应用功效函数图法评价常规生化室内质量控制方法

目的应用功效函数图法评价常规生化室内质量控制方法,获取适当的质量控制规则和质控测定个数的质控方案。方法确定临床允许分析总误差(TEa),实验室检测项目的实际不精密度,计算分析方法的临界系统误差(△SEc),利用功效函数图法,选择合适的质控规则和质控测定个数。结果分析项目性能不同,质控规则和控制测定个数有所不同。其中直接胆红素(DB)、丙氨酸氨基转移酶(ALT)、门冬氨酸氨基转移酶(AST)、总蛋白(TP)、白蛋白(ALB)、尿酸(UA)、甘油三酯 (TG)、胆固醇(CHOL)、碱性磷酸酶(ALP)、肌酸激酶(CK)、乳酸脱氢酶(LDH)、钠(Na)、钾(K)、氯 (CL)的质控规则为13s,质控测定个数N=2;总胆红素(TB)、糖(GLU)、磷(P)的质控规则13s或13s／ 22s／R4s／(41sW),质控测定个数N=2;尿素(Urea)、肌酐(Crea)、钙(Ca)、镁(Mg)的质控规则为12．5s 或13s／22s／R4s／(41sW),质控测定个数N=4或12．5s或13s／22s／R4s／(41sW),质控测定个数N=2。结论临床实验室要根据分析项目的性能选用不同的室内质控方法。利用功效函数图法计划制定质控规则,可满足临床质量要求,达到期望的误差检出率且控制假失控率,提高质控工作的有效性。     

分析批长度Westgard西格玛规则在酶学室内质量控制中的应用

目的 运用分析批长度 Westgard 西格玛(σ)规则为酶学室内质量控制设置合理的质控策略。 方法 收集盘锦辽油宝石花医院 2020 年参加国家临检中心室间质量评价数据及本实验室室内质量控制数据作为偏倚及变异系数。 采用国家卫健委颁布的《临床生物化学检验常规项目分析质量指标》(卫生行业标准 WS/T403-2012)提供的允许总误差作为规范,计算 σ 度量,根据分析批长度 Westgard σ 规则选择质控策略,计算质量目标指数,评价酶学的分析性能。 结果 酶学各项目中,2 个项目的 σ≥6,选择 1_3s规则,分析批长度为 1000;2 个项目的 σ 水平为 6>σ≥5,质控规则为 1_3s/2_2s/R_4s,每 450 个测试做一次质控;2 个项目的 σ 水平为 5>σ≥4,质控规 则 为 1_3s/2_2s/R_4s/4_1s,每200 个测试做一次质控;1 个项目的 σ 水平为4>σ≥3,质控规则为...     

血型血清学输血实验室室内质量控制策略探讨

<正>为将分析测试结果误差控制在允许限度内,实验室必须采取质量控制措施。室内质量控制是对测试过程进行自我控制的过程,目的在于判断检验结果的质量,结果是否可接受。对于定量检测项目,《临床实验室定量测定室内质量控制指南》(以下简称指南)从质控规则、质控品频次、失控判定规则、标准差、变异系数到质控图的绘制等都做了详细说明,然而对于定性实验,目前尚无可遵循的质控指南。血型血清学输血实验室的检测项目,主要是定性测定,     

不同生化检测系统测定电解质结果的可比性评价

目的探讨湿式化学法和干式化学法测定电解质结果的可比性,为同一实验室内两种不同测定系统检测结果提供可比性依据。方法用罗氏P800生化分析仪、原装试剂、校准品、正常和异常水平质控血清组成湿化学法为比较方法;以Fuji DRI-CHEM 7000型干式生化分析仪,原装测试片、校准品和质控品组成干化学法为对比方法,分别用质控血清及患者新鲜血清对钾(K+)、钠(Na+)、氯(Cl-)、钙(Ca2+)进行测定,判断两种检测方法测定结果的可比性。结果两种仪器测定K+,Na+,Cl-,Ca2+经配对t检验,各检测项目的P值均大于>0.05,两种系统的测定结果是一致的;检测结果的批间CV在2.1～4.63间,精密度良好;各项目检测结果偏倚在-1.2%～4.3%之间;各检测结果相关系数均大于0.95。结论此实验证实:两种仪器测定K+,Na+,Cl-,Ca2+,其检测结果具有很好的相关性,在临床患者标本测定结果之间存在较好的可比性,能够满足临床检验的要求。     

