Evidence-Based Physical Diagnosis_Lect. 1_ What is Evidence-Based Physical Diagnosis

Evidence-Based Physical Diagnosis Lecture 1:

What Is Evidence-Based Physical Diagnosis?

Marc Imhotep Cray, M.D.

Goals The goals of this presentation are  To elucidate the term evidence-based physical diagnosis.  To provide the learner with a first-layer understanding modern-day physical diagnosis.  To demonstrate how concepts in basic epidemiology, biostatistics and probability serve as requisites to applying clinical epidemiology, evidence-based medicine and thus, evidence-based physical diagnosis in core clerkships.

Marc Imhotep Cray, M.D.

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“Icebreaker Admonition” “Read with two objectives: First to acquaint yourself with the current knowledge on the subject and the steps by which it has been reached; and secondly, and more important, read to understand and analyze your cases.”

From: LeBlond RF, et al. DeGowin’s Diagnostic Examination, 10th Ed. New York: McGraw-Hill Education, 2015; xxxi.

Originally: Sir William Osler “The Student Life”

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Why is Diagnosis Important? Medical history and physical examination (H&P) are basis for diagnostic hypothesis generation the first step in the diagnostic process Accurate Dx precedes three tasks central to healing professions: explanation, prognostication & therapy  These three tasks provide answers to patient’s three fundamental questions: 1.

2.

3.

What is happening to me and why?

What does this mean or my future?

What can be done about it, how will that change my future?

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Why is Diagnosis Important cont’d.  Failure to pursue a Dx  may permit a disease to progress from curable to incurable  Contrastly, for many complaints, in otherwise healthy people w no alarm Sx or Sn a good prognosis can be determined w/o knowing exact cause of complaint 

For example, an upper respiratory infection (URI) o

Marc Imhotep Cray, M.D.

An experienced clinician can reassure pt. further testing is unnecessary and will not change Px or Tx

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Why is Diagnosis Important cont’d.  It takes…    

Experience, Knowledge of the medical literature, Good judgment, and Understanding of fundamentals of clinical epidemiology and decision making

…to determine when pursuit of specific Sx & Sn is warranted Note: For a first-rate review of principles of epidemiology, see Fletcher et al.

[Fletcher RH, Fletcher SW, Fletcher GS. Clinical Epidemiology, the Essentials. 5th ed. Baltimore, MD: Lippincott, Williams & Wilkens, 2012].

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Review of Diagnostic process: (9 Sequential Steps) Step 1: Take a History: Elicit symptoms and a timeline; begin a problem list.

Step 2: Develop Hypotheses: Generate a mental list of anatomic sites of disease, pathophysiologic processes, and diseases that might produce the symptoms.

Step 3: Perform a Physical Examination: Look for signs of physiologic processes and diseases suggested by history, and identify new findings for problem list.

Step 4: Make a Problem List: List ALL problems found during history and PE that require an explanation.

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Diagnostic process steps cont’d.

Step 5: Generate a Differential Diagnosis: List most probable diagnostic hypotheses with an estimate of their pretest probabilities.

Step 6: Test the Hypotheses: Select laboratory tests, imaging studies, and other procedures with appropriate likelihood ratios to evaluate your hypotheses.

Step 7: Modify Your Differential Diagnosis: Use test results to evaluate your hypotheses, eliminating some, adding others, and adjusting probabilities.

Step 8: Repeat Steps 1 to 7: Reiterate your process until you have reached a diagnosis or decided that a definite diagnosis is neither likely nor necessary.

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Diagnostic process steps cont’d.

Step 9: Make the Diagnosis or Diagnoses: When tests of your hypotheses are of sufficient certainty that they meet your stopping rule you have reached a diagnosis.  If uncertain, consider a provisional diagnosis or watchful waiting.  Decide whether more investigation (return to Step l), consultation, treatment, or watchful observation is best course based upon severity of illness, prognosis, and comorbidities.

To learn more: LeBlond RF, et al. Part 1: The Diagnostic Framework. In: DeGowin’s Diagnostic Examination, 9th Ed. New York: McGraw-Hill Education, 2009; 1-47.

