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Medically unexplained physical symptoms in primary care

den Boeft, M.

2016

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citation for published version (APA)den Boeft, M. (2016). Medically unexplained physical symptoms in primary care: Identification, management andsocietal aspects.

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1

CHAPTER3 Riskassessmentmodelsforpatientswithpersistentmedicallyunexplainedphysical

symptomsinprimarycareusingelectronicmedicalrecords

MadelondenBoeft

MarkHoogendoorn

JosWR.Twisk

SjanneNap

TimvanderNeut

JohannesC.vanderWouden

HenrietteE.vanderHorst

MattijsE.Numans

Submitted

2

ABSTRACT

Background

Patients with persistent medically unexplained physical symptoms (MUPS) often experience

limitationsduetotheirsymptoms,haveanimpairedqualityoflifeandincursubstantialhealth

carecosts. EarlyidentificationofpatientswithMUPSwhohaveahighriskofpersistentMUPS

could lead to early intervention by general practitioners (GPs), which could prevent the

transitiontopersistentMUPS.However, therearenoeffectiveandefficientscreeningmethods

to identifypatientswithahighriskofpersistentMUPS.Therefore, toenhanceidentificationof

theseriskpatientsandthussupportGPs,weaimedtodevelopariskassessmentmodelusinga

largedatasetextractedfromprimarycareelectronicmedicalrecords(EMRs).

Methods

WeusedanonymisedEMRsfrom22Dutchgeneralpractices(n=156.176)overafive-yearperiod

(2007-2011).WeoperationalizedpersistentMUPSby combining international classificationof

primarycare(ICPC)codesforirritablebowelsyndrome,fibromyalgia,chronicfatiguesyndrome

and lowbackpainwithout radiationas anendpoint.Webalanced thedatasetby includingall

patientswith persistentMUPS (n=7840) and a randomly selected sample of patientswithout

persistentMUPS(n=7988).Weappliedlogisticregressionanalysisanddecisiontreeanalysisto

assess thepersistentMUPS riskon80%of thedataset.Theperformancesof themodelswere

evaluatedwiththeareaunderthecurve(AUC)ontheremaining20%ofthedata,thevalidation

set.

Results

The variable best discriminating for persistent MUPS in both models was the number of

episodes.RiskassessmentofpersistentMUPSwasperformedgoodwiththedecisiontree(AUC

= 0.81) and moderate to good with logistic regression (AUC = 0.70). The validation showed

acceptablestability(0.78and0.70,respectively).

Conclusion

We explored the possibilities of assessment of patients’ individual risks for persistent MUPS

based on routine EMRs. We developed two moderate to good performing risk assessment

models, partly by using relatively new datamining techniques.While our algorithms require

further validation and fine-tuning, they provide a starting point fromwhichGPs could evolve

towardsamoreproactive,structuredMUPSmanagementtogetherwiththeirpatients.

3

BACKGROUND

Twenty to thirtyper centof all physical symptomspresented to thegeneralpractitioner (GP)

remains unsatisfactorily explained after adequate evaluation (1,2). Most of the so called

medicallyunexplainedphysicalsymptoms(MUPS)resolvespontaneously.However,aminority

of patients in whom MUPS become persistent suffer from major impairments in daily

functioning anda reducedqualityof life.Also, persistentMUPS lead tohigh costsdue tohigh

healthcareutilizationanddecreasedworkfunctioning(3,4).

Early assessment of the risk of patients developing persistent MUPS by the GP could create

possibilities for offering structured, proactive care and the prevention from the transition of

MUPS to persistentMUPS in subgroups of patientswith a high risk. This combination of risk

assessment followed by structured, specified proactive care interventions is called panel

management (5). Depending on the nature of the symptoms, functional limitations and the

preferences of the patient, different treatment options, for example cognitive behavioural

therapyorphysicaltherapy,canbediscussedandofferedatanearlystage(6,7).

TheidentificationofpatientsatriskforpersistentMUPSthatisneededforpanelmanagementis

not a trivial task as there are no generally accepted determinants to guide any selection

procedure. Other challenges include the heterogeneity amongMUPS patients (8), the varying

definitionsandclassificationsusedinliterature(9)andthelackofagenericMUPScodeinthe

international classification of primary care (ICPC) system, a commonly used classification

systeminprimarycare(10).OnlyafewsyndromescloselyrelatedtotheconceptofMUPSwitha

persistentcoursehaveaspecificICPCcode:irritablebowelsyndrome,chronicfatiguesyndrome,

fibromyalgiaand(persistentorrecurrent)lowbackpainwithoutradiation.

