Objective Establishing Nomogram to predict the overall survival (OS) rate of patients with gastric adenocarcinoma by utilizing the database of the Surveillance, Epidemiology, and End Results (SEER) Program. Methods Obtained the data of 3 272 gastric adenocarcinoma patients who were diagnosed between 2004 and 2014 from the SEER database. These patients were randomly divided into training (n=2 182) and validation (n=1 090) cohorts. The Cox proportional hazards regression model was performed to evaluate the prognostic effects of multiple clinicopathologic factors on OS. Significant prognostic factors were combined to build Nomogram. The predictive performance of Nomogram was evaluated via internal (training cohort data) and external validation (validation cohort data) by calculating index of concordance (C-index) and plotting calibration curves. Results In the training cohort, the results of Cox proportional hazards regression model showed that, age at diagnosis, race, grade, 6th American Joint Committee on Cancer (AJCC) stage, histologic type, and surgery were significantly associated with the survival prognosis (P<0.05). These factors were used to establish Nomogram. The Nomograms showed good accuracy in predicting OS rate, with C-index of 0.751 ［95%CI was (0.738, 0.764)］ in internal validation and C-index of 0.753 ［95% CI was (0.734, 0.772)］ in external validation. All calibration curves showed excellent consistency between prediction by Nomogram and actual observation. Conclusion Novel Nomogram for patients with gastric adenocarcinoma was established to predict OS in our study has good prognostic significance, it can provide clinicians with more accurate and practical predictive tools which can quickly and accurately assess the patients’ survival prognosis individually, and can better guiding clinicians in the follow-up treatment of patients.
ObjectiveBased on a large sample of data, study the factors affecting the survival and prognosis of patients with rectal cancer and construct a prediction model for the survival and prognosis.MethodsThe clinical data of 26 028 patients with rectal cancer were screened from the Surveillance, Epidemiology, and End Results (SEER) clinical database of the National Cancer Institute. Univariate and multivariate Cox proportional hazard regression analysis were used to screen related risk factors. Finally, the Nomogram prediction model was summarized and its accuracy was verified.ResultsResult of multivariate Cox proportional hazard regression analysis showed that the risk factors affecting the survival probability of rectal cancer included: age, gender, marital status, TMN staging, T staging, tumor size, degree of tissue differentiation, total number of lymph nodes removed, positive lymph node ratio, radiotherapy, and chemotherapy (P<0.05). Then we further built the Nomogram prediction model. The C index of the training cohort and the validation cohort were 0.764 and 0.770, respectively. The area under the ROC curve (0.777 and 0.762) for 3 years and 5 years, and the calibration curves of internal and external validation all indicated that the model could effectively predict the survival probability of rectal cancer.ConclusionThe constructed Nomogram model can predict the survival probability of rectal cancer, and has clinical guiding significance for the prognostic intervention of rectal cancer.
ObjectiveTo investigate the value of preoperative clinical data and computed tomography angiography (CTA) data in predicting perioperative mortality risk in patients with acute aortic dissection (AAD), and to construct a Nomogram prediction model. MethodsA retrospective study was conducted on AAD patients treated at Affiliated Hospital of Zunyi Medical University from February 2013 to July 2023. Patients who died during the perioperative period were included in the death group, and those who improved during the same period were randomly selected as the non-death group using a random number table method. The first CTA data and preoperative clinical data within the perioperative period of the two groups were collected, and related risk factors were analyzed to screen out independent predictive factors for perioperative death. The Nomogram prediction model for perioperative mortality risk in AAD patients was constructed using the screened independent predictive factors, and the effect of the Nomogram was evaluated by calibration curves and area under the curve (AUC). ResultsA total of 270 AAD patients were included. There were 60 patients in the death group, including 42 males and 18 females with an average age of (56.89±13.42) years. There were 210 patients in the non-death group, including 163 males and 47 females with an average age of (56.15±13.77) years. Multivariate logistic regression analysis showed that type A AAD [OR=4.589, 95%CI (2.273, 9.267), P<0.001], irregular tear morphology [OR=2.054, 95%CI (1.025, 4.117), P=0.042], decreased hemoglobin [OR=0.983, 95%CI (0.971, 0.995), P=0.007], increased uric acid [OR=1.003, 95%CI (1.001, 1.005), P=0.004], and increased aspartate aminotransferase [OR=1.003, 95%CI (1.000, 1.006), P=0.035] were independent risk factors for perioperative death in AAD patients. The Nomogram prediction model constructed using the above risk factors had an AUC of 0.790 for predicting perioperative death, indicating good predictive performance. ConclusionType A AAD, irregular tear morphology, decreased hemoglobin, increased uric acid, and increased aspartate aminotransferase are independent predictive factors for perioperative death in AAD patients. The Nomogram prediction model constructed using these factors can help assess the perioperative mortality risk of AAD patients.
