Fasting glucose: a cardiometabolic indicator for subclinical atherosclerosis on excess weight adolescents

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.

Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

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.

A prediction model for coronary artery abnormalities in children with Kawasaki disease older than 5 years

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.

Application of alpha-fetoprotein and IL-6 in prognostic prediction of patients with hepatitis B related liver failure / 公共卫生与预防医学

Objective To analyze the application value of alpha-fetoprotein (AFP) and interleukin-6 (IL-6) in prognosis prediction of hepatitis B virus (HBV) associated liver failure. Methods A total of 135 patients with HBV-related liver failure who underwent treatment at the Infection Department of the Second People's Hospital of Yibin City from July 2020 to June 2022 were selected as the study subjects (observation group). Additionally, 100 patients who underwent physical examination in the hospital during the same period with normal indicators were selected as the control group. Serum levels of AFP and IL-6 were compared between the two groups. Factors influencing the prognosis of HBV-related liver failure were analyzed. Multiple logistic regression was used to analyze the risk factors affecting the prognosis of HBV-related liver failure patients. Results The levels of serum AFP and IL-6 in the control group were lower than those in the control group, and the difference was statistically significant (P10.0 pg/mL were risk factors affecting the prognosis of HBV-related liver failure. Conclusion Serum AFP and IL-6 can predict the prognosis of patients with HBV-related liver failure, which is worthy of clinical study.

Predictive value of a new radiotherapy plan evaluation tool for bladder toxicity in cervical cancer / 中国辐射卫生

Objective To evaluate the predictive value of a dose-surface histogram (DSH) for radiation cystitis (RC) in patients with cervical cancer. Methods We retrospectively included 190 patients with cervical cancer who underwent image-guided radiotherapy (IGRT) from the HIS system of The First Affiliated Hospital of Hebei North University from May 2013 to May 2023. The patients were divided into test group (n = 100) and control group (n = 90). The dose distribution in the bladder was evaluated by using a DSH for the test group and using a dose-volume histogram (DVH) for the control group. Receiver operating characteristic curves were used to evaluate the predictive value of DSH for RC in comparison with DVH. Results There were no significant differences in baseline data and RC incidence between the two groups (all P＞0.05). All evaluation indicators were significantly different between DSH and DVH (all P＜0.05). The predictive value of S45 and V45 for the incidence of grade-I, -II, and -III RC was low (all P＜0.05). The predictive value of S50 and V50 for the incidence of grade-I, -II, and -III RC was moderate (all P＜0.05). S55−S57 and V55−V57 showed high value for predicting the incidence of grade-I, -II, and -III RC (all P＜0.05). Conclusion DSH shows basically the same predictive value for the incidence of RC caused by IGRT in cervical cancer as DVH, which is expected to become a new tool for evaluating radiotherapy plans.

Changes of Immunological Indexes and Blood Routine in Treatment of Aplastic Anemia by Yiqi Yangxue Prescription / 中国实验方剂学杂志

