iPathology

Thoughts on Pathology and Laboratory Medicine as a Career

As the Fall rolls on, senior medical students will be choosing their fields and applying for residency programs. It's an exciting and critically important time in their lives. The choices made between now and the Match day will determine the course of their lives.

What about Pathology as a career? Ever thought about it? What do Pathologists do?

Pathology is a very broad field that is commonly said to bridge the divide between basic science and clinical medicine. In fact, it's far more than that. Pathology is an incredibly dynamic area. This may come as a surprise to those who think of pathology as all about looking at slides and doing autopsies. While those are important and essential for patient care, which is the field that is taking the new genomics and applying to medicine? Or the new proteomics? The answer is these are being applied in pathology, particularly in the field known as clinical pathology. That's the field that includes the medical direction of clinical laboratories. It's also called laboratory medicine. A clinical pathologist may practice some anatomic pathology, but most clinical pathologists have a specialty interest in one of the areas of the clinical laboratories such as clinical chemistry, molecular diagnostics, hematology, cytogenetics, coagulation, blood banking, immunology, or microbiology. That's a lot of areas each of which has plenty of depth. Some of them involve primarily laboratory work, others, like coagulation and blood banking involve patient care to one degree or another. Molecular diagnostics is said to be the bridge between clinical pathology and anatomic pathology. Increasingly, tumors are being studied at a molecular level, providing data that complements morphology and offers information for diagnosis and sometimes prognosis. This is where the fruits of the human genome project are being applied. It's a very exciting field that is at the cutting edge of applying science to patient care.

What's the training like to become a pathologist? In the U.S., most people take 4 years of residency in anatomic and clinical pathology in accredited programs and are then eligible to apply to take the American Board of Pathology exams. Subspecialty training is available in academic medical centers around the country. To my mind, what distinguishes programs is usually not the anatomic pathology which tends to be of a uniformly good standard, but the level of involvement with clinical pathology. Does the program have full-time clinical pathologists? Who runs the clinical laboratories? Here are some questions to consider if looking at a pathology program: how many anatomic and clinical pathologists are directly involved in the teaching program? Is there a real interest in clinical pathology or is a part-time activity? What are the opportunities to be involved in cutting edge research? On anatomic pathology rotations, do residents get to see the cases from start to finish? Are the "most interesting" cases reserved for fellows? (A red flag if you're a resident). What is the activity in molecular pathology? If it doesn't exist, I'd look at other programs: it's not a good idea to begin a 30-40 year career in pathology today and not know about molecular pathology.

What are the job prospects like? What do pathologists earn? I'll talk about these more in later note.

For more information see the American Society for Investigative Pathology website.

The Effect of Disease Prevalence on the Predictive Value of Diagnostic Tests

This is an updated version of a note I first wrote in 1999. Because it has been cited in several publications, I am posting it again at the ipathology.com website. 

Standard Metrics of Diagnostic Tests 

When considering the diagnostic utility of tests to distinguish persons with disease from normal persons, reference is frequently made to common metrics, namely, sensitivity, specificity, positive predictive value, and negative predictive value of a test. Traditionally, these concepts are explained with a simple matrix to define the terms true positive (TP), false positive (FP), true negative (TN), and false negative (FN) as follows. 

 

	Test Positive	Test Negative
Disease	TP	FN
Normal	FP	TN

 

Using these terms, the following metrics can be defined. 

Sensitivity = TP/(TP+FN) 

Specificity = TN/(TN+FP) 

Positive predictive value (PPV) = TP/(TP+FP) 

Negative predictive value = TN/(TN+FN). 

 

What is not so clear from these definitions is the effect of disease prevalence on the predictive values of test results, particularly the more commonly-used positive predictive value. Although standard textbooks of clinical pathology make reference to the fact that disease prevalence influences the positive predictive value, this relationship is seldom explained. 

Effect of Overlapping Values 

Given the definition of PPV above, it is apparent that this metric is a function of the TP and FP values. If there were no false positives, the PPV would be TP/TP or 100%. This would occur if the test could reliably distinguish normal persons from persons with disease without any overlap of values. However, this is rarely the situation with laboratory results that fall on a continuous scale. Almost always there is some degree of overlap between results from normal and diseased subjects. For example, low levels of blood markers of myocardial infarction can be detected in healthy subjects. These may overlap the lower range of values found in patients with MI. When there is such overlap, some cut-off point must be established to distinguish a positive from a negative result. It is worth noting that even results which are reported as "postivie" or "negative" by analytical instruments are generated from continuous scales by using a cut-off point. An example of this is the reporting of HIV-1 antibody screening tests. In a popular assay system, these are detected by ELISA reactions which result in changes in absorbance as measured by a spectrophotometer. The lower end of absorbance readings in HIV-1 positive patients overlap those of normal subjects. The computer in the analytical instrument defines a cut-off point of absorbance readings and then reports the results as positive or negative based on the cut-off value. Given that most assays show some overlap of values between normal and diseased subjects, it is apparent that it is very unusual for assays to have no FP results. 

