The science of clinical practice: disease diagnosis or patient prognosis? Evidence about “what is likely to happen” should shape clinical practice

A useful diagnosis is defined by patient prognosis

Diagnosis classifies sick people into groups defined by disease and pathology 10]. The frameworks to explain illness have expanded from pathoanatomical to physiological-biochemical-psychological
and, more recently, genetic-molecular models, but the basic concept of diagnosis has
not changed. Diagnosis provides clinicians with the means to organise and interpret
a range of information provided by patient symptoms, signs, tests, and investigations
as the basis for decision-making.

The importance of diagnosis seems most obvious when there is an available treatment
which works by directly targeting a specific disease.

Example: The primary care physician faced with the common clinical problem of a child with
fever is concerned not to miss rare but serious diagnoses needing specific treatments.
A correct diagnosis of meningococcal meningitis will dictate life-saving, targeted
antibiotic treatment. Also, correctly classifying the majority of children with fever
who have self-limiting infections will underpin appropriate reassurance, and avoid
antimicrobial therapy and potentially harmful specialist investigations.

Diagnosis and disease mechanisms inform the decisions in this example, yet patient
prognosis is the underlying concern. The concern is firstly to correctly identify
the few children who, unless they receive urgent treatment targeted at a specific
disease, have a high likelihood of poor outcome (disease diagnosis being a highly
effective way to improve prognosis in these individuals) and, secondly, to allow the
many children with fever who have a high likelihood of a good outcome to recover without
disease-targeted interventions.

The science of diagnosis recognises the uncertainty clinicians face as they attempt
to classify people with and without disease. In primary care, this drives the search
for optimal combinations of symptoms and signs to identify and select patients with
high probability of the target condition to undergo further tests. The usefulness
of this strategy is judged by prognosis – are outcomes improved in those selected
for testing, and is it safe to avoid tests in persons with low disease probability
because their prognosis would not be altered by the test?

Example: Urinary tract infections in children present in ways which overlap with many other
acute childhood illnesses. The diagnostic challenge is to identify children whose
urine contains bacteria so that antibiotic therapy can be rationalised and kept to
a safe minimum. Carrying out high quality microbiology on urine samples from all acutely
unwell children is difficult and inefficient. Research seeks clinical prediction rules
that select children with an increased probability of bacteriologically-positive infection
for urine testing11], using information including sociodemographics and urinary and non-urinary symptoms.
The question is whether application of the prediction rule, plus targeted testing
and treatment of identified bacteriologically-positive children, improves outcomes
for those who are tested and does no harm to those who are not.

This example illustrates how a stratified process for diagnosis may support clinical
decisions, including avoiding unnecessary investigation and treatment in patients
with low disease probability. The usefulness of this process is defined by the prognosis
of all children engaged in it.

Diagnostic research emphasises the need for evidence that new tests improve outcomes
before adoption in practice 12]. This can be done by linking advances in disease detection with existing evidence
for treatment efficacy, or by demonstrating that the new test lowers costs or improves
safety. However, it may be necessary to evaluate whether the new diagnostic process
changes decision making, improves outcomes in persons classified with the disease,
and avoids unnecessary treatment in persons without the disease 13]. The justification for novelty in diagnostic practice is whether it improves patient
prognosis. Evidence for this may be lacking.

Example: A new source of information about suspected coronary heart disease is cardiovascular
magnetic resonance imaging. Medical imaging is the fastest growing physician-ordered
service in the US Medicare system14]. The most important indicator for cardiovascular imaging in Europe is suspected coronary
artery disease and myocardial ischaemia15]. The safety of cardiovascular imaging and its potential to change the diagnosis are
established, but whether its use improves patient outcomes has not been addressed,
although trials are underway16]. Evidence is needed since “…as an imaging community we have failed to demonstrate the added value of cardiac
imaging in terms of improved quality of care or improved outcomes” 17].

This demand for prognostic evidence of improved outcomes when evaluating new diagnostic
information poses substantial challenges of feasibility for the necessary research,
especially for studies of long-term impact and cost; this is a limitation on the prognostic
model. Information science, and its expanding reservoir of data linked to patient
outcomes, will need to drive novel methods to address these questions such as modelling
of long-term outcomes by combining data from cross-sectional diagnostic and short-term
effectiveness studies.

Prognosis identifies overdiagnosis

In all three examples above, the usefulness of diagnosis is defined by evidence about
patient prognosis, but the clinical process remains focused on disease. The assumption
is that identifying individuals with disease optimises their outcome. This assumption
may not always be justified.

