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 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 To establish a scoring system for patients withnon-small cell lung cancer (NSCLC), complicated by chemotherapy and myelosuppression based on Logistic regression analysis. Methods The clinical data of patients with lung cancer who received chemotherapy in our hospital from January 2018 to April 2024 were collected. The influencing factors of chemotherapy complicated with myelosuppression were analyzed by univariate and Logistic regression, and a nematographic model was established. Results Compared with non-myelosuppressive group, there were statistically significant differences in pre-chemotherapy leukocyte, pre-chemotherapy hemoglobin, ECOG score, use of platinum drugs, use of anti-metabolic drugs, use of anti-microtubule drugs in myelosuppressive group (P<0.05). WBC<4.0×109/L (OR:4.166, 95%CI: 1.521~11.410), hemoglobin<110g/L (OR: 6.926, 95%CI: 1.817~26.392), ECOG score ≥2 points (OR: 2.235, 95%CI: 1.032~4.840), platinum drugs (OR: 5.738, 95%CI: 2.514~13.097), anti-microtubule drugs (OR: 4.284, 95%CI: 1.853~9.905) and anti-metabolic drugs (OR: 7.180, 95%CI: 2.608~19.769) was an independent risk factor for chemotherapy complicated with myelopathic depression in lung cancer patients (P<0.05). Model verification results showed that the C-index was 0.817 (95%CI: 0.783~0.851), the calibration curve of the model was close to the ideal curve, and the AUC of the ROC curve was 0.811 (95%CI: 0.780~0.842), which showed a net benefit of the model within the range of 10% to 87.5%. Conclusion The constructed nomogram model can effectively predict the risk of chemotherapy complicated with myelosuppression in non-small cell lung cancer patients.
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.
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.
Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.
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 develop and validate a nomogram model that can be used to predict the prognosis of acute exacerbation of chronic obstructive pulmonary disease (AECOPD) patients with type II respiratory failure. Methods A retrospective analysis was conducted on the clinical data of 300 hospitalized AECOPD patients in the People’s Hosipital of Leshan from August 2016 to December 2021. Patients were grouped into a training cohort (n=210) and a validation cohort (n=90) in a 7:3 ratio. The variables for the patients in the training cohort were selected using the least absolute shrinkage and selection operator (LASSO), followed by multivariate logistic regression analysis to identify independent risk factors of poor prognosis in AECOPD with type II respiratory failure, and a nomogram model was constructed. Receiver operating characteristic (ROC) curves were plotted for the training and validation cohorts, and the area under ROC curve (AUC) was calculated.The model was validated by conducting the Hosmer-Lemeshow test, drawing calibration curves, and performing decision curve analysis(DCA).ResultsCardiovascular disease, lymphocyte percentage, and red cell distribution width-standard deviation(RDW-SD) were identified as independent risk factors of poor prognosis for AECOPD patients with type II respiratory failure (P<0.05). The AUC values for the training and validation cohorts were 0.742 (95%CI: 0.672-0.812) and 0.793 (95%CI: 0.699-0.888), respectively. The calibration curves of the two cohorts are close to the desirable curves.The Hosmer-Lemeshow test P-values were greater than 0.05, indicating good clinical practicality. The DCA curve indicates that the model has good clinical value. Conclusions The clinical prediction model, based on factors such as cardiovascular disease, lymphocyte percentage, and RDW-SD, showed good predictive value for AECOPD patients complicated by type II respiratory failure.
