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Abstract Objective: To build a model based on cardiometabolic indicators that allow the identification of overweight adolescents at higher risk of subclinical atherosclerotic disease (SAD). Methods: Cross-sectional study involving 161 adolescents with a body mass index ≥ + 1 z-Score, aged 10 to 19 years. Carotid intima-media complex thickness (IMT) was evaluated using ultrasound to assess subclinical atherosclerotic disease. Cardiometabolic indicators evaluated included nutritional status, central adiposity, blood pressure, lipidic profile, glycemic profile, as well as age and sex. Data was presented using measures of central tendency and dispersion, as well as absolute and relative frequency. The relationship between IMT measurement (outcome variable) and other variables (independent variables) was assessed using Pearson or Spearman correlation, followed by multiple regression modeling with Gamma distribution to analyze predictors of IMT. Statistical analysis was performed using SPSS and R software, considering a significance level of 5 %. Results: It was observed that 23.7 % had Carotid thickening, and the prevalence of abnormal fasting glucose was the lowest. Age and fasting glucose were identified as predictors of IMT increase, with IMT decreasing with age by approximately 1 % per year and increasing with glucose by around 0.24 % per mg/dL. Conclusion: The adolescent at higher risk is younger with higher fasting glycemia levels.
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Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.
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Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.
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Objective To study the factors affecting hospital death in elderly patients with novel coronavirus infec-tion/disease 2019(COVID-19),and to build a risk prediction model.Methods According to the diagnostic criteria of Diagnosis and Treatment Protocol for COVID-19 Infection(Trial 10th Edition).Totally 775 elderly patients(≥60 years old)diagnosed as COVID-19 infection in the emergency department and fever clinic of the First Hospital of Changsha were selected as the research objects.General data and serum biomarkers of patients were collected.After treatment,the patients'data were divided into survival group and hospital death group.Binary Logistic regres-sion was used to screen the independent influencing factors of death,and ROC curve was used to analyze the pre-dictive value of related indicators on hospital death.Results After treatment,712 patients(91.9%)survived and 63 patients(8.3%)died in hospital.Binary Logistic regression analysis showed that:≥90 years old[OR=5.065,95%CI(1.427,17.974)],type 2 diabetes mellitus[OR= 3.757,95%CI(1.649,8.559)],COPD[OR= 5.625,95%CI(2.357,13.421)],monocyte ratio[OR=0.908,95%CI(0.857,0.963)],plasma fibringen[OR=1.376,95%CI(1.053,1.800)]and lactate dehydrogenase[OR=1.005,95%CI(1.001,o1.008)]were independent factors of in-hospital death(P＜0.05).The predictive value of diabetes mellitus+COPD+age+monocyte ratio+plasma fibrinogen+lactate dehydrogenase was proved in hospital death from COVID-19 infected patients:the area under the curve(AUC)was 0.883(95%CI:0.827,0.940,P＜0.001),the critical value≥0.710 suggested the risk of death in hospital,the specificity was 0.851,the sensitivity was 0.857.Conclusions The hospital mortality of the elderly after COVID-19 infection is higher and closely related to type 2 diabetes,COPD,monocyte ratio,plasma fibrinogen and lactate dehydrogenase.
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Objective To analyze risk factors for perioperative blood transfusion in elderly patients undergoing uni-lateral primary total hip arthroplasty and develop a prediction model.Methods The study retrospectively collected 467 elderly patients receiving unilateral primary total hip arthroplasty between January 2013 and October 2021 at Peking Union Medical College Hospital.The 70%of the data were used as the training set and the 30%of the data were used as the testing set.Patients were divided into the transfusion and no-transfusion groups based on the presence or absence of perioperative blood transfusion.Univariate analysis and multivariable logistic regression were conducted to analyze patient demographic characteristics,surgical information,and preoperative laboratory tests for identifying risk factors.Clinical experience was combined to establish a prediction model and draw the nomogram.The receiver operating characteristic(ROC)curve and calibration curve were used to evaluate the model in the tes-ting set.Results A total of 91 patients(19.5%)received perioperative blood transfusion.Multivariable logistic re-gression suggested the history of coronary artery disease,prolonged operation time,and lower preoperative hemoglo-bin were risk factors for perioperative blood transfusion(P＜0.05).The prediction model was constructed based on the results of statistical analysis and clinical experience,including the history of coronary artery disease,operation time,preoperative hemoglobin,age,and American Society of Anesthesiologists(ASA)physical status＞Ⅱ.The area under the receiver operating characteristic curve(AUC)of the model was 0.809.Conclusions The prediction model for perioperative blood transfusion in elderly patients undergoing unilateral total hip arthroplasty had a good performance and could assist in clinical practice.
