62 results on '"Ge, Yi-Zhong"'
Search Results
52. One-Year Mortality in Patients with Cancer Cachexia: Association with Albumin and Total Protein
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Liu, Xiao-Yue, primary, Zhang, Xi, additional, Ruan, Guo-Tian, additional, Zhang, Kang-Ping, additional, Tang, Meng, additional, Zhang, Qi, additional, Song, Meng-Meng, additional, Zhang, Xiao-Wei, additional, Ge, Yi-Zhong, additional, Yang, Ming, additional, Xu, Hong-Xia, additional, Song, Chun-Hua, additional, and Shi, Han-Ping, additional
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- 2021
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53. Which anthropometric measurement is better for predicting survival of patients with cancer cachexia?
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Ge, Yi-Zhong, primary, Ruan, Guo-Tian, additional, Zhang, Kang-Ping, additional, Tang, Meng, additional, Zhang, Qi, additional, Zhang, Xi, additional, Song, Meng-Meng, additional, Zhang, Xiao-Wei, additional, Yang, Ming, additional, Shen, Xian, additional, Xu, Hong-Xia, additional, Song, Chun-Hua, additional, Wang, Chang, additional, and Shi, Han-Ping, additional
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- 2021
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54. Associations of Serum Total Bilirubin with Survival Outcomes in Patients with Cancer Cachexia: A Retrospective, Multicenter Cohort Study
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Li, Xiang-Rui, primary, Zhang, Qi, additional, Zhang, Kang-Ping, additional, Zhang, Xi, additional, Ruan, Guo-Tian, additional, Song, Meng-Meng, additional, Ge, Yi-Zhong, additional, Zhang, Xiao-Wei, additional, Song, Chun-Hua, additional, and Shi, Han ping, additional
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- 2021
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55. Association of Systemic Inflammation and Overall Survival in Elderly Patients with Cancer Cachexia – Results from a Multicenter Study
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Ruan,Guo-Tian, Yang,Ming, Zhang,Xiao-Wei, Song,Meng-Meng, Hu,Chun-Lei, Ge,Yi-Zhong, Xie,Hai-Lun, Liu,Tong, Tang,Meng, Zhang,Qi, Zhang,Xi, Zhang,Kang-Ping, Li,Xiang-Rui, Li,Qin-Qin, Chen,Yong-Bing, Yu,Kai-Ying, Cong,Ming-Hua, Wang,Kun-Hua, Shi,Han-Ping, Ruan,Guo-Tian, Yang,Ming, Zhang,Xiao-Wei, Song,Meng-Meng, Hu,Chun-Lei, Ge,Yi-Zhong, Xie,Hai-Lun, Liu,Tong, Tang,Meng, Zhang,Qi, Zhang,Xi, Zhang,Kang-Ping, Li,Xiang-Rui, Li,Qin-Qin, Chen,Yong-Bing, Yu,Kai-Ying, Cong,Ming-Hua, Wang,Kun-Hua, and Shi,Han-Ping
- Abstract
Guo-Tian Ruan,1â 3,* Ming Yang,1â 3,* Xiao-Wei Zhang,1â 3,* Meng-Meng Song,1â 3 Chun-Lei Hu,1â 3 Yi-Zhong Ge,1â 3 Hai-Lun Xie,1â 3 Tong Liu,1â 3 Meng Tang,1â 3 Qi Zhang,1â 3 Xi Zhang,1â 3 Kang-Ping Zhang,1â 3 Xiang-Rui Li,1â 3 Qin-Qin Li,1â 3 Yong-Bing Chen,1â 3 Kai-Ying Yu,1â 3 Ming-Hua Cong,4 Kun-Hua Wang,5 Han-Ping Shi1â 3 1Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, Peopleâs Republic of China; 2Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, Peopleâs Republic of China; 3Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, Peopleâs Republic of China; 4Comprehensive Oncology Department, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100038, Peopleâs Republic of China; 5Department of Surgery, The First