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Objective:To investigate the risk factors of esophageal stenosis after endoscopic submucosal dissection (ESD) of early esophageal cancer and precancerous lesions, and to construct and assess a predictive model for esophageal stenosis.Methods:Data of 421 patients with early esophageal cancer or precancerous lesions who underwent ESD and were confirmed by pathology in the First Affiliated Hospital of Zhengzhou University between January 2015 and April 2020 were retrospectively analyzed.Eighty-nine cases developed postoperative esophageal stenosis (stenosis group) and 332 cases did not (non-stenosis group). Risk factors of esophageal stenosis were investigated by univariate and multivariate logistic regression analysis. Independent risk factors were used as predictors to construct a nomogram model by using the lasso algorithm.The accuracy of the model was evaluated by the consistency index (C-index) and the calibration curve. Bootstrap was applied to internal verification to avoid over-fitting of the model.Results:Univariate analysis showed that postoperative pathology, depth of infiltration, median long and short diameters of the specimen, circumferential resection range, and muscularis propria injury were related to postoperative esophageal stenosis( P<0.05). Further multivariate logistic regression analysis showed that the resection range≥1/2 of the circumference (VS <1/2 circumference: P<0.01, OR=48.453, 95% CI: 11.288-207.983), muscularis propria injury( P<0.01, OR=4.671, 95% CI: 2.283-9.557)and longitudinal length≥50 mm (VS <50 mm: P=0.008, OR=2.741, 95% CI: 1.299-5.785) were independent risk factors for esophageal stenosis after ESD. The nomogram model was constructed through the lasso algorithm by taking the longitudinal length, circumferential resection range, and muscularis propria injury as the predictive factors. The C-index of the nomogram was 0.934 (95% CI: 0.909-0.959)and was 0.931 after 100 times of Bootstrap internal sampling, which meant the prediction probability of the model in the calibration curve was in good agreement with the actual observation probability. Conclusion:Circumferential resection range ≥1/2, muscularis propria injury, and longitudinal length≥50 mm are independent risk factors for post-ESD esophageal stenosis. The nomogram model constructed by the above three indicators as predictors shows an ideal prediction effect on esophageal stenosis after ESD for early esophageal cancer and precancerous lesions, which is helpful to establish a standard plan for early intervention in patients at high risk of stenosis after ESD.
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Objective:To analyze the correlation of the short diameter of residual lymph nodes with the efficacy and prognosis of patients with esophageal squamous cell carcinoma (ESCC) undergoing chemoradiotherapy (CRT), and establish a Nomogram prediction model to predict the prognosis of ESCC patients.Methods:Clinical data of 143 ESCC patients who underwent CRT in Second People′s Hospital of Huai′an from August 2018 to September 2020 were collected. The survival analysis was conducted with Kaplan- Meier method, log-rank test and univariate prognostic analysis. Multivariate prognostic analysis was performed with Cox models. Finally, a Nomogram prediction model was established to predict the 1-year and 2-year progression-free survival (PFS) of patients, and the C-index, AUC, and calibration curve were used to evaluate the performance of the model. Results:Logistic regression analysis results showed that differentiation, TNM staging, PG-SGA scores before and after radiotherapy (RT) and short diameter of residual lymph nodes were the independent predictors of clinical efficacy of ESCC patients treated with CRT. Cox regression analysis demonstrated that differentiation, TNM staging, PG-SGA scores before and after RT and short diameter of residual lymph nodes were the independent prognostic predictors of ESCC patients undergoing CRT. Conclusions:The short diameter of residual lymph nodes is significantly correlated with the efficacy and prognosis of ESCC patients undergoing CRT. The Nomogram prediction model established after comprehensive clinical baseline characteristics is a practical and reliable tool for predicting clinical prognosis of ESCC patients.