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Objective To analyze the pathogenic characteristics and drug sensitivity of candidaemia,and construct a short-term mortality risk prediction scoring model.Methods The clinical data of patients with candidaemia admitted to the 909 Hospital of Joint Logistics Support Force from January 2011 to December 2020 were retrospectively analyzed,and the composition of pathogen composition,drug sensitivity test results and incidence of hospitalized patients were analyzed.324 cases of candidaemia were randomly divided into modeling group(190 cases)and validation group(134 cases),and the risk factors were screened by binary logistic regression.According to the odds ratio(OR)score,the 30 day mortality risk prediction scoring model was constructed,and the predictive performance of the model was verified both in modeling and validation groups.Results 356 strains of Candida including 126 strains of C.albicans(35.39%),79 strains of C.tropicalis(22.19%),74 strains of C.parapsilosis(20.79%),48 strains of C.glabrata(13.48%),14 strains of C.guilliermondii(3.93%),8 strains of C.krusei(2.25%),and 7 strains of other Candida(1.97%)were detected in 336 patients with candidemia.The incidence of candidaemia among hospitalized patients increased from 0.20 ‰ in 2011 to 0.48 ‰ in 2020.The resistance rate of candida to amphotericin B was significantly lower than that of fluconazole,voriconazole and itraconazole(P＜0.05).Among the 324 cases included in the model,95 patients died in 30 days after diagnosis,and the mortality rate was 29.32%.The proportion of males,fever,and parenteral nutrition in modeling group was significantly higher than that in validation group(P＜0.05),while the proportion of chronic lung disease and surgical history within one month were lower than those in validation group(P＜0.05).Logistic regression analysis showed that chronic renal failure,mechanical ventilation,severe neutropenia,failure to receive anti-fungal treatment within 72 hours,and APACHE Ⅱ≥20 were risk factors for short-term death of candidaemia,the OR values were 3.179,1.970,2.979,2.080,and 2.399,and the risk scores were 6,4,6,4,and 5,respectively.The area under the curve(AUC)of the risk scoring model for modeling group was 0.792(95%CI 0.721-0.862),and the result of Hosmer-Lemeshow(H-L)test was P=0.305;The AUC of validation group was 0.796(95%CI 0.735-0.898),and the H-L test result was P=0.329.A risk score≤8 indicated a low risk group for short-term mortality,a score of 9-15 indicated a medium risk group,and a score≥16 indicated a high risk group.Conclusions The incidence of candidemia in hospitalized patients is increasing and the mortality is high.The risk prediction score model can effectively predict the short-term prognosis and facilitate the early identification of the prognosis.
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Hepatocellular carcinoma(HCC)is the fifth most common malignant tumor in the world and it is characterized by clinically insidious onset and high mortality rate.As a preferred treatment method for patients with moderate and advanced HCC,transcatheter arterial chemoembolization(TACE)has many advantages such as reducing tumor load and relieving patient pain,but the selection of the patients who may get benefits from TACE treatment remains a challenging issue.Therefore,it is essential to predict the efficacy of TACE.At present,various methods including clinical laboratory testing,imaging method,genetic-molecular method,etc.have been used to predict the therapeutic efficacy of TACE.Imaging prediction has the advantages of high visualization and strong interpretability,and MRI functional imaging sequence can better demonstrate the details of the lesion.Radiomics,as an emerging imaging field,can complement or even replace tumor biopsy by quantifying the tumor phenotypic variation.This paper aims to make a review concerning the correlation between the imaging radiomics and the prediction of TACE efficacy in patients with HCC,and to discuss whether MRI imaging radiomics can be used as a valid and reproducible method for predicting TACE efficacy for HCC.(J Intervent Radiol,2024,32:90-94)
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Objective To assess the value of CT image texture features in predicting the occurrence of hemorrhagic transformation(HT)in ischemic stroke,and to compare it with the traditional clinical prediction scores.Methods A total of 73 patients with acute anterior circulation ischemic stroke were enrolled in this study.All patients received reperfusion treatment.The region of interesting(ROI)of the infarction area was outlined according to the diffusion restricted area displayed on the follow-up ADC images,which were matched to the corresponding ischemic region on computed tomographic angiography(CTA)and on plain CT scan(non-contrast CT,NCCT).Five patients with HT and 5 patients with non-HT were randomly selected and used as the test set,and the remaining patients were assigned to the train set.The 6 texture features that had the most predictive value were separately selected from the CTA sets and NCCT train set,then the training of classifiers was earried out by using the 5-fold cross-validation method.Finally,the test set was evaluated according to the trained classifier.Besides,the determination of four clinical scores(HAT,SEDAN,HIAT2,THRIVE-c)was performed for all patients in the train set.Results The trained classifiers model performed well in not only CTA but also NCCT.In the CTA prediction model,linear SVM was chosen as the final classifier with 0.816 validation accuracy and 0.890 AUC value;and with 0.800 test accuracy,0.600 sensitivity,and 1.000 specificity in external test set Logistic regression(LR)was the best-performing classifier in NCCT.The predicted performance of HT was slightly worse than that of CTA,which had 0.697 validation accuracy and 0.763 AUC value.The test set of NCCT achieved 0.700 accuracy with 0.600 sensitivity and 0.800 specificity.Compared to the texture analysis models,all the four clinical scores showed a modest prediction efficiency in HT and AUC values,which were no more than 0.700.Conclusion Texture analysis of cerebral ischemic area based on CT images(CTA and NCCT)has the ability to predict HT after reperfusion treatment in AIS patients,and it is superior to traditional clinical scoring methods.(J Intervent Radiol,2024,33:230-235)
