Temporal Variations in Mortality after Liver Transplantation: Retrospective Investigation of Potential Risk Factors Using Propensity Score

Objective: We aim to detect over-time variations in mortality of liver transplant recipients stratified by the period of transplant. Since this is a retrospective investigation, bias reduction caused by possible confounding effects can be achieved by using propensity score weighting in a multivariate logistic regression model. Methods: Medical charts of all adult liver transplant recipients (n = 250) who were transplanted in three periods 2005-2009, 2010-2014 and 2015-2019 were retrospectively reviewed. The following recipient factors were analyzed: recipients and donors’ ages, sex, renal impairment, body mass index (BMI), presence of bacterial or viral infections, MELD (Model for end-stage diseases). Multivariate logistic model adjusted by Propensity Scores (PS) was used to identify the effect of the risk factors on mortality, and death within five years, in the targeted time frame. Patient outcomes are recorded as;(patient status = 1 if dead, or patient status = 0 if alive). Results: Meld score, recipient age, and renal impairments were shown to be predictors of mortality in transplanted patients. Multivariate regression model was used to identify the significance of the specified risk factors, followed by pairwise comparisons between periods. Pairwise comparisons between periods using logistic regression weighted by the inverse propensity score, correcting for the possible confounding effect of measured covariates showed that the death rate is significantly reduced in subsequent periods as compared to the initial period. Conclusions: The clinical implications of these findings are the ability to stratify patients at high risk of posttransplant death by planning more intensive and accurate management for them.

Laparoscopic vs.open surgery for gastrointestinal stromal tumors of esophagogastric junction:A multicenter,retrospective cohort analysis with propensity score weighting

Objective:Laparoscopic resection is increasingly performed for gastrointestinal stromal tumors(GISTs).However,the laparoscopic approach for GISTs located in the esophagogastric junction(EGJ-GIST)is surgically challenging.This study compares the efficacy of laparoscopic surgery and the open procedure for EGJ-GIST through the propensity score weighting(PSW)method.Methods:Between April 2006 and April 2018,1,824 surgical patients were diagnosed with primary gastric GIST at four medical centers in South China.Of these patients,228 were identified as EGJ-GISTs and retrospectively reviewed clinicopathological characteristics,operative information,and long-term outcomes.PSW was used to create the balanced cohorts.Results:PSW was carried out in laparoscopic and open-surgery cohorts according to year of surgery,sex,age,body mass index(BMI),tumor size,mitotic rates and recurrence risk.After PSW,438 patients consisting of 213 laparoscopic(L group)and 225 open surgery(O group)patients were enrolled.After PSW,the following measures in the L group were superior to those in the O group:median operative time[interquartile range(IQR)]:100.0(64.5-141.5)vs.149.0(104.0-197.5)min,P<0.001;median blood loss(IQR):30.0(10.0-50.0)vs.50.0(20.0-100.0)mL,P=0.002;median time to liquid intake(IQR):3.0(2.0-4.0)vs.4.0(3.0-5.0)d,P<0.001;median hospital stay(IQR):6.0(4.0-8.0)vs.7.0(5.0-12.0)d,P<0.001;and postoperative complications(10.3%vs.22.7%,P=0.001).The median follow-up was 55(range,2-153)months in the entire cohort.No significant differences were detected in either relapse-free survival(RFS)[hazard ratio(HR):0.372,95%confidence interval(95%CI):0.072-1.910,P=0.236]or overall survival(OS)(HR:0.400,95%CI:0.119-1.343,P=0.138)between the two groups.Conclusions:Laparoscopic surgery for EGJ-GIST is associated with the advantages of shorter operative time,reduced blood loss,shorter time to liquid intake,and shorter length of stay,all without compromising postoperative outcomes and long-term survival.