Affiliated Hospital of Kunming Medical University, Kunming, 650000, Peopleâs Republic of China*These authors contributed equally to this workCorrespondence: Han-Ping ShiDepartment of Gastrointestinal Surgery and Beijing Shijitan Hospital, Capital Medical University, 10 Tie Yi Road, Beijing, 100038, Peopleâs Republic of ChinaTel +86-10- 6392 6985Fax +86-10 -6392 6325Email shihp@ccmu.edu.cnBackground: Systemic inflammation and cachexia are associated with adverse clinical outcomes in elderly patients with cancer. The survival outcomes of elderly patients with cancer cachexia (EPCC) with high inflammation and a high risk of mortality are unknown. This study aimed to investigate the impact of high inflammation on the prognosis of EPCC patients with high mortality.Patients and Methods: This multicenter cohort study included 746 EPCC (age > 65 years) with a mean age of 72.00 ± 5.24 years, of whom 489 (65.5%) were male. The cut-off value
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- 2021
56. Relationship Between Prognostic Nutritional Index and Mortality in Overweight or Obese Patients with Cancer: A Multicenter Observational Study
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Zhang,Xi, Li,Jing-Hua, Zhang,Qi, Li,Qin-Qin, Zhang,Kang-Ping, Tang,Meng, Ge,Yi-Zhong, Li,Wei, Xu,Hong-Xia, Guo,Zeng-Qing, Shi,Han-Ping, Zhang,Xi, Li,Jing-Hua, Zhang,Qi, Li,Qin-Qin, Zhang,Kang-Ping, Tang,Meng, Ge,Yi-Zhong, Li,Wei, Xu,Hong-Xia, Guo,Zeng-Qing, and Shi,Han-Ping
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Xi Zhang,1â 3 Jing-Hua Li,4 Qi Zhang,1,3 Qin-Qin Li,1,3 Kang-Ping Zhang,1,3 Meng Tang,1,3 Yi-Zhong Ge,1,3 Wei Li,5 Hong-Xia Xu,6 Zeng-Qing Guo,7 Han-Ping Shi1,3 1Department of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, Peopleâs Republic of China; 2Department of Radiotherapy, Affiliated Hospital of Hebei University, Baoding, 071000, Peopleâs Republic of China; 3Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, Peopleâs Republic of China; 4Department of Hepatobiliary Surgery, Affiliated Hospital of Hebei University, Baoding, 071000, Peopleâs Republic of China; 5Cancer Center of the First Hospital of Jilin University, Changchun, 130021, Peopleâs Republic of China; 6Department of Clinical Nutrition, Daping Hospital, Third Military Medical University (Army Medical University), Chongqing, 400042, Peopleâs Republic of China; 7Department of Medical Oncology, Fujian Cancer Hospital, Fujian Medical University, Fujian, 350014, Peopleâs Republic of ChinaCorrespondence: Han-Ping ShiDepartment of Gastrointestinal Surgery/Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, Peopleâs Republic of ChinaTel +86-10-6392 6985Fax +86-10-6392 6325Email shihp@ccmu.edu.cnBackground: Overweight and obese patients with cancer present with chronic inflammation, dysfunctional antitumor immunity and malnutrition risk. Prognostic nutritional index (PNI) is a promising indicator for predicting inflammatory, immunological and nutritional states; however, its prognostic value in overweight and obese patients with cancer has not been explored. Therefore, the aim of the current study was to explore the prognostic value of PNI levels in overweight and obese patients with cancer.Methods: The current large-scale retrospective cohort multicenter study included 3532 patients. Time-depe