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Objective To construct and validate a predictive model based on preoperative inflammatory biomarkers,and to evaluate its ability in predicting the prognosis of patients with unresectable hepatocellular carcinoma(HCC)after receiving transcatheter arterial chemoembolization(TACE).Methods A total of 544 patients with HCC,who received TACE as the initial treatment at six medical institutions between January 2007 and December 2020,were retrospectively collected.The patients were divided into training cohort(n=376)and validation cohort(n=168).LASSO algorithm and Cox regression analysis were used to screen out the independent influencing factors and to make modelling.The model was validated based on the discrimination,calibration and clinical applicability,and the Kaplan-Meier risk stratification curves were plotted to determine the prognostic differences between groups.The likelihood ratio chi-square value,R2 value,akaike information criterion(AIC)value,C-index and AUROC value of the model were calculated to determine its accuracy and efficiency.Results The training cohort and validation cohort had 376 participants and 168 participants respectively.Multivariate analysis indicated that BCLC,tumor size,number of tumor lesions,neutrophil and prognostic nutritional index(PNI)were the independent influencing factors for postoperative overall survival(OS),with all P being＜0.05;the BCLC grade,tumor size,number of tumor lesions,NLR,PNI and PS score were the independent influencing factors for progression-free survival(PFS),with all P being＜0.05.The C-indexes of the OS and PFS models were 0.735(95％ CI=0.708-0.762)and 0.736(95％ CI=0.711-0.761)respectively,and the external validation was 0.721(95％ CI=0.680-0.762)and 0.693(95％ CI=0.656-0.730)respectively.Ideal discrimination ability of the nomogram was exhibited in time-dependent C-index,time-dependent ROC,and time-dependent AUC.The calibration curves significantly coincided with the ideal standard lines,indicating that the model had high stability and low over-fitting level.Decision curve analysis revealed that there was a wider range of threshold probabilities and it could augment net benefits.The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied dramatically between risk categories(P＜0.000 1).The Kaplan-Meier curves for risk stratification indicated that the prognosis of patients varied dramatically among different risk groups(P＜0.000 1).The likelihood ratio chi-square value,R2 value,AIC value,C-index and AUROC value of the model were better than those of other models commonly used in clinical practice.Conclusion The newly-developed prognostic nomogram based on preoperative inflammatory indicators has excellent accuracy as well as excellent prediction effect in predicting the prognosis of patients with unresectable HCC after receiving TACE,therefore,it can be used as an effective tool for guiding individualized treatment and for predicting prognosis.(J Intervent Radiol,2024,33:245-258)
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Objective:To establish a 14-day prognosis model for emergency patients with acute ischemic cerebral stroke and evaluate its predictive efficacy.Methods:A prospective cohort study was conducted. Patients with acute ischemic stroke admitted to the emergency department of Beijing Bo’ai Hospital within 72 hours of onset from October 2018 to December 2020 were enrolled. Univariate and multivariate logistic regression analysis were used to screen the risk factors of poor prognosis. The ROC curve was drawn to determine the cut-off value of continuous variables and discretise data with reference to clinical practice. The corresponding scores were set up according to the β regression coefficient of each variable, and the clinical scale prediction model of short-term prognosis of acute cerebral infarction was established. Patients with ischemic stroke in the hospital from January to December 2021 were selected as the internal validation, to verify the constructed predictive model.Results:A total of 321 patients were included in the study, including 223 in the training set and 98 in the internal validation set. Multivariate logistic regression analysis showed that age, hypersensitive C-reactive protein, prealbumin (PA), infarct volume, Frailty Screening Questionnaire (FSQ) and National Institute of Health Stroke Scale (NIHSS) were independent risk factors for poor short-term prognosis of acute cerebral infarction. The total score of the clinical prediction scoring system for short-term prognosis of acute cerebral infarction in the emergency department was 15 points, including age ≥74 years (1 point), PA ≤373 mg/L (2 points), large artery atherosclerosis (1 point), cardiogenic embolism (2 points), infarct volume ≥ 2.18 cm 3 (2 points), FSQ ≥3 points (1 point), NIHSS ≥4 points (6 points); The area under the ROC curve (AUC) of the scoring system for predicting short-term poor prognosis of acute cerebral infarction was 0.927 (95% CI: 0.894-0.960). The optimal cut-off value was ≥5 points, and the sensitivity and specificity were 0.770 and 0.976, respectively. In the internal validation set, the scoring system had similar predictive value for poor outcomes (AUC=0.892, 95% CI:0.827-0.957). Conclusion:The scoring system for short-term prognosis prediction of acute ischemic cerebral infarction has good diagnostic efficacy, and could guide clinicians to judge the prognosis of emergency patients in the early stage.
