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Laboratory test selection principles

Laboratory test selection principles

Laboratory test selection principles

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Test Selection Principles - The First Crucial Step

  • Pre-test Probability (PTP): The estimated likelihood of a disease before performing a test, based on patient history, exam, and prevalence. It's the cornerstone of Bayesian reasoning in medicine.
  • Test Purpose Dictates Choice:
    • Screening: Detects potential disease in asymptomatic individuals (e.g., Pap smear). Applied to populations with low PTP.
    • Diagnostic: Confirms or refutes a diagnosis in symptomatic individuals (e.g., biopsy). Used in high PTP settings.
    • Monitoring: Tracks disease progression or treatment response (e.g., HbA1c).

⭐ As Pre-test Probability increases, the Positive Predictive Value (PPV) of a test also increases, while the Negative Predictive Value (NPV) decreases.

Diagnostic Test Metrics - Decoding the Numbers

2x2 Contingency Table for Diagnostic Test Accuracy

Disease PresentDisease Absent
Test +veTrue Positive (TP)False Positive (FP)
Test -veFalse Negative (FN)True Negative (TN)
-   $Sensitivity = \frac{TP}{TP + FN}$
  • Specificity: Ability to correctly identify those without the disease.
    • $Specificity = \frac{TN}{TN + FP}$
  • 📌 Mnemonic: SPIN (Specific test, when Positive, rules IN) & SNOUT (Sensitive test, when Negative, rules OUT).
  • Positive Predictive Value (PPV): Probability that a patient with a positive test truly has the disease.
    • $PPV = \frac{TP}{TP + FP}$
  • Negative Predictive Value (NPV): Probability that a patient with a negative test is truly disease-free.
    • $NPV = \frac{TN}{TN + FN}$
  • Prevalence affects predictive values:
    • ↑ Prevalence → ↑ PPV, ↓ NPV

⭐ Sensitivity and Specificity are intrinsic properties of a test and are not affected by the prevalence of the disease in the population.

Likelihood Ratios & ROC - Odds, Ends, and Curves

  • Likelihood Ratios (LRs) quantify how much a test result changes the certainty of a diagnosis.

    • Positive LR (LR+): $ \frac{Sensitivity}{1 - Specificity} $. How much to increase the odds of disease given a positive test.
    • Negative LR (LR-): $ \frac{1 - Sensitivity}{Specificity} $. How much to decrease the odds of disease given a negative test.
    • Clinical application via odds: $Post-test odds = Pre-test odds \times LR$.
    • Interpretation: LR+ > 10 or LR- < 0.1 provide strong evidence.
  • Receiver Operating Characteristic (ROC) Curve:

    • Plots Sensitivity (True Positive Rate) vs. 1 - Specificity (False Positive Rate).
    • Area Under the Curve (AUC) measures overall diagnostic accuracy.
      • AUC = 1: Perfect test.
      • AUC = 0.5: Useless (chance).
      • AUC > 0.8: Good test.

ROC Curve and Fagan Nomogram for HCVcAg Assay

⭐ Likelihood Ratios are more robust than predictive values as they are not significantly affected by disease prevalence.

High‑Yield Points - ⚡ Biggest Takeaways

  • Always start with screening tests (high sensitivity) before using confirmatory tests (high specificity).
  • Test selection is guided by pre-test probability; high probability may warrant a more specific initial test.
  • Likelihood Ratios (LRs) are clinically superior; seek Positive LR >10 and Negative LR <0.1 for strong evidence.
  • Never treat the report; always correlate lab findings with the clinical picture.
  • Avoid the "shotgun approach"-order tests sequentially and logically.

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