones receive doses in the mGy–Gy range, small parts of the heart, lung and bone marrow often receive doses as high as 50 Gy. Contemporary treatment planning allows for considerable flexibility in the distribution of this exposure. To optimise treatment with regards to long-term health risks, evidence-based risk estimates are required for the entire broad range of exposures. Here, we thus propose an approach that combines data from medical and epidemiological studies with different exposure conditions. Approximating cancer induction as a local process, we estimate organ cancer risks by integrating organ-specific dose–response relationships over the organ dose distributions. For highly exposed organ parts, specific high-dose risk models based on studies with medical exposure are applied. For organs or their parts receiving relatively low doses, established dose–response models based on radiation-epidemiological data are used. Joining the models in the intermediate dose range leads to a combined, in general non-linear, dose response supported by data over the whole relevant dose range. For heart diseases, a linear model consistent with high- and low-dose studies is presented. The resulting estimates of long-term health risks are largely compatible with rate ratios observed in randomised breast cancer radiotherapy trials. The risk models have been implemented in a software tool PASSOS that estimates long-term risks for individual breast cancer patients.

Glycolysis-Based Genes Are Potential Biomarkers in Thyroid Cancer.

While increased glycolysis has been identified as a cancer marker and attracted much attention in thyroid cancer (THCA), the prognostic role of it remains to be further elucidated. Here we aimed to determine a specific glycolysis-associated risk model to predict THCA patients' survival. We also explored the interaction between this signature and tumor immune microenvironment and performed drug screening to identify specific drugs targeting the glycolysis-associated signature. Six genes (CHST6, POM121C, PPFIA4, STC1, TGFBI, and FBP2) comprised the specific model, which was an independent prognostic indicator in THCA patients determined by univariate, LASSO and multivariate Cox regression analyses. The receiver operating characteristic (ROC) curve analysis confirmed the excellent clinical performance of the prognostic signature. According to the specific gene signature, patients were categorized into high- and low-risk subgroups. The high-risk group was characterized by decreased immune score and elevated tumor purity, as well as worser survival prognosis compared to the low-risk group. We also validated the expression of these genes in clinical samples and in-vitro experiments. Lastly, we identified potential drugs targeting the glycolysis-associated signature. The derived glycolysis-related signature is an independent prognostic biomarker for THCA patients and might be used as an efficacy of biomarker for drug-sensitivity prediction.

Development and Validation of Extracorporeal Membrane Oxygenation Mortality-Risk Models for Congenital Diaphragmatic Hernia.

The purpose of our study was to develop and validate extracorporeal membrane oxygenation (ECMO)–specific mortality risk models for congenital diaphragmatic hernia (CDH). We utilized the data from the Extracorporeal Life Support Organization Registry (2000–2015). Prediction models were developed using multivariable logistic regression. We identified 4,374 neonates with CDH with an overall mortality of 52%. Predictive discrimination (C statistic) for pre-ECMO mortality model was C = 0.65 (95% confidence interval, 0.62–0.68). Within the highest risk group, based on the pre-ECMO risk score, mortality was 87% and 75% in the training and validation data sets, respectively. The pre-ECMO risk score included pre-ECMO ventilator settings, pH, prior diaphragmatic hernia repair, critical congenital heart disease, perinatal infection, and demographics. For the on-ECMO model, mortality prediction improved substantially: C = 0.73 (95% confidence interval, 0.71–0.76) with the addition of on-ECMO–associated complications. Within the highest risk group, defined by the on-ECMO risk score, mortality was 90% and 86% in the training and validation data sets, respectively. Mortality among neonates with CDH needing ECMO can be reliably predicted with validated clinical variables identified in this study. ECMO-specific mortality prediction tools can allow risk stratification to be used in research and quality improvement efforts, as well as with caution for individual case management.

Male-female differences in aortic valve and combined aortic valve/coronary surgery: a national cohort study in the Netherlands

ObjectiveThe outcome of female patients after adult cardiac surgery has been reported to be less favourable compared with the outcome of male patients. This study compares men with women with...

OA200] Age and gender specific radiation risk models as an alternative to effective dose

Purpose The purpose is to provide gender and age specific predictions of cancer risk to patients undergoing a variety of medical examinations and treatments using radiation. Methods Lifetime attributable risk (LAR) coefficients (the excess risk of receiving a cancer and the risk to die from it) have been applied to estimated organ absorbed dose data. The LAR coefficients were developed by the U.S. Environmental Protection Agency (EPA) and generated from three different variables, gender, attained age and age at exposure. Specifically, organ dose coefficients (mGy/MBq) from ICRP Publication 128 have been combined with recommended administered activities for a number of frequently used radiopharmaceuticals to get cancer risk predictions. Phantom measurements for clinically used exposure parameters have been performed to estimate organ dose values from different CT-examinations (as a complement to published data) as a base for cancer risk predictions. The most recent biokinetic model for iodide has been used for cancer risk predictions at therapy for thyreotoxicosis and thyroid cancer using 131I-iodide. Based on biokinetic data and organ dose estimates, cancer risks have been estimated for treatments using the α -emitting radiopharmaceutical 223Ra-chloride. Cancer risk predictions from the absorbed dose to tissues outside the treated volume at external beam radiation therapy, including radiation from treatment planning, and imaging for position control have been performed. Results Effective dose can – as expected – significantly underestimate the cancer risk for ages under 30 years and overestimate it for senior people. A limitation is that the risk projections published by EPA are derived for a US population and also for mostly healthy persons, and therefore might differ from patients exposed to radiation for medical reasons in Sweden. Ongoing work will adopt basic data to a Swedish population and later also for different patient populations. Conclusions The age and gender specific LAR risk estimations could be a valuable complement to effective dose for referring physicians, radiologists, oncologists, nuclear medicine physicians and medical physicists as well as ethical committees. It could also be a complement to current information material when notifying patients about benefits and risks connected to their radiological investigations or treatments.

