Evaluation of inter-batch variability in the establishing and quality control of glucose

www.elsevier.es/rmuanl

ORIGINAL

ARTICLE

Evaluation

of

inter-batch

variability

in

the

establishing

and

quality

control

of

glucose

J.

Moya-Salazar

_,

_L.

_Pio-Dávila

a

M.T.,FacultaddeCienciasyFilosofía,UniversidadPeruanaCayetanoHeredia,Lima,Peru b_{M.L.T.,HospitalNacionalDosdeMayo,Lima,Peru}

Received18October2015;accepted1March2016 Availableonline11June2016

KEYWORDS Glucose; Qualityrequirements; Inter-batch variability; Operativepoint

Abstract Thisresearchevaluatedtheinter-batchvariabilityintheidentificationandquality controlofglucose,accordingtointernationalspecificationsdetailedintheguideCLSIEP-15. Type ofqualitative research, analytical, notexperimental, prospective cross-sectional con-ductedattheDepartmentofClinicalLaboratoryatPolyclinic‘‘LaFe’’duringJanuary2015was performed.Theinter-batchvariabilityforglucoseinthesemi-automatedbiochemicalanalyzer URIT-810withliquidenzymeglucose-LSreagent(GOD-PAP)Valtek® batch140825was evalu-ated.Thecalibrators(CS)werethelotCS-A:140428,CS-B:120912andCS-C:131202.Data analysiswasperformedinSPSSversion20.0statisticalanalyzerandMicrosoftOfficeExcel2010 forWindows.Thevaluesfoundbycalculatingthesigmametricwere:2(SE−0.35),0(SE 1.65) −0.9(SE −0.75)toCS-A,CS-BandCS-C,respectively (p<0.05).OnlyCS-Amightbe abletoimprovetheirperformance,althoughwithgreatercost.Sub-optimalperformance char-acteristicsbyusingstandardcalibratorsshowhighinter-lotvariability,suggestingthechoice andsearchforanewandbettercalibrationmethodtoensureresultsthatcontainnomedically importanterrorsaffectingpatienthealth.

©2016UniversidadAut´onomadeNuevoLe´on.PublishedbyMassonDoymaM´exicoS.A.Thisis anopenaccessarticleundertheCCBY-NC-NDlicense(http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Introduction

Glucose is the main energetic biomolecule for most liv-ingsystems,responsible formaintainingcellularfunctions

∗_{Correspondingauthorat:Pacifico957Urb,SanFelipeLima07,} Peru.Tel.:+5114873681;mobile:+511986014954.

E-mail addresses: [email protected], [email protected] (J.Moya-Salazar).

through glucid catabolism. Since its isolation in 1747 by AndreasSigismund, and the discovery of its configuration in 1902 by Emil Fisher, the function of glucose has been explainedandcorrelatedwiththedevelopmentofdiverse disorders and homeostatic mechanisms for its control.1

Thus,glucoseallowsforthediagnosisofseveraldisorders, suchasdiabetes mellitus, Cushing’s syndrome,meningeal inflammatoryprocesses,metabolicsyndrome, heredofamil-ial intracorpuscular hemolysis, and even enzymopathies, intoleranceor improper carbohydrate enteric absorption, http://dx.doi.org/10.1016/j.rmu.2016.03.004

1665-5796/©2016UniversidadAut´onomadeNuevoLe´on.PublishedbyMassonDoymaM´exicoS.A.Thisisanopenaccessarticleunderthe CCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

kidney diseases, and in vitro tests for cellular functional statusevaluation,inadditiontobeingusedasa preserva-tionagent for haemocomponents, among others.2 _Hence,

glucoseistranscendentalfordiagnosisinalmostallclinical laboratoriesareas.

Itis mainlyusedfordiagnosing diabetesmellitus (DM), a metabolic disease which imposes a high economic and social cost in the world today, especially type II DM. Diagnosis is made through signs and symptoms and stim-uli/response tests, such as the oral glucose tolerance test (OGTT), among others.3,4 _{Moreover, explorations of}

carbohydrate metabolism are employed, such as random glycemias,fastingplasmatic glycemias,the glucose toler-ancewithcorticoids test and,recently, withtheglycated hemoglobinA1Ctest,aswellasplasmaticinsulinlevel eval-uations,peptideCandglucagondosing,andglucoseinurine andlipidprofiles.5---7

Determined valuesindicate how the organism controls glucose.Biochemicaldeterminationisgenerallyperformed with reducing methods, enzymatic methods (hexokinase, glucose-oxidase,andglucose-dehydrogenase),and commer-cialdeterminationsbydrychemistryforclinicaldiagnosis, aswell asfor in-homemonitoring andimmunoradiometric trials.Enzymaticmethodsincommercialpresentationsare the most utilized, followed by patient monitoring by dry chemistry, usually with acceptable sensitivity ranges and errormargins.8_{Nevertheless,glucoseisoneofthe}

metabo-liteswhichsuffersmorepre-analyticandanalyticchanges, and onewhich presents an elevated biological intra-and inter-individual variability. However, the reagent maker incorporates internationally-validated technical specifica-tions;duetodifferentfactors,thesecannotbereproduced intheroutinelaboratory,thusmakingqualitycontrolunder workconditionswhichguaranteethequalityoftheirresults necessary.9,10

Parameterstoensurequalityinclinicalbiochemistry,as described by the Clinical Laboratory Standards Institute

(CLSI), include analyticalmethods and verification guides which lead to a correct planning and choosing of qual-itycontrolrulesforcontinuousmonitoringofperformance underworkconditions. Obtainingof thesedatesthetotal erroroflaboratorymethod(Tea)iscomparabletothe max-imum permissible error designed from different sources

liketheClinicalLaboratoryImprovementAmendments1988

(CLIA’88), biological variability, RCPA regulatory require-ments,etc. Subsequently,planning andmethodcontrolas wellasqualitywillshowcontinuousimprovement.11

Quality verificationof amethod’s analytical character-isticsis applicable toallkindsof laboratories.In addition tomeasurable dataonthe system’sperformance (inaccu-racy and bias), quality requirements and real reference valuesarenecessary.12_{Qualityrequirementsforglucoseare}

diverse,withmaximumquantifiablepermissibleerrorvalues upto±10%(±6mg/dl)inmostsources.5,13

Subsequent to the establishment of internal quality control,thesamereagentlotshouldbemaintainedin bio-chemistry(andhematology)for ayear,inordertocontrol performance.14_{Likewise,variabilityofthecontrolmaterials}

fromlot-to-lotshouldbeminimal.Sinceitrepresentsasmall amountofobservedvariation,thisshouldnotexceed10%.15

Amongstthecommunity,thecirculationoflyophilized con-trolsispredominant,which,unlikeliquidcontrols,present

highererrorandstabilityandlowercost,butrequirecareful handlingofvolumetricmaterial,distilledwaterand recon-stitution.

Withinthecommunity,therearefewclinicalanalysis lab-oratories withquality systemsin biochemistry. Consistent with the evolution of laboratories, most National hospi-tals and some private Health Centers in Lima maintain internal,externalandinter-laboratoryquality implementa-tionwithoutacosmopolitanreachinthecapital.Thevast majority of new clinical laboratories with economic limi-tations, interest of profit, or those which are unfamiliar withtheclinicalimpactofresultswithoutaqualitysystem onlyuse‘‘standardcalibrator’’controls,usually semiauto-maticbiochemicalanalyzersprovidedbythemanufacturer asaqualityreferenceforworkperformanceevaluationand resultreliability.

Itisevidentthatthelackofconscienceaboutthissubject andtheampleuncertaintyproducedintheresult,sincethe errorisnotquantifiedorcorrected, willbemagnified pro-gressivelyandbecomeuncontrollable.Inthisregard,some of themost commonlyusedreagents inclinical chemistry amongstthecommunitydonothavethesamelotinsidethe kit(inotherwords,adifferentlotfortheenzymaticreagent andforthestandardcalibrator).Then,howcanthequality oftheresultsbeensuredintheseconditions?