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- 2021
57. Which anthropometric measurement is better for predicting survival of patients with cancer cachexia?
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Ge, Yi-Zhong, Ruan, Guo-Tian, Zhang, Kang-Ping, Tang, Meng, Zhang, Qi, Zhang, Xi, Song, Meng-Meng, Zhang, Xiao-Wei, Yang, Ming, Shen, Xian, Xu, Hong-Xia, Song, Chun-Hua, Wang, Chang, and Shi, Han-Ping
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RESEARCH ,CONFIDENCE intervals ,ANTHROPOMETRY ,SKIN ,MULTIVARIATE analysis ,CASE-control method ,CANCER patients ,TRICEPS ,SURVIVAL analysis (Biometry) ,MALNUTRITION ,DESCRIPTIVE statistics ,CACHEXIA ,LOGISTIC regression analysis ,ODDS ratio - Abstract
No relevant studies have yet been conducted to explore which measurement can best predict the survival time of patients with cancer cachexia. This study aimed to identify an anthropometric measurement that could predict the 1-year survival of patients with cancer cachexia. We conducted a nested case–control study using data from a multicentre clinical investigation of cancer from 2013 to 2020. Cachexia was defined using the Fearon criteria. A total of 262 patients who survived less than 1 year and 262 patients who survived more than 1 year were included in this study. Six candidate variables were selected based on clinical experience and previous studies. Five variables, BMI, mid-arm circumference, mid-arm muscle circumference, calf circumference and triceps skin fold (TSF), were selected for inclusion in the multivariable model. In the conditional logistic regression analysis, TSF (P = 0·014) was identified as a significant independent protective factor. A similar result was observed in all patients with cancer cachexia (n 3084). In addition, a significantly stronger positive association between TSF and the 1-year survival of patients with cancer cachexia was observed in participants aged > 65 years (OR: 0·94; 95 % CI 0·89, 0·99) than in those aged ≤ 65 years (OR: 0·96; 95 % CI 0·93, 0·99; P
interaction = 0·013) and in participants with no chronic disease (OR: 0·92; 95 % CI 0·87, 0·97) than in those with chronic disease (OR: 0·97; 95 % CI 0·94, 1·00; Pinteraction = 0·049). According to this study, TSF might be a good anthropometric measurement for predicting 1-year survival in patients with cancer cachexia. [ABSTRACT FROM AUTHOR]- Published
- 2022
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58. A Prospective and Nutrition-Related Nomogram Model for Prognosis in Colorectal Cancer Survival: A Multicenter Cohort Study in China
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Ruan, Guo-Tian, primary, Zhang, Kang-Ping, additional, Zhang, Qi, additional, Zhang, Xi, additional, Tang, Meng, additional, Song, Meng-Meng, additional, Zhang, Xiao-Wei, additional, Ge, Yi-Zhong, additional, Yang, Ming, additional, Song, Chun-Hua, additional, Cong, Ming-Hua, additional, Guo, Zeng-Qing, additional, Zhu, Li-Chen, additional, Braga, Marco, additional, Cederholm, Tommy E, additional, Xu, Hong-Xia, additional, Li, Wei, additional, Xiao, Chun-Yun, additional, and shi, hanping, additional
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- 2020
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59. Poor sleep quality association with higher lung cancer risk: a nested case-control study.
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Ruan GT, Wei YP, Ge YZ, Liu LS, Zhou ZY, Siddiqi SM, He QQ, Li SQ, Xu JF, Song Y, Zhang Q, Zhang X, Yang M, Chen P, Sun Y, Wang XB, Wang BY, and Shi HP
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- Humans, Male, Middle Aged, Aged, Female, Case-Control Studies, Sleep Quality, Risk Factors, Smoking adverse effects, Lung Neoplasms epidemiology
- Abstract
Background: Little is known about the relationship between sleep quality and lung cancer incidence. Thus, this study was conducted to investigate the potential connection between sleep quality and lung cancer incidence., Methods: We performed and selected a nested case-control study that included 150 lung cancer cases and 150 matched controls based on the Lianyungang cohort. Univariate and multivariate logistic regression was utilized to investigate the connection between potential risk factors and lung cancer incidence risk., Results: In this study, the average age of participants was 66.5 ± 9.1 years, with 58.7% being male, and 52.7% reportedly experiencing sleep quality problems. The results of multivariate logistic regression showed that poor sleep quality was connected to an increased lung cancer incidence risk ( P = 0.033, odds ratio = 1.83, 95% confidence interval = [1.05-3.19]) compared with those with good sleep quality. The stratified analyses showed a significantly positive connection between poor sleep quality ( vs . good sleep quality) and cancer risk in smokers ( vs . non-smoker, P for interaction = 0.085). The combined effect analysis indicated that smokers with poor sleep quality suffered from a 2.79-fold increase in cancer incidence rates when compared with non-smokers with good sleep quality., Conclusions: Poor sleep quality was positively connected to an increased lung cancer incidence risk. In addition, among those individuals with poor sleep quality, smoking increased the lung cancer incidence risk., Competing Interests: Yun Song is employed by Shenzhen AUSA Pharmed Co Ltd. All authors declare that they have no competing interests., (© 2023 Ruan et al.)
