scholarly journals Risk Score to Predict Dental Caries in Adult Patients for Use in the Clinical Setting

2019 ◽  
Vol 8 (2) ◽  
pp. 203 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Ana Sezinando ◽  
Inês Fernandes ◽  
Paulo Maló
Keyword(s):  
Dental Caries ◽  
Risk Score ◽  
Risk Model ◽  
Risk Groups ◽  
C Statistic ◽  
Risk Algorithm ◽  
Study Population ◽  
Level Of Significance ◽  
Bacterial Plaque ◽  
Systemic Condition

Background: There is a need for risk prediction tools in caries research. This investigation aimed to estimate and evaluate a risk score for prediction of dental caries. Materials and Methods: This case-cohort study included a random sample of 177 cases (with dental caries) and 220 controls (randomly sampled from the study population at baseline), followed for 3 years. The risk ratio (RR) for each potential predictor was estimated using a logistic regression model. The level of significance was 5%. Results: The risk model for dental caries included the predictors: “presence of bacterial plaque/calculus” (RR = 4.1), “restorations with more than 5 years” (RR = 2.3), “>8 teeth restored” (RR = 2.0), “history/active periodontitis” (RR = 1.7) and “presence of systemic condition” (RR = 1.4). The risk model discrimination (95% confidence interval) was 0.78 (0.73; 0.82) (p < 0.001, C-statistic). Patients were distributed into three risk groups based on the pre-analysis risk (54%): low risk (<half the pre-analysis risk; caries incidence = 6.8%), moderate risk (half-to-less than the pre-analysis risk; caries incidence = 20.4%) and high risk (≥the pre-analysis risk; caries incidence = 27%). Conclusions: The present study estimated a simple risk score for prediction of dental caries retrieved from a risk algorithm with good discrimination.

scholarly journals Adult Patient Risk Stratification Using a Risk Score for Periodontitis

2019 ◽  
Vol 8 (3) ◽  
pp. 307 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Ana Ferro ◽  
Paulo Maló
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Risk Groups ◽  
First Year ◽  
C Statistic ◽  
History Of ◽  
Study Population ◽  
Subgingival Calculus ◽  
Analytical Tools

Background: There is a need for analytical tools predicting the risk of periodontitis. The purpose of this study was to estimate and evaluate a risk score for prediction of periodontitis. Materials and methods: This case-cohort study included a random sample of 155 cases (with periodontitis) and 175 controls (randomly sampled from the study population at baseline) that were followed for 3-year. A logistic regression model was used with estimation of the risk ratio (RR) for each potential predictor. Results: The risk model included the predictors “age > 53 years” (RR = 0.53), “smoking” (RR = 2.9), “gingivitis at baseline” (RR = 3.1), “subgingival calculus at baseline” (RR = 1.9), “history of periodontitis” (RR = 2.3), and “less than 2 observations in the first year of follow-up” (RR = 3.7). Patients were distributed into three risk groups based on the preanalysis risk: low risk, moderate risk, and high risk. The risk score discrimination (95% confidence interval (CI)) was 0.75 (0.70; 0.80) (p < 0.001, C-statistic). Conclusions: The risk score estimated in the present study enabled to identify patients at higher risk of experiencing periodontitis and may be considered a useful tool for both clinicians and patients.

scholarly journals A Peri-Implant Disease Risk Score for Patients with Dental Implants: Validation and the Influence of the Interval between Maintenance Appointments

2019 ◽  
Vol 8 (2) ◽  
pp. 252 ◽  
Author(s):  
Miguel de Araújo Nobre ◽  
Francisco Salvado ◽  
Paulo Nogueira ◽  
Evangelista Rocha ◽  
Peter Ilg ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Disease Risk ◽  
Disease Incidence ◽  
Risk Groups ◽  
Risk Profile ◽  
Moderate Risk ◽  
C Statistic ◽  
Disease Risk Score ◽  
Very High

