In medicine, a differential diagnosis is the distinguishing of a
particular disease or condition from others that present similar
clinical features.[1] Differential diagnostic procedures are used by
physicians and other trained medical professionals to diagnose the
specific disease in a patient, or, at least, to eliminate any
imminently life-threatening conditions. Often, each individual option
of a possible disease is called a differential diagnosis (for example,
acute bronchitis could be a differential diagnosis in the evaluation
of a cough that ends up with a final diagnosis of common cold).
More generally, a differential diagnostic procedure is a systematic
diagnostic method used to identify the presence of a disease entity
where multiple alternatives are possible. This method is essentially a
process of elimination or at least a process of obtaining information
that shrinks the "probabilities" of candidate conditions to negligible
levels, by using evidence such as symptoms, patient history, and
medical knowledge to adjust epistemic confidences in the mind of the
diagnostician (or, for computerized or computer-assisted diagnosis,
the software of the system).
Contents 1 General components 2 Specific methods 2.1 Epidemiology-based method 2.1.1 Theory 2.1.2 Example 2.2 Likelihood ratio-based method 2.2.1 Theory 2.2.2 Example 3 Coverage of candidate conditions 4 Combinations 5 Machine differential diagnosis 6 History 7 Alternative medical meanings 8 Usage apart from in medicine 9 See also 10 References General components[edit] This paragraph needs additional citations for verification. Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. (October 2011) (Learn how and when to remove this template message) For additional, general, aspects, see Diagnostic procedure. A standard of care differential diagnosis has four steps. Patient safety requires that the physician: Gathers all information about the patient and creates a symptoms list. The list can be in writing or in the physician's head, as long as they make a list. Lists all possible causes (candidate conditions) for the symptoms. Again, this can be in writing or in the physician's head but it must be done. Prioritizes the list by placing the most urgently dangerous possible causes at the top of the list. Rules out or treats possible causes, beginning with the most urgently dangerous condition and working down the list. Rule out—practically—means use tests and other scientific methods to determine that a candidate condition has a clinically negligible probability of being the cause. If no diagnosis remains, it means either that the physician made an error, or that the condition is undocumented. The physician removes diagnoses from the list by observing and applying tests that produce different results, depending on which diagnosis is correct. A mnemonic to help in considering multiple possible pathological processes is VINDICATE'M: Vascular
Inflammatory / Infectious
Neoplastic
Degenerative / Deficiency / Drugs
Specific methods[edit] There are several methods for differential diagnostic procedures, and several variants among those. Furthermore, a differential diagnostic procedure can be used concomitantly or alternately with protocols, guidelines, or other diagnostic procedures (such as pattern-recognition or using medical algorithms). For example, in case of medical emergency, there may not be enough time to do any detailed calculations or estimations of different probabilities, in which case the ABC protocol (Airway, Breathing and Circulation) may be more appropriate. Later, when the situation is less acute, a more comprehensive differential diagnostic procedure may be adopted. The differential diagnostic procedure may be simplified if a "pathognomonic" sign or symptom is found (in which case it is almost certain that the target condition is present) or in the absence of a sine qua non sign or symptom (in which case it is almost certain that the target condition is absent). A diagnostician can be selective, considering first those disorders that are more likely (a probabilistic approach), more serious if left undiagnosed and untreated (a prognostic approach), or more responsive to treatment if offered (a pragmatic approach).