scholarly journals Construction and Validation of a Convenient Clinical Nomogram to Predict the Risk of Brain Metastasis in Renal Cell Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Yuexin Tong ◽  
Zhangheng Huang ◽  
Chuan Hu ◽  
Changxing Chi ◽  
Meng Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Brain Metastasis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Clinical Tool ◽  
Clinical Value

Brain metastasis (BM) is a typical type of metastasis in renal cell carcinoma (RCC) patients. The early detection of BM is likely a crucial step for RCC patients to receive appropriate treatment and prolong their overall survival. The aim of this study was to identify the independent predictors of BM and construct a nomogram to predict the risk of BM. Demographic and clinicopathological data were obtained from the Surveillance, Epidemiology, and End Results (SEER) database for RCC patients between 2010 and 2015. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors, and then, a visual nomogram was constructed. Multiple parameters were used to evaluate the discrimination and clinical value. We finally included 42577 RCC patients. Multivariate logistic regression analysis showed that histological type, tumor size, bone metastatic status, and lung metastatic status were independent BM-associated risk factors for RCC. We developed a nomogram to predict the risk of BM in patients with RCC, which showed favorable calibration with a C -index of 0.924 (0.903-0.945) in the training cohort and 0.911 (0.871-0.952) in the validation cohort. The calibration curves and decision curve analysis (DCA) also demonstrated the reliability and accuracy of the clinical prediction model. The nomogram was shown to be a practical, precise, and personalized clinical tool for identifying the RCC patients with a high risk of BM, which not only will contribute to the more reasonable allocation of medical resources but will also enable a further improvements in the prognosis and quality of life of RCC patients.

scholarly journals Serum markers change for intraocular metastasis in renal cell carcinoma

2021 ◽  
Author(s):  
Tie Sun ◽  
Jing Tang ◽  
Yi-Cong Pan ◽  
Chen-Yu Yu ◽  
Biao Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Renal Cell Carcinoma ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Risk Factor ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Intraocular Metastasis

Objective: Intraocular metastasis(IOM) of renal cell carcinoma is rare. In this study, we studied the relationship between different biochemical indicators and the occurrence of IOM in renal cancer patients, and identified the potential risk factors. Methods: A retrospective analysis of the clinical data of 214 patients with renal cell carcinoma from October 2001 to August 2016. Analyze the difference and correlation of various indicators between the two groups with or without IOM, and use binary logistic regression analysis to explore the risk factors of IOM in renal cancer patients. Calculate the diagnostic value of each independent related factor according to the receiver operating curve (ROC). Results: The level of neuron specific enolase （NSE） in renal cell carcinoma patients with IOM was significantly higher than that in patients without IOM (P < 0.05). There was no significant difference in ALP, Hb, serum calcium concentration, AFP, CEA, CA-125 etc. between IOM group and non-intraocular metastasis (NIOM) group (P > 0.05). Binary logistic regression analysis showed that NSE was an independent risk factor for IOM in renal cell carcinoma patients (P < 0.05). ROC curve shows that the factor has high accuracy in predicting IOM, and the area under the curve is 0.774. The cut-off value of NSE was 49.5U/L, the sensitivity was 72.2%, and the specificity was 80.1%. Conclusion：NSE concentration is a risk factor for IOM in patients with renal cell cancer. If the concentration of NSE in the patient's body is ≥49.5U/L, disease monitoring and eye scans should be strengthened.

scholarly journals Prediction and follow-up of risk factors for severe SARS-CoV-2 pneumonia and application of CT visual scoring

2021 ◽  
pp. 1-12
Author(s):  
Yingjian Ye ◽  
Xiaxia Wu ◽  
Xiumei Li ◽  
Chunmei Xu ◽  
Qingpeng Wang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Clinical Features ◽  
Lymphocyte Count ◽  
Clinical Symptoms ◽  
Multivariate Logistic Regression Analysis ◽  
Clinical Treatment ◽  
Severe Illness ◽  
Multivariate Logistic Regression ◽  
Clinical Value

