Differential diagnosis - Wikipedia

In healthcare, a differential diagnosis (abbreviated DDx) is a method of analysis of a patient's history and physical examination to arrive at the correct ... Differentialdiagnosis FromWikipedia,thefreeencyclopedia Jumptonavigation Jumptosearch Methodofanalysisofapatient'shistoryandphysicalexamination DifferentialdiagnosisMeSHD003937[editonWikidata] Inhealthcare,adifferentialdiagnosis(abbreviatedDDx)isamethodofanalysisofapatient'shistoryandphysicalexaminationtoarriveatthecorrectdiagnosis.Itinvolvesdistinguishingaparticulardiseaseorconditionfromothersthatpresentwithsimilarclinicalfeatures.[1]Differentialdiagnosticproceduresareusedbyclinicianstodiagnosethespecificdiseaseinapatient,or,atleast,toconsideranyimminentlylife-threateningconditions.Often,eachindividualoptionofapossiblediseaseiscalledadifferentialdiagnosis(e.g.,acutebronchitiscouldbeadifferentialdiagnosisintheevaluationofacough,evenifthefinaldiagnosisiscommoncold). Moregenerally,adifferentialdiagnosticprocedureisasystematicdiagnosticmethodusedtoidentifythepresenceofadiseaseentitywheremultiplealternativesarepossible.Thismethodmayemployalgorithms,akintotheprocessofelimination,oratleastaprocessofobtaininginformationthatshrinksthe"probabilities"ofcandidateconditionstonegligiblelevels,byusingevidencesuchassymptoms,patienthistory,andmedicalknowledgetoadjustepistemicconfidencesinthemindofthediagnostician(or,forcomputerizedorcomputer-assisteddiagnosis,thesoftwareofthesystem). Differentialdiagnosiscanberegardedasimplementingaspectsofthehypothetico-deductivemethod,inthesensethatthepotentialpresenceofcandidatediseasesorconditionscanbeviewedashypothesesthatcliniciansfurtherdetermineasbeingtrueorfalse. Adifferentialdiagnosisisalsocommonlyusedwithinthefieldofpsychiatry/psychology,wheretwodifferentdiagnosescanbeattachedtoapatientwhoisexhibitingsymptomsthatcouldfitintoeitherdiagnosis.Forexample,apatientwhohasbeendiagnosedwithbipolardisordermayalsobegivenadifferentialdiagnosisofborderlinepersonalitydisorder,[citationneeded]giventhesimilarityinthesymptomsofbothconditions. Strategiesusedinpreparingadifferentialdiagnosislistvarywiththeexperienceofthehealthcareprovider.Whilenoviceprovidersmayworksystemicallytoassessallpossibleexplanationsforapatient'sconcerns,thosewithmoreexperienceoftendrawonclinicalexperienceandpatternrecognitiontoprotectthepatientfromdelays,risks,andcostofinefficientstrategiesortests.Effectiveprovidersutilizeanevidence-basedapproach,complementingtheirclinicalexperiencewithknowledgefromclinicalresearch.[2] Contents 1Generalcomponents 2Specificmethods 2.1Epidemiology-basedmethod 2.1.1Theory 2.1.2Example 2.2Likelihoodratio-basedmethod 2.2.1Theory 2.2.2Example 3Coverageofcandidateconditions 4Combinations 5Machinedifferentialdiagnosis 6History 7Alternativemedicalmeanings 8Usageapartfrominmedicine 9Inart 10Seealso 11References Generalcomponents[edit] Thisparagraphneedsadditionalcitationsforverification.Pleasehelpimprovethisarticlebyaddingcitationstoreliablesources.Unsourcedmaterialmaybechallengedandremoved.Findsources: "Differentialdiagnosis" – news ·newspapers ·books ·scholar ·JSTOR(October2011)(Learnhowandwhentoremovethistemplatemessage) Forfurtheraspectsoftheprocess,seeDiagnosticprocedure. Adifferentialdiagnosishasfourgeneralsteps.Theclinicianwill: Gatherrelevantinformationaboutthepatientandcreateasymptomslist.[3] Listpossiblecauses(candidateconditions)forthesymptoms.[4]Thelistneednotbeinwriting. Prioritizethelistbybalancingtherisksofadiagnosiswiththeprobability.Thesearesubjective,notobjectiveparameters. Performteststodeterminetheactualdiagnosis.Thisisknownbythecolloquialphrase"toRuleOut".Evenaftertheprocess,thediagnosisisnotclear.Theclinicianagainconsiderstherisksandmaytreatthemempirically,oftencalled"EducatedBestGuess." AmnemonictohelpinconsideringmultiplepossiblepathologicalprocessesisVINDICATE'M:[citationneeded][clarificationneeded] Vascular Inflammatory/Infectious Neoplastic Degenerative/Deficiency/Drugs Idiopathic/Intoxication/Iatrogenic Congenital Autoimmune/Allergic/Anatomic Traumatic Endocrine/Environmental Metabolic[5] Specificmethods[edit] Thereareseveralmethodsfordifferentialdiagnosticproceduresandseveralvariantsamongthose.Furthermore,adifferentialdiagnosticprocedurecanbeusedconcomitantlyoralternatelywithprotocols,guidelines,orotherdiagnosticprocedures(suchaspatternrecognitionorusingmedicalalgorithms).[citationneeded] Forexample,incaseofmedicalemergency,theremaynotbeenoughtimetodoanydetailedcalculationsorestimationsofdifferentprobabilities,inwhichcasetheABCprotocol(Airway,BreathingandCirculation)maybemoreappropriate.Later,whenthesituationislessacute,amorecomprehensivedifferentialdiagnosticproceduremaybeadopted. Thedifferentialdiagnosticproceduremaybesimplifiedifa"pathognomonic"signorsymptomisfound(inwhichcaseitisalmostcertainthatthetargetconditionispresent)orintheabsenceofasinequanonsignorsymptom(inwhichcaseitisalmostcertainthatthetargetconditionisabsent). Adiagnosticiancanbeselective,consideringfirstthosedisordersthataremorelikely(aprobabilisticapproach),moreseriousifleftundiagnosedanduntreated(aprognosticapproach),ormoreresponsivetotreatmentifoffered(apragmaticapproach).