BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for...BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.展开更多
AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB bet...AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB between October 2008 and October 2013 at the Beijing Ditan Hospital, Capital Medical University, China. The patients were divided into two groups: severe acute exacerbation(SAE) group(n = 382) and non-SAE group(n = 1075). The SAE group was classified as the high-risk group based on the higher incidence of ACLF in this group than in the non-SAE group(13.6% vs 0.4%). Two-thirds of SAE patients were randomly assigned to risk-model derivation and the other one-third to model validation. Univariate risk factors associated with the outcome were entered into a multivariate logistic regression model for screening independent risk factors. Each variable was assigned an integer value based on the regression coefficients, and the final score was the sum of these values in the derivation set. Model discrimination and calibration were assessed using area under the receiver operating characteristic curve and the Hosmer-Lemeshow test. RESULTS: The risk prediction scoring model includedthe following four factors: age ≥ 40 years, total bilirubin ≥ 171 μmol/L, prothrombin activity 40%-60%, and hepatitis B virus DNA > 107 copies/m L. The sum risk score ranged from 0 to 7; 0-3 identified patients with lower risk of ACLF, whereas 4-7 identified patients with higher risk. The Kaplan-Meier analysis showed the cumulative risk for ACLF and ACLF-related death in the two risk groups(0-3 and 4-7 scores) of the primary cohort over 56 d, and log-rank test revealed a significant difference(2.0% vs 33.8% and 0.8% vs 9.4%, respectively; both P < 0.0001). In the derivation and validation data sets, the model had good discrimination(C index = 0.857, 95% confidence interval: 0.800-0.913 and C index = 0.889, 95% confidence interval: 0.820-0.957, respectively) and calibration demonstrated by the Hosmer-Lemeshow test(χ2 = 4.516, P = 0.808 and χ2 = 1.959, P = 0.923, respectively).CONCLUSION: Using the scoring model, clinicians can easily identify patients(total score ≥ 4) at high risk of ACLF and ACLF-related death early during SAE.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leadi...BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leading to poor prognosis.However,the effects of genes involved in TME on the prognosis of HCC patients remain unclear.Here,we investigated the HCC microenvironment to identify prognostic genes for HCC.AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm.Additionally,a risk score model was established based on Differentially Expressed Genes(DEGs)between high and lowimmune/stromal score patients.RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients.The patients were divided into high-or low-risk groups.The genes(Disabled homolog 2,Musculin,C-X-C motif chemokine ligand 8,Galectin 3,B-cell-activating transcription factor,Killer cell lectin like receptor B1,Endoglin and adenomatosis polyposis coli tumor suppressor)involved in our risk score model were considered to be potential immunotherapy targets,and they may provide better performance in combination.Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway,respectively,related to the immune-related genes in the DEGs between high-and low-risk groups.The receiver operating characteristic(ROC)curve analysis confirmed the good potency of the risk score prognostic model.Moreover,we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database.A nomogram was established to predict the overall survival of HCC patients.CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.展开更多
目的:心力衰竭(heart failure,HF)是住院患者死亡的重要原因。本研究旨在探究营养炎症风险评分(nutrition-inflammation risk score,NIRS)及其列线图模型对老年心力衰竭患者30d死亡率的预测价值。方法:本研究回顾性分析2018年1月至2020...目的:心力衰竭(heart failure,HF)是住院患者死亡的重要原因。本研究旨在探究营养炎症风险评分(nutrition-inflammation risk score,NIRS)及其列线图模型对老年心力衰竭患者30d死亡率的预测价值。方法:本研究回顾性分析2018年1月至2020年6月,青岛市胶州中心医院收治的老年HF患者。根据患者30d的存活情况,分为死亡组和存活组。采用单因素分析和多因素Logistic回归构建NIRS和死亡预测模型。采用ROC分析、校准曲线和决策曲线评估老年HF患者死亡预测列线图模型的预测能力、校准能力和临床净获益。结果:本研究共纳入797例老年HF患者,164例(20.6%)老年HF患者在30d内死亡。NIRS由预后营养指数(prognosis nutrition index,PNI)、HGB-白蛋白-淋巴细胞-血小板(hemoglobin,albumin,lymphocyte,platelet,HALP)评分、单核细胞-高密度脂蛋白比值(monocyte to high density lipoprotein ratio,MHR)和CRP-白蛋白比值(C-reaction protein to albumin ratio,CAR)组成。多因素Logstic回归结果表明,NIRS(OR=11.867,95%CI:7.681~18.333,P<0.001)、高血压(OR=1.935,95%CI:1.18~3.175,P<0.001)和慢性阻塞性肺疾病(OR=4.306,95%CI:2.611~7.1,P<0.001)是老年HF患者30d死亡的危险因素。此外,老年HF患者30d死亡列线图模型ACU为0.855;校准曲线显示该模型预测概率与实际概率基本吻合;决策曲线显示该模型净获益良好。结论:INRS是老年HF患者30d死亡的独立预测因素。此外,老年HF患者30d死亡列线图模型可个体化预测老年HF患者30d内的死亡风险,帮助临床医生早期识别死亡高风险个体。展开更多
文摘BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.
