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Diagnostic testing II

Diagnostic testing II. Previously…. Guidelines for evaluating tests have been discussed Population spectrum Reference standard Verification bias Masking readers of the test and outcome Review bias Quantitate uncertainty Confidence interval . Outline. Identifying potential tests

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Diagnostic testing II

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  1. Diagnostic testing II

  2. Previously…. • Guidelines for evaluating tests have been discussed • Population spectrum • Reference standard • Verification bias • Masking readers of the test and outcome • Review bias • Quantitate uncertainty • Confidence interval

  3. Outline • Identifying potential tests • Creating groups • Evaluating the validity of the tests/groups

  4. Identifying potential tests • Test refers to any independent variable / predictor: • Clinical, histological features • Immunohistochemical, biochemical, molecular tests, etc

  5. Identifying potential tests • Literature • Chart review • Expert opinion

  6. Examples

  7. I. Identifying bacteremia or bacterial meningitis in febrile infants • Review charts on febrile infants: • Identify cases with confirmed bacteremia/bacterial meningitis • Identify those without these disorders • Identify variables that occurred significantly more frequently in cases • Interview physicians • Review the literature • Create a panel of promising tests

  8. I. Identifying bacteremia or bacterial meningitis in febrile infants • Pantell RH et al. JAMA 2004;291:1203-12 • Prospective cohort from which “tests” identified and rule developed • Stepwise use of tests • Clinical appearance: moderately/very ill vs well or minimally ill • Age: <25d vs ≥25 days • Temperature: ≥38.6 vs <38.6/normal • Defined high risk febrile infant • at least moderately ill or <25d or To ≥38.6 • Training set • Next step: Evaluate this test panel in another cohort of febrile infants following diagnostic test methodology

  9. II. Ottawa ankle rules Stiell IG, et al. JAMA 1994;271:827-32 • To develop decision rule to predict fractures in patients with ankle injuries, at same time decrease use of radiology • Set sensitivity to identify fractures at 100% • 32 clinical variables, based on clinical experience and previous studies • Applied the 32 variables prospectively in 750 patients • Final rule had 4 variables; applied simultaneously; rule considered positive if any test positive • Shown prospectively in numerous settings to have high sensitivity for ankle fracture while reducing number of radiographs

  10. III. Distinguishing between serous and endometrioid endometrial carcinomaOnuma K et al. • Serous carcinoma requires staging, chemo • Can sometimes be difficult to tell apart • Clinical variables: age, BMI, stage, HRT • Any helpful? • 5 IHC markers: p16, p53, B-catenin, ER, PR • Did any provide additional information once clinical variables taken into account?

  11. Searched pathology database for hysterectomies diagnosed as serous / endometrioid • 46 confirmed endometrioid and 35 confirmed serous by 2 independent observers • Uncertain cases excluded*

  12. Training set • Univariate analysis of each variable against histologic type • Multivariate analysis • Incremental contribution of each variable • Final model of 4 independent variable • P16, p53, PR, ER

  13. Problem • We don’t need markers for histologically unequivocal cases • Really want to know if these markers are useful in equivocal cases

  14. Problem • Febrile infant and ankle studies, rule created in • infants known to be culture + or – • in patients with X-ray confirmed fracture or no fracture • Can evaluate the rule in unknown population (febrile infants / patients with ankle injury) and compare to gold standard of culture / X-ray • In equivocal serous / endometrioid cancers (the unknown population), what is the gold standard? • Consensus histology diagnosis? • Survival?

  15. Creating groups

  16. Colorectal carcinoma:Identifying prognostic groupsFurlan D, et al. Modern Pathol 2011;24:126-37 • Purpose: create morphomolecular classification for colorectal cancer • 13 routine clinicopathologic features • 5 molecular markers • Cluster analysis

  17. Cluster A • Right colon, special type (mucinous, medullary, papillary, cribriform), higher grade, microsattelite instability, low stage • Cluster B • Common type, left colon, LVSI, loss of heterozygosity, high stage • Cluster C • Special types, non LVSI, low stage, MGMT methylation

  18. Are these clusters useful? • Survival information (but given by stage) • Perhaps allow targeted therapy

  19. Summary • Methods to identify tests, create groups • This is preliminary information • Must be evaluated in patients with proper test methodology before information is ready for clinical use

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