Week 14, Monday

Evidence Informed Practice 24 pdf

50-60% of people will have a disc bulge with no Back Pain

30+% of people will have a Disc protrusion with no Back Pain

0% of people will have a Disc Rupture with no Back Pain

Clinical Indic actions for imaging - Radiology classes

  • Plain films:

    • Significant trauma at any age

    • Mild trauma if over 50 y/o

    • History of prolonged steroid use

    • History or evidence of osteoporosis

    • Over the age of 70 y/o

    • A child who may have been abused

  • See “ACA choosing Wisely Guidelines” in Canvas

Imaging for Low Back pain:

  • patient with LBP in primary care settings:

    • ~0.7% have metastatic cancer

    • ~ 0.01% have spinal infection

    • ~0.04% have CES

    • ~4% have vertebral compression fractures

    • ~< 5% have inflammatory back disease (generally lower diagnostic urgency)

  • Almost all have an identifiable risk factor

Decision making:

  • if a clinical problem lacks technological standards, evidence-based reasoning is applicable

  • Rely on empirical evidence - what the doctor sees, feels, hears, experiences

Statistical Tools: improves accuracy of your diagnosis:

  • Sensitivity

  • Specificity

  • Likelihood ratio

  • Prevalence

  • Predictive value

  • Reliability

  • Validity

Probability:

  • is the number between 0 and 1 that quantifies the likelihood that something exists

  • If certain it exists, probability=1

  • If certain it doesn’t exist, probability=0

  • Findings:

    • True Positive=

      • Clinical finding is present, and the patient has the disease

    • False Positive=

      • Clinical finding is present, but the patient does not have the disease

    • False Negative=

      • Clinical finding is absent, and the patient has the disease

    • True Negative=

      • Clinical finding is absent, and the patient does not have the disease

Sensitivity:

  • True Positive Rate=

    • Proportion of people with the disease that have a clinical finding or positive test

  • Good observations/ tests have sensitivity of more than 90%

  • As sensitivity goes up, false negatives decrease (low false negatives)

  • When a high sensitivity test is negative, the disease is unlikely

  • High sensitivity useful for excluding/ ruing out disease

Specificity:

  • Proportion of people without the disease that do NOT have a clinical finding, or they have a negative test

  • True Negative Rate=

    • The likelihood of a non-diseased individual of getting a negative test

  • Beneficial observations or tests with specificity of 90% or higher (rarely positive when disease is absent)

  • As specificity increases, false positives decrease, and the disease is likely

    • Therefore, high specificity useful to confirm/ rule in dense when there is a positive test/ clinical finding

  • With low false positives, clinical finding is only seen in those with the disease

  • Equations to find specificity of test:

    • True Positives/ all diseased patients

    • True Negatives/all healthy patients

Sensitivity and Specificity:

  • Best is both are over 90%

  • Example: patient with shoulder pain

    • Arm squeeze test, when positive indicates cervical root compression and not a Shoulder lesion

    • Sensitivity= 0.96

    • Specificity= 0.96

    • Positive likelihood ratio= 24

  • Example: headache and nauseas

    • 81% sensitivity and 90% specificity for migraine

      • High specificity- the presence of nausea, tends to rule in migraine

  • Cervical fracture-rust sign

    • Plain film radiography- 31% specificity

    • CT- 99% specificity

  • SnNout

    • Sensitivity is Negative, rule out

  • SpPin

    • Specificity is Positive, rule in

Likelihood Ratio (LR):

  • likelihood of a given test result in patient with a disease compared with the likelihood of the same result in a patient with the disease

  • Based on sensitivity and specificity

  • When >1.0= probability of disease goes up

  • When <1.0= probability of disease goes down

  • Is the likelihood that a given test result would be expected in a Patient with the target disorder compared to the likelihood that the same result would be expected in a patient without the target disorder

  • Used to assess how good a diagnostic test is

  • Ratio meaning:

    • 0= no change

    • 1-2= rarely important

    • 2-5= increases probability

    • 5-10= moderately helpful

    • >10= largely helpful

  • The LR of any clinical finding is the probability of that finding in patients of the same finding in patients without disease

  • It always links a test or risk factor to a particular condition

  • Example:

    • Age 60 y/o+ with a positive drop arm test most likely identifies a degenerative rotator cuff tear

    • LR of 3.2 and 5.0

    • Add in supraspinatus and infraspinatus weakness (RROM) and a positive impingement sign- LR= 48.0 (you have your DX)

    • If all three tests are negative, the LR is 0.02 (rule out RTC as a DDX)

  • Example:

    • Thoracic pain- compression fracture

    • Corticosteroid use +LR 25 ad over age 70, LR=2.2

  • Example:

    • Knee Pain- ACL tear

    • Positive anterior prayer sign= ACL tear 11.5X more likely

    • Significant forward excursion causes the LR to increase by 17.0

  • Example:

    • Knee Osteoarthritis

      • Tenderness along bony ridges of joint, LR 11.8

      • Genu Varum deformity, LR 3.4

      • Stiffness lasting <30 minutes, LR 3.0

      • Crepitus

Prevalence:

  • the proportion of people with the diseases in question in a given population

    • I.e. how widespread the disease is

      • Among 25y/o women, prevalence of migraine is 18%

      • Among 65y/o Men, prevalence of migraine is 7%

Incidence:

  • is the number of newly diagnosed cases of a disease, your risk of getting the disease

Validity:

  • how close an observation agrees with the true state of affairs- best measure of reality

    • I.e. BP by Sphygmomanometers are less valid than intra-arterial pressures