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Objective:To observe the expression level of bone morphogenetic protein 9 (bone morphogenetic protein 9,BMP9) in patients with sepsis-associated acute respiratory distress syndrome (acute respiratory distress syndrome,ARDS), and to explore the role of BMP9 in early recognition and prognosis prediction of sepsis-associated ARDS.Methods:From May 2022 to May 2023, total of 56 patients with sepsis-associated ARDS in Shanxi Bethune Hospital were selected as the ARDS group, 49 patients with cardiogenic pulmonary edema as the case control group, and 46 adults who underwent physical examination in the physical examination center of our hospital as the healthy control group.The patients in the ARDS group were followed up for 28 days and divided into survival group ( n = 26) and death group ( n = 30). The expression level of serum BMP9 and its correlation with clinical indicators in each group were analyzed and compared. The risk factors of sepsis-associated ARDS were analyzed by Logistic regression, and the diagnostic efficacy and prognostic value of related indicators were analyzed. Results:The serum level of BMP9 in sepsis-associated ARDS group [1401.14 (856.59,1982.86) ]pg/mL was significantly higher than that in case control group (438.26±128.52) pg/mL and healthy control group (398.96±96.55)pg/mL, the differences were statistically significant ( P<0.01). In addition, BMP9 expression significantly correlated with procalcitonin, lymphocyte count and SOFA score ( P < 0.05, P < 0.01, respectively). Multivariate Logistic regression analysis showed that BMP9 was a high risk factor for the development of sepsis-associated ARDS ( P<0.01). The area under the ROC curve (area under the ROC curve,AUC) of BMP9 to predict the occurrence of sepsis-associated ARDS was 0.930. The specificity was 100.0% and the sensitivity was 80.4%, which was significantly higher than the specificity (89.8%) and sensitivity (67.9%) of the oxygenation index. Follow-up and comparison of BMP9 levels in patients with different prognosis of sepsis-associated ARDS showed that the expression level of BMP9 in the death group was higher than that in the survival group, and the difference was statistically significant ( P < 0.05). The ROC curve of BMP9 in predicting the prognosis of patients with sepsis-associated ARDS. The area under the ROC curve was 0.699, the sensitivity was 43.3%, and the specificity was 100.0%. Conclusions:The expression of BMP9 in sepsis-associated ARDS patients significantly increased, and its high expression was significantly correlated with inflammatory markers such as procalcitonin, lymphocyte count and SOFA score. BMP9 is an independent risk factor in patients with sepsis-associated ARDS, and it is promising as a new biomarker for early identification of sepsis-associated ARDS. However, it do not show a good predictive effect on the prognosis of the disease.