Background: There is a need for tools that provide prediction of peri-implant disease. The purpose of this study was to validate a risk score for peri-implant disease and to assess the influence of the recall regimen in disease incidence based on a five-year retrospective cohort. Methods: Three hundred and fifty-three patients with 1238 implants were observed. A risk score was calculated from eight predictors and risk groups were established. Relative risk (RR) was estimated using logistic regression, and the c-statistic was calculated. The effect/impact of the recall regimen (≤ six months; > six months) on the incidence of peri-implant disease was evaluated for a subset of cases and matched controls. The RR and the proportional attributable risk (PAR) were estimated. Results: At baseline, patients fell into the following risk profiles: low-risk (n = 102, 28.9%), moderate-risk (n = 68, 19.3%), high-risk (n = 77, 21.8%), and very high-risk (n = 106, 30%). The incidence of peri-implant disease over five years was 24.1% (n = 85 patients). The RR for the risk groups was 5.52 (c-statistic = 0.858). The RR for a longer recall regimen was 1.06, corresponding to a PAR of 5.87%. Conclusions: The risk score for estimating peri-implant disease was validated and showed very good performance. Maintenance appointments of < six months or > six months did not influence the incidence of peri-implant disease when considering the matching of cases and controls by risk profile.

Venous Thromboembolism Risk Model Predicts Early Progression and Overall Mortality in Cancer Patients Receiving Chemotherapy

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 172-172 ◽  
Author(s):  
Nicole M. Kuderer ◽  
Alok A. Khorana ◽  
Charles W. Francis ◽  
Eva Culakova ◽  
Thomas L. Ortel ◽  
...  
Keyword(s):  
Overall Survival ◽  
Prognostic Factors ◽  
Venous Thromboembolism ◽  
Disease Progression ◽  
Cancer Patients ◽  
Risk Score ◽  
Risk Model ◽  
Risk Groups ◽  
Group I ◽  
All Cause Mortality

Abstract Background: Venous Thromboembolism (VTE) is a common complication of cancer and is strongly associated with early all-cause mortality during the course of cancer chemotherapy (Kuderer et al. ASCO 2008). A clinical model for predicting the risk of VTE in cancer patients initiating chemotherapy has been recently developed and validated (Khorana et al. Blood 2008). Risk of VTE in low (group I), intermediate (group II) and high risk patients (group III) was 0.8%, 1.8% and 7.1%, respectively. The aim of current study is to evaluate the ability of the VTE risk model to predict disease progression and early all-cause mortality. Methods: A prospective study of 4,458 adult cancer patients with solid tumors or malignant lymphoma initiating a new chemotherapy regimen was conducted between 2002 and 2006 at 115 randomly selected practice sites throughout the USA. Demographic, clinical and treatment-related information was captured prospectively at baseline and during the first four cycles of chemotherapy, including rates of documented VTE, disease recurrence and deaths from all causes. Progression-free survival (PFS) and overall survival (OS) within 4 months of starting chemotherapy were estimated by the method of Kaplan-Meier and adjusted hazard ratios (HR ± 95% CI) were estimated by a Cox regression model, incorporating VTE as a time-dependent covariate. Results: Patient age ranged from 18–97 with a mean of 60 years. VTE occurred in 3% of patients by 4 months with a median of 38 days following initiation of chemotherapy. The HR for VTE occurrence among risk score groups II and III, compared to group I, were 3.07 [1.39–6.77] and 11.73 [5.22–16.37], (P&lt;0.0001) respectively. Within 4 months, disease progression occurred in 298 patients and 137 patients died. Death or disease progression was reported in 7%, 18% and 28% of risk score groups I, II and III, respectively. HR for reduced PFS among risk groups II and III compared to group I were 2.77 [1.97–3.87] and 4.27 [2.90–6.27], respectively (P&lt;0.0001). Death from all causes within 4 months of treatment initiation was reported in 1.2%, 5.9% and 12.7% patients for risk groups I, II and III. HR estimates for mortality among groups II and III were 3.56 [1.91–6.66] and 6.89 [3.50–13.57], respectively (P&lt;0.0001). In multivariate analysis, the risk score and VTE occurrence were both significant independent predictors for early mortality and reduced PFS after adjusting for major prognostic factors including: age, stage, cancer type, ECOG performance status, Charlson comorbidity index, body mass index, relative dose intensity, and year of enrollment. Conclusions: VTE is strongly associated with increased early all-cause mortality during the course of cancer chemotherapy. A recently validated risk score is not only predictive of VTE occurrence, but also of progression-free and overall survival demonstrating a strong association with prognostic factors for disease progression and mortality.

