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Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy 被引量:7
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作者 Fang-Ze Wei Shi-Wen Mei +6 位作者 Jia-Nan Chen Zhi-Jie Wang Hai-Yu Shen Juan Li Fu-Qiang Zhao Zheng Liu Qian Liu 《World Journal of Gastroenterology》 SCIE CAS 2020年第42期6638-6657,共20页
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
关键词 Neoadjuvant therapy Rectal cancer NOMOGRAM Overall survival Diseasefree survival Risk factor score prediction model
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A risk prediction score model for predicting occurrence of post-PCI vasovagal reflex syndrome: a single center study in Chinese population 被引量:3
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作者 Hai-Yan LI Yu-Tao GUO +4 位作者 Cui TIAN Chao-Qun SONG Yang MU Yang LI Yun-Dai CHEN 《Journal of Geriatric Cardiology》 SCIE CAS CSCD 2017年第8期509-514,共6页
Background The vasovagal reflex syndrome (VVRS) is common in the patiems undergoing percutaneous coronary intervemion (PCI) However, prediction and prevention of the risk for the VVRS have not been completely fulf... Background The vasovagal reflex syndrome (VVRS) is common in the patiems undergoing percutaneous coronary intervemion (PCI) However, prediction and prevention of the risk for the VVRS have not been completely fulfilled. This study was conducted to develop a Risk Prediction Score Model to identify the determinants of VVRS in a large Chinese population cohort receiving PCI. Methods From the hos- pital electronic medical database, we idemified 3550 patients who received PCI (78.0% males, mean age 60 years) in Chinese PLA General Hospital from January 1, 2000 to August 30, 2016. The multivariate analysis and receiver operating characteristic 01OC) analysis were performed. Results The adverse events of VVRS in the patients were significantly increased after PCI procedure than before the operation (all P 〈 0.001). The rate of VVRS [95% confidence interval (CI)] in patients receiving PCI was 4.5% (4.1%-5.6%). Compared to the patients suffering no VVRS, incidence of VVRS involved the following factors, namely female gender, primary PCI, hypertension, over two stems im- plantation in the left anterior descending (LAD), and the femoral puncture site. The multivariate analysis suggested that they were independ- ent risk factors for predicting the incidence of VVRS (all P 〈 0.001). We developed a risk prediction score model for VVRS. ROC analysis showed that the risk prediction score model was effectively predictive of the incidence of VVRS in patients receiving PCI (c-statistic 0.76, 95% CI: 0.72-0.79, P 〈 0.001). There were decreased evems of VVRS in the patients receiving PCI whose diastolic blood pressure dropped by more than 30 mmHg and heart rate reduced by 10 times per minute (AUC: 0.84, 95% CI: 0.81-0.87, P 〈 0.001). Conclusion The risk prediction score is quite efficient in predicting the incidence of VVRS in patients receiving PCI. In which, the following factors may be in- volved, the femoral puncture site, female gender, hypertension, primary PCI, and over 2 stents implanted in LAD. 展开更多
关键词 Post-percutaneous coronary intervention Risk prediction score model Vasovagal reflex syndrome
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A good prognostic predictor for liver transplantation recipients with benign end-stage liver cirrhosis 被引量:3
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作者 Qiang Wei Rahmeet Singh Nemdharry +9 位作者 Run-Zhou Zhuang Jie Li Qi Ling Jian Wu Tian Shen Lin Zhou Hai-Yang Xie Min Zhang Xiao Xu Shu-Sen Zheng 《Hepatobiliary & Pancreatic Diseases International》 SCIE CAS CSCD 2017年第2期164-168,共5页