[6]Sincethesubjectiveprobabilityofthepresenceofaconditionisneverexactly100%or0%,thedifferentialdiagnosticproceduremayaimatspecifyingthesevariousprobabilitiestoformindicationsforfurtheraction. Thefollowingaretwomethodsofdifferentialdiagnosis,beingbasedonepidemiologyandlikelihoodratios,respectively. Epidemiology-basedmethod[edit] Onemethodofperformingadifferentialdiagnosisbyepidemiologyaimstoestimatetheprobabilityofeachcandidateconditionbycomparingtheirprobabilitiestohaveoccurredinthefirstplaceintheindividual.Itisbasedonprobabilitiesrelatedbothtothepresentation(suchaspain)andprobabilitiesofthevariouscandidateconditions(suchasdiseases).[citationneeded] Theory[edit] ThestatisticalbasisfordifferentialdiagnosisisBayes'theorem.Asananalogy,whenadiehaslandedtheoutcomeiscertainby100%,buttheprobabilitythatitWouldHaveOccurredintheFirstPlace(hereafterabbreviatedWHOIFP)isstill1/6.Inthesameway,theprobabilitythatapresentationorconditionwouldhaveoccurredinthefirstplaceinanindividual(WHOIFPI)isnotsameastheprobabilitythatthepresentationorconditionhasoccurredintheindividual,becausethepresentationhasoccurredby100%certaintyintheindividual.Yet,thecontributiveprobabilityfractionsofeachconditionareassumedthesame,relatively: Pr ( Presentationiscausedbyconditioninindividual ) Pr ( Presentationhasoccurredinindividual ) = Pr ( PresentationWHOIFPIbycondition ) Pr ( PresentationWHOIFPI ) {\displaystyle{\begin{aligned}&{\frac{\Pr({\text{Presentationiscausedbyconditioninindividual}})}{\Pr({\text{Presentationhasoccurredinindividual}})}}={\frac{\Pr({\text{PresentationWHOIFPIbycondition}})}{\Pr({\text{PresentationWHOIFPI}})}}\end{aligned}}} where: Pr(Presentationiscausedbyaconditioninindividual)istheprobabilitythatthepresentationiscausedbyconditionintheindividualconditionwithoutfurtherspecificationreferstoanycandidatecondition Pr(Presentationhasoccurredinindividual)istheprobabilitythatthepresentationhasoccurredintheindividual,whichcanbeperceivedandtherebysetat100% Pr(PresentationWHOIFPIbycondition)istheprobabilitythatthepresentationWouldHaveOccurredintheFirstPlaceintheIndividualbycondition Pr(PresentationWHOIFPI)istheprobabilitythatthepresentationWouldHaveOccurredintheFirstPlaceintheIndividual Whenanindividualpresentswithasymptomorsign,Pr(Presentationhasoccurredinindividual)is100%andcanthereforebereplacedby1,andcanbeignoredsincedivisionby1doesnotmakeanydifference: Pr ( Presentationiscausedbyconditioninindividual ) = Pr ( PresentationWHOIFPIbycondition ) Pr ( PresentationWHOIFPI ) {\displaystyle\Pr({\text{Presentationiscausedbyconditioninindividual}})={\frac{\Pr({\text{PresentationWHOIFPIbycondition}})}{\Pr({\text{PresentationWHOIFPI}})}}} Thetotalprobabilityofthepresentationtohaveoccurredintheindividualcanbeapproximatedasthesumoftheindividualcandidateconditions: Pr ( PresentationWHOIFPI ) = Pr ( PresentationWHOIFPIbycondition1 ) + Pr ( PresentationWHOIFPIbycondition2 ) + Pr ( PresentationWHOIFPIbycondition3 ) + etc. {\displaystyle{\begin{aligned}\Pr({\text{PresentationWHOIFPI}})&=\Pr({\text{PresentationWHOIFPIbycondition1}})\\&{}+\Pr({\text{PresentationWHOIFPIbycondition2}})\\&{}+\Pr({\text{PresentationWHOIFPIbycondition3}})+{\text{etc.}}\end{aligned}}} Also,theprobabilityofthepresentationtohavebeencausedbyanycandidateconditionisproportionaltotheprobabilityofthecondition,dependingonwhatrateitcausesthepresentation: Pr ( PresentationWHOIFPIbycondition ) = Pr ( ConditionWHOIFPI ) ⋅ r condition → presentation , {\displaystyle\Pr({\text{PresentationWHOIFPIbycondition}})=\Pr({\text{ConditionWHOIFPI}})\cdotr_{{\text{condition}}\rightarrow{\text{presentation}}},} where: Pr(PresentationWHOIFPIbycondition)istheprobabilitythatthepresentationWouldHaveOccurredintheFirstPlaceintheIndividualbycondition Pr(ConditionWHOIFPI)istheprobabilitythattheconditionWouldHaveOccurredintheFirstPlaceintheIndividual rCondition→presentationistherateatwhichaconditioncausesthepresentation,thatis,thefractionofpeoplewithconditionsthatmanifestswiththepresentation. Theprobabilitythataconditionwouldhaveoccurredinthefirstplaceinanindividualisapproximatelyequaltothatofapopulationthatisassimilartotheindividualaspossibleexceptforthecurrentpresentation,compensatedwherepossiblebyrelativerisksgivenbyknownriskfactorthatdistinguishtheindividualfromthepopulation: Pr ( ConditionWHOIFPI ) ≈ R R condition ⋅ Pr ( Conditioninpopulation ) , {\displaystyle\Pr({\text{ConditionWHOIFPI}})\approxRR_{\text{condition}}\cdot\Pr({\text{Conditioninpopulation}}),} where: Pr(ConditionWHOIFPI)istheprobabilitythattheconditionWouldHaveOccurredintheFirstPlaceintheIndividual RRconditionistherelativeriskforconditionconferredbyknownriskfactorsintheindividualthatarenotpresentinthepopulation Pr(Conditioninpopulation)istheprobabilitythattheconditionoccursinapopulationthatisassimilartotheindividualaspossibleexceptforthepresentation Thefollowingtabledemonstrateshowtheserelationscanbemadeforaseriesofcandidateconditions: Candidatecondition1 Candidatecondition2 Candidatecondition3 Pr(Conditioninpopulation) Pr(Condition1inpopulation) Pr(Condition2inpopulation) Pr(Condition3inpopulation) RRcondition RR1 RR2 RR3 Pr(ConditionWHOIFPI) Pr(Condition1WHOIFPI) Pr(Condition2WHOIFPI) P(Condition3WHOIFPI) rCondition→presentation rCondition1→presentation rCondition2→presentation rCondition3→presentation Pr(PresentationWHOIFPIbycondition) Pr(PresentationWHOIFPIbycondition1) Pr(PresentationWHOIFPIbycondition2) Pr(PresentationWHOIFPIbycondition3) Pr(PresentationWHOIFPI)=thesumoftheprobabilitiesinrowjustabove Pr(Presentationiscausedbyconditioninindividual) Pr(Presentationiscausedbycondition1inindividual) Pr(Presentationiscausedbycondition2inindividual) Pr(Presentationiscausedbycondition3inindividual) Oneadditional"candidatecondition"istheinstanceoftherebeingnoabnormality,andthepresentationisonlya(usuallyrelativelyunlikely)appearanceofabasicallynormalstate.Itsprobabilityinthepopulation(P(Noabnormalityinpopulation))iscomplementarytothesumofprobabilitiesof"abnormal"candidateconditions. Example[edit] Thisexamplecasedemonstrateshowthismethodisappliedbutdoesnotrepresentaguidelineforhandlingsimilarreal-worldcases.Also,theexampleusesrelativelyspecifiednumberswithsometimesseveraldecimals,whileinreality,thereareoftensimplyroughestimations,suchasoflikelihoodsbeingveryhigh,high,loworverylow,butstillusingthegeneralprinciplesofthemethod.[citationneeded] Foranindividual(whobecomesthe"patient"inthisexample),abloodtestof,forexample,serumcalciumshowsaresultabovethestandardreferencerange,which,bymostdefinitions,classifiesashypercalcemia,whichbecomesthe"presentation"inthiscase.Aclinician(whobecomesthe"diagnostician"inthisexample),whodoesnotcurrentlyseethepatient,getstoknowabouthisfinding. Bypracticalreasons,theclinicianconsidersthatthereisenoughtestindicationtohavealookatthepatient'smedicalrecords.Forsimplicity,let'ssaythattheonlyinformationgiveninthemedicalrecordsisafamilyhistoryofprimaryhyperparathyroidism(hereabbreviatedasPH),whichmayexplainthefindingofhypercalcemia.Forthispatient,let'ssaythattheresultanthereditaryriskfactorisestimatedtoconferarelativeriskof10(RRPH=10). Theclinicianconsidersthatthereisenoughmotivationtoperformadifferentialdiagnosticprocedureforthefindingofhypercalcemia.Themaincausesofhypercalcemiaareprimaryhyperparathyroidism(PH)andcancer,soforsimplicity,thelistofcandidateconditionsthatthecliniciancouldthinkofcanbegivenas: Primaryhyperparathyroidism(PH) Cancer Otherdiseasesthatthecliniciancouldthinkof(whichissimplytermed"otherconditions"fortherestofthisexample) Nodisease(ornoabnormality),andthefindingiscausedentirelybystatisticalvariability Theprobabilitythat'primaryhyperparathyroidism'(PH)wouldhaveoccurredinthefirstplaceintheindividual(P(PHWHOIFPI))canbecalculatedasfollows: Let'ssaythatthelastbloodtesttakenbythepatientwashalfayearagoandwasnormalandthattheincidenceofprimaryhyperparathyroidisminageneralpopulationappropriatelymatchestheindividual(exceptforthepresentationandmentionedheredity)is1in4000peryear.Ignoringmoredetailedretrospectiveanalyses(suchasincludingspeedofdiseaseprogressandlagtimeofmedicaldiagnosis),thetime-at-riskforhavingdevelopedprimaryhyperparathyroidismcanroughlyberegardedasbeingthelasthalf-yearbecauseapreviouslydevelopedhypercalcemiawouldprobablyhavebeencaughtupbythepreviousbloodtest.Thiscorrespondstoaprobabilityofprimaryhyperparathyroidism(PH)inthepopulationof: Pr ( PHinpopulation ) = 0.5  years ⋅ 1 4000peryear = 1 8000 {\displaystyle\Pr({\text{PHinpopulation}})=0.5{\text{years}}\cdot{\frac{1}{\text{4000peryear}}}={\frac{1}{8000}}} Withtherelativeriskconferredfromthefamilyhistory,theprobabilitythatprimaryhyperparathyroidism(PH)wouldhaveoccurredinthefirstplaceintheindividualgivenfromthecurrentlyavailableinformationbecomes: Pr ( PHWHOIFPI ) ≈ R R P H ⋅ Pr ( PHinpopulation ) = 10 ⋅ 1 8000 = 1 800 = 0.00125 {\displaystyle\Pr({\text{PHWHOIFPI}})\approxRR_{PH}\cdot\Pr({\text{PHinpopulation}})=10\cdot{\frac{1}{8000}}={\frac{1}{800}}=0.00125} Primaryhyperparathyroidismcanbeassumedtocausehypercalcemiaessentially100%ofthetime(rPH→hypercalcemia=1),sothisindependentlycalculatedprobabilityofprimaryhyperparathyroidism(PH)canbeassumedtobethesameastheprobabilityofbeingacauseofthepresentation: Pr ( HypercalcemiaWHOIFPIbyPH ) = Pr ( PHWHOIFPI ) ⋅ r PH → hypercalcemia = 0.00125 ⋅ 1 = 0.00125 {\displaystyle{\begin{aligned}\Pr({\text{HypercalcemiaWHOIFPIbyPH}})&=\Pr({\text{PHWHOIFPI}})\cdotr_{{\text{PH}}\rightarrow{\text{hypercalcemia}}}\\&=0.00125\cdot1=0.00125\end{aligned}}} Forcancer,thesametime-at-riskisassumedforsimplicity,andlet'ssaythattheincidenceofcancerintheareaisestimatedat1in250peryear,givingapopulationprobabilityofcancerof: Pr ( cancerinpopulation ) = 0.5  years ⋅ 1 250peryear = 1 500 {\displaystyle\Pr({\text{cancerinpopulation}})=0.5{\text{years}}\cdot{\frac{1}{\text{250peryear}}}={\frac{1}{500}}} Forsimplicity,let'ssaythatanyassociationbetweenafamilyhistoryofprimaryhyperparathyroidismandriskofcancerisignored,sotherelativeriskfortheindividualtohavecontractedcancerinthefirstplaceissimilartothatofthepopulation(RRcancer=1): Pr ( cancerWHOIFPI ) ≈ R R cancer ⋅ Pr ( cancerinpopulation ) = 1 ⋅ 1 500 = 1 500 = 0.002. {\displaystyle\Pr({\text{cancerWHOIFPI}})\approxRR_{\text{cancer}}\cdot\Pr({\text{cancerinpopulation}})=1\cdot{\frac{1}{500}}={\frac{1}{500}}=0.002.