In healthcare, a differential diagnosis (DDx) is a method of analysis that distinguishes a particular disease or condition from others that present with similar clinical features. Differential diagnostic procedures are used by clinicians to diagnose the specific disease in a patient, or, at least, to consider any imminently life-threatening conditions. Often, each possible disease is called a differential diagnosis (e.g., acute bronchitis could be a differential diagnosis in the evaluation of a cough, even if the final diagnosis is 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 may employ algorithms, akin to the process of elimination, or at least a process of obtaining information that decreases 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).
Differential diagnosis can be regarded as implementing aspects of the hypothetico-deductive method, in the sense that the potential presence of candidate diseases or conditions can be viewed as hypotheses that clinicians further determine as being true or false.
A differential diagnosis is also commonly used within the field of psychiatry, where two different diagnoses can be attached to a patient who is exhibiting symptoms that could fit into either diagnosis. For example, a patient who has been diagnosed with bipolar disorder may also be given a differential diagnosis of attention deficit hyperactivity disorder, major depressive disorder, post-traumatic stress disorder, anxiety disorders, and borderline personality disorder, given the overlap and similarity of signs and symptoms across the conditions.
Strategies used in preparing a differential diagnosis list vary with the experience of the healthcare provider. While novice providers may work systemically to assess all possible explanations for a patient's concerns, those with more experience often draw on clinical experience and pattern recognition to protect the patient from delays, risks, and cost of inefficient strategies or tests. Effective providers utilize an evidence-based approach, complementing their clinical experience with knowledge from clinical research.
General components
A differential diagnosis has four general steps. The clinician will: Gather relevant information about the person's medical history and present signs and/or symptoms list. List possible causes (candidate conditions) for the symptoms. The list need not be in writing. Prioritize the list by balancing the risks of a diagnosis with the probability. These are subjective, not objective parameters. Perform tests to determine the actual diagnosis. This is known by the colloquial phrase "to Rule Out". Even after the process, the diagnosis is not clear. The clinician again considers the risks and may treat them empirically, often called "Educated Best Guess."
A mnemonic to help in considering multiple possible pathological processes is VINDICATEM: ascular nflammatory / nfectious eoplastic egenerative / / rugs diopathic / ntoxication / atrogenic ongenital utoimmune / llergic / natomic raumatic ndocrine / nvironmental etabolicCf. VINDICATE – Mnemonic for differential diagnosis at PG Blazer.com.
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 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). 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 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 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:
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:
The total probability of the presentation to have occurred in the individual can be approximated as the sum of the individual candidate conditions:
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:
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 at which a condition causes the presentation, that is, the fraction of people with conditions that manifests 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:
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 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 clinician (who becomes the "diagnostician" in this example), who does not currently see the patient, gets to know about his finding.
By practical reasons, the clinician 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 clinician 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 clinician could think of can be given as: Primary hyperparathyroidism (PH) Cancer Other diseases that the clinician 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: