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. 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=0.218, 95%CI (0.108, 0.440), 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.
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.
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.
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.
Objective To explore the influencing factors of visual prognosis of macular edema secondary to branch retinal vein occlusion (BRVO-ME) after treatment with ranibizumab, and construct and verify the nomogram model. MethodsA retrospective study. A total of 130 patients with BRVO-ME diagnosed by ophthalmology examination in the Department of Ophthalmology, Liuzhou Red Cross Hospital from January 2019 to December 2021 were selected in this study. All patients received intravitreal injection of ranibizumab. According to the random number table method, the patients were divided into the training set and the test set with a ratio of 3:1, which were 98 patients (98 eyes) and 32 patients (32 eyes), respectively. According to the difference of logarithm of the minimum angle of resolution (logMAR) best corrected visual acuity (BCVA) at 6 months after treatment and logMAR BCVA before treatment, 98 patients (98 eyes) in the training set were divided into good prognosis group (difference ≤－0.3) and poor prognosis group (difference ＞－0.3), which were 58 patients (58 eyes) and 40 patients (40 eyes), respectively. The clinical data of patients in the two groups were analyzed, univariate and multivariate logistic regression analysis were carried out for the different indicators, and the visualization regression analysis results were obtained by using R software. The consistency index (C-index), convolutional neural network (CNN), calibration curve and receiver operating characteristic (ROC) curve were used to verify the accuracy of the nomogram model. ResultsUnivariate analysis showed that age, disease course, outer membrane (ELM) integrity, elliptical zone (EZ) integrity, BCVA, center macular thickness (CMT), outer hyperreflective retinal foci (HRF), inner retina HRF, and the blood flow density of retinal deep capillary plexus (DCP) were risk factors affecting the visual prognosis after treatment with ranibizumab in BRVO-ME patients (P＜0.05). Multivariate logistic regression analysis showed that course of disease, ELM integrity, BCVA and outer HRF were independent risk factors for visual prognosis after ranibizumab treatment for BRVO-ME patients (P＜0.05). The ROC area under the curve of the training set and the test set were 0.846[95% confidence interval (CI) 0.789-0.887) and 0.852 (95%CI 0.794 -0.873)], respectively; C-index were 0.836 (95%CI 0.793-0.865) and 0.845 (95%CI 0.780-0.872), respectively. CNN showed that the error rate gradually stabilized after 300 cycles, with good model accuracy and strong prediction ability. ConclusionsCourse of disease, ELM integrity, BCVA and outer HRF were independent risk factors of visual prognosis after ranibizumab treatment in BRVO-ME patients. The nomogram model based on risk factors has good differentiation and accuracy.
ObjectiveTo develop and validate a nomogram for predicting the cancer-specific survival in patients with intrahepatic cholangiocarcinoma (ICC) after hepatectomy. MethodsSuitable patient cases were selected from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were established based on the independent prognostic factors identified by COX and Lasso regression models. The performance of the nomograms was validated internally and externally by using the concordance index (c-index), calibration plot, and decision curve analysis. ResultsThe multi factor COX regression results showed that: age, gender, T stage, tumor grade, tumour diameter and number of positive lymph nodes were independent prognostic predictors for cancer-specific survival (CSS) in ICC patients. Nomogram predicting CSS had a c-index of 0.66 (95%CI 0.64 to 0.69) in the training cohort and 0.67 (95%CI 0.63 to 0.72) in the internal validation cohort. The 1-, 3- and 5-year areas under the curve (AUC) of nomogram were 0.68, 0.74 and 0.75 in the training cohort respectively. In the validation cohort, the 1-, 3- and 5-year AUC of nomogram were 0.69, 0.68 and 0.71, respectively. ConclusionThe prediction model constructed based on six factors, including age, gender, pathological stage, T-stage, tumour diameter and number of positive lymph nodes, shows good prediction accuracy.