The objectiveof thisinvestigation wastoassess inter-batch variabilityin the determination andquality control of glucose, according to the international specifications detailedintheCLSIEP-15-A3guide.

Method

and

materials

A qualitative, analytic, non-experimental, prospective cross-sectional study wasconducted in the Clinical Labo-ratoryDepartmentatthebiochemicalareaofthe‘‘LaFe’’ PolyclinicHealthCareCenterduringJanuary,2015.Glucose inter-batch variabilitywas assessed according tothe CLSI EP15-A3guide,highlightingtheusefulnessof‘‘control let-ters’’ (metric sigma, power control chartand Normalized OPSpecschart).12

Sample

On-probabilistic,samplingintentionallybyconvenience.

Biochemicalanalyzer

In order to perform determinations, we used aURIT-810 MedicalElectronic(Guangxi,PRChina)semiautomatic bio-chemicalanalyzer,whichwasstabilizedto220V,100VA.

Reagents

Valtek®_{Glucose-LSenzymaticliquidreagent(Valtek} diagno-sis,SantiagodeChile,Chile)lot140825,withastoragerange between 2 and 8◦C, 50ml bottle, with glucose oxidase-peroxidase (GOD-PAP) enzymatic colorimetric method, modifiedbasedonthepublicationbyTrinderin1969.16_First

levelcalibratorkit(calibratorstandard glucose‘‘CS’’ (CS-A))andnormalfixedvalue(100±1mg/dl)oflot140428,B

A

B

C

1 ml Reactive

0.1 ml Standard calibrator.

Lot: 140 825

Lot: 140 428 Lot: 120 912 Lot: 131 202 Analytic trial: 5 days i triplicate.*

BIAS %CV BIAS %CV BIAS %CV Control letters Sigma metric, ΔSE

Westgard control rules (Monorregla-multirules)

Result quality Verification Planning Control * Total management of quality

Established underthe work protocol forGlucosedetermination–LS Valtek®

Analytictrials describedbytheCLSI EP-15 guide.

Figure1 Workschemewithintheglucoseserummatrix. andCcalibrators(CS-BandCS-C)oflot120912and131202,

respectively(100±1mg/dl).

Controlreagent

Inordertoperformmethodverification,thekit’s‘‘standard calibrators’’wereutilizedascontrolsaccordingtooperative needs.

Datarecollectiontechniqueandsampleprocessing

Accordingtothemaker’srecommendations,CSswere sta-bilizedat roomtemperature(roomtemperature25±2◦C) for10minbeforeprocessing.Theequipmentstabilizedfor 10min beforetheanalyticruns.Ablankreagentwasused beforetheanalysisverificationfromlot-to-lot. Inorderto evaluateprecision,threesetsofmeasurementsweremade fivedaysinarow.Fortheevaluationofveracity,duplicates ofthe5daysoftheprecisionprotocolofthe3control mate-rials for glucose were necessary (CS-A, CS-B and CS-C).12

Analyticrunswereconductedbytheinstitution’spersonnel underthe work’s protocol specifications.17 _{Procedures for}

glucoseverification wereconducted usingtheCLSI EP-15-A3guide,measuredinstandarddeviation(SD)andvariance coefficient (%VC) toasses bias and imprecision. With the dataobtainedfrom%VC,biasandTea, thedotand opera-tivelinesweredeterminedtoevaluatetheperformanceof themethodology, demonstratedingraphics withincontrol letters.Thesedeterminetheamountofcontrolsnecessary

fortheanalyticalrun,aswellastheclinical assaycontrol rules.18_{TheworkoutlineisshowninFig.1.}

Dataanalysistechnique

Dataanalysis wasdone using the statistical analyzer IBM SPSS, version 21 (Armonk, USA) and MS Excel 2010 (Red-mond,USA)forwindows.Thequalitymatrixwasdeveloped inanExcelsheetshowingSD,%VC,Tea,sixsigmaandcritical systemicerror(SDEorSE).

