【作者】 刘莹；

【导师】 张涛；

【作者基本信息】 山东大学 ， 流行病与卫生统计学， 2023， 硕士

【摘要】 研究背景:脓毒症为机体由于感染而出现的炎症和免疫失调反应,严重者会发生多器官功能衰竭甚至死亡。近年来对脓毒症的早期识别和治疗水平有所提高,但其死亡率仍保持在20%以上。加强脓毒症患者的治疗和管理,准确评估其病情和预后十分重要。序贯器官衰竭评分(sequential organ failure assessment,SOFA)可以评估脓毒症患者器官功能障碍的数量和严重程度,综合反映患者病情,且对患者预后具有良好的预测性。目前关于危重症评分系统的研究大多数集中在病人危重症评分基线值、住院期间的最差值或者delta值来对患者预后进行预测,忽略了治疗过程中患者生理状况和器官功能的动态变化。基于脓毒症患者SOFA评分纵向数据探索其动态轨迹变化模式、识别高危的轨迹亚组具有重要的临床实际意义。然而SOFA评分包括六项亚评分指标,各亚评分指标常由于测量困难、记录错误、仪器故障等原因存在一定的缺失,在计算综合评分时缺失率进一步提高,给SOFA评分的轨迹分析带来了挑战。目前SOFA评分的缺失处理常采用末次观测结转法(last observation coming forward,LOCF),该方法简单直接但忽略了数据的变异性。联合多元线性混合效应模型(joint multivariate linear mixed effects model,JM-MLMM)和完全条件定义混合效应模型(fully conditional specification linear mixed effects model,FCS-LMM)是常用的多重填补方法,考虑了缺失数据的不确定性,已被广泛用于纵向缺失数据的处理,但以上三种方法在纵向数据的填补性能比较研究尚未充分。本研究旨在通过模拟比较LOCF、JM-MLMM和FCS-LMM在纵向缺失数据中的填补性能,为纵向缺失数据的填补提供有价值的参考;继而从中选择较优的填补方法对SOFA评分各亚评分指标进行填补,识别脓毒症患者SOFA评分轨迹亚组,并探索轨迹分组和不良临床预后结局的关联关系。材料与方法:通过模拟生成纵向生存数据,在完全随机缺失机制(missing completely at random,MCAR)和随机缺失(missing at random,MAR)机制下生成不同缺失比例(5%、10%、15%、20%、30%、40%和50%)的缺失数据,每种模拟情形重复100次。采用LOCF、JM-MLMM和FCS-LMM对以上缺失数据集进行填补,其中JM-MLMM和FCS-LMM填补次数分别设置为1次、3次和5次,得到“完整数据集”后构建混合效应模型进行参数估计。通过模型参数评价指标(绝对偏倚、绝对百分比偏倚、标准误、均方根误差和覆盖率)和缺失值填补效果评价指标(均方根误差)两方面综合评价上述三种方法的填补准确性和精确性,从而选择较优的填补方法用于SOFA评分的轨迹分析。基于美国重症监护医学信息数据库-Ⅳ(medical information mart for intensive care Ⅳ,MIMIC-Ⅳ),纳入年龄18-90岁且在重症监护病房(intensivecareunit,ICU)停留时间为2天及以上的脓毒症患者,以入ICU当天为第一天,之后以24小时为周期计算每日SOFA评分。在计算SOFA评分之前选择模拟比较得出的较优填补方法对各个SOFA亚评分指标进行填补。基于潜在类别增长混合模型(latent class growth mixed model,LCGMM)识别ICU脓毒症患者SOFA评分轨迹分组,通过Cox比例风险回归模型评估轨迹分组与出院结局的关联;采用logistic回归模型探索SOFA评分增长曲线下面积(area under the growth curve,AUC)与出院结局之间的关系。研究结果:模拟实验结果显示:①随着缺失率的升高,LOCF、JM-MLMM和FCS-LMM三种填补方法对真实参数的估计均逐渐偏离真值,模型估计参数的准确度和精度均降低。②随着填补次数的增加,JM-MLMM和FCS-LMM对固定效应参数估计的准确性提高。③对于固定时间效应项的参数估计,JM-MLMM和FCS-LMM估计较为准确,以FCS-LMM表现最佳,LOCF对时间项参数估计表现最差。即使在缺失率为5%的情形下,LOCF填补后估计的时间项参数也明显偏离真值。④对于截距和连续型协变量的参数估计,在缺失率>20%时,LOCF和JM-MLMM估计参数值明显偏离真值,模型估计参数的准确度和精度迅速降低,FCS-LMM在三种方法中表现最优。⑤相比于其它固定效应参数的估计,LOCF对二分类协变量的参数估计较为准确,但从估计的准确性和精确性方面,仍是FCS-LMM表现最优,JM-MLMM次之。⑥从缺失值填补效果来看,JM-MLMM填补较为准确,FCS-LMM次之,LOCF填补效果最差。实例研究共纳入MIMIC-Ⅳ数据库ICU脓毒症患者2734人,其中男性1572人(57.5%),出院死亡结局共1131例(41.4%)。选择FCS-LMM填补SOFA各亚评分指标后计算综合评分,基于LCGMM识别出三个SOFA评分轨迹分组,分别是高升高组(n=337,12.3%)、中升高组(n=1531,56.0%)和下降组(n=866,31.7%)。在高升高组,基线SOFA评分较低,但在入ICU后前4天迅速升高并在第2天以后分值明显超过另外两组。在中升高组,SOFA评分在入ICU的前两天稍微升高,之后表现出平缓的下降趋势,但SOFA评分一直在下降组之上。在下降组中,SOFA评分基线值在三组中最高,但是在前4天内,该组SOFA评分迅速下降。与下降组相比,中升高组和升高组患者死亡风险均较高,出院死亡的风险比(hazardratio,HR)及95%置信区间(confidence interval,CI)分别为1.62(1.37～1.92)和3.11(2.55～3.79)。SOFA评分轨迹曲线下面积—AUC与出院结局呈现剂量-反应关系。与SOFA评分增长AUC的第一分位数相比,第二、三和四分位数的患者死亡比值比(oddsratio,OR)及95%CI分别为2.90(2.25～3.76)、3.90(3.02～5.07)和 16.22(12.13～21.85)。研究结论:无论从分析模型参数估计还是缺失值填补准确性来看,FCS-LMM填补性能最佳,表现较为稳定,优于JM-MLMM和LOCF。在假设纵向指标随时间变化的纵向缺失数据中以及不同的缺失率条件下,FCS-LMM是三种填补方法中最为推荐的选择。ICU脓毒症患者的SOFA评分存在三个不同的轨迹分组,不同轨迹组患者的死亡风险不同。纵向SOFA轨迹的动态变化可以包含更多的患者病情信息,在脓毒症患者预后监测中具有重要意义,住院期间SOFA评分呈上升趋势的患者应给予密切关注。更多还原

【Abstract】 Backgrounds:Sepsis is an inflammatory and immune disorder caused by infection.Severe cases can lead to multiple organ failure or even death.Early identification and treatment of sepsis have improved in recent years,but its mortality rate remains above 20%.Strengthening the treatment and management of sepsis patients and accurately evaluate their condition and prognosis are quite important.The sequential organ failure assessment(SOFA)can assess the number and severity of organ dysfunction in sepsis patients,comprehensively reflect the condition of sepsis patients and have good prognostic prediction.Most studies of critical illness score systems focused on baseline scores,the worst-case