} However,hypercalcemiaonlyoccursin,veryapproximately,10%ofcancers,[7](rcancer→hypercalcemia=0.1),so: Pr ( HypercalcemiaWHOIFPIbycancer ) = Pr ( cancerWHOIFPI ) ⋅ r cancer → hypercalcemia = 0.002 ⋅ 0.1 = 0.0002. {\displaystyle{\begin{aligned}&\Pr({\text{HypercalcemiaWHOIFPIbycancer}})\\=&\Pr({\text{cancerWHOIFPI}})\cdotr_{{\text{cancer}}\rightarrow{\text{hypercalcemia}}}\\=&0.002\cdot0.1=0.0002.\end{aligned}}} Theprobabilitiesthathypercalcemiawouldhaveoccurredinthefirstplacebyothercandidateconditionscanbecalculatedinasimilarmanner.However,forsimplicity,let'ssaythattheprobabilitythatanyofthesewouldhaveoccurredinthefirstplaceiscalculatedat0.0005inthisexample. Fortheinstanceoftherebeingnodisease,thecorrespondingprobabilityinthepopulationiscomplementarytothesumofprobabilitiesforotherconditions: Pr ( nodiseaseinpopulation ) = 1 − Pr ( PHinpopulation ) − Pr ( cancerinpopulation ) − Pr ( otherconditionsinpopulation ) = 0.997. {\displaystyle{\begin{aligned}\Pr({\text{nodiseaseinpopulation}})&=1-\Pr({\text{PHinpopulation}})-\Pr({\text{cancerinpopulation}})\\&{}\quad-\Pr({\text{otherconditionsinpopulation}})\\&{}=0.997.\end{aligned}}} Theprobabilitythattheindividualwouldbehealthyinthefirstplacecanbeassumedtobethesame: Pr ( nodiseaseWHOIFPI ) = 0.997. {\displaystyle\Pr({\text{nodiseaseWHOIFPI}})=0.997.\,} Therateatwhichthecaseofnoabnormalconditionstillendsupinmeasurementofserumcalciumofbeingabovethestandardreferencerange(therebyclassifyingashypercalcemia)is,bythedefinitionofstandardreferencerange,lessthan2.5%.However,thisprobabilitycanbefurtherspecifiedbyconsideringhowmuchthemeasurementdeviatesfromthemeaninthestandardreferencerange.Let'ssaythattheserumcalciummeasurementwas1.30 mmol/L,which,withastandardreferencerangeestablishedat1.05to1.25 mmol/L,correspondstoastandardscoreof3andacorrespondingprobabilityof0.14%thatsuchdegreeofhypercalcemiawouldhaveoccurredinthefirstplaceinthecaseofnoabnormality: r nodisease → hypercalcemia = 0.0014 {\displaystyler_{{\text{nodisease}}\rightarrow{\text{hypercalcemia}}}=0.0014} Subsequently,theprobabilitythathypercalcemiawouldhaveresultedfromnodiseasecanbecalculatedas: Pr ( HypercalcemiaWHOIFPIbynodisease ) = Pr ( nodiseaseWHOIFPI ) ⋅ r nodisease → hypercalcemia = 0.997 ⋅ 0.0014 ≈ 0.0014 {\displaystyle{\begin{aligned}&\Pr({\text{HypercalcemiaWHOIFPIbynodisease}})\\=&\Pr({\text{nodiseaseWHOIFPI}})\cdotr_{{\text{nodisease}}\rightarrow{\text{hypercalcemia}}}\\=&0.997\cdot0.0014\approx0.0014\end{aligned}}} Theprobabilitythathypercalcemiawouldhaveoccurredinthefirstplaceintheindividualcanthusbecalculatedas: Pr ( hypercalcemiaWHOIFPI ) = Pr ( hypercalcemiaWHOIFPIbyPH ) + Pr ( hypercalcemiaWHOIFPIbycancer ) + Pr ( hypercalcemiaWHOIFPIbyotherconditions ) + Pr ( hypercalcemiaWHOIFPIbynodisease ) = 0.00125 + 0.0002 + 0.0005 + 0.0014 = 0.00335 {\displaystyle{\begin{aligned}&\Pr({\text{hypercalcemiaWHOIFPI}})\\=&\Pr({\text{hypercalcemiaWHOIFPIbyPH}})+\Pr({\text{hypercalcemiaWHOIFPIbycancer}})\\&{}+\Pr({\text{hypercalcemiaWHOIFPIbyotherconditions}})+\Pr({\text{hypercalcemiaWHOIFPIbynodisease}})\\=&0.00125+0.0002+0.0005+0.0014=0.00335\end{aligned}}} Subsequently,theprobabilitythathypercalcemiaiscausedbyprimaryhyperparathyroidism(PH)intheindividualcanbecalculatedas: Pr ( hypercalcemiaiscausedbyPHinindividual ) = Pr ( hypercalcemiaWHOIFPIbyPH ) Pr ( hypercalcemiaWHOIFPI ) = 0.00125 0.00335 = 0.373 = 37.3 % {\displaystyle{\begin{aligned}&\Pr({\text{hypercalcemiaiscausedbyPHinindividual}})\\=&{\frac{\Pr({\text{hypercalcemiaWHOIFPIbyPH}})}{\Pr({\text{hypercalcemiaWHOIFPI}})}}\\=&{\frac{0.00125}{0.00335}}=0.373=37.3\%\end{aligned}}} Similarly,theprobabilitythathypercalcemiaiscausedbycancerintheindividualcanbecalculatedas: Pr ( hypercalcemiaiscausedbycancerinindividual ) = Pr ( hypercalcemiaWHOIFPIbycancer ) Pr ( hypercalcemiaWHOIFPI ) = 0.0002 0.00335 = 0.060 = 6.0 % , {\displaystyle{\begin{aligned}&\Pr({\text{hypercalcemiaiscausedbycancerinindividual}})\\=&{\frac{\Pr({\text{hypercalcemiaWHOIFPIbycancer}})}{\Pr({\text{hypercalcemiaWHOIFPI}})}}\\=&{\frac{0.0002}{0.00335}}=0.060=6.0\%,\end{aligned}}} andforothercandidateconditions: Pr ( hypercalcemiaiscausedbyotherconditionsinindividual ) = Pr ( hypercalcemiaWHOIFPIbyotherconditions ) Pr ( hypercalcemiaWHOIFPI ) = 0.0005 0.00335 = 0.149 = 14.9 % , {\displaystyle{\begin{aligned}&\Pr({\text{hypercalcemiaiscausedbyotherconditionsinindividual}})\\=&{\frac{\Pr({\text{hypercalcemiaWHOIFPIbyotherconditions}})}{\Pr({\text{hypercalcemiaWHOIFPI}})}}\\=&{\frac{0.0005}{0.00335}}=0.149=14.9\%,\end{aligned}}} andtheprobabilitythatthereactuallyisnodisease: Pr ( hypercalcemiaispresentdespitenodiseaseinindividual ) = Pr ( hypercalcemiaWHOIFPIbynodisease ) Pr ( hypercalcemiaWHOIFPI ) = 0.0014 0.00335 = 0.418 = 41.8 % {\displaystyle{\begin{aligned}&\Pr({\text{hypercalcemiaispresentdespitenodiseaseinindividual}})\\=&{\frac{\Pr({\text{hypercalcemiaWHOIFPIbynodisease}})}{\Pr({\text{hypercalcemiaWHOIFPI}})}}\\=&{\frac{0.0014}{0.00335}}=0.418=41.8\%\end{aligned}}} Forclarification,thesecalculationsaregivenasthetableinthemethoddescription: PH Cancer Otherconditions Nodisease P(Conditioninpopulation) 0.000125 0.002 - 0.997 RRx 10 1 - - P(ConditionWHOIFPI) 0.00125 0.002 - - rCondition→hypercalcemia 1 0.1 - 0.0014 P(hypercalcemiaWHOIFPIbycondition) 0.00125 0.0002 0.0005 0.0014 P(hypercalcemiaWHOIFPI)=0.00335 P(hypercalcemiaiscausedbyconditioninindividual) 37.3% 6.0% 14.9% 41.8% Thus,thismethodestimatesthattheprobabilitythatthehypercalcemiaiscausedbyprimaryhyperparathyroidism,cancer,otherconditionsornodiseaseatallare37.3%,6.0%,14.9%,and41.8%,respectively,whichmaybeusedinestimatingfurthertestindications. Thiscaseiscontinuedintheexampleofthemethoddescribedinthenextsection. Likelihoodratio-basedmethod[edit] Theprocedureofdifferentialdiagnosiscanbecomeextremelycomplexwhenfullytakingadditionaltestsandtreatmentsintoconsideration.Onemethodthatissomewhatatradeoffbetweenbeingclinicallyperfectandbeingrelativelysimpletocalculateisonethatuseslikelihoodratiostoderivesubsequentpost-testlikelihoods. Theory[edit] Theinitiallikelihoodsforeachcandidateconditioncanbeestimatedbyvariousmethods,suchas: Byepidemiologyasdescribedintheprevioussection. Byclinic-specificpatternrecognition,suchasstatisticallyknowingthatpatientscomingintoaparticularclinicwithaparticularcomplaintstatisticallyhasaparticularlikelihoodofeachcandidatecondition. Onemethodofestimatinglikelihoodsevenafterfurthertestsuseslikelihoodratios(whichisderivedfromsensitivitiesandspecificities)asamultiplicationfactoraftereachtestorprocedure.Inanidealworld,sensitivitiesandspecificitieswouldbeestablishedforalltestsforallpossiblepathologicalconditions.Inreality,however,theseparametersmayonlybeestablishedforoneofthecandidateconditions.Multiplyingwithlikelihoodratiosnecessitatesconversionoflikelihoodsfromprobabilitiestooddsinfavor(hereaftersimplytermed"odds")by: odds = probability 1 − probability {\displaystyle{\text{odds}}={\frac{\text{probability}}{1-{\text{probability}}}}} However,onlythecandidateconditionswithknownlikelihoodrationeedthisconversion.Aftermultiplication,conversionbacktoprobabilityiscalculatedby: probability = odds odds + 1 {\displaystyle{\text{probability}}={\frac{\text{odds}}{{\text{odds}}+1}}} Therestofthecandidateconditions(forwhichthereisnoestablishedlikelihoodratioforthetestathand)can,forsimplicity,beadjustedbysubsequentlymultiplyingallcandidateconditionswithacommonfactortoagainyieldasumof100%. Theresultingprobabilitiesareusedforestimatingtheindicationsforfurthermedicaltests,treatmentsorotheractions.Ifthereisanindicationforanadditionaltest,anditreturnswitharesult,thentheprocedureisrepeatedusingthelikelihoodratiooftheadditionaltest.Withupdatedprobabilitiesforeachofthecandidateconditions,theindicationsforfurthertests,treatments,orotheractionschangeaswell,andsotheprocedurecanberepeateduntilanendpointwheretherenolongerisanyindicationforcurrentlyperformingfurtheractions.Suchanendpointmainlyoccurswhenonecandidateconditionbecomessocertainthatnotestcanbefoundthatispowerfulenoughtochangetherelativeprobabilityprofileenoughtomotivateanychangeinfurtheractions.Tacticsforreachingsuchanendpointwithasfewtestsaspossibleincludesmakingtestswithhighspecificityforconditionsofalreadyoutstandinglyhigh-profile-relativeprobability,becausethehighlikelihoodratiopositiveforsuchtestsisveryhigh,bringingalllesslikelyconditionstorelativelylowerprobabilities.Alternatively,testswithhighsensitivityforcompetingcandidateconditionshaveahighlikelihoodrationegative,potentiallybringingtheprobabilitiesforcompetingcandidateconditionstonegligiblelevels.Ifsuchnegligibleprobabilitiesareachieved,thecliniciancanruleouttheseconditions,andcontinuethedifferentialdiagnosticprocedurewithonlytheremainingcandidateconditions. Example[edit] Thisexamplecontinuesforthesamepatientasintheexamplefortheepidemiology-basedmethod.Aswiththepreviousexampleofepidemiology-basedmethod,thisexamplecaseismadetodemonstratehowthismethodisappliedbutdoesnotrepresentaguidelineforhandlingsimilarreal-worldcases.Also,theexampleusesrelativelyspecifiednumbers,whileinreality,thereareoftenjustroughestimations.Inthisexample,theprobabilitiesforeachcandidateconditionwereestablishedbyanepidemiology-basedmethodtobeasfollows: PH Cancer Otherconditions Nodisease Probability 37.3% 6.0% 14.9% 41.8% Thesepercentagescouldalsohavebeenestablishedbyexperienceattheparticularclinicbyknowingthatthesearethepercentagesforfinaldiagnosisforpeoplepresentingtotheclinicwithhypercalcemiaandhavingafamilyhistoryofprimaryhyperparathyroidism. Theconditionofhighestprofile-relativeprobability(except"nodisease")isprimaryhyperparathyroidism(PH),butcancerisstillofmajorconcern,becauseifitistheactualcausativeconditionforthehypercalcemia,thenthechoiceofwhethertotreatornotlikelymeanslifeordeathforthepatient,ineffectpotentiallyputtingtheindicationatasimilarlevelforfurthertestsforbothoftheseconditions. Here,let'ssaythattheclinicianconsiderstheprofile-relativeprobabilitiesofbeingofenoughconcerntoindicatesendingthepatientacallforaclinicianvisit,withanadditionalvisittothemedicallaboratoryforanadditionalbloodtestcomplementedwithfurtheranalyses,includingparathyroidhormoneforthesuspicionofprimaryhyperparathyroidism. Forsimplicity,let'ssaythattheclinicianfirstreceivesthebloodtest(informulasabbreviatedas"BT")resultfortheparathyroidhormoneanalysisandthatitshowedaparathyroidhormonelevelthatiselevatedrelativetowhatwouldbeexpectedbythecalciumlevel. Suchaconstellationcanbeestimatedtohaveasensitivityofapproximately70%andaspecificityofapproximately90%forprimaryhyperparathyroidism.