ABSTRACT
AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P<0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P<0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.
ABSTRACT
Objective To investigate the importance of a nomogram model based on biomarkers and CT signs in the prediction of the invasive risk of ground glass nodules. Methods A total of 322 patients with ground glass nodule, including 240 and 82 patients in the model and verification groups, respectively, were retrospectively analyzed. Independent risk factors for the invasive risk of ground glass nodules were screened out after using single and multiple Logistic analysis. R software was used to construct the nomogram model, and clinical decision curve analysis (DCA), receiver operating curve (ROC), and calibration curve were used for internal and external verification of the model. Results In this study, the independent risk factors for the invasive risk of ground glass nodules included systemic immune-inflammation index (SII), CYFRA21-1, edge, vascular cluster sign, and nodular consolidation tumor ratio (CTR). The area under the ROC curve of the constructed nomogram model had a value of 0.946, and that of the external validation group reached 0.932, which suggests the good capability of the model in predicting the invasive risk of ground glass nodules. The model was internally verified through drawing of calibration curves of Bootstrap 1000 automatic sampling. The results showed that the consistency index between the model and actual curves reached 0.955, with a small absolute error and good fit. The DCA curve revealed a good clinical practicability. In addition, nodule margin, vascular cluster sign, and CTR were correlated with the grade of pathological subtype of invasive adenocarcinoma. Conclusion A nomogram model based on biomarkers and CT signs has good value and clinical practicability in the prediction of the invasive risk of ground glass nodules.
ABSTRACT
Objective To explore the development and validation of a prediction model for severe communi-ty-acquired pneumonia in adults based on peripheral blood inflammatory indicators.Methods Venous blood samples of 204 community-acquired pneumonia in adults patients admitted to 7 hospitals in Chongqing area from April 2021 to August 2022 were collected to detect C-reactive protein(CRP),peripheral white blood cell count(WBC),neutrophil to lymphocyte ratio(NLR),cytokines,lymphocyte subgroups and neutrophil CD64 index.All of patients were divided into a training group and a validation group according to the time of admis-sion.Univariate and multivariate Logistic regression were used to analyze the data of the training group,the characteristic factors of severe progression for pneumonia were selected to construct the nomogram model,and the data of the validation group was used to verify the model.The receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to evaluate the prediction ability of the model for severe community-acquired pneumonia in adults.Results Logistic regression analysis showed that age,CRP,WBC,interleukin(IL)-4/interferon gamma ratio and IL-6/IL-10 ratio were independent risk factors for severe community-acquired pneumonia in adults.The area under the ROC curve of the nomogram model in the training group and the validation group was 0.893 and 0.880,respectively.The calibration curve and DCA results shown that the model had a good prediction effect for severe community-acquired pneumonia in adults.Conclusion The inflammatory indicators included in this model are simple and easy to obtain clinically.This model with good differentiation and accuracy,it can be used as a practical tool to predict severe community-ac-quired pneumonia in adults,and has certain clinical application value.
ABSTRACT
Objective To establish a Nomogram model for assessing the risk of intestinal colonization by Carbapenem-Resistant Klebsiella pneumoniae(CRKP)to determine the specific probability of colonization and adopt individualized prevention strategies for the purpose of reducing the occurrence of colonization and secondary infection of neonatal CRKP.Methods A total of 187 neonates hospitalized between January 2021 and October 2022 and diagnosed with CRKP colonization by rectal swab/fecal culture as well drug sensitivity identification 48 h after admission were assigned to the CRKP group.Another 187 neonates without non-CRKP colonization during the same period were set as the non-CRKP group.All the data of the two groups were used for a retrospective analysis.The caret package in R 4.2.1 was used to randomly divide the 374 cases into the model group and validation group at a ratio of 3∶1.Then the glmnet package in R 4.2.1 was used to conduct a LASSO regression analysis over the data from the model group to determine the predictive factors for modeling and the rms software package was used to build a Nomogram model.The pROC and rms packages in R 4.2.1 were used to examine the data,analyzing the consistency indexes(Cindex),receiver operating characteristic curves(ROC),and area under the curves(AUC)and performing the internal and external validation of the efficacy of the Nomogram model via the calibration curves.Results LASSO regression analysis determined eight predictors from the 35 factors probably affecting neonatal CRKP colonization:gender,cesarean section,breastfeeding,nasogastric tube,enema,carbapenems,probiotics,and hospital stay.The Nomogram model constructed using these eight predictors as variables could predict CRKP colonization to a moderate extent,with the area under the ROC curve of 0.835 and 0.800 in the model and validation group,respectively.The Hos-mer-Lemeshow test showed that the predicted probability was highly consistent with the actual probability(the modeling group:P = 0.678>0.05;the validation group:P = 0.208>0.05),presenting a higher degree of fitting.Conclusion The Nomogram model containing such variables as gender,cesarean section,breastfeeding,nasogastric tube,enema,carbapenems,probiotics,and hospital stay is more effective in predicting the risk of neonatal CRKP colonization.Therefore,preventive measures should be individualized based on the colonization probability predicted by the Nomogram model in order to keep neonates from CRKP colonization and reduce the incidence of secondary CRKP infections among them.