BACKGROUND: Post-transplant model for predicting mortality(PMPM, calculated as-5.359+1.988×ln(serum creatinine [mg/d L])+1.089×ln(total bilirubin [mg/d L])) score has been proved to be a simple and ... BACKGROUND: Post-transplant model for predicting mortality(PMPM, calculated as-5.359+1.988×ln(serum creatinine [mg/d L])+1.089×ln(total bilirubin [mg/d L])) score has been proved to be a simple and accurate model for predicting the prognosis after liver transplantation(LT) in a single center study. Here we aim to verify this model in a large cohort of patients.METHODS: A total of 2727 patients undergoing LT with endstage liver cirrhosis from January 2003 to December 2010 were included in this retrospective study. Data were collected from the China Liver Transplant Registry(CLTR). PMPM score was calculated at 24-h and 7-d following LT. According to the PMPM score at 24-h, all patients were divided into the low-risk group(PMPM score ≤-1.4, n=2509) and the high-risk group(PMPM score 〉-1.4, n=218). The area under receiver operator characteristic curve(AUROC) was calculated for evaluating the prognostic accuracy.RESULTS: The 1-, 3-, and 5-year patient survival rates in the low-risk group were significantly higher than those in the high-risk group(90.23%, 88.01%, and 86.03% vs 63.16%, 59.62%, and 56.43%, respectively, P〈0.001). In the high-risk group, 131 patients had a decreased PMPM score(≤-1.4) at 7-d, and their cumulative survival rate was significantly higher than the other 87 patients with sustained high PMPM score(〉-1.4)(P〈0.001). For predicting 3-month mortality, PMPM score showed a much higher AUROC than post-transplant MELD score(P〈0.05).CONCLUSION: PMPM score is a simple and effective tool to predict short-term mortality after liver transplantation in patients with benign liver diseases, and an indicator for prompt salvaging treatment as well. 展开更多
关键词 cirrhosis liver transplantation post-transplant model for predicting mortality score prognosis
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Predictive scores for mortality in full-term infants with necrotizing enterocolitis: experience of a tertiary hospital in Southwest China 被引量:3
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作者 Yu Zhang Ji-Kun Ma +3 位作者 Hong Wei Xiao-Wen Li Lu-Quan Li Jia-Lin Yu 《World Journal of Pediatrics》 SCIE CSCD 2016年第2期202-208,共7页
Background: Although many risk factors for mortalityof necrotizing enterocolitis (NEC) were investigated,most of them were obtained from preterm infants, andfew works focused on the prognostic risk factors in fullterm... Background: Although many risk factors for mortalityof necrotizing enterocolitis (NEC) were investigated,most of them were obtained from preterm infants, andfew works focused on the prognostic risk factors in fullterminfants. This study aimed to identify risk factors anddevelop a prediction score model for mortality in fulltermneonates with NEC.Methods: The risk factors were analyzed retrospectivelyby bivariate and multivariate logistic regression analysis in153 full-term neonates with NEC, who were hospitalizedin Children's Hospital of Chongqing Medical Universityfrom 2000 to 2013. A prediction score model was developedaccording to the regression coeffi cients of risk factors.Results: The mortality of the infants was 19.6%(30/153). The non-survivors had a younger age of diagnosisand advanced stage of NEC (P<0.05). They had a higherprevalence of respiratory failure, intestinal perforation,peritonitis and other complications, compared with thesurvivors (P<0.05). On the day of diagnosis, the nonsurvivorswere more likely to have abnormal laboratoryindicators than survivors (P<0.05). Age at diagnosis [oddsratio (OR)=0.91, 95% confidence interval (CI)=0.836-0.99], respiratory failure (OR=2.76, 95% CI=1.10-6.92),and peritonitis (OR=26.36, 95% CI=7.52-173.92) hadsignificant independent contributions to death. A scoremodel predicting death was developed, and the area underthe receiver operating characteristic curve was 0.869 (95%CI=0.803-0.935). All infants with scores ≥8 died.Conclusions: Younger age at diagnosis, peritonitis,and respiratory failure might be risk factors for themortality of full-term infants with NEC. Infants with apredictive score of 8 were at high risk for death. 展开更多
关键词 necrotizing enterocolitis NEONATE predictive score model prognosis
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