Data mining in clinical big data:the frequently used databases,steps,and methodological models

Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients.

Analysis of the correlation between the longitudinal trajectory of SOFA scores and prognosis in patients with sepsis at 72 hour after admission based on group trajectory modeling

Background:To identify the distinct trajectories of the Sequential Organ Failure Assessment(SOFA)scores at 72 h for patients with sepsis in the Medical Information Mart for Intensive Care(MIMIC)-IV database and determine their effects on mortality and adverse clinical outcomes.Methods:A retrospective cohort study was carried out involving patients with sepsis from the MIMIC-IV database.Group-based trajectory modeling(GBTM)was used to identify the distinct trajectory groups for the SOFA scores in patients with sepsis in the intensive care unit(ICU).The Cox proportional hazards regression model was used to investigate the relationship between the longitudinal change trajectory of the SOFA score and mortality and adverse clinical outcomes.Results:A total of 16,743 patients with sepsis were included in the cohort.The median survival age was 66 years(interquartile range:54-76 years).The 7-day and 28-day in-hospital mortality were 6.0%and 17.6%,respectively.Five different trajectories of SOFA scores according to the model fitting standard were determined:group 1(32.8%),group 2(30.0%),group 3(17.6%),group 4(14.0%)and group 5(5.7%).Univariate and multivariate Cox regression analyses showed that,for different clinical outcomes,trajectory group 1 was used as the reference,while trajectory groups 2-5 were all risk factors associated with the outcome(P<0.001).Subgroup analysis revealed an interaction between the two covariates of age and mechanical ventilation and the different trajectory groups of patients’SOFA scores(P<0.05).Conclusion:This approach may help identify various groups of patients with sepsis,who may be at different levels of risk for adverse health outcomes,and provide subgroups with clinical importance.

Red blood cell distribution width improves the prediction of 28-daymortality for patients with sepsis-induced acute kidney injury:A retrospective analysis from MIMIC-IV database usingpropensity score matching

Background:The predictive value of red blood cell distribution width(RDW)for mortality in patients withsepsis-induced acute kidney injury(SI-AKI)remains unclear.The present study aimed to investigate the potentialassociation between RDW at admission and outcomes in patients with SI-AKI.Methods:The Medical Information Mart for Intensive Care(MIMIC)-IV(version 2.0)database,released in Juneof 2022,provides medical data of SI-AKI patients to conduct our related research.Based on propensity scorematching(PSM)method,the main risk factors associated with mortality in SI-AKI were evaluated using Coxproportional hazards regression analysis to construct a predictive nomogram.The concordance index(C-index)and decision curve analysis were used to validate the predictive ability and clinical utility of this model.Patientswith SI-AKI were classified into the high-and low-RDW groups according to the best cut-off value obtained bycalculating the maximum value of the Youden index.Results:A total of 7574 patients with SI-AKI were identified according to the filter criteria.Compared withthe low-RDW group,the high-RDW group had higher 28-day(9.49%vs.31.40%,respectively,P<0.001)and7-day(3.96%vs.13.93%,respectively,P<0.001)mortality rates.Patients in the high-RDW group were moreprone to AKI progression than those in the low-RDW group(20.80%vs.13.60%,respectively,P<0.001).Basedon matched patients,we developed a nomogram model that included age,white blood cells,RDW,combinedhypertension and presence of a malignant tumor,treatment with vasopressor,dialysis,and invasive ventilation,sequential organ failure assessment,and AKI stages.The C-index for predicting the probability of 28-day survivalwas 0.799.Decision curve analysis revealed that the model with RDW offered greater net benefit than that withoutRDW.Conclusion:The present findings demonstrated the importance of RDW,which improved the predictive ability ofthe nomogram model for the probability of survival in patients with SI-AKI.