scores during hospitalization,or deltas to predict patient outcomes that ignored dynamic changes in patient’s physiological status and organ function during treatment.Based on the longitudinal SOFA score of patients with sepsis,it is of great clinical significance to explore the dynamic trajectory pattern of SOFA score and identify the poorprognostic and high-risk trajectory subgroups.SOFA score includes six sub-score indexes.Each sub-score index would be missed due to measurement difficulties,recording errors,instrument failure and other reasons.The miss rate in the calculation of comprehensive score is further increased,which brings challenges to the longitudinal trajectory analysis of SOFA score.The last observation coming forward(LOCF)method is often used to deal with missing SOFA scores,which is simple and direct but ignores the variability of data.Joint multivariate linear mixed effects model(JM-MLMM)and fully conditional specification linear mixed effects model(FCS-LMM)are commonly used multiple imputation methods,which consider the uncertainty of missing data and has been widely used in the processing of longitudinal missing data.However,the comparative study of imputing performance within the above three methods in longitudinal missing data has not been sufficient.The study aimed to compare the imputing performance of LOCF,JM-MLMM and FCSLMM in longitudinal missing data through simulation,in order to provide valuable reference for the imputation of longitudinal missing data.Using the better and appropriate imputation method to impute SOFA sub-scores,identify SOFA score trajectory groups in sepsis patients,and explore the relationship between trajectory groups and clinical outcomes.Methods:Firstly,longitudinal survival data was generated by simulation.In the missing completely at random(MCAR)and the missing at random(MAR)mechanism,generating missing data with different miss rates(5%,10%,15%,20%,30%,40%and 50%),and each simulation situation was repeated 100 times.Using LOCF,JM-MLMM and FCS-LMM to impute the above missing data sets.The imputation times of JM-MLMM and FCS-LMM were set as 1,3 and 5 times,respectively.After obtaining the "complete data set",the linear mixed effect model was constructed for parameter estimation.The imputation accuracy of the above three methods were comprehensively evaluated from two aspects including model parameter(bias,relative bias,standard error,root mean square error and coverage rate)and imputed missing value(root mean square error).The better imputation method was selected for the trajectory analysis of SOFA score.Based on the medical information mart for intensive care Ⅳ(MIMIC-Ⅳ),sepsis patients aged 18-90 years who stayed in intensive care unit(ICU)for 2 days or more were included.The day admitted to ICU was recorded as the first day,and then the daily SOFA score was calculated on a 24-hour period.Before calculating SOFA score,the optimal imputation method obtained by the simulation experiment was used to impute each SOFA sub-score.Using the latent class growth mixed model(LCGMM)to identify SOFA score trajectory groups in ICU sepsis patients and Cox proportional hazard regression model was used to evaluate the association between the trajectory groups and discharge outcome.Logistic regression model was used to explore the relationship between SOFA score area under the growth curve(AUC)and discharge outcome.Results:Simulation experiment results show that ① With the increase of the overall data miss rate,the estimates of the real parameters of the three imputation methods including LOCF,JM-MLMM and FCS-LMM,gradually deviated from the true values,and the accuracy and precision of the model estimation parameters decreased.