scholarly journals Adjusting preoperative risk models of post heart transplant survival to a European cohort in the age of a new cardiac allocation score in Europe

2018 ◽  
Vol 21 (6) ◽  
pp. E527-E533
Author(s):  
Tarik Alp Sargut ◽  
Panagiotis Pergantis ◽  
Christoph Knosalla ◽  
Jan Knierim ◽  
Manfred Hummel ◽  
...  
Keyword(s):  
Heart Transplant ◽  
Scoring System ◽  
Graft Failure ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Scores ◽  
Risk Models ◽  
Adjusted Risk ◽  
The Impact

Background Several risk models target the issue of posttransplant survival, but none of them have been validated in a large European cohort. This aspect is important, in a time of the planned change of the Eurotransplant allocation system to a scoring system. Material and Methods Data of 761 heart transplant recipients from the Eurotransplant region with a total follow up of 5027 patient-years were analyzed. We assessed 30-day to 10-year freedom from graft failure. Existing post-transplant mortality risk models, IMPACT, Meld-XI and Columbia Risk Stratification Score were (RSS) were evaluated. A new risk model was created and the predictive accuracy was compared with the existing risk scores, with a focus on LVAD patients. Results Thirty-day, 1-year, 5-year and 10-year rates of freedom from graft failure were 78.3±1.5%, 68.8±1.71%, 59.1±1.8% and 44.1±1.9. The 1-year incidence of graft failure varied from 14.1% to 50% (RSS), from 22.9% to 57.1 (IMPACT) and from 24.9% to 42.6% using MELD-XI. Our newly adjusted risk score showed an improved area under the curve (AUC) of 0.69 (95% CI 0.64-0.72) with better discrimination in the intermediate to moderate risk cohort (CABDES Score). Conclusion IMPACT, Meld-XI and RSS were suitable to predict posttransplant graft failure only in a high and low risk cohort. CABDES Score, might be an alternative scoring system, with donor age and eGFR beeing the strongest predictors. Implementation of the IMPACT score within the new Eurotransplant Cardiac Allocation Score for patient prioritization for heart transplantation, should be reevaluated.

scholarly journals Prognostic Value of Plasma hPG80 (Circulating Progastrin) in Metastatic Renal Cell Carcinoma

Cancers ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 375
Author(s):  
Manish Kohli ◽  
Winston Tan ◽  
Bérengère Vire ◽  
Pierre Liaud ◽  
Mélina Blairvacq ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Risk Groups ◽  
Prognostic Impact ◽  
Significant Difference

Precise management of kidney cancer requires the identification of prognostic factors. hPG80 (circulating progastrin) is a tumor promoting peptide present in the blood of patients with various cancers, including renal cell carcinoma (RCC). In this study, we evaluated the prognostic value of plasma hPG80 in 143 prospectively collected patients with metastatic RCC (mRCC). The prognostic impact of hPG80 levels on overall survival (OS) in mRCC patients after controlling for hPG80 levels in non-cancer age matched controls was determined and compared to the International Metastatic Database Consortium (IMDC) risk model (good, intermediate, poor). ROC curves were used to evaluate the diagnostic accuracy of hPG80 using the area under the curve (AUC). Our results showed that plasma hPG80 was detected in 94% of mRCC patients. hPG80 levels displayed high predictive accuracy with an AUC of 0.93 and 0.84 when compared to 18–25 year old controls and 50–80 year old controls, respectively. mRCC patients with high hPG80 levels (>4.5 pM) had significantly lower OS compared to patients with low hPG80 levels (<4.5 pM) (12 versus 31.2 months, respectively; p = 0.0031). Adding hPG80 levels (score of 1 for patients having hPG80 levels > 4.5 pM) to the six variables of the IMDC risk model showed a greater and significant difference in OS between the newly defined good-, intermediate- and poor-risk groups (p = 0.0003 compared to p = 0.0076). Finally, when patients with IMDC intermediate-risk group were further divided into two groups based on hPG80 levels within these subgroups, increased OS were observed in patients with low hPG80 levels (<4.5 pM). In conclusion, our data suggest that hPG80 could be used for prognosticating survival in mRCC alone or integrated to the IMDC score (by adding a variable to the IMDC score or by substratifying the IMDC risk groups), be a prognostic biomarker in mRCC patients.

scholarly journals Prognostic model and immune-infiltrating cell landscape based on differentially expressed autophagy-related genes in TP53-mutated multiple myeloma