scholarly journals m5C-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Junfan Pan ◽  
Zhidong Huang ◽  
Yiquan Xu
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Prognostic Value ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Rna Methylation

Long non-coding RNAs (lncRNAs), which are involved in the regulation of RNA methylation, can be used to evaluate tumor prognosis. lncRNAs are closely related to the prognosis of patients with lung adenocarcinoma (LUAD); thus, it is crucial to identify RNA methylation-associated lncRNAs with definitive prognostic value. We used Pearson correlation analysis to construct a 5-Methylcytosine (m5C)-related lncRNAs–mRNAs coexpression network. Univariate and multivariate Cox proportional risk analyses were then used to determine a risk model for m5C-associated lncRNAs with prognostic value. The risk model was verified using Kaplan–Meier analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic curve analysis. We used principal component analysis and gene set enrichment analysis functional annotation to analyze the risk model. We also verified the expression level of m5C-related lncRNAs in vitro. The association between the risk model and tumor-infiltrating immune cells was assessed using the CIBERSORT tool and the TIMER database. Based on these analyses, a total of 14 m5C-related lncRNAs with prognostic value were selected to build the risk model. Patients were divided into high- and low-risk groups according to the median risk score. The prognosis of the high-risk group was worse than that of the low-risk group, suggesting the good sensitivity and specificity of the constructed risk model. In addition, 5 types of immune cells were significantly different in the high-and low-risk groups, and 6 types of immune cells were negatively correlated with the risk score. These results suggested that the risk model based on 14 m5C-related lncRNAs with prognostic value might be a promising prognostic tool for LUAD and might facilitate the management of patients with LUAD.

scholarly journals Development and validation of a predictive model for chemotherapy-associated thrombosis

Blood ◽  
2008 ◽  
Vol 111 (10) ◽  
pp. 4902-4907 ◽  
Author(s):  
Alok A. Khorana ◽  
Nicole M. Kuderer ◽  
Eva Culakova ◽  
Gary H. Lyman ◽  
Charles W. Francis
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Risk Model ◽  
High Risk Group ◽  
Individual Risk ◽  
Derivation Cohort ◽  
Predictive Variables ◽  
C Statistic ◽  
Multiple Variables ◽  
Individual Risk Factors

Abstract Risk of venous thromboembolism (VTE) is elevated in cancer, but individual risk factors cannot identify a sufficiently high-risk group of outpatients for thromboprophylaxis. We developed a simple model for predicting chemotherapy-associated VTE using baseline clinical and laboratory variables. The association of VTE with multiple variables was characterized in a derivation cohort of 2701 cancer outpatients from a prospective observational study. A risk model was derived and validated in an independent cohort of 1365 patients from the same study. Five predictive variables were identified in a multivariate model: site of cancer (2 points for very high-risk site, 1 point for high-risk site), platelet count of 350 × 109/L or more, hemoglobin less than 100 g/L (10 g/dL) and/or use of erythropoiesis-stimulating agents, leukocyte count more than 11 × 109/L, and body mass index of 35 kg/m2 or more (1 point each). Rates of VTE in the derivation and validation cohorts, respectively, were 0.8% and 0.3% in low-risk (score = 0), 1.8% and 2% in intermediate-risk (score = 1-2), and 7.1% and 6.7% in high-risk (score ≥ 3) category over a median of 2.5 months (C-statistic = 0.7 for both cohorts). This model can identify patients with a nearly 7% short-term risk of symptomatic VTE and may be used to select cancer outpatients for studies of thromboprophylaxis.