[3] Since the subjective probability of the presence of a condition is never exactly 100% or 0%, the differential diagnostic procedure may aim at specifying these various probabilities to form indications for further action. The following are two methods of differential diagnosis, being based on epidemiology and likelihood ratios, respectively. Epidemiology-based method[edit] One method of performing a differential diagnosis by epidemiology aims to estimate the probability of each candidate condition by comparing their probabilities to have occurred in the first place in the individual. It is based on probabilities related both to the presentation (such as pain) and probabilities of the various candidate conditions (such as diseases). Theory[edit] The statistical basis for differential diagnosis is Bayes' theorem. As an analogy, when a die has landed the outcome is certain by 100%, but the probability that it Would Have Occurred In the First Place (hereafter abbreviated WHOIFP) is still 1/6. In the same way, the probability that a presentation or condition would have occurred in the first place in an individual (WHOIFPI) is not same as the probability that the presentation or condition has occurred in the individual, because the presentation has occurred by 100% certainty in the individual. Yet, the contributive probability fractions of each condition are assumed the same, relatively: Pr ( Presentation is caused by condition in individual ) Pr ( Presentation has occurred in individual ) = Pr ( Presentation WHOIFPI by condition ) Pr ( Presentation WHOIFPI ) displaystyle begin aligned & frac Pr( text Presentation is caused by condition in individual ) Pr( text Presentation has occurred in individual ) = frac Pr( text Presentation WHOIFPI by condition ) Pr( text Presentation WHOIFPI ) end aligned where: Pr(Presentation is caused by condition in individual) is the probability that the presentation is caused by condition in the individual condition without further specification refers to any candidate condition Pr(Presentation has occurred in individual) is the probability that the presentation has occurred in the individual, which can be perceived and thereby set at 100% Pr(Presentation WHOIFPI by condition) is the probability that the presentation Would Have Occurred in the First Place in the Individual by condition Pr(Presentation WHOIFPI) is the probability that the presentation Would Have Occurred in the First Place in the Individual When an individual presents with a symptom or sign, Pr(Presentation has occurred in individual) is 100% and can therefore be replaced by 1, and can be ignored since division by 1 does not make any difference: Pr ( Presentation is caused by condition in individual ) = Pr ( Presentation WHOIFPI by condition ) Pr ( Presentation WHOIFPI ) displaystyle Pr( text Presentation is caused by condition in individual )= frac Pr( text Presentation WHOIFPI by condition ) Pr( text Presentation WHOIFPI ) The total probability of the presentation to have occurred in the individual can be approximated as the sum of the individual candidate conditions: Pr ( Presentation WHOIFPI ) = Pr ( Presentation WHOIFPI by condition 1 ) + Pr ( Presentation WHOIFPI by condition 2 ) + Pr ( Presentation WHOIFPI by condition 3 ) + etc. displaystyle begin aligned Pr( text Presentation WHOIFPI )&=Pr( text Presentation WHOIFPI by condition 1 )\& +Pr( text Presentation WHOIFPI by condition 2 )\& +Pr( text Presentation WHOIFPI by condition 3 )+ text etc. end aligned Also, the probability of the presentation to have been caused by any candidate condition is proportional to the probability of the condition, depending on what rate it causes the presentation: Pr ( Presentation WHOIFPI by condition ) = Pr ( Condition WHOIFPI ) ⋅ r condition → presentation , displaystyle Pr( text Presentation WHOIFPI by condition )=Pr( text Condition WHOIFPI )cdot r_ text condition rightarrow text presentation , where: Pr(Presentation WHOIFPI by condition) is the probability that the presentation Would Have Occurred in the First Place in the Individual by condition Pr(Condition WHOIFPI) is the probability that the condition Would Have Occurred in the First Place in the Individual rCondition → presentation is the rate for which a condition causes the presentation, that is, the fraction of people with condition that manifest with the presentation. The probability that a condition would have occurred in the first place in an individual is approximately equal to that of a population that is as similar to the individual as possible except for the current presentation, compensated where possible by relative risks given by known risk factor that distinguish the individual from the population: Pr ( Condition WHOIFPI ) ≈ R R condition ⋅ Pr ( Condition in population ) , displaystyle Pr( text Condition WHOIFPI )approx RR_ text condition cdot Pr( text Condition in population ), where: Pr(Condition WHOIFPI) is the probability that the condition Would Have Occurred in the First Place in the Individual RRcondition is the relative risk for condition conferred by known risk factors in the individual that are not present in the population