Limitations

Severallimitations oughttobeaddressed before interpre-tingresults.

First,theremaybefailures inthe conservationor sta-bilityofstandard calibrators, withindomesticdistribution byfranchisesorthebrand’scommercialdistributors.These maybethecauseforerroneousresults.Asecondlimitation is the fact that we were not able to compare the inter-batch glucose variability results found in this study with thosedescribed by Valtek reagentsfor glucose,which do notshow significant systemic differences in accuracy and imprecisionwhencomparedtoothercommercialreagents, obtainedusingtheBSseriesMINDRAYauto-analyzer.16_Athird

limitationisthattheclinicalbiochemistrylaboratorywhere thestudy wasconducted is not accredited under the ISO 15189 norm,but it does have the technical and manage-rialrequirementsoftheISO 9001normimplemented.The fourthlimitationiscontrolseraVALTROL-N(code210-100)

Table1 Averagebias%,%CV,teaforglucosewiththreeCS. CS-A CS-B CS-C bias% 0 10 21 %CV 6 11 12 Teaa _{10 10 10} Sigma 2 0 0.9 SE 0.35 −1.65 −0.75

a_{QualityrequirementsunderCLIA’88.}

and VALTROL-P (code210-110) areused in the biochemi-calfieldonaweeklybasis.Lastly,notalllinkedprotocols wereanalyzedwithmethodverification(linearity,detection limit,referencevalues,etc.)11,17_{.Despitetheselimitations,}

thisresearchisthefirsttodescribeverificationprocesses, planningandqualitycontrolusingstandardcalibratorssuch ascontrolserainclinicalbiochemistry.

Results

Oftheconductedresearch,thevaluesfoundbythesigma metriccalculationwere2,0and−0.9,forCS-A,CS-Band CS-C,respectively.Theseindicateaverypoorperformance, whichcannotbecontrolledormaintainedwithinthe appli-cationofastatisticalcontrol(p<0.05).19_(Table1)

Sigma metric is a process improvement methodology. Itsonly goal is toreach less than one defect per million (99.9997% successes). Although quality requirements are different for each magnitude, these unify with six sigma inordertocomparemethodswithasinglevalue.20 _Inthis

sense,we areable toexpressvalues andknow statistical

controlstrategiesbasedonsigma,asshowninFig.2asan example.

Othercontrollettersareoperativespecificationgraphics (OPSpecs) and power control chart. Power control chart represent the most powerful graphic because of the vast amount of data which can be obtainedfrom them during quality planning. These requires SE calculations, based onthemethod’smaximumpermissibleerror,whichshould not be more than the quality requirements (up to 90% of quality assurance), which equals the statistical value of 1.65 standard deviations.21 _{These functions present}

the information about the performance of a rejection probability graphic control rule versus the analytic error measures,asshowninFig.3.

Every one of the cases underwent 2 control levelsfor every analytical run (n=2). Choosing one control rulefor every analyte,therearefeweralarms ininternal control, thusfreeingtheanalyticalrunwithagoodchanceoferror detectionandalowprobabilityofrejectinggoodruns.Every letterbeginswiththechoiceofcontrolrules,accordingto each case (mono-rules or multi-rules) whichevaluate and controlimmeasurablesystemicandrandommistakes.