ABSTRACT
Objective:To investigate the influencing factors for microvascular invasion (MVI) in hepatocellular carcinoma based on three-dimensional visualization and the construction of its nomogram model.Methods:The retrospective cohort study method was conducted. The clinico-pathological data of 190 patients with hepatocellular carcinoma who were admitted to Henan University People′s Hospital from May 2018 to May 2021 were collected. There were 148 males and 42 females, aged (58±12)years. The 190 patients were randomly divided into the training set of 133 cases and the validation set of 57 cases by the method of random number table in the ratio of 7:3. The abdominal three-dimensional visualization system was used to characterize the tumor morphology and other imaging features. Observation indicators: (1) analysis of influencing factors for MVI in hepatocellular carcinoma; (2) construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. Measurement data with normal distribution were expressed as Mean± SD, and independent sample t test was used for comparison between groups. Measurement data with skewed distribution were expressed as M( Q1, Q3), and non-parametric rank sum test was used for comparison between groups. Count data were expressed as absolute numbers, and the chi-square test was used for comparison between groups. Corresponding statistical methods were used for univariate analysis. Binary Logistic regression model was used for multivariate analysis. Receiver operator characteristic (ROC) curves were plotted, and the nomogram model was assessed by area under the curve (AUC), calibration curve, and decision curve. Results:(1) Analysis of influencing factors for MVI in hepatocellular carcinoma. Among 190 patients with hepatocellular carcinoma, there were 97 cases of positive MVI (including 63 cases in the training set and 34 cases in the validation set) and 93 cases of negative MVI (including 70 cases in the training set and 23 cases in the validation set). Results of multivariate analysis showed that alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology were independent factors affecting the MVI of patients with hepatocellular carcinoma ( odds ratio=5.06, 3.62, 1.00, 2.02, 2.59, 95% confidence interval as 1.61-15.90, 1.28-10.20, 1.00-1.01, 1.02-3.98, 1.03-6.52, P<0.05). (2) Construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. The results of multivariate analysis were incorporated to construct a nomogram prediction model for MVI of hepatocellular carcinoma. ROC curves showed that the AUC of the training set of nomogram model was 0.85 (95% confidence interval as 0.79-0.92), the optimal fractional cutoff based on the Jordon′s index was 0.51, the sensitivity was 0.71, and the specificity was 0.84. The above indicators of validation set were 0.92 (95% confidence interval as 0.85-0.99), 0.50, 0.90, and 0.82, respectively. The higher total score of the training set suggested a higher risk of MVI in hepatocellular carcinoma. The calibration curves of both training and validation sets of nomogram model fitted well with the standard curves and have a high degree of calibration. The decision curve showed a high net gain of nomogram model. Conclusions:Alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology are independent influencing factors for MVI in patients with hepatocellular carcinoma. A nomogram model constructed based on three-dimensional visualized imaging features can predict MVI in hepatocellular carcinoma.
ABSTRACT
Objective To analyze the influencing factors of choroidopathy(choroidal atrophy and choroidal neovas-cularization)secondary to high myopia based on Logistic regression analysis and to construct a Nomogram risk prediction model based on the related factors,so as to provide guidance for clinical treatment.Methods A total of 340 patients(680 eyes)with high myopia admitted to Beijing Jishuitan Hospital from January 2021 to January 2023 were selected and di-vided into group A(170 patients,340 eyes)and group B(170 patients,340 eyes).The incidence of choroidopathy in the two groups was compared.The groups A and B were divided into two subgroups,subgroup a and subgroup b,according to whether choroidopathy occurred or not.Multivariate Logistic regression analysis was carried out to explore the influencing factors of choroidopathy secondary to high myopia.A Nomogram risk prediction model for choroidopathy secondary to high myopia was constructed based on the influencing factors and externally validated.Results In groups A and B,the age,proportion of diabetes mellitus,axial length,and level of seruim transforming growth factor β1(TGF-β1)of patients in subgroup a were higher than those in the subgroup b,and the diopter was lower than that in the subgroup b(all P<0.05).The Logistic regression analysis showed that age,diabetes mellitus,axial length and serum TGF-β1 level were independent risk factors for choroidopathy secondary to high myopia,and diopter was a protective factor(all P<0.05).Age,diabetes mellitus,axial length and serum TGF-β1 level were positively correlated risk factors for choroidopathy secondary to high myopia,and diopter was a negatively correlated risk factor(all P<0.05).The area under the curve of the Nomogram risk prediction model for predicting choroidopathy secondary to high myopia was 0.818,and the calibration was good.Con-clusion Age,diabetes mellitus,axial length,diopter and serum TGF-β1 level are the influential factors for choroidopa-thy secondary to high myopia.The Nomogram risk prediction model established based on these factors has a certain value for predicting choroidopathy secondary to high myopia.The clinical therapeutic schedules should be made based on this model to reduce the risk of secondary choroidopathy.
ABSTRACT
Objective:To investigate the relationship between spread through air spaces(STAS) of peripheral stage ⅠA small adenocarcinoma of the lung(≤2 cm) and related factors such as clinical and CT morphological features, and to construct a nomogram model.Methods:Relevant clinical, pathological and imaging data of patients who underwent lung surgery and were diagnosed as peripheral stage ⅠA small lung adenocarcinoma by postoperative pathology in the Affiliated Hospital of Nantong University from 2017 to 2022 were collected, of which cases that met the inclusion criteria from 2017 to 2021 served as the training group, and those that met the inclusion criteria in 2022 served as the validation group. The independent risk factors for the occurrence of STAS in peripheral stage ⅠA lung small adenocarcinoma were investigated by using univariate analysis and multifactorial logistic regression analysis, based on which a nomogram prediction model was constructed, and the subjects were analyzed by using the receiver operating characteristic curve( ROC), correction model, etc. were used to evaluate the model. Results:A total of 430 patients who met the criteria were included, including 351 patients in the training group(109 STAS-positive and 242 STAS-negative) and 79 patients in the validation group(23 STAS-positive and 56 STAS-negative). Univariate analysis showed that the patients in the two groups showed a significant difference in age(>58 years old), gender, smoking history, tumor location(subpleural, non-subpleural), pleural pull, nodule type, nodule maximal diameter, solid component maximal diameter, consolidation tumor ratio(CTR), lobulation sign, burr sign, bronchial truncation sign, vascular sign(includes thickening and distortion of blood vessels in/around the nodes), satellite lesions, and ground-glass band sign were statistically significant( P<0.05). The results of multifactorial logistic regression analysis showed that CTR( OR=4.98, P<0.001), lobulation sign( OR=4.07, P=0.013), burr sign( OR=3.66, P<0.001), and satellite lesions( OR=3.56, P=0.009) were the independent risk factors for the occurrence of STAS. Applying the above factors to construct the nomogram model and validate the model, the results showed that the ROC curve was plotted by the nomogram prediction model, and the area under the ROC curve( AUC) of the training set was 0.840(sensitivity 0.835, specificity 0.734), and the validation set had an AUC value of 0.852(sensitivity 0.786, specificity 0.783), and the training set and validation set calibration curves have good overlap with the ideal curve. Conclusion:CTR, lobular sign, burr sign, and satellite lesions are independent risk factors for STAS, and the nomogram model constructed in this study has good predictive value.
ABSTRACT
【Objective】 To investigate the prognostic value of tumor location in patients with upper tract urothelial carcinoma (UTUC) treated with radical nephroureterectomy (RNU), and to develop and validate a nomogram model for predicting the overall survival (OS). 【Methods】 UTUC patients undergoing RUN at our hospital during Jan.2010 and Dec.2022 were retrospectively collected, 70% of whom were included in the training group and 30% in the validation group.According to the tumor location, patients were divided into renal pelvis tumor (RPT) group and ureteral tumor (UT) group.The differences in clinicopathological features and prognosis were analyzed.Based on multivariate Cox results, a nomogram model for predicting OS was developed and validated. 【Results】 A total of 366 patients (196 RPT and 170 UT) were included in this study.There were statistically significantly differences in urine cytology (P=0.001), hydronephrosis (P<0.001), history of bladder tumor (P=0.021), pathological T stage (P<0.001) and histological structure (P=0.037) between the two groups.Multivariate Cox results showed that patients with UT had a worse prognosis (HR=2.00, 95%CI: 1.22-3.27, P=0.006).Factors of the nomogram for predicting OS included age, tumor location, lymphovascular invasion and pathological T stage.The model showed good discrimination and calibration, and performed well in internal verification. 【Conclusion】 Compared with RPT, UT has a worse prognosis and the fat around the tumor should be surgically removed more thoroughly to avoid micro-residual.We successfully coustructed a nomogram model that can be used to predict the OS of UTUC patients after RNU surgery.