基金Supported by Grants from National Natural Science Foundation of China,No.81273743,No.81473641and 215 Program,No.2013-2-11
文摘AIM: To establish a clinical scoring model to predict risk of acute-on-chronic liver failure(ACLF) in chronic hepatitis B(CHB) patients.METHODS: This was a retrospective study of 1457 patients hospitalized for CHB between October 2008 and October 2013 at the Beijing Ditan Hospital, Capital Medical University, China. The patients were divided into two groups: severe acute exacerbation(SAE) group(n = 382) and non-SAE group(n = 1075). The SAE group was classified as the high-risk group based on the higher incidence of ACLF in this group than in the non-SAE group(13.6% vs 0.4%). Two-thirds of SAE patients were randomly assigned to risk-model derivation and the other one-third to model validation. Univariate risk factors associated with the outcome were entered into a multivariate logistic regression model for screening independent risk factors. Each variable was assigned an integer value based on the regression coefficients, and the final score was the sum of these values in the derivation set. Model discrimination and calibration were assessed using area under the receiver operating characteristic curve and the Hosmer-Lemeshow test. RESULTS: The risk prediction scoring model includedthe following four factors: age ≥ 40 years, total bilirubin ≥ 171 μmol/L, prothrombin activity 40%-60%, and hepatitis B virus DNA > 107 copies/m L. The sum risk score ranged from 0 to 7; 0-3 identified patients with lower risk of ACLF, whereas 4-7 identified patients with higher risk. The Kaplan-Meier analysis showed the cumulative risk for ACLF and ACLF-related death in the two risk groups(0-3 and 4-7 scores) of the primary cohort over 56 d, and log-rank test revealed a significant difference(2.0% vs 33.8% and 0.8% vs 9.4%, respectively; both P < 0.0001). In the derivation and validation data sets, the model had good discrimination(C index = 0.857, 95% confidence interval: 0.800-0.913 and C index = 0.889, 95% confidence interval: 0.820-0.957, respectively) and calibration demonstrated by the Hosmer-Lemeshow test(χ2 = 4.516, P = 0.808 and χ2 = 1.959, P = 0.923, respectively).CONCLUSION: Using the scoring model, clinicians can easily identify patients(total score ≥ 4) at high risk of ACLF and ACLF-related death early during SAE.
基金Supported by National Natural Science Foundation of China,No.81972255,No.81772597 and No.81672412Guangdong Natural Science Foundation,No.2017A030311002+4 种基金Guangdong Science and Technology Foundation,No.2017A020215196Fundamental Research Funds for the Central Universities of Sun YatSen University,No.17ykpy44Science Foundation of Jiangxi,No.20181BAB214002Education Department Science and Technology Foundation of Jiangxi,No.GJJ170936Grant from Guangdong Science and Technology Department,No.2017B030314026
文摘BACKGROUND Hepatocellular carcinoma(HCC)is a common cancer with a poor prognosis.Previous studies revealed that the tumor microenvironment(TME)plays an important role in HCC progression,recurrence,and metastasis,leading to poor prognosis.However,the effects of genes involved in TME on the prognosis of HCC patients remain unclear.Here,we investigated the HCC microenvironment to identify prognostic genes for HCC.AIM To identify a robust gene signature associated with the HCC microenvironment to improve prognosis prediction of HCC.METHODS We computed the immune/stromal scores of HCC patients obtained from The Cancer Genome Atlas based on the ESTIMATE algorithm.Additionally,a risk score model was established based on Differentially Expressed Genes(DEGs)between high and lowimmune/stromal score patients.RESULTS The risk score model consisting of eight genes was constructed and validated in the HCC patients.The patients were divided into high-or low-risk groups.The genes(Disabled homolog 2,Musculin,C-X-C motif chemokine ligand 8,Galectin 3,B-cell-activating transcription factor,Killer cell lectin like receptor B1,Endoglin and adenomatosis polyposis coli tumor suppressor)involved in our risk score model were considered to be potential immunotherapy targets,and they may provide better performance in combination.Functional enrichment analysis showed that the immune response and T cell receptor signaling pathway represented the major function and pathway,respectively,related to the immune-related genes in the DEGs between high-and low-risk groups.The receiver operating characteristic(ROC)curve analysis confirmed the good potency of the risk score prognostic model.Moreover,we validated the risk score model using the International Cancer Genome Consortium and the Gene Expression Omnibus database.A nomogram was established to predict the overall survival of HCC patients.CONCLUSION The risk score model and the nomogram will benefit HCC patients through personalized immunotherapy.
文摘目的:心力衰竭(heart failure,HF)是住院患者死亡的重要原因。本研究旨在探究营养炎症风险评分(nutrition-inflammation risk score,NIRS)及其列线图模型对老年心力衰竭患者30d死亡率的预测价值。方法:本研究回顾性分析2018年1月至2020年6月,青岛市胶州中心医院收治的老年HF患者。根据患者30d的存活情况,分为死亡组和存活组。采用单因素分析和多因素Logistic回归构建NIRS和死亡预测模型。采用ROC分析、校准曲线和决策曲线评估老年HF患者死亡预测列线图模型的预测能力、校准能力和临床净获益。结果:本研究共纳入797例老年HF患者,164例(20.6%)老年HF患者在30d内死亡。NIRS由预后营养指数(prognosis nutrition index,PNI)、HGB-白蛋白-淋巴细胞-血小板(hemoglobin,albumin,lymphocyte,platelet,HALP)评分、单核细胞-高密度脂蛋白比值(monocyte to high density lipoprotein ratio,MHR)和CRP-白蛋白比值(C-reaction protein to albumin ratio,CAR)组成。多因素Logstic回归结果表明,NIRS(OR=11.867,95%CI:7.681~18.333,P<0.001)、高血压(OR=1.935,95%CI:1.18~3.175,P<0.001)和慢性阻塞性肺疾病(OR=4.306,95%CI:2.611~7.1,P<0.001)是老年HF患者30d死亡的危险因素。此外,老年HF患者30d死亡列线图模型ACU为0.855;校准曲线显示该模型预测概率与实际概率基本吻合;决策曲线显示该模型净获益良好。结论:INRS是老年HF患者30d死亡的独立预测因素。此外,老年HF患者30d死亡列线图模型可个体化预测老年HF患者30d内的死亡风险,帮助临床医生早期识别死亡高风险个体。