Objective To establish a predictive model for long-term tumor-specific survival after surgery for patients with intermediate to advanced medullary thyroid cancer (MTC) based on American Joint Committee on Cancer (AJCC) TNM staging, by using the Surveillance, Epidemiology, and End Results (SEER) Database. Methods The data of 692 patients with intermediate to advanced MTC who underwent total thyroidectomy and cervical lymph node dissection registered in the SEER database during 2004–2017 were extracted and screened, and were randomly divided into 484 cases in the modeling group and 208 cases in the validation group according to 7∶3. Cox proportional hazard regression was used to screen predictors of tumor-specific survival after surgery for intermediate to advanced stage MTC and to develop a Nomogram model. The accuracy and usefulness of the model were tested by using the consistency index (C-index), calibration curve, time-dependent ROC curve and decision curve analysis (DSA). Results In the modeling group, the multivariate Cox proportional hazard regression model indicated that the factors affecting tumor-specific survival after surgery in patients with intermediate to advanced MTC were AJCC TNM staging, age, lymph node ratio (LNR), and tumor diameter, and the Nomogram model was developed based on these results. The modeling group had a C-index of 0.827 and its area under the 5-year and 10-year time-dependent ROC curves were 0.865 [95%CI (0.817, 0.913)], 0.845 [95%CI (0.787, 0.904)], respectively, and the validation group had a C-index of 0.866 and its area under the 5-year and 10-year time-dependent ROC curves were 0.866 [95%CI (0.798, 0.935)] and 0.923 [95%CI (0.863, 0.983)], respectively. Good agreement between the model-predicted 5- and 10-year tumor-specific survival rates and the actual 5- and 10-year tumor-specific survival rates were showed in both the modeling and validation groups. Based on the DCA curve, the new model based on AJCC TNM staging was developed with a significant advantage over the former model containing only AJCC TNM staging in terms of net benefits obtained by patients at 5 years and 10 years after surgery. Conclusion The prognostic model based on AJCC TNM staging for predicting tumor-specific survival after surgery for intermediate to advanced MTC established in this study has good predictive effect and practicality, which can help guide personalized, precise and comprehensive treatment decisions and can be used in clinical practice.
Objective To analyze the risk factors associated with retinal detachment in patients with myopia, and to establish and validate the predictive column-line diagram model. MethodsA cross-sectional clinical study. From January 2020 to November 2021, 90 patients with myopia combined with retinal detachment who were diagnosed by ophthalmologic examination in the People's Hospital of Ningxia Hui Autonomous Region were included in the study (observation group). Ninety myopic patients with age- and gender-matched myopia who underwent ophthalmologic examination for myopia during the same period were selected as the control group. The clinical data of the two groups were analyzed, and the indicators with differences were subjected to univariate and multivariate logistic regression analyses. The results of the regression analyses were visualized by using R software to obtain the column charts, and the accuracy of the column charts was verified by the ROC curves of the subjects' work characteristics; the clinical efficacy of the column chart model was verified by the internal data. ResultsCompared with the control group, patients in the observation group were older, had higher myopic refraction, had more visual fatigue, ocular trauma, and cataracts, had lower choroidal and retinal thickness, and had more history of ophthalmic surgery, and the differences were statistically significant (P＜0.05). The area under the ROC curve (AUC) for age, myopic refraction, retinal thickness, and choroidal thickness were 0.612, 0.613, 0.720, and 0.704, respectively; the optimal cutoff values were 43 years old, －3.5 D, 225 μm, and 144 μm. the ROC values were 0.612, 0.613, 0.720, and 0.704 for age (＞43 years old), myopic refraction (＞－3.5 D), visual fatigue (yes), ocular trauma (yes), cataracts (yes), retinal thickness (≤225 μm), and choroidal thickness (≤144 μm) were the risk factors affecting the development of retinal detachment in myopic patients (P＜0.05). The consistency index of the column chart model for predicting the risk of retinal detachment in patients with myopia was 0.731 (95% confidence interval 0.665-0.824); the risk threshold for predicting the development of retinal detachment in patients was ＞0.07. ConclusionsAge ＞43 years, myopic refraction >－3.5 D, presence of visual fatigue, ocular trauma, cataract, retinal thickness ≤225 μm, choroidal thickness ≤144 μm are the risk factors affecting the development of retinal detachment in myopic patients. The column-line diagram model constructed on the basis of the risk factors has good accuracy.