Discussion

Performancecharacteristicswerenotoptimalwiththeuse of standardcalibratorsfor glucosedetermination, accord-ingtothesigmametricevaluation(Table1).Thesedeficient performances(sigma<3)suggestachoiceandsearchfora newandimprovedcalibrationmethodwhichimproves pre-cision,sincethereisnominimumqualitycontrolunderthe studied conditions. The analysisof results with statistical controlstrategychartsbasedonsigmametricsprovesthat

Manual selection TEa=10.00 %(SE)

Probability f

or rejection (P)

Process metric (Sigma-scale)

1.65 2.65 3.65 4.65 5.65 P ed P fr 1 1 2 3s 0.03 0.04 1 2.5s 3s 2s 4s 1.00 1.00 4 1 4 1 0.03 1.00 4 1 0.01 1.00 4 1 0.01 0.99 4 1 0.03 0.01 0.96 0.94 2 2 1 1 0.01 4.0 3.0 2.0 1.0

Control metric (ΔSE, multiples of s)

0.0 0.0 0.1 0.2 0.3 Sigma = 2 ΔSEcrit = 0.35 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.94 2 1 2s 4s 1s / 2 / 13s 13s 13s 12.5s 2s 2 / 2s 2 / 13s 2s/2 R / 4s R / R / /4 N R

Figure2 SigmametricforglucoseCS-A.Thedeficientperformancesshownnotoptimalqualitycontrolprocesswithcalibrator standardglucose,noneofthemqualitystimations(NationalTestQuality,NationalMethodQualityandLocalMethodQuality)provides arobustenesswithCSforglucosecontrol.

1.0 TEa 10% Pfr Ped N R 0.9 0.8 0.7 0.6 0.5 Probability f or rejection (P) 0.4 0.3 0.2 0.1 0.0 0.0 1.0 2.0

Sistematic error (ΔSE)

SEcrit = 0.35 3.0 0.00 1 0.91 4 1 0.00 0.92 2 1 1_3s 1 2 2 R 2s 2s/ 4s 3s/ 1 1 3s/ 22s 1_3s/ 1_3s 2.5s 1_2.5s 0.01 0.96 2 1 0.01 0.96 2 1 0.03 0.97 2 1 0.01 0.98 4 1 0.01 1.00 4 1 0.04 1.00 4 1 4.0 3.5s

Figure3 PowercontrolchartforglucoseCS-A.Showthateventhoughwith10%ofprobabilityforrejectionbeableestablisha correctplanningandchoosingofqualitycontrolruleswithstatisticalcontrolchart.

analytical qualitycontrol management with standard cal-ibrators providedby the manufacturer are not enough to ensure results free of medically significant errors affect-ingthehealthofthepatient.Moreover,statisticsshowthe specificconcentrationtestperformanceinworkconditions inherenttothementionedlaboratory.22

The vast majority of clinical chemistry laboratories in Lima,which usemanualor semiautomaticmethodologies, presentahighdegreeofinaccuracyinglucoseand choles-teroldeterminations,highlightingnotonlythepoorquality oflabresults,butalsotheneed fordynamicandefficient controls which ensurequality inprocesses.23 _{Bynot using}

Good ClinicalLaboratory Practices, theerror is not quan-tified,thusuncertaintyintheresultcreatesabadclinical diagnosis. Itis worth notingthatqualityis nota common characteristicamongstbiochemicallaboratoriesinLima.A

contrariosensu,withthedevelopmentandnormalizationof

clinicalmedicine,doctorsandpatients areexpectinghigh qualityresults,whichis achallengefor alllaboratoriesin thecommunity.

Results provided during the calibrator’s analytical run shouldbetruthfulandpreciseinordertocertifythatroutine analyticaldeterminationsensureaminimumqualityanda correctclinical interpretation, in additiontobeinguseful fortheirinter-laboratorycomparability.22,23

OPSpecs’ statistical control letters as well, as power control chart, describe the acceptable imprecision and inaccuracy for a method and qualitycontrol necessary to supervise performance, as well as the test performance understableconditions,andwarnifthereareanychanges thatmayaffectthereport.Thegoalistoobtain<90% prob-ability of error detection (Ped or AQA) and less than 5% probabilityof false rejection (Pfr),with an N(amount of controlsbeinganalyzed)aslowaspossibleandinasingle analytical run.24 _{In the example shown in Fig. 3,none of}

therules is eligible, withtwocontrols peranalytical run. Through the use of control materials ‘‘CS’’ anda proper statisticalmanagement,weareabletoprovethatnorule orcontrolisusefulintheverificationofthemethod.25,26,27

