Experimental Medicine Notes lab diagnosis

Introduction to Experimental Medicine

  • Presenter: Shun Li, affiliated with the First Affiliate Hospital of Xiaomi University
  • Experience: 15 years in the field since starting career in 2012; over 10 years of teaching the course
  • Focus of Course: Understanding the use of laboratory tests in aiding physicians in clinical decision-making

Course Outline

  • Topics to be covered:
    • Principles of laboratory tests
    • Importance and use cases of tests in clinical practice
  • Topics taught by Shun Li are marked in red; others by different instructors in black

Importance of Laboratory Tests

Statistical Relevance

  • In 2003, studies revealed that 70-85% of medical decisions are influenced by laboratory test results
  • Increasing reliance on tests in modern clinical practice:
    • Decision-making is based on gathered evidence from patients and lab results

Understanding Clinical Probabilities

  • Clinical history and physical examinations help form a guess about potential diseases
  • Diagnosis is often probabilistic:
    • High certainty (80-99% probability) may allow immediate treatment
    • Lower certainty (20-40% probability) necessitates laboratory tests for clarification

Uses of Laboratory Tests

Screening

  • Definition: Identifying asymptomatic individuals who may have risk factors or undiagnosed diseases
  • Objective: Allow for early interventions to prevent disease
  • Example:
    • HDL and LDL testing indicate cardiovascular disease risk
    • Early detection of tumors can improve treatment outcomes

Diagnosis

  • A critical function of laboratory tests is to establish or exclude a diagnosis based on symptoms
  • Symptoms alone are insufficient for definitive diagnosis in many cases
  • Example:
    • Diabetes diagnosis typically requires fasting glucose tests
    • Assessment of chest pain utilizes CKMB levels to diagnose myocardial infarction
    • For colds, various tests (antigens, antibodies, nucleic acids) identify causative pathogens

Patient Management

  • Laboratory tests are used for:
    • Evaluating severity of disease
    • Monitoring progression or resolution of conditions
    • Noteworthy markers:
    • ESR (Erythrocyte Sedimentation Rate) and CRP (C-Reactive Protein) indicate inflammation but are not definitive for diagnosis
    • Monitoring tumor markers (e.g., CA125 for ovarian cancer) can help in detecting recurrence

Personalized Medicine

  • Emphasis on tailoring treatment protocols based on individual genetic profiles
  • Analyzing genomic data helps in selecting appropriate drugs for treatments like colon cancer therapy
  • Specific tests for lung cancer (e.g., EGFR, ALK, ROS1 mutations) inform targeted therapies

Disadvantages and Challenges of Laboratory Tests

  • Invasive testing may cause discomfort or harm; some tests carry morbidity risk
  • Costs associated with extensive testing can be substantial
    • Example: A positive result may lead to a cascade of additional testing
  • Tests may yield false positives or negatives, which can lead to misdiagnosis or unnecessary distress
    • Example: Early detection of low-grade prostate cancer in patients with severe comorbidities might pose more harm than benefit

Clinical Decision-Making Framework

Thresholds in Testing

  • Definitions:
    • Treatment Threshold: Probability level above which immediate treatment is warranted
    • Test Threshold: Probability level at which further testing is needed to make a diagnosis
  • Pre-test probability is assessed based on history and physical examination
  • Outcomes based on probability ranges:
    • If above treatment threshold, go directly to treatment
    • If between thresholds, laboratory tests are required

Mathematical Considerations

  • Conversion of probabilities:
    • Pre-test probability translates to post-test probability through laboratory testing, impacting clinical decisions
    • Likelihood ratios based on sensitivity and specificity help in adjusting probabilities

Test Characteristics

Key Concepts

  • Accuracy: How close laboratory results are to the true values
  • Precision: Consistency of test results upon repeated measures
  • Sensitivity: Ability of a test to correctly identify those with a disease (true positive rate)
  • Specificity: Ability of a test to correctly identify those without the disease (true negative rate)

Implications of Testing

  • Reference Intervals:
    • Established from healthy population data, includes a 5% margin for false abnormal results
  • False Results:
    • Risk increases with the number of tests ordered
    • Example: 1 test has a 5% chance of abnormal result, while 20 tests reach over 60% chance of false results

Conclusion

  • In summary, the necessity of laboratory tests is emphasized in enabling accurate clinical decision-making
  • Importance of understanding and applying statistical and mathematical principles in healthcare settings
  • Ongoing focus on personalized medicine to optimize patient outcomes through the analysis of test results and individual variability.