診斷試驗(yàn)(英文)ppt課件

1、Analysis and evaluation of the evidence of diagnostic test Clinical Trail Study CenterCao SumeiDiagnostic test are not just about diagnosisScreeningDetermining severityOptimally therapyPrognosisMonitor ExampleCarotid ultrasound can tell you the severity of the patients carotid stenosis Carotid ultra

2、sound can tell you the patients prognosis for stroke and deathCarotid ultrasound can predict your patients likely responsiveness to therapyBasic principles of conducting diagnostic studiesApply the gold standard to determine whether or not the target condition is present Gold standard: The most reco

3、gnized standard for clinician to diagnose the target conditionPathological measurementOperation findingSpecial imaging detectionLong-term follow-up Recognized standardWhat if your test is more gold than the standardMay lead to underestimate of the diagnostic power of the evaluatingOne strategy for d

4、ealing with this problem is to use long-term follow-up as a gold standardTo Whom Should the Gold Standard Be Applied?to everyoneselective performing the gold standard on patients may result in “ verification bias or “workup biasRecruit your participantsRecruit the target-negative and target-positive

5、 participants identified by gold standard characteristic of those to whom you will want to apply the test in clinical practiceIncluding a broad spectrum of the diseasedcase：from mildly to severelycontrol：a broad spectrum of competing conditionsAn alternative approach is that recruiting a consecutive

6、 sample of patientsMeasurement proceduresSpecifying test techniqueReproducibilityBlinding of the individual conducting or interpreting the test to the gold standardSelect statistical procedureCalculating sample size Example: Assuming a sensitivity of 80%, specificity of 60% of ultrasonography for di

7、agnosis of cholecystolithiasis. How many samples are needed ? Result evaluation indexExample: 126 patients underwent independent, blind BNP measurement and echocardiography for diagnosis of LVD.sensitivity：a/(a+c)=35/40=0.88, or 88%specificity：d/(b+d)=29/86=0.34, or 34%positive predictive value (PPV

8、)：a/(a+b)=35/92=0.38, or 38%negative predictive value (NPV)：d/(c+d)=29/34=0.85, or 85%prevalence: (a+c) / (a+b+c+d)=40/126=0.32, or 32%Pre-test odds: pre-test probability/(1- pre-test probability) =32% / 68%=0.47positive likelihood ratio ( LR+ ): Sen/1-Spe=88% /(100%-34%)=1.3Multilevel likelihood ra

9、tiosStability of the indexStable : Sen, SpeRelatively stable: LR+, LR- Unstable : PPV, NPV, prevalence:Receiver operating characteristic curves(ROC)It illustrates the performance of a diagnostic test when you select different cut-points to distinguish “normal from “abnormal It demonstrates the fact

10、that any increase in sensitivity will be accompanied by a decrease in specificityThe closer the curve gets to the upper left corner of the display, the more the overall accuracy of the testThe closer the curve comes to the 45-degree diagonal of the ROC space ,the less accurate the testThe area under

11、 the curve provides an overall measure of a tests accuracy Fig A ROC for BNP as a diagnostic test for LVD Parallel test A test B test Result + + + + + + + -Reduction miss diagnosisExclude some disease When prevalence is low, as the primary screening methodSerial test A test B test Result + + + + - +

12、 - -Sen = Sen A SenBSpe = Spe A + (1-Spe A) Spe BMisdiagnosis may cause nuisance effect Confirmatory diagnosis Serial test with enzyme labeled compound assay for diagnosis of myocardial infarctionEnzyme labeled compound assaySenSpeCPK9657SGOT9174LDH879191Multivariate analysisSEN SPE single variable

13、analysismarkermethodsSEN (%)spe (%)cutoffAREAaELISA90.788.80.19150.926bELISA77.373.20.20350.802cELISA74.270.90.09050.762dELISA78.481.61.080.836eELISA90.784.40.3560.932fELISA84.581.60.7990.899multivariate analysis using logistic regression Combined markersSEN（）SPE（）AREAa and b88.882.50.926a and c87.7

14、82.50.927a and e91.690.070.974a and f95.590.070.967a and d87.285.6.0936b and c78.876.30.837b and d87.786.60.934b and e83.882.50.900b and f82.781.40.863C and d87.785.60.946C and e88.385.60.926C and f81.679.40.854d and e89.486.60.946d and f88.386.60.952e and f87.785.60.933Prediction the probability of

15、 a diseaseLogit(P)= -0.934+4.797 x a +2.203 x eAvoiding overfitting Overfitting occurs when a computer model identifies a “chance pattern that discriminates cancer patients from non-cancer patients, perfectly fitting that dataset but not reproducible in other data sets.One way to avoiding overfittin

16、g is to randomly split the data into separate training and test samples.The EBM steps for diagnostic testsLooking for the most suitable study papers according to the clinical questionBring forward the question in clinicExample 2 : if detection of serum forritin can diagnose Iron deficiency anemia?Se

17、arch the computer information using the apposite key word“diagnose Iron deficiency anemia and “diagnostic test and “humanEvaluation of the scientificity of the papersIf compared with the gold standard independently and blindlyExample 2: Iron stain with myeloid biopsyIf detected with the control test

18、 for every quizzeeGold standardTotal （No.）Results+-New diagnostic test +351550sensitivity=46%New diagnostic test -40460500specificity=96.8%Verification biasGold standardTotal （No.）Results+-New diagnostic test +3515 50sensitivity=90%New diagnostic test -446 50specificity=75%If the patient samples inc

19、luded a broad spectrum of the diseaseIf the disease spectrum uniform Whats the objective question that the investigator concerned aboutIf the study sample and the quizzee is uniformSpectrum bias：overstate the performance parameter of the diagnostic test because of excluding the “grey zone patientsTh

20、e precision of the diagnostic tests Definition: in the same condition, degree of stability of achieving the same result when repeating the operation Detailed introduction the procedurestandards of reporting diagnostic accuracy, STARDEstimate the significance of clinical applicationEstimate the pre-t

21、est probabilityPre-test probability：Estimating the suffering probability before diagnostic testMedical recordMedical examination Individual experience Epidemiology dataThe predictive value depend on the pre-test probability of illnessExplain and use of sensitivity and specificityHigh sensitivity tes

22、t (negative test, rule out)High specificity test (positive test, rule in )Use likelihood ratio, LRLikelihood ratio： provides a direct estimate of how much a test result will change the probability of having a diseaseLR+ = sensitivity/(1- specificity)Likelihood ratio unchanged with the prevalence rat

23、e Overcoming deficiency with only sensitivity or specificity to expresspost-test probability post-test probability = post-test odds / 1 + post-test odds post-test odds = pre-test odds LR pre-test odds = pre-test probability 1- pre-test probability Crude principle: LR 10 or 0.1 affirm or denial 5 LR 10 or 0.1 LR 0.2 moderate probability 2 LR 5 or 0.2 LR 0.5 minor probability 1 LR 2 or 0.5 LR 100 81080.13denial45-100 7270.45minor probability18-45 23133.10moderate probability=18 47242.5 affirmTotal 85150The LR with serum ferritin for diagnosis of iron-deficiency anemiaAppl