Prevalence of Disease Influences Positive Predictive Value 

There is another situation in which there are no false positive results, namely, if everyone in the population had the disease. In this situation, every positive result would be a true positive. There could be no false positive results, and the PPV would be 100%. Conversely, if no one in the population had the disease, every positive result would be a false positive. There could be no true positives, and the PPV would be 0%. This leads us to conclude that the disease prevalence influences the PPV by influencing the true positive and false positive rates. 

Bayesian Analysis to Determine Positive Predictive Value 

The effect of disease prevalence may be determined by performing a Bayesian analysis. Consider a disease with a prevalence of 10% in a given population. A diagnostic test for the disease has both a sensitivity and a specificity of 95%. What is the positive predictive value of the test? 

Using Bayesian analysis we can determine the likelihood that a person with a positive test result has the disease as follows. For those not familiar with Bayesian calculations, the steps are outlined here and shown in the following tables. 

The two mutually exclusive possibilities are that the person has disease or is normal (row A). The a priori probability of having disease is 10% (0.1) and of being normal is 90% (0.9) (row B). If a person has the disease, the probability of having a positive test result is 0.95. If a person is normal, the probability of having a positive test result is 0.05 (row C). These probabilities are obtained from the sensitivity and specificity values above. If the sensitivity is 95%, then FN is 5%. If the specificity is 95%, then FP is 5%. 

The joint probabilities are the products of multiplying values in rows B and C (row D). The final probability for each of the two mutually exclusive states is obtained by dividing each value in row D by the sum of the two values in row D. This is shown in row E. 

 

A		Disease	Normal
B	A priori probability	0.1	0.9
C	Probability of a positive test result	0.95	0.05
D	Joint probability	0.1 × 0.95 = a	0.9 × 0.05 = b
E	Final probability	a/(a+b) = 0.68	b/(a+b) = 0.32

 

In this case, with a disease prevalence of 10%, a person with a positive test result has a 68% probability of having disease, and a 32% probability of being normal. 

In a population in which the disease prevalence is 0.1% (0.001), the calculations are as follows: 

 

A		Disease	Normal
B	A priori probability	0.001	0.999
C	Probability of a positive test result	0.95	0.05
D	Joint probability	0.001 × 0.95 = a	0.999 × 0.05 = b
E	Final probability	a/(a+b) = 0.019	b/(a+b) = 0.981

 

In this case, with a disease prevalence of 0.1%, a person with a positive test result has a 1.9% probability of having disease and a 98.1% probability of being normal. 

The effect of disease prevalence on the positive predictive value for a test with sensitivity and specificity of 95% is shown in the following table. 

 

Prevalence	PPV
0.1%	1.9%
1%	16%
10%	68%
20%	83%
50%	95%

Conclusion

The prevalence of a disease in the population has an important influence on the positive predictive value of a diagnostic test. With increasing disease prevalence, the more likely it becomes that a person with a positive test result has the disease, and the less likely it becomes that a positive result is a false positive. The effect of disease prevalence can be easily determined by Bayesian analysis.

 

Copyright, © Anthony A. Killeen, 1999-2007. All rights reserved.

Molecular Testing for Cardiac Rejection

Believe it or not it's been 40 years since the first human to human heart transplant was performed by Christiaan Barnard in South Africa.  The success of this procedure is now attested to by the fact that over 2000 patients in the United States receive a heart transplant each year with an estimated 50% 10 year survival.  In 1967 the first patient to receive a heart transplant survived only 18 days.

 

One of the main medical concerns following heart transplantation is rejection of the organ by the recipient immune system.  A new test, AlloMap, is now available that can detect the presence of cardiac rejection noninvasively [Starling RC et al., J Heat Lung Transplant, 25:1389, 2006].  The test is based on the measurement of expression of certain genes in peripheral blood mononuclear cells.  A panel of 11 genes is reliably informative for the identification of patients who have a very low risk of organ rejection.  While the negative predictive value of the test is extremely high the positive predictive value is quite low, in part because of the fact that with modern immunosuppressive therapies, relatively few patients develop acute cellular rejection particularly after more than a year post-transplantation. It is possible that the new test will reduce the number of cardiac biopsies that are performed posttransplantation.  If so, this will be probably be welcomed by patients.

Presumably the AlloMap test will be applied to other organ transplants such as liver, kidney and lung.  At the time of the first human heart transplant the procedure seemed extraordinary.  In many ways, the ability to detect organ rejection by gene expression profiling in a blood sample seems equally extraordinary.

How much was that test?

The issue of marking up (i.e., increasing) the charge for lab tests continues to smolder. In a typical mark up, a clinician sends a patient's sample to a lab for testing. The lab performs the test, reports the results to the clinician, sends its bill to that same clinician, who then re-bills the patient at a higher rate. Can this be ethical?? Lab tests, whether in anatomic or clinical pathology, should be billed to the patient (or his insurance company) at the rate they were billed to the ordering physician with a reasonable allowance for costs of transportation of samples. In some states the practice of marking up tests is illegal. Medicare requires that the performing lab send them the bill directly. It's surprising that insurance companies don't look into this practice.

Next time you get a bill for a lab test from your doctor, ask how much it really cost.