A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

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POS1439 A MULTI-PREDICTOR MODEL TO PREDICT RISK OF SCLERODERMA RENAL CRISIS IN SYSTEMIC SCLEROSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.3613 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1004.1-1004

Author(s):

D. Xu ◽

R. Mu

Keyword(s):

Systemic Sclerosis ◽

Prediction Model ◽

Renal Involvement ◽

Area Under The Curve ◽

Individual Risk ◽

Multivariable Logistic Regression Analysis ◽

Scleroderma Renal Crisis ◽

Lasso Regression ◽

Patients At Risk ◽

Renal Crisis

Background:Scleroderma renal crisis (SRC) is a life-threatening syndrome. The early identification of patients at risk is essential for timely treatment to improve the outcome[1].Objectives:We aimed to provide a personalized tool to predict risk of SRC in systemic sclerosis (SSc).Methods:We tried to set up a SRC prediction model based on the PKUPH-SSc cohort of 302 SSc patients. The least absolute shrinkage and selection operator (Lasso) regression was used to optimize disease features. Multivariable logistic regression analysis was applied to build a SRC prediction model incorporating the features of SSc selected in the Lasso regression. Then, a multi-predictor nomogram combining clinical characteristics was constructed and evaluated by discrimination and calibration.Results:A multi-predictor nomogram for evaluating the risk of SRC was successfully developed. In the nomogram, four easily available predictors were contained including disease duration <2 years, cardiac involvement, anemia and corticosteroid >15mg/d exposure. The nomogram displayed good discrimination with an area under the curve (AUC) of 0.843 (95% CI: 0.797-0.882) and good calibration.Conclusion:The multi-predictor nomogram for SRC could be reliably and conveniently used to predict the individual risk of SRC in SSc patients, and be a step towards more personalized medicine.References:[1]Woodworth TG, Suliman YA, Li W, Furst DE, Clements P (2016) Scleroderma renal crisis and renal involvement in systemic sclerosis. Nat Rev Nephrol 12 (11):678-91.Disclosure of Interests:None declared

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Validation of the Zwolle score for selection of very low-risk STEMI patients treated with primary angioplasty

European Heart Journal ◽

10.1093/ehjci/ehaa946.1840 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

A Cordero ◽

B Cid ◽

P Monteiro ◽

J.M Garcia-Acuna ◽

M Rodriguez-Manero ◽

...

Keyword(s):

Hospital Mortality ◽

Risk Score ◽

Mortality Risk ◽

Area Under The Curve ◽

Coronary Intervention ◽

Low Risk ◽

Individual Risk ◽

Care Organization ◽

Grace Score ◽

Selection Of

Abstract Background The Zwolle risk score was designed to stratify the actual in-hospital mortality risk of ST-elevation myocardial infarction (STEMI) patients treated with primary percutaneous coronary intervention (p-PCI) but, also, for decision-making related to patients location in an intensive care unit or not. Since the GRACE score continues being the gold-standard for individual risk assessment in STEMI in most institutions we assessed the specificity of both scores for in-hospital mortality. Methods We assessed the accuracy of Zwolle risk score for in-hospital mortality estimation as compared to the GRACE score in all patients admitted for STEMI in 3 tertitary hospitals. Patients with Zwolle risk score <3 would qualify as “low risk”, 3–5 as “intermediate risk” and ≥6 as “high risk”. Patients with GRACE score <140 were classified as low-risk. Specificity, sensitivity and classification were assessed by ROC curves and the area under the curve (AUC). Results We included 4,446 patients, mean age 64.7 (13.6) years, 24% women and 39% with diabetes. Mean GRACE score was 157.3 (4.9) and Zwolle was 2.8 (3.3). In-hospital mortality was 10.6% (471 patients). Patients who died had higher GRACE score (218.4±4.9 vs. 149.6±37.5; p<0.001) and Zwolle score (7.6±4.3 vs. 2.3±2.18; p<0.001); a statistically significant increase of in-hospital mortality risk, adjusted adjusted by age, gender and revascularization, was observed with both scores (figure). A total of 1,629 patients (40.0%) were classified as low risk by the GRACE score and 2,962 (66.6%) by the Zwolle score; in-hospital mortality was 1.6% and 2.7%, respectively. Moreover, the was a significant increase of in-hospital mortality rate according to Zwolle categories (2.7%; 13.0%; 41.6%)The AUC of both score was the same (p=0.49) but the specificity of GRACE score <140 was 43.1% as compared to 72.6% obtained by Zwolle score <3; patients accurately classified was also lower with the GRACE score threshold (48.8% vs. 73.7%). Conclusions Selection of low-risk STEMI patients treated with p-PCI based on the Zwolle risk score has higher specificity than the GRACE score and might be useful for the care organization in clinical practice. Funding Acknowledgement Type of funding source: None

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Polygenic risk scores predict diabetic complications and their response to therapy

10.1101/19010785 ◽

2019 ◽

Author(s):

J. Tremblay ◽

M. Haloui ◽

F. Harvey ◽

R. Tahir ◽

F.-C. Marois-Blanchet ◽

...