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- 2023
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60. The combination of hand grip strength and modified Glasgow prognostic score predicts clinical outcomes in patients with liver cancer.
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Chen Y, Ruan GT, Shi JY, Liu T, Liu CA, Xie HL, Song MM, Wang ZW, Hu CL, Zhang HY, Zhang XW, Tian HY, Ge YZ, Yang M, Liu YY, Lin SQ, Liu XY, Zheng X, Wang KH, Cong MH, Shen X, Wang X, Deng L, and Shi HP
- Abstract
Purpose: Previous studies have shown that both hand grip strength (HGS) and the modified Glasgow Prognostic Score (mGPS) are associated with poor clinical outcomes in patients with liver cancer. In spite of this, no relevant studies have been conducted to determine whether the combination of HGS and mGPS can predict the prognosis of patients with liver cancer. Accordingly, this study sought to explore this possibility., Methods: This was a multicenter study of patients with liver cancer. Based on the optimal HGS cutoff value for each sex, we determined the HGS cutoff values. The patients were divided into high and low HGS groups based on their HGS scores. An mGPS of 0 was defined as low mGPS, whereas scores higher than 0 were defined as high mGPS. The patients were combined into HGS-mGPS groups for the prediction of survival. Survival analysis was performed using Kaplan-Meier curves. A Cox regression model was designed and adjusted for confounders. To evaluate the nomogram model, receiver operating characteristic curves and calibration curves were used., Results: A total of 504 patients were enrolled in this study. Of these, 386 (76.6%) were men (mean [SD] age, 56.63 [12.06] years). Multivariate analysis revealed that patients with low HGS and high mGPS had a higher risk of death than those with neither low HGS nor high mGPS (hazard ratio [HR],1.50; 95% confidence interval [CI],1.14-1.98; p = 0.001 and HR, 1.55; 95% CI, 1.14-2.12, p = 0.001 respectively). Patients with both low HGS and high mGPS had 2.35-fold increased risk of death (HR, 2.35; 95% CI, 1.52-3.63; p < 0.001). The area under the curve of HGS-mGPS was 0.623. The calibration curve demonstrated the validity of the HGS-mGPS nomogram model for predicting the survival of patients with liver cancer., Conclusion: A combination of low HGS and high mGPS is associated with poor prognosis in patients with liver cancer. The combination of HGS and mGPS can predict the prognosis of liver cancer more accurately than HGS or mGPS alone. The nomogram model developed in this study can effectively predict the survival outcomes of liver cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Chen, Ruan, Shi, Liu, Liu, Xie, Song, Wang, Hu, Zhang, Zhang, Tian, Ge, Yang, Liu, Lin, Liu, Zheng, Wang, Cong, Shen, Wang, Deng and Shi.)
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- 2023
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61. Value of the Controlling Nutritional Status score in predicting the prognosis of patients with lung cancer: A multicenter, retrospective study.
