Cox regression analysis of postoperative prognostic factors and construction of nomogram prediction model for patients with aortic dissection / 中国综合临床

Objective:To explore the related prognostic factors in patients with aortic dissection (AD) after surgery or interventional therapy, and to construct a multi-factor Cox regression analysis nomogram prediction model.Methods:The clinical data of patients diagnosed with AD in the emergency department of Affiliated Hospital of Jining Medical University of Shandong Province from January 2019 to December 2021 were collected to analyze retrospectively. The survival time was followed up, the survival curve was drawn by Kaplan-Meier method, the difference of survival rate among different Stanford types was tested by Log-Rank test, and Cox regression univariate and multivariate survival analysis was used to screen the prognostic factors. According to the results of multivariate analysis, the death risk nomogram model after AD was established, the differentiation of the model was evaluated by C-index, the accuracy of the model was evaluated by calibration curve, and the clinical benefit of the model was evaluated by decision curve analysis (DCA).Results:A total of 256 patients with AD were included, the mortality rate was 4.45% (37/256), and the overall survival rates at 3 months, 6 months and 12 months after operation were 90.23%, 87.50% and 85.55%, respectively. There was no significant difference in postoperative survival rate among AD patients with different Stanford types (χ 2=2.30, P=0.13). Cox multivariate regression analysis showed that history of hypertension ( HR=3.791, 95% CI 1.150-12.501, P=0.029), number of branch vessels involved ( HR=1.210, 95% CI 1.029-1.422, P=0.021), preoperative left ventricular ejection fraction (LVEF) ( HR=0.936, 95% CI 0.880-0.996, P=0.038) and perioperative complications ( HR=4.024, 95% CI 1.839-8.807, P<0.001) were independent prognostic factors in patients with AD. Furthermore, the predictive nomogram model of 3-month, 6-month and 12-month survival rate in patients with AD was constructed, and the C-index was 0.778. The calibration curve indicated that the accuracy of the model was good, and the clinical benefit of the DCA model was good. Conclusions:Hypertension history, the number of branch vessels involved, preoperative LVEF and perioperative complications are independent risk factors of postoperative death in patients with AD. The nomogram prediction model based on the above factors can be used to evaluate the postoperative survival of patients with AD.

Analysis of clinical characteristics and the related factors to flare in patients with systemic lupus erythematosus / 中华风湿病学杂志

Objective:To summarize the clinical characteristics and factors that may affect the flare of patients with systemic lupus erythematosus (SLE).Methods:A total of 300 patients with SLE who were treated with standard treatment in the outpatient clinic of the department of rheumatology and immunology of the Second Affiliated Hospital of Air Force Military Medical University of PLA, were enrolled, and the patients were divided into 24 patients in the complete response group, 40 cases in the no response group, 192 cases in the treatment response group, and 44 cases in the low disease activity group according to the response to treatment. The differences in clinical characteristics and survival rates between the groups were compared and analyzed. Comparisons of count data were made using analysis of variance (ANOVA), comparisons of measurement data were made using the chi-square test or the Fisher′s ecact test, and survival rates were expressed as Kaplan-Meier curves. Cox regression analysis was adapted to explore risk factors for flare in these patients.Results:A total of 300 patients were followed. With a median follow-up time of 18 (1, 36) months, a total of 42 patients experienced flare. The clinical characteristics of the four groups were compared, and there were significant differences in age ( F=4.39, P=0.005), the presence of lupus nephritis ( χ2=12.66, P=0.005), hemoglobin level ( F=2.73, P=0.044), NLR level( F=3.88, P=0.010), cystatin C level( F=3.11, P=0.027), anti-RNP antibody ( χ2=12.04, P=0.007), anti-Sm antibody ( χ2=8.33, P=0.040), anti-SSB antibody ( P=0.014), anti-nucleosome antibody ( P=0.014), and anti-ribosomal P protein antibody ( χ2=11.83, P=0.008). There was no significant difference in survival between the four groups. Cox analysis showed that the combination of other autoimmune diseases [ HR(95%CI)=3.23(1.58, 6.57), P=0.001], anti-Sm antibody [ HR(95%CI)=2.15(1.04, 4.43), P=0.038], and anti-RNP antibody [ HR(95%CI)=2.54(1.13, 5.68), P=0.023] were risk factors for flare in patients with SLE who could reach the treatment target. Conclusion:Patients with SLE with different treatment responses have different clinical features, and all treatment can significantly improve the recurrence rate no matter what level of response to treatment. Patients concurrent with other autoimmune diseases, positive anti-Sm antibodies, and positive anti-RNP antibodies are at highrisk of flare.