Keyword(s):

High Risk ◽

Prediction Model ◽

Risk Group ◽

Intensive Therapy ◽

Cardiovascular Death ◽

Response To Therapy ◽

Low Risk ◽

Number Needed To Treat ◽

Genome Wide Association Studies ◽

Increased Risk

AbstractType 2 diabetes increases the risk of cardiovascular and renal complications, but early risk prediction can lead to timely intervention and better outcomes. Through summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals, we combined 9 PRS gathering genomic variants associated to cardiovascular and renal diseases and their key risk factors into one logistic regression model, to predict micro- and macrovascular endpoints of diabetes. Its clinical utility in predicting complications of diabetes was tested in 4098 participants with diabetes of the ADVANCE trial followed during a period of 10 years and replicated it in three independent non-trial cohorts. The prediction model adjusted for ethnicity, sex, age at onset and diabetes duration, identified the top 30% of ADVANCE participants at 3.1-fold increased risk of major micro- and macrovascular events (p=6.3×10−21 and p=9.6×10−31, respectively) and at 4.4-fold (p=6.8×10−33) increased risk of cardiovascular death compared to the remainder of T2D subjects. While in ADVANCE overall, combined intensive therapy of blood pressure and glycaemia decreased cardiovascular mortality by 24%, the prediction model identified a high-risk group in whom this therapy decreased mortality by 47%, and a low risk group in whom the therapy had no discernable effect. Patients with high PRS had the greatest absolute risk reduction with a number needed to treat of 12 to prevent one cardiovascular death over 5 years. This novel polygenic prediction model identified people with diabetes at low and high risk of complications and improved targeting those at greater benefit from intensive therapy while avoiding unnecessary intensification in low-risk subjects.

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An immune-related lncRNA signature as a prognostic immunotherapy target for colon cancer

10.21203/rs.3.rs-35120/v1 ◽

2020 ◽

Author(s):

Bin Wu ◽

Yi Yao ◽

Yi Dong ◽

Si Qi Yang ◽

Deng Jing Zhou ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Risk Score ◽

Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Risk Scores ◽

Gene Set Enrichment ◽

Immunotherapy Target

Abstract Background：We aimed to investigate an immune-related long non-coding RNA (lncRNA) signature that may be exploited as a potential immunotherapy target in colon cancer. Materials and methods: Colon cancer samples from The Cancer Genome Atlas (TCGA) containing available clinical information and complete genomic mRNA expression data were used in our study. We then constructed immune-related lncRNA co-expression networks to identify the most promising immune-related lncRNAs. According to the risk score developed from screened immune-related lncRNAs, the high-risk and low-risk groups were separated on the basis of the median risk score, which served as the cutoff value. An overall survival analysis was then performed to confirm that the risk score developed from screened immune-related lncRNAs could predict colon cancer prognosis. The prediction reliability was further evaluated in the independent prognostic analysis and receiver operating characteristic curve (ROC). A principal component analysis (PCA) and gene set enrichment analysis (GSEA) were performed for functional annotation. Results: Information for a total of 514 patients was included in our study. After multiplex analysis, 12 immune-related lncRNAs were confirmed as a signature to evaluate the risk scores for each patient with cancer. Patients in the low-risk group exhibited a longer overall survival (OS) than those in the high-risk group. Additionally, the risk scores were an independent factor, and the Area Under Curve (AUC) of ROC for accuracy prediction was 0.726. Moreover, the low-risk and high-risk groups displayed different immune statuses based on principal components and gene set enrichment analysis.Conclusions: Our study suggested that the signature consisting of 12 immune-related lncRNAs can provide an accessible approach to measuring the prognosis of colon cancer and may serve as a valuable antitumor immunotherapy.

