Differential diagnosis
Template:Interventions infobox A differential diagnosis (sometimes abbreviated DDx, ddx, DD, D/Dx, or ΔΔ) is a systematic diagnostic method used to identify the presence of an entity where multiple alternatives are possible (and the process may be termed differential diagnostic procedure), and may also refer to any of the included candidate alternatives (or candidate conditions).
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 computerassisted diagnosis, the software of the system).
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 lifethreatening conditions. Often each individual option of a possible disease is called a differential diagnosis (for example, bronchitis could be a differential diagnosis in the evaluation of a cough that ends up with a final diagnosis of common cold).
Differential diagnosis can be regarded as implementing aspects of the hypotheticodeductive method, in the sense that the potential presence of candidate diseases or conditions can be viewed as hypotheses that physicians further determine as being true or false.
General components
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There are various methods of performing a differential diagnostic procedure, but in general, it is based on the idea that one begins by considering the most common diagnosis first: a head cold versus meningitis, for example. As a reminder, medical students are taught the Occam's razor adage, "When you hear hoofbeats, look for horses, not zebras," which means look for the simplest, most common explanation first. Only after ruling out the simplest diagnosis should the clinician consider more complex or exotic diagnoses.
Differential diagnosis has four steps. 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.
In some cases, there remains no diagnosis. This suggests the physician has made an error, or that the true diagnosis is unknown to medicine. The physician removes diagnoses from the list by observing and applying tests that produce different results, depending on which diagnosis is correct.
Medical students routinely learn mnemonics that ensure they consider all possible pathological processes. For example VINDICATE'M:
 Vascular
 Inflammatory/Infectious
 Neoplastic
 Degenerative/Deficiency/Drugs
 Idiopathic/Intoxication/Iatrogenic
 Congenital
 Autoimmune/Allergic/Anatomic
 Traumatic
 Endocrine/Environmental
 Metabolic^{[1]}
Specific methods
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 patternrecognition 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 may be more appropriate. At a later, less acute, situation, there may be a switch to a more comprehensive differential diagnostic procedure.
The differential diagnostic procedure may be easier in the finding of a 'pathognomonic' sign or symptom, in which it is almost certain that the target condition is present, and in the absence of finding a sine qua non sign or symptom, in which case it is almost certain that the target condition is absent. In reality, however, the subjective probability of the presence of a condition is never exactly 100% or 0%, so in reality the procedure is usually aimed at specifying these various probabilities to form indications for further action.
Following are two methods of differential diagnosis, being based on epidemiology and likelihood ratios, respectively.
An epidemiologybased method
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
The statistical basis for differential diagnosis is Bayes' theorem. As an analogy, when a dice 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:
 <math>
\begin{align} & \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{align} </math>
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:
 <math> \Pr(\text{Presentation is caused by condition in individual}) = \frac {\Pr(\text{Presentation WHOIFPI by condition})}{\Pr(\text{Presentation WHOIFPI})}</math>
The total probability of the presentation to have occurred in the individual can be approximated as the sum of the individual candidate conditions:
 <math> \begin{align} \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{align}</math>
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:
 <math> \Pr(\text{Presentation WHOIFPI by condition}) = \Pr(\text{Condition WHOIFPI}) \cdot r_{\text{condition} \rightarrow \text{presentation}},</math>
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
 r_{Condition → 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:
 <math> \Pr(\text{Condition WHOIFPI}) \approx RR_\text{condition} \cdot \Pr(\text{Condition in population}),</math>
where:
 Pr(Condition WHOIFPI) is the probability that the condition Would Have Occurred in the First Place in the Individual
 RR_{condition} 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) 
RR_{condition}  RR_{ 1}  RR_{ 2}  RR_{ 3} 
Pr(Condition WHOIFPI)  Pr(Condition 1 WHOIFPI)  Pr(Condition 2 WHOIFPI)  P(Condition 3 WHOIFPI) 
r_{Condition → presentation}  r_{Condition 1 → presentation}  r_{Condition 2 → presentation}  r_{Condition 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
This example case demonstrates how this method is applied, but does not represent a guideline for handling similar realworld 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 (RR_{PH} = 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:
 Primary hyperparathyroidism (PH)
 Cancer