Comparison of the effects of Cox regression analysis model and decision tree model in identifying risk factors for the occurrence of hypertension in the elderly / 公共卫生与预防医学

Objective To explore the risk factors for the occurrence of hypertension in middle-aged and elderly residents in China using the Cox regression analysis model and decision tree model, and compare the differences between the two methods. Methods The 2011-2015 China Health and Retirement Longitudinal Study data were used. The study investigated the risk factors for hypertension using both a multivariate Cox regression model and a decision tree model. Results The results showed that the incidence rate of hypertension between 2011-2015 was 22.79%. Both the Cox regression model and decision tree model identified age, education level, body mass index, and diabetes as risk factors for hypertension. The Cox regression model also identified drinking status as a risk factor, while the decision tree model identified gender and marital status as additional risk factors. The area under the curve (AUC) suggested that the Cox regression model and decision tree model had comparable ability to predict hypertension. Conclusions The risk factors for hypertension include gender, age, education level, marital status, alcohol consumption, body mass index, and history of diabetes. The effectiveness of the hypertension prediction model established based on Cox regression model and decision tree model results is not different.

Construction and validation of rectal cancer prognostic model by LASSO-Cox regression / 安徽医科大学学报

Objective To construct and appraise a new model for predicting the prognosis of rectal cancer patients using the Lasso-Cox strategy.Methods The clinical pathological data of 599 rectal cancer patients who underwent radical resection were analyzed.Comparison between groups,Lasso and Cox regression were used to select varia-bles and construct a model,and its discrimination,consistency,and clinical benefits were appraised by the receiv-er operating characteristic(ROC),calibration curve,and decision curve analysis.Results Comparison between groups showed that age,body mass index(BMI),preoperational nutrition status,carbohydrate antigen199(CA199),preoperative chemotherapy,intraoperative blood transfusion,vascular or nerve invasion,cancer nod-ules,pathologic T,N,and TNM stages,tumor recurrence or metastasis,radiotherapy and postoperative survival time were associated with grouping of death or survival in rectal cancer patients.Among them,8 variables were se-lected by lasso and contained into the Cox regression model.Age(HR=1.04,P＜0.05),BMI(HR=0.89,P＜0.05),blood transfusion(HR=2.29,P＜0.05),postoperative chemotherapy(HR=0.16,P＜0.01),recur-rence(HR=43.67,P＜0.01),and metastasis(HR=2.75,P＜0.05)were identified as independent prognostic factors,which were used to construct a nomogram model.The area under the curve(AUC)and the 95％confi-dence interval of the receiver operating characteristic(ROC)curve of the predictive model was 0.95(0.91-0.99),P＜0.01.The predicted probability of 1-year and 3-year survival was close to the actual probability.The DCA curve of the model was far away from a decision line parallel to the X-axis and another line with a negative slope.Conclusion The newly established nomogram has good discrimination,consistency and clinical benefits,which help predict the prognosis of rectal cancer after surgery.

Influencing factors of death in patients with MDR-TB based on Bayesian Cox regression model / 中南大学学报(医学版)

Objective:Multidrug-resistant tuberculosis(MDR-TB)has a high mortality and is always one of the major challenges in global TB prevention and control.Analyzing the factors that may impact the adverse outcomes of MDR-TB patients is helpful for improving the systematic management and optimizing the treatment strategies for MDR-TB patients.For follow-up data,the Cox proportional hazards regression model is an important multifactor analysis method.However,the method has significant limitations in its application,such as the fact that it is difficult to deal with the impacts of small sample sizes and other practical issues on the model.Therefore,Bayesian and conventional Cox regression models were both used in this study to analyze the influencing factors of death in MDR-TB patients during the anti-TB therapy,and compare the differences between these 2 methods in their application. Methods:Data were obtained from 388 MDR-TB patients treated at Lanzhou Pulmonary Hospital from November 1,2017 to March 31,2021.Survival analysis was employed to analyze the death of MDR-TB patients during the therapy and its influencing factors.Conventional and Bayesian Cox regression models were established to estimate the hazard ratios(HR)and their 95% confidence interval(95% CI)for the factors affecting the death of MDR-TB patients.The reliability of parameter estimation in these 2 models was assessed by comparing the parameter standard deviation and 95% CI of each variable.The smaller parameter standard deviation and narrower 95% CI range indicated the more reliable parameter estimation. Results:The median survival time(1st quartile,3rd quartile)of the 388 MDR-TB patients included in the study was 10.18(4.26,18.13)months,with the longest survival time of 31.90 months.Among these patients,a total of 12 individuals died of MDR-TB and the mortality was 3.1%.The median survival time(1st quartile,3rd quartile)for the deceased patients was 4.78(2.63,6.93)months.The majority of deceased patients,accounting for 50%,experienced death within the first 5 months of anti-TB therapy,with the last mortality case occurring within the 13th month of therapy.The results of the conventional Cox regression model showed that the risk of death in MDR-TB patients with comorbidities was approximately 6.96 times higher than that of patients without complications(HR=6.96,95% CI 2.00 to 24.24,P=0.002)and patients who received regular follow-up had a decrease in the risk of death by approximately 81% compared to those who did not receive regular follow-up(HR=0.19,95% CI 0.05 to 0.77,P=0.020).In the results of Bayesian Cox regression model,the iterative history plot and Blue/Green/Red(BGR)plot for each parameter showed the good model convergence,and parameter estimation indicated that the risk of death in patients with a positive first sputum culture was lower than that of patients with a negative first sputum culture(HR=0.33,95% CI 0.08 to 0.87).Additionally,compared to patients without complications,those with comorbidities had an approximately 6.80-fold increase in the risk of death(HR=7.80,95% CI 1.90 to 21.91).Patients who received regular follow-up had a 90% reduction in the risk of death compared to those who did not receive regular follow-up(HR=0.10,95% CI 0.01 to 0.30).The comparison between these 2 models showed that the parameter standard deviations and corresponding 95% CI ranges of other variables in the Bayesian Cox model were significantly smaller than those in the conventional model,except for parameter standard deviations of receiving regular follow-up(Bayesian model was 0.77;conventional model was 0.72)and pulmonary cavities(Bayesian model was 0.73;conventional model was 0.73). Conclusion:The first year of anti-TB therapy is a high-risk period for mortality in MDR-TB patients.Complications are the main risk factors of death in MDR-TB patients,while patients who received regular follow-up and had positive first sputum culture presented a lower risk of death.For data with a small sample size and low incidence of outcome,the Bayesian Cox regression model provides more reliable parameter estimation than the conventional Cox model.