ObjectiveTo investigate the immunological characteristics of the patients with aplastic anemia （AA） and elevated hemogram parameters treated with Yiqi Yangxue prescription combined with Western medicine and the predictive effects of immunological indexes on elevated hemogram parameters， thus providing a reference for the prediction of the treatment efficacy and the adjustment of the treatment regimen. MethodA retrospective study was conducted， involving 77 AA patients treated with Yiqi Yangxue prescription combined with Western medicine for 6 months in 19 medical institutions including Xiyuan Hospital， China Academy of Chinese Medical Sciences from September 2018 to March 2021. The patients were assigned into two groups according to the elevations in hemogram parameters ［including hemoglobin （HGB）， white blood cell count （WBC）， platelet （PLT）， and absolute neutrophil count （ANC）］ after 6 months of treatment. One group had the elevation <50%， and the other group had the elevation ≥50% compared with the baseline. The clinical and immunological characteristics were compared between the two groups. Result① Compared with the group with HGB elevation<50%， the group with HGB elevation≥50% showed elevated level of CD3+ human leukocyte antigen-DR （HLA-DR）+ and increased proportion of patients with T-helper cell type 2 （Th2）<5%， CD8+≥50%， and CD3+HLA-DR+≥9% before treatment （P<0.05， P<0.01）. The multivariate Logistic regression analysis showed that CD8+≥50% before treatment was the independent influencing factor for HGB elevation ≥50% ［odds ratio （OR）=12.000， 95% confidence interval （CI） 2.218， 64.928， P<0.01］. ② Compared with the group with WBC elevation<50%， the group with WBC elevation≥50% showed increased proportion of patients with CD3+HLA-DR+<6% and T-box transcription factor （T-bet）≥200% before treatment （P<0.05）. The multivariate Logistic regression analysis showed that CD3+HLA-DR+<6% （OR=2.998， 95%CI 1.036， 8.680， P<0.05） and T-bet≥200% （OR=3.634， 95%CI 1.076， 12.273， P<0.05） before treatment were independent influencing factors for WBC elevation≥50%. ③ Compared with the group with PLT elevation<50%， the group with PLT elevation≥50% presented lowered Th1 and CD3+HLA-DR+ levels and increased proportion of patients with Th1<12%， CD4+≥6%， and CD3+HLA-DR+<5% before treatment （P<0.05， P<0.01）. The multivariate Logistic regression analysis showed that CD3+HLA-DR+<5% before treatment was the independent influencing factor for PLT elevation≥50% （OR=16.190， 95%CI of 3.430 to 76.434， P<0.01）. ④ Compared with the group with ANC elevation<50%， the group with ANC elevation≥50% showed no significant changes in the hemogram parameters before treatment. ConclusionAs for the AA patients with rapid elevation in HGB， Yiqi Yangxue prescription combined with Western medicine demonstrate significant effects in the patients with Th2<5% and CD3+HLA-DR+≥9%， especially those with CD8+≥50%. As for the AA patients with rapid elevation in WBC， the therapy was particularly effective in the patients with CD3+HLA-DR+<6% and T-bet≥200%. As for the AA patients with rapid growth in PLT， the therapy was particularly effective in the patients with Th1<12% and CD4+≥6%， especially those with CD3+HLA-DR+<5%.

Randomized Controlled Trial and Correlation Analysis of Gandouling Tablet in Treatment of Abnormal Lipid Metabolism of Wilson's Disease with Turbid Phlegm and Blood Stasis / 中国实验方剂学杂志

ObjectiveTo investigate the clinical efficacy of Gandouling tablet （GDL） on abnormal lipid metabolism in Wilson's disease （WD） and the correlation between the prediction model of hepatic steatosis and the related indexes of lipid metabolism in WD. MethodA total of 86 patients with abnormal lipid metabolism in WD were selected. The 24-hour urine copper， alanine aminotransferase （ALT）， aspartate aminotransferase （AST）， serum triglyceride （TG）， total cholesterol （TC）， apolipoprotein B （ApoB）， low density lipoprotein cholesterol （LDL-C）， bile acid （BA）， γ-glutamyl transferase （GGT）， prediction model of hepatic steatosis ［hepatic steatosis index （HSI） and Zhejiang University index （ZJU index）］， ultrasonic attenuation coefficient imaging （ATT）， and traditional Chinese medicine （TCM） syndrome score were statistically analyzed before treatment. Pearson correlation test was used to analyze the correlation between TG， TC， LDL-C， ApoB， ALT， AST， ALT/AST， BA， GGT， TCM syndrome score， ATT， and HIS and ZJU. The patients were divided into an observation group and a control group by random number table method， with 43 cases in each group. The observation group was treated with GDL combined with sodium dimercaptopropane sulfonate （DMPS）， while the control group was only treated with DMPS as a control. After six courses of treatment， 24-hour urine copper， TC， TG， LDL-C， ApoB， HSI， ZJU， ATT， TCM syndrome score， and clinical efficiency before and after treatment were observed and compared between the two groups. The correlation between HSI and ZJU and serum TC， TG， LDL-C， ApoB， ALT， AST， ALT/AST， BA， GGT， TCM syndrome scores， and ATT was analyzed. ResultPearson correlation analysis showed that serum TC （r = 0.811）， TG （r = 0.826）， LDL-C （r = 0.802）， ApoB （r = 0.820）， ALT （r = 0.497）， ALT/AST （r = 0.826）， TCM syndrome score （r = 0.716）， and ATT （r = 0.736） were positively correlated with HSI （P<0.01）， while AST， BA， and GGT had no significant correlation with HSI. TC （r = 0.718）， TG （r = 0.765）， LDL-C （r = 0.667）， ApoB （r = 0.699）， ALT/AST （r = 0.403）， TCM syndrome score （r = 0.666）， and ATT （r = 0.684） were positively correlated with ZJU （P<0.01）. ALT， AST， BA， and GGT had no significant correlation with ZJU. The total effective rate of the observation group was 86.05 （37/43）， and that of the control group was 72.09% （31/43）. The total effective rate of the observation group was higher than that of the control group （Z = -2.301， P<0.05）. After treatment， the 24-hour urine copper of the two groups increased significantly. The levels of TC， TG， LDL-C， and ApoB were significantly decreased， and the HSI， ZJU， and ATT were significantly decreased （P<0.01）. Compared with those in the control group after treatment， the above indexes improved better in the observation group （P<0.05， P<0.01）. ConclusionGDL can effectively improve the level of copper and lipid metabolism in patients with WD， with high clinical safety and good clinical application value. The prediction model of hepatic steatosis can effectively reflect the degree of abnormal lipid metabolism in WD.