2021 ◽  
Author(s):  
Yan-Hua Zheng ◽  
Hong-Yuan Shen ◽  
Xiang Chen ◽  
Juan Feng ◽  
Guang-Xun Gao
Keyword(s):  
Multiple Myeloma ◽  
Prognostic Model ◽  
Risk Model ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Threshold Value ◽  
Differentially Expressed ◽  
Term Survival ◽  
Tp53 Mutations ◽  
Infiltrating Cells

IntroductionAutophagy functions as a prosurvival mechanism in multiple myeloma (MM).The objective of this research was to establish an autophagy-related gene (ARG) signature for predicting the survival outcomes of MM patients with TP53 mutations.Material and methodsInformation about MM patients with TP53 mutations was downloaded from Gene Expression Omnibus (GEO) database. Cox proportional hazard regression was employed to determine the independent prognostic ARG and construct a risk signature. Time-dependent receiver-operating characteristic (t ROC) curve was used to explore the predictive accuracy of the prognostic model. A nomogram was constructed to give a more precise prediction of the probability of 5-year, 8-year and 10-year overall survival (OS). In addition, we utilized the CIBERSORT algorithm to explore the distribution difference of 22 immune-infiltrating cells.ResultsThree differentially expressed ARGs (CASP8, MAPK8, RB1CC1) were finally incorporated to construct the risk model. Area under the curve (AUC) of corresponding tROC curve for 5-year,8-year and 10-year OS were 0.735, 0.686 and 0.662, respectively. MM patients were categorized into high and low-risk group in accordance with the median threshold value (-1.724549). ARG-based risk score model was an independent prognostic element correlated with OS, giving an hazard ratio (HR) of 3.29 (95%CI 2.35-4.60, P<0.001). 13 immune infiltrating cells were found to have distribution differences between the two groups.ConclusionsWe established a three-ARGs risk signature which manifested an independent prognostic factor. The nomogram was testified to perform well in forecasting the long-term survival of TP53-mutated MM patients.

scholarly journals Assessing the Clinical Impact of Risk Prediction Models With Decision Curves: Guidance for Correct Interpretation and Appropriate Use

2016 ◽  
Vol 34 (21) ◽  
pp. 2534-2540 ◽  
Author(s):  
Kathleen F. Kerr ◽  
Marshall D. Brown ◽  
Kehao Zhu ◽  
Holly Janes
Keyword(s):  
Prostate Cancer ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Model ◽  
Characteristic Curve ◽  
Clinical Impact ◽  
Adjuvant Radiation ◽  
Risk Models ◽  
Personal Benefit ◽  
The Impact

The decision curve is a graphical summary recently proposed for assessing the potential clinical impact of risk prediction biomarkers or risk models for recommending treatment or intervention. It was applied recently in an article in Journal of Clinical Oncology to measure the impact of using a genomic risk model for deciding on adjuvant radiation therapy for prostate cancer treated with radical prostatectomy. We illustrate the use of decision curves for evaluating clinical- and biomarker-based models for predicting a man’s risk of prostate cancer, which could be used to guide the decision to biopsy. Decision curves are grounded in a decision-theoretical framework that accounts for both the benefits of intervention and the costs of intervention to a patient who cannot benefit. Decision curves are thus an improvement over purely mathematical measures of performance such as the area under the receiver operating characteristic curve. However, there are challenges in using and interpreting decision curves appropriately. We caution that decision curves cannot be used to identify the optimal risk threshold for recommending intervention. We discuss the use of decision curves for miscalibrated risk models. Finally, we emphasize that a decision curve shows the performance of a risk model in a population in which every patient has the same expected benefit and cost of intervention. If every patient has a personal benefit and cost, then the curves are not useful. If subpopulations have different benefits and costs, subpopulation-specific decision curves should be used. As a companion to this article, we released an R software package called DecisionCurve for making decision curves and related graphics.

A novel risk score for prediction of hypersensitivity reactions in cancer patients receiving carboplatin: Retrospective observational analysis.

2016 ◽  
Vol 34 (3_suppl) ◽  
pp. e282-e282
Author(s):  
Orawan Suppramote ◽  
Prapatsara Pongpunpisand ◽  
Kanlaya Ladkam ◽  
Somkiat Rujirawat
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Scoring System ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Cancer Type ◽  
Hypersensitivity Reactions ◽  
Risk Patients ◽  
Carboplatin Hypersensitivity