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Objective To explore the influencing factors of spontaneous bacterial peritonitis in patients with primary liver cancer complicated with ascites and establish a prediction model.Methods A total of 292 patients with primary liver cancer complicated with ascites who were hospitalized for the first time in the Third People's Hospital of Kunming from January 2012 to December 2021 were selected as the study objects.General data,etiological indicators,serological indicators and complications of these subjects were collected.Then they were divided into the infection group(n = 114)and the control group(n = 178)according to whether spontaneous bacterial peritonitis(SBP)was complicated.Univariate and multivariate logistic regression were used to analyze the influencing factors of SBP in patients with primary liver cancer complicated with ascites.Finally,ROC curves were constructed to more intuitively represent the individual and combined predictive value of these targets.Results Am-ong 292 hepatocellular carcinoma patients with ascites,there were 235 males(80.48%)and 57 females(19.52%),among which 114 patients with SBP were in the infection group and 178 patients without SBP were in the control group.The results of univariate analysis showed that compared with the control group,the levels of WBC,neutrophils,prothrombin time,total bilirubin,albumin,CD3,CD4,CD8,CD4/CD8 ratio,CD19 procalcitonin,serum amyloid A,hypersensitive C-reactive protein,sodium,chlorine,alcohol consumption,shock,hepatorenal syndrome,hepatic encephalopathy,massive ascites in the infection group had statistically significant difference(P<0.05).Multi-factor analysis revealed that CD8,CD4/CD8 ratio were protective factors for SBP in patients with liver cancer ascites,CD19,procalcitonin,serum amyloid A,and massive ascites were risk factors for SBP in patients with ascites.ROC curve construction showed that serum amyloid A,CD8,CD4/CD8 ratio,CD19,procalcitonin,massive ascites area under curve(AUC)of massive ascites were 0.724,0.637,0.653,0.820,0.705,0.686,respectively.Conclusion CD8,CD4/CD8 ratio,CD19,procalcitonin,serum amyloid A,and a large volume of ascites are significant factors contributing to the development of spontaneous bacterial peritonitis(SBP)in patients with hepatocellular carcinoma ascites.The predictive value of combination is substantial,demonstrating a level of accuracy in forecasting SBP occurrence
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Objective To construct and validate a clinical prediction model for delayed extubation undergoing non-emergency major surgery based on the random forest algorithm.Methods Clinical data of 7 528 patients undergoing non-emergency major surgery under general anesthesia from January 2018 to De-cember 2022 were retrospectively collected.The patients were divided into two groups according to whether extubation was performed within 2 hours after surgery:non-delayed extubation group(≤2 hours)and de-layed extubation group(＞2 hours).All the patients were randomly divided into a training set and a valida-tion set in a ratio of 7 ∶ 3.The predictive factors for delayed extubation after surgery were screened through LASSO regression and Logistic regression.The random forest model was established and verified by random forest algorithm.Results There were 123 patients(1.6%)experienced delayed extubation after surgery.ASA physical status,department,intraoperative use of flurbiprofen ester,dexmedetomidine,glucocorticoid,hypocalcemia,severe anemia,intraoperative blood transfusion,and airway spasm were identified as inde-pendent predictive factors for delayed extubation.The area under curve(AUC)value of the random forest prediction model in the validation set was0.751(95%CI0.742-0.778),and the sensitivity was98.1%,and the specificity was 41.9%.Conclusion The predictive model of delayed extubation undergoing non-e-mergency major surgery based on random forest algorithm has a good predictive value,which may be helpful to prevent delayed extubation undergoing non-emergency major surgery.
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Objective To explore the related risk factors of postoperative venous thromboembolism(VTE)in patients with gastric cancer,establish a prediction model and verify the predictive value of the model.Methods 160 gastric cancer patients who underwent radical surgery at the First Affiliated Hospital of Hainan Medical College from January 2019 to June 2021 were included as the modeling group,167 cases as validation group.Their clinicopathological data were collected.All modeling group patients were divided into VTE group and N-VTE group according to the occurrence of VTE within 6 months after operation.The clinicopathological factors of the two groups were analyzed by univariate analysis.Then,the statistically significant indexes in the univariate analysis were substituted into the multivariate logistic regression model for multivariate analysis to obtain the independent risk factors affecting the postoperative VTE of patients with gastric cancer.The independent risk factors obtained based on the results of multivariate analysis were combined p Value,assign scores to independent risk factors according to the principle of nomogram,construct the nomogram model,draw the nomogram with R software,internal and external validation of nomogram model with Bootstrap method and calibration curve,calculate the discrimination evaluation Index C index,and evaluate the calibration ability of the prediction model through goodness of fit(H-L).Results 160 modeling group patients with gastric cancer underwent radical gastrectomy.According to the occurrence of VTE within 6 months after operation,they were divided into VTE group(23 cases)(14.38％)and N-VTE group(137 cases)(85.62％).Multivariate analysis showed that the age of 60 years old,the diameter of the lesion was more than 5 cm,the stage of diabetes,the TNM/T stage was 3-4,and the lymph node metastasis was the independent risk factors affecting the postoperative VTE of patients with gastric cancer(P＜0.05).Construct nomogram:P=1/(1+e-x),X=1.885 × Age(≥ 60 years=1,＜60 years=0)+2.051 × Diabetes mellitus(=1,no=0)+2.646 × Lesion diameter(≥ 5 cm=1,＜5 cm=0)+2.952 × TNM/T stage(stage 1-2=0,stage 3-4=1)+0.694 × Lymph node metastasis(yes=1,no=0)-0.436.The C index of nomogram model was 847(95％CI:0.784-0.932)and 0.832(95％CI:0.772-0.910).H-L test showed that the predicted value of postoperative VTE in patients with gastric cancer was in good agreement with the actual value(P＞0.05).Conclusion A nomogram model for predicting the risk of postoperative VTE in patients with gastric cancer was established.It was verified that the model can accurately predict the risk of postoperative VTE in patients with gastric cancer.