Inprimarycareresearch,routineelectronicmedicalrecords(EMRs)canbeusedforidentifying

subgroupsofpatientsatrisk.Thereisevidencethatsuggeststhatidentifyingsubgroupsatrisk

followed by panel management can improve quality of life, for example in the frail elderly

population (11,12). Only a few previous studies developed risk assessment or identification

modelsforMUPSusingEMRs,buteitherthemethoddidnotprovetobeeffectiveoritwasnot

directlysuitableforusageforprimarycare(13,14).

4

In the light of the need to identify potentially new risk variables for MUPS in large

heterogeneousEMRdatasets,more traditionalhypothesisdrivenapproachesmightnotbe the

mostsuitableones.Nowadays,relativelynewdataminingtechniquesareavailableforanalysing

large heterogeneous databases, such as EMR databases, to unravel relationships between

potentialriskvariablesandoutcomesandtogaindeeperunderstandingofthedata(15–17).In

thepresentobservationalstudyweaimedtoexplorealternativeapproachesindevelopingrisk

assessment models based on the analysis of primary care EMRs to enhance identification of

patientsatriskforpersistentMUPSusingtwodifferentstatisticaltechniques.

METHODS

Designandstudypopulation

We used an observational study design. We were able to use a database consisting of fully

anonymisedextractedroutineEMRsfrom22generalpracticecenters(156176patients)inthe

areaofUtrecht, TheNetherlands, covering a five-yearperiod (January2007-December2011).

All general practice centers represented in this dataset participate in the Julius General

Practitioners’Network(JGPN)andsharetheiranonymisedroutinehealthcaredatathroughthe

JGPNdatabase,connectedwiththeJuliusCenter,UniversityMedicalCenterUtrecht(UMCU)and

approvedbythemedicalethicalcommitteeoftheUMCU(file#99-240).

Alldataare codedusing the ICPC for registrationof symptomsanddiagnoses, theAnatomical

Therapeutic Chemical (ATC) classification system for drug prescription and various

measurement codes for biometry, interventions and referrals. All practice centers involved in

theJGPNroutinelyinformtheirenlistedpatientsthatEMRsareanonymisedandsharedthrough

theJGPNdatabaseandthatresearchiscarriedoutwiththiscodeddatabase.Individualpatients

whodonotwanttheirdatatobeusedforthispurposeareenabledtoopt-out.Sincepatientdata

arecompletelyanonymised,nofurtherinformedconsentornewapprovalofthemedicalethics

committeewasneeded.

5

Definitionofoutcome

SinceMUPSisnotaclearhomogeneousconditionordiagnosisandweaimedatdiscriminatinga

subgroupofpatientsat risk forpersistentMUPS,weoperationalized theconceptofpersistent

MUPS by choosing any combination of ICPC codes for irritable bowel syndrome (D93),

fibromyalgia(L18.01),chronicfatiguesyndrome(A04.01)andlowbackpainwithoutradiation

(L03) as thepotential outcome.We consideredpatients registeredwith anyof these codes to

closely represent the concept of persistent MUPS, since all four tend to develop into chronic

symptomatologywithoutstraightforwardpathophysiologicalexplanation.

Thedataset

ThedatabaseincludedindividualpatientrecordseachwiththefollowingroutineEMRvariables:

patient characteristics (gender, age), consultation characteristics (date of consultation, ICPC

code assigned per consultation, to which episode the consultation belongs), medication

(prescription date, type of medication using ATC code), referrals (date of referral to any

specialtytype)andlaboratoryresults(datesoftests,typeoftests,testresults,frequencyofeach

test per patient). The first occurrence of any of the four mentioned MUPS ICPC codes was

consideredtobethefirstregistrationofpersistentMUPSinourdefinition.Foreachpatientwith

persistentMUPS inourdefinition,alldataregisteredduring theyearprior to the firstdefined

ICPC codewere selected. For the patients in the dataset without one of the persistentMUPS

codes, a one-year period was randomly selected. As the number of patients with persistent

MUPSinthedatasetwassmall(n=7840),webalancedthedatasetbytakingarandomsampleof

patients without persistent MUPS (n=7988). From this balanced dataset, only patients aged

between18and65wereincludedintheanalyses(6398patientswithpersistentMUPSand5988

patientswithoutpersistentMUPS).

