Differential diagnosis


In healthcare, a differential diagnosis 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.
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.
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 ADHD, Major depressive disorder, Posttraumatic 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:
  1. Gather relevant information about the person's medical history and present signs and/or symptoms list.
  2. List possible causes for the symptoms. The list need not be in writing.
  3. Prioritize the list by balancing the risks of a diagnosis with the probability. These are subjective, not objective parameters.
  4. 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:
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.
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. 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 or in the absence of a sine qua non sign or symptom.
A diagnostician can be selective, considering first those disorders that are more likely, more serious if left undiagnosed and untreated, or more responsive to treatment if offered. 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 and probabilities of the various candidate conditions.

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 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 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 is the probability that the presentation is caused by condition in the individual; condition without further specification refers to any candidate condition
  • Pr is the probability that the presentation has occurred in the individual, which can be perceived and thereby set at 100%
  • Pr is the probability that the presentation Would Have Occurred in the First Place in the Individual by condition
  • Pr 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 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 is the probability that the presentation Would Have Occurred in the First Place in the Individual by condition
  • Pr 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 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 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:
One additional "candidate condition" is the instance of there being no abnormality, and the presentation is only a appearance of a basically normal state. Its probability in the 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, 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 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, 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.
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 and cancer, so for simplicity, the list of candidate conditions that the clinician could think of can be given as:
  • Primary hyperparathyroidism
  • Cancer
  • Other diseases that the clinician could think of
  • No disease, and the finding is caused entirely by statistical variability
The probability that 'primary hyperparathyroidism' would have occurred in the first place in the individual 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 appropriately matches the individual is 1 in 4000 per year. Ignoring more detailed retrospective analyses, 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 in the population of:
With the relative risk conferred from the family history, the probability that primary hyperparathyroidism would have occurred in the first place in the individual given from the currently available information becomes:
Primary hyperparathyroidism can be assumed to cause hypercalcemia essentially 100% of the time, so this independently calculated probability of primary hyperparathyroidism can be assumed to be the same as the probability of being a cause of the presentation:
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:
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 :
However, hypercalcemia only occurs in, very approximately, 10% of cancers,, so:
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:
The probability that the individual would be healthy in the first place can be assumed to be the same:
The rate at which the case of no abnormal condition still ends up in measurement of serum calcium of being above the standard reference range 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:
Subsequently, the probability that hypercalcemia would have resulted from no disease can be calculated as:
The probability that hypercalcemia would have occurred in the first place in the individual can thus be calculated as:
Subsequently, the probability that hypercalcemia is caused by primary hyperparathyroidism in the individual can be calculated as:
Similarly, the probability that hypercalcemia is caused by cancer in the individual can be calculated as:
and for other candidate conditions:
and the probability that there actually is no disease:
For clarification, these calculations are given as the table in the method description:
Thus, this method estimates that the probability 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.