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Objective:To analyze the risk factors for central lymph node metastasis (CLNM) in patients with papillary thyroid cancer (PTC) aged 55 years and above, and to construct a predictive model with columnar graph.Methods:This retrospective study included 406 PTC patients aged 55 and above, treated at the First Affiliated Hospital of Zhengzhou University from Nov. 2019 to Feb. 2022. Data on demographic characteristics, disease features, and laboratory test results were collected. Univariate and multivariate logistic regression analyses were used to identify risk factors for CLNM and develop a clinical prediction model and nomogram.Results:The study involved 406 patients, divided into a modeling group (285 patients) and a validation group (121 patients). The predictive model identified independent risk factors for CLNM. In the modeling group, the model demonstrated a ROC AUC of 0.769, with 82.6% sensitivity, 63.0% specificity, and 67.7% accuracy. The validation group showed 66.7% sensitivity, 74.5% specificity, and 72.7% accuracy, with an AUC of 0.760. Hosmer-Lemeshow tests indicated good fit in both groups. Decision curve analysis confirmed the model's clinical decision-making value, showing better performance than traditional strategies and good generalizability and reliability.Conclusions:Sex, maximum tumor diameter, bilateral involvement of thyroid lobes, clinically evident cervical lymph nodes, and local invasion are independent predictive factors for CLNM in patients over 55 with papillary thyroid carcinoma (PTC). A clinical risk stratification nomogram model based on these risk factors demonstrates good predictive performance.
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Objective The study aimed to construct and validate a predictive model for pulmonary nodules(PN)nature based on clinicopa-thological features,imaging,and serum biomarkers,so as to provide scientificdecision-making for early diagnosis and treatment of lung cancer.Methods A retrospective was performed on 816 PN patients with definited pathological diagnosis who received surgical resection analysisor lung biopsy in the Department of Thoracic Surgery and Oncology of Shenzhen Traditional Chinese Medicine Hospital from January 2019 to February 2023.Among them,113 cases that did not meet the inclusion criteria were excluded,and the remaining 703 cases were included in the study.The study based on the clinicopathologic features(age,gender,smoking history,smoking cessation history and family history of cancer),chest imaging(maximum diameter of nodule,location of lesion,clear border,Lobulation,spiculation,vascular convergence sign,vacuole,calcification,air bronchial sign,emphysema,nodule type and pleural indentation,nodule number)and serum carcinoembryonic antigen(CEA),cytokeratin 19 fragment(CYFRA21-1),squamous cell carcinoma antigen(SCCA)in patients with PN.These cases were randomly divided into a modeling group(n=552,237 benign,315 malignant)and a validation group(n=151,85 benign,66 malignant).First,univariate analysis was performed to screen for statistically significant predictors of nodules nature.Then,multivariate regression analysis was performed to screen for independent predictors of nodules nature.Finally,the prediction model of PN nature was constructed by logistic regression analysis.Subsequently,the validation group data were entered into the proposed model and Mayo clinic(Mayo)model,veterans affairs(VA)model,Brock University(Brock)model,Peking University(PKU)model and Guangzhou Medical University(GZMU)model,respectively.PN malignancy probability was calculated.The receiver operating characteristic(ROC)curves were plotted.The diagnostic efficiency of each model was compared according to the area under the curve(AUC).Results There were statistically significant variables including age,family history of cancer,maximum nodule diameter,nodule type,upper lobe of lung,calcification,vascular convergence sign,lobulation,clear border,spiculation,and serum CEA,SCCA,CYFRA21-1 using univariate analysis.Multiple regression analysis showed that age,CEA,clear border,CYFRA21-1,SCCA,upper lobe of lung,maximum nodule diameter,family history of cancer,spiculation and nodule type were independent predictors of PN nature.The prediction model equation constructed in this study is as follows:f(x)= ex/(1+ex),X=(-6.318 8+0.020 8×Age+0.527 4×CEA-0.928 4×clear border+0.294 6×Cyfra21-1+0.294×maximum nodule diameter+1.220 1×family history of cancer +0.573 2×upper lobe of lung +0.064 8×SCCA +1.461 5×Spiculation +1.497 6×nodule type).The AUC(0.799 vs 0.659,0.650)of the proposed model was significantly higher compared with Mayo model and VA model,and there were statistically significant differences(Z=3.029,2.638,P=0.003,0.008).However,compared with Brock model,PKU model and GZMU model,the differences of AUC(0.799 vs 0.762,0.773,0.769)were not statistically significant(Z=1.063,0.686,0.757,P=0.288,0.493,0.449).Conclusion The prediction model for PN nature established in this study is accurate and reliable,which can help clinics with early diagnosis and early intervention,and this prediction model deserves to be popularized.