scholarly journals A Novel Model of Tumor-Infiltrating B Lymphocyte Specific RNA-Binding Protein-Related Genes With Potential Prognostic Value and Therapeutic Targets in Multiple Myeloma

2021 ◽  
Vol 12 ◽  
Author(s):  
JingJing Zhang ◽  
Pengcheng He ◽  
Xiaoning Wang ◽  
Suhua Wei ◽  
Le Ma ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
High Risk ◽  
B Cell ◽  
Risk Score ◽  
Drug Sensitivity ◽  
Rna Binding ◽  
Risk Model ◽  
Risk Group ◽  
Risk Groups ◽  
Low Risk

Background: RNA-binding proteins (RBPs) act as important regulators in the progression of tumors. However, their role in the tumorigenesis and prognostic assessment in multiple myeloma (MM), a B-cell hematological cancer, remains elusive. Thus, the current study was designed to explore a novel prognostic B-cell-specific RBP signature and the underlying molecular mechanisms.Methods: Data used in the current study were obtained from the Gene Expression Omnibus (GEO) database. Significantly upregulated RBPs in B cells were defined as B cell-specific RBPs. The biological functions of B-cell-specific RBPs were analyzed by the cluster Profiler package. Univariate and multivariate regressions were performed to identify robust prognostic B-cell specific RBP signatures, followed by the construction of the risk classification model. Gene set enrichment analysis (GSEA)-identified pathways were enriched in stratified groups. The microenvironment of the low- and high-risk groups was analyzed by single-sample GSEA (ssGSEA). Moreover, the correlations among the risk score and differentially expressed immune checkpoints or differentially distributed immune cells were calculated. The drug sensitivity of the low- and high-risk groups was assessed via Genomics of Drug Sensitivity in Cancer by the pRRophetic algorithm. In addition, we utilized a GEO dataset involving patients with MM receiving bortezomib therapy to estimate the treatment response between different groups.Results: A total of 56 B-cell-specific RBPs were identified, which were mainly enriched in ribonucleoprotein complex biogenesis and the ribosome pathway. ADAR, FASTKD1 and SNRPD3 were identified as prognostic B-cell specific RBP signatures in MM. The risk model was constructed based on ADAR, FASTKD1 and SNRPD3. Receiver operating characteristic (ROC) curves revealed the good predictive capacity of the risk model. A nomogram based on the risk score and other independent prognostic factors exhibited excellent performance in predicting the overall survival of MM patients. GSEA showed enrichment of the Notch signaling pathway and mRNA cis-splicing via spliceosomes in the high-risk group. Moreover, we found that the infiltration of diverse immune cell subtypes and the expression of CD274, CD276, CTLA4 and VTCN1 were significantly different between the two groups. In addition, the IC50 values of 11 drugs were higher in the low-risk group. Patients in the low-risk group exhibited a higher complete response rate to bortezomib therapy.Conclusion: Our study identified novel prognostic B-cell-specific RBP biomarkers in MM and constructed a unique risk model for predicting MM outcomes. Moreover, we explored the immune-related mechanisms of B cell-specific RBPs in regulating MM. Our findings could pave the way for developing novel therapeutic strategies to improve the prognosis of MM patients.

scholarly journals An immune-related long non-coding RNA signature predicts prognosis in glioblastoma patients

2021 ◽  
Author(s):  
Xiangdong Lu ◽  
Siquan Zhu ◽  
Shouqing Zhang ◽  
Feng Si ◽  
Yunfeng Ma ◽  
...  
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Univariate Analysis ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Risk Groups ◽  
Low Risk ◽  
Risk Scores ◽  
Normal Brain ◽  
Vesicle Cycle