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Development and Validation of a Clostridium difficile Infection Risk Prediction Model

Infection Control and Hospital Epidemiology ◽

10.1086/658944 ◽

2011 ◽

Vol 32 (4) ◽

pp. 360-366 ◽

Cited By ~ 62

Author(s):

Erik R. Dubberke ◽

Yan Yan ◽

Kimberly A. Reske ◽

Anne M. Butler ◽

Joshua Doherty ◽

...

Keyword(s):

High Risk ◽

Clostridium Difficile ◽

Prediction Model ◽

Risk Prediction ◽

Predictive Accuracy ◽

Medical Center ◽

Tertiary Care ◽

Risk Prediction Model ◽

Brier Score ◽

Tertiary Care Medical Center

Objective.To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.Design and Setting.Retrospective cohort study in a tertiary care medical center.Patients.Patients admitted to the hospital for at least 48 hours during the calendar year 2003.Methods.Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.Results.A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).Conclusions.The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.

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P0894OBESITY IMPAIRS THE ACUTE RESPONSE TO AN ORAL PHOSPHATE CHALLENGE

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa142.p0894 ◽

2020 ◽

Vol 35 (Supplement_3) ◽

Author(s):

Mandy Turner ◽

Christine White ◽

Patrick Norman ◽

Corinne Babiolakis ◽

Michael Adams ◽

...

Keyword(s):

Insulin Resistance ◽

High Risk ◽

Kidney Function ◽

Area Under The Curve ◽

Fractional Excretion ◽

Low Risk ◽

Normal Kidney ◽

Phosphate Excretion ◽

Fractional Excretion Of Phosphate ◽

Age And Sex

Abstract Background and Aims T Obesity is an increasing health problem world-wide. People who are overweight or obese are at greater risk of developing chronic diseases including cardiovascular disease (CVD). Factors associated with dysregulated phosphate metabolism have been linked to the presence of vascular calcification in people with type 2 diabetes (T2D) with normal kidney function. Insulin resistance and abdominal obesity are associated with increased circulating levels of phosphaturic hormones including fibroblast growth factor 23 (FGF-23) and parathyroid hormone (PTH). Abnormalities in phosphate regulation may not be reflected in single circulating measurements of serum phosphate, but can be revealed by the acute circulating and mineral response to an oral challenge of phosphate. The aim of this study was to determine if obesity and insulin resistance impact the acute capacity to excrete an oral phosphate challenge. Method Community-dwelling people (N=78) free of T2D and symptomatic CVD (∼10 males and ∼10 females from each decade between 40 and 80 years) with normal kidney function were recruited from Kingston, Ontario, Canada. Following a 12-hour fast, participants consumed a 1250 mg phosphate drink (sodium phosphate) where blood and urine were collected at baseline, 1, 2 and 3 hours following the oral challenge. Participants with a high-risk metabolic profile characterized by an elevated waist-to-height ratio (WHtR) (> 0.58) were matched by age and sex to participants with a low risk WHtR (<0.5). Results The results reveal a significant impact of obesity on phosphate excretion in response to an oral phosphate challenge. There was an association between WHtR ratio and the level of iFGF-23 (R=-0.34 p<0.01) but not PTH. After adjustment for age and sex, WHtR ratio was inversely correlated with urinary phosphate excretion in response to the phosphate challenge (R=-0.29, p=0.02) and the change in fractional excretion of phosphate (r=-0.34, p=0.007). From the larger cohort, an age- and sex- matched subset was selected for 12 high risk and 12 low risk metabolic profiles with WHtR of 0.66±0.02 and 0.46±0.01, respectively. Kidney function was the same between the two groups (eGFR 92.3±13.1 versus 95.8±13.6 ml/min/1.73m2 respectively) but high risk participants had significantly higher homeostatic model assessment of insulin resistance (HOMA-IR) (1.61±0.81 versus 0.68±0.3, p<0.01). Participants with a high risk metabolic profile had a greater increase in serum phosphate from baseline (29% increase in the area under the curve, p=0.04) and a significantly blunted increase in the fractional excretion of phosphate in response to the oral phosphate challenge (35% reduction in area under the curve [AUC], p=0.03) compared to the matched low risk profile participants. Conclusion Overweight/obese individuals demonstrate impaired response to an oral phosphate challenge, whereby phosphate excretion was impaired and there was increased exposure to new circulating phosphate. An impaired acute phosphate response may contribute to the initiation or propagation of vascular calcification. Dysregulated phosphate homeostasis may be an under-recognized cardiovascular risk factor in obese people that could be modified by diet and weight loss. Whether insulin enhances renal phosphate reabsorption requires further study.