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Objective:To investigate the influencing factors for lung metastasis of hepato-cellular carcinoma after liver transplantation and application value of its nomogram prediction model.Methods:The retrospective cohort study was conducted. The clinicopathological data of 339 hepatocellular carcinoma patients with lung metastasis after liver transplantation who were admitted to Zhongshan Hospital of Fudan University from January 2015 to June 2019 were collected. There were 299 males and 40 females, aged from 23 to 73 years, with a median age of 54 years. According to the random numbers showed in the computer, all 339 patients were divided into training dataset consisting of 226 and validation dataset consisting of 113, with a ratio of 2:1. All patients underwent classic orthotopic liver transplantation. Observation indicators: (1) analysis of clinicopathological data of patients in the training dataset and validation dataset; (2) follow-up; (3) analysis of influencing factors for lung metastasis of hepatocellular carcinoma after liver transplanta-tion; (4) construction and evaluation of nomogram prediction model for lung metastasis of hepatocellular carcinoma after liver transplantation. Follow-up was conducted using outpatient examination and telephone interview to detect lung metastasis of patients up to November 2020. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the paired t test. Measurement data with skewed distribution were represented as M( P25, P75) or M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute number or percentages, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to calculate lung metastasis rate and draw lung metastasis curve. The Log-rank test was used for survival analysis. The COX proportional hazard model was used for univariate and multivariate analysis. Based on the results of multivariate analysis, the nomogram prediction model was constructed. The prediction accuracy of the nomogram model was evaluated using C-index and receiver operating characteristic (ROC) curve. The calibration curve was used to evaluate the prediction error of the model. Results:(1) Analysis of clinicopathological data of patients in the training dataset and validation dataset: there was no significant difference in general data between patients in the training dataset and validation dataset ( P>0.05). (2) Follow-up: 226 patients in training dataset and 113 patients in validation dataset were followed up. The follow-up time of training dataset was 5.2 to 69.0 months, with a median follow-up time of 29.3 months, and the follow-up time of validation dataset was 4.3 to 69.0 months, with a median follow-up time of 30.4 months. Up to the last follow-up, 48 cases of the training dataset and 22 cases of the validation dataset had lung metastasis, with the incidence and median time of lung metastasis were 21.24%(48/226), 19.47%(22/113) and 8.5 months, 7.8 months, respectively. There was no significant difference in lung metastasis between patients in the training dataset and validation dataset ( χ2=0.144, P>0.05). (3) Analysis of influencing factors for lung metastasis of hepatocellular carcinoma after liver transplantation: results of univariate analysis showed that age, alpha fetoprotein, tumor diameter, tumor differentiation degree, vascular invasion, systemic immune inflammation index and postoperative treatment were related factors for lung metastasis of hepatocellular carcinoma after liver transplantation ( hazard ratio=0.465, 3.413, 1.140, 3.791, 2.524, 2.053, 1.833, 95% confidence interval as 0.263?0.822, 1.740?6.695, 1.091?1.191, 1.763?8.154, 1.903?3.349, 1.047?4.027, 1.038?3.238, P<0.05) . Results of multivariate analysis showed that age, tumor diameter and vascular invasion were independent influencing factors for lung metastasis of hepatocellular carcinoma after liver transplantation ( hazard ratio=0.462, 1.076, 2.170, 95% confidence interval as 0.253?0.843, 1.013?1.143, 1.545?3.048, P<0.05). (4) Construction and evaluation of nomogram prediction model for lung metastasis of hepatocellular carcinoma after liver transplantation: the C-index was 0.810 (95% confidence interval as 0.758?0.863) and 0.802 (95% confidence interval as 0.723?0.881) of the nomogram prediction model for lung metastasis of hepatocellular carcinoma after liver transplanta-tion in the training dataset and validation dataset, respectively, showing good discrimination ability. The area under ROC of 0.5-, 1- and 2-year nomogram prediction model in the training dataset and the validation dataset were 0.815(95% confidence interval as 0.725?0.905), 0.863(95% confidence interval as 0.809?0.917), 0.835(95% confidence interval as 0.771?0.900)and 0.873(95% confidence interval as 0.801?0.945), 0.858(95% confidence interval as 0.760?0.956), 0.841(95% confidence interval as 0.737?0.945), respectively, which illustrated that the model had good predictive ability. The formula of nomogram prediction model=33.300 06+(?33.300 06)×age(≤50 years=0, >50 years=1)+2.857 14×tumor diameter (cm)+31.585 71×vascular invasion (M0 stage of microvascular invasion staging=0, M1 stage of microvascular invasion staging=1, M2 stage of microvascular invasion staging=2, visible tumor thrombus=3). The optimal threshold of nomogram risk score was 77.5. Patients with risk score ≥77.5 were assigned into high risk group, and patients with risk score <77.5 were assigned into low risk group. The 0.5-,1- and 2-year lung metastasis rate of patients in the high risk group and low risk group of the training dataset were 16.7%, 39.2%, 46.4% and 1.4%, 4.1%, 6.9%, respectively, showing a significant difference between the two groups ( χ2=54.86, P<0.05). The 0.5-,1- and 2-year lung metastasis rate of patients in the high risk group and low risk group of the validation dataset were 17.6%, 29.0%, 39.5% and 0, 3.1%, 4.8%, respectively, showing a significant difference between the two groups ( χ2=25.29, P<0.05). Conclusions:Age, tumor diameter and vascular invasion are independent influencing factors for lung metastasis of hepatocellular carcinoma after liver transplantation. The nomogram prediction model based on age, tumor diameter and vascular invasion can predict risk of lung metastasis for hepatocellular carcinoma patients after liver transplantation accurately.