Establishment and verification of a prognostic nomogram for survival of tongue squa-mous cell carcinoma patients who underwent cervical dissection / 北京大学学报(医学版)

Objective:To evaluate the prognostic significance of inflammatory biomarkers,prognostic nutritional index and clinicopathological characteristics in tongue squamous cell carcinoma(TSCC)patients who underwent cervical dissection.Methods:The retrospective cohort study consisted of 297 patients undergoing tumor resection for TSCC between January 2017 and July 2018.The study population was divided into the training set and validation set by 7:3 randomly.The peripheral blood indices of interest were preoperative neutrophil-to-lymphocyte ratio(NLR),lymphocyte-to-monocyte ratio(LMR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),systemic inflammation score(SIS)and prognostic nutritional index(PNI).Kaplan-Meier survival analysis and multivariable Cox regression analysis were used to evaluate independent prognostic factors for overall survival(OS)and disease-specific survival(DSS).The nomogram's accuracy was internally validated using concordance index,receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration plot and decision curve analysis.Results:According to the univariate Cox regression analysis,clinical TNM stage,clinical T category,clinical N category,differentiation grade,depth of invasion(DOI),tumor size and pre-treatment PNI were the prognostic factors of TSCC.Multivariate Cox regression analysis revealed that pre-treatment PNI,clinical N category,DOI and tumor size were independent prognostic factors for OS or DSS(P＜0.05).Positive neck nodal status(N≥1),PNI≤50.65 and DOI＞2.4 cm were associated with the poorer 5-year OS,while a positive neck nodal status(N≥1),PNI≤50.65 and tumor size＞3.4 cm were associated with poorer 5-year DSS.The concordance index of the nomograms based on independent prognostic factors was 0.708(95％CI,0.625-0.791)for OS and 0.717(95％CI,0.600-0.834)for DSS.The C-indexes for external validation of OS and DSS were 0.659(95％CI,0.550-0.767)and 0.780(95％CI,0.669-0.890),respectively.The 1-,3-and 5-year time-dependent ROC analyses(AUC=0.66,0.71 and 0.72,and AUC=0.68,0.77 and 0.79,respec-tively)of the nomogram for the OS and DSS pronounced robust discriminative ability of the model.The calibration curves showed good agreement between the predicted and actual observations of OS and DSS,while the decision curve confirmed its pronounced application value.Conclusion:Pre-treatment PNI,clinical N category,DOI and tumor size can potentially be used to predict OS and DSS of patients with TSCC.The prognostic nomogram based on these variables exhibited good accurary in predicting OS and DSS in patients with TSCC who underwent cervical dissection.They are effective tools for predicting sur-vival and helps to choose appropriate treatment strategies to improve the prognosis.

Establishment and validation of nomogram of cancer specific survival of patients with hepatocellular carcinoma with negative alpha fetoprotein based on SEER Database / 吉林大学学报(医学版)