Pr(Condition in population) is the probability that the condition occurs in a population that is as similar to the individual as possible except for the presentation The following table demonstrates how these relations can be made for a series of candidate conditions: Candidate condition 1 Candidate condition 2 Candidate condition 3 Pr(Condition in population) Pr(Condition 1 in population) Pr(Condition 2 in population) Pr(Condition 3 in population) RRcondition RR 1 RR 2 RR 3 Pr(Condition WHOIFPI) Pr(Condition 1 WHOIFPI) Pr(Condition 2 WHOIFPI) P(Condition 3 WHOIFPI) rCondition → presentation rCondition 1 → presentation rCondition 2 → presentation rCondition 3 → presentation Pr(Presentation WHOIFPI by condition) Pr(Presentation WHOIFPI by condition 1) Pr(Presentation WHOIFPI by condition 2) Pr(Presentation WHOIFPI by condition 3) Pr(Presentation WHOIFPI) = the sum of the probabilities in row just above Pr(Presentation is caused by condition in individual) Pr(Presentation is caused by condition 1 in individual) Pr(Presentation is caused by condition 2 in individual) Pr(Presentation is caused by condition 3 in individual) One additional "candidate condition" is the instance of there being no abnormality, and the presentation is only a (usually relatively unlikely) appearance of a basically normal state. Its probability in the population (P(No abnormality in population)) is complementary to the sum of probabilities of "abnormal" candidate conditions. Example[edit] This example case demonstrates how this method is applied, but does not represent a guideline for handling similar real-world cases. Also, the example uses relatively specified numbers with sometimes several decimals, while in reality, there are often simply rough estimations, such as of likelihoods being very high, high, low or very low, but still using the general principles of the method. For an individual (who becomes the "patient" in this example), a blood test of, for example, serum calcium shows a result above the standard reference range, which, by most definitions, classifies as hypercalcemia, which becomes the "presentation" in this case. A physician (who becomes the "diagnostician" in this example), who does not currently see the patient, gets to know about his finding. By practical reasons, the physician considers that there is enough test indication to have a look at the patient’s medical records. For simplicity, let’s say that the only information given in the medical records is a family history of primary hyperparathyroidism (here abbreviated as PH), which may explain the finding of hypercalcemia. For this patient, let’s say that the resultant hereditary risk factor is estimated to confer a relative risk of 10 (RRPH = 10). The physician considers that there is enough motivation to perform a differential diagnostic procedure for the finding of hypercalcemia. The main causes of hypercalcemia are primary hyperparathyroidism (PH) and cancer, so for simplicity, the list of candidate conditions that the physician could think of can be given as:
The probability that 'primary hyperparathyroidism' (PH) would have occurred in the first place in the individual (P(PH WHOIFPI)) can be calculated as follows: Let’s say that the last blood test taken by the patient was half a year ago and was normal, and that the incidence of primary hyperparathyroidism in a general population that appropriately matches the individual (except for the presentation and mentioned heredity) is 1 in 4000 per year. Ignoring more detailed retrospective analyses (such as including speed of disease progress and lag time of medical diagnosis), the time-at-risk for having developed primary hyperparathyroidism can roughly be regarded as being the last half-year, because a previously developed hypercalcemia would probably have been caught up by the previous blood test. This corresponds to a probability of primary hyperparathyroidism (PH) in the population of: Pr ( PH in population ) = 0.5 years ⋅ 1 4000 per year = 1 8000 displaystyle Pr( text PH in population )=0.5 text years cdot frac 1 text 4000 per year = frac 1 8000 With the relative risk conferred from the family history, the probability that primary hyperparathyroidism (PH) would have occurred in the first place in the individual given from the currently available information becomes: Pr ( PH WHOIFPI ) ≈ R R P H ⋅ Pr ( PH in population ) = 10 ⋅ 1 8000 = 1 800 = 0.00125 displaystyle Pr( text PH WHOIFPI )approx RR_ PH cdot Pr( text PH in population )=10cdot frac 1 8000 = frac 1 800 =0.00125
Pr (
) = Pr ( PH WHOIFPI ) ⋅ r PH → hypercalcemia = 0.00125 ⋅ 1 = 0.00125 displaystyle begin aligned Pr( text