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@#AIM:To investigate the risk factors of high intraocular pressure after cataract surgery for phacolytic glaucoma(PLG). <p>METHODS: The clinical data of 148 PLG patients(148 eyes)who underwent cataract surgery were analyzed retrospectively. According to the occurrence of postoperative high intraocular pressure, the patients were divided into non-high intraocular pressure group and high intraocular pressure group. Logistic regression analysis was used to evaluate the risk factors of high intraocular pressure after cataract surgery in PLG patients. Nomogram model was constructed, whose predictive ability was evaluated by receiver operating characteristic curve(ROC). <p>RESULTS: There was no significant difference in age, gender, BMI, cataract lens nucleus grade, combined with hypertension, and surgical methods between two groups(<i>P</i>>0.05); the proportion of diabetes, high myopia, preoperative uveitis, preoperative ocular trauma and intraoperative complications in high intraocular pressure group were higher than those of non-high intraocular pressure group, with the difference was statistically significant(<i>P</i><0.05). Logistic regression analysis showed that diabetes, high myopia, preoperative uveitis, preoperative ocular trauma and intraoperative complications were independent influencing factors for high intraocular pressure after cataract surgery in PLG patients. The nomogram model constructed in this study had good fitting effect, and the area under the ROC curve was 0.906(0.890-0.921), indicating that it had strong predictive ability. <p>CONCLUSION: PLG patients with intraoperative complications, preoperative uveitis, ocular trauma, diabetes and high myopia had higher risk of high intraocular pressure after cataract surgery. Clinical doctors should pay more attention to these patients in order to improve the treatment prognosis.
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Objective The SEER database was utilized to analyze the prognosis and related factors for patients with locally resectable esophageal cancer undergoing preoperative chemoradiotherapy.A nomogram for predicting survival was established to provide reference for screening patients receiving preoperative chemoradiotherapy.Methods Patients diagnosed with stage T1b-4aN0-3M0(7th version AJCC in 2010) resectable esophageal cancer receiving preoperative chemoradiotherapy between 2010 and 2015 were selected from the SEER database.The survival rate was determined by Kaplan-Meier method.The univariate analysis was performed by log-rank test.The multivariate analysis was conducted by Cox test.The nomogram for survival prediction was established by using R software.The predicting accuracy of the nomogram was evaluated by C-index and calibration curve.Results A total of 1 697 eligible patients were included.Univariate analysis showed that sex,T stage,N stage and tumor differentiation were significantly associated with overall survival (OS) and cancer-specific survival rate (CSS)(all P＜0.001),and age (P=0.027) was significantly correlated with OS.Multivariate analysis demonstrated that age,sex,tumor differentiation and N stage were significantly associated with OS.Sex,tumor differentiation,T stage and N stage were significantly correlated with CSS (all P＜0.05).After the prognostic factors were included into the nomogram,the C-index for 5-year OS and CSS was 0.60 and 0.61.The model for predicting survival of patients with esophageal squamous cell cancer was established by using the same method.The C-index for the OS and CSS was 0.62 and 0.64.Conclusions Sex,clinical stage and tumor differentiation are prognostic factors of CSS in patients with locally resectable esophageal cancer undergoing preoperative chemoradiotherapy followed by surgery.The nomogram established according to the data above can provide certain reference for the selection of preoperative chemoradiotherapy combined with surgery.
