Table 1. Annual incidences of selected pulmonary diseases.
*Incidence per 100,000 person-years. †UpToDate (www.utdol.com) was
accessed on January 13, 2020 and was used as the default source for
incidence data. Additional sources were consulted (as referenced) if
UpToDate did not provide an estimate of incidence for the disease. CTPA,
Computed Tomography Pulmonary Angiography
As is evident from Table 1, there is no clear dividing line between
common and rare diseases, which exist on a continuum. Likewise, there is
no precise method or formula for taking the raw incidence rate in an
unselected member of the population from Table 1 and transforming it
into a probability of that disease for a specific
patient14,15,26. However, some back-of-the-envelope
calculations are illuminating. Suppose that a patient is being admitted
through the emergency department with symptoms compatible with pneumonia
and the physician estimates, based on experience, that the probability
of pneumonia is on the order of 65%; two out of three times he admits a
similar patient, the diagnosis is ultimately confirmed to be pneumonia.
The remaining 35% of the diagnostic probability
space4 is shared among several less likely
possibilities including lung cancer, atelectasis, infarction, etc.
Since, as seen in Table 1, the annual incidence of lung cancer is
approximately one tenth that of CAP, we could estimate that the
probability of lung cancer is on the order of 6.5%, or one tenth of the
65% probability of CAP. This crude approximation will pass muster with
physicians who regularly admits patients with findings compatible with
pneumonia – a handful of them are ultimately diagnosed with lung cancer
rather than (or in addition to) pneumonia. Similarly, if a medical
student included tuberculosis on the differential diagnosis for this
patient, and she were pressed on how likely it is, she could respond
that the incidence of tuberculosis is just 1/200ththat of CAP making the probability in this patient, ceteris paribus,
65%/200 or about 0.33%. This number also has face validity for
experienced clinicians: the authors, pulmonologists at an academic
medical center, admit on the order of one hundred or more cases of
community acquired pneumonia for every case of tuberculosis they
ultimately diagnose.
With this background, we may now attempt to answer the questions posed
at the outset. What diseases are common? Diseases with the highest
incidences, (e.g., community acquired pneumonia) on the order of
hundreds of cases per 100,000 person-years; practitioners are likely to
encounter these diseases commonly – daily or weekly – in general
medical practice. What diseases are rare? Diseases such as
pheochromocytoma, with an incidence on the order of one case or fewer
per 100,000 person-years; diagnosticians are likely to encounter new
cases of such diseases on the order of once during their entire
career14,36. Is sarcoidosis common? This question is
more difficult since sarcoidosis does not fall on the extremes of the
incidence continuum. What we can say is that community acquired
pneumonia is 65 times more common; diagnosticians are likely to
encounter something like 65 new cases of pneumonia for each newly
diagnosed case of sarcoidosis they encounter. Should commonness be
assessed according to incidence or prevalence? For the purposes of
diagnosis, it is related to the incidence of new, previously undiagnosed
disease. (For the purposes of healthcare expenditures or burden of
disease, it is better assessed by prevalence.) Can the notion of
commonness be operationalized in a practicable way to assist in the
assignment of diagnostic probabilities? We hope to have shown that it
can.
What are the implications of these answers? Despite decades of articles
and dozens of books on clinical problem solving claiming that rational
diagnosis and therapeutics require formal probabilities of
disease14-16,37-41, controversy about the role of
probability in diagnosis is ongoing6,10,11,42-48. This
may stem from the fact that the formal systems proposed for
probabilistic problem solving are too complicated and cumbersome for
day-to-day use in the hustle and bustle of medical
practice15,26,40,41,49. Thus, experts typically arrive
at a diagnosis by pattern recognition, and any use of probabilistic
reasoning is intuitive rather than explicit, rarified exceptions
(including one of the authors)
notwithstanding22,50,51. But perhaps the baby has been
discarded with the bathwater: the unsuitability of complex decision
trees for everyday use does not mean that probability is irrelevant to
diagnosis, rather that its use must be simplified to be practicable.
The enduring popularity of the CTC axiom and its metaphorical variants
is a tacit acknowledgment of the importance of probability in diagnosis.
However, CTC is often invoked after the fact as a corrective (as when
thrombotic thrombocytopenic purpura is disproved by positive blood
cultures) rather than as a general guide for estimating pre-test
probability of diseases. Resolving ambiguity about how to determine what
is common may make the axiom more practicable and the notion of
probability more tractable for diagnosticians. We propose that using
incidence to compare the relative frequencies of competing diseases
early in the differential diagnosis may avert base rate neglect and
related biases from the first. This advice may prove especially useful
for students and trainees who do not yet have an intuitive sense of
disease frequencies. For them, knowing that the incidence of pneumonia
is an order of magnitude greater than that of pulmonary embolism will
represent a pedagogical leap over the tautological admonition that
“common things are common.”
There are several limitations to the use of incidence as a starting
point for estimating the probability of disease. As noted, Table 1 shows
raw incidences in unselected persons in the population. For many
diseases, the incidence varies markedly in subsets of patients
stratified by age, gender, race, geography, and other factors;
diagnosticians must attend to these differences to find an incidence
rate appropriate for a specific (as opposed to unselected) patient. For
diseases such as HIV where screening programs are in place, the
incidence will reflect asymptomatic cases detected by screening in
addition to those diagnosed because of symptoms, comingling incidence
and prevalence. For specialists and those practicing at specialized
centers, patients referred from other physicians or facilities will
likely have enriched probability of rare disease, sometimes remarkably
so. For many diseases, unique combinations of presenting features or
pathognomonic signs and symptoms will give large probability boosts to
otherwise rare diseases – i.e., the rare disease is not rare in the
specific clinical scenario. Some rare diseases, e.g., vibrio vulnificus
sepsis, are not rare in the presence of strong risk factors such as
hemochromatosis and consumption of raw oysters. Lastly, incidence
estimates are susceptible to a host of epidemiological biases including
over- and under-diagnosis and their internal and external validity can
be uncertain. Most of these issues, once acknowledged, can be accounted
for, or serve as an injunction against relying too heavily upon
incidence in specific cases.
Because of its immanent probabilistic and stochastic nature, diagnosis
is a special type of forecasting. Expert forecasters of all stripes use
base rates and probabilistic reasoning explicitly in the development of
their forecasts9,13,52. In addition to improving
accuracy, doing so enables feedback to be more easily brought to bear on
the process, improving subsequent forecasts8,13,52.
Failure to make explicit probabilistic predictions hinders calibration
by fostering the “I knew it all along” effect of hindsight
bias52-55. Physicians, when they have been studied,
have shown unimpressive proficiency at forecasting and
calibration7,10,53. Whether explicit consideration of
numerical disease incidences can improve diagnostic accuracy must await
empirical research dedicated to that question. Such research will
require new paradigms of physician evaluation that depart from the
traditional use of material focused on rare diseases and recondite
knowledge, untethered to base rates, to assess medical competence and
clinical acumen. Meanwhile, we see little downside to considering
incidence as a starting point for gauging what diseases are common for
the purpose of diagnosis. “Common diseases have the highest
incidences” merely revises the tautology, and admittedly makes for a
less euphonious axiom. Nonetheless, being more explicit and concrete, it
holds the promise of providing practicable guidance for how to integrate
probability into diagnosis.
1. Hutchison R. An Address on THE PRINCIPLES OF DIAGNOSIS. British
Medical Journal. 1928;1(3504):335-337.
2. Tumulty PA. The effective clinician: His methods and approach
to diagnosis and care. Philadelphia: Saunders; 1973.
3. Harvey AM, Bordley, J., Barondess, J.A. Differential Diagnosis:
The Interpretation of Clinical Evidence. Philadelphia: W.B. Saunders;
1979.
4. Aberegg SK, Johnson SA. When Alternative Diagnoses Are More Likely
Than Pulmonary Embolism: A Paradox. Ann Am Thorac Soc. 2020.
5. Dolan JG, Bordley DR, Mushlin AI. An evaluation of clinicians’
subjective prior probability estimates. Medical decision making :
an international journal of the Society for Medical Decision Making.1986;6(4):216-223.
6. Puhan MA, Steurer J, Bachmann LM, ter Riet G. Variability in
diagnostic probability estimates. Ann Intern Med.2004;141(7):578-579.
7. Bushyhead JB, Christensen-Szalanski JJ. Feedback and the illusion of
validity in a medical clinic. Medical decision making : an
international journal of the Society for Medical Decision Making.1981;1(2):115-123.