Objective To establish a prediction model for the 1-, 3-, and 5-year survival rates in patients with gastric cancer liver metastases (GCLM) by analyzing prognostic factors based on the Surveillance, Epidemiology, and End Results (SEER) database. Methods Clinical and pathological data from 591 patients diagnosed with GCLM between 2010 and 2015 were obtained from the SEER database. The population was randomly divided into a training cohort and an internal validation cohort at a 7 to 3 ratio. Independent predictors of GCLM were analyzed using univariate and multifactorial Cox regression. Consequently, nomograms were constructed. The model's accuracy was verified by calibration curve, ROC curve, and the C-index, and the clinical utility of the model was analyzed through decision curve analysis. Results Tumor differentiation grade, surgical status, and chemotherapy were significantly associated with the prognosis of GCLM patients, and these three factors were included in constructing the prognostic model and plotting the nomogram. The C-index was 0.706 (95%CI 0.677 to 0.735) and 0.749 (95%CI 0.710 to 0.788) for the training set and the internal validation cohort, respectively. The results of the ROC curve analysis indicated that the area under the curve (AUC) was over 0.7 at 1, 3, and 5 years for both the training and validation cohorts. Conclusion The prediction model of the GCLM is developed based on the 3 factors, i.e., tumor differentiation grade, surgery, and chemotherapy, and shows good prediction accuracy and thus may promote clinical decision making and individualized treatment of GCLM patients.
ObjectiveTo analyze the relevant risk factors affecting postoperative relapse-free survival (RFS) in the primary gastrointestinal stromal tumors (GIST) and develop a Nomogram predictive model of postoperative RFS for the GIST patients. MethodsThe patients diagnosed with GIST by postoperative pathology from January 2011 to December 2020 at the First Hospital of Lanzhou University and Gansu Provincial People’s Hospital were collected, and then were randomly divided into a training set and a validation set at a ratio of 7∶3 using R software function. The univariate and multivariate Cox regression analysis were used to identify the risk factors affecting the RFS for the GIST patients after surgery, and then based on this, the Nomogram predictive model was constructed to predict the probability of RFS at 3- and 5-year after surgery for the patients with GIST. The effectiveness of the Nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), consistency index (C-index), and calibration curve, and the clinical utility of the Nomogram and the modified National Institutes of Health (M-NIH) classification standard was evaluated using the decision curve analysis (DCA). ResultsA total of 454 patients were included, including 317 in the training set and 137 in the validation set. The results of multivariate Cox regression analysis showed that the tumor location, tumor size, differentiation degree, American Joint Committee onCancer TNM stage, mitotic rate, CD34 expression, treatment method, number of lymph node detection, and targeted drug treatment time were the influencing factors of postoperative RFS for the GIST patients (P<0.05). The Nomogram predictive model was constructed based on the influencing factors. The C-index of the Nomogram in the training set and validation set were 0.731 [95%CI (0.679, 0.783)] and 0.685 [95%CI (0.647, 0.722)], respectively. The AUC (95%CI) of distinguishing the RFS at 3- and 5-year after surgery were 0.764 (0.681, 0.846) and 0.724 (0.661, 0.787) in the training set and 0.749 (0.625, 0.872) and 0.739 (0.647, 0.832) in the validation set, respectively. The calibration curve results showed that a good consistency of the 3-year and 5-year recurrence free survival rates between the predicted results and the actual results in the training set, while which was slightly poor in the validation set. There was a higher net benefit for