J R Soc Med 2001;94:617-619
© 2001 Royal Society of Medicine
Aiming at averages
E J Will BM FRCP
St James's University Hospital, Leeds LS9 7TF, UK
 |
INTRODUCTION
|
|---|
This much, then, is clear—that the mean state is in every case to be
praised, but that sometimes we must incline towards the excess, sometimes
towards the deficiency, because in this way we shall most easily hit the mean,
namely what is good—Aristotle, Nicomachean Ethics
All the business of war, and indeed all the business of life, is to
endeavour to find out what you don't know by what you do; that's what I called
‘guessing what was at the other side of the hill’—Duke of
Wellington, The Croker Papers
If anything has characterized the professions in the past decade it is the
proliferation of guidelines, targets and standards. These are beginning to act
as a kind of scaffold—in both senses, some fear—for medical
practice. In this modern setting it is increasingly important, for clinicians,
patients and payers, to know the consequences of clinical intervention.
However, guessing what is on the ‘other side of the hill’ is
arguably not good enough, and ad hoc clinical encounters are not the
best basis for predictable clinical
outcomes1. In renal
medicine, my own specialty, the quantitative components of clinical practice
are particularly suitable for audit purposes, in regard to both treatment
processes and outcomes. It is not surprising that some of the issues in
predicting clinical outcomes are being explored first in a renal context.
|
AIMING POINTS
|
|---|
Current documents recommend a variety of renal standards, for
blood
pressure, biochemistry, dialysis dose and so
on
2,3.
A
minimum or maximum value is declared typically as a limit for
desirable
clinical results. When outcome data are presented—for
example, in the UK
Renal Registry (UKRR) report—they are
given as distributions for any
given renal unit patient cohort.
Depending on where the standard limits have
been pitched for
the variable there will be an overlap, since the outcome
distributions
have an inevitable dispersion (standard deviation, SD)
(
Figure 1).
Minimizing the
dispersion of results is desirable for clinical
and economic reasons, but,
contrary to intuition, the mere declaration
of a ‘target’ value
does not necessarily narrow
the eventual distribution. Much effort may be
required to do
so, even in areas of technical control such as
dialysis
4. One
may
of course choose to aggregate the mean values of repeated
measurements in
individual patients, so as to narrow any given
range—a tactic that has
been little debated. For a majority
of values to fall on the desired side of
the declared minimum/maximum,
the mean/median of the outcome distribution must
to a varying
degree exceed the guideline value. The ‘target’
aiming
point for management therefore also needs to exceed the standard
limit
and the necessary mean by some uncertain amount. This
‘something in
excess’ of desirable results does
not have a name, nor do we have a
simple phrase to convey the
necessity (except perhaps Robert Browning's
‘a man's reach
should exceed his grasp’). The excess allows for
the inevitable
under-achievements of practice, whether due to problems of
patient
ascertainment or inadequacy of delivered treatment. Perhaps
because we
are always opposing a pathological ‘pressure’,
it is usual for the
factors that impede therapy to far outweigh
those that facilitate it. In other
words, our processes do not
lead to failure or success at random, but are
biased to
under-performance
5,6,7.
Both
under-aspiration and miscellaneous practical factors underlie
this
phenomenon, as demonstrated in studies of dialysis dosing
in the
US
8,9.
View larger version (18K):
[in this window]
[in a new window]
|
Figure 1. Any set of results will be aligned in relation to a declared standard
minimum or maximum, overlapping to a variable extent because of the inevitable
dispersion of results. In this example the distribution overlaps a desired
minimum value in a particular way. The mathematical consequence of any
Gaussian curve divided at one standard deviation below the mean is to give 85%
of readings above the dividing value. This allows some prediction of the
characteristics necessary to comply at the 85% level with any given
‘standard’ value (Ref.
7, by permission, Oxford
University Press)
|
|
|
DEFINING OUTCOME DISTRIBUTIONS
|
|---|
The more sophisticated ‘standards’ may take this
into account
by specifying that physician compliance need only
involve, say, 85% of the
patient group. Such an allowance still
implies that the distribution of
results must assume a certain
position in relation to the limit. As shown in
Figure 1, when
results are
Gaussian in distribution 85% will be above a given
minimum if that is one SD
below the mean, a property of classical
statistics. This gives a lead to
achieving the standard, as
demonstrated in Box 1 (Nos 1-3). Such specific
positioning of
the distribution of results is difficult to achieve by design.
The
unthinking use of ‘target’ values seems to lead
to
distributions that straddle the limit, as illustrated by
the ESAM study of
renal anaemia, where the minimum haemoglobin
standard of 11 g/dL is also the
outcome mean
value
10. Perhaps
clinical
effort falls away once the value is achieved, or perhaps pathological
‘pressure’
causes an undesirable drift in the population under
stable therapy.