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Objective@#To evaluate the risk factors of postoperative upgrade to pT3a of cT1 renal cell carcinoma, and to establish a nomogram prediction model to improve the ability of predicting locally advanced renal cell carcinoma and provide a reference for clinical surgical decision-making.@*Methods@#Clinical data of 1 376 patients with cT1 (diameter ≤ 7 cm) renal tumor hospitalized for surgery from January 2010 to December 2016 were retrospectively analyzed. There were 979 males and 397 females, with the mean age of (57.65±10.92) years. The mean body mass index (BMI) was (25.47±3.27) kg/m2 and the average tumor size was (4.02±1.52) cm. There were 711 tumors on the left and 665 on the right. There were 363 cases with clinical symptoms, 567 patients with smoking history , 732 cases with history of chronic disease. There were 289 cases with tumor necrosis, 636 cases with tumor protrusion, 822 cases with irregular tumor , and 738 cases with renal sinus compression. Partial nephrectomy and radical nephrectomy were performed in 396 cases and 980 cases respectively. Mann-whitney U test and chi-square test were used for univariate analysis, logistic regression analysis was used for multivariate analysis to analyze the predictors of upgrading, R software was used to construct the nomogram predictive model, C-index was used to evaluate the model discrimination, and calibration curve method was used to evaluate the consistency of the model.@*Results@#Postoperative pathology of total 1 376 cases showed that there were 1 195 cases of clear cell carcinoma of kidney, 48 cases of papillary cell carcinoma, 57 cases of chromophobe cell carcinoma, and 76 cases of other types. Among the 1 376 patients with cT1 renal tumor, 75 patients were upgraded to pT3a, accounting for 5.5% of all patients. Univariate analysis showed that the patients who upgraded to pT3a were older [(63.08±10.17) years old and (57.34±10.88) years old], and the tumor length and diameter were larger [(5.24±1.35) cm and (3.95±1.51) cm]. Patients with clinical symptoms [46.7% (35/75) vs. 25.2%(328/1 301)], patients with CT indication of tumor necrosis [40.0%(30/25 975) vs. 19.9% (259/1 301)], patients with irregular tumor contour [73.3%(55/76 775) vs. 59.0%(767/1 301)], and patients with radical nephrectomy were higher [(92.1% (70/91 075) vs. 70%(910/1 301)]. All the differences were statistically significant (P<0.01). Multivariate analysis showed that the independent predictors of upgrading were age (OR=1.046, P<0.001), larger tumor (OR=1.504, P<0.001), clinically symptom (OR=2.153, P=0.004), irregular tumor profile (OR=2.466, P=0.002), and tumor necrosis on CT (OR=2.588, P<0.001). The C-index was 0.808, the calibration curve of forecasting curve with the standard curve fit was good, and the prediction of renal cancer are better in predict consistency.@*Conclusions@#Based on the five preoperative predictors, including age, tumor size, clinical presence or absence of symptoms, tumor profile, and whether or not the tumor necrosis indicated by CT, this study developed a nomogram of cT1 renal cancer upgrade to pT3a. This nomogram has a good statistical significance, and this model can provide prognosis consultation for patients and provide reference for doctors to make decisions before treatment.
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Objective To evaluate the risk factors of postoperative upgrade to pT3a of cT1 renal cell carcinoma,and to establish a nomogram prediction model to improve the ability of predicting locally advanced renal cell carcinoma and provide a reference for clinical surgical decision-making.Methods Clinical data of 1 376 patients with cT1 (diameter ≤ 7 cm) renal tumor hospitalized for surgery from January 2010 to December 2016 were retrospectively analyzed.There were 979 males and 397 females,with the mean age of (57.65 ± 10.92) years.The mean body mass index (BMI) was (25.47 ± 3.27) kg/m2 and the average tumor size was (4.02 ±1.52) cm.There were 711 tumors on the left and 665 on the right.There were 363 cases with clinical symptoms,567 patients with smoking history,732 cases with history of chronic disease.There were 289 cases with tumor necrosis,636 cases with tumor protrusion,822 cases with irregular tumor,and 738 cases with renal sinus compression.Partial nephrectomy and radical nephrectomy were performed in 396 cases and 980 cases respectively.Mann-whitney U test and chi-square test were used for univariate analysis,logistic regression analysis was used for multivariate analysis to analyze the predictors of upgrading,R software was used to construct the nomogram predictive model,C-index was used to evaluate the model discrimination,and calibration curve method was used to evaluate the consistency of the model.Results Postoperative pathology of total 1 376 cases showed that there were 1 195 cases of clear cell carcinoma of kidney,48 cases of papillary cell carcinoma,57 cases of chromophobe cell carcinoma,and 76 cases of other types.Among the 1 376 patients with cT1 renal tumor,75 patients were upgraded to pT3a,accounting for 5.5％ of all patients.Univariate analysis showed that the patients who upgraded to pT3a were older [(63.08 ± 10.17) years old and (57.34 ± 10.88) years old],and the tumor length and diameter were larger [(5.24 ± 1.35) cm and (3.95 ± 1.51) cm].Patients with clinical symptoms [46.7％ (35/75) vs.25.2％ (328/1 301)],patients with CT indication of tumor necrosis [40.0％ (30/25 975) vs.19.9％ (259/1 301)],patients with irregular tumor contour [73.3％ (55/76 775) vs.59.0％ (767/1 301)],and patients with radical nephrectomy were higher [(92.1％ (70/91 075) vs.70％ (910/ 1 301)].All the differences were statistically significant (P ＜0.01).Multivariate analysis showed that the independent predictors of upgrading were age (OR =1.046,P ＜0.001),larger tumor (OR =1.504,P ＜0.001),clinically symptom (OR =2.153,P =0.004),irregular tumor profile (OR =2.466,P =0.002),and tumor necrosis on CT (OR =2.588,P ＜ 0.001).The C-index was 0.808,the calibration curve of forecasting curve with the standard curve fit was good,and the prediction of renal cancer are better in predict consistency.Conclusions Based on the five preoperative predictors,including age,tumor size,clinical presence or absence of symptoms,tumor profile,and whether or not the tumor necrosis indicated by CT,this study developed a nomogram of cT1 renal cancer upgrade to pT3a.This nomogram has a good statistical significance,and this model can provide prognosis consultation for patients and provide reference for doctors to make decisions before treatment.