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Objective To explore the risk factors of gestational diabetes mellitus(GDM)in the first trimester(12～13+6 weeks)of pregnancy,build a prediction model and verify it.Methods 433 singleton pregnant women delivered in the First People's Hospital of Shanghai from January 2020 to December 2020 were selected.They were divided into GDM group(n=188)pregnant women and non-GDM group(n=245)pregnant women according to a 75g glucose tolerance test results at 24～28 weeks of gestation.The electrochemiluminescence method measured serum biochemical indexes in early pregnancy,and glycosylated hemoglobinAlc was measured by ion exchange high-performance liquid chromatography.Using logistic regression analysis to screen the risk factors of GDM and construct a predictive model,draw the subject's work characteristic curve to analyze the model's predictive value.Ninety-five pregnant women who underwent prenatal examinations at Shanghai First People's Hospital from January 2021 to June 2021 were selected to validate the model's effectiveness.Results Compared with the non-GDM group,the level of body mass index(BMI)(23.41±11.17 kg/m2 vs 21.18±2.88 kg/m2),gamma-glutamyl transpeptidase(γ-GGT)(16.61±10.62 U/L vs 14.00±8.35 U/L),triacylglycerol(TG)(1.90±0.58 mmol/L vs 1.57±0.55 mmol/L),glycosylated hemoglobinAlc(HbAlc)(5.25%±0.47%vs 5.07±0.34%),fasting blood glucose(FBG)(4.68±0.47 mmol/L vs 4.36±0.36 mmol/L),LDL/HDL(1.53±0.49 vs 1.41±0.50),TG/HDL(2.93±0.59 vs 2.71±0.58),and TC/HDL(1.19±0.49 vs 0.95±0.45)in GDM group was increased,and the level of highdensity lipoprotein-cholesterol(HDL)(1.69±0.39 mmol/L vs 1.77±0.41 mmol/L)was decreased,the differences were statistically significant(t=2.613,2.818,5.874,4.582,17.701,2.458,3.815,5.310,-2.187,all P＜0.05).Logistic regression analysis showed that pre-pregnancy BMI,FBG,HbAlc,TG,and TC/HDL were all independent risk factors for predicting gestational diabetes(Waldχ2=4.48～35.549,all P＜0.05).The prediction model constructed based on the selected risk factors was as follows:Logit(P)=-20.562+0.085(BMI)+1.921(FBG)+1.57(HbAlc)+2.248(TG)-2.302(TC/HDL).The model predicts that the area under the curve of GDM was 0.800(95%CI:0.757～0.842),the optimal cutoff value was 0.352,and the sensitivity and specificity were 80.00%and 66.00%,respectively.Ninety-five pregnant women validated the model,and its sensitivity,specificity,and accuracy were 84.50%,91.00%,and 85.30%,respectively.Conclusion The prediction model constructed by BMI combined with FBG,HbAlc,TG and TC/HDL in the first trimester(12～13+6 weeks)of pregnancy has a high predictive value for GDM.
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Objective:To evaluate the efficacy of cerebral-placental-uterine ratio(CPUR)in predicting late-on-set fetal growth restriction(FGR).Methods:From May 2020 to May 2021,1255 women with singleton pregnancy who underwent prenatal examinations at the University of Hong Kong Shenzhen Hospital were selected for fetal growth and Doppler measurements at 35-37 +6 weeks of gestation.Pregnant women with birth weight of newbo-rns＜the 10th percentile were the FGR group.The pulsatility index(PI)of uterine artery(UtA),umbilical artery(UA)and fetal middle cerebral artery(MCA)were analyzed separately and in combination.ROC curve was used to analyze the cerebral-placental-uterine ratio(CPUR),cerebral-placental ratio(CPR),cerebral-uterine ratio(C-UtA)for predicting late-onset FGR;and to evaluate the sensitivity,positive and negative predictive value and of CPUR in the prediction of late-onset FGR.Results:The area under the curve(AUC)of CPUR,CPR,C-UtA and mean UtA-PI for FGR grope were 0.88,0.86,0.84 and 0.72.Under certain cut-off values and 87% specificity,the specificity of CPUR,CPR,C-UtA and mean UtA-Pifor predicting FGR group was 43.2%,46.6%,39.8% and 23.9%,respectively.The positive predictive values of CPUR,CPR,C-UtA and mean UtA-PI,UA-PI for predicting FGR group were 90.5%,71.9%,83.3%,63.6%and 5.2%,respectively.Conclusions:CPUR is more effective in predicting late onset FGR than CPR,C-UtA and mean UtA-PI.It can effectively increase the detection rate of fetal growth restrictionand reduce the FGR risk.