For cancer, the same time-at-risk is assumed for simplicity, and let’s say that the incidence of cancer in the area is estimated at 1 in 250 per year, giving a population probability of cancer of: Pr ( cancer in population ) = 0.5 years ⋅ 1 250 per year = 1 500 displaystyle Pr( text cancer in population )=0.5 text years cdot frac 1 text 250 per year = frac 1 500 For simplicity, let’s say that any association between a family history of primary hyperparathyroidism and risk of cancer is ignored, so the relative risk for the individual to have contracted cancer in the first place is similar to that of the population (RRcancer = 1): Pr ( cancer WHOIFPI ) ≈ R R cancer ⋅ Pr ( cancer in population ) = 1 ⋅ 1 500 = 1 500 = 0.002. displaystyle Pr( text cancer WHOIFPI )approx RR_ text cancer cdot Pr( text cancer in population )=1cdot frac 1 500 = frac 1 500 =0.002. However, hypercalcemia only occurs in, very approximately, 10% of cancers,[4] (rcancer → hypercalcemia = 0.1), so: Pr (
) = Pr ( cancer WHOIFPI ) ⋅ r cancer → hypercalcemia = 0.002 ⋅ 0.1 = 0.0002. displaystyle begin aligned &Pr( text
The probabilities that hypercalcemia would have occurred in the first place by other candidate conditions can be calculated in a similar manner. However, for simplicity, let’s say that the probability that any of these would have occurred in the first place is calculated at 0.0005 in this example. For the instance of there being no disease, the corresponding probability in the population is complementary to the sum of probabilities for other conditions: Pr ( no disease in population ) = 1 − Pr ( PH in population ) − Pr ( cancer in population ) − Pr ( other conditions in population ) = 0.997. displaystyle begin aligned Pr( text no disease in population )&=1-Pr( text PH in population )-Pr( text cancer in population )\& quad -Pr( text other conditions in population )\& =0.997.end aligned The probability that the individual would be healthy in the first place can be assumed to be the same: Pr ( no disease WHOIFPI ) = 0.997. displaystyle Pr( text no disease WHOIFPI )=0.997., The rate at which the case of no abnormal condition still ends up in a measurement of serum calcium of being above the standard reference range (thereby classifying as hypercalcemia) is, by the definition of standard reference range, less than 2.5%. However, this probability can be further specified by considering how much the measurement deviates from the mean in the standard reference range. Let’s say that the serum calcium measurement was 1.30 mmol/L, which, with a standard reference range established at 1.05 to 1.25 mmol/L, corresponds to a standard score of 3 and a corresponding probability of 0.14% that such degree of hypercalcemia would have occurred in the first place in the case of no abnormality: r no disease → hypercalcemia = 0.0014 displaystyle r_ text no disease rightarrow text hypercalcemia =0.0014 Subsequently, the probability that hypercalcemia would have resulted from no disease can be calculated as: Pr (
) = Pr ( no disease WHOIFPI ) ⋅ r no disease → hypercalcemia = 0.997 ⋅ 0.0014 ≈ 0.0014 displaystyle begin aligned &Pr( text
The probability that hypercalcemia would have occurred in the first place in the individual can thus be calculated as: Pr ( hypercalcemia WHOIFPI ) = Pr ( hypercalcemia WHOIFPI by PH ) + Pr ( hypercalcemia WHOIFPI by cancer ) + Pr ( hypercalcemia WHOIFPI by other conditions ) + Pr ( hypercalcemia WHOIFPI by no disease ) = 0.00125 + 0.0002 + 0.0005 + 0.0014 = 0.00335 displaystyle begin aligned &Pr( text hypercalcemia WHOIFPI )\=&Pr( text hypercalcemia WHOIFPI by PH )+Pr( text hypercalcemia WHOIFPI by cancer )\& +Pr( text hypercalcemia WHOIFPI by other conditions )+Pr( text hypercalcemia WHOIFPI by no disease )\=&0.00125+0.0002+0.0005+0.0014=0.00335end aligned Subsequently, the probability that hypercalcemia is caused by primary hyperparathyroidism (PH) in the individual can be calculated as: Pr ( hypercalcemia is caused by PH in individual ) = Pr ( hypercalcemia WHOIFPI by PH ) Pr ( hypercalcemia WHOIFPI ) = 0.00125 0.00335 = 0.373 = 37.3 % displaystyle begin aligned &Pr( text hypercalcemia is caused by PH in individual )\=& frac Pr( text hypercalcemia WHOIFPI by PH ) Pr( text hypercalcemia WHOIFPI ) \=& frac 0.00125 0.00335 =0.373=37.3%end aligned Similarly, the probability that hypercalcemia is caused by cancer in the individual can be calculated as: Pr ( hypercalcemia is caused by cancer in individual ) = Pr ( hypercalcemia WHOIFPI by cancer ) Pr ( hypercalcemia WHOIFPI ) = 0.0002 0.00335 = 0.060 = 6.0 % , displaystyle begin aligned &Pr( text hypercalcemia is caused by cancer in individual )\=& frac Pr( text hypercalcemia WHOIFPI by cancer ) Pr( text hypercalcemia WHOIFPI ) \=& frac 0.0002 0.00335 =0.060=6.0%,end