BACKGROUND: The SARS-CoV-2 pneumonia infection is associated with high rates of hospitalization and mortality and this has placed healthcare systems under strain. Our study provides a novel method for the progress prediction, clinical treatment and prognosis of NCP, and has important clinical value for timely treatment of severe NCP patients. OBJECTIVE: To summarize the clinical features and severe illness risk factors of the patients with novel coronavirus pneumonia (NCP), in order to provide support for the progression prediction, clinical treatment and prognosis of NCP patients. MATERIALS AND METHODS: A total of 196 NCP patients treated in our hospital from January 25, 2020 to June 21, 2020 were divided into the severe group and the mild group. The clinical features of the two groups were analyzed and compared. The risk factors were explored by using multivariate logistic regression, and the receiver operating characteristic (ROC) curve was obtained. The correlations of the risk factors with the prognosis of NCP were investigated combined with the lung function test. RESULTS: The primary clinical symptoms of 196 cases of NCP included fever in 167 cases (85.2%) and cough in 121 cases (61.73%). The chest computed tomography (CT) scans of the 178 cases (90.81%) showed a typical ground-glass opacification. In 149 cases, the lymphocyte count was decreased, while the levels of creatine kinase (CK), lactate dehydrogenase (LDH), c-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and D-dimer (D-D) increased. 44 cases (22.45%) were found to be severely ill. The multivariate logistic regression analysis demonstrated that age, underlying disease, length of hospital stay, body mass index (BMI), LDH, chest CT visual score, absolute lymphocyte count (ALC) and CRP were risk factors for severe

scholarly journals Risk factors for tissue expander infection in scar reconstruction: a retrospective cohort study of 2374 consecutive cases

2020 ◽  
Vol 8 ◽  
Author(s):  
Chen Dong ◽  
Minhui Zhu ◽  
Luguang Huang ◽  
Wei Liu ◽  
Hengxin Liu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Treatment Failure ◽  
Logistic Regression Analysis ◽  
Disease Duration ◽  
Multivariate Logistic Regression Analysis ◽  
Tissue Expansion ◽  
Multivariate Logistic Regression ◽  
Hematoma Evacuation

Abstract Background Tissue expansion is used for scar reconstruction owing to its excellent clinical outcomes; however, the complications that emerge from tissue expansion hinder repair. Infection is considered a major complication of tissue expansion. This study aimed to analyze the perioperative risk factors for expander infection. Methods A large, retrospective, single-institution observational study was carried out over a 10-year period. The study enrolled consecutive patients who had undergone tissue expansion for scar reconstruction. Demographics, etiological data, expander-related characteristics and postoperative infection were assessed. Univariate and multivariate logistic regression analysis were performed to identify risk factors for expander infection. In addition, we conducted a sensitivity analysis for treatment failure caused by infection as an outcome. Results A total of 2374 expanders and 148 cases of expander infection were assessed. Treatment failure caused by infection occurred in 14 expanders. Multivariate logistic regression analysis identified that disease duration of ≤1 year (odds ratio (OR), 2.07; p < 0.001), larger volume of expander (200–400 ml vs <200 ml; OR, 1.74; p = 0.032; >400 ml vs <200 ml; OR, 1.76; p = 0.049), limb location (OR, 2.22; p = 0.023) and hematoma evacuation (OR, 2.17; p = 0.049) were associated with a high likelihood of expander infection. Disease duration of ≤1 year (OR, 3.88; p = 0.015) and hematoma evacuation (OR, 10.35; p = 0.001) were so related to high risk of treatment failure. Conclusions The rate of expander infection in patients undergoing scar reconstruction was 6.2%. Disease duration of <1 year, expander volume of >200 ml, limb location and postoperative hematoma evacuation were independent risk factors for expander infection.

scholarly journals A convention-radiomics CT nomogram for differentiating fat-poor angiomyolipoma from clear cell renal cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yanqing Ma ◽  
Weijun Ma ◽  
Xiren Xu ◽  
Zheng Guan ◽  
Peipei Pang
Keyword(s):  
Risk Factors ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Logistic Model ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Entire Cohort ◽  
Conventional Ct ◽  
Radiomics Signature