 Other diseases that the physician could think of (which is simply termed "other conditions" for the rest of this example)
 No disease (or no abnormality), and the finding is caused entirely by statistical variability
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 timeatrisk for having developed primary hyperparathyroidism can roughly be regarded as being the last halfyear, 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:
 <math> \Pr(\text{PH in population}) = 0.5\text{ years} \cdot \frac{1}{\text{4000 per year}} = \frac{1}{8000}</math>
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:
 <math> \Pr(\text{PH WHOIFPI}) \approx RR_{PH}\cdot \Pr(\text{PH in population}) = 10 \cdot \frac {1}{8000} = \frac {1}{800} = 0.00125 </math>
Primary hyperparathyroidism can be assumed to cause hypercalcemia essentially 100% of the time (r_{PH → hypercalcemia} = 1), so this independently calculated probability of primary hyperparathyroidism (PH) can be assumed to be the same as the probability of being a cause of the presentation:
 <math>\begin{align} \Pr(\text{Hypercalcemia WHOIFPI by PH}) & = \Pr(\text{PH WHOIFPI}) \cdot r_{\text{PH} \rightarrow \text{hypercalcemia}} \\
& = 0.00125 \cdot 1 = 0.00125 \end{align}</math>
For cancer, the same timeatrisk 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 an population probability of cancer of:
 <math> \Pr(\text{cancer in population}) = 0.5\text{ years} \cdot \frac{1}{\text{250 per year}} = \frac{1}{500}</math>
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 (RR_{cancer} = 1):
 <math> \Pr(\text{cancer WHOIFPI}) \approx RR_\text{cancer} \cdot \Pr(\text{cancer in population}) = 1 \cdot \frac{1}{500} = \frac{1}{500} = 0.002. </math>
However, hypercalcemia only occurs in, very approximately, 10% of cancers,^{[2]} (r_{cancer → hypercalcemia} = 0.1), so:
 <math>\begin{align}
& \Pr(\text{Hypercalcemia WHOIFPI by cancer}) \\ = & \Pr(\text{cancer WHOIFPI}) \cdot r_{\text{cancer} \rightarrow \text{hypercalcemia}} \\ = & 0.002 \cdot 0.1 = 0.0002. \end{align} </math>
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:
 <math>\begin{align}
\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{align}</math>
The probability that the individual would be healthy in the first place can be assumed to be the same:
 <math> \Pr(\text{no disease WHOIFPI}) = 0.997. \, </math>
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:
 <math> r_{\text{no disease} \rightarrow \text{hypercalcemia}} = 0.0014 </math>
Subsequently, the probability that hypercalemia would have resulted from no disease can be calculated as:
 <math> \begin{align} & \Pr(\text{Hypercalcemia WHOIFPI by no disease}) \\
= & \Pr(\text{no disease WHOIFPI}) \cdot r_{\text{no disease} \rightarrow \text{hypercalcemia}} \\ = & 0.997 \cdot 0.0014 \approx 0.0014 \end{align}</math>
The probability that hypercalcemia would have occurred in the first place in the individual can thus be calculated as:
 <math>\begin{align}
& \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 = 0.00335 \end{align} </math>
Subsequently, the probability that hypercalcemia is caused by primary hyperparathyroidism (PH) in the individual can be calculated as:
 <math>\begin{align} & \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{align}</math>
Similarly, the probability that hypercalcemia is caused by cancer in the individual can be calculated as:
 <math> \begin{align} & \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{align}</math>
and for other candidate conditions:
 <math>\begin{align} & \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{align}</math>
and the probability that there actually is no disease:
 <math>\begin{align} & \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{align}</math>
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 
RR_{x}  10  1     
P(Condition WHOIFPI)  0.00125  0.002     
r_{Condition →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.
A likelihood ratiobased method
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 posttest likelihoods.
Theory
The initial likelihoods for each candidate condition can be estimated by various methods, such as:
 By epidemiology as described in previous section.
 By clinicspecific 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:
 <math>\text{odds} = \frac{\text{probability}}{1\text{probability}}</math>
However, only the candidate conditions with known likelihood ratio need this conversion. After multiplication, conversion back to probability is calculated by:
 <math> \text{probability} = \frac{\text{odds}}{\text{odds}+1} </math>
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 probabilityprofile 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 profilerelative 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
This example continues for the same patient as in the example for the epidemiologybased method. As with the previous example of epidemiologybased method, this example case is made to demonstrate how this method is applied, but does not represent a guideline for handling similar realworld 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 epidemiologybased 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 profilerelative 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 profilerelative 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.^{[3]} This confers a likelihood ratio positive of 7 for primary hyperparathyroidism.
