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. 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.
ObjectiveTo analyze independent factors for treatment-requiring retinopathy of prematurity (TR-ROP) and establish a predictive nomogram model for TR-ROP. MethodA retrospective cohort study. A total of 6 998 preterm infants who were born at Guangdong Women's and Children's Hospital between January 1, 2012 and March 31, 2022 and were screened for retinopathy of prematurity (ROP) were included in the study. TR-ROP was defined as type 1 ROP and aggressive ROP; 22 independent factors including general information, maternal perinatal conditions, interventions and neonatal diseases related to ROP were collected. The infants were divided at the level at an 8:2 ratio according to clinical experience, with 5 598 in the training cohort and 1 400 in the validation cohort. t test was used for comparison of quantitative data and χ2 test was used for comparison of counting data between groups. Multivariate logistic regression analysis was carried out for the indicators with differences in the univariate analysis. The visualized regression analysis results of R software were used to obtain the histogram. The accuracy of the nomogram was verified by C-index and receiver operating characteristic curve (ROC curve). ResultsAmong the 6 998 children tested, 4 069 were males and 2 920 were females. Gestational age was (33.69±3.19) weeks; birth weight was (2 090±660) g. There were 376 cases of TR-ROP (5.4%, 376/6 998). The results of multivariate logistic regression analysis showed that gestational age [odds ratio (OR) =0.63, 95% confidence interval (CI) 0.47-0.85, P=0.002], intrauterine distress (OR=0.30, 95%CI 0.10-0.99, P=0.048), bronchopulmonary dysplasia (OR=0.23, 95%CI 0.09-0.60, P=0.003), hypoxic-ischemic encephalopathy (OR=5.40, 95%CI 1.45-20.10, P=0.012), blood transfusion history (OR=4.05, 95%CI 1.50-10.95, P=0.006) were the independent influencing factors of TR-ROP. Based on this and combined with birth weight, a nomogram prediction model was established. The C-index of the training set and validation set were 0.940 and 0.885, respectively, and the area under ROC curve were 0.945 (95%CI 0.930-0.961) and 0.931 (95%CI 0.876-0.986), respectively. The sensitivity and specificity were 86.2%, 94.0% and 83.2%, 93.3%, respectively. ConclusionsGestational age, intrauterine distress, bronchopulmonary dysplasia, hypoxic-ischemic encephalopathy and blood transfusion history are the independent factors influencing the occurrence of TR-ROP. The TR-ROP nomogram prediction model based on independent influencing factors has high sensitivity and specificity.
ObjectiveTo construct a nomogram prediction model for pain crisis occurrence based on clinical data of patients with advanced non-small cell lung cancer (NSCLC), with the aim of providing a scientific basis for clinical decision-making.MethodsA total of patients with advanced non-small cell lung cancer (NSCLC) admitted to our hospital from January 2022 to January 2024 were selected as the study subjects. Demographic data, disease information, pain severity (assessed using the Numerical Rating Scale, NRS), psychological status (anxiety and depression assessed using the Self-Rating Anxiety Scale, SAS, and the Self-Rating Depression Scale, SDS), and social support (assessed using the Perceived Social Support Scale, PSSS) were collected. Univariate and multivariate Logistic regression analyses were performed to identify independent factors influencing pain crisis. The R software was used to visualize the nomogram, and the Receiver Operating Characteristic (ROC) curve, calibration curve, and Hosmer-Lemeshow test were employed to evaluate the discrimination and calibration of the model.ResultsA total of 500 questionnaires were distributed, and 448 qualified questionnaires were collected, with a qualification rate of 89.6%. The patients were divided into a modeling group (n=314) and a validation group (n=134). Univariate analysis showed significant differences between the pain crisis group and the pain-free group in terms of gender, age, education level, PSSS score, bone metastases, pleural metastases, depression and anxiety levels, and antitumor efficacy (P<0.05). Multivariate Logistic regression analysis showed that bone metastasis, PSSS score, age, depression, and anxiety levels were independent factors influencing pain crisis in patients with advanced NSCLC. Based on the results of the multivariate Logistic regression analysis, a nomogram prediction model for pain crisis occurrence in patients with advanced NSCLC was constructed. The Area Under the Curve (AUC) of the ROC curve in the modeling and validation groups was 0.948 and 0.921, respectively, indicating high discrimination of the model. The calibration curve and Hosmer-Lemeshow test results showed good consistency of the model.ConclusionThis study successfully constructed and validated a nomogram prediction model based on independent factors such as bone metastasis, social support (PSSS score), age, depression, and anxiety levels. This model can objectively and quantitatively predict the risk of pain crisis occurrence in patients with advanced NSCLC, providing a scientific basis for clinical decision-making. It helps identify high-risk patients with pain crisis in advance and optimize pain management strategies, thereby improving patient prognosis and quality of life.
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.
ObjectiveTo investigate the influencing factors of unplanned readmission in patients with chronic obstructive pulmonary disease (COPD) within 1 year, construct a risk prediction model and evaluate its effect. MethodsClinical data of 403 inpatients with COPD were continuously collected from January 2023 to May 2023, including 170 cases in the readmission group and 233 cases in the non readmission group. LASSO regression was applied to screen the optimized variables and multivariate logistic regression analyses were applied to explore the risk factors of unplanned readmission in patients with COPD within 1 year. After that a nomogram prediction model was constructed and evaluated its discrimination, calibration, and clinical applicability. ResultsThe incidence of unplanned readmission in patients with COPD within 1 year was 42.2%. Respiratory failure, number of acute exacerbation in the last year, creatinine and white blood cell count were risk factors for unplanned admission of patients with COPD within one year (P<0.05). Creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid were included in the nomogram model, the area under curve (AUC) and its 95% confidential interval (CI) of the nomogram model was 0.687 (0.636 - 0.739), with the sensitivity, specificity, and accuracy were 0.824, 0.742 and 0.603, respectively. The AUC of the nomogram after re-sampling 1 000 times was 0.687 (0.634 - 0.739). The calibration curve showed a high degree of three line overlap and the clinical decision curve showed that the nomogram model provided better net benefits than the treat-all tactics or the treat-none tactics with threshold probabilities of 15.0% - 55.0%. ConclusionThe nomogram model constructed based on creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid has good predictive value for unplanned readmission in patients with COPD within 1 year.
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.
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.
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.