Constructingvariables

The one-year history per patient is event-based, while the construction of a risk assessment

modelrequiresasinglerecordperpatientthatsummarizestheeventsovertheentireyear. In

order tocreatesucha summarization,wederivedvariables fromtheeventsbycounting their

frequencywithinthegivenperiod.Theincludedvariablesaredemographicdata,summationsof

ICPC codes belonging to a chapter during the given period (both the number of unique ICPC

codes assigned from the chapter and the total number of consultations associated with each

chapter),numberofepisodes,numberofprescriptionsofmedicationsperATCgroup,aswellas

variables about laboratory results and referrals which are represented as the number of

referralsperspecialism.

6

Modelling

Weperformed a pre-selectionprocedure to reduce the number of variables to be used in the

modelling. In thispre-selection first thevariableswith less than10caseswereexcluded.This

wasdoneinthefulldataset.Second,becausethenumberofuniqueICPCcodesbelongingtoan

ICPC chapter and the number of consultations are obviously correlated, we only used the

number of consultations or contacts per chapter in the modelling procedure. In exploring

possibilities to generate an algorithm, we used two statistical methods to generate a risk

assessmentmodel for persistentMUPS. Firstwe used a relatively classical logistic regression

analysisapproachwithaforwardselectionprocedure.Second,weappliedarelativelyadvanced

decisiontreeanalysismorecommonlyusedindatamining.Thedecisiontreeanalysis isbased

onthemethodofrecursive-partitioninganalysis(18,19).Inthisanalysis,thetotalgroupissplit

into subgroupsbasedon the variable,whichdistinguishes thepersistentMUPS from thenon-

MUPS patients best. These subgroups are again partitioned, until no further significant

partitioningispossible.Incaseofcontinuouscandidateriskvariables,groupsweresplitatthe

cut-offpoint(s)withthehighestdiscriminativevalueforpersistentMUPS.Theterminalnodesin

thedecision treewereused toclassify thepatient’s risk forpersistentMUPS.For thedecision

treeanalysis,thechi-squaredautomaticinteractiondetector(CHAID)algorithmwasused(20),

with amaximum tree depth of 10 splits.Wedeveloped themodel in 80% of the dataset and

validatedthemodelintheremaining20%.Weassessedtheperformanceoftheriskassessment

models by computing the area under the curve (AUC) of the receiver operating characteristic

curve (ROC), which is also known as the C-statistic. All analyses were performed in SPSS

ModelerVersion16(IBM).

7

RESULTS

Thedataset

Table1showsdescriptiveinformationforthevariablesinthecomposeddataset,whichweused

todevelopourriskassessmentmodels.InthepersistentMUPSgroup62%werefemaleandthe

meanageofthegroupwas41years.Fortypercenthadoneepisode,26%hadtwoepisodesand

32%hadmorethantwoepisodes.MedicationfromATCchapterS(sensoryorgans)was

prescribedmostfrequentlyfollowedbymedicationfromchapterL(antineoplasticandimmune-

modulatingagents),R(respiratorysystem)andA(alimentarytractandmetabolism).Inthenon-

persistentMUPSgroups,57%werefemaleandthemeanageofthegroupwas40years.Fifty-

ninepercenthadzeroepisodes,24%hadoneepisodeand17%hadmorethanoneepisode.

MedicationfromATCChapterA(Alimentarytractandmetabolism)wasprescribedmost

frequently,followedbymedicationfromChapterSandLandR.Bothgroupswerereferredin

threepercentforadditionalexaminationortosecondarycarespecialisms.Mostreferralswere

notincludedinthepredictionmodellingbecausethenumberofpatientsreferredtosecondary

carespecialismswaslessthan10,toolowtoallowmeaningfulanalyses.

