BACKGROUND Perihilar cholangiocarcinoma(pCCA)has a poor prognosis and urgently needs a better predictive method.The predictive value of the age-adjusted Charlson comorbidity index(ACCI)for the long-term prognosis of p...BACKGROUND Perihilar cholangiocarcinoma(pCCA)has a poor prognosis and urgently needs a better predictive method.The predictive value of the age-adjusted Charlson comorbidity index(ACCI)for the long-term prognosis of patients with multiple malignancies was recently reported.However,pCCA is one of the most surgically difficult gastrointestinal tumors with the poorest prognosis,and the value of the ACCI for the prognosis of pCCA patients after curative resection is unclear.AIM To evaluate the prognostic value of the ACCI and to design an online clinical model for pCCA patients.METHODS Consecutive pCCA patients after curative resection between 2010 and 2019 were enrolled from a multicenter database.The patients were randomly assigned 3:1 to training and validation cohorts.In the training and validation cohorts,all patients were divided into low-,moderate-,and high-ACCI groups.Kaplan-Meier curves were used to determine the impact of the ACCI on overall survival(OS)for pCCA patients,and multivariate Cox regression analysis was used to determine the independent risk factors affecting OS.An online clinical model based on the ACCI was developed and validated.The concordance index(C-index),calibration curve,and receiver operating characteristic(ROC)curve were used to evaluate the predictive performance and fit of this model.RESULTS A total of 325 patients were included.There were 244 patients in the training cohort and 81 patients in the validation cohort.In the training cohort,116,91 and 37 patients were classified into the low-,moderate-and high-ACCI groups.The Kaplan-Meier curves showed that patients in the moderate-and high-ACCI groups had worse survival rates than those in the low-ACCI group.Multivariable analysis revealed that moderate and high ACCI scores were independently associated with OS in pCCA patients after curative resection.In addition,an online clinical model was developed that had ideal C-indexes of 0.725 and 0.675 for predicting OS in the training and validation cohorts.The calibration curve and ROC curve indicated that the model had a good fit and prediction performance.CONCLUSION A high ACCI score may predict poor long-term survival in pCCA patients after curative resection.High-risk patients screened by the ACCI-based model should be given more clinical attention in terms of the management of comorbidities and postoperative follow-up.展开更多
We aimed to develop a disease risk comorbidity index(DRCI)based on disease risk index(DRI)and Hematopoietic Cell Transplantation-Specific Comorbidity Index(HCT-CI)in patients receiving haploidentical hematopoietic ste...We aimed to develop a disease risk comorbidity index(DRCI)based on disease risk index(DRI)and Hematopoietic Cell Transplantation-Specific Comorbidity Index(HCT-CI)in patients receiving haploidentical hematopoietic stem cell transplantation(haplo-HSCT).We identified the prognostic factors of disease-free survival(DFS)in a training subset(n=593),then assigned a weighted score using these factors to the remaining patients(validation subset;n=296).The multivariable model identified two independent predictors of DFS:DRI and HCT-CI before transplantation.In this scoring system,we assigned a weighted score of 2 to very high-risk DRI,and assigned a weighted score of 1 to high-risk DRI and intermediate-and high-risk HCT-CI(i.e.,haplo-DRCI).In the validation cohort,the three-year DFS rate was 65.2%(95%confidence interval(CI),58.2%–72.2%),55.8%(95%CI,44.9%–66.7%),and 32.0%(95%CI,5.8%–58.2%)for the low-,intermediate-,and high-risk group,respectively(P=0.005).Haplo-DRCI can also predict DFS in disease-specific subgroups,particularly in acute leukemia patients.Increasing score was also significantly predictive of increased relapse,increased non-relapse mortality(NRM),decreased DFS,and decreased overall survival(OS)in an independent historical cohort(n=526).These data confirmed that haplo-DRCI could effectively risk stratify haplo-HSCT recipients and provide a tool to better predict who will best benefit from haplo-HSCT.展开更多