[8]Thisconfersalikelihoodratiopositiveof7forprimaryhyperparathyroidism. TheprobabilityofprimaryhyperparathyroidismisnowtermedPre-BTPHbecauseitcorrespondstobeforethebloodtest(Latinprepositionpraemeansbefore).Itwasestimatedat37.3%,correspondingtoanoddsof0.595.Withthelikelihoodratiopositiveof7forthebloodtest,thepost-testoddsiscalculatedas: Odds ⁡ ( PostBT P H ) = Odds ⁡ ( PreBT P H ) ⋅ L H ( B T ) = 0.595 ⋅ 7 = 4.16 , {\displaystyle\operatorname{Odds}({\text{PostBT}}_{PH})=\operatorname{Odds}({\text{PreBT}}_{PH})\cdotLH(BT)=0.595\cdot7=4.16,} where: Odds(PostBTPH)istheoddsforprimaryhyperparathyroidismafterthebloodtestforparathyroidhormone Odds(PreBTPHistheoddsinfavorofprimaryhyperparathyroidismbeforethebloodtestforparathyroidhormone LH(BT)isthelikelihoodratiopositiveforthebloodtestforparathyroidhormone AnOdds(PostBTPH)of4.16isagainconvertedtothecorrespondingprobabilityby: Pr ( PostBT P H ) = Odds ⁡ ( PostBT P H ) Odds ⁡ ( PostBT P H ) + 1 = 4.16 4.16 + 1 = 0.806 = 80.6 % {\displaystyle\Pr({\text{PostBT}}_{PH})={\frac{\operatorname{Odds}({\text{PostBT}}_{PH})}{\operatorname{Odds}({\text{PostBT}}_{PH})+1}}={\frac{4.16}{4.16+1}}=0.806=80.6\%} Thesumoftheprobabilitiesfortherestofthecandidateconditionsshouldthereforebe: Pr ( PostBT r e s t ) = 100 % − 80.6 % = 19.4 % {\displaystyle\Pr({\text{PostBT}}_{rest})=100\%-80.6\%=19.4\%} Beforethebloodtestforparathyroidhormone,thesumoftheirprobabilitieswere: Pr ( PreBT rest ) = 6.0 % + 14.9 % + 41.8 % = 62.7 % {\displaystyle\Pr({\text{PreBT}}_{\text{rest}})=6.0\%+14.9\%+41.8\%=62.7\%} Therefore,toconformtoasumof100%forallcandidateconditions,eachoftheothercandidatesmustbemultipliedbyacorrectingfactor: Correctingfactor = Pr ( PostBT rest ) Pr ( PreBT rest ) = 19.4 62.7 = 0.309 {\displaystyle{\text{Correctingfactor}}={\frac{\Pr({\text{PostBT}}_{\text{rest}})}{\Pr({\text{PreBT}}_{\text{rest}})}}={\frac{19.4}{62.7}}=0.309} Forexample,theprobabilityofcancerafterthetestiscalculatedas: Pr ( PostBT cancer ) = Pr ( PreBT cancer ) ⋅ Correctingfactor = 6.0 % ⋅ 0.309 = 1.9 % {\displaystyle\Pr({\text{PostBT}}_{\text{cancer}})=\Pr({\text{PreBT}}_{\text{cancer}})\cdot{\text{Correctingfactor}}=6.0\%\cdot0.309=1.9\%} Theprobabilitiesforeachcandidateconditionsbeforeandafterthebloodtestaregiveninfollowingtable: PH Cancer Otherconditions Nodisease P(PreBT) 37.3% 6.0% 14.9% 41.8% P(PostBT) 80.6% 1.9% 4.6% 12.9% These"new"percentages,includingaprofile-relativeprobabilityof80%forprimaryhyperparathyroidism,underlieanyindicationsforfurthertests,treatments,orotheractions.Inthiscase,let'ssaythatthecliniciancontinuestheplanforthepatienttoattendaclinicianvisitforafurthercheckup,especiallyfocusedonprimaryhyperparathyroidism. Aclinicianvisitcan,theoretically,beregardedasaseriesoftests,includingbothquestionsinamedicalhistory,aswellascomponentsofaphysicalexamination,wherethepost-testprobabilityofaprevioustest,canbeusedasthepre-testprobabilityofthenext.Theindicationsforchoosingthenexttestaredynamicallyinfluencedbytheresultsofprevioustests. Let'ssaythatthepatientinthisexampleisrevealedtohaveatleastsomeofthesymptomsandsignsofdepression,bonepain,jointpainorconstipationofmoreseveritythanwhatwouldbeexpectedbythehypercalcemiaitself,supportingthesuspicionofprimaryhyperparathyroidism,[9]andlet'ssaythatthelikelihoodratiosforthetests,whenmultipliedtogether,roughlyresultsinaproductof6forprimaryhyperparathyroidism. Thepresenceofunspecificpathologicsymptomsandsignsinthehistoryandexaminationareoftenconcurrentlyindicativeofcanceraswell,andlet'ssaythatthetestsgaveanoveralllikelihoodratioestimatedat1.5forcancer.Forotherconditions,aswellastheinstanceofnothavinganydiseaseatall,let'ssaythatit'sunknownhowtheyareaffectedbythetestsathand,asoftenhappensinreality.Thisgivesthefollowingresultsforthehistoryandphysicalexamination(abbreviatedasP&E): PH Cancer Otherconditions Nodisease P(PreH&E) 80.6% 1.9% 4.6% 12.9% Odds(PreH&E) 4.15 0.019 0.048 0.148 LikelihoodratiobyH&E 6 1.5 - - Odds(PostH&E) 24.9 0.0285 - - P(PostH&E) 96.1% 2.8% - - SumofknownP(PostH&E) 98.9% SumoftherestP(PostH&E) 1.1% SumoftherestP(PreH&E) 4.6%+12.9%=17.5% Correctingfactor 1.1%/17.5%=0.063 Aftercorrection - - 0.3% 0.8% P(PostH&E) 96.1% 2.8% 0.3% 