ABSTRACT
@#Objective To investigate the prognostic value of preoperative serum albumin-to-globulin ratio (AGR) and neutrophil-lymphocyte ratio (NLR) in the overall survival (OS) of patients with esophageal squamous cell carcinoma (ESCC), and to establish an individualized nomogram model and evaluate its efficacy, in order to provide a possible evaluation basis for the clinical treatment and postoperative follow-up of ESCC patients. Methods AGR, NLR, clinicopathological and follow-up data of ESCC patients diagnosed via pathology in the Department of Thoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University from 2010 to 2017 were collected. The correlation between NLR/AGR and clinicopathological data were analyzed. Kaplan-Meier analysis and log-rank test were used for survival analysis. The optimal cut-off values of AGR and NLR were determined by X-tile software, and the patients were accordingly divided into a high-level group and a low-level group. At the same time, univariate and multivariate Cox regression analyses were used to identify independent risk factors affecting OS in the ESCC patients, and a nomogram prediction model was constructed and internally verified. The diagnostic efficacy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was evaluated by decision curve analysis. Results A total of 150 patients were included in this study, including 105 males and 45 females with a mean age of 62.3±9.3 years, and the follow-up time was 1-5 years. The 5-year OS rate of patients in the high-level AGR group was significantly higher than that in the low-level group (χ2=6.339, P=0.012), and the median OS of the two groups was 25 months and 12.5 months, respectively. The 5-year OS rate of patients in the high-level NLR group was significantly lower than that in the low-level NLR group (χ2=5.603, P=0.018), and the median OS of the two groups was 18 months and 39 months, respectively. Multivariate Cox analysis showed that AGR, NLR, T stage, lymph node metastasis, N stage, and differentiation were independent risk factors for the OS of ESCC patients. The C-index of the nomogram model was 0.689 [95%CI (0.640, 0.740)] after internal validation. The area under the ROC curve of predicting 1-, 3-, and 5-year OS rate was 0.773, 0.724 and 0.725, respectively. At the same time, the calibration curve and the decision curve suggest that the model had certain efficacy in predicting survival and prognosis. Conclusion Preoperative AGR and NLR are independent risk factors for ESCC patients. High level of AGR and low level of NLR may be associated with longer OS in the patients; the nomogram model based on AGR, NLR and clinicopathological features may be used as a method to predict the survival and prognosis of ESCC patients, which is expected to provide a reference for the development of personalized treatment for patients.
ABSTRACT
Objective To evaluate the predictive value of serum carbohydrate antigen 50(CA50),tumor specific growth factor(TSGF),and tissue polypeptide antigen(TPA)levels for sensitivity to radiochemotherapy in patients with middle-and advanced-stage breast cancer using a nomogram model.Methods Eighty-two patients with middle-and advanced-stage breast cancer were selected as the study sub-jects.All patients received paclitaxel chemotherapy combined with radiotherapy and were divided into sensitive(n= 57)and insensitive(n= 25)groups according to the Response Evaluation Criteria in Solid Tumors.The general information of the patients,serum expression of CA50,TSGF,and TPA,and their differences before and after treatment were recorded.A nomogram model was constructed,and cali-bration curves,receiver operating characteristic(ROC)curves,and decision curves were used to evaluate the predictive power and clinical utility of the nomogram model.Results Significant differences were observed in tumor diameter,vascular invasion,TNM stage,lymph node metastasis,and degree of differentiation between the two groups(P<0.05).Compared to those in the sensitive group,the serum expression of CA50,TSGF,and TPA after treatment was higher,and the difference in CA50,TSGF,and TPA was smaller in the insensitive group(P<0.05).Three predictive variables were identified in the LASSO regression:differences in CA50,TSGF,and TPA.The logistic regression results showed that differences in CA50,TSGF,and TPA influenced sensitivity to radiochemotherapy in middle-and advanced-stage breast cancer(P<0.05).A nomogram model was constructed using differences in CA50,TSGF,and TPA.Calibration,ROC,and decision curves showed the model's good predictive accuracy and clinical utility.Conclusion Serum expression of CA50,TSGF,and TPA is high in patients with middle-and advanced-stage breast cancer who are insensitive to radiochemotherapy,and differences in CA50,TSGF,and TPA affect their sensitivity to radiochemotherapy.The nomogram model had good predictive value and clinical utility.