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Diagnosis When clinicians diagnose disease, their intent is to place patient’s experience into a particular category (or diagnosis) a process implying specific pathogenesis, prognosis, and treatment  This procedure allows clinicians to explain what is happening to patients and to identify best way to restore pt’s health

 A century ago, such categorization of disease rested almost entirely on empiric observation—what clinicians saw, heard, and felt at patient’s bedside  almost all diagnoses were based on traditional physical examination

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Diagnosis cont’d. For example, if patients presented a century ago w complaints of fever and cough,  Dx of lobar pneumonia rested on presence of characteristic PE findings of pneumonia =  fever, tachycardia, tachypnea, grunting respirations, cyanosis, ↓ excursion of affected side, dullness to percussion, ↑ tactile fremitus, ↓ breath sounds (later bronchial breath sounds), abnormalities of vocal resonance (bronchophony, pectoriloquy, and egophony), and crackles o If findings were absent pt. did not have pneumonia •

Chest radiography played no role in diagnosis b/c it was not widely available until early 1900s

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Diagnosis cont’d.  Modern medicine, of course, relies on technology much more than medicine did a century ago (to our patients’ advantage) and for many modern categories of disease, diagnostic standard is a technologic test  For example, if patients present today with fever and cough Dx of pneumonia is based on presence of an infiltrate on chest radiograph  Similarly, Dx of systolic murmurs depends on echocardiography and that of ascites on abdominal ultrasonography o In these disorders, clinician’s principal interest is result of technologic

test and decisions about Tx depend much more on tech result than on, o whether pt. exhibits egophony, radiation of murmur into neck, or shifting dullness Marc Imhotep Cray, M.D.

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Dx a Century Ago “EVOLUTION OF THE DIAGNOSTIC STANDARD”…

McGee S, Steven R. Evidence-based Physical Diagnosis, 4th Ed. Philadelphia, PA: Elsevier, 2018; Fig..1.1, 2.

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Dx in Modern Times …“EVOLUTION OF THE DIAGNOSTIC STANDARD”  One century ago, most Dx were defined by bedside observation, whereas  Today technologic standards have a much greater diagnostic role  Nonetheless, there are many examples today of Dx based solely on bedside findings (Ex. appear in large gray shaded box)

 Evidence-based physical diagnosis, on other hand, principally addresses those Dx defined by technologic standards b/c it identifies those traditional findings that accurately predict result of technologic test McGee S, Steven R. Evidence-based Physical Diagnosis, 4th Ed. Philadelphia, PA: Elsevier, 2018; Fig..1.1, 2.

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Evidence-based medicine  EBM is a modern term for application of clinical epidemiology to care of patients it includes:    

Formulating specific “answerable” clinical questions, Finding best available research evidence bearing on those questions, Judging evidence for its validity, and Integrating critical appraisal w clinician’s expertise & patient’s situation and values

See previous lecture: Introduction to Evidence Based Medicine (EBM) .Ppt

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Dx in Modern Times cont’d. Reliance on technology creates tension for medical students* b/c they spend hours mastering traditional exam yet later learn (when first appearing wards) traditional exam pales in importance compared to technology  A realization prompting a fundamental question: o What is true diagnostic value of traditional physical examination? Is it outdated and best discarded? Is it completely accurate and underutilized? Is the truth somewhere between these two extremes?

* This tension applies most in Western and other highly technologically reliant and wealthy societies.

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Dx in Modern Times cont’d.  “EVOLUTION OF THE DIAGNOSTIC STANDARD”

discussed above indicates

Dx today is split into two parts  For some categories of disease diagnostic standard still remains empiric observation— what clinician sees, hears, and feels—just as it was for all diagnoses a century ago  For example, how does a clinician know pt. has cellulitis? Ans: Only way is to go to patient’s bedside and observe fever and localized bright erythema, warmth, swelling, and tenderness on leg o There is no other way to make this diagnosis (technologic or not)

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“Understanding the Evidence�

NB: The purpose of the follow snippet of concepts in basic epidemiology, biostatistics and probability are entirely select, and only intended to support understanding of how these concepts are applied in clinical epidemiology, evidence-based medicine and thus, evidence-based physical diagnosis.

To learn more student is referred to Public Health Sciences lectures and the Sociology, Epidemiology/Population Health (SPH) & Interpretation of the Medical Literature (EBM) cloud folders.