Predictive value of stress-induced hyperglycemia on 28 d risk of all-cause death in intensive care patients / 北京大学学报(医学版)

OBJECTIVE@#To investigate the relationship between stress glucose elevation and the risk of 28 d all-cause mortality in intensive care unit (ICU) patients, and to compare the predictive efficacy of different stress glucose elevation indicators.@*METHODS@#ICU patients who met the inclusion and exclusion criteria in the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ) database were used as the study subjects, and the stress glucose elevation indicators were divided into Q1 (0-25%), Q2 (>25%- 75%), and Q3 (>75%-100%) groups, with whether death occurred in the ICU and the duration of treatment in the ICU as outcome variables, and demographic characteristics, laboratory indicators, and comorbidities as covariates, Cox regression and restricted cubic splines were used to explore the association between stress glucose elevation and the risk of 28 d all-cause death in ICU patients; and subject work characteristics [receiver operating characteristic (ROC) and the area under curve (AUC)] were used to evaluate the predictive efficacy of different stress glucose elevation indicators, The stress hyperglycemia indexes included: stress hyperglycemia ratio (SHR1, SHR2), glucose gap (GG); and the stress hyperglycemia index was further incorporated into the Oxford acute severity of illness score (OASIS) to investigate the predictive efficacy of the improved scores: the AUC was used to assess the score discrimination, and the larger the AUC indicated, the better score discrimination. The Brier score was used to evaluate the calibration of the score, and a smaller Brier score indicated a better calibration of the score.@*RESULTS@#A total of 5 249 ICU patients were included, of whom 7.56% occurred in ICU death. Cox regression analysis after adjusting for confounders showed that the HR (95%CI) for 28 d all-cause mortality in the ICU patients was 1.545 (1.077-2.217), 1.602 (1.142-2.249) and 1.442 (1.001-2.061) for the highest group Q3 compared with the lowest group Q1 for SHR1, SHR2 and GG, respectively, and The risk of death in the ICU patients increased progressively with increasing indicators of stressful blood glucose elevation (Ptrend < 0.05). Restricted cubic spline analysis showed a linear relationship between SHR and the 28 d all-cause mortality risk (P>0.05). the AUC of SHR2 and GG was significantly higher than that of SHR1: AUCSHR2=0.691 (95%CI: 0.661-0.720), AUCGG=0.685 (95%CI: 0.655-0.714), and AUCSHR1=0.680 (95%CI: 0.650-0.709), P < 0.05. The inclusion of SHR2 in the OASIS scores significantly improved the discrimination and calibration of the scores: AUCOASIS=0.820 (95%CI: 0.791-0.848), AUCOASIS+SHR2=0.832 (95%CI: 0.804-0.859), P < 0.05; Brier scoreOASIS=0.071, Brier scoreOASIS+SHR2=0.069.@*CONCLUSION@#Stressful glucose elevation is strongly associated with 28 d all-cause mortality risk in ICU patients and may inform clinical management and decision making in intensive care patients.

Development and validation of a prognostic model for patients with sepsis in intensive care unit / 中华危重病急救医学