the 3-year recurrence free survival rate after GIST surgery when the threshold probability range was 0.19 to 0.57. When the threshold probability range was 0.44 to 0.83, there was a higher net benefit for the 5-year recurrence free survival rate after GIST surgery. And within the threshold probability ranges, the net benefit of the Nomogram was better than the M-NIH classification system at the corresponding threshold probability. ConclusionsThe results of this study suggest that the patients with GIST located in the other sites (mainly including the esophagus, duodenum, and retroperitoneum), with tumor size greater than 5 cm, poor or undifferentiated differentiation, mitotic rate lower than 5/50 HPF, negative CD34 expression, ablation treatment, number of lymph nodes detected more than 4, and targeted drug treatment time less than 3 months need to closely pay attentions to the postoperative recurrence. The discrimination and clinical applicability of the Nomogram predictive model are good.
ObjectiveTo establish and preliminarily validate a nomogram model for predicting the risk of retinal vein occlusion (RVO). MethodsA retrospective clinical study. A total of 162 patients with RVO (RVO group) diagnosed by ophthalmology examination in The Second Affiliated Hospital of Xi'an Jiaotong University from January 2017 to April 2022 and 162 patients with age-related cataract (nRVO group) were selected as the modeling set. A total of 45 patients with branch RVO, 45 patients with central RVO and 45 patients with age-related cataract admitted to Xi 'an Fourth Hospital from January 2022 to February 2023 were used as the validation set. There was no significant difference in gender composition ratio (χ2=2.433) and age (Z=1.006) between RVO group and nRVO group (P=0.120, 0.320). Age, gender, blood routine (white blood cell count, hemoglobin concentration, platelet count, neutrophil count, monocyte count, lymphocyte count, erythrocyte volume, mean platelet volume, platelet volume distribution width), and four items of thrombin (prothrombin time, activated partial thrombin time, fibrinogen, and thrombin time) were collected in detail ), uric acid, blood lipids (total cholesterol, triglyceride, high-density lipoprotein, low-density lipoprotein, lipoprotein a), hypertension, diabetes mellitus, coronary heart disease, and cerebral infarction. Neutrophil/lymphocyte ratio and platelet/lymphocyte ratio were calculated. The single logistic regression was used to analyze the clinical parameters of the two groups of patients in the modeling set, and the stepwise regression method was used to screen the variables, and the column graph for predicting the risk of RVO was constructed. The Bootstrap method was used to repeated sample 1 000 times for internal and external verification. The H-L goodness-of-fit test and receiver operating characteristic (ROC) curve were used to evaluate the calibration and discrimination of the nomogram model. ResultsAfter univariate logistic regression and stepwise regression analysis, high density lipoprotein, neutrophil count and hypertension were included in the final prediction model to construct the nomogram. The χ2 values of the H-L goodness-of-fit test of the modeling set and the validation set were 0.711 and 4.230, respectively, and the P values were 0.701 and 0.121, respectively, indicating that the nomogram model had good prediction accuracy. The area under the ROC curve of the nomogram model for predicting the occurrence of post-stroke depression in the modeling set and the verification set was 0.741 [95% confidence interval (CI) 0.688-0.795] and 0.741 (95%CI 0.646-0.836), suggesting that the nomogram model had a good discrimination. ConclusionsLow high density lipoprotein level, high neutrophil count and hypertension are independent risk factors for RVO. The nomogram model established based on the above risk factors can effectively assess and quantify the risk of post-stroke depression in patients with cerebral infarction.