The probability of primary hyperparathyroidism is now termed PreBT_{PH} 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 posttest odds is calculated as:
 <math> \operatorname{Odds}(\text{PostBT}_{PH}) = \operatorname{Odds}(\text{PreBT}_{PH}) \cdot LH(BT) = 0.595 \cdot 7 = 4.16,</math>
where:
 Odds(PostBT_{PH}) is the odds for primary hyperparathyroidism after the blood test for parathyroid hormone
 Odds(PreBT_{PH} 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(PostBT_{PH}) of 4.16 is again converted to the corresponding probability by:
 <math> \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%</math>
The sum of the probabilities for the rest of the candidate conditions should therefore be:
 <math> \Pr(\text{PostBT}_{rest}) = 100\%  80.6\% = 19.4\%</math>
Before the blood test for parathyroid hormone, the sum of their probabilities were:
 <math> \Pr(\text{PreBT}_\text{rest}) = 6.0\% + 14.9\% + 41.8\% = 62.7\% </math>
Therefore, to conform to a sum of 100% for all candidate conditions, each of the other candidates must be multiplied by a correcting factor:
 <math> \text{Correcting factor} = \frac{\Pr(\text{PostBT}_\text{rest})}{\Pr(\text{PreBT}_\text{rest})} = \frac{19.4}{62.7} = 0.309</math>
For example, the probability of cancer after the test is calculated as:
 <math> \Pr(\text{PostBT}_\text{cancer}) = \Pr(\text{PreBT}_\text{cancer}) \cdot \text{Correcting factor} = 6.0\% \cdot 0.309 = 1.9\%</math>
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 profilerelative 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 posttest probability of a previous test can be used as the pretest 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,^{[4]} 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
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's 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
If two conditions get high posttest probabilities, especially if the sum of the probabilities for conditions with known likelihood ratios become higher than 100%, then there is an the actual condition consists of a combination of the two. In such cases, that combined condition can be added to the list of candidate condition, 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 hormoneproducing 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,^{[5]} giving a very low probability before taking any tests into consideration. In comparison, the probability that a nonmalignant primary hyperparathyroidism would have occurred at the same time as an unrelated noncarcinoma 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
Template:Further 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,^{[6]} Lyme disease^{[7]} or ventilatorassociated pneumonia.^{[8]} Others such as ESAGIL,^{[9]} Iliad, QMR, DiagnosisPro,^{[10]} VisualDx,^{[11]} Isabel,^{[12]} ZeroMD^{[13]} and DxMate^{[14]} are designed to cover all major clinical and diagnostic findings to assist physicians with faster and more accurate diagnosis.
However, these tools all still require advanced medical skills to rate symptoms and choose additional tests to deduce the probabilities of different diagnoses. Thus, nonprofessionals should still see a health care provider for a proper diagnosis.
History
The method of differential diagnosis was first suggested for use in the diagnosis of mental disorders by Emil Kraepelin. It is more systematic than the oldfashioned method of diagnosis by gestalt (impression).
Alternative medical meanings
'Differential diagnosis' is also used more loosely, to refer simply to a list of the most common causes of a given symptom, to a list of disorders similar to a given disorder, or to such lists when they are annotated with advice on how to narrow the list down (the book 'French's Index of Differential Diagnosis', ISBN 0340810475, is an example). Thus, a differential diagnosis in this sense is medical information specially organized to aid in diagnosis.
Usage apart from in medicine
Methods similar to those of differential diagnostic processes in medicine are also is used by biological taxonomists to identify and classify organisms, living and extinct. For example, after finding an unknown species, there can first be a listing of all potential species, followed by ruling out of one by one until, optimally, only one potential choice remains.
See also
References
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References
Featured disease
Metabolic syndrome is a cluster of the most dangerous heart attack risk factors: diabetes and prediabetes, abdominal obesity, high triglycerides, low HDL cholesterol and high blood pressure.
Affects one in three adults
Affecting about 35 percent of all adults in the United States according to the CDC, metabolic syndrome contributes to weight gain, by causing a state of internal starvation called metabolic starvation. This in turn leads to increases hunger, sugar cravings and increased portions leading to overeating and weight gain.
Cause and effect misunderstood
Since we traditionally thought that the portion control (which in turn was attributed wrongly to poor will power)is the cause of weight gain, rather than the effect of this metabolic starvation, all our traditional ideas about cause and effect of obesity were not only wrong but lead to the “blame the victim” attitude when it comes to obesity.
Secret of weight gain revealed
Secret of weight gain, and metabolic syndrome revealed  it has been recently proven that metabolic syndrome, and the weight gain itself are caused by a process called insulin resistance. Check your metabolic syndrome risk using the free Metabolic syndrome meter. Watch this amazing Ted Med video that reveals the secret of weight loss  Stop blaming the victim for obesity
 ↑ Cf. VINDICATE – Mnemonic for differential diagnosis at PG Blazer.com.
 ↑ [1] [2]
 ↑ [3]
 ↑
 ↑ Parathyroid Cancer Treatment at National Cancer Institute. Last Modified: 03/11/2009
 ↑
 ↑
 ↑ "Evaluation of a Computer Assisted Decision Support System (DSS) for Diagnosis and Treatment of Ventilator Associated Pneumonia (VAP) in Intensive Care Unit (ICU)". nih.gov. Retrieved 3 October 2008.
 ↑ esagil.org
 ↑ "DiagnosisPro differential diagnosis reminder tool". diagnosispro.com. Retrieved 3 October 2008.
 ↑ visualdx.com
 ↑ isabelhealthcare.com
 ↑ "ZeroMD Diagnosis Tool and Community". www.ZeroMD.com. Retrieved 12 July 2013.
 ↑ dxmate.com
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