AbstractThis study aimed to construct convention-radiomics CT nomogram containing conventional CT characteristics and radiomics signature for distinguishing fat-poor angiomyolipoma (fp-AML) from clear-cell renal cell carcinoma (ccRCC). 29 fp-AML and 110 ccRCC patients were enrolled and underwent CT examinations in this study. The radiomics-only logistic model was constructed with selected radiomics features by the analysis of variance (ANOVA)/Mann–Whitney (MW), correlation analysis, and Least Absolute Shrinkage and Selection Operator (LASSO), and the radiomics score (rad-score) was computed. The convention-radiomics logistic model based on independent conventional CT risk factors and rad-score was constructed for differentiating. Then the relevant nomogram was developed. Receiver operation characteristic (ROC) curves were calculated to quantify the accuracy for distinguishing. The rad-score of ccRCC was smaller than that of fp-AML. The convention-radioimics logistic model was constructed containing variables of enhancement pattern, VUP, and rad-score. To the entire cohort, the area under the curve (AUC) of convention-radiomics model (0.968 [95% CI 0.923–0.990]) was higher than that of radiomics-only model (0.958 [95% CI 0.910–0.985]). Our study indicated that convention-radiomics CT nomogram including conventional CT risk factors and radiomics signature exhibited better performance in distinguishing fp-AML from ccRCC.

Brain Metastasis From Renal-Cell Carcinoma: An Institutional Study

2019 ◽  
Vol 17 (6) ◽  
pp. e1163-e1170 ◽  
Author(s):  
Alfredo Suarez-Sarmiento ◽  
Kevin A. Nguyen ◽  
Jamil S. Syed ◽  
Adam Nolte ◽  
Kamyar Ghabili ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Brain Metastasis ◽  
Cell Carcinoma ◽  
Renal Cell

Risk Factors for Ipsilateral Adrenal Involvement in Renal Cell Carcinoma

Urology ◽  
2008 ◽  
Vol 72 (2) ◽  
pp. 354-358 ◽  
Author(s):  
Keiichi Ito ◽  
Hayakazu Nakazawa ◽  
Ken Marumo ◽  
Seiichiro Ozono ◽  
Tatsuo Igarashi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell

scholarly journals BRAIN METASTASIS FROM RENAL CELL CARCINOMA

1984 ◽  
Vol 75 (2) ◽  
pp. 278-282
Author(s):  
Fujio Masuda ◽  
Yoshikazu Arai ◽  
Tetsuro Ohnishi ◽  
Jyojiro Nakada ◽  
Masayasu Suzuki ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Brain Metastasis ◽  
Cell Carcinoma ◽  
Renal Cell

scholarly journals Predictive risk factors for worse outcomes in COVID-19 patients with different clinical features at baseline

2021 ◽  
Author(s):  
Elisabetta Schiaroli ◽  
Anna Gidari ◽  
Giovanni Brachelente ◽  
Sabrina Bastianelli ◽  
Alfredo Villa ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Critical Illness ◽  
Serum Ferritin ◽  
Logistic Regression Analysis ◽  
Severe Disease ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Neutrophil Lymphocyte Ratio

IntroductionCOVID-19 is characterized by a wide range of clinical expression and by possible progression to critical illness and death. Therefore it is essential to identify risk factors predicting progression towards serious and fatal diseases. The aim of our study was to identify laboratory predictive markers of clinical progression in patients with moderate/severe disease and in those with acute respiratory distress syndrome (ARDS).Material and methodsUsing electronic medical records for all demographic, clinical and laboratory data, a retrospective study on all consecutive patients with COVID-19 admitted to the Infectious Disease Clinic of Perugia was performed. The PaO2/FiO2 ratio (P/F) assessment cut‑off of 200 mm Hg was used at baseline to categorize the patients into two clinical groups. The progression towards invasive ventilation and/or death was used to identify critical outcome. Statistical analysis was performed. Multivariate logistic regression analysis was adopted to identify risk factors of critical illness and mortality.ResultsIn multivariate logistic regression analysis neutrophil/lymphocyte ratio (NLR) was the only significant predictive factor of progression to a critical outcome (p = 0.03) and of in-hospital mortality (p = 0.03). In ARDS patients no factors were associated with critical progression. Serum ferritin > 1006 ng/ml was the only predictive value of critical outcome in COVID-19 subjects with moderate/severe disease (p = 0.02).ConclusionsNeutrophil/lymphocyte ratio and serum ferritin are the only biomarkers that can help to stratify the risk of severity and mortality in patients with COVID-19.

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