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Objective To establish a preoperative evaluation model and evaluate the accuracy of postoperative complications by analyzing the risk factors of postoperative complications in hip arthroplasty in elderly patients, and to provide a basis for grading nursing. Methods From January 2012 to August 2018, 148 patients with elderly patients undergoing hip arthroplasty admitted to People′s hospital of Wanning were included in the study. Among them, 23 patients with postoperative complications were included as complications and 125 patients without complications served as a control group. Univariate analysis of the factors associated with complications, statistically significant indicators were included in the multivariate logistic regression model, and the risk factors for postoperative complications in hip arthroplasty were analyzed. Based on the results of the regression analysis, a preoperative evaluation model was established. The ROC curve and the Hosmer_Lemeshow test were used to evaluate the model prediction performance. Results Multivariate analysis showed age ( OR=1.239, 95% CI : 1.002-1.531, P=0.048), Glu ( OR=2.961, 95% CI : 1.055-8.311, P=0.039), surgical size ( OR=4.997, 95% CI : 1.269-19.686, P=0.021) were risk factors for postoperative complications, Albumin ( OR=0.696, 95% CI : 0.535-0.904, P=0.007) was a protective factor for postoperative complications (P<0.05). The ROC curve showed a nomogram model predicting the area under the complication curve (AUC) of 0.988 (95% CI :0.975-1.000), with a specificity of 96.77% and a sensitivity of 99.82%. Conclusion A nomogram model based on age, Glu, albumin, surgery size to predict the risk of complications after hip arthroplasty in elderly patients has a good specificity and sensitivity, high clinical value.
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Objective@#To establish a preoperative evaluation model and evaluate the accuracy of postoperative complications by analyzing the risk factors of postoperative complications in hip arthroplasty in elderly patients, and to provide a basis for grading nursing.@*Methods@#From January 2012 to August 2018, 148 patients with elderly patients undergoing hip arthroplasty admitted to People′s hospital of Wanning were included in the study. Among them, 23 patients with postoperative complications were included as complications and 125 patients without complications served as a control group. Univariate analysis of the factors associated with complications, statistically significant indicators were included in the multivariate logistic regression model, and the risk factors for postoperative complications in hip arthroplasty were analyzed. Based on the results of the regression analysis, a preoperative evaluation model was established. The ROC curve and the Hosmer-Lemeshow test were used to evaluate the model prediction performance.@*Results@#Multivariate analysis showed age (OR=1.239, 95%CI: 1.002-1.531, P=0.048), Glu (OR=2.961, 95%CI: 1.055-8.311, P=0.039), surgical size (OR=4.997, 95%CI: 1.269-19.686, P=0.021) were risk factors for postoperative complications, Albumin (OR=0.696, 95%CI: 0.535-0.904, P=0.007) was a protective factor for postoperative complications (P<0.05). The ROC curve showed a nomogram model predicting the area under the complication curve (AUC) of 0.988 (95%CI:0.975-1.000), with a specificity of 96.77% and a sensitivity of 99.82%.@*Conclusion@#A nomogram model based on age, Glu, albumin, surgery size to predict the risk of complications after hip arthroplasty in elderly patients has a good specificity and sensitivity, high clinical value.