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.
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.
Objective The purpose of the current research was to analyze the relevant risk factors for short-term death in patients with chronic obstructive pulmonary disease (COPD) and heart failure (HF), and to build a predictive nomogram. Methods We conducted a retrospective analysis of clinical data from 1 323 COPD and HF comorbidity patients who were admitted to the Affiliated Hospital of Southwest Medical University from January 2018 to January 2022. Samples were divided into survival and death groups based on whether they died during the follow-up. General data and tested index of both groups were analyzed, and the discrepant index was analyzed by single factor and multiple factor Logistic regression analysis. R software was applied to create the nomogram by visualizing the results of the regression analysis. The accuracy of the results was verified by C index, calibration curve, and ROC curve. Results The results from the multiple factor Logistic regression analysis indicated that age (OR=1.085, 95%CI 1.048 to 1.125), duration of smoking (OR=1.247, 95%CI 1.114 to 1.400), duration of COPD (OR=1.078, 95%CI 1.042 to 1.116), comorbidity with respiratory failure (OR=5.564, 95%CI 3.372 to 9.329), level of NT-proBNP (OR=1.000, 95%CI 1.000 to 1.000), level of PCT (OR=1.153, 95%CI 1.083 to 1.237), and level of D-dimer (OR=1.205, 95%CI 1.099 to 1.336) were risk factors for short-term death of COPD and HF comorbidity patients. The level of ALB (OR=0.892, 95%CI 0.843 to 0.942) was a protective factor that was used to build the predictive nomogram with the C index of 0.874, the square under the working characteristics curve of the samples of 0.874, the specify of 82.5%, and the sensitivity of 75.0%. The calibration curve indicated good predictive ability of the model. Conclusion The nomogram diagram built by the current research indicated good predictability of short-term death in COPD and HF comorbidity patients.
ObjectiveThis study aimed to construct a Nomogram predictive model to assess the prognosis of patients with viral pneumonia complicated by diabetes mellitus.MethodsWe retrospectively collected data from patients with viral pneumonia who visited our hospital from January 2023 to February 2024 and divided them into diabetes and non-diabetes groups based on the presence of diabetes. Clinical data were collected and intergroup differences were analyzed. Subsequently, factors with statistical significance (P<0.05) were selected for univariate and multivariate Logistic regression analysis in the diabetes group to identify risk factors affecting patient survival. Based on the regression analysis results, a linear model was constructed to predict the survival risk of patients. Additionally, calibration curves and decision curve analysis (DCA) were plotted to assess the predictive accuracy and clinical net benefit of the model.ResultsThe study found significant intergroup differences in age (age), cough, dyspnea, respiratory rate at admission, heart rate, body temperature, and laboratory test results (including blood glucose Glu, glycated hemoglobin HbA1c, neutrophil ratio Neu, C-reactive protein Crp, etc.). Multivariate Logistic regression analysis confirmed that age (age), B-type natriuretic peptide (Bnp), neutrophil ratio (Neu), and lactate (Lac) are independent risk factors affecting the survival of patients with viral pneumonia and diabetes.The constructed nomogram prediction model was evaluated. The calibration curve demonstrated a high degree of consistency between the predicted probabilities and actual outcomes, with a non-significant Hosmer-Lemeshow test result (P>0.05). Decision curve analysis further showed that the model yielded no significant clinical net benefit at extreme probability thresholds, whereas it provided substantial clinical net benefit across all other threshold ranges. Collectively, these findings indicate that the model exhibits high predictive accuracy and holds significant value for clinical application. ConclusionsAge, serum B-type natriuretic peptide, neutrophil ratio, and lactate are independent risk factors for the survival of patients with viral pneumonia complicated by diabetes. The Nomogram predictive model constructed based on these factors has clinical value for prognosis assessment.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.
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 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.
ObjectiveTo investigate the risk factors of high intraocular pressure (IOP) after femtosecond laser in situ keratomileusis (FS-LASIK) in patients with high myopia, and construct and verify nomogram model. MethodsA retrospective clinical study. From January 2019 to January 2021, 327 patients (654 eyes) with high myopia treated with FS-LASIK in the Department of Ophthalmology of the 910th Hospital of the People's Liberation Army Coalition Security Force were included in the study. The patients were categorized into high IOP group and non-high IOP group according to whether high IOP occurred after surgery, which were 60 cases and 120 eyes (18.35%, 60/327) and 267 cases and 534 eyes (81.65%, 267/327), respectively. The clinical data of patients in the two groups were analyzed and observed, and the indicators with differences were subjected to one-way and multifactorial logistic regression analyses, and the results of the regression analyses were visualized to obtain the column line graphs using R3.5.3 software, and the accuracy of the column line graphs was verified by the consistency index (C-index), the calibration curves, and the subject's work characteristic curves (ROC curves). ResultsComparison of the number of cases of affected corneal thickness (χ2=7.424), corneal curvature (χ2=9.849), glucocorticoid treatment (χ2=7.222), intraoperative IOP fluctuation (χ2=11.475), corneal hysteresis (χ2=6.368), and the incidence of intraoperative complications (χ2=6.673) in the hypertensive IOP group and the nonvisualized IOP group were statistically significant (P＜0.05). Binary logistic regression analysis showed that corneal thickness ＞450 μm, corneal curvature≤38 D, glucocorticoid treatment, intraoperative IOP fluctuation, corneal hysteresis ≤8.0 mm Hg (1 mm Hg=0.133 kPa), and intraoperative complications were the risk factors for the occurrence of high IOP after FS-LASIK surgery in patients with high myopia (P＜0.05). The C-index of the column-line graph prediction model based on this was 0.722 (95% confidence interval 0.684-0.760), the calibration curve and the ideal curve were basically the same, and the area under the ROC curve was 0.709. ConclusionsCorneal thickness＞450 μm, keratometric curvature ≤38 D, glucocorticoid treatment, intraoperative fluctuation of intraocular pressure, and corneal hysteresis ≤8.0 mm Hg are the risk factors for the development of hyperopic IOP in highly risk factors for the development of high IOP after FS-LASIK surgery in myopic patients. The column-line diagram model constructed on the basis of the risk factors hava good accuracy.
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.