e282 Background: Hypersentitivity reactions (HSRs) from carboplatin are high incidence and most severity in Chulabhorn hospital. These reactions are associated with several causes including patient factors and experience in drug used. A reliable and valid tool for evaluated risk of HSRs before started carboplatin infusion should lead to prevent or decrease severity of the reactions. We innovated risk score to screen patient at high risk of HSRs. Methods: From October 2013 to September 2014, all cancer patients who received carboplatin in Chulabhorn hospital were included. A retrospective study design to developed risk scoring system for prediction of patients at high risk of carboplatin hypersensitivity called “Hypersensitivity risk score”. The hypersensitivity risk score was calculated for all patients receiving carboplatin and data for carboplatin hypersensitivity were obtained from medical records. Expected and observed HSRs were analyzed by using receiver operating characteristic (ROC) curve. Results: Seventy-three cancer patients received carboplatin and five (7%) patients had HSRs. Our scoring algorithm based on cancer type, number of carboplatin retreatment, duration between each retreatment, and number of carboplatin infusions prior to first reaction. All significant predictors were weighted into points and categorized to risk group which ranged from 0 to 8 . The ROC analysis for hypersensitivity risk score indicated good predictive accuracy with an area under the curve of 0.96 (95 %CI: 0.91-1.00). Data showed high sensitivity (80%) and specificity (94.85%) for a risk score cut-off of 4. The hypersensitivity risk score clearly differentiated the low (0-1), intermediate (2-3) and intermediate-high (4-5) and high (6-8) risk patients. Conclusions: The hypersensitivity risk score is a simple scoring system with high predictive value and differentiates low versus high risk patients. This score should be used for screen high risk of hypersensitivity reactions in patients receiving carboplatin.

scholarly journals Discrete-Time Risk Models with Claim Correlated Premiums in a Markovian Environment

Risks ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 26
Author(s):  
Dhiti Osatakul ◽  
Xueyuan Wu
Keyword(s):  
Discrete Time ◽  
Risk Model ◽  
Insurance Industry ◽  
External Environment ◽  
Ruin Probabilities ◽  
Risk Models ◽  
Markovian Environment ◽  
Life Insurance Industry ◽  
Claim Frequency ◽  
The Impact

In this paper we consider a discrete-time risk model, which allows the premium to be adjusted according to claims experience. This model is inspired by the well-known bonus-malus system in the non-life insurance industry. Two strategies of adjusting periodic premiums are considered: aggregate claims or claim frequency. Recursive formulae are derived to compute the finite-time ruin probabilities, and Lundberg-type upper bounds are also derived to evaluate the ultimate-time ruin probabilities. In addition, we extend the risk model by considering an external Markovian environment in which the claims distributions are governed by an external Markov process so that the periodic premium adjustments vary when the external environment state changes. We then study the joint distribution of premium level and environment state at ruin given ruin occurs. Two numerical examples are provided at the end of this paper to illustrate the impact of the initial external environment state, the initial premium level and the initial surplus on the ruin probability.

Neutrophil-lymphocyte score: A novel prognostic scoring system that utilized the dynamic change of neutrophil, lymphocyte, and albumin and its comparison to other indices.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 3048-3048
Author(s):  
Songzhu Zhao ◽  
Mingjia Li ◽  
Daniel Spakowicz ◽  
Sandip H. Patel ◽  
Andrew Johns ◽  
...  
Keyword(s):  
Scoring System ◽  
Prognostic Value ◽  
Predictive Accuracy ◽  
Dynamic Change ◽  
Area Under The Curve ◽  
Roc Curves ◽  
Time Dependent ◽  
Advanced Lung Cancer ◽  
Lymphocyte Ratio ◽  
Lymphocyte To Monocyte Ratio