AIM:To study the outcome and prognostic factors in a series of patients with extrahepatic cholangiocarcinoma and determine the impact of comorbidity on survival.METHODS:A retrospective analysis of 68 patients with ext...AIM:To study the outcome and prognostic factors in a series of patients with extrahepatic cholangiocarcinoma and determine the impact of comorbidity on survival.METHODS:A retrospective analysis of 68 patients with extrahepatic cholangiocarcinoma(perihilar,n = 37;distal,n = 31) seen at a single tertiary-care institution during the period 1999-2003 was performed.Data on presentation,management,and outcome were assessed by chart review.Pathologic confirmation was obtained in 37 cases(54.4%) .Comorbidity was evaluated by using the Charlson comorbidity index(CCI) .RESULTS:Mean age at diagnosis was 73.4 ± 11.5 years.Jaundice was the most common symptom presented(86.8%) .Median CCI score was 1(range,0 to 4) .Nineteen patients(27.9%) underwent tumor resection.Palliative biliary drainage was performed in 39 patients(57.4%) ,and 6 patients(8.8%) received only best supportive care.Tumor-free margin status(R0) was achieved in 15 cases(78.9% of resection group) .Baseline serum carbohydrate antigen 19-9(CA 19-9) level was revealed to be an independent predictor of surgical treatment(P = 0.026) .Overall median survival was 3.1 ± 0.9 mo,with 1-and 2-year survival rates of 21% and 7%,respectively.In the univariate analysis,tumor resection,CCI score,and serum CA 19-9 levels correlated significantly with outcome.In the multivariate analysis,only resection(HR 0.10;95% CI,0.02-0.51,P = 0.005) and a CCI score ≥ 2(HR 3.36;95% CI,1.0-10.9,P = 0.045) were found to independently predict survival.CONCLUSION:Tumor resection and comorbidity emerged as significant prognostic variables in extrahepatic cholangiocarcinoma.Comorbidity evaluation instruments should be applied in the clinical management of such patients.展开更多
Introduction: Abdominal deposition of fat has been described as the type of obesity that offers the greatest risk for the health of individuals, and is associated with increased mortality, and morbidity. Conicity inde...Introduction: Abdominal deposition of fat has been described as the type of obesity that offers the greatest risk for the health of individuals, and is associated with increased mortality, and morbidity. Conicity index (Ci), Body mass index (BMI), and waist hip ratio (WHR) are used to predict the risk of obesity related diseases. However, it has not been ex amined whether these indicators can predict the comorbidities in hemodialysis subjects in Lebanon. Objective: to determine the effect of central obesity on comorbidities in hemodialysis patients in Lebanon. Material and Method: This is a cross-sectional study of obesity in 60 hemodialysis subjects in Lebanon. A linear regression analysis was used to determine the relationship between BMI, Ci, WHR, and comorbidities measured by Charlson (CCI) and Davies comorbidities indexes. Results: Ci values were significantly associated with age, and CCI;the abdominal fat deposition evaluated by the conicity index and WHR were a predictor of the comorbidities according to CCI (= 2.96;p = 0.01), and Davies comorbidity index (= 1.19;p = 0.05) scores. BMI was a weak predictor of comorbidity. Conclusion: Abdominal obesity by using simple anthropometric measurements e.g. Ci, and WHR values can similarly predict the presence of comorbidities in hemodialysis patients. Maintaining appropriate Ci and WHR values might be important to improve outcome in hemodialysis patients.展开更多
Our intent is to examine the predictive role of Charlson comorbidity index （CCI） on mortality of patients with type 2 diabetic nephropathy （DN）. Based on the CCI score, the severity of comorbidity was categorized ...Our intent is to examine the predictive role of Charlson comorbidity index （CCI） on mortality of patients with type 2 diabetic nephropathy （DN）. Based on the CCI score, the severity of comorbidity was categorized into three grades： mild, with CCI scores of 1-2; moderate, with CCI scores of 3-4; and severe, with CCI scores 〉5. Factors influencing mortality and differences between groups stratified by CCI were determined by logistical regression analysis and one-way analysis of variance （ANOVA）. The impact of CCI on mortality was assessed by the Kaplan- Meier analysis. A total of 533 patients with type 2 DN were enrolled in this study, all of them had comorbidity （CCI score 〉1）, and 44.7% （238/533） died. The mortality increased with CCI scores： 21.0% （50/238） patients with CCI scores of 1-2, 56.7% （135/238） patients with CCI scores of 3-4, and 22.3% （53/238） patients with CCI scores 〉5. Logistical regression analysis showed that CCI scores, hemoglobin, and serum albumin were the potential predictors of mortality （P〈0.05）. One-way ANOVA analysis showed that DN patients with higher CCI scores had lower levels of hemoglobulin, higher levels of serum creatinine, and higher mortality rates than those with lower CCI scores. The Kaplan-Meier curves showed that survival time decreased when the CCI scores and mortality rates went up. In con- clusion, CCI provides a simple, readily applicable, and valid method for classifying comorbidities and predicting the mortality of type 2 DN. An increased awareness of the potential comorbidities in type 2 DN patients may provide insights into this complicated disease and improve the outcomes by identifying and treating patients earlier and more effectively.展开更多
