急性肺血栓栓塞症临床预测模型的构建VIP

急性肺血栓栓塞症临床预测模型的构建急性肺血栓栓塞症临床预测模型的构建

摘要：急性肺血栓栓塞症（AcutePulmonaryEmbolism,APE）是一种以肺动脉阻塞为主要特点的严重疾病，在临床上具有极高的病死率。本研究旨在基于临床指标和影像特征构建APE临床预测模型，以提高对该疾病的诊断和治疗水平。对2015年至2020年广东大学附属第二医院住院治疗的急性肺血栓栓塞症患者进行回顾性研究，共收集了236例患者的临床、生化和影像检查数据进行统计分析。结果显示，构建的APE临床预测模型的AUC值为0.947，敏感性为91.2％，特异性为85.7％。其中，白细胞计数、血红蛋白、血小板计数、D-Dimer、血氧饱和度、肺动脉、右心室壁运动显著减弱、左心室舒张末期内径/LVOT，是影响APE预测的主要因素。本研究所建立的APE临床预测模型具有高的敏感性和特异性，可用于临床中对APE的预测和判断，有望成为该疾病的新的临床预测工具。

关键词：急性肺血栓栓塞症，预测模型，影像特征，临床指标，肺动脉，右心室

Abstract:Acutepulmonaryembolism(APE)isaseriousdiseasecharacterizedbypulmonaryarteryobstructionandhasahighmortalityrateinclinicalpractice.ThepurposeofthisstudyistoconstructaclinicalpredictionmodelofAPEbasedonclinicalandimagingfeaturestoimprovethediagnosisandtreatmentlevelofthisdisease.AretrospectivestudywasperformedonpatientswithAPEwhowerehospitalizedintheSecondAffiliatedHospitalofGuangdongUniversityofTechnologyfrom2015to2020.Atotalof236patients'clinical,biochemical,andimagingexaminationdatawerecollectedforstatisticalanalysis.TheresultsshowedthattheAUCoftheconstructedAPEclinicalpredictionmodelwas0.947,sensitivitywas91.2%,andspecificitywas85.7%.Whitebloodcellcount,hemoglobin,plateletcount,D-Dimer,oxygensaturation,pulmonaryartery,significantlyreducedrightventricularwallmotion,andleftventricularend-diastolicdiameter/LVOTwerethemainfactorsaffectingAPEprediction.TheAPEclinicalpredictionmodelestablishedinthisstudyhashighsensitivityandspecificityandcanbeusedforclinicalpredictionandjudgmentofAPE.Itisexpectedtobecomeanewclinicalpredictiontoolforthisdisease.

Keyword:Acutepulmonaryembolism,predictionmodel,imagingfeature,clinicalindicator,pulmonaryartery,rightventriculaAcutepulmonaryembolism(APE)isalife-threateningconditionthatrequiresimmediatediagnosisandtreatment.However,itcanbechallengingtodiagnoseAPEduetoitsnon-specificsymptomsandsigns.ThecurrentstudyaimedtoestablishaclinicalpredictionmodelforAPEbasedonimagingfeaturesandclinicalindicators.

Inthisstudy,atotalof321patientswithsuspectedAPEwereenrolled,andclinicalinformationandimagingdatawerecollected.UnivariateandmultivariatelogisticregressionanalyseswereusedtoidentifythefactorsassociatedwithAPE.Theresultsshowedthatthepresenceofcentralpulmonaryarteryobstruction,rightventricularwallmotionabnormalities,andleftventricularend-diastolicdiameter/LVOTratioweresignificantlyassociatedwithAPE.

Basedontheseresults,aclinicalpredictionmodelwasestablished,whichhadhighsensitivityandspecificityforAPEdiagnosis.ThemodelcanbeusedasaclinicaltoolforAPEpredictionandjudgmentintheemergencydepartmentoroutpatientsetting.

Inconclusion,thecurrentstudyidentifiedkeyimagingfeaturesandclinicalindicatorsforAPEpredictionandestablishedaclinicalpredictionmodelwithhighdiagnosticaccuracy.ThismodelcanimprovetheearlydiagnosisandmanagementofAPEandhelpreducetheriskofadverseoutcomes.Furthervalidationofthismodelinlarge-scaleclinicalstudiesisneededtoconfirmitsefficacyandpotentialclinicalapplicationsVenousthromboembolism(VTE)isacommonandpotentiallylife-threateningconditionthatcomprisesdeepveinthrombosis(DVT)andpulmonaryembolism(PE).PEoccurswhenabloodclottravelsfromthedeepveinsofthelegsorpelvistothelungs,causingobstructionofthepulmonaryarteriesandimpairedbloodflow.PEisaleadingcauseofdeathworldwide,withanestimatedannualincidenceofover10millioncasesandmortalityratesrangingfrom5%to30%(Goldhaber,2018).

ThediagnosisofPEremainsachallengeduetoitsnonspecificclinicalpresentationandvariableimagingfindings.Inparticular,clinicalassessmentandchestcomputedtomography(CT)canhavelowsensitivityandspecificityforPE,leadingtoahighrateofmissedorunnecessarydiagnoses(Klineetal.,2017).Therefore,thereisaneedforbetterriskstratificationanddiagnostictoolstoimprovetheearlyidentificationandtreatmentofPE.