In the past, disease diagnosis often occurred without effective treatments or any
evidence that diagnosis changed outcomes. Even now the culture of ‘diagnosis as an
end in itself’, without evidence of its prognostic or practical value for patients,
may at best be unnecessary and at worst do harm. There is increasing concern about
such ‘overdiagnosis’, in which a pathological lesion or state is identified, and the
patient is defined as having a disease, in the absence of any evidence that this state
either leads to a poor outcome or defines a pathway of investigation or treatment
that clearly advantages the patient. Evidence is accruing that overdiagnosis is not
only inefficient in its creation of unnecessary health care, but harmful in the effects
which the investigations and treatments generated can have on patients 8].

Example: A patient presenting with mild urinary symptoms has prostate cancer diagnosed by a
test and histopathology. The grading of his cancer places him at one end of the spectrum
of risk of future poor outcome, with a low probability that he will die prematurely
and evidence that surgical treatment would not alter this probability18]. Furthermore, if surgically treated, there is a risk of undesirable outcomes such
as reduced genito-urinary function. The evidence base to inform clinical, personal,
and policy decisions needs to show how use of diagnostic tests to identify and classify
prostate cancer links to outcomes with and without treatment8],9], i.e., decisions should be informed by evidence about patient prognosis. We cannot
assume the pursuit of diagnosis and disease is beneficial in the absence of evidence
about future outcomes.

Overdiagnosis flourishes in the vacuum created by a culture of ‘underprognosis’, i.e.,
lack of critical enquiry, information, or evidence about the likely future benefits
or harms of identifying a condition as an abnormal disease state. A prognostic framework
for clinical practice would help to resist evidence-free diagnostic novelty. Prognostic
evidence highlights when overenthusiastic search for pathology leads to irrelevant
treatments and needless anxiety, such as disc anomalies on MRI of the spine 19], but can reassure people who need neither active intervention nor a diagnosis, and
identify those in whom diagnosis does guide decisions that improve outcomes.

Concerns about overdiagnosis are often generated by screening programmes which diagnose
early or latent disease in healthy people. There is debate, for example, about how
much breast cancer screening programmes reduce premature mortality, and to what extent
the nature and rate of adverse consequences are acceptable, i.e., how population prognosis
changes as a result of a screening programme 20]. Overdiagnosis of lesions that do not confer poor prognosis or affect future outcomes
is an important adverse consequence of breast cancer screening because of the implications
of unnecessary anxiety, investigation, and treatment in patients with such lesions.

Patient prognosis is determined by more than disease diagnosis

The traditional model of clinical practice assumes that prognosis is inferred only
after the diagnosis has been made – presence or absence of disease determines prognosis.
However, in the absence of effective treatment, clinicians have always understood
that prognosis can be highly variable in persons with a particular diagnosis.

Example: A physician, working during a typhoid outbreak in the UK in the 1930’s, provided care
for the many who recovered and the few who did not21]. He wrote “A patient with typhoid fever usually inclines to recovery: it is a natural proclivity
in one with the disease”. Diagnosis characterised the sick group but was less important to the people involved
than knowledge that most were likely to get better.

The science of prognosis is concerned with improving the precision, accuracy, and
usefulness of measures of likely future outcomes. Modelling an individual’s prognosis
can draw on the full range of relevant and available information, both clinical and
non-clinical. In the diagnostic model, this may appear as an interaction of disease
with non-disease factors in determining outcome such as the influence of psychological
health on surgical outcomes. Prognosis offers an alternative starting point with wider
incorporation of factors relevant to patient outcomes than diagnosis alone.

Example: The potential benefit of early diagnosis of type 2 diabetes was investigated in persons
identified from primary care records as being at risk of the condition22]. Outcomes were compared between groups invited and not invited for diabetes screening.
Management of persons diagnosed with diabetes in the screened group focused on improving
prognosis by attempting to reduce their risk of future cardiovascular disease, targeting
blood pressure, and cholesterol as well as blood glucose. Ten-year mortality was similar
in the screened and unscreened groups. One explanation for this finding was that systematic
cardiovascular risk factor management in the screened population was only offered
to persons diagnosed with diabetes rather than to everyone invited for screening.

It is difficult to avoid the conclusion in this example that the focus on diagnosis
has obstructed a coherent approach to improving outcomes for people at elevated risk
of future cardiovascular events. A focus on improving prognosis regardless of ‘diagnosis’,
with blood glucose as one contributor to the probability of poor outcomes, integrated
with other risk measures to derive estimates of individual prognosis, would provide
a less selective, more productive approach to improving health outcomes. Such an approach
may be particularly relevant for patients with multiple health problems.