冷藏和冷冻生化质控品的结果对比

目的探讨冷藏和冷冻生化质控品的结果。方法每天取冷冻管一支置室温待完全溶解,冷藏管一支恢复至室温,两管同时测定,连续测定60d,一共60次。结果冷藏质控品的TBIL,DBIL,LDH几个项目与冷冻质控品比较,差异有统计学意义(P0.05),说明这些项目适合冷冻。TP、ALB、BUN、T-CHOL、TG、CA各个项目比较,差异无统计学意义(P0.05);冷藏质控品的SD、CV%除CA之外其他项目均大于冷冻质控品,尤其是酶类和胆红素差异很大,胆红素的CV%28.753。质控品避免了分装、冻干和复溶等操作过程所引起的误差,所以各项RCV%均明显较小(RCV%3),表明该质控品稳定性好。结论冷冻质控品的方法所得数据的SD、CV%相对比冷藏质控品的小,稳定性好,可作为一种理想的保存生化质控品的方法,值得在室内质控时应用。     

实验室内质量控制图在血站检验数据质量控制中的应用

《血站实验室质量管理规范》规定:建立和实施与检测项目相适应的室内质量控制程序,以保证检验结果达到预期的质量标准,其中包括质控品的技术要求、质控品常规使用前的确认、实施质控的频次、质控品检测数据的适当分析方法、质控规则的选定等。本文对实验室内质量控制图在血站实验室检验数据质量控制中的应用做了系统阐述。     

日立7170A、COBAS检测系统间血清酶测定结果的可比性研究

目的:对同一生化实验室两台分析仪进行方法对比和偏差评估,探讨不同仪器间血清酶测定结果是否具有可比性。方法:以可溯源的日立7170A生化分析仪,罗氏原装试剂及程序,c.f.a.s校准品和质控品组成的检测系统作为目标检测系统(Y),罗氏COBAS Integra 400 Plus为待评系统(X),在20个工作日内分别测定罗氏正常、病理水平质控物各20次和新鲜血清样本40份。计算目标检测系统与待评系统之间的相对偏差(%SE),以美国临床实验室修正法规规定的室间质量评价允许误差范围的1/2为标准,判断不同检测系统之间的可比性。结果:经随机区组资料设计的方差分析,罗氏质控物和新鲜样本天门冬氨酸氨基转移酶(AST)、乳酸脱氢酶(LDH)、肌酸激酶(CK)、肌酸激酶同功酶(CK-MB)测定值二检测系统间的总体差异无显著性(P>0.05),系统间的相关系数均大于0.975。两系统所有项目测量结果的系统误差临床可接受。结论:二检测系统4种酶测定结果之间具有可比性,能满足临床的需求。     

自建色谱法血药浓度监测靶值建立及与不同方法测定水平的比较——以硫唑嘌呤代谢物和卡马西平为例

目的：探索自建色谱法血药浓度监测靶值的建立；对比色谱法和免疫法在血药浓度测定的水平差异。方法：选择安徽医科大学第一附属医院硫唑嘌呤代谢物和卡马西平血药浓度监测分别作为实验室自建色谱法和不同测定方法的项目代表。回顾性分析硫唑嘌呤代谢物部分质控数据，以即刻质控法初步建立室内靶值，Westgard多规则质控对该项目开展常规质控；43例卡马西平样本分别由实验室HPLC和全自动生化分析仪以及第三方医学实验室，通过相关和回归分析评价检验方法一致性。结果：硫唑嘌呤代谢物项目在第16次质控监测提示告警，第17次失控记录也证实其存在异常离群。常规检测过程中累积失控2次即2_2s及1_3s记录各1次。三水平质控品累积变异系数分别为8.85%、7.46%和4.73%。3组卡马西平血药浓度测定数据均为非正态分布，其中实验室免疫法测定质量浓度结果最高（中位数为6.00 μg·mL^-1），第三方实验室测定结果次之（中位数为5.50 μg·mL^-1），而实验室色谱法测定结果最小（中位数为3.80 μg·mL^-1）。第三方实验室与实验室免疫法、第三方实验室与实验室色谱法以及实验室色谱法与免疫法其3组方法比较的秩相关系数分别为0.984、0.975和0.956，Passing-Bablok回归方程分别为：y=0.298+0.817x，y=-0.246+1.539x，y=-1.009+1.947x。结果表明，以上测定方法的一致性较好。结论：即刻质控法可用于自建色谱法血药浓度监测的简易室内质控程序。色谱法和免疫法在卡马西平血药浓度测定中存在基线水平差异。     

环孢素治疗药物监测质控规则的比较与评价

目的通过不同质量控制方法的比较,确定本院实验室条件下环孢素治疗药物监测的质量控制规则,及时发现测定误差成因,确保血药浓度监测质量。方法对本院实验室2009年质控数据进行统计学分析,并使用不同的质控方法评价,根据临床检验质控设计评价方法选择最佳质控规则。结果 Levey-Jennings控制方法的结果均在控,累计和质量控制方法检出3次失控,Z-分数控制图法检出8次失控,修改的Westgard多规则控制方法达到了极高的误差检出率和较低的假失控概率。结论修改的Westgard多规则控制方法简便易行,误差检出率高,成本低,符合本院实验室的质量控制要求,提高了实验室测定数据的准确性,能为临床提供更加真实有效的数据。     