It remains the case that the ‘target’ aiming point
towards
which effort must be applied is uncertain in current systems—to
what
pressure below 140/80 mmHg should one pursue values in order
to achieve a
high rate of correspondence with a 140/80 maximum?
Moreover, even in the best
studies of treatment efficacy the
declared treatment aims may prove
unachievable
11.
Studies from the UKRR suggest another way to assess the
‘over-achievement’ necessary for complete correspondence with
‘standards’. The outcome distributions for haemoglobin and
dialysis dose, measured as urea reduction ratio, are Gaussian, with rather
uniform dispersion of data (SDs). This allows the use of data from several
renal units to explore the relation of mean/median and per cent satisfaction
with a guideline minimum/maximum. A plot of the mean/median of each unit
against the per cent compliance with any standard min/max indicates the
mean/median of the necessary distribution
(Figure
2)12.
In this case a median unit haemoglobin of about 11.5 g/dL would be necessary
to comply with 85% above 10 g/dL. An essential caveat is that this reflects
current procedures, since a systematic narrowing of outcome ranges would give
different necessary values.
View larger version (21K):
[in this window]
[in a new window]
|
Figure 2. Plot of median renal unit haemoglobin (Hb) against per cent values
greater than 10 g/dL (the Renal Association ‘standard’). A
median of 11.5 g/dL will be necessary, under current clinical conditions, to
achieve the ‘standard’ outcome (UK Renal Registry Report,
2000)
|
|
| Box 1 Strategy for achieving desired minimum haemoglobin in
dialysis patients
- For haemoglobin in haemodialysis patients the Renal Association
‘standards document’ recommended a minimum value of 10 g/dL in
>85% of patients after 3 months of treatment (Ref.
2)
- The UK Renal Registry 2000 records that the average standard deviation of
haemoglobin values (single point data) in data-sets from several renal units
is 1.7
- The mean of the distribution of haemoglobin in any current cohort needs to
be desired minimum plus SD (i.e. 10+1.7=11.7 g/dL) to give 85% above 10 g/dL
(Ref. 17)
- This was achieved and sustained in one study by using 11 g/dL as the lower
intervention value and 12 g/dL as the ceiling, for treatment with intravenous
iron and subcutaneous erythropoietin. This intervention model was based on
local clinical research in a particular dialysis patient cohort that examined
paired intervention values of 10.5/11.5 and 12.0/13.0 g/dL (Ref.
13).
|
|
ACHIEVING PARTICULAR DISTRIBUTIONS
|
|---|
Having decided what specific distribution, how should clinicians
then
proceed? Can we manage a patient cohort so as to produce
a predictable
distribution of results? In other words, can we
aim at averages? The usual
technique of declaring progressively
more extreme ‘aiming-points’
(say lower blood pressure
or higher haemoglobin) may drive outcome
distributions in the
desired direction, but this is scarcely a predictable
methodology.
There could be other approaches, but it begs a method that is
more
explicit than an
ad hoc approach to individual patients (the
usual
gold standard of
practice
7).
Sociology exists partly because
people tend to behave differently in groups
than as individuals,
so we might reasonably ask whether patient groups might
be handled
in the whole, rather than as simply an aggregate ‘sum
of
parts’. Since we manage individual patients by shifting
treatment doses
to adjust towards desirable results, what would
be the effect of doing the
same systematically to a large population?
This implies fixed intervention
points—in the case of
renal anaemia, say, one ‘threshold’
value below
the desired mean/median and one ‘ceiling’ value
above.
This has been attempted in a large unselected dialysis
cohort over several
years for the management of renal anaemia
with erythropoietin and iron and
seems to produce reliable distributions
that can be made to comply with
‘standard’
recommendations
13,14,15
(see
Box 1, No. 4). As it happens, UKRR data show that in practice
we can
always know what is ‘on the other side of the hill’,
month on
month, year on year. The outcome of clinical management
is very stable when
reflected in large groups, and shifts only
with major changes of procedure or
case-mix. What are required
are treatment technologies to allow the
determination of distributions
in response to best practice
guidelines
16.
|
IMPLICATIONS FOR GUIDELINES
|
|---|
We do not have the means of predicting the distribution of results
unless
we adopt some new approaches, where the ‘aiming-point’
is likely
to be less important than the threshold/ceiling values
for intervention. These
need to be defined through clinical
research in each case. The further
implication is that recommendations
should in future not only contain the
desirable limits but also
attempt to define the features of the anticipated
outcome distributions
in mean/median and range. They should also, for best,
indicate
the costs and safety of achieving them, in case of hazard at
the
extremes of predictable outcome ranges and futile expenditure
in the course of
over-compensation for
under-achievement
17.
This
reflects the fact that guidelines and standards are the basis
of
treatment policies that should be subject to explicit risk
analysis before
implementation. Although the fanfares of the
guideline culture were not
entirely without justification, it
appears that we know better where to go
than how to get there.