Example: Although a person with multiple diseases will benefit from optimal care for each separate
condition by disease-based specialists, the multimorbidity state itself contributes
to poor prognosis, for example a higher probability of unplanned hospitalisation,
and outcomes are improved if there is additional integrated care from generalists23]. However, much multimorbidity concerns risk-based measures (blood sugar, kidney function,
blood pressure), which are more usefully considered as continuous variables rather
than disease states6]. In constructing prognostic models to support decision-making for people with multimorbidity,
such biological measures can be integrated with subjective measures, such as mood
state, pain severity, and mobility limitation, to create quantitative estimates of
prognosis to inform care for people with multiple long-term conditions.

Traditional disease-based classification systems are being challenged by the quest
for new ways to classify persons with multimorbidity and to incorporate new information
about health, such as genomics, into such systems 24]. This new information is undermining the idea that medicine only starts when there
is a diagnostic label. Biomedical diagnoses have also traditionally encouraged isolated
disease-based measures of outcome, such as normal blood glucose, to assess the success
of health care. The acceptance that patient-focused measures, such as improved or
maintained social participation, are realistic and desirable outcomes of health care
for patients with long-term conditions 25] is subverting the idea that good prognosis is only judged by disease cure.

Not “have you got it or not?” but “how much have you got?”

Clinical decisions are often dichotomous (does this person have something serious
or not? should this patient be allowed to drive or not?). Diagnosis as “either you
have it or you don’t” (is it a heart attack or not?) aligns with such yes/no decisions,
but the diagnostic process itself is often more probabilistic and uncertain – a series
of decisions, guided at each stage by the changed probability of a diagnosis being
present or not and designed to gradually reduce uncertainty 26]. However, this process still assumes there is an underlying dichotomous disease state
(yes or no); this assumption may be flawed.

The underlying ‘disease’ is often a continuous distribution of probability for future
health states. Diagnosis is then not “have you got it?” but “how much of it have you
got?” Gale 27], for example, has argued that there is no single pathology underlying diabetes, and
that its identification and diagnosis subsumes much heterogeneity, given blood glucose
is a continuously distributed variable. Vickers et al. 6] highlight that many such risk variables are artificially dichotomised and treated
as disease states rather than as sources of information about probability of future
events, which provide a quantitative estimate of individual risk for particular outcomes.

It can be argued that diagnosis does incorporate variability under the concept of
disease severity. The continuous distribution of blood pressure, for example, aligns
with severity of the ‘disease’ (or unhealthy state). The diagnostic paradigm, however,
focuses on classification and misclassification of ‘hypertension’. The prognostic
approach more naturally accommodates the idea of severity by relating increasing levels
of blood pressure to contrasting outcomes.

A potential downside of the prognostic approach is whether clinical models in the
real world can incorporate the continuous nature and variability of risk of future
avoidable outcomes into decision making. Calculation of prognostic likelihoods and
treatment responsiveness can combine all relevant information, but validation and
translation of prognostic models into practice often relies still on categorisation
(high-medium-low risk of a particular outcome) to drive stratified care 28],29]. Evidence about the clinical usefulness of such new categorisations is essential
– prognostic classification for its own sake should not replace diagnosis for its
own sake 30]. Nor should individual patient prognosis be a static classification in time – it
needs to be updated with recent predictor values, or a profile of values over time,
to reflect how clinical practice works in the real world by using new information
and repeat consultations to modify treatment.

There are practical challenges to introducing an information-rich prognosis framework
for a real-world clinical practice already struggling with volumes of guidelines,
decision-aids, and protocols of care. Technological and statistical advances to make
calculation and presentation of prognostic information more accessible and research
into ways in which health care professionals and patients can better assimilate, use,
and share prognostic information could help to meet these challenges.

The pros and cons of labelling people

Diagnosis and disease do not represent unchanging truth. Historians and sociologists
have described how apparently robust diagnostic entities, such as coronary heart disease,
conceal a history of shifting boundaries and definitions 10],31],32]. These shifts reflect not only scientific advance and expanding knowledge (e.g.,
the identification of Helicobacter as a cause of peptic ulcer), but also the recurring need to organise medical knowledge
in practical and pragmatic ways to aid clinical and public health decision-making.
However, it is equally clear that such shifts are taken up, shaped, and driven by
professional ambition, pharmaceutical industry interests, and the commercial drive
for profit, and that they influence and are influenced by changing social context
and cultural norms, for example, changes in mental illness classification. “Even US law now recognizes that disease is no longer a unique collection of symptoms
equalling a given condition, but rather a constellation of current symptoms, previous
exposures, and future potential manifestations, all of which make the art of diagnosis
even more precarious” 33].