3048 Background: Indications for immune checkpoint inhibitor (ICI) in cancer care are expanding rapidly. There is increasing need for accurate decision tool to better guide treatment. We have constructed a new prognostic scoring system, neutrophil-lymphocyte score (NRS), based on the nonlinear dynamic change of neutrophil to lymphocyte ratio (NLR) in relation to survival over the first cycle of ICI treatment. We compared this novel system to existing indices such as NLR, lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR), Advanced Lung Cancer Inflammation Index (ALI), and Systemic Immune-inflammation Index (SII). Methods: This is a retrospective analysis of 837 patients at Ohio State University from 2011-18. Neutrophil (ANC), lymphocyte (ALC), platelet (plt), monocyte (AMC), albumin (alb), and body mass index (BMI) were collected at baseline. Repeat labs were collected at cycle 2. NLR = ANC/ALC, ALI = BMI x alb / NLR, LMR = ALC/AMC, SII = platelet x NLR, PLR = plt/ALC. NLR Ratio = baseline NLR / repeat NLR. Based on the association between NLR and the overall survival, we assigned 1 point (p) for baseline NLR < 0.7, 6p for 0.7 to < 2, 5p for 2 to < 3, 4 p for 3 to < 4, 3 for 4 to 5, 2p for 5 to < 9, and 1p for ≥9. We also assigned 1p for NLR ratio < 0.6, 2p for 0.6 to < 0.8, 3p for 0.8 to < 1.2, 5p for 1.25 to < 1.4, 3p for 1.4 to < 1.6, and 2p for ≥1.6. NLS = sum of these 2 scores . NLS_A = NLS*alb. Time-dependent receiver operator characteristic (ROC) curves with integrated time-dependent area under the curve (TD AUC) values were used to evaluate the predictive accuracy of each index for survival. Results: For baseline and repeat values, all indices were statistically significant (P < 0.001) in predicting survival. Baseline integrated TD AUC were: ALI 0.704, NLR 0.692, SII 0.663, LMR 0.645, and PLR 0.612. All of the repeat indices at cycle 2 had higher prognostic value than their baseline counterparts. Integrated TD AUC for indices at cycle 2 were: ALI 0.740 (with baseline BMI), NLR 0.729, SII 0.694, LMR 0.671, and PLR 0.652. NLS_A was a composite score based on the dynamic change of NLR from cycle 1 to 2 and the treatment alb with integrated TD-AUC at 0.754. Conclusions: Indices constructed from ANC, ALC, AMC, Plt, alb, and BMI can be obtained inexpensively and provide great prognostic value for pts on ICI. We have constructed a novel scoring system (NLS_A) and demonstrated its improvement over the current prognostic indices. Studies with a larger cohort are needed to further improve and validate this system.

Estimation of cardiovascular risk: a comparison between the Framingham and the SCORE model in people under 60 years of age

2008 ◽  
Vol 15 (5) ◽  
pp. 562-566 ◽  
Author(s):  
Tjarda Scheltens ◽  
W.M. Monique Verschuren ◽  
Hendriek C. Boshuizen ◽  
Arno W. Hoes ◽  
Nicolaas P. Zuithoff ◽  
...  
Keyword(s):  
Cohort Study ◽  
Cardiovascular Risk ◽  
Goodness Of Fit ◽  
Risk Model ◽  
Treatment Guidelines ◽  
Risk Function ◽  
Area Under The Curve ◽  
Risk Models ◽  
Discriminative Ability ◽  
Score Model

Background The Framingham Heart Study risk model has been used in the majority of cardiovascular risk management guidelines. Recently, a new model based on the SCORE system has been proposed. We compared both risk models with regard to their ability to predict cardiovascular mortality in the Netherlands. Design Cohort study. Methods In a Dutch cohort study of 39 719 persons, three properties of the risk models were investigated: discriminating ability (ranking persons in order of risks, expressed in area under the curve); calibrating ability (prediction of events compared with actual events expressed in goodness of fit); and the number of persons assigned to treatment according to the guideline. Results The discriminative ability of both models was similar: the area under the curve of Framingham was 0.86 and of SCORE 0.85. Calibration of both functions was inadequate. The goodness of fit of the SCORE model was 35 and of the Framingham model 64, whereas a goodness of fit less than 20 is considered acceptable. Using the Dutch guideline treatment threshold of 10% mortality risk, the SCORE risk function assigned 0.4% of the population to drug treatment where the Framingham function assigned 0.7%. Conclusion The findings of this study show that both the SCORE and the Framingham model function have a good discriminative ability but are insufficient in predicting absolute risks. SCORE assigned fewer participants to treatment than Framingham. If a new risk model is implemented in treatment guidelines, comparison with the model in use and evaluation of calibrating features is needed.

scholarly journals Risk models and scores for metabolic syndrome: systematic review protocol

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e027326 ◽  
Author(s):  
Musa Saulawa Ibrahim ◽  
Dong Pang ◽  
Gurch Randhawa ◽  
Yannis Pappas
Keyword(s):  
Metabolic Syndrome ◽  
Adult Population ◽  
Quality Criteria ◽  
Risk Scores ◽  
Primary Data ◽  
Cochrane Library ◽  
Risk Models ◽  
Manual Search ◽  
The Metabolic Syndrome ◽  
The Impact