血清钾钠氯测定室内质控结果分析

为评价血清钾、钠、氯测定室内质控效果 ,采用 DSI-90 3 Na/ K/ Cl电解质分析仪检测血清钾、钠、氯 ,分析五位操作者对高低质控物的检测情况 ,对同期各位操作者临床血清标本相应指标之间及其阳离子与阴离子之差 (钾 +钠 -氯 )与样本数 /质控数之比进行了统计分析。结果显示 ,钾、钠、氯高低质控无操作者差别 ,且与定值无统计差别 ;临床标本钾、氯无操作者差别 ,但钠具有操作者间高度显著性差异 ,血清标本 (钾 +钠 -氯 )与样本数 /质控数之比之间呈显著负相关。     

A “Backward” Bayesian Method for Determination of Criteria for Making Go/No-Go Decisions in the Early Phases

We introduce a “backward” Bayesian method to assist sponsors formulating early phase Go/No-Go criteria based on the ultimate efficacy or safety target, which is usually clearer for Phase 3. Derived from the definition of success for Phase 3, involving prior information and cost of later phases, this work presents the quantitative relationships among the following factors: previous and current study results, study designs (e.g., sample size, duration, or dose), true effect, target probability of success (PoS), expected financial loss, expected probability of terminating a potentially successful asset. An example is given to demonstrate how to accomplish these objectives for an exponential model describing the trajectory of weight loss. The expected loss and the probability of terminating a valuable compound are plotted against a range of criteria. The sponsors can then optimize the Go/No-Go criteria based on their tolerance for their objectives. This method can also be generalized to other nonlinear models. A byproduct of this work is to highlight the naivety of conventional gut feeling approaches in early internal decision-making process by explicitly identifying the necessary, albeit elaborate, information, and assumptions. Supplementary materials for this article are available online.     

Probability of passing dissolution acceptance criteria for an immediate release tablet

During development of solid dosage products, a pharmaceutical manufacturer is typically required to propose dissolution acceptance criteria unless the product falls into Biopharmaceutics Classification System (BCS) class I, in which case a disintegration test may be used. At the time of filing the new drug application (NDA) or common technical document (CTD), the manufacturer has already met with regulatory agencies to discuss and refine dissolution strategy. The dissolution acceptance criteria are based on stability and batch history data and are often arrived at by considering the percentage of batches that pass United States Pharmacopeia (USP) criteria at Stage 1 (S(1)), when in fact, the product is deemed unacceptable only when a batch fails USP criteria at Stage 3 (S(3)) [H. Saranadasa, Disso. Technol. 7 (2000) 6-7, 18 [1]]. Calculating the probability of passing (or failing) dissolution criteria at S(1), S(2), or S(3) can assist a manufacturer in determining appropriate acceptance criteria. This article discusses a general statistical method that was developed to assess the probability of passing the multistage USP test for dissolution and how it was applied to an immediate release tablet formulation. In this case, acceptance criteria were set and the analysis was conducted to assess the probabilities of passing or failing based on this acceptance criterion. Whether the acceptance criteria were relevant to the product was also considered. This mathematical approach uses a Monte Carlo simulation and considers a range of values for standard deviation and mean of historical data.     

一室模型药物吸收计算的概率论法

目的:从概率论角度为吸收动力学研究提供两种具体方法。方法:在一室模型时,药物分子从给药部位进入体循环和从体内消除的过程为相互独立的随机过程。将一次血管外给药后的总滞留时间(Th)视为在给药部位的滞留时间(Tf)与在体内的滞留时间(Tg)之和,Tf与Tg相互独立,且Th、Tf及Tg均为非负连续型随机变量。根据卷积公式及一室模型特征,得到一室模型药物吸收计算的概率论方法:概率法(A法)与数值反卷积分法(B法)。结果:概率论方法所需条件与Wagner-Nelson(W-N)法一致。A法所求吸收分数的准确度与W-N法相同。在已知消除速率常数K的准确值时,B法所求吸收分数的准确度低于W-N法,但B法尚可推测出同一药物注射剂型在血管内快速给药后的概率密度函数及相关信息。在K估计值有误差(±10%)时,B法所求吸收分数的准确度略高于W-N法。结论:概率论方法可用于计算吸收分数及有关概率。     

Optimal Two-Stage Designs to Evaluate a Series of New Agents or Treatments

Recent developments in cancer vaccines and other research programs have prompted the need for screening a large number of agents (or treatments) to identify promising ones for further clinical investigation. This article proposes optimal two-stage group sequential designs that minimize the expected number of patients exposed to inactive agents. The proposed three-outcome, two-stage designs include some region of uncertainty which enables factors such as cost of the agent and convenience of administration to be used in the decision making when the observed response rate falls into the uncertainty region. In the special case of two-outcome designs, our proposal extends Simon’s optimal two-stage design and other existing screening trial designs that use available distributional information on response rates. It does so by allowing early termination of the trial when the observed response rate is below a certain threshold and by providing more choices of error rates to control. A computer algorithm was developed to effectively calculate the three-outcome and/or two-outcome optimal designs. It is demonstrated via numerical studies and a cancer vaccine trial.     