In the same manner, the error in Valtek’s critical glucose-LSlevelswereestimated.Thesystemicerroristhe differencebetween the conventionallytruthful value and the median value of a number of determinations which are experimentally measured. Within this investigation, we were able to find a 0%, 10% and 20% of bias for CS-A, CS-B and CS-C, respectively. On the other hand, the systemic error, that is to say the resultant precision of the approximation’s repeated measured values, were 6%, 11% and 12% of CS-A, CS-B and CS-C, respectively. These continuous quantitativevariables, when expressed graph-ically, show thewide inter-batchvariability and error for glucose.Of these, only lot140428 (CS-A) could be capa-ble of improving its performance of being indispensable for analysis in the laboratory, in other words, capa-bleof recognizing, monitoring, minimizing and correcting errors, although it would be costly to maintain it within quality.14,15,17

Thecausesofthisvariabilitycouldbelinkedtodifferent motives,whichitisnotanobjectiveofthisinvestigationto describe.Butitistoconsiderwhetherornotsomefactors aredirectlyinterfering,suchthematrixtype,signal varia-tionbetweenequipment,lackofnoisecontrol,CSbehavior inworkconditions,measuredcommutability,transportation and control storage and lack of preventive maintenance, toname afew.In consequence, fullroutinemonitoring is ineludible.

Glucose concentration is very important for endocri-nologists,diabetologistsand diabeticpatients,evenwhen dysglycemicor apparently healthy, particularly when the concentrationisclosetothe upperlimitof thereference interval(±10%or6.1mmol/L).28_{Theconcerningprevalence}

ofdiabetesandprediabetessuggeststheimmediate prioriti-zationofhealthcareinordertoavoidfuturecomplications. Diagnosis by stimuli-response and/or explorations of the carbohydrate metabolism ought to be evaluated for all phenomenawhichmayinterveneduringeveryanalysisand generate non-quantifiable errors, which will not ensure qualityinresults.3,29

Conclusions

In this investigation, the measure of standard glucose calibrators was performed in order to prove inter-batch variability and the application of control letters, follow-ingevaluationproceduresrecommendedby theCLSIEP15 guide.Totalerrorwasidentifiedandqualitycontrol neces-sarytocontrolthemethodperformancewasevaluated.

Thewideinter-batchvarietyworksagainstresult repro-ducibility and quality, hence we must opt for a different controlmaterialforqualitymonitoringinglucose-LS deter-mination with Valtek® _{reagents. Quality planning helps} clinicalanalysislaboratorieswhichdon’tquantifyerrorsor guaranteeatrustworthy diagnosisfacequalitychallenges. However,theproblem withvariabilityand qualitycontrol estimationsisthatmostlaboratoryusersarenotfamiliarized withtheconcepts and themeasuringprocess isstill hier-archically restrained toreference methods, which ensure processtraceability,butarenotaccessibleforeveryclinical laboratorynationwide.

Itis notaproblemfor ourmethodtoworkwitha cer-taindegree of error. The problem is for this error tobe greaterthanthe maximumpermissibleamount, according tothemethod’squalityspecifications.Inthissense,quality ofresultsisnotguaranteedbyusingonlystandard calibra-torsprovidedby themanufacturer; thisevaluationshould bethestartingpointtodevelopqualityprocesses.

Analytical chemistry shouldnot only equilibrate a col-lection of data and methods, but obtain representative samples,optimizemethodsand interferencemanagement andguaranteequalityof data.We hope thisinvestigation isabletopromotetheuseofoptimalcontrolmaterialsfor correctplanningandqualitycontrolinclinicalanalysis lab-oratories.

Funding

sources

Self-financedbytheauthors.

Conflict

of

interest

Theauthorsdeclarenottohaveanyconflictsofinterest.

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