ObjectiveTo investigate the risk factors affecting the occurrence of infectious complications after radical gastrectomy for gastric cancer, and to establish a risk prediction Nomogram model. MethodsThe clinicopathologic data of 429 primary gastric cancer patients who underwent radical resection for gastric cancer at the Second Department of General Surgery of Shaanxi Provincial People’s Hospital between January 2018 and December 2020 were retrospectively collected to explore the influencing factors of infectious complications using multivariate logistic regression analyses, and to construct a prediction model based on the results of the multivariate analysis, and then to further validate the differentiation, consistency, and clinical utility of the model. ResultsOf the 429 patients, infectious complications occurred in 86 cases (20.05%), including 53 cases (12.35%) of pulmonary infections, 16 cases (3.73%) of abdominal infections, 7 cases (1.63%) of incision infections, and 10 cases (2.33%) of urinary tract infections. The results of multivariate logistic analysis showed that low prognostic nutritional index [OR=0.951, 95%CI (0.905, 0.999), P=0.044], long surgery time [OR=1.274, 95%CI (1.069, 1.518), P=0.007], American Society of Anesthesiologists physical status classification (ASA) grade Ⅲ–Ⅳ [OR=9.607, 95%CI (4.484, 20.584), P＜0.001] and alcohol use [OR=3.116, 95%CI (1.696, 5.726), P＜0.001] were independent risk factors for the occurrence of infectious complications, and a Nomogram model was established based on these factors, with an area under the ROC of 0.802 [95%CI (0.746, 0.858)]; the calibration curves showed that the probability of occurrence of infectious complications after radical gastrectomy predicted by the Nomogram was in good agreement with the actual results; the decision curve analysis showed that the Nomogram model could obtain clinical benefits in a wide range of thresholds and had good practicality.ConclusionsClinicians need to pay attention to the perioperative management of gastric cancer patients, fully assess the patients’ own conditions through the prediction model established by prognostic nutritional index, surgery time, ASA grade and alcohol use, and take targeted interventions for the patients with higher risks, in order to reduce the risk of postoperative infectious complications.
Objective To investigate the factors influencing the occurrence of postoperative pulmonary complications (PPCs) in liver transplant recipients and to construct Nomogram model to identify high-risk patients. Methods The clinical data of 189 recipients who underwent liver transplantation at the General Hospital of Eastern Theater Command from November 1, 2019 to November 1, 2022 were retrospective collected, and divided into PPCs group (n=61) and non-PPCs group (n=128) based on the occurrence of PPCs. Univariate and multivariate logistic regression analyses were used to determine the risk factors for PPCs, and the predictive effect of the Nomogram model was evaluated by receiver operator characteristic curve (ROC) and calibration curve. Results Sixty-one of 189 liver transplant patients developed PPCs, with an incidence of 32.28%. Univariate analysis results showed that PPCs were significantly associated with age, smoking, Child-Pugh score, combined chronic obstructive pulmonary disease (COPD), combined diabetes mellitus, prognostic nutritional index (PNI), time to surgery, amount of bleeding during surgery, and whether or not to diuretic intraoperatively (P<0.05). Multivariate logistic regression analysis showed that age [OR=1.092, 95%CI (1.034, 1.153), P=0.002], Child-Pugh score [OR=1.575, 95%CI (1.215, 2.041), P=0.001], combined COPD [OR=4.578, 95%CI (1.832, 11.442), P=0.001], combined diabetes mellitus [OR=2.548, 95%CI (1.024, 6.342), P=0.044], preoperative platelet count (PLT) [OR=1.076, 95%CI (1.017, 1.138), P=0.011], and operative time [OR=1.061, 95%CI (1.012, 1.113), P=0.014] were independent risk factors for PPCs. The prediction model for PPCs which constructed by using the above six independent risk factors in Nomogram had an area under the ROC curve of 0.806. Hosmer and Lemeshow goodness of fit test (P=0.129), calibration curve, and decision curve analysis showed good agreement with Nomogram model. Conclusion The Nomogram model constructed based on age, Child-Pugh score, combined COPD, combined diabetes mellitus, preoperative PLT, and time of surgery can better identify patients at high risk of developing PPCs after liver transplantation.