Statistical Inference for the Within-Device Precision of Quantitative Measurements in Assay Validation

Intermediate precision is one of the most important characteristics for evaluation of precision in assay validation. The current methods for evaluation of within-device precision recommended by the Clinical Laboratory Standard Institute (CLSI) guideline EP5-A2 are based on the point estimator. On the other hand, in addition to point estimators, confidence intervals can provide a range for the within-device precision with a probability statement. Therefore, we suggest a confidence interval approach for assessment of the within-device precision. Furthermore, under the two-stage nested random-effects model recommended by the approved CLSI guideline EP5-A2, in addition to the current Satterthwaite's approximation and the modified large sample (MLS) methods, we apply the technique of generalized pivotal quantities (GPQ) to derive the confidence interval for the within-device precision. The data from the approved CLSI guideline EP5-A2 illustrate the applications of the confidence interval approach and comparison of results between the three methods. Results of a simulation study on the coverage probability and expected length of the three methods are reported. The proposed method of the GPQ-based confidence intervals is also extended to consider the between-laboratories variation for precision assessment.     

Sample Size Calculation for Poisson Endpoint Using the Exact Distribution of Difference Between Two Poisson Random Variables

We propose a method that compares Poisson distributed outcomes. Our method uses the exact distribution of the difference between two Poisson variables to calculate the sample size required to detect a given difference with prespecified power. When the true difference between the two Poisson rates is more than 1.2 units, the number of subjects and events needed at the desired power and Type I error rate is 5–10% less than that computed by simulation based on the normal approximation method. The normal approximation method is more comparable to the exact sample size method when the difference between the rates is less than 1.2 units. The proposed method is more intuitive, efficient, and less subjective than the normal approximation method. A simple code is developed in R to estimate the sample size and critical values.     

基于Schweizer算子簇的柔性概率逻辑算子的研究

Schweizer算子簇是泛逻辑学研究零级非相容T/S范数完整簇的数学基础,由它构造的与/或运算具有连续单调可变性.基于Schweizer算子簇构造的概率逻辑算子,既可满足概率测度的基本公理,又可实现概率逻辑运算的连续单调可变.     

急诊内科应用APACHEⅡ与SAPSⅡ评分在对患者重症评估时的价值比较

目的比较急性生理与慢性健康状况评分系统Ⅱ(APACHEⅡ)与简化急性生理学评分系统Ⅱ(SAPSⅡ)在临床急诊内科重症患者评估时的临床价值差异。方法收集本院2011年～2012年急诊内科收治的重症患者157例的临床资料,分别依据APACHEⅡ评分系统和SAPSⅡ系统对其预计病死率进行预计,并与真实病死率进行比较。结果两组患者的APACHEⅡ和SAPSⅡ评分和预计病死率方面均以死亡组较高(P<0.01),且SAPSⅡ评分法计算的两组预计死亡概率均高于APACHEⅡ法(P<0.05),两种分析方法的AUROCC分别为0.874和0.798(P<0.05)。随着入院时分值增加,病死率亦增加,且SAPSⅡ预计病死率高于实际病死率(P<0.05)。结论两种评价系统均适用于急诊内科重症患者的评估,其中APACHEⅡ校准度高,宜为首选。     

An Extension of Bayesian Expected Power and Its Application in Decision Making

Whether and how to conduct a clinical study is one of the critical decisions to be made every day in the pharmaceutical industry. Robust measurements on the probability of success (POS) of a study are crucial in the decision-making process. Among many factors that can affect the POS, establishment of the statistical alternative hypothesis ( H ₁ ) is arguably the most important consideration. The classical power is universally used for assessing the probability of H ₁ being accepted, given a certain value of the parameter of interest. If H ₁ is composite, power is often provided for a range of plausible values of the parameter to reflect our uncertainty about the parameter. This could become inconvenient for decision makers and potentially introduce biases into the decision-making process. This paper proposes an extension of Bayesian expected power (eBEP) as a single metric for assessing POS defined as the probability that H ₁ is accepted in a study as a tool for decision-making. eBEP is convenient, driven by scientific evidence, and systematically integrates the uncertainty, thus facilitates robust decision-making. The computation procedure of eBEP via Monte Carlo methods is provided. The application of eBEP is illustrated using a real-life bioequivalence study.