Development of diabetic complications and influencing factors among 32653 type 2 diabetes patients:retrospective cohort study using a multi-state Markov

Background:Patients with type 2 diabetes are at high risk for developing multiple chronic complications.However,there is a lack of studies of the cumulative number of diabetic complications in China.Methods:A retrospective cohort study was performed from 2009 to 2021.Type 2 diabetes patients who were first diagnosed after the age of 35 years between January 1,2009,and December 31,2017,were included.Five states were defined according to the number of chronic complications:no(S0),one(S1),two(S2),three(S3),and four or more complications(S4).A multi-state Markov model was constructed to estimate transition probability,transition intensity,mean sojourn time,and the possible factors for each state.Results:The study included 32653 type 2 diabetes patients(mean age,59.59 years;15929(48.8%)male),and mean follow-up time of 7.75 years.In all,4375 transitions were observed.The 12-year transition probability of from state S0 to S1 was the lowest at 16.4%,while that from S2 to S3 was the highest,at 45.6%.Higher fasting blood glucose,lower high-density lipoprotein cholesterol,higher total cholesterol,and an unhealthy diet were associated with higher risk of progression from S0 to S1.Being female,less than 60 years old,weekly physical activity,and vegetarian diet decreased this risk.Being female and less than 60 years old reduced the likelihood of transition from S1 to S2,whereas lower high-density lipoprotein cholesterol increased this likelihood.Conclusions:Following the occurrence of two complications in type 2 diabetes patients,the risk for accumulating a third complication within a short time is significantly increased.It is important to take advantage of the stable window period when patients have fewer than two complications,strengthen the monitoring of blood glucose and blood lipids,and encourage patients to maintain good living habits to prevent further deterioration.

机器学习方法在预测麻精药品不合理使用风险中的应用现状和思考

麻醉药品、精神药品不合理用药在欧美国家已造成了严重的公共卫生问题,评估麻精药品的滥用及其他不合理用药模式风险、监督麻精药品全流程合理合规使用是监管工作的重点难点。近年来,国外借助真实世界数据,采用机器学习方法构建预测模型以快速识别药物滥用和药物使用障碍,以及预测药物依赖、持续使用等不合理使用模式和不良反应等研究日益增多,然而我国学者对类似研究范式关注仍较少。通过梳理麻精药品预测模型研究现状,集中关注阿片类药物用药风险预测相关研究,概括研究场景和研究设计要点,提出对模型转化和我国监管重点的思考,以期为将机器学习用于中国麻精药品监管领域提供思路。

Prediction for risk of disease progression among hospitalized COVID-19 patients

Objective:The COVID-19 pandemic poses a significant threat to global health.Given the lack of studies on risk factors for COVID-19 progression at present,this study aimed to build a predictive model to predict the progression risk among hospitalized COVID-19 patients.Methods:We extracted data from 1074 mild and moderate COVID-19 patients from Electronic Health Records(EHRs)in a designated Wuhan hospital including demographic characteristics and clinical and laboratory information.Disease progression was defined as progressing to severe critical illness after admission.The LASSO regression was used to select the predicted variables and a logistic regression model was applied to build the predictive model.Nomogram was used to show the results.Results:Seven variables were included in the predictive model:age per 10 years(OR,1.15;95%CI,1.03-1.29),lactate dehydrogenase(OR,1.73;95%CI,1.14-2.62),neutrophil-to-lymphocyte ratio(OR,2.07;95%CI,1.42-3.02),eosinophil count(OR,2.10;95%CI,1.20-3.69),albumin(OR,2.37;95%CI,1.65-3.45),hemoglobin(OR,1.50;95%CI,1.10-2.05),D-dimer(OR,1.63;95%CI,1.19-2.23).The mean area under the receiver operating characteristic curve of the predictive model was 0.72(95%CI,0.69-0.76).Conclusions:This study built a predictive model that could effectively predict the progression risk among hospitalized COVID-19 patients.

Comparative effectiveness and safety of 32 pharmacological interventions recommended by guidelines for coronavirus disease 2019:a systematic review and network meta-analysis combining 66 trials

Background:The global pandemic coronavirus disease 2019(COVID-19)has become a major public health problem and presents an unprecedented challenge.However,no specific drugs were currently proven.This study aimed to evaluate the comparative efficacy and safety of pharmacological interventions in patients with COVID-19.Methods:Medline,Embase,the Cochrane Library,and clinicaltrials.gov were searched for randomized controlled trials(RCTs)in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)/SARS-CoV.Random-effects network metaanalysis within the Bayesian framework was performed,followed by the Grading of Recommendations Assessment,Development,and Evaluation system assessing the quality of evidence.The primary outcome of interest includes mortality,cure,viral negative conversion,and overall adverse events(OAEs).Odds ratio(OR)with 95%confidence interval(CI)was calculated as the measure of effect size.Results:Sixty-six RCTs with 19,095 patients were included,involving standard of care(SOC),eight different antiviral agents,six different antibiotics,high and low dose chloroquine(CQ_HD,CQ_LD),traditional Chinese medicine(TCM),corticosteroids(COR),and other treatments.Compared with SOC,a significant reduction of mortality was observed for TCM(OR=0.34,95%CI:0.20–0.56,moderate quality)and COR(OR=0.84,95%CI:0.75–0.96,low quality)with improved cure rate(OR=2.16,95%CI:1.60–2.91,low quality for TCM;OR=1.17,95%CI:1.05–1.30,low quality for COR).However,an increased risk of mortality was found for CQ_HD vs.SOC(OR=3.20,95%CI:1.18–8.73,low quality).TCM was associated with decreased risk of OAE(OR=0.52,95%CI:0.38–0.70,very low quality)but CQ_HD(OR=2.51,95%CI:1.20–5.24)and interferons(IFN)(OR=2.69,95%CI:1.02–7.08)vs.SOC with very low quality were associated with an increased risk.Conclusions:COR and TCM may reduce mortality and increase cure rate with no increased risk of OAEs compared with standard care.CQ_HD might increase the risk of mortality.CQ,IFN,and other antiviral agents could increase the risk of OAEs.The current evidence is generally uncertain with low-quality and further high-quality trials are needed.