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Biostatistics Definitions Incidence: The number of new cases of a disease in a population over a specific period of time (=longitudinal) Prevalence: The total number of people in a population affected by a condition at one point in time (=cross-sectional) Duration relates incidence to prevalence For example:  Upper respiratory infections (URIs) have a high incidence every year during winter months but a low prevalence b/c most URIs resolve quickly Contrastly,  Diabetes mellitus (DM) has a relatively low incidence but high prevalence b/c a patient who has diabetes has it for life Marc Imhotep Cray, M.D.

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Statistics for Diagnostic Tests  True positive (Tp): Disease is present and diagnostic test is positive  a correct result  True negative (Tn): Disease is absent and diagnostic test is negative  a correct result  False positive (Fp): Disease is absent and diagnostic test is positive  an incorrect result  False negative (Fn): Disease is present and diagnostic test is negative  an incorrect result

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Statistics for Diagnostic Tests: Sensitivity  Sensitivity: Given disease is present, probability that test will be positive  Stated another way, sensitivity is ability of a test to become positive in presence of the disease  It is defined as Tp/(Tp + Fn) or Tp/(total number of people with disease)  Sensitive tests are useful for screening b/c there are few false negatives  A highly sensitive test can, therefore, rule out the disease o Consider the mnemonic SN-N-OUT= for a test that is SeNsitive, a Negative result rules OUT a disease Marc Imhotep Cray, M.D.

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Statistics for Diagnostic Tests: Sensitivity cont’d.  Example: An HIV test with 98% sensitivity means that, when a disease is present, it will be detected 98% of time  Example: Consider a test that was positive 100% of time regardless of presence or absence of disease  It would technically have 100% sensitivity b/c it would be positive in all patients with disease (but would be clinically useless b/c it would be positive in all patients without disease, too) o Therefore, sensitivity is not whole picture when it comes to test characteristics specificity is also important

Marc Imhotep Cray, M.D.

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Statistics for Diagnostic Tests  Specificity: Given disease is absent, probability that test will be negative  Stated another way specificity is ability of a test to remain negative in absence of disease  It is defined as Tn/(Tn + Fp) or Tn/(total number of people without disease)  Tests with high specificity are useful to confirm a diagnosis b/c there are few false positives  A highly specific test can, therefore, rule in the disease o Consider the mnemonic SP-P-IN—for a test that is SPecific, a Positive result rules IN a disease For Example: An HIV test with 98% specificity means that, when a disease is absent, test will be negative 98% of time Marc Imhotep Cray, M.D.

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Statistics for Diagnostic Tests: PPV & NPV Positive Predictive Value (PPV): Given test is positive, probability that disease is present  PPV = TP/(TP + FP) or TP/(total number of positive tests) o For example, if a computed tomography (CT) scan has 98% specificity for appendicitis, then given a positive finding of appendicitis, patient will truly have disease 98% of time

 Negative Predictive Value (NPV): Given test is negative, probability that the disease is absent  NPV = TN/(TN + FN) or TN/(total number of negative tests) o For example, if an HIV test has 98% NPV, then given a negative test, patient will truly be HIV negative 98% of time

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Evaluation of diagnostic tests  Uses 2 × 2 table comparing test results w actual presence of disease TP, FP, TN, FN

 Sensitivity and specificity are fixed properties of a test  PPV and NPV vary depending on disease prevalence in population being tested Marc Imhotep Cray, M.D.

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Pre- and post-test probability Pre-test probability and post-test probability (pretest and posttest probability) are probabilities of presence of a condition (such as a disease) before and after a diagnostic test, respectively Post-test probability, in turn, can be positive or negative depending on whether test falls out as a positive test or a negative test, respectively Ability to make a difference betw. pre- and post-test probabilities of various conditions is a major factor in indication of medical tests

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Pre- and post-test probability cont’d. Estimation of post-test probability: In clinical practice, post-test probabilities are often just roughly estimated  This is acceptable in finding of a pathognomonic Sn or Sx in which case it is almost certain target condition is present; or  In absence of finding a sine qua non Sn or Sx in which case it is almost certain target condition is absent

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Pre- and post-test probability cont’d. In reality, subjective probability of presence of a condition is never exactly 0 or 100%  Yet, there are several systematic methods to estimate that probability (eg. likelihood ratios [next slide]) methods are based on previously having performed test on a reference group in which presence or absence of condition is known (a test that is considered highly accurate= "Gold standard") o These data are used to interpret test result of any individual tested by method

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Likelihood ratios (LRs) in diagnostic testing  In EBM, likelihood ratios are used for assessing value of performing a diagnostic test (=PE, Lab or other Dx study)  Use sensitivity and specificity of test to determine whether a test result usefully changes probability that a condition (such as a disease state) exists

Application  A likelihood ratio of greater than 1 indicates test result is associated with disease  A likelihood ratio less than 1  indicates test result is assoc. w absence of disease  Tests where likelihood ratios lie close to 1 have little practical significance as post-test probability (odds) is little different from pretest probability Marc Imhotep Cray, M.D.