aligned and for other candidate conditions: Pr ( hypercalcemia is caused by other conditions in individual ) = Pr ( hypercalcemia WHOIFPI by other conditions ) Pr ( hypercalcemia WHOIFPI ) = 0.0005 0.00335 = 0.149 = 14.9 % , displaystyle begin aligned &Pr( text hypercalcemia is caused by other conditions in individual )\=& frac Pr( text hypercalcemia WHOIFPI by other conditions ) Pr( text hypercalcemia WHOIFPI ) \=& frac 0.0005 0.00335 =0.149=14.9%,end aligned and the probability that there actually is no disease: Pr ( hypercalcemia is present despite no disease in individual ) = Pr ( hypercalcemia WHOIFPI by no disease ) Pr ( hypercalcemia WHOIFPI ) = 0.0014 0.00335 = 0.418 = 41.8 % displaystyle begin aligned &Pr( text hypercalcemia is present despite no disease in individual )\=& frac Pr( text hypercalcemia WHOIFPI by no disease ) Pr( text hypercalcemia WHOIFPI ) \=& frac 0.0014 0.00335 =0.418=41.8%end aligned For clarification, these calculations are given as the table in the method description: PH Cancer Other conditions No disease P(Condition in population) 0.000125 0.002 - 0.997 RRx 10 1 - - P(Condition WHOIFPI) 0.00125 0.002 - - rCondition →hypercalcemia 1 0.1 - 0.0014 P(hypercalcemia WHOIFPI by condition) 0.00125 0.0002 0.0005 0.0014 P(hypercalcemia WHOIFPI) = 0.00335 P(hypercalcemia is caused by condition in individual) 37.3% 6.0% 14.9% 41.8% Thus, this method estimates that the probabilities that the hypercalcemia is caused by primary hyperparathyroidism, cancer, other conditions or no disease at all are 37.3%, 6.0%, 14.9% and 41.8%, respectively, which may be used in estimating further test indications. This case is continued in the example of the method described in the next section. Likelihood ratio-based method[edit] The procedure of differential diagnosis can become extremely complex when fully taking additional tests and treatments into consideration. One method that is somewhat a tradeoff between being clinically perfect and being relatively simple to calculate is one that uses likelihood ratios to derive subsequent post-test likelihoods. Theory[edit] The initial likelihoods for each candidate condition can be estimated by various methods, such as: By epidemiology as described in previous section. By clinic-specific pattern recognition, such as statistically knowing that patients coming into a particular clinic with a particular complaint statistically has a particular likelihood of each candidate condition. One method of estimating likelihoods even after further tests uses likelihood ratios (which is derived from sensitivities and specificities) as a multiplication factor after each test or procedure. In an ideal world, sensitivities and specificities would be established for all tests for all possible pathological conditions. In reality, however, these parameters may only be established for one of the candidate conditions. Multiplying with likelihood ratios necessitates conversion of likelihoods from probabilities to odds in favor (hereafter simply termed “odds”) by: odds = probability 1 − probability displaystyle text odds = frac text probability 1- text probability However, only the candidate conditions with known likelihood ratio need this conversion. After multiplication, conversion back to probability is calculated by: probability = odds odds + 1 displaystyle text probability = frac text odds text odds +1 The rest of the candidate conditions (for which there is no established likelihood ratio for the test at hand) can, for simplicity, be adjusted by subsequently multiplying all candidate conditions with a common factor to again yield a sum of 100%. The resulting probabilities are used for estimating the indications for further medical tests, treatments or other actions. If there is an indication for an additional test, and it returns with a result, then the procedure is repeated using the likelihood ratio of the additional test. With updated probabilities for each of the candidate conditions, the indications for further tests, treatments or other actions changes as well, and so the procedure can be repeated until an end point where there no longer is any indication for currently performing further actions. Such an end point mainly occurs when one candidate condition becomes so certain that no test can be found that is powerful enough to change the relative probability-profile enough to motivate any change in further actions. Tactics for reaching such an end point with as few tests as possible includes making tests with high specificity for conditions of already outstandingly high-profile-relative probability, because the high likelihood ratio positive for such tests is very high, bringing all less likely