8. The Cambridge Handbook of Expertise and Expert Performance.Cambridge: Cambridge University Press; 2006.
9. Tetlock PaG, D. Superforecasting: The art and science of prediction.
2015.
10. Morgan DJ, Pineles L, Owczarzak J, et al. Accuracy of Practitioner
Estimates of Probability of Diagnosis Before and After Testing.JAMA internal medicine. 2021.
11. Cahan A. Diagnosis is driven by probabilistic reasoning:
counter-point. Diagnosis. 2016;3(3):99-101.
12. Cahan A, Gilon D, Manor O, Paltiel O. Probabilistic reasoning and
clinical decision-making: do doctors overestimate diagnostic
probabilities? QJM : monthly journal of the Association of
Physicians. 2003;96(10):763-769.
13. Dhaliwal G, Detsky AS. The evolution of the master diagnostician.Jama. 2013;310(6):579-580.
14. Kassirer JP, Wong, J., Kopelman, R. Learning CLinical
Reasoning. Second ed: Lippincott Williams Wilkins; 2009.
15. Ledley RS, Lusted LB. Reasoning Foundations of Medical Diagnosis.Symbolic logic, probability, and value theory aid our
understanding of how physicians reason. 1959;130(3366):9-21.
16. O’Connor GT, Sox HC. Bayesian Reasoning in Medicine:The
Contributions of Lee B. Lusted, MD. Medical Decision Making.1991;11(2):107-111.
17. BURCHELL HB. Unusual Forms of Heart Disease. Circulation.1954;10(4):574-579.
18. Montgomery K. How doctors think: Clinical judgment and the
practice of medicine. Oxford: Oxford University Press; 2006.
19. Imperato PJ. Medical Detective. New York: R Marek; 1979.
20. Sotos JG. Zebra Cards: An Aid to Obscure Diagnosis. Third ed.
USA: Mt Vernon Book Systems; 1991.
21. Bar-Hillel M. The base-rate fallacy in probability judgments.Acta Psychologica. 1980;44(3):211-233.
22. Weber EU, Böckenholt U, Hilton DJ, Wallace B. Determinants of
diagnostic hypothesis generation: Effects of information, base rates,
and experience. Journal of Experimental Psychology: Learning,
Memory, and Cognition. 1993;19(5):1151-1164.
23. Tversky A, Kahneman D. Judgment under uncertainty: Heuristics and
biases. Science. 1974;185(4157):1124-1131.
24. Elstein AS. Heuristics and biases: selected errors in clinical
reasoning. Academic medicine : journal of the Association of
American Medical Colleges. 1999;74(7):791-794.
25. Meador CK. A Little Book of Doctors’ Rules I. Hanley &
Belfus; 1992.
26. Balla JI, Iansek R, Elstein A. Bayesian diagnosis in presence of
pre-existing disease. Lancet. 1985;1(8424):326-329.
27. Salive ME. Referral Bias in Tertiary Care: The Utility of Clinical
Epidemiology. Mayo Clinic Proceedings. 1994;69(8):808-809.
28. Eddy DM, Clanton CH. The art of diagnosis: solving the
clinicopathological exercise. N Engl J Med.1982;306(21):1263-1268.
29. Dela Cruz CS, Tanoue LT, Matthay RA. Lung cancer: epidemiology,
etiology, and prevention. Clin. Chest Med. 2011;32(4):605-644.
30. Stojan G, Petri M. Epidemiology of systemic lupus erythematosus: an
update. Curr. Opin. Rheumatol. 2018;30(2):144-150.
31. Pérez ERF, Kong AM, Raimundo K, Koelsch TL, Kulkarni R, Cole AL.
Epidemiology of Hypersensitivity Pneumonitis among an Insured Population
in the United States: A Claims-based Cohort Analysis. Annals of
the American Thoracic Society. 2018;15(4):460-469.
32. Mohammad AJ, Jacobsson LTH, Westman KWA, Sturfelt G, Segelmark M.
Incidence and survival rates in Wegener’s granulomatosis, microscopic
polyangiitis, Churg–Strauss syndrome and polyarteritis nodosa.Rheumatology. 2009;48(12):1560-1565.
