Biomedical Engineering Reference
In-Depth Information
CHAPTER 11
Clinical Examples: A Biomedical
Informatics Approach
Michael N. Liebman
As stated in Chapter 1, biomedical informatics can play a key role in driving the
evolution of patient care by addressing those issues that appear when a physician is
faced with a patient and the need to make clinical choices that will impact the
patient, the patient's quality of life, and the patient's family. Bridging the gap
between clinical need and the available technologies requires being able to define a
clinical problem within a biomedical informatics framework, that is, with data,
analytical tools, and interpretive tools [1]. Three examples are presented in this
chapter that attempt to provide this perspective: (1) the use of Her2/neu as a diag-
nostic for Herceptin treatment in breast cancer, (2) stratification of patients pro-
gressing through perimenopause, and (3) analysis of blood coagulation disorders
including DIC.
11.1
Understanding the Role of Biomarkers and Diagnostics
The introduction of high-throughput, omics-based technologies has led to the iden-
tification and publication of more than 4,000 biomarkers but relatively few of them
have potential relevance as diagnostics and even fewer as causal diagnostics [2]. It is
important to clarify the differences among these concepts in light of the goal of
using them to improve patient care.
A
biomarker
refers to a measurable quantity that reflects a change in state of a
biological process and can range from the presence/absence/increase/decrease of a
molecular entity to the presence/absence of a fever in a patient. The association of a
biomarker with a specific disease process typically reflects a correlative relationship
between marker and condition.
A
diagnostic
is a biomarker that more specifically describes one of several
stages in the premanagement of the disease. For example, from a patient perspec-
tive, there are five major stages starting with presymptomatic detection and extend-
ing to postintervention, where diagnostics are relevant: risk detection, early
detection of disease, stratification for intervention, adherence, and control [3]. A
diagnostic can play a significant role in clinical decision making and typically
requires FDA review and approval although many are used in an off-label manner.
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