ABSTRACT
【Objective】 To analyze the risk factors of postpartum stress urinary incontinence (SUI) and to establish a nomogram model. 【Methods】 A total of 278 puerpera who gave birth at our hospital during Dec.2018 and Aug.2020 were selected as the modeling group, and 132 puerpera who gave birth during Sep.2020 and Sep.2021 were involved in the verification group. Factors affecting postpartum SUI were identified with univariate and multivariate logistic regression, and a nomogram prediction model was constructed with R software. The predictive effectiveness and discrimination of the model were assessed, and the decision curve analysis (DCA) was drawn to evaluate the clinical application value of the model. 【Results】 A total of 84 cases (30.22%) in the modeling group developed SUI 2 months after delivery. Fetal weight, delivery method, maternal age, mobility (Δhy) and rotation Angle (Δβ) were factors affecting postpartum SUI (P<0.05). Multivariate logistic regression analysis showed that increased fetal weight, normal delivery, increased Δhy, and increased Δβ were independent risk factors of postpartum SUI (P<0.05). The constructed nomogram fitted well. The H-L fit curve of the modeling group and verification group were (χ2=7.514, P=0.312) and (χ2=6.157, P=0.267), respectively. The area under the receiver operating characteristic curve of the modeling group and verification group were 0.815 and 0.760, respectively, indicating high specificity and consistency. DCA indicated that when the high-risk threshold probability of the model was between 0.06-0.80, the nomogram model had a high clinical value. 【Conclusion】 Increased fetal weight, normal delivery, increased Δhy and elevated Δβ are independent risk factors that affect postpartum SUI. The nomogram model constructed has good predictive effectiveness and discrimination, and high clinical application value.
ABSTRACT
Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.
Subject(s)
Humans , Retrospective Studies , Lymphoma, Large B-Cell, Diffuse/drug therapy , Algorithms , Niacinamide , Tomography, X-Ray ComputedABSTRACT
Objective To analyze the risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic surgery and establish a predictive nomogram model. Methods A total of 315 patients with primary lung cancer who had undergone thoracoscopic surgery from January 2018 to October 2021 in our hospital were divided into two groups according to the incidence of pulmonary infection. Two groups of clinical data were collected for single-factor and regression analyses, and independent predictors were obtained. On this basis, a risk model was constructed and its predictive effectiveness was evaluated. Results The independent risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic radical operation were as follows: age≥62.5 years, smoking index≥100, PEF≤72.1 ml/s, TNM stage Ⅲ/Ⅳ, and operation time≥188.5 min (P < 0.05). Based on the above factors, the risk model of the column chart was established. Model-verification results showed that the C-index of the model was 0.909, and the correction curve showed that the column chart model had good differentiation and consistency. Conclusion Lung cancer patients' age, smoking index, TNM stage, PEF, and operation time are closely related to pulmonary infection after thoracoscopic radical operation. The nomogram model is useful for identifying high-risk patients and reducing postoperative complications.
ABSTRACT
@#Abstract: Objective To analyze the risk factors for neonatal preterm birth in 12 hospitals in Yunnan Province from 2016 to 2017, and to establish a nomogram prediction model for neonatal preterm birth, providing scientific evidence for the prevention of preterm birth. Methods A total of 20 445 pregnant women who gave birth in 12 hospitals in Yunnan Province from 2016 to 2017 were collected and grouped into a preterm group (n=1 186) and a full-term group (n=19 259) according to whether they had a premature delivery. The general information questionnaire of pregnant women designed by the research team was applied to understand the basic conditions and pregnancy information of the two groups, and the risk factors of preterm birth were determined by logistic regression analysis, R software was applied to draw a nomogram prediction model of neonatal preterm birth, and its predictive performance was tested. Results There were significant differences in the proportions of twins and above (9.11% vs 7.10%), pregnancy-induced hypertension (21.67% vs 18.57%), gestational diabetes mellitus (18.21% vs 15.90%), anemia (24.28% vs 20.70%), premature rupture of membranes (11.64% vs 9.76%), and abnormal placenta (7.08% vs 5.51%) between the preterm group and the full-term group (χ2=6.731, 7.055, 4.441, 8.691, 4.437, 5.232, all P<0.05); the logistic regression analysis showed that the risk factors for neonatal preterm birth were twins and above (OR=2.378), pregnancy-induced hypertension (OR=2.039), gestational diabetes mellitus (OR=1.824), anemia (OR=1.825), and premature rupture of membranes (OR=2.313) (all P<0.05); the discrimination (area under the curve was 0.794, 95%CI=0.738-0.850) and precision (goodness of fit HL test, χ2=8.864, P=0.312) of the nomogram model constructed to predict the occurrence of neonatal preterm birth were both good. Conclusions The nomogram model for preterm birth constructed based on 5 factors including number of fetuses, pregnancy-induced hypertension, gestational diabetes mellitus, anemia and premature rupture of membranes can predict the occurrence of neonatal preterm birth well, thus providing reference for the prevention of neonatal preterm birth.