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Objective:To investigate the predictive value of pregnancy-associated plasma protein A(PAPP-A),fasting blood glucose(FPG),body mass index(BMI)and age in gestational diabetes mellitus(GDM)during the first trimester.Methods:A retrospective analysis was performed on 792 pregnant women who underwent pre-natal examination and delivered in Sichuan Provincial Maternal and Child Health Care Hospital from December 2021 to June 2022.They were divided into GDM group(232 cases)and control group(560 cases)according to whether they had GDM.The clinical data,serum PAPP-A median multiple(PAPP-A MoM)in early pregnancy and FPG levels were compared between the two groups.The indicators with statistical significance in univariate analy-sis were included in multivariate Logistic regression analysis to analyze the related factors affecting the occurrence of GDM.The receiver operating curve(ROC)and area under the curve(AUC)of different indexes were plotted to compare the efficacy of GDM prediction.Results:①The age,pre pregnancy BMI,early pregnancy FPG and the proportion of assisted reproductive technology in GDM group were higher than those in control group,and the differences were statistically significant(P＜0.05).The early pregnancy PAPP-A MoM level in GDM group was lower than that in control group,and the difference was statistically significant(P＜0.05).②Multivariate Logistic regression analysis showed that older age,lager pre-pregnancy BMI and lager FPG in the first trimester were in-dependent risk factors for GDM occurrence(OR＞1,P＜0.05),while an increase of PAPP-A MoM in the first tri-mester was a protective factor(OR＜1,P＜0.05).③ROC showed that the combination of PAPP-A MoM in early pregnancy,FPG in early pregnancy,BMI in pre-pregnancy and age had the highest AUC(0.752)when predicting GDM,with a sensitivity of 55.6％and a specificity of 84.3％.Conclusions:The combined screening of serologi-cal(PAPP-A +FPG)and clinical data(pre-pregnancy BMI +age)in early pregnancy has a high clinical application prospect and can be popularized.
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Objective:To compare the accuracy of 11 ultrasound parameters prediction formulas for fetal body mass,and to explore the effect of gestational weight gain(GWG)on the accuracy of ultrasound prediction of fetal body mass.Methods:A total of 502 single and full-term postpartum women who gave birth from August 2020 to December 2020 at Jinshan Hospital,Fudan University were collected.The gestational weight gain,fetal ultrasound measurement indicators within 7 days before delivery,and newborn birth weight were calculated and analyzed.The accuracy of multiple ultrasound prediction formulas were calculated and analyzed.According to the criteria for weight gain during pregnancy,the reasonable weight gain during pregnancy was 12.5-18.0 kg for singleton pregnancies with pre-pregnancy(body mass index)BMI<18.5 kg/m2,11.5-16.0 kg for those with BMI 18.5-24.9 kg/m2,7.0-11.5 kg for those with BMI 25.0-29.9 kg/m2,and 5.0-9.0 kg for those with BMI≥30.0 kg/m2.The cases were divided into the group with insufficient GWG(125 cases),the normal group(202 cases),and the group with too much GWG(175 cases)to analyze the effect of different GWG on the accuracy of ultrasound pre-diction of fetal body mass.Results:Among the 11 ultrasound parameter formulas for predicting fetal body mass,the HadlockⅢformula predicted fetal body mass with an absolute error of 186.64±149.28 g and a relative error of(5.52±4.18)%,which was the smallest error among 11 prediction formulas,with a statistically significant difference(P<0.05).The absolute and relative error compliance rates were 72.31%,86.25%,respectively,both of which were the highest,and the difference was statistically significant(P<0.05).When the HadlockⅢformula was used to predict birth weight in the insufficient GWG group,the normal group,and the group with too much GWG,the absolute errors were 190.23±136.69 g,148.12±99.39 g,228.54±189.57 g,and the relative errors were(5.95±4.25)%,(4.40±2.78)%,(6.49±5.09)%,respectively,and the differences were statistically significant(P<0.05).Conclusions:The accuracy of Hadlock Ⅲ formula in predicting fetal body mass is better than that of other formulas,but its accuracy can be affected by GWG,and it is necessary to consider multiple as-pects when estimating fetal body mass in clinical practice.