Objective:To analyze the risk factors related to the prognosis of patients with sepsis in intensive care unit (ICU), construct a nomogram model, and verify its predictive efficacy.Methods:A retrospective cohort study was conducted using data from Medical Information Mart for Intensive Care-Ⅳ 0.4 [MIMIC-Ⅳ (version 2.0)]. The information of 6 500 patients with sepsis who meet the diagnostic criteria of Sepsis-3 were collected, including demography characteristics, complications, laboratory indicators within 24 hours after ICU admission, and final outcome. Using a simple random sampling method, the patients were divided into a training set and a validation set at a ratio of 7∶3. The restricted cubic spline (RCS) was used to explore whether there was a linear relationship between each variable and the prognosis, and the nonlinear variables were truncated into categorical variables. All variables were screened by LASSO regression and included in multivariate Cox regression analysis to analyze the death risk factors in ICU patients with sepsis, and construct a nomograph. The consistency index, calibration curve and receiver operator characteristic curve (ROC curve) were used to evaluate the prediction efficiency of nomogram model. The decision curve analysis (DCA) was used to validate the clinical value of the model and its impact on actual decision-making.Results:Among 6 500 patients with sepsis, 4 551 were in the training set and 1 949 were in the validation set. The 28-day, 90-day and 1-year mortality in the training set were 27.73% (1?262/4?551), 34.76% (1?582/4?551), and 42.98% (1?956/4?551), respectively, those in the validation set were 27.24% (531/1?949), 33.91% (661/1?949), and 42.23% (823/1?949), respectively. Both in training set and the validation set, compared with the final survival patients, the death patients were older, and had higher sequential organ failure assessment (SOFA) score and simplified acute physiology scoreⅡ (SAPSⅡ), more comorbidities, less urine output, and more use of vasoactive drugs, kidney replacement therapy, and mechanical ventilation. By RCS analysis, the variables with potential nonlinear correlation with the prognosis risk of septic patients were transformed into categorical variable. The variables screened by LASSO regression were enrolled in the multivariate Cox regression model. The results showed that age [hazard ratio ( HR) = 1.021, 95% confidence interval (95% CI) was 1.018-1.024], SOFA score ( HR = 1.020, 95% CI was 1.000-1.040), SAPSⅡ score > 44 ( HR = 1.480, 95% CI was 1.340-1.634), mean arterial pressure (MAP) ≤ 75 mmHg (1 mmHg ≈ 0.133 kPa; HR = 1.120, 95% CI was 1.026-1.222), respiratory rate (RR; HR = 1.044, 95% CI was 1.034-1.055), cerebrovascular disease ( HR = 1.620, 95% CI was 1.443-1.818), malignant tumor ( HR = 1.604, 95% CI was 1.447-1.778), severe liver disease ( HR = 1.330, 95% CI was 1.157-1.530), use of vasoactive drugs within 24 hours ( HR = 1.213, 95% CI was 1.101-1.336), arterial partial pressure of oxygen (PaO 2; HR = 0.999, 95% CI was 0.998-1.000), blood lactic acid (Lac; HR = 1.066, 95% CI was 1.053-1.079), blood urea nitrogen (BUN) > 8.9 mmol/L ( HR = 1.257, 95% CI was 1.144-1.381), total bilirubin (TBil; HR = 1.023, 95% CI was 1.015-1.031), and prothrombin time (PT) > 14.5 s ( HR = 1.232, 95% CI was 1.127-1.347) were associated with the death of ICU patients with sepsis (all P < 0.05). Based on the above factors, a nomogram model was constructed, and the model validation results showed that the consistency index was 0.730. The calibration curve showed a good consistency between the predicted results of the nomogram model and observed results in the training and validation sets. ROC curve analysis showed that the area under the ROC curve (AUC) predicted by the nomogram model in the training set and the validation set for 28-day, 90-day and 1-year death risk was 0.771 (95% CI was 0.756-0.786) and 0.761 (95% CI was 0.738-0.784), 0.777 (95% CI was 0.763-0.791) and 0.765 (95% CI was 0.744-0.787), 0.677 (95% CI was 0.648-0.707) and 0.685 (95% CI was 0.641-0.728), respectively. DCA analysis showed that the nomogram model had significant net benefits in predicting 28-day, 90-day, and 1-year death risk, verifying the clinical value of the model and its good impact on actual decision-making. Conclusions:The death risk factors related to ICU patients with sepsis include age, SOFA score, SAPSⅡ score > 44, MAP ≤ 75 mmHg, RR, cerebrovascular disease, malignant tumors, severe liver disease, use of vasoactive drugs within 24 hours, PaO 2, Lac, BUN, TBil, PT > 14.5 s. The nomogram model constructed based on this can predict the death risk of ICU patients with sepsis.

Study on risk factors of recurrent Henoch-Schonlein purpura recurrence in children based on Cox regression analysis / 西安交通大学学报(医学版)

【Objective】 To explore the relevant risk factors of Henoch-Schonlein purpura (HSP) recurrence so as to provide some theoretical basis for early identification of children prone to recurrence. 【Methods】 The clinical data of 417 children with HSP hospitalized in Department of Pediatrics, The First Affiliated Hospital of Xi’an Jiaotong University, in the past five years were collected and followed up. They were divided into recurrent group and non-recurrent group. Cox regression analysis was used for univariate and multivariate analysis, and finally the independent risk factors for HSP recurrence were screened. 【Results】 A total of 417 children with initial onset of HSP were included in the study. During the follow-up period of 14 to 60 months, 78 cases recurred, and the recurrence rate was 18.7%. 94.9% of the children had relapse within 1 year. The results of univariate Cox regression analysis showed that age >7 years old at the time of onset, history of infection, history of strenuous exercise, duration of rashes more than 4 weeks, high level of neutrophil-to-lymphocyte ratio (NLR), and high level of platelet-to-lymphocyte ratio (PLR) were all risk factors for HSP recurrence (P7 years old at the time of onset, history of infection, history of strenuous exercise, duration of rashes for more than 4 weeks at the first onset, and high PLR level were independent risk factors for HSP recurrence (P 7 years at the time of onset, with a history of infection, vigorous exercise, rashes lasting more than 4 weeks, and high PLR level, nursing should be strengthened after discharge to avoid infection and vigorous exercise and increase the frequency of follow-up.