ABSTRACT
ObjectiveTo investigate the risk factors of fertility behaviors with preterm birth and low birth weight, and to develop a nomogram model to predict the occurrence of low birth weight. MethodsBirth registration information in Shanghai from 2010 to 2020 was collected, and ANOVA and Chi-square tests were used to compare the differences in reproductive behavior factors and newborn health status across time. The odds ratio (OR) value and 95%CI were calculated by a multi-classification logistic regression model to determine the association between reproductive behavior factors and preterm birth or low birth weight infants. A nomogram model was established based on logistic model and the area under the ROC curve was used to assess the effect of the model. ResultsThis analysis included 2 089 384 live newborns. The incidence of full-term low birth weight, preterm normal weight and preterm low birth weight in Shanghai was 0.94%, 2.48% and 2.01%, respectively. From 2010 to 2020, 40.00% women had a history of abortion, the proportion of women who gave birth at age ≥40 years old increased from 1.05% to 2.24%, the proportion of fathers aged ≥40 years increased from 4.79% to 7.48%, and the proportion of women with postgraduate or above increased from 4.81% to 11.74%. The incidence of preterm low birth weight in Shanghai showed an increasing trend over time. Logistic regression analysis showed that the risk of preterm low birth weight was lower in female than in male infants (OR=0.97, 95%CI: 0.95‒0.98), and the risk of full-term low birth weight was higher than in male infants (OR=1.85, 95%CI: 1.80‒1.90). The risk of preterm birth and low birth weight was lower for couples of childbearing age with higher education. The risk of preterm low birth weight in newborns tended to increase with maternal age at childbirth >30 years, paternal age ≥40 years, and the number of abortions >2 times. Mother <25 or >35 years, father aged 30‒34 years, and the number of abortions >3 times were the risk factors of full-term low birth weight infants. ConclusionCouples of childbearing age who choose to have children at too high or too low age may increase the risk of preterm birth or low birth weight, so it is necessary to strengthen population awareness and promote age-appropriate childbirth. Multiple abortions are also associated with preterm birth and low birth weight, and it is advisable to popularize the scientific knowledge of contraception and birth control to reduce unnecessary abortions. The nomogram in the study can visualize the risk of full-term and low birth weight infant at different levels of factors, which can assist couples preparing for pregnancy in making decisions about the timing of childbirth and understanding the level of risk.
ABSTRACT
Objective To construct and validate a nomogram risk model for predicting concurrent aspi-ration.Methods Fifty-five stroke patients with aspiration(the aspiration group)and 55 patients without aspi-ration(the control group)admitted to this hospital from April 2021 to April 2023 were selected as the study objects.Clinical data of the two groups of patients were collected,univariate and multivariate logistic regres-sion were used to analyze the risk factors of stroke with aspiration,and R software was used to construct a no-mogram risk model of stroke with aspiration based on the screened risk factors,and the accuracy of this model in predicting stroke complicated aspiration was validated.Results Age(≥60 years),infarct site(brain stem),lesion site(multiple),hypertension component ratio,NIHSS score and Hcy and hs-CRP levels in the aspiration group were higher than those in the control group,with statistical significance(P<0.05).Multiva-riate logistic regression analysis showed that age(≥60 years old),infarct site(brainstem),lesion site(multi-ple),hypertension and NIHSS score(high)were the risk factors affecting aspiration in stroke patients(P<0.05).The results of the nomogram model showed that NIHSS score was the strongest predictor of aspiration in stroke patients,followed by age(≥60 years old),infarct site(brain stem),lesion site(multiple),and hy-pertension.Model validation results showed that the area under the curve of receiver operating characteristic(ROC)curve of the nomogram was 0.885(95%CI:0.701-0.945),and the consistency index was 0.831.H-L goodness of fit test showed that there was no significant difference between the predicted value and the actu-al observed value(x2=4.112,P=0.459),indicating that the prediction accuracy and differentiation of the model were good.Conclusion Age(≥60 years old),infarct site(brainstem),lesion site(multiple),hypertension,NIHSS score(high),Hcy(high)and hs-CRP(high)were the risk factors affecting aspiration in stroke pa-tients.The nomogram model could effectively predict the risk of aspiration in stroke patients.