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Prediction of Ischemic Heart Disease and Stroke in Survivors of Childhood Cancer

Journal of Clinical Oncology ◽

10.1200/jco.2017.74.8673 ◽

2018 ◽

Vol 36 (1) ◽

pp. 44-52 ◽

Cited By ~ 35

Author(s):

Eric J. Chow ◽

Yan Chen ◽

Melissa M. Hudson ◽

Elizabeth A.M. Feijen ◽

Leontien C. Kremer ◽

...

Keyword(s):

Heart Disease ◽

High Risk ◽

Ischemic Heart Disease ◽

Childhood Cancer ◽

Area Under The Curve ◽

Risk Groups ◽

Ischemic Heart ◽

Risk Scores ◽

Individual Risk ◽

Survivors Of Childhood Cancer

Purpose We aimed to predict individual risk of ischemic heart disease and stroke in 5-year survivors of childhood cancer. Patients and Methods Participants in the Childhood Cancer Survivor Study (CCSS; n = 13,060) were observed through age 50 years for the development of ischemic heart disease and stroke. Siblings (n = 4,023) established the baseline population risk. Piecewise exponential models with backward selection estimated the relationships between potential predictors and each outcome. The St Jude Lifetime Cohort Study (n = 1,842) and the Emma Children’s Hospital cohort (n = 1,362) were used to validate the CCSS models. Results Ischemic heart disease and stroke occurred in 265 and 295 CCSS participants, respectively. Risk scores based on a standard prediction model that included sex, chemotherapy, and radiotherapy (cranial, neck, and chest) exposures achieved an area under the curve and concordance statistic of 0.70 and 0.70 for ischemic heart disease and 0.63 and 0.66 for stroke, respectively. Validation cohort area under the curve and concordance statistics ranged from 0.66 to 0.67 for ischemic heart disease and 0.68 to 0.72 for stroke. Risk scores were collapsed to form statistically distinct low-, moderate-, and high-risk groups. The cumulative incidences at age 50 years among CCSS low-risk groups were < 5%, compared with approximately 20% for high-risk groups ( P < .001); cumulative incidence was only 1% for siblings ( P < .001 v low-risk survivors). Conclusion Information available to clinicians soon after completion of childhood cancer therapy can predict individual risk for subsequent ischemic heart disease and stroke with reasonable accuracy and discrimination through age 50 years. These models provide a framework on which to base future screening strategies and interventions.

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Risk category system to identify pituitary adenoma patients with AIP mutations

Journal of Medical Genetics ◽

10.1136/jmedgenet-2017-104957 ◽

2018 ◽

Vol 55 (4) ◽

pp. 254-260 ◽

Cited By ~ 16

Author(s):

Francisca Caimari ◽

Laura Cristina Hernández-Ramírez ◽

Mary N Dang ◽

Plamena Gabrovska ◽

Donato Iacovazzo ◽

...

Keyword(s):

Pituitary Adenoma ◽

Family History ◽

High Risk ◽

Pituitary Adenomas ◽

Univariate Analysis ◽

Low Risk ◽

Individual Risk ◽

Risk Category ◽

Moderate Risk ◽

Category System

BackgroundPredictive tools to identify patients at risk for gene mutations related to pituitary adenomas are very helpful in clinical practice. We therefore aimed to develop and validate a reliable risk category system for aryl hydrocarbon receptor-interacting protein (AIP) mutations in patients with pituitary adenomas.MethodsAn international cohort of 2227 subjects were consecutively recruited between 2007 and 2016, including patients with pituitary adenomas (familial and sporadic) and their relatives. All probands (n=1429) were screened for AIP mutations, and those diagnosed with a pituitary adenoma prospectively, as part of their clinical screening (n=24), were excluded from the analysis. Univariate analysis was performed comparing patients with and without AIP mutations. Based on a multivariate logistic regression model, six potential factors were identified for the development of a risk category system, classifying the individual risk into low-risk, moderate-risk and high-risk categories. An internal cross-validation test was used to validate the system.Results1405 patients had a pituitary tumour, of which 43% had a positive family history, 55.5% had somatotrophinomas and 81.5% presented with macroadenoma. Overall, 134 patients had an AIP mutation (9.5%). We identified four independent predictors for the presence of an AIP mutation: age of onset providing an odds ratio (OR) of 14.34 for age 0-18 years, family history (OR 10.85), growth hormone excess (OR 9.74) and large tumour size (OR 4.49). In our cohort, 71% of patients were identified as low risk (<5% risk of AIP mutation), 9.2% as moderate risk and 20% as high risk (≥20% risk). Excellent discrimination (c-statistic=0.87) and internal validation were achieved.ConclusionWe propose a user-friendly risk categorisation system that can reliably group patients into high-risk, moderate-risk and low-risk groups for the presence of AIP mutations, thus providing guidance in identifying patients at high risk of carrying an AIP mutation. This risk score is based on a cohort with high prevalence of AIP mutations and should be applied cautiously in other populations.