On fair reinsurance premiums; Capital injections in a perturbed risk model

We consider a risk model where deficits after ruin are covered by a new type of reinsurance contract that provides capital injections. To allow the insurance company’s survival after ruin, the reinsurer injects capital only at ruin times caused by jumps larger than a chosen retention level. Otherwise capital must be raised from the shareholders for small deficits. The problem here is to determine adequate reinsurance premiums. It seems fair to base the net reinsurance premium on the discounted expected value of any future capital injections. Inspired by the results of Huzak et al. (2004) and Ben Salah (2014) on successive ruin events, we show that an explicit formula for these reinsurance premiums exists in a setting where aggregate claims are modeled by a subordinator and a Brownian perturbation. Here ruin events are due either to Brownian oscillations or jumps and reinsurance capital injections only apply in the latter case. The results are illustrated explicitly for two specific risk models and in some numerical examples.

A Risk Model for Prediction of 1-Year Mortality in Patients Undergoing MitraClip Implantation

There is a lack of specific tools for risk stratification in patients who undergo MitraClip implantation. We aimed at combining preprocedural variables with prognostic impact into a specific risk model for the prediction of 1-year mortality in patients undergoing MitraClip implantation. A total of 311 consecutive patients who underwent MitraClip implantation were included. A lasso-penalized Cox-proportional hazard regression model was used to identify independent predictors of 1-year all-cause mortality. A nomogram (GRASP [Getting Reduction of mitrAl inSufficiency by Percutaneous clip implantation] nomogram) was obtained from the Cox model. Validation was performed using internal bootstrap resampling. Forty-two deaths occurred at 1-year follow-up. The Kaplan-Meier estimate of 1-year survival was 0.845 (95% confidence interval, 0.802 to 0.895). Four independent predictors of mortality (mean arterial blood pressure, hemoglobin natural log-transformed pro-brain natriuretic peptide levels, New York Heart Association class IV at presentation) were identified. At internal bootstrap resampling validation, the GRASP nomogram had good discrimination (area under receiver operating characteristic curve of 0.78, Somers' Dxy statistic of 0.53) and calibration (le Cessie-van Houwelingen-Copas-Hosmer p value of 0.780). Conversely, the discriminative ability of the EuroSCORE II (the European System for Cardiac Operative Risk Evaluation II) and the STS-PROM (the Society of Thoracic Surgeons Predicted Risk of Mortality score) was fairly modest with area under the curve values of 0.61 and 0.55, respectively. A treatment-specific risk model in patients who undergo MitraClip implantation may be useful for the stratification of mortality at 1year. Further studies are needed to provide external validation and support the generalizability ofthe GRASP nomogram.

Whipple-specific complications result in prolonged length of stay not accounted for in ACS-NSQIP Surgical Risk Calculator

BackgroundThe American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Surgical Risk Calculator was developed to help counsel patients regarding estimated postoperative risk for a variety of surgical complications. This retrospective single institutional study examined the calculator's ability to accurately predict complications and length of hospital stay (LOS) in patients who had undergone a Pancreaticoduodenectomy (PD) at our institution. Methods165 patients at Washington University School of Medicine who underwent a PD from 8/2011 to 7/2013 were included. Surgical complication risk as determined by the ACS-NSQIP Surgical Risk Calculator were compared to actual 30 day complications. PD complications not accounted for by the calculator were compared to those without PD-specific complications. ResultsOverall predicted LOS was significantly shorter than actual duration of hospitalization (median 8.5 vs. 8.0 days; p < 0.001). 38% patients (n = 62) with Whipple-specific complication demonstrated a significant increase in LOS (8.0 vs. 12.2 days; p < 0.0001). DiscussionA large proportion of complications experienced after PD are pancreas-specific, accounting for the difference in predicted vs. actual LOS and providing rationale for future development of PD specific risk models.

Outcome of allogeneic stem cell transplantation for AML and myelodysplastic syndrome in elderly patients (⩾60 years).

Allogeneic stem cell transplantation (SCT) remains the best curative option for patients with refractory AML or with high-risk myelodysplastic syndrome (MDS). For decades, age alone had been widely used as the primary criterion to assess eligibility for allogeneic SCT; however, prospective studies to evaluate allogeneic SCT in elderly patients are still limited. A total of 187 patients (median age of 64 years, range 60-77 years) with AML (87%) or MDS (13%) transplanted between 1999 and 2014 were included in this retrospective analysis. Relapse-free survival (RFS) and overall survival (OS) at 3 years were 32% (95% confidence interval (CI): 25-39%) and 35% (95%CI: 27-42%), respectively. Overall survival was 49% (95%CI: 35-64%) in AML patients who were transplanted in first complete remission (CR1), but even patients with active disease did benefit from transplantation, showing an OS at 3 years of 30% (95%CI: 20-40%). Multivariate analysis revealed disease- and patient-specific risk indices as independent prognostic factors for OS and non-relapse mortality (NRM). In conclusion, our monocenter results indicate that patients should not be generally withheld from allogeneic SCT because of age or disease status only. Specific risk models incorporating disease status and disease-specific risk factors at the time of transplantation as well as existing comorbidities are helpful tools to assess transplantation-associated risk factors of elderly patients.