Clinical characteristics and risk factors of renal function deterioration in children with renal dysplasia complicated with chronic kidney disease / 现代泌尿外科杂志

【Objective】 To explore the clinical characteristics and risk factors of renal function deterioration in children with renal dysplasia and chronic kidney disease (CKD), so as to provide a basis for the diagnosis, treatment, and management. 【Methods】 The clinical data of children with renal dysplasia complicated with CKD treated in the Children’s Hospital of Chongqing Medical University during 2012 and 2022 were retrospectively analyzed, including the gender, age of diagnosis, growth index, concomitant malformation and complications. According to the diagnostic criteria and staging standard of KDIGO2020 guidelines, patients with disease deteriorated to CKD stage 4-5 were enrolled into the regression group. Factors affecting the deterioration of renal function were determined with Cox regression analysis. 【Results】 A total of 122 children were involved, including 66 (54.1%) with CKD stag 4-5. There were more boys than girls. Bilateral and unilateral renal dysplasia occurred in 88 (72.13%) and 34 (27.87%) cases, respectively, and 64 (52.46%) cases were complicated with other urinary diseases. There were significant differences in weight, height and body mass index (BMI) among patients with CKD stage 1-5 (P<0.01). The age of onset of CKD <10 years, BMI lower than the 3rd percentile of the same sex and age, bilateral renal dysplasia, and one or more complications of congenital renal and urinary tract abnormalities (CAKUT) were the risk factors of deterioration of renal function (P<0.05). 【Conclusion】 Renal dysplasia complicated with CKD are more common in boys, with high incidence of bilateral renal dysplasia. Bilateral renal dysplasia, age of onset of CKD <10 years, BMI lower than 3% and complications are important influencing factors of renal dysplasia in children with CKD.

Screening ferroptosis related genes influencing prognosis of colon cancer through bioinformatics analysis / 解剖学报

Objective To explore ferroptosis-related long non-coding RNAs (lncRNAs) with prognostic significance in colon cancer (CC), and then construct a prognosis-related predictive scoring model. To search for ferroptosis-related differential expressed genes co-expressed with prognosis-related lncRNAs. Methods Ferroptosis-related genes (FGs) were downloaded from FerrDb database; The expression data of 41 adjacent normal tissues and 473 tumor tissues, and clinical data of 452 patients were successfully downloaded. Co-expression and differential expression analysis was performed to identify differentially expressed ferroptosis-related lncRNAs (DEFlncRNAs), and univariate Cox regression analysis was used to screen statistically significant prognosis-related DEFlncRNAs, and then multivariate Cox regression analysis was used to construct a prognostic model, calculate risk score among CC patients and divide patients by the median risk score. Kaplan-Meier curves, univariate and multivariate Cox regression analyses, and receiver operationg characteristic(ROC) curve were used to reveale great accuracy of the model. Then, a nomogram was drawed to predict the survival among CC patients. Finally, the differentially expressed ferroptosis-related genes regulating DEFlncRNAs were found by co-expression analysis, and the different expression was verified by immunohistochemical experiments. Result Expression and clinical data among colon cancer (CC) patients were downloaded from TCGA database. A risk prognostic model containing 28 lncRNAs to predict the prognosis among CC patients was successfully constructed. An effective clinical nomogram for predicting the overall survival of CC patients was successfully constructed. Finally, the co-expression analysis of DEFlncRNAs and differentially expressed ferroptosis-related genes (DEFGs) was preformed to obtain a co-expression network, including17 key DEFGs, with the correlation coefficient filter criteria (| corFilter |) > 0.4 and P value filter criteria (P value filter) < 0.05. Immunohistochemical experiments confirmed ANGPTL7 was highly expressed in the adjacent tissues among CC patients. Conclusion Successfully constructed a prognostic-related model among CC patients containing 28 DEFlncRNAs, and 17 DEFGs was finally obtained.

What is the best method for long?term survival analysis?