Blockchain-based Bank-to-Personal Credit Decisions

With the continuous integration of Internet technology and people's lives,blockchain technology provides more possibilities for the development of the banking industry.Blockchain is a distributed database.It has the characteristics of non-tampering,openness,transparency,decentralization,and good anonymity.It can well solve the shortcomings of the current personal credit evaluation system,thus proposing corresponding strategies for banks.Firstly,this paper proposes the challenges and difficulties in building a personal credit data sharing system based on blockchain,designs a personal credit data sharing model based on blockchain technology,and proposes a personal credit evaluation mechanism based on blockchain,including three parts:credit mechanism,credit index,and weighted scoring algorithm.Finally,through the linear regression model,corresponding credit strategies are proposed for banks.

Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform

An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor set.Subsequently,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items.This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness.Furthermore,a weighting function is employed to combine the various improved algorithms.The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list.To address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing framework.Experiments were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single machine.The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency.

Consensus on diagnostic criteria for Yin deficiency syndrome in hypertension:a modified Delphi study

OBJECTIVE:To reach consensus on the diagnostic criteria for Yin deficiency syndrome in hypertension(YDSH)patients by a modified Delphi method.METHODS:Our study was consistent with T/CACM 1032-2017.The methodology of RAND/UCLA appropriateness was used to develop consensus guidance statements.A nationwide panel of experienced clinical experts from 19 provinces was constructed.These experts were all prominent in Traditional Chinese Medicine(TCM)of cardiovascular diseases.This consensus process consisted of two rounds of questionnaires and a final round of consultation to analyze the weight score of each item.Moreover,the data extraction process is carried out independently by third-party researchers(LIANG Junya,SUN Yang,and DU Xiaona).When there is disagreement in all three rounds,the expert panel group(odd number)are invited to vote,and the one with more votes wins.In the questionnaires,participants were asked to rate the appropriateness of each syndrome item using a nine-point Likert scale.The consensus was defined as a panel median rating 1-3 or 7-9 without disagreement.And then the diagnostic criteria of YDSH were formed according to the weight score in the final round.RESULTS:Twenty-eight experts(84.8%)participated in the first round,and thirty-one(93.9%)finished the second round.After two rounds,the consensus of YDSH was reached on 11 items(25.6%),including symptoms,signs,and pulse condition.Twenty-one experts(63.6%)completed the final round in which they used a grading system for each item.Red tongue with scanty fur had the highest weighting(22.8%),followed by heat in the palms and soles(20.1%).CONCLUSIONS:The consensus-based diagnostic criteria for YDSH,formed by a modified Delphi method,can be widely incorporated in TCM.A further clinical study will be conducted to analyze the diagnosis value and cut-off score of our YDSH criteria.

Law of iterated logarithm and model selection consistency for generalized linear models with independent and dependent responses

We study the law of the iterated logarithm (LIL) for the maximum likelihood estimation of the parameters (as a convex optimization problem) in the generalized linear models with independent or weakly dependent (ρ-mixing) responses under mild conditions. The LIL is useful to derive the asymptotic bounds for the discrepancy between the empirical process of the log-likelihood function and the true log-likelihood. The strong consistency of some penalized likelihood-based model selection criteria can be shown as an application of the LIL. Under some regularity conditions, the model selection criterion will be helpful to select the simplest correct model almost surely when the penalty term increases with the model dimension, and the penalty term has an order higher than O(log log n) but lower than O(n). Simulation studies are implemented to verify the selection consistency of Bayesian information criterion.

Some Properties of A Lack-of-Fit Test for a Linear Errors in Variables Model

The relationship between the linear errors-in-variables model and the corresponding ordinary linear model in statistical inference is studied. It is shown that normality of the distribution of covariate is a necessary and sufficient condition for the equivalence. Therefore, testing for lack-of-fit in linear errors-in-variables model can be converted into testing for it in the corresponding ordinary linear model under normality assumption. A test of score type is constructed and the limiting chi-squared distribution is derived under the null hypothesis. Furthermore, we discuss the power of the test and the choice of the weight function involved in the test statistic.