0.8% Theseprobabilitiesafterthehistoryandexaminationmaymakethephysicianconfidentenoughtoplanthepatientforsurgeryforaparathyroidectomytoresecttheaffectedtissue. Atthispoint,theprobabilityof"otherconditions"issolowthatthephysiciancannotthinkofanytestforthemthatcouldmakeadifferencethatwouldbesubstantialenoughtoformanindicationforsuchatest,andthephysiciantherebypracticallyregards"otherconditions"asruledout,inthiscasenotprimarilybyanyspecifictestforsuchotherconditionsthatwerenegative,butratherbytheabsenceofpositivetestssofar. For"cancer",thecutoffatwhichtoconfidentlyregarditasruledoutmaybemorestringentbecauseofsevereconsequencesofmissingit,sothephysicianmayconsiderthatatleastahistopathologicexaminationoftheresectedtissueisindicated. ThiscaseiscontinuedintheexampleofCombinationsinthecorrespondingsectionbelow. Coverageofcandidateconditions[edit] Thevalidityofboththeinitialestimationofprobabilitiesbyepidemiologyandfurtherworkupbylikelihoodratiosaredependentontheinclusionofcandidateconditionsthatareresponsibleforalargepartaspossibleoftheprobabilityofhavingdevelopedthecondition,anditisclinicallyimportanttoincludethosewhererelativelyfastinitiationoftherapyismostlikelytoresultinthegreatestbenefit.Ifanimportantcandidateconditionismissed,nomethodofdifferentialdiagnosiswillsupplythecorrectconclusion.Theneedtofindmorecandidateconditionsforinclusionincreaseswiththeincreasingseverityofthepresentationitself.Forexample,iftheonlypresentationisadeviatinglaboratoryparameterandallcommonharmfulunderlyingconditionshavebeenruledout,thenitmaybeacceptabletostopfindingmorecandidateconditions,butthiswouldmuchmorelikelybeunacceptableifthepresentationwouldhavebeenseverepain. Combinations[edit] Iftwoconditionsgethighpost-testprobabilities,especiallyifthesumoftheprobabilitiesforconditionswithknownlikelihoodratiosbecomeshigherthan100%,thentheactualconditionisacombinationofthetwo.Insuchcases,thatcombinedconditioncanbeaddedtothelistofcandidateconditions,andthecalculationsshouldstartoverfromthebeginning. Tocontinuetheexampleusedabove,let'ssaythatthehistoryandphysicalexaminationwereindicativeofcanceraswell,withalikelihoodratioof3,givinganOdds(PostH&E)of0.057,correspondingtoaP(PostH&E)of5.4%.Thiswouldcorrespondtoa"SumofknownP(PostH&E)"of101.5%.Thisisanindicationforconsideringacombinationofprimaryhyperparathyroidismandcancer,suchas,inthiscase,aparathyroidhormone-producingparathyroidcarcinoma.Arecalculationmaythereforebeneeded,withthefirsttwoconditionsbeingseparatedinto"primaryhyperparathyroidismwithoutcancer","cancerwithoutprimaryhyperparathyroidism"aswellas"combinedprimaryhyperparathyroidismandcancer",andlikelihoodratiosbeingappliedtoeachconditionseparately.Inthiscase,however,tissuehasalreadybeenresected,whereinahistopathologicexaminationcanbeperformedthatincludesthepossibilityofparathyroidcarcinomaintheexamination(whichmayentailappropriatesamplestaining). Let'ssaythatthehistopathologicexaminationconfirmsprimaryhyperparathyroidism,butalsoshowedamalignantpattern.Byaninitialmethodbyepidemiology,theincidenceofparathyroidcarcinomaisestimatedat1in6millionpeopleperyear,[10]givingaverylowprobabilitybeforetakinganytestsintoconsideration.Incomparison,theprobabilitythatnon-malignantprimaryhyperparathyroidismwouldhaveoccurredatthesametimeasanunrelatednon-carcinomacancerthatpresentswithmalignantcellsintheparathyroidglandiscalculatedbymultiplyingtheprobabilitiesofthetwo.Theresultantprobabilityis,however,muchsmallerthanthe1in6million.Therefore,theprobabilityofparathyroidcarcinomamaystillbecloseto100%afterhistopathologicexaminationdespitethelowprobabilityofoccurringinthefirstplace. Machinedifferentialdiagnosis[edit] Furtherinformation:Clinicaldecisionsupportsystem Machinedifferentialdiagnosisistheuseofcomputersoftwaretopartlyorfullymakeadifferentialdiagnosis.Itmayberegardedasanapplicationofartificialintelligence.Alternatively,itmaybeseenas"AugmentedIntelligence"ifitmeetstheFDAcriteria,namelythat(1)itrevealstheunderlyingdata,(2)revealstheunderlyinglogic,and(3)leavestheclinicianinchargetoshapeandmakethedecision.MachineLearningAIisgenerallyseenasadevicebytheFDA,whereasAugmentedIntelligenceapplicationsarenot. Manystudiesdemonstrateimprovementofqualityofcareandreductionofmedicalerrorsbyusingsuchdecisionsupportsystems.Someofthesesystemsaredesignedforaspecificmedicalproblemsuchasschizophrenia,[11]Lymedisease[12]orventilator-associatedpneumonia.[13]Othersaredesignedtocoverallmajorclinicalanddiagnosticfindingstoassistphysicianswithfasterandmoreaccuratediagnosis. However,thesetoolsallstillrequireadvancedmedicalskillstoratesymptomsandchooseadditionalteststodeducetheprobabilitiesofdifferentdiagnoses.Machinedifferentialdiagnosisisalsocurrentlyunabletodiagnosemultipleconcurrentdisorders.[14]Thus,non-professionalsshouldstillseeahealthcareproviderforaproperdiagnosis. History[edit] ThemethodofdifferentialdiagnosiswasfirstsuggestedforuseinthediagnosisofmentaldisordersbyEmilKraepelin.Itismoresystematicthantheold-fashionedmethodofdiagnosisbygestalt(impression).