In the Cox proportional hazards regression model, which is the most commonly used model in survival analysis, the effects of independent variables on survival may not be constant over time and proportionality cannot be achieved, especially when long-term follow-up is required. When this occurs, it would be better to use alternative methods that are more powerful for the evaluation of various effective independent variables, such as milestone survival analysis, restricted mean survival time analysis (RMST), area under the survival curve (AUSC) method, parametric accelerated failure time (AFT), machine learning, nomograms, and offset variable in logistic regression. The aim

Effect of frailty on the risk of all-cause mortality —a 12-year follow-up study of community residents aged 45 years and above in Shanghai / 上海预防医学

ObjectiveTo analyze the effect of frailty status on the risk of mortality in a community-based population aged 45 years and above in Shanghai with different characteristics， and to provide further basis for population-based interventions for frailty and prevention of adverse outcomes. MethodsData were derived from baseline data from the Shanghai prospective study on AGEing and adult health （2009-2010） and cohort follow-up of causes of death up to October 30， 2021. Frailty index （FI） scores were constructed from 40 variables. Those with frailty index FI≥0.2 were judged to be in a frail state， and a multifactorial Cox regression model was used to calculate the hazard ratio （HR） to evaluate the effect of frailty status on the risk of death in different age groups by gender. Socioeconomic characteristics （age， residence， marital status， education and family economic level， etc.） and health-related behaviors （smoking， alcohol consumption， fruit and vegetable intake， social participation， etc.） were included as control variables. ResultsThe study included 7 978 subjects， 777 （9.7%） of whom were in a frail state. After （11.3±1.8） years of follow-up， 1 043 （13.1%） individuals were dead， including 214 （27.5%） who were frail. The results of the multifactorial Cox regression analysis showed that the effect of frailty on the risk of death in each subgroup was in descending order of men in the middle-aged group （45‒ years） （HR=2.92， 95%CI： 1.38-6.19）， women in the low-aged elderly group （60‒ years） （HR=1.68， 95% CI： 1.08-2.60）， and women in the old-aged elderly group （≥75 years and older） （HR=1.59， 95%CI： 1.22‒2.06）. ConclusionFrailty is associated with the risk of death， and we should focus on the frailty status of men aged 45~59 years and women aged 60 years and above. Early screening and assessment of frailty status and taking appropriate preventive interventions may reduce the occurrence of adverse outcomes and premature death.

Influencing factors and risk prediction model for cervical cancer recurrence / 中南大学学报(医学版)

OBJECTIVES@#Cervical cancer is the most common malignant tumor in the female reproductive system worldwide. The recurrence rate for the treated cervical cancer patients is high, which seriously threatens women's lives and health. At present, the risk prediction study of cervical cancer has not been reported. Based on the influencing factors of cervical cancer recurrence, we aim to establish a risk prediction model of cervical cancer recurrence to provide a scientific basis for the prevention and treatment of cervical cancer recurrence.@*METHODS@#A total of 4 358 cervical cancer patients admitted to the Hunan Cancer Hospital from January 1992 to December 2005 were selected as research subjects, and the recurrence of cervical cancer patients after treatment was followed up. Univariate analysis was used to analyze the possible influencing factors. Variables that were significant in univariate analysis or those that were not significant in univariate analysis but may be considered significant were included in multivariate Cox regression analysis to establish a cervical cancer recurrence risk prediction model. Line graphs was used to show the model and it was evaluated by using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.@*RESULTS@#Univariate analysis showed that the recurrence rates of cervical cancer patients with different age, age of menarche, parity, miscarriage, clinical stage, and treatment method were significantly different (all P<0.05). Multivariate Cox regression analysis showed that RR=-0.489×(age≥55 years old)+0.481×(age at menarche >15 years old)+0.459×(number of miscarriages≥3)+0.416×(clinical stage II)+0.613×(clinical stage III/IV)+0.366×(the treatment method was surgery + chemotherapy) + 0.015×(the treatment method was chemotherapy alone). The area under the ROC curve (AUC) of the Cox risk prediction model for cervical cancer recurrence constructed was 0.736 (95% CI 0.684 to 0.789), the best prediction threshold was 0.857, the sensitivity was 0.576, and the specificity was 0.810. The accuracy of the Cox risk model constructed by this model was good. From the clinical decision curve, the net benefit value was high and the validity was good.@*CONCLUSIONS@#Patient age, age at menarche, miscarriages, clinical stages, and treatment methods are independent factors affecting cervical cancer recurrence. The Cox proportional hazards prediction model for cervical cancer recurrence constructed in this study can be better used for predicting the risk of cervical cancer recurrence.

A novel defined pyroptosis-related genes prognostic risk model for predicting the prognosis of kidney renal clear cell carcinoma / 解剖学报