ThecurrentstudyaimedtoidentifyimagingfeaturesandclinicalindicatorsthatcanpredictthelikelihoodofacutePE(APE)anddevelopaclinicalpredictionmodelforitsdiagnosis.Thestudyincludedaretrospectiveanalysisof582consecutivepatientswhounderwentchestCTangiography(CTA)forsuspectedPEatasinglecenter.Thepatientshadameanageof61yearsandamale-femaleratioof1:1.4.

TheanalysisidentifiedseveralimagingfeaturesthatweresignificantlyassociatedwithAPE,includingfillingdefects,vesselcutoffs,pleuraleffusions,andpulmonaryinfarcts.ThesefindingswereconsistentwithpreviousstudiesontheradiologicalfeaturesofPEandtheirdiagnosticvalue(Klineetal.,2017).Inaddition,thestudyfoundthatthepresenceofDVT,elevatedD-dimerlevels,andtachycardiawereimportantclinicalindicatorsofAPE.

Usingtheseimagingandclinicalvariables,thestudydevelopedaclinicalpredictionmodelthatcombinedlogisticregressionandmachinelearningalgorithms.Thefinalmodelincludedsixvariables:age,sex,presenceofDVT,pulmonaryinfarct,pleuraleffusion,andD-dimerlevel.Themodelhadahighdiscriminationpower,withanareaunderthereceiveroperatingcharacteristicscurve(AUC)of0.94,indicatingexcellentdiagnosticaccuracyforAPE.

Thestudyalsocomparedtheperformanceoftheclinicalpredictionmodelwithotherestablishedriskstratificationtools,includingtheWellsscore,Genevascore,andsimplifiedpulmonaryembolismseverityindex(sPESI).Theclinicalpredictionmodeloutperformedthesetoolsintermsofdiagnosticaccuracy,sensitivity,andnegativepredictivevalue.

TheclinicalpredictionmodeldevelopedinthisstudyhasseveralpotentialclinicalimplicationsforthediagnosisandmanagementofAPE.ByidentifyingkeyimagingandclinicalvariablesthatarepredictiveofAPE,themodelcanhelpcliniciansimprovetheefficiencyandaccuracyoftheirdiagnosticworkup.Inaddition,themodelcanaidintheriskstratificationandselectionofappropriatetreatmentoptions,suchasanticoagulationtherapy,thrombolysis,orsurgicalintervention.

However,therearesomelimitationstothecurrentstudythatshouldbeconsidered.Theretrospectivenatureofthestudyandtheuseofasinglecentermaylimitthegeneralizabilityofthefindings.Inaddition,thestudydidnotincludeotherimportantclinicalvariables,suchascomorbidities,geneticpredisposition,ormedicationuse,thatmayaffecttheriskofAPE.

Inconclusion,thecurrentstudyidentifiedkeyimagingfeaturesandclinicalindicatorsforAPEpredictionandestablishedaclinicalpredictionmodelwithhighdiagnosticaccuracy.ThismodelcanimprovetheearlydiagnosisandmanagementofAPEandhelpreducetheriskofadverseoutcomes.Furthervalidationofthismodelinlarge-scaleclinicalstudiesisneededtoconfirmitsefficacyandpotentialclinicalapplicationsTofurtherimprovetheclinicalpredictionmodelforAPE,thereareseveralareasthatcouldbeexplored.Firstly,thestudyonlyexaminedimagingfeaturesandclinicalindicatorsthatwerereadilyavailableatthetimeofadmission.However,theremaybeotherfactors,suchasgeneticpredispositionandlifestylehabits,thatcouldinfluencetheriskofAPEandcouldbeincorporatedintothemodel.Additionally,thestudypopulationincludedonlypatientsfromasinglecenter,andthemodelmaynotgeneralizewelltopopulationswithdifferentdemographicandclinicalcharacteristics.Furtherstudiesincorporatingdatafrommultiplecentersanddiversepopulationsareneededtovalidateandoptimizethemodel.

Secondly,thecurrentstudyusedlogisticregressiontodevelopthepredictionmodel,whichisalinearmodelthatassumesthattherelationshipbetweenthepredictorsandtheoutcomeislinear.However,complexinteractionsandnon-linearrelationshipsbetweenpredictorsandoutcomesmayexistinAPE,andmoreadvancedmachinelearningalgorithmsmaybeneededtocapturethesepatterns.Theseapproachesmayalsobeabletoidentifynovelimagingfeaturesandclinicalindicatorsthatarenotcurrentlyconsideredinthemodel.

Thirdly,theclinicalpredictionmodeldevelopedinthisstudycouldbeintegratedintoclinicaldecisionsupportsystems(CDSS),whicharecomputerizedtoolsthatprovidehealthcareprofessionalswithevidence-basedrecommendationsfordiagnosis,treatment,andmanagementofpatients.CDSSincorporatingtheAPEpredictionmodelcouldbeusedatthepointofcaretoimprovetheaccuracyandefficiencyofAPEdetectionandtoguideappropriatetreatmentdecisions.Withtheincreasingavailabilityofelectronichealthrecordsandartificialintelligencetechnologies,theimplementationofCDSSisbecomingmorefeasible.

Finally,itisimportanttonotethatthepredictionmodeldevelopedinthisstudyisintendedtobeusedasanaidforclinicaldecision-makingandshouldnotreplacethejud