33. Prins KW, Thenappan T. World Health Organization Group I Pulmonary
Hypertension: Epidemiology and Pathophysiology. Cardiol. Clin.2016;34(3):363-374.
34. Calamia KT, Wilson FC, Icen M, Crowson CS, Gabriel SE, Kremers HM.
Epidemiology and clinical characteristics of Behcet’s disease in the US:
a population-based study. Arthritis Rheum. 2009;61(5):600-604.
35. Harknett EC, Chang WY, Byrnes S, et al. Use of variability in
national and regional data to estimate the prevalence of
lymphangioleiomyomatosis. QJM. 2011;104(11):971-979.
36. Lau ES, Scirica B, Schaefer I-M, Miller AL, Loscalzo J. Hypertensive
Heartbreak. New England Journal of Medicine.2021;384(22):2145-2152.
37. Pauker SG, Kassirer JP. The threshold approach to clinical decision
making. N Engl J Med. 1980;302(20):1109-1117.
38. Pauker SG, Kassirer JP. Therapeutic Decision Making: A Cost-Benefit
Analysis. New England Journal of Medicine. 1975;293(5):229-234.
39. Djulbegovic B, van den Ende J, Hamm RM, Mayrhofer T, Hozo I, Pauker
SG. When is rational to order a diagnostic test, or prescribe treatment:
the threshold model as an explanation of practice variation. Eur J
Clin Invest. 2015;45(5):485-493.
40. Weinstein MC, Fineberg, H. V. Clinical Decision AnalysisPhiladelphia: W.B. Saunders; 1980.
41. Sox HC, Higgins, M.C., Owens, D.K. Medical Decision Making.second ed. UK: John Wiley & Sons, Ltd.; 2013.
42. Jain BP. Why is diagnosis not probabilistic in clinical-pathological
conference (CPCs): Point. Diagnosis (Berlin, Germany).2016;3(3):95-97.
43. Reid MC, Lane DA, Feinstein AR. Academic calculations versus
clinical judgments: practicing physicians’ use of quantitative measures
of test accuracy. Am J Med. 1998;104(4):374-380.
44. Manrai AK, Bhatia G, Strymish J, Kohane IS, Jain SH. Medicine’s
uncomfortable relationship with math: calculating positive predictive
value. JAMA internal medicine. 2014;174(6):991-993.
45. Casscells W, Schoenberger A, Graboys TB. Interpretation by
physicians of clinical laboratory results. N Engl J Med.1978;299(18):999-1001.
46. Richardson WS. Five uneasy pieces about pre-test probability.J Gen Intern Med. 2002;17(11):882-883.
47. Sanders S, Doust J, Glasziou P. A systematic review of studies
comparing diagnostic clinical prediction rules with clinical judgment.PLoS One. 2015;10(6):e0128233.
48. Goodman SN. Toward evidence-based medical statistics. 1: The P value
fallacy. Ann Intern Med. 1999;130(12):995-1004.
49. Berwick DM, Fineberg HV, Weinstein MC. When doctors meet numbers.Am J Med. 1981;71(6):991-998.
50. Norman G. Building on experience–the development of clinical
reasoning. N Engl J Med. 2006;355(21):2251-2252.
51. Brush JE, Jr, Brophy JM. Sharing the Process of Diagnostic Decision
Making. JAMA internal medicine. 2017;177(9):1245-1246.
52. Arkes HR. Overconfidence in judgmental forecasting. In: Armstrong
JS, ed. Principles of Forecasting: A Handbook for Researchers and
Practitioners . Massachusetts: Kluwer Academic Publishers; 2001.
53. Dawson NV, Connors AF, Jr., Speroff T, Kemka A, Shaw P, Arkes HR.
Hemodynamic assessment in managing the critically ill: is physician
confidence warranted? Medical decision making : an international
journal of the Society for Medical Decision Making. 1993;13(3):258-266.
54. Dawson NV, Arkes HR, Siciliano C, Blinkhorn R, Lakshmanan M,
Petrelli M. Hindsight bias: an impediment to accurate probability
estimation in clinicopathologic conferences. Medical decision
making : an international journal of the Society for Medical Decision
Making. 1988;8(4):259-264.
55. Arkes HR, Wortmann RL, Saville PD, Harkness AR. Hindsight bias among
physicians weighing the likelihood of diagnoses. J Appl Psychol.1981;66(2):252-254.