Objective To establish a novel defined pyroptosis-related genes risk model of kidney renal clear cell carcinoma. Methods Data of 522 patients with KIRC and 72 normal tissue samples were respectively downloaded from the Cancer Genome Atlas ( TCGA) database and Genotype-Tissue Expression ( GTEx) database. Differential analysis was performed between data of TCGA and GTEx. Univariate Cox regression analysis, multivariate Cox regression analyses and LASSO Cox regression analysis were used to establish a prognostic risk model. Data from the International Cancer Genome Consortium (ICGC) database was used as an external validation cohort. Gene ontology ( GO) enrichment analysis and Kyoto Encylopedia of Genes and Genomes (KEGG) pathway analysis were used to explore the differences of gene functions and pathways between high-risk and low-risk groups. The CIBERSORT database was used to explore the immune infiltration of high-risk and low-risk groups. Results Through differential analysis, we obtained 13 differentially expressed pyroptosis-related genes. Univariate Cox regression analysis, multivariable Cox regression analyses and LASSO Cox regression analysis were used to establish a 6-gene risk model. Kaplan-Meier analysis indicated that survival time in high-risk group was shorter than low-risk group in both cohorts. The area under the curve ( AUC) was 0. 710 for 1-year, 0. 683 for 2-year, and 0. 727 for 3-year survival in the TCGA_KIRC cohort. The AUC was 0. 592 for 1-year, 0. 531 for 2-year, and 0. 545 for 3-year survival in the ICGC_RECA cohort. Independent prognostic analysis indicated that risk score was an independent prognostic factor. GO enrichment analysis and KEGG pathway analysis showed that it was mainly associated with immune and inflammatory responses. The result of tumor immune infiltration showed that the high-risk group had low infiltration levels of regulatory T cells , natural killer cells, monocytes, M2 macrophages and eosinophils and high infiltration level of B cells, CD8+T cells and follicular helper T cells. Conclusion Pyrolysis-related genes may play an important role in KIRC tumor immunity, and the 6-gene risk model can provide a forecast basis for personalized treatment of patients with KIRC.

Construction and Validation of Prognostic Risk Score Model of Autophagy Related Genes in Lung Adenocarcinoma / 中国肺癌杂志

BACKGROUND@#Autophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.@*METHODS@#5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.@*RESULTS@#Survival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.@*CONCLUSIONS@#We constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

Relationship between serum uric acid level and hyperglycemia: a prospective cohort study / 中华健康管理学杂志

Objective:To investigate the correlation between serum uric acid level and hyperglycemia.Methods:A medical examination cohort of the staff of our hospital was constructed. From February 1 st, 2011, to December 31 st, 2011, 3 937 staff members without hyperglycemia were selected, and baseline data were collected through a questionnaire survey, physical examination, measurement of blood lipid and blood glucose, assessment of kidney function, and other laboratory tests. The subjects were followed up during the annual physical examination for 7 years, from January 1 st, 2012, to December 31 st, 2018. They were divided into four groups according to serum uric acid level: uric acid<360 μmol/L, 360≤uric acid<420 μmol/L, 420≤uric acid<480 μmol/L, and uric acid≥ 480 μmol/L. With the occurrence of hyperglycemia as the outcome indicator; uric acid level as the observation index; uric acid<360 μmol/L as the control group; and gender, age, body mass index, smoking, hypertension, dyslipidemia as confounding factors, Cox regression was performed before and after adjusting confounding factors to analyze the relationship between different uric acid levels and the incidence of hyperglycemia in the entire sample, in the male staff, and in the female staff. Results:The 7-year cumulative incidence of hyperglycemia in the four groups were 15.7%, 34.0%, 38.8%, and 43.8%, respectively ( Z=148.94, P<0.01). In the male staff, the 7-year cumulative incidence rates in the four groups were 23.4%, 29.9%, 34.7%, and 35.8%, respectively ( Z=11.17, P<0.01). In the female staff, the 7-year cumulative incidence rates in the four groups were 14.2%, 42.5%, 52.2%, and 65.0%, respectively ( Z=141.84, P<0.01. After adjusting for gender, age, body mass index, smoking, hypertension, and dyslipidemia, the risk of hyperglycemia in the 360≤uric acid<420 μmol/L, 420≤uric acid<480 μmol/L, and uric acid≥ 480 μmol/L groups were 1.73 (1.39-2.15), 1.86 (1.42-2.45), and 1.95 (1.34-2.85) times higher than that in the control group (all P<0.05). Among female staff, the risk of hyperglycemia in the 360≤uric acid<420 μmol/L, 420≤uric acid<480 μmol/L, and uric acid≥ 480 μmol/L groups were 2.18 (1.62-2.94), 3.41 (2.24-5.20), and 3.02 (1.69-5.40) times, respectively, and were also higher than those in the control group (all P<0.01). Conclusion:With the increase of serum uric acid level, the risk of hyperglycemia in medical staff increases, which is mainly manifested in female staff.

MicroRNA model that can predict the prognosis of oral squamous cell carcinoma based on bioinformatics analysis / 华西口腔医学杂志