Table1.Descriptiveinformationregardingthevariablesusedtobuildpredictionmodels

forpersistentMUPS

Variabledescription PersistentMUPS

(n=6398)Non-persistent

MUPS(n=5988)Patient

Female,% 62 57Age,mean(SD) 41(13) 40(13)

Consultations

Totalnumberofepisodes,%

0 42 591 26 24

>1 32 17ICPCChapters,oneormorecontactsin%

A(GeneralandUnspecified) 14 23B(BloodandBloodformingorgans) 1 2D(Digestive) 9 10

F(Eye) 3 5H(Ear) 4 6K(Circulatory) 5 5

8

L(Musculoskeletal) 15 21

N(Neurological) 4 4P(Psychological) 10 8R(Respiratory) 14 18

S(Skin) 17 21T(Endocrine,metabolicandnutritional) 4 4U(Urology) 4 4

W(Pregnancy,childbirth,familyplanning) 6 8X(Femalegenitalsystemandbreasts) 8 10Y(Malegenitalsystem) 2 3

Z(Socialproblems) 3 3Referrals,oneormorein%

Total 3 3Imagingdiagnostics 1 1

Physiotherapy 1 0Laboratory 2 0ATCMedicationprescriptions,oneormorein%

A(Alimentarytractandmetabolism) 24 15B(Bloodandbloodformingorgans) 4 4

C(Cardiovascularsystem) 11 11D(Dermatologicals) 14 15

G(Genito-urinarysystemandsex-hormones) 11 13

J(Anti-infectivesforsystemicuse) 11 12H(Systemichormonalpreparations) 3 3L(Anti-neoplasticandimmune-modulating) 1 1

M(Musculoskeletalsystem) 24 10N(Nervoussystem) 21 16

P(Anti-parasiticproducts) 1 1R(Respiratorysystem) 16 16S(Sensoryorgans) 6 6

Productpp(numberofprescriptionproductswith

regardstomedication)58 57

Producttp(numberoftradeproductswithregardsto

medication)15 15

Productmp(numberofmedicalproductsfreely

available)16 9

Productcpp(numberofproductscustompreparedby

pharmacist)2 2

9

Totalmedication 66 64

Laboratory,oneormorein%

QuestionD(diagnosticquestions) 3 2

QuestionY(yes/noquestions) 2 2QuestionMC(multiplechoicequestions) 1 1QuestionM(measurements) 30 29

QuestiontypeFT(freetextquestions) 27 26Totallabresults 44 42

ICPCinternationalclassificationofprimarycare;ATCanatomicaltherapeuticclassification

Modelling

Table2showsthefinalriskassessmentmodelbasedonthelogisticregressionanalysis.Based

ontheWaldstatisticitcanbeseenthatthetotalnumberofepisodeswasthemost

discriminativevariableofthelogisticmodel,followedbymedicationforATCchapterM

(musculoskeletalsystem).Theoddsratioforthenumberofepisodes(i.e.1.486)indicatesthat

whenapatienthasonemoreepisode,theoddsofhavingpersistentMUPSbecomes1.486higher.

TheoddsratioformedicationforATCchapterMindicatesthatonemoremedicationinATC

chapterMisassociatedwitha1.935timeshigheroddsofhavingpersistentMUPS.Figure1(A

tillD)showsthemostoptimaldecisiontreebasedontheCHAIDalgorithm.Thefirstsplitinthe

decisiontreewasalsobasedthetotalnumberofepisodes.Thehighestprobabilityofhaving

persistentMUPS(figure1D),withaprobabilityofpersistentMUPSof96%wasobtainedvia

numberofepisodesmorethan3,numberofcontactsinICPCChapterA=zero,numberof

contactsinICPCChapterL=zeroandnumberofprescriptionproductsw.r.t.medication=zero.

ThelowestprobabilityofhavingpersistentMUPS(6%)wasobtainedbythefollowingsteps:

numberofepisodes=one,numberofcontactsinICPCChapterA=zero,numberofcontactsin

ICPCChapterLmorethanzeroandgender=male(figure1A).TheAUCcomputedforthefinal

logisticregressionpredictionmodelwas0.70andforthedecisiontree,theAUCwas0.81.The

validationprocedureofbothmodelsprovidedAUCvaluesof0.70forthelogisticregression

modeland0.78forthedecisiontree.Figure2showstheROCcurvesforthetworiskassessment

modelsinthevalidationset.