基金Supported by National Natural Science Foundation of China,No. 81874211Chongqing Technology Innovation and Application Development Special Key Project,No. CSTC2021jscx-gksb-N0009
文摘BACKGROUND Perihilar cholangiocarcinoma(pCCA)has a poor prognosis and urgently needs a better predictive method.The predictive value of the age-adjusted Charlson comorbidity index(ACCI)for the long-term prognosis of patients with multiple malignancies was recently reported.However,pCCA is one of the most surgically difficult gastrointestinal tumors with the poorest prognosis,and the value of the ACCI for the prognosis of pCCA patients after curative resection is unclear.AIM To evaluate the prognostic value of the ACCI and to design an online clinical model for pCCA patients.METHODS Consecutive pCCA patients after curative resection between 2010 and 2019 were enrolled from a multicenter database.The patients were randomly assigned 3:1 to training and validation cohorts.In the training and validation cohorts,all patients were divided into low-,moderate-,and high-ACCI groups.Kaplan-Meier curves were used to determine the impact of the ACCI on overall survival(OS)for pCCA patients,and multivariate Cox regression analysis was used to determine the independent risk factors affecting OS.An online clinical model based on the ACCI was developed and validated.The concordance index(C-index),calibration curve,and receiver operating characteristic(ROC)curve were used to evaluate the predictive performance and fit of this model.RESULTS A total of 325 patients were included.There were 244 patients in the training cohort and 81 patients in the validation cohort.In the training cohort,116,91 and 37 patients were classified into the low-,moderate-and high-ACCI groups.The Kaplan-Meier curves showed that patients in the moderate-and high-ACCI groups had worse survival rates than those in the low-ACCI group.Multivariable analysis revealed that moderate and high ACCI scores were independently associated with OS in pCCA patients after curative resection.In addition,an online clinical model was developed that had ideal C-indexes of 0.725 and 0.675 for predicting OS in the training and validation cohorts.The calibration curve and ROC curve indicated that the model had a good fit and prediction performance.CONCLUSION A high ACCI score may predict poor long-term survival in pCCA patients after curative resection.High-risk patients screened by the ACCI-based model should be given more clinical attention in terms of the management of comorbidities and postoperative follow-up.
基金This work was supported by the National Key Research and Development Program of China(2017YFA0104500)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(81621001)+6 种基金the Key Program of the National Natural Science Foundation of China(81930004)Capital’s Funds for Health Improvement and Research(2018-4-4089)CAMS Innovation Fund for Medical Sciences(CIFMS)(2019-I2M-5-034)the Science and Technology Project of Guangdong Province of China(2016B030230003)the Project of Health Collaborative Innovation of Guangzhou City(201704020214)Peking University Clinical Scientist Program(BMU2019LCKXJ003)supported by the Fundamental Research Funds for the Central Universities.
文摘We aimed to develop a disease risk comorbidity index(DRCI)based on disease risk index(DRI)and Hematopoietic Cell Transplantation-Specific Comorbidity Index(HCT-CI)in patients receiving haploidentical hematopoietic stem cell transplantation(haplo-HSCT).We identified the prognostic factors of disease-free survival(DFS)in a training subset(n=593),then assigned a weighted score using these factors to the remaining patients(validation subset;n=296).The multivariable model identified two independent predictors of DFS:DRI and HCT-CI before transplantation.In this scoring system,we assigned a weighted score of 2 to very high-risk DRI,and assigned a weighted score of 1 to high-risk DRI and intermediate-and high-risk HCT-CI(i.e.,haplo-DRCI).In the validation cohort,the three-year DFS rate was 65.2%(95%confidence interval(CI),58.2%–72.2%),55.8%(95%CI,44.9%–66.7%),and 32.0%(95%CI,5.8%–58.2%)for the low-,intermediate-,and high-risk group,respectively(P=0.005).Haplo-DRCI can also predict DFS in disease-specific subgroups,particularly in acute leukemia patients.Increasing score was also significantly predictive of increased relapse,increased non-relapse mortality(NRM),decreased DFS,and decreased overall survival(OS)in an independent historical cohort(n=526).These data confirmed that haplo-DRCI could effectively risk stratify haplo-HSCT recipients and provide a tool to better predict who will best benefit from haplo-HSCT.