This is partly because efficacy studies (can it be
done?) greatly
exceed effectiveness studies (does it
work?)
18.
Declarations
of ideal intent imply the need for research into calibrated
clinical
interventions, to put the achievement of clinical outcomes into
a
predictable, safe and cost-effective
mould
19. The fusion
of
clinical aspiration, basic medical science and statistics in
this exercise
represents a novel response to the recent call
for integration of these
elements of
medicine
20.
|
REFERENCES
|
|---|
-
Wyatt JC. Management of explicit and tacit knowledge. J
R Soc Med 2000;94:6
-9[Medline]
-
Renal Association. Treatment of Adult Patients with
Renal Failure. Recommended Standards and Audit Measures. London:
Royal College of Physicians of London, 1997
-
NKF-DOQI clinical practice guidelines. Am J Kidney
Dis 2001; 37(suppl 1):S1
-S238
-
Depner T, Beck G, Daugirdas J, Kusek J, Eknoyan G. Lessons from the
hemodialysis (HEMO) study: an improved measure of the actual hemodialysis
dose. Am J Kidney Dis1999; 33:142
-9[Medline]
-
Smith WCS, Lee AJ, Crombie IK, Tunstall-Pedoe H. Control of blood
pressure in Scotland: the rule of halves. BMJ1990; 300:981
-3
-
Mead A, Burnett S, Davey C. Diabetic retinal screening in the UK.
J R Soc Med2001; 94:127
-9[Free Full Text]
-
Will EJ. Target practice. Int J Artif
Organs 1998;21:433
-6[Medline]
-
Seghal AR, Snow RJ, Singer ME, et al. Barriers to adequate
delivery of hemodialysis. Am J Kidney Dis1998; 31:593
-601[Medline]
-
Palevsky PM, Washington MS, Stevenson JA, et al. Barriers
to the delivery of adequate hemodialysis in ESRD network 4. Adv
Renal Replace Ther2000; 7(suppl 1):S11
-S20[Medline]
-
Jacobs C, Horl WH, Macdougall IC, et al. European best
practice guidelines 5: target haemoglobin. Nephrol Dial
Transplant 2000;
15(suppl 4):15
-19[Free Full Text]
-
Hansson L, Zanchetti A, Carruthers SG, et al. Effects of
intensive blood-pressure lowering and low-dose aspirin in patients with
hypertension: principal results of the Hypertension Optimal Treatment (HOT)
randomised trial. Lancet1998; 351:1755
-62[Medline]
-
Rose G, Day S. The population mean predicts the number of deviant
individuals. BMJ1990; 301:1031
-4
-
Richardson D, Bartlett C, Will EJ. Intervention thresholds and
ceilings can determine the haemoglobin outcome distribution in a haemodialysis
population. Nephrol Dial Transplant2000; 15:2007
-13[Abstract/Free Full Text]
-
Richardson D, Bartlett C, Jolly H, Will EJ. Intravenous iron for
CAPD populations: proactive or reactive strategies? Nephrol Dial
Transplant 2001;16:115
-19[Abstract/Free Full Text]
-
Richardson D, Bartlett C, Will EJ. Optimizing erythropoietin
therapy in hemodialysis patients. Am J Kidney Dis2001; 38:109
-17[Medline]
-
Will EJ. Targets and targeting. Am J Kidney
Dis 2001;38:411
-14[Medline]
-
Will EJ, Cameron JS. European guidelines for renal anaemia—
predicting 85% compliance. Nephrol Dial Transplant2000; 15:439
-40[Free Full Text]
-
Haynes B. Can it work? Does it work? Is it worth it?
BMJ1999; 319:652
-3[Free Full Text]
-
Chalmers I. It's official: evaluative research must become part of
routine care in the NHS. J R Soc Med2000; 93:555
-6[Free Full Text]
-
Swales J. The troublesome search for evidence: three cultures in
need of integration. J R Soc Med2000; 93:402
-7[Free Full Text]
CiteULike 
Complore 
Connotea 
Del.icio.us 
Digg 
Reddit 
Technorati What's this?
This article has been cited by other articles:
|
|
|
|
 
E. Will
Intention and outcome in guideline-based nephrological practice: a suitable space for 'clinical technology'
Nephrol. Dial. Transplant.,
November 1, 2007;
22(11):
3110 - 3114.
[Full Text]
[PDF]
|
|
|
|
|
|
|
P. Barany and H.-J. Muller
Maintaining control over haemoglobin levels: optimizing the management of anaemia in chronic kidney disease
Nephrol. Dial. Transplant.,
June 1, 2007;
22(suppl_4):
iv10 - iv18.
[Abstract]
[Full Text]
[PDF]
|
|
|
TOP
INTRODUCTION
AIMING POINTS
