PATH 381 - Module 5: Biomarker Evaluation and Acute Coronary Syndrome

NIH definition of a biomarker

characteristic that is objectively measured and evaluated as an indicator of healthy biological processes, pathogenic processes, or pharmacologic responses to therapeutic intervention

IPCS definition of a biomarker

any measurable substance/structure/process or its products that can influence/predict the incidence of an outcome/disease

WHO definition of a biomarker

- any measurement reflecting interaction between biological system and a poptential hazard (chemical, physical, biological)

- measured response may be functional and physiological, biochemical, or molecular

4 criteria of an ideal/clinically useful biomarker (and explain)

1. measureable and interpretive: specific, sensitive, quantifiable results/processes that can be interpreted

2. cost efficient and safe: (decision triad) fast, affordable, safe, meaningful results

3. consistent and accurate: reliably predict disease course and outcomes, measured using test performance metrics

(can act as surrogate marker)

4. applicable: effective for wide-scale use, safe and consistent across multiple patient populations

surrogate marker

biomarker that stands in for a real clinical outcome because the real outcome takes too long or is too hard to measure (eg. blood pressure in suspected stroke)

limitations of biomarkers (5 and explain)

1. cost:

- can be expensive, must fit budget and clinical need

2. analyte stability and storage:

- Some analytes degrade or change over time

- Poor storage = inaccurate results

3. standardization:

- different manufacturers use different methods

- no standardization = results won’t match across labs

4. measurement errors:

- test may lack accuracy or precision

- equipment/transport issues can = inaccurate levels

- even a “good biomarker” is useless if the measurement method is bad

5. confounding factors:

- biomarker levels vary by age, sex, ethnicity, weight, etc.

- need adjusted reference ranges

- if not accounted for → misinterpretation

3 steps in biomarker evaluation process (and what its for)

1. analytical validation: is the test accurate?

2. qualification: does the biomarker actually relate to the disease?

3. utilization: given the evidence, can it be used in practice?

step 1: analytical validation (5 specifications)

→ Must determine specifications of biomarker:

1. limit of detection

2. limit of quantification

3. reference value

4. cut-off conc

5. total imprecision at cut-off conc

- all specifications must be determined before biomarker data is relevant

step 2: qualification

→ look at research showing:

- biomarker levels change with disease

- biomarker is involved in the disease pathway

- treatments that change the biomarker also change real clinical outcomes

(Checks the strength of evidence linking biomarker ↔ disease)

step 3: utilization

→ Decide if:

- the validation and qualification evidence is enough

- the biomarker fits the intended use (e.g., diagnosis, monitoring, prognosis)

what are 3 sources of biomarker variation

- bias

- measurement errors

- confounding factors

stages of variability for biomarkers

1. pre-analytical: biological variation and sample collection

2. analytical (during measurements)

pre-analytical: biological variations and 3 ex

diff in health, disease, stress, dehydration, age, sex, diet, etc.

eg:

- random vs fasting glucose

- supine vs standing BP

- morning vs afternoon cortisol levels

pre-analytical: sample collection variation

issues with tube type, mislabelling, needle size, transport/storage temp

eg:

- hemolyzed sample (shear force→lysed RBC)

- biomarker instability at wrong temp

analytical variation

- during measurement

- interferences from drugs/endogenous compounts

- instrument performance changes

- errors in post-test calculations

eg. precision and accuracy problems (can get worse as more widely used → even in same lab, lot-to-lot variation = differences between batches)

how do labs minimize analytical variability?

total quality management framework

goal of evidence-based lab medicine and biomarker qualification

use high-quality evidence to decide whether a biomarker/lab test actually improves patient outcomes

5 principles of evidence-based lab medicine

1. asking the question

2. searching for evidence

3. appraising the evidence

4. applying the evidence

5. assessing the experience

evidence-based lab medicine: asking the question (and 6 common study themes)

identify the real unmet clinical needs/problems → common study themes:

1. pre-analytical issues (timing, sample type)

2. analytical/test performance (accuracy, imprecision)

3. diagnostic value (usefulness of screening

4. prognosis/treatment selection

5. cost-effectiveness

6. whether test should be used/discontinued

evidence-based lab medicine: searching for evidence (requires what?)

- use existing strong evidence (eg. systematic reviews, meta analysis) or develop new evidence

- evidence must reflect routine lab practice AND research settings

- to prove test improves outcomes, need intervenion/"test-and-act" study, often using an RCT = (test result + resulting clinical action).

evidence-based lab medicine: appraising the evidence (and 4 questions)

critically evaluate research/evidence to judge validity and usefulness → ask critical appraisal questions:

- is the question clear?

- what are the results?

- are the results valid? (internal validity)

- are the results relevent to patient/population? (external validity)

evidence-based lab medicine: applying the evidence (and 4 questions)

decide if evidence fits real clinical context, and if the question was right → question if evidence is relevant to problem:

- does test meet clinical needs?

- is the analytical performance good enough?