10

Table2.FinalpredictionmodelforpersistentMUPSbasedonalogisticregression

analysiswithaforwardselectionprocedure

Variabledescription Oddsratio Wald P-value

Female 1.253 21.649 <0.001

Age 1.014 51.334 <0.001

Numberofepisodes 1.486 311.645 <0.001

Numberofcontactsperchapter

D(Digestive) 0.974 6.228 0.013

F(Eye) 0.898 8.473 0.004

H(Ear) 0.957 3.202 0.074

L(Musculoskeletal) 0.912 63.265 <0.001

R(Respiratory) 0.981 4.516 0.034

S(Skin) 0.952 15.871 <0.001

W(Pregnancy,childbirth,familyplanning) 0.942 11.119 0.001

X(Femalegenitalsystemandbreasts) 0.912 23.169 <0.001

Y(Malegenitalsystem) 0.952 3.582 0.058

Z(Socialproblems) 0.889 7.833 0.005

Physiotherapy 9.747 24.085 <0.001

Numberofreferrals 0.754 5.667 0.017

Medication

A(Alimentarytractandmetabolism) 1.243 51.479 <0.001

J(Anti-infectivesforsystemicuse) 0.859 8.238 0.004

L(Anti-neoplasticand

Immune-modulatingagents) 0.578 8.438 0.004

M(Musculoskeletalsystem) 1.935 173.626 <0.001

N(Nervoussystem) 1.056 9.862 0.002

R(Respiratorysystem) 1.069 6.756 0.009

Productpp(numberofprescriptionproductswith

regardstomedication) 0.934 29.336 <0.001

Producttp(numberoftradeproductswith

regardstomedication) 0.912 21.054 <0.001

QuestiontypeFT(freetextquestions) 1.037 11.905 0.001

Numberoflaboratoryresults 0.988 48.002 <0.001

ICPCinternationalclassificationofprimarycare;ATCanatomicaltherapeuticclassification

Figure1.(AtoD)MostoptimalriskassessmentdecisiontreeforpersistentMUPS

11

1A

1B

12

1C

1D

13

MUPSmedicallyunexplainedphysicalsymptoms;ICPCinternationalclassificationofprimary

care;ATCanatomicaltherapeuticclassification;#episodes=numberofepisodes;*_contacts=

numberofcontactsinICPCchapter*;ATC_*;numberofmedicationsinATCchapter*;Product

pp=numberofprescriptionproductswithregardstomedication;#labresults=numberof

laboratoryresults

Figure2.Receiveroperatingcharacteristicsforthelogisticregressionmodelanddecision

treeonvalidationset

DISCUSSION

Mainfindings

Theobjectiveofourstudywastodevelopriskassessmentmodelsthatpotentiallycouldbeused

toidentifypatientsatriskforpersistentMUPSfromroutineprimarycareEMRs.Wecompared

the results froma relatively classical logistic regressionprocedurewith those fromadecision

tree model. We were able to assess the risk of persistent MUPS by using the final logistic

regressionmodelwithamoderateAUC,whilethedecisiontreeanalysesperformedsomewhat

betterwith amoderate to goodAUC.Thevariablemost clearlydiscriminating inbothmodels

wasthetotalnumberofepisodesamongpatientswithpersistentMUPS.

14

Comparisonwithliterature

Only fewstudiesaimed todevelopa riskassessmentor identificationmodel forpatientswith

MUPS from EMRs. They showed that there is no simple or straightforward method. Smith’s

model, containing gender, total number of consultations andpercentage of consultationswith

MUPS potential, initially predicted MUPS with an AUC of 0.90. Upon external validation, the

valuedecreasedto0.78(13).MorrissaimedtoestimatetheprevalenceofMUPSfromprimary

care EMRs (14). Theirmodels, related toMUPS and severeMUPS, hadAUCs of 0.70 and 076,

respectively.However,themodelsalsorequiredfurthervalidationandtheyconcludedthatthe

modelsdidnotyetprove tobeeffective forclinical screeningpurposesdue to lowsensitivity.

Rosmalenetalconductedalatentclassanalysistoidentifypatientswithsomatization,aconcept

closely related toMUPS, and found that a simple symptom count could support the diagnosis

somatization(21).Ourstudyisessentiallymoreorlessaconfirmationofthesepreviousstudies

because we also found that the number of episodes was the most important discriminative

variableforpersistentMUPSinbothourmethods.

Althoughnotentirelycomparablewithourstudy,otherstudiesidentifyingsubgroupsofMUPS

showed varying results. Tiandeveloped a risk assessmentmodel for chronic painwherepain

scores,painmedicationandspecificinternationalstatisticalclassificationofdiseasesandrelated

health problems (ICD-9) scores were combined. They found a high AUC of 0.98 (22). Viniol

performedk-meansclusteranalysisandstudiedpatientswithchroniclowbackpain(23).They

found three clusters specifically characterized by age and psychological variables such as

distress.Harkness et al usedREAD codes, standard clinical terminology system codesused in

generalpracticeintheUnitedKingdom,toidentifypatientsatriskforirritablebowelsyndrome.