conditions to relatively lower probabilities. Alternatively, tests with high sensitivity for competing candidate conditions have a high likelihood ratio negative, potentially bringing the probabilities for competing candidate conditions to negligible levels. If such negligible probabilities are achieved, the physician can rule out these conditions, and continue the differential diagnostic procedure with only the remaining candidate conditions. Example[edit] This example continues for the same patient as in the example for the epidemiology-based method. As with the previous example of epidemiology-based method, this example case is made to demonstrate how this method is applied, but does not represent a guideline for handling similar real-world cases. Also, the example uses relatively specified numbers, while in reality, there are often just rough estimations. In this example, the probabilities for each candidate condition were established by an epidemiology-based method to be as follows: PH Cancer Other conditions No disease Probability 37.3% 6.0% 14.9% 41.8% These percentages could also have been established by experience at the particular clinic by knowing that these are the percentages for final diagnosis for people presenting to the clinic with hypercalcemia and having a family history of primary hyperparathyroidism. The condition of highest profile-relative probability (except “no disease”) is primary hyperparathyroidism (PH), but cancer is still of major concern, because if it is the actual causative condition for the hypercalcemia, then the choice of whether to treat or not likely means life or death for the patient, in effect potentially putting the indication at a similar level for further tests for both of these conditions. Here, let’s say that the physician considers the profile-relative probabilities of being of enough concern to indicate sending the patient a call for a doctor's visit, with an additional visit to the medical laboratory for an additional blood test complemented with further analyses, including parathyroid hormone for the suspicion of primary hyperparathyroidism. For simplicity, let’s say that the doctor first receives the blood test (in formulas abbreviated as “BT”) result for the parathyroid hormone analysis, and that it showed a parathyroid hormone level that is elevated relatively to what would be expected by the calcium level. Such a constellation can be estimated to have a sensitivity of approximately 70% and a specificity of approximately 90% for primary hyperparathyroidism.[5] This confers a likelihood ratio positive of 7 for primary hyperparathyroidism. The probability of primary hyperparathyroidism is now termed Pre-BTPH because it corresponds to before the blood test (Latin preposition prae means before). It was estimated at 37.3%, corresponding to an odds of 0.595. With the likelihood ratio positive of 7 for the blood test, the post-test odds is calculated as: Odds ( PostBT P H ) = Odds ( PreBT P H ) ⋅ L H ( B T ) = 0.595 ⋅ 7 = 4.16 , displaystyle operatorname Odds ( text PostBT _ PH )=operatorname Odds ( text PreBT _ PH )cdot LH(BT)=0.595cdot 7=4.16, where: Odds(PostBTPH) is the odds for primary hyperparathyroidism after the blood test for parathyroid hormone Odds(PreBTPH is the odds in favor of primary hyperparathyroidism before the blood test for parathyroid hormone LH(BT) is the likelihood ratio positive for the blood test for parathyroid hormone An Odds(PostBTPH) of 4.16 is again converted to the corresponding probability by: Pr ( PostBT P H ) = Odds ( PostBT P H ) Odds ( PostBT P H ) + 1 = 4.16 4.16 + 1 = 0.806 = 80.6 % displaystyle Pr( text PostBT _ PH )= frac operatorname Odds ( text PostBT _ PH ) operatorname Odds ( text PostBT _ PH )+1 = frac 4.16 4.16+1 =0.806=80.6% The sum of the probabilities for the rest of the candidate conditions should therefore be: Pr ( PostBT r e s t ) = 100 % − 80.6 % = 19.4 % displaystyle Pr( text PostBT _ rest )=100%-80.6%=19.4% Before the blood test for parathyroid hormone, the sum of their probabilities were: Pr ( PreBT rest ) = 6.0 % + 14.9 % + 41.8 % = 62.7 % displaystyle Pr( text PreBT _ text rest )=6.0%+14.9%+41.8%=62.7% Therefore, to conform to a sum of 100% for all candidate conditions, each of the other candidates must be multiplied by a correcting factor: Correcting factor = Pr ( PostBT rest ) Pr ( PreBT rest ) = 19.4 62.7 = 0.309 displaystyle text Correcting factor = frac Pr( text PostBT _ text rest ) Pr( text PreBT _ text rest ) = frac 19.4 62.7 =0.309 For example, the probability of cancer after the test is calculated as: Pr ( PostBT cancer ) = Pr ( PreBT cancer ) ⋅ Correcting factor = 6.0 % ⋅ 0.309 = 1.9 % displaystyle Pr( text PostBT _ text cancer )=Pr( text PreBT _ text cancer )cdot text Correcting factor =6.0%cdot 0.309=1.9% The probabilities for each candidate conditions before and after the blood test are given in following table: PH Cancer Other conditions No disease P(PreBT) 37.3% 6.0% 14.9% 41.8% P(PostBT) 80.6% 1.9% 4.6% 12.9% These “new” percentages, including a profile-relative probability of 80% for primary hyperparathyroidism, underlie any indications for further tests, treatments or other actions. In this case, let's say that the physician continues the plan for the patient to attend a doctor's visit for further checkup, especially focused at primary hyperparathyroidism. A doctor's visit can, theoretically, be regarded as a series of tests, including both questions in a medical history as well as components of a physical examination, where the post-test probability of a previous test can be used as the pre-test probability of the next. The indications for choosing the next test is dynamically influenced by the results of previous tests. Let's say that the patient in this example is revealed to have at least some of the symptoms and signs of depression, bone pain, joint pain or constipation of more severerity than what would be expected by the hypercalcemia itself, supporting the suspicion of primary hyperparathyroidism,[6] and let's say that the likelihood ratios for the tests, when multiplied together, roughly results in a product of 6 for primary hyperparathyroidism. The presence of unspecific pathologic symptoms and signs in the history and examination are often concurrently indicative of cancer as well, and let's say that the tests gave an overall likelihood ratio estimated at 1.5 for cancer. For other conditions, as well as the instance of not having any disease at all, let’s say that it’s unknown how they are affected by the tests at hand, as often happens in reality. This gives the following results for the history and physical examination (abbreviated as P&E): PH Cancer Other conditions No disease P(PreH&E) 80.6% 1.9% 4.6% 12.9% Odds(PreH&E) 4.15 0.019 0.048 0.148 Likelihood ratio by H&E 6 1.5 - - Odds(PostH&E) 24.9 0.0285 - - P(PostH&E) 96.1% 2.8% - - Sum of known P(PostH&E) 98.9% Sum of the rest P(PostH&E) 1.1% Sum of the rest P(PreH&E) 4.6% + 12.9% = 17.5% Correcting factor 1.1% / 17.5% = 0.063 After correction - - 0.3% 0.8% P(PostH&E) 96.1% 2.8% 0.3% 0.8% These probabilities after the history and examination may make the
physician confident enough to plan the patient for surgery for a
parathyroidectomy to resect the affected tissue.
At this point, the probability of "other conditions" is so low that
the physician cannot think of any test for them that could make a
difference that would be substantial enough to form an indication for
such a test, and the physician thereby practically regards "other
conditions" as ruled out, in this case not primarily by any specific
test for such other conditions that were negative, but rather by the
absence of positive tests so far.
For "cancer", the cutoff at which to confidently regard it as ruled
out may be more stringent because of severe consequences of missing
it, so the physician may consider that at least a histopathologic
examination of the resected tissue is indicated.
This case is continued in the example of Combinations in corresponding
section below.
Coverage of candidate conditions[edit]
The validity of both the initial estimation of probabilities by
epidemiology and further workup by likelihood ratios are dependent of
inclusion of candidate conditions that are responsible for as large
part as possible of the probability of having developed the condition,
and it is clinically important to include those where relatively fast
initiation of therapy is most likely to result in greatest benefit. If
an important candidate condition is missed, no method of differential
diagnosis will supply the correct conclusion. The need to find more
candidate conditions for inclusion increases with increasing severity
of the presentation itself. For example, if the only presentation is a
deviating laboratory parameter and all common harmful underlying
conditions have been ruled out, then it may be acceptable to stop
finding more candidate conditions, but this would much more likely be
unacceptable if the presentation would have been severe pain.
Combinations[edit]
If two conditions get high post-test probabilities, especially if the
sum of the probabilities for conditions with known likelihood ratios
become higher than 100%, then the actual condition is a combination of
the two. In such cases, that combined condition can be added to the
list of candidate conditions, and the calculations should start over
from the beginning.