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Calculation of likelihood ratio Two versions of likelihood ratio exist  one for positive and one for negative test results respectively, known as o positive likelihood ratio (LR+, likelihood ratio positive, likelihood ratio for positive results) and o negative likelihood ratio (LR–, likelihood ratio negative, likelihood ratio for negative results)

Positive likelihood ratio is calculated as which is equivalent to  Or " probability of a person who has disease testing positive divided by probability of a person who does not have disease testing positive“ o o

"T+" or "T−" denote that result of test is positive or negative, respectively "D+" or "D−" denote that disease is present or absent, respectively

 "true positives" are those that test positive (T+) and have disease (D+), and  "false positives" are those that test positive (T+) but do not have disease (D−) Marc Imhotep Cray, M.D.

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Calculation of likelihood ratio cont’d. Negative likelihood ratio is calculated as which is equivalent to  or "probability of a person who has disease testing negative divided by probability of a person who does not have disease testing negative."

Pretest odds of a particular diagnosis X multiplied by likelihood ratio= determines post-test odds (calculation is based on Bayes' theorem )  MKSAP Audio 1-07 – General Medicine_The Bayes Theorem (Offline)

Note : Odds can be calculated from, and then converted to, probability Marc Imhotep Cray, M.D.

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Likelihood ratios: Key Points Likelihood ratios (LRs) are diagnostic weights= numbers that quickly convey to clinicians how much a physical sign argues for or against disease  LRs have possible values between 0 and ∞ o Values greater than 1 ↑probability of disease (greater value of LR, greater ↑in probability)

 LRs less than 1 decrease probability of disease (closer number is to zero, more probability of disease ↓)  LRs that equal 1 do not change probability of disease at all

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LRs Key Points cont’d.  LRs of 2, 5, and 10 increase probability of disease about 15%, 30%, and 45%, respectively (in absolute terms)

 LRs of 0.5, 0.2, and 0.1 (i.e., reciprocals of 2, 5, and 10) decrease probability 15%, 30%, and 45%, respectively  Tables comparing LRs of different physical signs quickly inform clinicians about which findings have greatest diagnostic value See: Medical Likelihood Ratio Repository The Likelihood Ratio Database

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Easy Estimation Table  Use this table to estimate how likelihood ratio changes probability without needing a calculator

Likelihood ratios in diagnostic testing. Article is issued from WikiMed Medical Encyclopedia - version 9/28/2016.

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Estimation Example 1.Pre-test probability: For example, if about 2 out of every 5 patients with abdominal distension have ascites, then pretest probability is 40% 2.Likelihood Ratio: An example "test" is that the physical exam finding of bulging flanks has a positive likelihood ratio of 2.0 for ascites 3.Estimated change in probability: Based on table of previous slide, a likelihood ratio of 2.0 corresponds to an approximately + 15% increase in probability 4.Final (post-test) probability: Therefore, bulging flanks increases probability of ascites from 40% to about 55% (i.e., 40% + 15% = 55%, which is within 2% off exact probability of 57%) Marc Imhotep Cray, M.D.

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Calculation Example ď ą An example is likelihood that a given test result would be expected in a patient with a certain disorder compared to likelihood that same result would occur in a patient without target disorder  A worked example: A diagnostic test w sensitivity 67% and specificity 91% is applied to 2, 030 people to look for a disorder with a population prevalence of 1.48%

Marc Imhotep Cray, M.D. Likelihood ratios in diagnostic testing. Article is issued from WikiMed Medical Encyclopedia - version 9/28/2016.

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THE END

See next slide for further study tools.

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Further study: Recommended textbook reading  McGee S, Steven R. Evidence-based Physical Diagnosis, 4th Ed. Philadelphia, PA: Elsevier, 2018; 1-18.

Cloud folders  Sociology, Epidemiology/Population Health (SPH)  Interpretation of the Medical Literature (EBM)

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