Particular challenges occur when no pathological explanation for the patient’s symptoms
is currently available.

Example: Chronic fatigue syndrome is a ‘contested diagnosis’31]. The drive for pathological explanations led to its initial characterisation in the
UK as myalgic encephalomyelitis, with little evidence that such a pathological inflammatory
process was responsible for the symptoms. The failure of clinical science to identify
a mechanism to support the diagnosis means that patients with chronic fatigue often
perceive their symptoms are not believed. The outcome of patients with symptoms of
persistent fatigue, however, is of high relevance, regardless of the biomedical status
of the syndrome.

Whilst biomedical science often ignores such problems, patients believe biomedicine
could and should come up with the answer 32]. Prognostic classification provides a practical way forward. Modifiable patient characteristics
that contribute to poor prognosis in chronic fatigue (e.g., low activity level, depression,
insomnia) provide targets for intervention in the absence of a definitive biomedical
explanation. Targeted exercise programmes, for example, improve prognosis in persons
at risk of persisting problems 34].

Diagnosis, however, has functions other than revealing pathological truth. A diagnostic
label provides the patient with meaning and value for symptoms regardless of whether
these have a biomedical explanation 31]. Diagnosis legitimates the sickness state, and gives access to support and benefits
32]. Prognostic statements may not provide the same immediate value as a diagnostic label,
and some patients may be more interested in ‘have I got high cholesterol?’ than in
the language of risk. A culture is needed in which clinicians and patients can work
together on improving outcomes in the absence of the apparent certainty provided by
a diagnostic label.

A hazard of applying a prognostic model in clinical practice relates to the very thing
it is designed to reduce, namely over-medicalization of daily life. We have discussed
how excessive diagnostic zeal in the absence of improved outcomes for the patient,
and the wish to find a pathology to explain every symptom, and the application of
diagnostic labels to asymptomatic risk factors, may all lead to ineffective and inefficient
care, and harmful side-effects for the patient. The potential for a prognostic model
of care to solve these problems has to be weighed against the possibility that such
a model may create its own version of over-medicalization. Aronowitz points to healthy
individuals locked rather fearfully into long-term surveillance of risk markers, believing
this is the way to ensure continuing good prognosis, even if their risk of death and
other adverse outcomes is low 35]. The anxieties, and unnecessary and inefficient health care, and commercial and professional
interests involved look remarkably similar to those associated with unevidenced diagnostic
excess. The resolution lies in demanding high quality evidence of what is and is not
useful for improving outcomes, i.e., pursuit of the best prognostic evidence as the
scientific basis for resisting excessive medicalization.

Prognosis provides a natural framework for modern clinical practice

Disease diagnosis is a crucial component of modern medicine but fails to provide a
sufficient framework for a modern clinical practice which must incorporate variability
in individual patient risk of different outcomes, influences on patient outcome which
extend beyond disease, and avoidance of harm; prognosis provides such a framework.
Clinicians often think in terms of prognosis, especially the primary care physician
who may start by judging if the patient is going to get better or not 36]. Decisions about individual patients in primary care are informed by available evidence
about likely future outcomes, and a clinician’s own judgement on likely outcome has
prognostic value and helps to guide decision-making 37]. Shared exploration and understanding between clinician and patient of which outcomes
are wanted or needed, achieved through patients being able to voice their own priorities
and goals for care and treatment in the consultation, supports a prognostic framework
for the clinical encounter, particularly for the patient with long-term conditions
and multimorbidity 38].

Example: Evidence that clinicians and patients can integrate disease-based explanation within
a broader framework of prognosis is provided by back pain. Primary care practitioners
undertake initial triage in a diagnostic framework to identify rare underlying conditions
which have a poor immediate prognosis unless treated (e.g., cord compression from
a tumour). Once these are excluded, the task diverts from diagnosis and considers
the clinical problem as the risk of poor long-term outcomes (work loss, persistent
pain). Activity limitation, psychological distress, and capacity to cope are used
to classify people into prognostic categories that drive treatment decisions39]. The many at low risk of a poor outcome are managed without referral or investigation,
whereas more intense care is targeted at those with poorer prognosis. This exemplifies
the principle of ‘stratified care’. Use of this prognostic approach to select back
pain patients for different treatment programmes was effective and cost effective
in a randomised controlled trial40].

Such personalised medicine is likely to herald preferential expansion of prognostic
modelling of individual risk for future health outcomes over new diagnostic tests
of current disease status. However, research to inform and justify a prognostic model
of clinical practice is crucial, including the important uncertainties about the application
of this model represented by clinical, patient, and public understanding of risk and
probability.