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 Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Primary thromboprophylaxis in ambulatory cancer patients with a high Khorana score: a systematic review and meta-analysis

2020 ◽  
Vol 4 (20) ◽  
pp. 5215-5225
Author(s):  
Floris T. M. Bosch ◽  
Frits I. Mulder ◽  
Pieter Willem Kamphuisen ◽  
Saskia Middeldorp ◽  
Patrick M. Bossuyt ◽  
...  
Keyword(s):  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Major Bleeding ◽  
Profile Likelihood ◽  
Intermediate Risk ◽  
High Risk Patients ◽  
Significant Difference ◽  
All Cause Mortality ◽  
Risk Patients

Abstract Guidelines suggest thromboprophylaxis for ambulatory cancer patients starting chemotherapy with an intermediate to high risk of venous thromboembolism (VTE) according to Khorana score. Data on thromboprophylaxis efficacy in different Khorana score risk groups remain ambiguous. We sought to evaluate thromboprophylaxis in patients with an intermediate- to high-risk (≥2 points) Khorana score and an intermediate-risk score (2 points) or high-risk score (≥3 points) separately. MEDLINE, Embase, and CENTRAL were searched for randomized controlled trials (RCTs) comparing thromboprophylaxis with placebo or standard care in ambulatory cancer patients. Outcomes were VTE, major bleeding, and all-cause mortality. Relative risks (RRs) were calculated in a profile-likelihood random-effects model. Six RCTs were identified, involving 4626 cancer patients. Thromboprophylaxis with direct oral anticoagulants (DOACs) or low molecular weight heparin (LMWH) significantly reduced VTE risk in intermediate- to high-risk (RR, 0.51; 95% confidence interval [CI], 0.34-0.67), intermediate-risk (RR, 0.58; 95% CI, 0.36-0.83), and high-risk patients (RR, 0.45; 95% CI, 0.28-0.67); the numbers needed to treat (NNTs) were 25 (intermediate to high risk), 34 (intermediate risk), and 17 (high risk), respectively. There was no significant difference in major bleeding (RR, 1.06; 95% CI, 0.69-1.67) or all-cause mortality (RR, 0.90; 95% CI, 0.82-1.01). The numbers needed to harm (NNHs) for major bleeding in intermediate- to high-risk, intermediate-risk, and high-risk patients were 1000, −500, and 334, respectively. The overall NNH was lower in DOAC studies (100) versus LMWH studies (−500). These findings indicate thromboprophylaxis effectively reduces the risk of VTE in patients with an intermediate- to high-risk Khorana score, although the NNT is twice as high for intermediate-risk patients compared with high-risk patients.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and AGRs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development and Validation of a Post-Operative Non-Union Risk Score for Subtrochanteric Femur Fractures

2021 ◽  
Vol 10 (23) ◽  
pp. 5632
Author(s):  
Michalis Panteli ◽  
James S. H. Vun ◽  
Robert M. West ◽  
Anthony J. Howard ◽  
Ippokratis Pountos ◽  
...  
Keyword(s):  
Logistic Regression ◽  
High Risk ◽  
Risk Score ◽  
Scoring System ◽  
Subtrochanteric Fracture ◽  
Non Union ◽  
High Risk Patients ◽  
Femur Fractures ◽  
Post Operative Period ◽  
Risk Patients

Background: Our objective was to develop and validate a predictive model for non-union following a subtrochanteric fracture of the femur. Methods: Following institutional board approval, 316 consecutive patients presenting to our institution (84 non-unions) who fulfilled the inclusion criteria were retrospectively identified. To identify potential unadjusted associations with progression to non-union, simple logistic regression models were used, followed by a revised adjusted model of multiple logistic regression. Results: Having established the risk factors for non-union, the coefficients were used to produce a risk score for predicting non-union. To identify the high-risk patients in the early post-operative period, self-dynamisation was excluded. The revised scoring system was the sum of the following: diabetes (6); deep wound infection (35); simple or severe comminution (13); presence of an atypical fracture (14); lateral cortex gap size ≥5 mm (11), varus malreduction (5–10 degrees) (9); varus malreduction (>10 degrees) (20). On the ROC (receiver operating characteristic) curve, the area under the curve (0.790) demonstrated very good discriminatory capability of the scoring system, with good calibration (Hosmer–Lemeshow test; p = 0.291). Moreover, 5-fold cross validation confirmed good fit of the model and internal validity (accuracy 0.806; Kappa 0.416). The cut-point determined by Youden’s formula was calculated as 18. Conclusion: This study demonstrates that the risk of non-union can be reliably estimated in patients presenting with a subtrochanteric fracture, from the immediate post-operative period. The resulting non-union risk score can be used not only to identify the high-risk patients early, offering them appropriate consultation and in some cases surgical intervention, but also informs surgeons of the modifiable surgery related factors that contribute to this risk.

