Laboratory test selection principles

Laboratory test selection principles

Laboratory test selection principles

On this page

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.

Practice Questions: Laboratory test selection principles

Test your understanding with these related questions

A scientist in Chicago is studying a new blood test to detect Ab to EBV with increased sensitivity and specificity. So far, her best attempt at creating such an exam reached 82% sensitivity and 88% specificity. She is hoping to increase these numbers by at least 2 percent for each value. After several years of work, she believes that she has actually managed to reach a sensitivity and specificity much greater than what she had originally hoped for. She travels to China to begin testing her newest blood test. She finds 2,000 patients who are willing to participate in her study. Of the 2,000 patients, 1,200 of them are known to be infected with EBV. The scientist tests these 1,200 patients' blood and finds that only 120 of them tested negative with her new exam. Of the patients who are known to be EBV-free, only 20 of them tested positive. Given these results, which of the following correlates with the exam's specificity?

1 of 5

Enjoying this lesson?

Get full access to all lessons, practice questions, and more.

Start Your Free Trial