Objective:To discuss the factors related to the prognosis in the alpha fetoprotein(AFP)negative hepatocellular carcinoma(HCC)patients,and to construct the nomogram for predicting the survival time of the patients.Methods:The retrospective analysis on data of 2 064 cases of AFP negative HCC patients extracted from the Surveillance,Epidemiology,and End Results(SEER)Database was conducted,and all the patients were divided into training cohort and internal validation cohort at a ratio of 7∶3,and 101 AFP negative HCC patients from the Integrated Traditional Chinese and Western Medicine Hospital in Hunan Province were regarded as the external validation cohort.The univariate Cox regression analysis results were incorporated into the multivariate analysis,and the independent risk factors for the AFP negative HCC patients were obtained by multivariate Cox analysis to build a cancer specific survival(CSS)prognosis nomogram for the AFP negative HCC patients.The predictive efficacy and clinical utility of the nomogram were evaluated by time-dependent receiver operating characteristic curve(ROC),calibration plots,and decision curve analysis(DCA).The total score obtained from the nomogram was used for the risk stratification to compare the degree of risk discrimination between the nomogram and the American Joint Committee on Cancer(AJCC)staging system.Results:Ten independent risk factors were selected by multivariate Cox regression analysis to construct 3-year,4-year,and 5-year CSS prognostic nomograms for the AFP negative HCC patients,including the patient's age,pathological grade,surgical status,radiotherapy status,chemotherapy status,lung metastasis status,tumor size,tumor T stage,tumor M stage,and marital status.The area under curve(AUC)for the 3-year,4-year,and 5-year time-dependent ROC in the training cohort were 0.807(95%CI:0.786-0.828),0.804(95%CI:0.782-0.826),and 0.813(95%CI:0.790-0.835),respectively.In the internal validation cohort,they were 0.776(95%CI:0.743-0.810),0.772(95%CI:0.737-0.808),and 0.789(95%CI:0.752-0.826),and in the external validation cohort,they were 0.773(95%CI:0.677-0.868),0.746(95%CI:0.620-0.872),and 0.736(95%CI:0.577-0.895).The calibration plots verified that the nomogram fitted well with the perfect line.The DCA curve revealed that the net benefit of the nomogram was significatly higer than that of the AJCC staging system at certain probability thresholds compared with AJCC staging,the nomogram had a better ability to identify high-risk individuals.Conclusion:The serum AFP expression is one of the prognostic markers for the HCC patients.For those patients with AFP negative expression in serum,different considerations should be taken.The nomogram model based on multiple risk factors is a promising clinical tool for assessing the CSS in the AFP negative HCC patients.

Construction of unplanned offline prediction model for IHD two-stage citrate anticoagulation / 重庆医学

Objective To study the risk factors of two-stage citrate anticoagulation in intermittent he-modialysis(IHD)and to establish an unplanned offline prediction model.Methods A retrospective analysis was conducted to include 34 patients and 118 times of treatment with two-stage citrate anticoagulation for IHD in the hospital from January 2019 to February 2023.According to whether the treatment did not reach the treatment time due to the coagulation of the extracorporeal circulation pipeline,118 treatments were divid-ed into the planned units(n=111)and the unplanned units(n=7).Univariate and multivariate logistic re-gression analysis were used to analyze the risk factors of unplanned weaning,and a risk prediction model was established.The receiver operating characteristic(ROC)curve was used to analyze the predictive value of the regression model.Results Univariate analysis showed that there were statistically significant differences in hematocrit(HCT),platelet count(PLT),activated partial thromboplastin time(APTT),and treatment mode between the planned and unplanned units(P<0.05).Multivariate logistic regression analysis showed that HCT and APTT were independent influencing factors for unplanned weaning(P<0.05).The HCT level was represented by A,the APTT level was represented by B,and the prediction model was:Logit(P)=1.304+ 0.206×A-0.378×B.The area under the ROC curve(AUC)of the prediction model was 0.912(95%CI:0.825-0.995,P<0.001),the maximum Youden index was 0.782,the cut off value was 0.113,the sensitivity was 85.7%,and the specificity was 92.5%.Conclusion The prediction model established by multivariate logistic regression analy-sis can make a preliminary judgment on whether coagulation occurs in two-stage IHD treatment.

Construction of risk prediction model for predicting death or readmission in acute heart failure patients during vulnerable phase based on machine learning / 陆军军医大学学报

Objective To construct risk prediction models of death or readmission in patients with acute heart failure(AHF)during the vulnerable phase based on machine learning algorithms and screen the optimal model.Methods A total of 651 AHF patients with admitted to Department of Cardiology of the Second Affiliated Hospital of Army Medical University from October 2019 to July 2021 were included.The clinical data consisting of admission vital signs,comorbidities and laboratory results were collected from electronic medical records.The composite endpoint was defined as all-cause death or readmission for worsening heart failure within 3 months after discharge.The patients were divided into a training set(521 patients)and a test set(130 patients)in a ratio of 8:2 through the simple random sampling.Six machine learning models were developed,including logistic regression(LR),random forest(RF),decision tree(DT),light gradient boosting machine(LGBM),extreme gradient boosting(XGBoost)and neural networks(NN).Receiver operating characteristic(ROC)curve and decision curve analysis(DCA)were used to evaluate the predictive performance and clinical benefit of the models.Shapley additive explanation(SHAP)was used to explain and evaluate the effect of different clinical characteristics on the models.Results A total of 651 AHF patients were included,of whom 203 patients(31.2%)died or were readmitted during the vulnerable phase.ROC curve analysis showed that the AUC values of the LR,RF,DT,LGBM,XGBoost and NN model were 0.707,0.756,0.616,0.677,0.768 and 0.681,respectively.The XGBoost model had the highest AUC value.DCA showed that the XGBoost model exhibited greater clinical net benefit compared with other models,with the best predictive performance.SHAP algorithm analysis showed that the clinical features that had the greatest impact on the output of the model were serum uric acid,D-dimer,mean arterial pressure,B-type natriuretic peptide,left atrial diameter,body mass index,and New York Heart Association(NYHA)classification.Conclusion The XGBoost model has the best predictive performance in predicting the risk of death or readmission of AHF patients during the vulnerable phase.