IntroductionMetabolic syndrome ‘a clustering of risk factors which includes hypertension central obesity, impaired glucose metabolism with insulin resistance and dyslipidaemia’ affects approximately 20%–25% of the global adult population. Individuals with metabolic syndrome have two to threefold risk of developing cardiovascular disease and a fivefold risk of developing developing diabetes and death from all causes. Although there is rapid proliferation of risk scores for predicting the risk of developing metabolic syndrome later in life, yet, these are seldom used in the practice. Therefore, the purpose of this review is to determine the performance of risk models and scores for predicting the metabolic syndrome.Methods and analysisArticles will be sought for from electronic databases (MEDLINE, CINAHL, PubMed and Web of Science) as well as the Cochrane Library. Further manual search of reference lists and grey literatures will be conducted. The search will cover from the start of indexing to 3 October 2018. Identified studies will be included if they fulfil the study selection criteria. Quality of studies will be appraised using suitable criteria for the risk models. The risk scores in the final sample of the review will be ranked/prioritised based on previous quality criteria for prognostic risk models. Lastly, the impact of the models will be ascertained by tracking citations on Google Scholar.Ethics and disseminationThis study does not require formal ethical approval as primary data will not be collected. The results will be disseminated through a peer-reviewed publication and relevant conference presentations.PROSPERO registration numberCRD42019139326

scholarly journals Multiple SNP testing improves risk prediction of first venous thrombosis

Blood ◽  
2012 ◽  
Vol 120 (3) ◽  
pp. 656-663 ◽  
Author(s):  
Hugoline G. de Haan ◽  
Irene D. Bezemer ◽  
Carine J. M. Doggen ◽  
Saskia Le Cessie ◽  
Pieter H. Reitsma ◽  
...  
Keyword(s):  
Venous Thrombosis ◽  
Risk Prediction ◽  
Genetic Risk ◽  
Risk Model ◽  
Cost Effective ◽  
Risk Scores ◽  
Nucleotide Polymorphisms ◽  
Risk Models ◽  
Genetic Risk Scores ◽  
Risk Alleles

Abstract There are no risk models available yet that accurately predict a person's risk for developing venous thrombosis. Our aim was therefore to explore whether inclusion of established thrombosis-associated single nucleotide polymorphisms (SNPs) in a venous thrombosis risk model improves the risk prediction. We calculated genetic risk scores by counting risk-increasing alleles from 31 venous thrombosis-associated SNPs for subjects of a large case-control study, including 2712 patients and 4634 controls (Multiple Environmental and Genetic Assessment). Genetic risk scores based on all 31 SNPs or on the 5 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves [AUCs] of 0.70 and 0.69, respectively). For the 5-SNP risk score, the odds ratios for venous thrombosis ranged from 0.37 (95% confidence interval [CI], 0.25-0.53) for persons with 0 risk alleles to 7.48 (95% CI, 4.49-12.46) for persons with more than or equal to 6 risk alleles. The AUC of a risk model based on known nongenetic risk factors was 0.77 (95% CI, 0.76-0.78). Combining the nongenetic and genetic risk models improved the AUC to 0.82 (95% CI, 0.81-0.83), indicating good diagnostic accuracy. To become clinically useful, subgroups of high-risk persons must be identified in whom genetic profiling will also be cost-effective.

scholarly journals A Smoothed Version of the Lassosum Penalty for Fitting Integrated Risk Models Using Summary Statistics or Individual-Level Data

Genes ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 112
Author(s):  
Georg Hahn ◽  
Dmitry Prokopenko ◽  
Sharon Lutz ◽  
Kristina Mullin ◽  
Rudolph Tanzi ◽  
...  
Keyword(s):  
Risk Model ◽  
Disease Risk ◽  
Risk Scores ◽  
Chronic Obstructive ◽  
Summary Statistics ◽  
Risk Models ◽  
Polygenic Risk ◽  
Individual Level ◽  
Integrated Risk ◽  
Genotype Information

Polygenic risk scores are a popular means to predict the disease risk or disease susceptibility of an individual based on its genotype information. When adding other important epidemiological covariates such as age or sex, we speak of an integrated risk model. Methodological advances for fitting more accurate integrated risk models are of immediate importance to improve the precision of risk prediction, thereby potentially identifying patients at high risk early on when they are still able to benefit from preventive steps/interventions targeted at increasing their odds of survival, or at reducing their chance of getting a disease in the first place. This article proposes a smoothed version of the “Lassosum” penalty used to fit polygenic risk scores and integrated risk models using either summary statistics or raw data. The smoothing allows one to obtain explicit gradients everywhere for efficient minimization of the Lassosum objective function while guaranteeing bounds on the accuracy of the fit. An experimental section on both Alzheimer’s disease and COPD (chronic obstructive pulmonary disease) demonstrates the increased accuracy of the proposed smoothed Lassosum penalty compared to the original Lassosum algorithm (for the datasets under consideration), allowing it to draw equal with state-of-the-art methodology such as LDpred2 when evaluated via the AUC (area under the ROC curve) metric.

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