[citationneeded] Alternativemedicalmeanings[edit] 'Differentialdiagnosis'isalsousedmoreloosely,torefersimplytoalistofthemostcommoncausesofagivensymptom,toalistofdisorderssimilartoagivendisorder,ortosuchlistswhentheyareannotatedwithadviceonhowtonarrowthelistdown(French'sIndexofDifferentialDiagnosisisanexample).Thus,adifferentialdiagnosisinthissenseismedicalinformationspeciallyorganizedtoaidindiagnosis. Usageapartfrominmedicine[edit] Methodssimilartothoseofdifferentialdiagnosticprocessesinmedicinearealsousedbybiologicaltaxonomiststoidentifyandclassifyorganisms,livingandextinct.Forexample,afterfindinganunknownspecies,therecanfirstbealistingofallpotentialspecies,followedbyrulingoutofonebyoneuntil,optimally,onlyonepotentialchoiceremains. Similarproceduresmaybeusedbyplantandmaintenanceengineersandautomotivemechanicsandusedtobeusedindiagnosingfaultyelectroniccircuitry. Inart[edit] TheAmericantelevisionmedicaldramaHouse,featuringHughLaurieasthemainprotagonistDr.GregoryHousewholeadsateamofdiagnosticiansatthefictionalPrinceton–PlainsboroTeachingHospitalinNewJersey,revolvesaroundusingdifferentialdiagnosticsproceduresinabidtocomeupwiththerightdiagnosis. Throughouttheseries,thedoctorshavediagnosedsuchdiseasesaslupus,mastocytosis,Plummer'sdisease,rabies,Kawasaki'ssyndrome,smallpox,Rickettsialpox,anddozensofothers. Seealso[edit] Comorbidity Diagnosisofexclusion Dualdiagnosis Gender-biasinmedicaldiagnosis Listofmedicalsymptoms References[edit] ^"differentialdiagnosis".Merriam-Webster(Medicaldictionary).Retrieved30December2014. ^Wilson,MC(2012).ThePatientHistory:Evidence-BasedApproachToDifferentialDiagnosis.NewYork,NY:McGrawHill.ISBN 9780071804202. ^Siegenthaler,Walter(2011).Differentialdiagnosisininternalmedicine :fromsymptomtodiagnosis.Thieme.p. 6.ISBN 978-1604062199. ^Lim,EricKS;Oster,AndrewJK;Rafferty,AndrewT(2014).Churchill'spocketbookofdifferentialdiagnosis(Fourth ed.).ElsevierHealthSciences.ISBN 978-0702054044. ^Cf.VINDICATE–MnemonicfordifferentialdiagnosisArchived20December2012attheWaybackMachineatPGBlazer.com. ^Richardson,WS.(March1999)."Users'GuidestotheMedicalLiterature:XV.Howtouseanarticleaboutdiseaseprobabilityfordifferentialdiagnosis".JAMA.281(13):1214–1219.doi:10.1001/jama.281.13.1214.PMID 10199432.S2CID 2389981.[1] ^Seccareccia,D.(March2010)."Cancer-relatedhypercalcemia".CanFamPhysician.56(3):244–6,e90–2.PMC 2837688.PMID 20228307.[2][3] ^Lepage,R.;d'Amour,P.;Boucher,A.;Hamel,L.;Demontigny,C.;Labelle,F.(1988)."Clinicalperformanceofaparathyrinimmunoassaywithdynamicallydeterminedreferencevalues".ClinicalChemistry.34(12):2439–2443.doi:10.1093/clinchem/34.12.2439.PMID 3058363. ^Bargren,A.E.;Repplinger,D.;Chen,H.;Sippel,R.S.(2011)."CanBiochemicalAbnormalitiesPredictSymptomatologyinPatientswithPrimaryHyperparathyroidism?".JournaloftheAmericanCollegeofSurgeons.213(3):410–414.doi:10.1016/j.jamcollsurg.2011.06.401.PMID 21723154. ^ParathyroidCancerTreatmentatNationalCancerInstitute.LastModified:03/11/2009 ^Razzouk,D.;Mari,J.J.;Shirakawa,I.;Wainer,J.;Sigulem,D.(January2006)."Decisionsupportsystemforthediagnosisofschizophreniadisorders".BrazilianJournalofMedicalandBiologicalResearch.39(1):119–28.doi:10.1590/s0100-879x2006000100014.PMID 16400472. ^HejlesenOK,OlesenKG,DessauR,BeltoftI,TrangeledM(2005)."Decisionsupportfordiagnosisoflymedisease".StudiesinHealthTechnologyandInformatics.116:205–10.PMID 16160260. ^"EvaluationofaComputerAssistedDecisionSupportSystem(DSS)forDiagnosisandTreatmentofVentilatorAssociatedPneumonia(VAP)inIntensiveCareUnit(ICU)".nih.gov.Archivedfromtheoriginalon10February2009.Retrieved3October2008. ^Wadhwa,R.R.;Park,D.Y.;Natowicz,M.R.(2018)."Theaccuracyofcomputer‐baseddiagnostictoolsfortheidentificationofconcurrentgeneticdisorders".AmericanJournalofMedicalGeneticsPartA.176(12):2704–2709.doi:10.1002/ajmg.a.40651.PMID 30475443.S2CID 53758271. vteBasicmedicaltermsusedtodescribediseaseconditions SignsandsymptomsSyndromeDisease MedicaldiagnosisDifferentialdiagnosisPrognosis AcuteChronicCure EponymousdiseaseAcronymorabbreviationRemission vteMedicalexaminationandhistorytakingMedicalhistory Chiefcomplaint Historyofthepresentillness Systemsreview Nursingassessment Allergies Medications Pastmedicalhistory Familyhistory Socialhistory Psychiatrichistory Progressnotes Mnemonics SAMPLE OPQRST SOAP COASTMAP PhysicalexaminationGeneral/IPPA Inspection Auscultation Palpation Percussion Vitalsigns Temperature Heartrate Bloodpressure Respiratoryrate HEENT Eyeexamination Ophthalmoscopy Swinginglighttest Hearing Weber Rinne Respiratory Respiratorysounds Cardiovascular Cardiacexamination Peripheralvascularexamination Heartsounds Other Abdominojugulartest Ankle-brachialpressureindex Abdominal Liverspan Rectalexamination Bowelsounds Musculoskeletal Kneeexamination Hipexamination Shoulderexamination Elbowexamination GALSscreen Neurological Mentalstatus Mini–mentalstateexamination Cranialnerveexamination Upperlimbneurologicalexamination Neonatal Apgarscore BallardMaturationalAssessment Gynecological Well-womanexamination Vaginalexamination Breastexamination Sports Athleticheartsyndrome Suddencardiacdeath RED-S Assessmentandplan Medicaldiagnosis Differentialdiagnosis Authoritycontrol:Nationallibraries France(data) 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