- does it meet standard of care?

- will using this test actually improve outcomes?

evidence-based lab medicine: assessing the experience (3)

- learning experience

- promotes applying the principles into daily practice = quality improvement

- after implementing test, evaluate if it actually improved outcomes

utilization/evidence of performance of a biomarker (5)

technical performance > diagnostic performance > clinical impact > organizational impact > cost-effectiveness > decisions

(must work on the lower level to work at any of the higher levels)

evaluation tools/test performance metrics (4)

1. NPV and PPV

2. Sensitivity and Specificity

3. Odds Ratio (OR)

4. Receiver Operating Characteristics (ROC)

PPV and NPV (what is it, equation, when does it inc/dec)

PPV:

- Probability a positive result is a true positive.

- PPV = TP / (TP + FP)

- Increases when disease prevalence ↑

NPV:

- Probability a negative result is a true negative.

- NPV = TN / (TN + FN)

- Decreases when disease prevalence ↑

Sensitivity and Specificity (what is it, equation)

Sensitivity:

- "True positive rate": Ability to correctly detect people with the disease

- Sens = TP / (TP + FN)

Specificity:

- "True negative rate": Ability to correctly identify people without the disease.

- Spec = TN / (TN + FP)

Odds Ratio (OR)

strength of association between exposure and event:

- OR < 1 → exposure lowers odds

- OR = 1 → no effect

- OR > 1 → exposure increases odds

LRs (equations, what it measures, how to interpret)

LR+: Sensitivity / (1 - Specificity)

- how does pos test impact probability of disease?

- larger = more likely

LR- : (1 - Sensitivity) / Specificity

- how does neg test impact probability of disease?

- smaller = less likely

why are LRs useful? (3)

- do NOT depend on prevalence (don't vary in diff pop)

- Can apply directly to one patient

- Turn test result into a probability

LRs and Baye's Theorum

- translates LRs into probability of disease

- post-test probability = pre-test probability × LR

why use ROC curves?

to visualize performance of biomarker at various cut-off settings:

- evaluate overall test performance

- decide the best cut-off point

- compare multiple biomarkers/tests

- understand how test accuracy changes depending on the threshold used

axes in ROC curve

Y axis: true positive rate (sensitivity)

X axis: false positive rate (1-specificity)

ROC curve: area under the curve (AUC)

- summarizes ROC curve into one number that represents how good the test is (higher AUC = better test at distinguishing)

AUC values (ideal, typical, poor)

ideal: 1.0 (100% distinguishablity)

typical: ~0.7 (~70% distinguishability)

poor: 0.5 (50% distinguishability = no better than flipping a coin)

ideal biomarker ROC curve

- population distributions (disease vs non-disease) have no overlap

- AUC = 1.0 (100% chance distinguishability)

- ROC reaches top left corner (ie full area covered)

poor biomarker ROC curve

- complete overlap between disease and non-disease distrib.

- ROC curve is a diagonal line (y = x)

- AUC = 0.5 (50% of distinguishability)

typical biomarker ROC curve

- some overlap between groups (realistic scenario)

- ROC curve goes upward but not perfectly

- AUC ~0.7 (moderate-to-good discrimination)

How to create an ROC curve (6 steps)

1. choose possible cut-off

2. calculate spec and sensitivity at that cut-off

3. plot sensitivity vs false positive rate (1-specificity)

4. repeat for may different cut-offs

5. connect points = make curve

6. measure AUC to summarize test performance

why are randomized control trials (RCTs) needed (3)

- biomarker may look good in research but not acc improve patient outcomes

- to prove biomarker is clinically useful, must compare to current gold standard

- RCTs are the only way to whether changes in biomarker actually cause better outcomes

RCT overview (3)

- patients randomly assigned to treatment (new biomarker) or control (current gold standard)

- may be blinded or double-blinded (prevents bias)

- outcomes compared → tells you if the new biomarker actually improves patient care

Phases of RCTs and explain

I: 'first in human'

- first patient group, examines potential use using earlier work

II: 'first in patient'

- randomized, controlled testing (use earlier work/phase I)

- validating biomarker in real clinical pop

- identifies promising approaches to be used for phase III

III: 'multi-site trials'

- large, randomized, can be placebo-controlled/uncontrolled, blinded

- confirms biomarker performance across labs and populations

- needed before widespread use

designs for cancer biomarker trials

1. target/enrichment: only biomarker+ (or -) patients

2. allcomers: everyone regardless of biomarker

3. adaptive: trial adjusts as it goes

target/enrichment designs (3)

- only include patients with (or without) the biomarker of interest.

