A family doctor in a rural area is treating a patient for dyspepsia. The patient had chronic heartburn and abdominal pain for the last 2 months and peptic ulcer disease due to a suspected H. pylori infection. For reasons relating to affordability and accessibility, the doctor decides to perform a diagnostic test in the office that is less invasive and more convenient. Which of the following is the most likely test used?

Culture of organisms from gastric specimen

Detection of the breakdown products of urea in biopsy

A research team is working on a new assay meant to increase the sensitivity of testing in cervical cancer. Current sensitivity is listed at 77%. If this research team's latest work culminates in the following results (listed in the table), has the sensitivity improved, and, if so, then by what percentage? Research team's latest results: | | Patients with cervical cancer | Patients without cervical cancer | |--------------------------|-------------------------------|----------------------------------| | Test is Positive (+) | 47 | 4 | | Test is Negative (-) | 9 | 44 |

Yes, the research team has seen an improvement in sensitivity of almost 7% according to the new results listed.

No, the research team has seen a decrease in sensitivity according to the new results listed.

No, the research team has not seen any improvement in sensitivity according to the new results listed.

Yes, the research team has seen an improvement in sensitivity of more than 10% according to the new results listed.

Yes, the research team has seen an improvement in sensitivity of less than 2% according to new results listed; this improvement is negligible and should be improved upon for significant contribution to the field.

A pharmaceutical company develops a sequential testing protocol for a rare genetic disorder (prevalence 0.01%). Initial screening test has sensitivity 95% and specificity 90%. Positive results undergo confirmatory testing with sensitivity 99% and specificity 99.5%. The company claims this approach achieves PPV >80% for the final positive result. Evaluate this claim and the rationale for sequential testing in this context.

The claim is true; sequential testing increases PPV by enriching the population tested in the second step

The claim is false; sensitivity decreases with sequential testing, reducing PPV

Sequential testing is unnecessary; the first test alone achieves adequate PPV

The claim is false; sequential testing cannot achieve PPV >80% with such low prevalence

The claim is true; the high specificity of the confirmatory test ensures high PPV regardless of prevalence

A hospital system is implementing a sepsis screening algorithm using clinical criteria with sensitivity of 92% and specificity of 75%. False positives result in unnecessary antibiotics, cultures, and ICU evaluations costing $3,000 per case. Missing true sepsis cases (false negatives) results in average increased mortality and morbidity costs of $50,000 per case. Hospital sepsis prevalence is 8%. Evaluate the optimal threshold adjustment strategy.

Decrease threshold to improve sensitivity despite more false positives

Maintain current threshold as it balances sensitivity and specificity equally

Implement risk stratification with different thresholds for different populations

Abandon screening due to unacceptable false positive rate

Increase threshold to improve specificity and reduce costs from false positives

Sensitivity calculation and interpretation | Sensitivity/Specificity

Sensitivity - The Disease Detective

Definition: The ability of a test to correctly identify individuals who have the disease (True Positive Rate).
Calculation:
- Formula: $Sensitivity = \frac{TP}{(TP + FN)}$
- TP = True Positives; FN = False Negatives.
Interpretation:
- High sensitivity tests are good at finding the disease. If the result is negative, you can be confident the person doesn't have it.
- 📌 SNOUT: A highly Sensitive test, when Negative, rules OUT the disease.

⭐ High-sensitivity tests have a low False Negative rate. They are crucial for screening in conditions where a missed diagnosis has severe consequences (e.g., initial HIV screen).

Sensitivity calculation with 2x2 contingency table

The 2x2 Table - Calculation Central

Sensitivity calculation with 2x2 contingency table

Sensitivity: The probability of a test correctly identifying individuals who have the disease (True Positive Rate).
- Answers: "Of all people with the disease, how many will test positive?"
- Calculation: $Sensitivity = \frac{TP}{(TP + FN)}$
Interpretation
- High sensitivity tests are used to screen for diseases.
- A negative result in a highly sensitive test is useful for ruling out a disease.
- High sensitivity corresponds to a low false-negative rate (FN).
Mnemonic
- 📌 SN-N-OUT: A highly Sensitive test, when Negative, helps to rule OUT the disease.

⭐ Screening tests, like the initial ELISA for HIV, demand high sensitivity to ensure that very few cases are missed.

Clinical Interpretation - To Screen or Not

High-Sensitivity Tests: Best for "ruling out" a disease. If the result is negative, you can be confident the person does not have the disease.
📌 SNOUT: Sensitive test, when Negative, rules OUT the disease.
Primary Use: Screening. Ideal for diseases that are dangerous but treatable, where missing a case is unacceptable.
- Examples: Initial HIV screening (ELISA), cancer screening.
- A negative result is reassuring.
Trade-off: High sensitivity can lead to a higher number of false positives, requiring confirmatory testing (often with a high-specificity test).
Formula: $Sensitivity = \frac{TP}{TP + FN}$

⭐ High-sensitivity tests are crucial for conditions with low prevalence. They are designed to cast a wide net and identify nearly everyone who might have the disease, minimizing false negatives.

High‑Yield Points - ⚡ Biggest Takeaways

Sensitivity is the test's ability to correctly identify true positives (those with the disease).

The formula is TP / (TP + FN), representing the fraction of diseased individuals who test positive.

A negative result in a high-sensitivity test helps rule out the disease (mnemonic: SNOUT).

Crucial for screening tests where missing a diagnosis is unacceptable (e.g., initial HIV test).

It is an intrinsic property of a test and is not affected by disease prevalence.