IntroductionThis study aimed to explore the prognostic value of immune-related long non-coding RNAs (lncRNAs) in glioblastoma (GBM).Material and methodsExpression and clinical data were acquired, including GSE111260 dataset: 67 GBM and 3 normal brain samples; GSE103227 dataset: 5 GBM and 5 normal brain samples; and TCGA data: 187 GBM samples. Immune-related genes were retrieved from ImmPort database. Immune-related differentially expressed genes (DEGs) and lncRNAs were screened. Prognostic lncRNAs were then screened to establish prognostic risk score model. Survival analysis and differential expression analysis were performed in high- vs. low-risk groups, followed by protein-protein interaction network and lncRNA-mRNA co-expression network.ResultsA total of 251 immune-related DEGs were screened. After correlation analysis, 387 immune-related lncRNAs that co-expressed with 140 immune-related DEGs were screened. Univariate analysis identified 18 lncRNAs that were significantly associated with prognosis. The prognostic risk score could be able to stratify GBM patients into high- and low-risk groups, and patients with high risk scores displayed worse outcomes than those with low risk scores in both training set and validation set. A total of 272 genes had abnormal expression between high- and low-risk groups. Of which, 22 genes were immune-related, such as SNAP25, SNAP91, SNCB, and RAB3A. These genes were mainly enriched in synaptic vesicle cycle/exocytosis and insulin secretion. The co-expression network contained 22 genes and 11 lncRNAs, and lncRNA LINC01574 co-expressed with the great number of mRNAs.ConclusionsWe identified 18 immune-related prognostic lncRNAs, and the established lncRNAs-based prognostic risk model could stratify GBM patients into different risks.

scholarly journals The Diagnostic and Prognostic Value of BEN-Domain (BEND) Family Genes in Gastric Cancer

2022 ◽  
Author(s):  
Jiaxin Fan ◽  
Min Yang ◽  
Chaojie Liang ◽  
Chaowei Liang ◽  
Jiansheng Guo
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Prognostic Value ◽  
Risk Model ◽  
Malignant Tumors ◽  
Risk Groups ◽  
Risk Scores ◽  
Gastric Cancer Patients

Abstract BEND(BEN domain-containing protein)is a domain protein-coding gene, whose abnormal expression is related to the occurrence of malignant tumors. But studies on gastric cancer are rare. We attempted to investigate the role of BEND family genes in evaluating the prognosis of gastric cancer and guiding clinical treatment. We analyzed the BEND family genes expression, prognostic value, and drug sensitivity in pan-cancer, and the correlation between their expression and tumor microenvironment of gastric cancer, stemness index, immune subtypes, and clinicopathological characteristics were analyzed. We constructed a model using BEND3P1 and BEND6 to evaluate the prognosis of gastric cancer patients. Multivariate Cox proportional risk model analysis showed that risk score is an independent risk factor for gastric cancer patients. To assess the value of risk score for prognosis, patients were divided into high-risk and low-risk groups based on median risk scores, and survival analyses were performed. The results showed that the OS of patients with high-risk scores is significantly lower. We also constructed a nomogram to predict individual survival probability using the BEND risk score and clinical case characteristics. In conclusion, the BEND family genes can predict the prognosis and guide the treatment of gastric cancer patients.

P1656Incremental value of interleukin-6 and C-reactive protein to the MEESSI acute heart failure risk score

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
D N Wussler ◽  
N Kozhuharov ◽  
Z Sabti ◽  
J Walter ◽  
I Strebel ◽  
...  
Keyword(s):  
Heart Failure ◽  
Risk Score ◽  
Interleukin 6 ◽  
Goodness Of Fit ◽  
Risk Groups ◽  
Original Model ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
C Statistic ◽  
Incremental Value