ObjectiveTo predict the probability of lymph node metastasis after thoracoscopic surgery in patients with lung adenocarcinoma based on nomogram. MethodsWe analyzed the clinical data of the patients with lung adenocarcinoma treated in the department of thoracic surgery of our hospital from June 2018 to May 2021. The patients were randomly divided into a training group and a validation group. The variables that may affect the lymph node metastasis of lung adenocarcinoma were screened out by univariate logistic regression, and then the clinical prediction model was constructed by multivariate logistic regression. The nomogram was used to show the model visually, the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve to evaluate the calibration degree and practicability of the model. ResultsFinally 249 patients were collected, including 117 males aged 53.15±13.95 years and 132 females aged 47.36±13.10 years. There were 180 patients in the training group, and 69 patients in the validation group. There was a significant correlation between the 6 clinicopathological characteristics and lymph node metastasis of lung adenocarcinoma in the univariate logistic regression. The area under the ROC curve in the training group was 0.863, suggesting the ability to distinguish lymph node metastasis, which was confirmed in the validation group (area under the ROC curve was 0.847). The nomogram and clinical decision curve also performed well in the follow-up analysis, which proved its potential clinical value. ConclusionThis study provides a nomogram combined with clinicopathological characteristics, which can be used to predict the risk of lymph node metastasis in patients with lung adenocarcinoma with a diameter≤3 cm.
Objective To analyze the risk factors and prognosis of acute gastrointestinal injury (AGI) early after acute type A aortic dissection (ATAAD) repair, and develop the Nomogram prediction model of AGI. Methods The patients who underwent ATAAD cardiopulmonary bypass surgery in our hospital from 2016 to 2021 were collected and divided into an AGI group and a non-AGI group. The clinical data of the two groups were compared. A Nomogram prediction model was established by using R language. Results A total of 188 patients were enrolled, including 166 males and 22 females, aged 22-70 (49.70±9.96) years. Through multivariate logistic regression analysis, the aortic dissection (AD) risk score, poor perfusion of superior mesenteric artery (SMA), duration of aortic occlusion and intraoperative infusion of red blood cells were the predictors for AGI (P<0.05). There were statistical differences in the ventilator-assisted duration, ICU stay time, liver dysfunction, renal insufficiency, parenteral nutrition, nosocomial infection and death within 30 days after the operation between the two groups (P<0.05). The Nomogram prediction model was established by using the prediction factors, and the C index was 0.888. Through internal verification, the C index was 0.848. The receiver operating characteristic curve was used to evaluate the discrimination of the model, and the area under the curve was 0.888. Conclusion The AD risk score after ATAAD, poor perfusion of SMA, duration of aortic occlusion and intraoperative infusion of red blood cells are independent predictors for AGI. The Nomogram model has good prediction ability.
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.
ObjectivesTo compare the survival outcomes between hepatocellular carcinoma and hepatic angiosarcoma, and to develop and validate a nomogram predicting the outcome of hepatic angiosarcoma.MethodsThe Surveillance, Epidemiology and End Results (SEER) database was electronically searched to collect the data of hepatic angiosarcoma patients and hepatocellular carcinoma patients from 2004 to 2016. Propensity score matching (PSM) was used to match the two groups by the ratio of 1:3. Cox regression analysis was used to compare the survival outcomes between hepatic angiosarcoma and HCC. In the angiosarcoma group, population was divided into training set and validation set by 6:4. Nomograms were built for the prediction of half- and one- year survival, and validated by concordance index (C-index) and calibration plots.ResultsA total of 210 histologically confirmed hepatic angiosarcoma patients and 630 hepatocellular carcinoma patients were included. The overall survival of HCC was significantly longer than angiosarcoma (3-year survival: 18.4% vs. 6.7%, median survival: 5 months vs. 1 month, P<0.001), and the nomogram achieved good accuracy with an internal C-index of 0.751 and an external C-index of 0.737.ConclusionsThe overall survival of HCC is significantly longer than angiosarcoma. The proposed nomograms can assist to predict survival probability in patients with hepatic angiosarcoma. Due to limitation of the data of included patients, more high-quality studies are required to verify above conclusions.