文摘AIM:To study the outcome and prognostic factors in a series of patients with extrahepatic cholangiocarcinoma and determine the impact of comorbidity on survival.METHODS:A retrospective analysis of 68 patients with extrahepatic cholangiocarcinoma(perihilar,n = 37;distal,n = 31) seen at a single tertiary-care institution during the period 1999-2003 was performed.Data on presentation,management,and outcome were assessed by chart review.Pathologic confirmation was obtained in 37 cases(54.4%) .Comorbidity was evaluated by using the Charlson comorbidity index(CCI) .RESULTS:Mean age at diagnosis was 73.4 ± 11.5 years.Jaundice was the most common symptom presented(86.8%) .Median CCI score was 1(range,0 to 4) .Nineteen patients(27.9%) underwent tumor resection.Palliative biliary drainage was performed in 39 patients(57.4%) ,and 6 patients(8.8%) received only best supportive care.Tumor-free margin status(R0) was achieved in 15 cases(78.9% of resection group) .Baseline serum carbohydrate antigen 19-9(CA 19-9) level was revealed to be an independent predictor of surgical treatment(P = 0.026) .Overall median survival was 3.1 ± 0.9 mo,with 1-and 2-year survival rates of 21% and 7%,respectively.In the univariate analysis,tumor resection,CCI score,and serum CA 19-9 levels correlated significantly with outcome.In the multivariate analysis,only resection(HR 0.10;95% CI,0.02-0.51,P = 0.005) and a CCI score ≥ 2(HR 3.36;95% CI,1.0-10.9,P = 0.045) were found to independently predict survival.CONCLUSION:Tumor resection and comorbidity emerged as significant prognostic variables in extrahepatic cholangiocarcinoma.Comorbidity evaluation instruments should be applied in the clinical management of such patients.
文摘Introduction: Abdominal deposition of fat has been described as the type of obesity that offers the greatest risk for the health of individuals, and is associated with increased mortality, and morbidity. Conicity index (Ci), Body mass index (BMI), and waist hip ratio (WHR) are used to predict the risk of obesity related diseases. However, it has not been ex amined whether these indicators can predict the comorbidities in hemodialysis subjects in Lebanon. Objective: to determine the effect of central obesity on comorbidities in hemodialysis patients in Lebanon. Material and Method: This is a cross-sectional study of obesity in 60 hemodialysis subjects in Lebanon. A linear regression analysis was used to determine the relationship between BMI, Ci, WHR, and comorbidities measured by Charlson (CCI) and Davies comorbidities indexes. Results: Ci values were significantly associated with age, and CCI;the abdominal fat deposition evaluated by the conicity index and WHR were a predictor of the comorbidities according to CCI (= 2.96;p = 0.01), and Davies comorbidity index (= 1.19;p = 0.05) scores. BMI was a weak predictor of comorbidity. Conclusion: Abdominal obesity by using simple anthropometric measurements e.g. Ci, and WHR values can similarly predict the presence of comorbidities in hemodialysis patients. Maintaining appropriate Ci and WHR values might be important to improve outcome in hemodialysis patients.
基金Project supported by the Science and Technology Research Projects of Sichuan Province(No.2011SZ0215),China
文摘Our intent is to examine the predictive role of Charlson comorbidity index （CCI） on mortality of patients with type 2 diabetic nephropathy （DN）. Based on the CCI score, the severity of comorbidity was categorized into three grades： mild, with CCI scores of 1-2; moderate, with CCI scores of 3-4; and severe, with CCI scores 〉5. Factors influencing mortality and differences between groups stratified by CCI were determined by logistical regression analysis and one-way analysis of variance （ANOVA）. The impact of CCI on mortality was assessed by the Kaplan- Meier analysis. A total of 533 patients with type 2 DN were enrolled in this study, all of them had comorbidity （CCI score 〉1）, and 44.7% （238/533） died. The mortality increased with CCI scores： 21.0% （50/238） patients with CCI scores of 1-2, 56.7% （135/238） patients with CCI scores of 3-4, and 22.3% （53/238） patients with CCI scores 〉5. Logistical regression analysis showed that CCI scores, hemoglobin, and serum albumin were the potential predictors of mortality （P〈0.05）. One-way ANOVA analysis showed that DN patients with higher CCI scores had lower levels of hemoglobulin, higher levels of serum creatinine, and higher mortality rates than those with lower CCI scores. The Kaplan-Meier curves showed that survival time decreased when the CCI scores and mortality rates went up. In con- clusion, CCI provides a simple, readily applicable, and valid method for classifying comorbidities and predicting the mortality of type 2 DN. An increased awareness of the potential comorbidities in type 2 DN patients may provide insights into this complicated disease and improve the outcomes by identifying and treating patients earlier and more effectively.