Theyconcludedthat theresultswerea largeunderestimationof thecommunityprevalenceof

irritablebowelsyndromeflawingtheiranalysis(24).

Strengthsandlimitations

Themainstrengthofourstudy is thatweuseda largedatabasewithroutineEMRs, reflecting

what is registered indailyclinicalpractice.Tobe included in theregistration,apatienthas to

consulttheGPandtheGPhastocodethecomplaintasasymptomoradiagnosis.Byusingdata

frommanypracticesandfrommanypatients,theriskofselectionbiasandinformationbiasis

reduced and the influence of loss of detail is reduced. This study potentially provides new

insights inresearchmethodologybecauseofapplyingadvancedbigdataanalysisonrelatively

rawdatathataregatheredfromroutineprimarycare.

15

Ourresultsshouldbeinterpretedinthelightofsomelimitations.Aswithallroutinelycollected

EMRs, we could not verify the correctness and completeness of the coded information and

different GPs have different coding patterns. Also, by using four ICPC codes to conceptualize

persistentMUPS,wemighthavemissedsomepotentialothercharacteristicsofMUPSpatients.

But because GPs apply these codes only after medical evaluation thereby ruling out other

diseases,we are confident thatwe really identified patients resembling thosewith persistent

MUPSasthereference,thusenablingustoassesstheriskofotherstoevolvetothatcondition.

Anotherlimitationisthat,bybalancingthedatasetinordertoorganizeouranalysisstatistically

efficient, theprevalenceofMUPSchangedtomoreor less50%,whichpotentiallyhampersthe

generalizability of the results in a way that we look at relative risks and not absolute risks.

Finally, inthepresentstudy,weusedtheAUCtoquantifythequalityofthepredictionmodels.

The AUC is an indicator for the internal validity of the model. External validity should be

assessedby testing thealgorithms inanotherEMRdataset. In thepresentstudywemimicked

external validation by performing a cross-validation of the risk assessment models in an

independentpartoftheoriginaldataset.ForthelogisticregressionmodeltheAUCremainedthe

same, while for the decision tree, the AUC became slightly lower. This is not a big surprise,

becausethebuildingofthetreeishighlydatadriven.Nevertheless,thevalidationAUCremained

moderatetogood.

Implicationsforclinicalpracticeandresearch

Manueletalwhostatedthattheuseofriskassessmentmodelsinclinicalmedicinehasincreased

overthepast20yearsandthattheyhavethepotentialtosupporttheprocessofdecisionmaking

andmanagement of population health (16). Christensen et al confirmed that recognition and

managementbyGPsof commonprimary carementalhealthdisorders includingMUPScanbe

positivelyinfluencedbyusingscreeningquestionnaires(25).Riskassessmentamongsubgroups

ofpatientspotentiallydevelopingpersistentMUPScouldbeausefulfirststepforimplementing

panel management. By deriving a software algorithm from the risk assessment model to be

applied in routine primary care, the tendency to move towards persistent MUPS could be

increased.PatientsatriskcouldbeapproachedproactivelybytheirGPs.Andwhenapatientat

risk for persistent MUPS presents, GPs are potentially enabled to adjust their consultation

strategies by paying attention to empathic communication, exploring the patients’ functional

limitations and providing rational explanations before evolving targeted interventions in

consultationwiththepatient(26,27).Infutureresearch,firstthemodelshavetobeexternally

validated and a sensitivity analyses should be performedwhere the ratio between persistent

MUPSandnon-MUPSisvariedtoincreasethegeneralizability.

16

CONCLUSIONS

We explored the possibilities of assessment of patients’ individual risks for persistent MUPS

based on routineEMRs andwedeveloped twomoderate to goodperforming risk assessment

models, partly by using relatively new datamining techniques.While our algorithms require

further validation and fine-tuning, they provide a starting point fromwhichGPs could evolve

towardsamoreproactiveandstructuredMUPSmanagementtogetherwiththeirpatients.

ACKNOWLEDGEMENTS

TheauthorswouldliketothankthegeneralpractitionersfromtheJGPNUMCUandtheir

patientsforsharingtheiranonymouselectronicmedicalrecords.

17

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