To continue the example used above, let's say that the history and
physical examination was indicative of cancer as well, with a
likelihood ratio of 3, giving an Odds(PostH&E) of 0.057,
corresponding to a P(PostH&E) of 5.4%. This would correspond to a
“Sum of known P(PostH&E)” of 101.5%. This is an indication for
considering a combination of primary hyperparathyroidism and cancer,
such as, in this case, a parathyroid hormone-producing parathyroid
carcinoma. A recalculation may therefore be needed, with the first two
conditions being separated into “primary hyperparathyroidism without
cancer”, “cancer without primary hyperparathyroidism” as well as
“combined primary hyperparathyroidism and cancer”, and likelihood
ratios being applied to each condition separately. In this case,
however, tissue has already been resected, wherein a histopathologic
examination can be performed that includes the possibility of
parathyroid carcinoma in the examination (which may entail appropriate
sample staining). Let’s say that the histopathologic examination
confirms primary hyperparathyroidism, but also showed a malignant
pattern. By an initial method by epidemiology, the incidence of
parathyroid carcinoma is estimated at about 1 in 6 million people per
year,[7] giving a very low probability before taking any tests into
consideration. In comparison, the probability that a non-malignant
primary hyperparathyroidism would have occurred at the same time as an
unrelated non-carcinoma cancer that presents with malignant cells in
the parathyroid gland is calculated by multiplying the probabilities
of the two. The resultant probability is, however, much smaller than
the 1 in 6 million. Therefore, the probability of parathyroid
carcinoma may still be close to 100% after histopathologic examination
despite the low probability of occurring in the first place.
Let's finally say that the diagnosis of parathyroid carcinoma resulted
in an extended surgery that removed remaining malignant tissue before
it had metastasized, and the patient lived happily ever after.
Machine differential diagnosis[edit]
Further information: Clinical decision support system
Machine differential diagnosis is the use of computer software to
partly or fully make a differential diagnosis. It may be regarded as
an application of artificial intelligence.
Many studies demonstrate improvement of quality of care and reduction
of medical errors by using such decision support systems. Some of
these systems are designed for a specific medical problem such as
schizophrenia,[8] Lyme disease[9] or ventilator-associated
pneumonia.[10] Others such as ESAGIL,[11] Iliad, QMR,
DiagnosisPro,[12] VisualDx,[13] Isabel,[14] ZeroMD,[15] DxMate,[16]
Symptoma, and
Comorbidity Diagnosis of exclusion Dual diagnosis Gender-bias in medical diagnosis List of medical symptoms References[edit] ^ "differential diagnosis". Merriam-Webster (Medical dictionary).
Retrieved 30 December 2014.
^ Cf. VINDICATE –
v t e Basic medical terms used to describe disease conditions
Medical sign Symptom Syndrome Medical diagnosis Differential diagnosis Prognosis Acute Chronic Cure/Remission Disease Eponymous disease Acronym or abbreviation v t e Medical examination and history taking Medical history Chief complaint History of the present illness Systems review Nursing assessment Allergies Medications Past medical history Surgical history Family history Social history Psychiatric history Progress notes Mnemonics SAMPLE OPQRST SOAP COASTMAP Physical examination General/IPPA Inspection Auscultation Palpation Percussion Vital signs Temperature Heart rate Blood pressure Respiratory rate HEENT Oral mucosa TM Eyes (Ophthalmoscopy, Swinging-flashlight test) Hearing (Weber, Rinne) Respiratory Respiratory sounds Cyanosis Clubbing Cardiovascular Precordial examination Peripheral vascular examination Heart sounds Other Jugular venous pressure Abdominojugular test Carotid bruit Ankle-brachial pressure index Abdominal Digestive Liver span Rectal Murphy's sign Bowel sounds Urinary Murphy's punch sign Extremities/Joint Back (Straight leg raise)
Knee (McMurray test)
Hip
Neurological Mental state Mini–mental state examination Cranial nerve examination Upper limb neurological examination Neonatal Apgar score Ballard Maturational Assessment Gynecological Well-woman examination Vaginal examination Breast examination Cervical motion tenderness Assessment and plan Medical diagnosis Differential diagnosis Authority control LCCN: sh85037493 GND: 4113309-2 BNF: |