ABSTRACT
Objective To explore the risk factors for renal injury in tumors patients treated with programmed death receptor-1(PD-1)inhibitor,and further construct a column chart model to predict the likelihood of renal injury in patients.Methods The present study is a single center retrospective analysis.447 patients with tumors treated with PD-1 inhibitors in the Third Affiliated Hospital of Soochow University between January 2018 and January 2021 were included and followed up until January 2022.Kidney injury was defined as acute kidney disease(AKD).All patients were divided into AKD group(n=71)and non-AKD group(n=376 according to whether PD-1 inhibitor associated with AKD development at the end of follow-up.Basic information,disease and medication situation,laboratory indicators,and the incidence of extrarenal immune related adverse events(irAEs)during follow-up period were compared between the two groups.Univariate and multivariate logistic regression models were used to identify independent risk factors for PD-1 inhibitor associated AKD.The present study randomly divided all samples(n=447)into training set(n=313)and validation set(n=134)in a 7:3 ratio,built nomogram prediction models in the training set according to the screened independent risk factors,drawn the receiver operating characteristic(ROC)curves to evaluate the discrimination of the models,drawn calibration curves to evaluate the calibration of the models,and drawn clinical decision curve analysis(DCA)to explore the clinical validity and benefit rate of the models.Results The combination of antibiotics,diabetes,hypertension,extrarenal irAEs and cystatin C(Cys C)in AKD group were significantly higher than those in non-AKD group(P<0.05),but hemoglobin(Hb)was significantly lower than that in non-AKD group(P<0.05).Single factor logistic regression analysis showed that combination of antibiotics,diabetes,hypertension,extrarenal irAEs,lower Hb,estimated glomerular filtration rate(eGFR),higher blood urea nitrogen(BUN),serum creatinine(SCr),Cys C,fasting blood glucose(FBG),and alanine transaminase(ALT)were risk factors for PD-1 inhibitor related AKD(P<0.05).Multivariate logistic regression analysis showed that concomitant extrarenal irAEs,lower Hb,higher SCr,and direct bilirubin(DBIL)were independent risk factors for PD-1 inhibitor associated AKD(P<0.05).Based on the independent risk factors mentioned above,a column chart prediction model was further established and validated.The results showed that the area under the ROC curve(AUC)of the training and validation sets of the model were 0.703(95%CI 0.628-0.777)and 0.791(95%CI 0.671-0.911),respectively,indicating good discrimination.The calibration curves of both the training and validation sets hover around the ideal line of 45°,indicating that the model has good calibration.DCA shows that the constructed model curve is far away from the two polar lines(the curve with a net benefit of 0 and the curve with all samples being positive),indicating that the model has good clinical benefits.Conclusion The combination of extrarenal irAEs,lower Hb,higher SCr,and higher DBIL are independent risk factors for the occurrence of PD-1 inhibitor related AKD;The established column chart model has good discrimination and calibration,which can provide guidance for clinical practice.
ABSTRACT
Objective:To explore the influencing factors of postoperative recurrence in patients with complex anal fistula, and to construct a nomogram model to predict the risk of postoperative recurrence and verify it.Methods:Clinical data of 310 patients with complex anal fistula who underwent fistulectomy in the hospital from Aug. 2019 to Mar. 2023 were retrospectively selected and divided into modeling group (93 cases) and validation group (217 cases) in a 3∶7 ratio according to system randomization method. Hospital electronic medical record system was used to collect patient baseline data and calculate the recurrence rate of patients 6 months after surgery. According to the data of the modeling group, multivariate Logistic regression was used to analyze the influencing factors of postoperative recurrence in patients with complex anal fistula. Based on the influencing factors, a nomogram model was established to predict the risk of postoperative recurrence, and external verification was performed based on the data of the validation group.Results:The recurrence rate at 6 months after operation was 20.43% (19/93) in the modeling group and 17.51% (38/217) in the validation group. There was no significant difference in recurrence rate between the two groups ( χ2=0.370, P=0.543) . The proportion of male, smoking history, diabetes mellitus, high anal fistula and unclear position of internal orifice in the recurrence group was higher than that in the non-recurrence group, and the body mass index and course of disease were higher than those in the non-recurrence group ( P<0.05) . Based on the above seven influencing factors, a nomogram model of the risk of recurrence of complex anal fistula after surgery was established. C index of the modeling group and the validation group was 0.984 and 0.798 respectively, the calibration curve was close to the ideal curve, and the Receiver operating characteristic AUC of the nomogram prediction model was>0.70, indicating that model consistency, prediction efficiency and differentiation were good. Conclusion:The nomogram prediction model based on gender, body mass index, smoking history, diabetes mellitus, course of disease, high anal fistula and internal orifice position can effectively predict the risk of postoperative recurrence in patients with complex anal fistula.