② With the increase of imputation times,the accuracy of fixed effect parameter estimation increased when imputed by JMMLMM and FCS-LMM.③For the parameter estimation of fixed time effect term,JMMLMM and FCS-LMM were more accurate,and FCS-LMM had the best performance,while LOCF had the worst performance.Even when the missing rate was only 5%,the estimation of fixed time effect term was obviously deviated from the true value when imputed by LOCF.④For the parameter estimation of intercept and continuous covariable,when the miss rate was greater than 20%,the estimated parameter values of LOCF and JM-MLMM were apparently deviated from the true value,and the accuracy and precision of model estimation parameters descended rapidly.FCS-LMM had the best performance among the three methods.⑤Compared with other fixed effect parameters,LOCF was more accurate in the estimation of binary covariable parameter,but in terms of accuracy and precision of estimation,FCS-LMM was still the best,followed by JM-MLMM.⑥ From the evaluation of imputing missing values,JM-MLMM was more accurate,FCS-LMM followed,and LOCF had the worst imputing performance.A total of 2734 sepsis patients in ICU in MIMIC-Ⅳ database were included in the study,including 1572 males(57.5%).The discharge outcome was 1131 cases(41.4%)of death.The comprehensive SOFA score was calculated after imputing SOFA sub-score with FCS-LMM.Three SOFA score trajectory groups were identified based on LCGMM,including high increasing group(n=337,12.3%),moderate increasing group(n=1531,56.0%)and decline group(n=866,31.7%).In the high increasing group,baseline of SOFA scores was low,but increased rapidly in the first four days after ICU admission and exceeded the other two groups after day two.In the moderate increasing group,SOFA scores increased slightly in the first two days,and then showed a descending trend,but SOFA scores were always above the decline group.In the decline group,SOFA score baseline was the highest among the three groups,but in the first four days,SOFA scores decreased rapidly.Compared with the decline group,the moderate increasing and high increasing group had higher risks of discharge to death with the hazard ratios(HR)and 95%confidence intervals(CI)for death were 1.62(1.37～1.92)and 3.11(2.55～3.79),respectively.There was a dose-response relationship between the AUC of SOFA score trajectory and hospital discharge outcome.Compared with the first quartile of increasing SOFA score AUC,the odds ratios(OR)and 95%CIs of the second,third and fourth quartile were 2.90(2.25～3.76),3.90(3.02～5.07)and 16.22(12.13～21.85),respectively.Conclusions:From the imputation accuracy and precision of analysis model parameter estimation and missing value,FCS-LMM has the best imputation performance and remains stable,which is better than JM-MLMM and LOCF.With the assumption of longitudinal missing data with the longitudinal indicator changing over time and under the condition of different miss rates,FCS-LMM is the most recommended choice among the three imputation methods.Three different trajectory groups identified in SOFA score of ICU sepsis patients,and the risks of death were different among these trajectory groups.The dynamic changes of longitudinal SOFA trajectory contain more information about the condition of sepsis patient,which is of great significance in monitoring the prognosis of patients with sepsis.More attention should be paid to sepsis patients with an upward trend in SOFA score during hospitalization.更多还原