scholarly journals Cellular components in tumor microenvironment of neuroblastoma and the prognostic value

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8017 ◽  
Author(s):  
Xiaodan Zhong ◽  
Yutong Zhang ◽  
Linyu Wang ◽  
Hao Zhang ◽  
Haiming Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Tumor Microenvironment ◽  
Risk Score ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Mycn Amplification ◽  
Cox Regression Analysis ◽  
High Risk Patients ◽  
Risk Patients ◽  
Cellular Components

Background Tumor microenvironment (TME) contributes to tumor development, progression, and treatment response. In this study, we detailed the cell composition of the TME in neuroblastoma (NB) and constructed a cell risk score model to predict the prognosis of NB. Methods xCell score was calculated through transcriptomic data from the datasets GSE49711 and GSE45480 based on the xCell algorithm. The random forest method was employed to select important features and the coefficient was obtained via multivariate cox regression analysis to construct a prognostic model, and the performance was validated in another two independent datasets, GSE16476 and TARGET-NBL. Results We found that both immune and non-immune cells varies significantly in different prognostic groups, and were correlated with survival time. The proposed prognostic cell risk score (pCRS) model we constructed can be an independent prognostic indicator for overall survival (OS) and event-free survival (EFS) (training: OS, HR 1.579, EFS, HR 1.563; validation: OS, HR 1.665, 3.848, EFS, HR 2.203, all p-values < 0.01) and only independent prognostic factor in International Neuroblastoma Risk Group high risk patients (HR 1.339, 3.631; p-value 1.76e–2, 3.71e–5), rather than MYCN amplification. Besides, pCRS model showed good performance in grouping, in discriminating MYCN status, the area under the curve (AUC) was 0.889, 0.933, and 0.861 in GSE49711, GSE45480, and GSE16476, respectively. In separating high risk groups, the AUC was 0.904 in GSE49711. Conclusion This study details the cellular components in the TME of NB through gene expression data, the proposed pCRS model might provide a basis for treatment selection of high risk patients or targeting cellular components of TME in NB.

scholarly journals Progression Risk Score Estimation Based on Immunostaining Data in Oral Cancer Using Unsupervised Hierarchical Clustering Analysis: A Retrospective Study in Taiwan

2021 ◽  
Vol 11 (9) ◽  
pp. 908
Author(s):  
Hui-Ching Wang ◽  
Leong-Perng Chan ◽  
Chun-Chieh Wu ◽  
Hui-Hua Hsiao ◽  
Yi-Chang Liu ◽  
...  
Keyword(s):  
High Risk ◽  
Oral Cancer ◽  
Cancer Patients ◽  
Hierarchical Clustering ◽  
Risk Score ◽  
Clustering Analysis ◽  
Hierarchical Clustering Analysis ◽  
Oral Cancer Patients ◽  
Risk Patients ◽  
Progression Risk

This study aimed to investigate whether the progression risk score (PRS) developed from cytoplasmic immunohistochemistry (IHC) biomarkers is available and applicable for assessing risk and prognosis in oral cancer patients. Participants in this retrospective case-control study were diagnosed between 2012 and 2014 and subsequently underwent surgical intervention. The specimens from surgery were stained by IHC for 16 cytoplasmic target markers. We evaluated the results of IHC staining, clinical and pathological features, progression-free survival (PFS), and overall survival (OS) of 102 oral cancer patients using a novel estimation approach with unsupervised hierarchical clustering analysis. Patients were stratified into high-risk (52) and low-risk (50) groups, according to their PRS; a metric consisting of cytoplasmic PLK1, PhosphoMet, SGK2, and SHC1 expression. Moreover, PRS could be extended for use in the Cox proportional hazard regression model to estimate survival outcomes with associated clinical parameters. Our study findings revealed that the high-risk patients had a significantly increased risk in cancer progression compared with low-risk patients (hazard ratio (HR) = 2.20, 95% confidence interval (CI) = 1.10–2.42, p = 0.026). After considering the influences of demographics, risk behaviors, and tumor characteristics, risk estimation with PRS provided distinct PFS groups for patients with oral cancer (p = 0.017, p = 0.019, and p = 0.020). Our findings support that PRS could serve as an ideal biomarker for clinical use in risk stratification and progression assessment in oral cancer.

scholarly journals Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

2020 ◽  
Vol 30 (5) ◽  
pp. 746-753
Author(s):  
Ning Dong ◽  
Hulin Piao ◽  
Yu Du ◽  
Bo Li ◽  
Jian Xu ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Type A ◽  
Postoperative Management ◽  
Prediction Score ◽  
Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age &gt;45 years; (ii) body mass index &gt;25 kg/m2; (iii) white blood cell count &gt;13.5 × 109/l; and (iv) lowest perioperative haemoglobin &lt;100 g/l, cardiopulmonary bypass duration &gt;150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

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