Abstract Background The MEESSI-acute heart failure (AHF) risk score has high accuracy in the prediction of 30-day mortality in patients presenting with AHF and may be considered the current gold standard for this indication. Purpose As the original MEESSI model does not include measurements of inflammatory biomarkers, the impact of interleukin-6 or C-reactive protein (CRP) on the model's goodness of fit is unknown. Methods In a prospective multicenter diagnostic study the presence of AHF was centrally adjudicated by two independent cardiologists among patients presenting with acute dyspnea to the ED. The MEESSI-AHF risk score was calculated using a recalibrated model containing 12 independent risk factors. The incremental value of interleukin-6 and CRP was examined by the use of logistic regression analysis and enter method variable selection with an entry criterion of p<0.05. Goodness of fit tests were performed to measure the updated model's discrimination and calibration. Results In 1247 patients with adjudicated AHF, the MEESSI-AHF risk score was calculated. Of these, 1113 patients (89.3%) had available measurements of interleukin-6 and CRP. In the logistic regression analysis both biomarkers had a highly significant impact on the MEESSI model (p<0.001, respectively). Compared to the original MEESSI-Model (c-statistic, 0.79 (95% CI, 0.75–0.83)) the addition of interleukin-6 (c-statistic, 0.81 (95% CI, 0.77–0.85)) or CRP (c-statistic, 0.83 (95% CI, 0.79–0.86)) significantly improved the model's discrimination (p=0.022 and p=0.011, respectively). When assessing the cumulative mortality, the gradient in 30-day mortality over six predefined risk groups was increased by addition of interleukin-6 or CRP. 30-day mortality rates in the lowest and highest risk groups of the original model were 0.4% and 32.5% compared to 0% and 34.9% in the model updated with interleukin-6 and 0.6% and 37.6% in the model updated with CRP. All compared models showed good overall calibration (Hosmer-Lemeshow p=0.302 (original model), p=0.136 (model updated by interleukin-6) and p=0.902 (model updated by CRP)). Discrimination original_updated Conclusion There is significant incremental value of interleukin-6 and CRP to the MEESSI score as indicated by the improved goodness of fit compared to the original model. Acknowledgement/Funding European Union, the Swiss National Science Foundation, the Swiss Heart Foundation, the Cardiovascular Research Foundation Basel,

scholarly journals A Prognostic Pyroptosis-Related lncRNAs Risk Model Correlates With the Immune Microenvironment in Colon Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Fada Xia ◽  
Yuanliang Yan ◽  
Cong Shen
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Clinical Information ◽  
Risk Groups ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Non Coding Rnas ◽  
Prognostic Risk Factors

Recent studies have indicated that long non-coding RNAs (lncRNAs) may participate in the regulation of tumor cell proptosis. However, the connection between lncRNA expression and pyroptosis remains unclear in colon adenocarcinoma (COAD). This study aims to explore and establish a prognostic signature of COAD based on the pyroptosis-related lncRNAs. We identify 15 prognostic pyroptosis-related lncRNAs (ZNF667-AS1, OIP5-AS1, AL118506.1, AF117829.1, POC1B-AS1, CCDC18-AS1, THUMPD3-AS1, FLNB-AS1, SNHG11, HCG18, AL021707.2, UGDH-AS1, LINC00641, FGD5-AS1 and AC245452.1) from the TCGA-COAD dataset and use them to construct the risk model. After then, this pyroptosis-related lncRNA signature is validated in patients from the GSE17536 dataset. The COAD patients are divided into low-risk and high-risk groups by setting the median risk score as the cut-off point and represented differences in the immune microenvironment. Hence, we construct the immune risk model based on the infiltration levels of ssGSEA immune cells. Interestingly, the risk model and immune risk model are both independent prognostic risk factors. Therefore, a nomogram combined risk score, immune risk score with clinical information which is meaningful in univariate and multivariate Cox regression analysis is established to predict the overall survival (OS) of COAD patients. In general, the signature consisted of 15 pyroptosis-related lncRNAs and was proved to be associated with the immune landscape of COAD patients.

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