- used when believe only a specific subgroup will benefit

- aim to understand safety, tolerability, clinical benefit in specific subgroup

allcomer designs and when are they used

Include all eligible patients regardless of biomarker status → Used when:

- Evidence for the biomarker is unclear

- Biomarker prevalence is high (>50%)

- No cut-off is established yet

- Biomarker takes too long to measure

adaptive designs

- multiple biomarkers tested under one big protocol

- trial adjusts as it runs (more patients moved to promising subgroups, and weak subgroups dropped)

acute coronary syndrome (ACS)

- umbrella term for conditions where blood flow to heart decreases

- includes myocardial infarction (MI) and unstable angina

- responsible for ~1/3 deaths in ppl >35

Unstable Angina

ischemic symptoms without elevations in biomarkers (cTn) or ECG changes

myocardial infarction (MI)

- 'heart attack'

- when heart doesn't get enough oxygen due to blocked blood flow (blocked coronary artery)

- term used when evidence of myocardial necrosis (cell death) due to acute ischemia (reduced blood flow)

- either STEMI or NSTEMI

types of MI

STEMI (ST elevation MI):

- ST elevation on ECG

- total occlusion/blockage of coronary artery

NSTEMI (non ST elevation MI):

- no ST elevation on ECG

- partial occlusion/blockage of coronary artey

- troponin (cTn) elevated

criteria for diagnosing MI (3)

1. acute myocardial injury with clinical evidence of myocardial ischemica

2. detection of rise/fall in cTn

3. at least one clinical sign of acute ischemia

clinical signs of acute ischemia (5)

- typical ischemic symptoms (chest pain, jaw/arm pain, dyspnea, nausea, fainting)

- imaging evidence of new loss of myocardiam or new wall motion

- pathological Q waves

- coronary thrombus (blood clot) on angiography/autopsy

- ECG changes indicative of ischemia

pathophysiology of ACS

usually following plaque rupture, formation of thrombus, occlusion of vessel → decreased bloodflow to part of heart → ischemia and then infarction

general qualities of an ideal biomarker (5)

- accurate diagnosis/prediction

- fast, affordable, meaningful results

- value beyond existing tests

- easy to measure

- can act as a surrogate marker

specific qualities for an MI biomarker (6)

- accurately distinguish MI from other causes

- high sensitivity and specitivity for MI

- clear separation between MI and non-MI levels

- useful in staging, prognosis, intervention

- direct link to pathophysiology of MI

- can predict occurrence

History of ACS Biomarkers

1950s: AST, LDH

1960s: CK, discovery of troponins

1970s: cTnT, cTnI, CKMB, myoglobin released early after MI

1980s: CKMB 'mass' immunoassay = more sensitive

1990s: rapid CKMB mass assays, 3rd-gen cTnT assay

2000s-2010s: high-sensitivity troponin assays

rise and fall curves for biomarkers

- help compare biomarkers

- myoglobin, CK, CKMB rise fast but low specificity

- troponin has slightly later response but much more specific → best overall marker

myoglobin (timing, pros, cons)

Timing: rises quickly (1-3h), falls early

Pros: high sensitivity, good for early detection and ruling out MI

Cons: low specificity (also elevated in skel muscle damage → rhabdomyolosis)

creatine kinsase (CK)

- older biomarker

- not specific

CKMB (timing, pros, cons)

cardiac-associated isoform of CK

Timing: rises 4-6h after MI, stays elevated for 24-48h

Pros: detect early refraction, rapid, cost-efficient, better specificity than CK/myoglobin

Cons: much less specific than cTn, can still rise with skel muscle injury

cardiac troponin (cTnI, cTnT) - what is it, pros, cons

proteins released from necrotic myocytes when irreversible damage occurs

Pros:

- highest sensitivity and specificity for MI

- prognostic indicators

- can detect recent MI up to 2 weeks after onset

Cons:

- slightly later rise (but outweighed by high accuracy)

what does detection of cTn mean? what does it not mean?

- indicates and quantifies cardiomyocyte damage and injury

- does not indicate underlying mechanisms, or ischemic/nonischemic causes

- does not automatically mean ACS (just detects damage)

why is cTn preferred?

- cTnI and cTnT highly specific and sensitive biomarkers of myocardial injury because unique to the heart

- remain as intact proteins and degradation products

- can detect MI for up to 2 weeks after onset

what is considered elevated cTn

defined as value that exceeds 99th percentile of normal reference populations

when are troponin levels measured

first presentation (within 6h)

AND

6-12 hours after pain onset (bc cTn release is delayed)

what is the next step if cTn levels are inconsistant with clinical symptoms?

CKMB testing may be used to assess differences

3 challenges of MI biomarkers

1. biotin use

2. muscle damage

3. chronic kidney disease

MI biomarkers and biotin use

- many immunoassays (including cTn) use streptavidin beads

- high biotin intake (hair/skin supplements) → excess biotin in blood → disrupts assay binding → falsely low/high troponin results depending on the assay design

MI biomarkers and muscle damage

- CK and myoglobin not specific

- trauma → muscle damage → elevated CK/myoglobin (even without MI)

- lowers specificity, can falsely suggest MI

MI biomarkers and CKD and how can you distinguish?

patients with CKD often have elevated cTnT (bc reduced clearence, mild myocardial stress)

key distinguisher: NO rise and fall in cTnT due to CKD