OBJECTIVE@#The microRNA (miRNA) prognostic model can predict the prognosis of patients with oral squamous cell carcinoma (OSCC) on the basis of bioinformatics. Moreover, it can accurately group OSCC patients to improve targeted treatment.@*METHODS@#We downloaded the miRNA and mRNA expression profile and clinical data of OSCC from The Cancer Genome Atlas (TCGA). The risk score model of miRNA was screened and established by univariate and multivariate Cox regression models. The performance of this prognostic model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). The target genes of six miRNAs were predicted and intersected with differential mRNA for enrichment analysis by Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway and gene ontology (GO) enrichment analysis. A protein protein interaction network (PPI) was constructed to screen hub genes.@*RESULTS@#By using univariate and multivariate Cox regression analyses, the prognostic risk model was obtained. The AUC of the ROC curve for predicting 5-year survival in the training group, test group, and whole cohort were 0.757, 0.673, and 0.724, respectively. Furthermore, univariate Cox regression and multivariate Cox regression considering other clinical factors showed that the six-miRNAs signature could serve as an independent prognostic factor (P<0.001). The top 10 hub genes in the PPI network screened by intersecting target genes include CCNB1, EGF, KIF23, MCM10, ITGAV, MELK, PLK4, ADCY2, CENPF, and TRIP13. EGF and ADCY2 were associated with survival prognosis (P<0.05).@*CONCLUSIONS@#The six-miRNAs signature could efficiently function as a novel and independent prognostic model for OSCC patients, which may be a new method to guide the accurate targeting treatment of OSCC.

Internal limiting membrane peel: Does it change the success rate of primary vitrectomy without belt buckle in rhegmatogenous retinal detachments?

Purpose: To compare the anatomic success of pars plana vitrectomy (PPV) after internal limiting membrane (ILM) peeling at macular area and macular plus peripapillary area versus no peeling in rhegmatogenous retinal detachments (RRD). Methods: A prospective observational study between July 2014 and March 2017 conducted on 289 eyes of 287 patients with RRD were randomly assigned to three treatment procedures, viz., PPV with no ILM peeling, PPV with macular peeling, and PPV with macular plus peripapillary peeling. Recurrent RD (ReRD) was treated as an event and accordingly the overall primary (PS) and final success (FS) rates were obtained. The risk of ReRD associated with peeling procedures after adjusting for risk factors were obtained using Cox-proportional hazard analysis. Results: The PS percentage for no peel, macular, and macular plus peripapillary procedures were 77.78% (70/90), 82.18% (83/101), and 94.89% (93/98; maximum), respectively, which was statistically significant with a P value of 0.003. The FS percentage for no peel, macular, and macular plus peripapillary were 93.33%, 95.04%, and 100%, respectively, which was significantly different with a P value of 0.048. With reference to no peeling, the adjusted hazard ratio for macular peeling was 0.841 [95% CI: 0.44–1.60] while 0.235 [95% CI: 0.088–0.626] for macular plus peripapillary peeling. Conclusion: The anatomic success rate of PPV with macular plus peripapillary ILM peeling was significantly higher as compared to no peel category. The hazard of ReRD in patients undergoing macular plus peripapillary peel was significantly reduced as compared to no peel procedure.

Impact Of Methadone Maintenance Therapy On Hiv Incidence In Perak, 2008 - 2017: A Cox Regression Analysis

@#In 2005, Ministry of Health introduced the Needle Syringe Exchange Program (NSEP) and Methadone Maintenance Therapy (MMT) program as a part of Harm Reduction program to combat HIV infection in Malaysia among people who inject drug (PWIDs). Expenditures were estimated approximately RM10 millions per year to establish and sustain the NSEP and MMT centres. This study examined the impact of MMT program on preventing HIV seroconversion among registered MMT clients that are people who inject drug (PWIDs), and to identify other predictors of HIV seroconversion among this group. This was a retrospective cohort study done in the state of Perak involving a total of 212 randomly selected MMT clients registered between 2008-2017 in 6 clinics. This study looks at data collected from the last ten-year cohort from baseline to follow-up. A questionnaire was used to obtain socio-demographic data, sexual and drugs abuse history. Test results for HIV were obtained from medical records. Cox regression analysis was performed to examine factors associated with seroconversion and Kaplan-Meier analysis to estimate HIV survival time. This study displayed that both Methadone take home supply (HR 10.4, 95% CI: 1.6 – 68.8) and unprotected sexual practice (HR 5.9, 95%CI: 1.1 – 31.5) shown higher risks of HIV seroconversion compared to DOTS and condom practice among MMT clients respectively. Mean survival for HIV seroconversion among MMT clients was 104.44 (95%CI: 101.85 – 107.04) months. This study provides reliable evidence that MMT program markedly reduces incidence of HIV infection among people who inject drug (PWIDs).

Survival analysis: part II – applied clinical data analysis / 대한마취과학회지

As a follow-up to a previous article, this review provides several in-depth concepts regarding a survival analysis. Also, several codes for specific survival analysis are listed to enhance the understanding of such an analysis and to provide an applicable survival analysis method. A proportional hazard assumption is an important concept in survival analysis. Validation of this assumption is crucial for survival analysis. For this purpose, a graphical analysis method and a goodness-of-fit test are introduced along with detailed codes and examples. In the case of a violated proportional hazard assumption, the extended models of a Cox regression are required. Simplified concepts of a stratified Cox proportional hazard model and time-dependent Cox regression are also described. The source code for an actual analysis using an available statistical package with a detailed interpretation of the results can enable the realization of survival analysis with personal data. To enhance the statistical power of survival analysis, an evaluation of the basic assumptions and the interaction between variables and time is important. In doing so, survival analysis can provide reliable scientific results with a high level of confidence.