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Liu XY, Zhang X, Zhang Q, Xie HL, Ruan GT, Liu T, Song MM, Ge YZ, Xu HX, Song CH, and Shi HP
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- Humans, Middle Aged, Predictive Value of Tests, Prognosis, Retrospective Studies, Carcinoma, Non-Small-Cell Lung therapy, Lung Neoplasms pathology, Lung Neoplasms therapy, Nutritional Status, Small Cell Lung Carcinoma therapy
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Background: The body's immune-nutrition status affects prognosis in patients with lung cancer. The Controlling Nutritional Status (CONUT) score is an immune-nutrition-related index associated with prognosis in other tumors. We aimed to assess the value of CONUT scores in predicting prognosis in patients with lung cancer., Methods: In this retrospective, multicenter study, 1339 patients with lung cancer were divided into low and high CONUT score groups. The relationship between CONUT scores and overall survival (OS) was assessed by survival curves and Cox proportional hazards regression modeling. A nomogram, including CONUT scores and other clinical variables, was established., Results: There were 659 (49.2%; mean age, 59.91 years) low and 680 (50.8%; mean age, 62.23 years) high CONUT score patients. OS was significantly worse in patients with high than in those with low CONUT scores (P < 0.001), even after stratification by pathological types (non-small-cell lung cancer and small-cell lung cancer) and Tumor, Node, Metastasis (TNM) stages. A high CONUT score independently predicted risk in patients with lung cancer (adjusted hazard ratio, 1.48; 95% CI, 1.26-1.73; P < 0.001). The CONUT-based nomogram could predict prognosis well (C-index, 0.701), with better resolution and accuracy than TNM staging for predicting OS at 1, 2, and 3 years (area under the receiver operating characteristic curve, 0.735 vs 0.678, 0.742 vs 0.696, and 0.768 vs 0.743, respectively)., Conclusion: The CONUT score can predict prognosis in patients with lung cancer. A CONUT-based nomogram can improve the accuracy of survival prediction in such patients., (© 2021 American Society for Parenteral and Enteral Nutrition.)
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- 2022
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62. Prevalence and Prognostic Value of Malnutrition Among Elderly Cancer Patients Using Three Scoring Systems.
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Zhang Q, Qian L, Liu T, Ding JS, Zhang X, Song MM, Wang ZW, Ge YZ, Hu CL, Li XR, Tang M, Wang KH, Barazzoni R, Song CH, Xu HX, and Shi HP
- Abstract
Background: Malnutrition is common in patients with cancer and is associated with adverse outcomes, but few data exist in elderly patients. The aim of this study was to report the prevalence of malnutrition using three different scoring systems and to examine the possible clinical relationship and prognostic consequence of malnutrition in elderly patients with cancer. Methods: Nutritional status was assessed by using controlling nutritional status (CONUT), the prognostic nutritional index (PNI), and the nutritional risk index (NRI). Quality-of-life (Qol) was assessed during admission by using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire C-30. Performance status (PS) was assessed by using the Eastern Cooperative Oncology Group (ECOG) classification. The relationship between nutritional status and overall survival and Qol were examined. Results: Data were available for 1,494 elderly patients with cancer (63.65% male), the mean age was 70.76 years. According to the CONUT, NRI, and PNI, 55.02, 58.70, and 11.65% patients were diagnosed with malnutrition, respectively. Worse nutritional status was related to older, lower BMI, lower hand grip strength, and more advanced tumor stage. All malnutrition indexes were correlated with each other (CONUT vs. PNI, r = -0.657; CONUT vs. NRI scores, r = -0.672; PNI vs. NRI scores, r = 0.716, all P < 0.001). During a median follow-up of 43.1 months, 692 (46.32%) patients died. For patients malnourished, the incidence rate (events-per-1,000person-years) was as follows: CONUT (254.18), PNI (429.91), and NRI (261.87). Malnutrition was associated with increased risk for all-cause mortality (adjust HR [95%CI] for CONUT: 1.09 [1.05-1.13], P < 0.001; PNI: 0.98[0.97-0.99], P < 0.001; NRI: 0.98 [0.98-0.99], P < 0.001). All malnutrition indexes improved the predictive ability of the TNM classification system for all-cause mortality. Deterioration of nutritional status was associated with deterioration in Qol parameters and immunotherapeutic response ( P < 0.001). Conclusions: Malnutrition was prevalent in elderly patients with cancer, regardless of the assessment tools used, and associated with lower Qol and the immunotherapy response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Zhang, Qian, Liu, Ding, Zhang, Song, Wang, Ge, Hu, Li, Tang, Wang, Barazzoni, Song, Xu, Shi and Investigation on Nutrition Status and Its Clinical Outcome of Common Cancers (INSCOC) Group.)
- Published
- 2021
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