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A Novel Seventeen-Gene Metabolic Signature for Predicting Prognosis in Colon Cancer

BioMed Research International ◽

10.1155/2020/4845360 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Dakui Luo ◽

Zezhi Shan ◽

Qi Liu ◽

Sanjun Cai ◽

Qingguo Li ◽

...

Keyword(s):

Colon Cancer ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Differentially Expressed ◽

Metabolic Signature ◽

Metabolic Genes ◽

Prognostic Prediction

A metabolic disorder is considered one of the hallmarks of cancer. Multiple differentially expressed metabolic genes have been identified in colon cancer (CC), and their biological functions and prognostic values have been well explored. The purpose of the present study was to establish a metabolic signature to optimize the prognostic prediction in CC. The related data were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) database, and Gene Expression Omnibus (GEO) combined with GSE39582 set, GSE17538 set, GSE33113 set, and GSE37892 set. The differentially expressed metabolic genes were selected for univariate Cox regression and lasso Cox regression analysis using TCGA and GTEx datasets. Finally, a seventeen-gene metabolic signature was developed to divide patients into a high-risk group and a low-risk group. Patients in the high-risk group presented poorer prognosis compared to the low-risk group in both TCGA and GEO datasets. Moreover, gene set enrichment analyses demonstrated multiple significantly enriched metabolism-related pathways. To sum up, our study described a novel seventeen-gene metabolic signature for prognostic prediction of colon cancer.

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Development and validation of a robust prognostic and predictive signature for colorectal cancer (CRC) patients

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.4036 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. 4036-4036

Author(s):

A. M. Glas ◽

P. Roepman ◽

R. Salazar ◽

G. Capella ◽

V. Moreno ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

High Risk ◽

Adjuvant Chemotherapy ◽

Cancer Patients ◽

Gene Signature ◽

Low Risk ◽

Stage Ii ◽

Significant Difference ◽

Independent Cohort

4036 Background: Between 25 and 35% of stage II CRC patients will experience a recurrence of their disease and may benefit from adjuvant chemotherapy. Official guidelines give suggestions but no clear recommendation for best risk stratification. Here we describe the development a robust signature that predicts disease relapse and can assist in treatment decisions. Methods: Fresh frozen tumor tissues from 180 patients with stage I, II and III colorectal cancer undergoing surgery were analyzed using high density Agilent 44K oligonucleotide arrays. Median FU was 70.2 months; 85% of patients did not receive adjuvant chemotherapy. Unsupervised hierarchical clustering based on full-genome gene expression measurement indicated the existence of 3 main colon molecular subclasses. Survival analysis of the 3 classes showed that subtype C (n= 27) had a poor outcome and subtype A (n= 48) good outcome. Only the intermediate group B (n=104) was used to develop a signature by using a cross validation procedure to score all genes for their association with 5-yr distant metastasis free survival (DMFS) and subsequently applied to all samples (n=180). The obtained gene signature was further validated on an independent cohort of 178 stage II + III colon samples. Results: A set of 38 prognosis related gene probes showed robust DMFS association in over 50% of all iterations in the Training Set of 180 samples. The gene signature was validated on an independent cohort of 178 samples from stage II + III colon cancer patients. The profile classified 61% of the validation samples as low-risk and 39% as high-risk. The low- and high-risk samples showed a significant difference in DMFS with a HR of 3.19 (P= 8.5e-4). Five-year DMFS rates were 89% (95%CI 83–95) for low-risk and 62% (95%CI 50–77) for high-risk samples. Moreover, the profile showed a significant performance within stage II (P=0.0058) and III (P=0.036) only samples. The performance of the profile was significant for both untreated (P=0.0082) and treated patients (P=0.016) suggesting that its power is independent of treatment benefits. Conclusions: ColoPrint is able to predict the prognosis of stage II and III colon cancer patients and facilitates the identification of patients who would benefit from adjuvant chemotherapy. [Table: see text]

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