Construction of predictive model for early allograft dysfunction after liver transplantation / 陆军军医大学学报

Objective To analyze the factors related to early allograft dysfunction(EAD)after liver transplantation and to construct a predictive model.Methods A total of 375 patients who underwent liver transplantation in our hospital from December 2008 to December 2021 were collected,including 90 patients with EAD and 266 patients without EAD.Thirty items of baseline data for the 2 groups were compared and analyzed.Aftergrouping in a ratio of 7∶3,univariate and multivariate logistic regression analyses were used in the training set to evaluate the factors related to EAD and construct a nomogram.Receiver operating characteristic(ROC)curve,decision curve analysis(DCA),sensitivity,specificity,positive predictive value,negative predictive value,Kappa value and other indicators were used to evaluate the model performance.Results The incidence of EAD after liver transplantation was 24%.Multivariate logistic regression analysis showed that preoperative tumor recurrence history(OR=3.15,95%CI:1.28～7.77,P=0.013)and operation time(OR=1.22,95%CI:1.04～1.42,P=0.015)were related to the occurrence of EAD after surgery.After predicting the outcome according to the cut-off point of 0.519 identified by the Youden index,the model performance in the both training set and validation set was acceptable.DCA suggested the model has good clinical applicability.Conclusion The risk factors for EAD after liver transplantation are preoperative tumor recurrence history and operation time,and the established model has predictive effect on prognosis.

Progress of early prediction methods for refractory Mycoplasma pneumoniae pneumonia in children / 国际儿科学杂志

Mycoplasma pneumoniae is one of the main pathogen of community-acquired pneumonia in children in China. Although most children with Mycoplasma pneumoniae pneumonia have a good prognosis，a small number of children can progress to refractory Mycoplasma pneumoniae pneumonia. Compared with general mycoplasma pneumoniae pneumonia，the clinical symptoms and lung imaging findings of refractory Mycoplasma pneumoniae pneumonia are more serious. Fever and treatment time are longer and extrapulmonary complications are more likely to occur. In order to better identify and treat refractory Mycoplasma pneumoniae pneumonia at an early stage，some scholars have carried out studies on early prediction of refractory Mycoplasma pneumoniae pneumonia by using biomarkers，imaging findings and nomogram. This paper reviews relevant studies in recent years，to provide reference for early prediction of refractory Mycoplasma pneumoniae pneumonia.

Progress in apllication of machine learning evaluation and prediction models in cardiovascular disease / 国际生物医学工程杂志

Artificial intelligence has been a huge success and contributes to the workplace. In the digital era, the amount of data in clinical practice is increasing, which requires healthcare workers to integrate and interpret the various information generated during clinical work. With the help of artificial intelligence techniques, especially machine learning techniques, researchers in cardiovascular medicine have developed a variety of predictive models to improve the efficiency of clinical work and treatment outcomes. The types of machine learning models were introduced, and the current prediction models of cardiovascular diseases using machine learning technology were summarized. The purpose of this paper is to facilitate accurate diagnosis of cardiovascular diseases and to provide a clearer direction for future development of cardiovascular disease prediction models using machine learning techniques.

Factors affecting nosocomial death in elderly patients with COVID-19 and construction of a risk predictive model / 基础医学与临床

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.

Development of a predictive model for perioperative blood transfusion in elderly patients undergoing unilateral total hip arthroplasty / 基础医学与临床

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.

Construction and validation of a scoring model for pathogen characteristics and short-term prognosis risk prediction of candidemia / 解放军医学杂志

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.

Research progress in MRI-based radiomics for predicting the efficacy of transcatheter arterial chemoembolization for hepatocellular carcinoma / 介入放射学杂志

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)

CT images texture analysis versus clinical scores in predicting hemorrhagic transformation of acute ischemic stroke / 介入放射学杂志

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)

The nomogram based on preoperative inflammatory biomarkers used for predicting the prognosis of HCC patients treated with transcatheter arterial chemoembolization:its construction and validation / 介入放射学杂志

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)