ABSTRACT
Objective:To investigate the high-risk factors affecting the prognosis of patients with pT 1-2N 1M 0 after mastectomy, establish a nomogram prediction model, perform risk stratification, and screen the radiotherapy benefit populations. Methods:Clinical data of 936 patients with pT 1-2N 1M 0 breast cancer undergoing mastectomy in the Fourth Hospital of Hebei Medical University from January 2010 to December 2016 were retrospectively analyzed and 908 cases had complete follow-up data. They were divided into the radiotherapy (RT) group ( n=583) and non radiotherapy (NRT) group ( n=325) according to the radiotherapy. After propensity score matching (PSM) was performed 1 vs. 1, 298 cases were assigned into the RT group and 298 in the NRT group. Overall survival (OS) and disease-free survival (DFS) were compared between two groups using log-rank test. Nomogram prediction model was established, the survival differences were compared among different risk groups, and the radiotherapy benefit populations were screened. Results:Univariate analysis showed that the 5- and 8-year OS and DFS in the RT group were significantly better than those in the NRT group (both P<0.001). Multivariate analysis showed that age, tumor quadrant, number of lymph node metastases, T staging, and Ki-67 level were the independent prognostic factors for OS. Age, tumor quadrant, and T staging were the independent prognostic factors for DFS. The OS nomogram analysis showed that the OS of patients in the high-risk group was significantly improved by post-mastectomy radiotherapy (PMRT) ( P=0.001), while PMRT did not show an advantage in the low- and medium-risk groups ( P=0.057, P=0.099). The DFS nomogram analysis showed that DFS was significantly improved by PMRT in patients in the medium- and high-risk groups ( P=0.036, P=0.001), whereas the benefits from PMRT were not significant in the low-risk group ( P=0.475). Conclusions:For patients with pT 1-2N 1M 0 breast cancer after mastectomy, age ≤ 40 years, tumor located in the inner quadrant or central area, T 2 staging, 2-3 lymph node metastases, Ki-67>30% are the high-risk factors affecting clinical prognosis. The nomogram prediction model can screen the populations that can benefit from PMRT, providing reference for clinical decision-making.
ABSTRACT
Objective:To investigate the risk factors for concomitant cardiac autonomic neuropathy in diabetic patients and to develop a Nomogram prediction model.Methods:One hundred and fifty-eight diabetic patients admitted to in Southern Hospital Zengcheng Branch from March 2019 to March 2021 were selected. Patients with normal heart rate variability were the diabetic group, and patients with abnormal heart rate variability were the group with diabetes mellitus complicated by cardiac autonomic neuropathy. Logistic regression analysis was used to analyze the risk factors of cardiac autonomic neuropathy. Nomogram models were developed and model performance was evaluated. Decision curve analysis (DCA) was used to assess the net clinical benefit of the Nomogram model.Results:Comparison of general data showed that fasting blood glucose, tumour necrosis factor-α (TNF-α), glomerular filtration rate (eGER), uric acid, C-reactive protein (CRP), interleukin-6 (IL-6), free fatty acids (FFA), standard deviation of sinus heart beat RR interval (SDNN), and duration of diabetes compared to the diabetic group had statistically significant ( P<0.05); the results of the subject work characteristics (ROC) curve analysis showed that the best cut-off values for fasting glucose, TNF-α, eGFR, uric acid, CRP, IL-6, FFA, SDNN and duration of diabetes were >7.53 mmol/L, >98.45 ng/L, ≤94.79 ml/(min·1.73 m 2), > 87.3 μmol/L, >6.22 μmol/L, >37.84 ng/L, >839.19 μmol/L, ≤ 95.88 ms, >9 years; multi-factorial Logistic regression analysis showed that fasting glucose (>7.53 mmol/L), TNF-α (>98.45 ng/L), CRP (>6.22 μmol/L), IL-6 (>37.84 ng/L), FFA (>839.19 μmol/L), SDNN (≤95.88 ms), and duration of diabetes (>9 years) were risk factors for the development of cardiac autonomic neuropathy in diabetic patients; internal validation showed that the Nomogram model predicted a C-index of 0.706 (95% CI 0.668 - 0.751) for the risk of cardiac autonomic neuropathy. The DCA results showed that the Nomogram model predicted a risk threshold of >0.25 for the development of cardiac autonomic neuropathy and that the Nomogram model provided a net clinical benefit. Conclusions:There are many risk factors for cardiac autonomic neuropathy, and the nomogram model based on risk factors in this study has good predictive power and may provide a reference for clinical screening of high-risk patients and further improvement of treatment planning.