Risk and Uncertainty in Anesthesia

doi:10.4236/ojanes.2013.37072

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Open Journal of Anesthesiology, 2013, 3, 338-344

http://dx.doi.org/10.4236/ojanes.2013.37072 Published Online September 2013 (http://www.scirp.org/journal/ojanes)

Risk and Uncertainty in Anesthesia

Roman Pohorecki1*, Gary E. Hill2

1Department of Anesthesiology, Southwest Medical Center, Liberal, USA; 2Department of Anesthesiology and Pain Management,

The University of Texas Southwestern Medical Center, Dallas, USA.

Email: *[email protected]

Received May 17th, 2013; revised June 19th, 2013; accepted July 15th, 2013

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

This article discusses risk and uncertainty, both stochastic and epistemic, as it applies to anesthesia. It shows the diffi-

culty in quantifying risk of ind ividual case and the somewhat arbitrary and even incorrect and naïve assignment of risk

in individual patient care management. Effective and honest communication remains at the core of physician-patient

relationship in discussing, evaluating and managing the individual case for optimum outcome as well as patients’ and

their families’ satisfaction and understanding.

Keywords: Risk; Uncertainty; Communication

1. Introduction

Risk is an integral part of life that is brought by natural

forces as well as human activity. Though it is reasonable

to assume that many people pondered on the nature of

the risk, one can, somewhat arbitrarily, trace the begin-

nings to 16th century Italian mathematician and physi-

cian Gorelamo Cardano, who was more interested in risk

related to gambling than to outcomes in medicine [1].

The assessment of risk of anesthesia became possible

only after the introduction of anesthesia record over 100

years ago, which allowed more substantiated and repro-

ducible comparisons [2]. Once risk is quantified it can

and should be used to guide the decision process through

the meaningful narrative. It is common to express risk as

a probability or probability distribution. That method,

even in simple models, forces us to make certain as-

sumptions and often tends to obscure the difference be-

tween the uncertainty about the model and the uncer-

tainty about the knowledg e.

In this review, we attempt to broadly define the risk in

anesthesia and discuss the relation between risk and un-

certainty. We also want to bring to light some imperfec-

tions of human mind that are relevant in addressing risk.

The decision process, studied by cognitiv e psychologists,

has innate flaws that, even with obvious data limit our

ability to recognize, to address, and to properly react to

issues related to risk.

2. Risk Measures Often Used in Medicine

In medicine, the most common practice is the estimation

of probability or, equivalently, the relative frequency of

some event. There are several measures that can be fur-

ther derived and we summarize them below.

Absolute risk reduc tion is a sub traction. It answers the

question: how much the risk increases or decreases as a

result of treatment? In more general terms: how much

does being a member of the group changes the risk when

compared to a different group or population?

Another, often used measure in medicine, is a number

needed to treat. It is a reciprocal of absolute risk and as

the term implies it measures the number of patients that

ne ed to b e t r eated to achieve a target outco me. O b v io u s l y,

the smaller the number the better.

Example. Lets assume that the rate of myocardial in-

farction in general male population is 0.0217 and in a

male population receiving a small dose of aspirin is

0.0126. The difference is 0.0091. The number needed to

treat to avoid 1 case of infarction is 1/0.0091 or about

110 patie nt s .

Relative risk is a division or a ratio: how many more

times is the outcome or characteristic prevalent in one

group compared to another. Since it is often reported

without base rates of particular variables, it tends to ex-

aggerate the small differences. It is calculated over time

to a defined endpoint.

The term hazard ratio is derived from survival analysis.

It is a ratio of two outcomes over a period of time. One

*Corresponding a uthor.

Risk and Uncertainty in Anesthesia 339

can think about relative risk as a cumulative form of

hazard ratio. Alternatively, the hazard ratio is an instan-

taneous measure of relative risk, determined before the

endpoint of the study.

Using the above example of infarction and aspirin we

can determine that relative risk red uction due to asp irin is

0.0091/0.0 217 = 0.42% or 42%.

We included those rudimentary calculations here to

show that when dealing with relative measures it helps to

refer to absolute values or, to so called, base. Otherwise,

the exclusive reliance on relative differences may be mis-

leading. The following 2 examples should make it clear.

When the Department of Justice reports [3] that His-

panic registered voters were at least a 46.5% more likely

to lack the necessary ID, one may arrive at conclusion

that requiring the ID amounts to discrimination. Fortu-

nately, the DoJ cites also two additional measures: the

percentage of ID lacking voters among Hispanics is 6.3%

and among non-Hispanics is 4.3%. The difference, 2%,

indeed amounts to 46.5% (2/4.3).

When it was reported that daily consumption of proc-

essed meat increases a risk of death by 13% [4] it does

not mean that after 10 years of such diet one faces certain

death. The analysis expanded by David Spiegelhalter

translates the relative risk into absolute one and clearly

shows that the person eating processed meat daily may

live 1 year less than the person who moderates the diet

(79 years vs 80 years) [5].

Once the risk is known, and a very large body of such

measures is indeed completed, we face immediate obsta-

cle: how to clearly present it.

3. The Importance of Communication

Even when we, physicians, develop a fairly good know-

ledge and a command of assessing risk, we face another

very important threshold: how to communicate it clearly

to the patients. The difficulty arises from the fact that

they constitute a group with diverse literacy and cogni-

tive abilities, most often tainted by some degree of anxi-

ety dictated by their circumstance. In addition language

introduces imprecise terms as “likely” or “probable”.

Even if such statements are supported by more exacting

numerical information, the information may be misinter-

preted by a large fraction of patients [6]. Nevertheless

good risk communication should be the integral part of

our strategy.

There are several methods aimed at helping physicians

and patients to discuss the subject of risk. One such

method is putting things in perspective. If we know the

chances of winning any prize in a lottery like Power Ball

(1:32), dying of any cause during next year (1:100), be-

ing struck by lightning (1:280,000), we can communicate

the estimated risk of anesthesia in reference to those

recognizable events [7].

4. The Importance of Distribution

The risk may be viewed as acceptable and unacceptable.

When we try to determine the risk of vomiting after

laparoscopic cholecystectomy or of bradycardia during

colonoscopy, we have to start form counting su ch events.

We may quickly realize that there are two major factors

that one needs to consider: prevalence and severity. Most

frequently occurring events cary small consequences. On

the other hand infrequent events often have large cones-

quences. The graph below illustrates that concept: even ts

in the increasing order of severity are paired with their

corresponding frequency. It shows the events carrying

the largest consequences are infrequent. It also helps to

visualize that events carrying relatively small cones-

quences may be unacceptable if they happen often (Fig-

ure 1).

The concept of viewing risk as acceptable and unac-

ceptable may refer to different categories of risk. For

example, the failure risk of anesthesia machine [8] and,

unrelated, the risk of nausea after cholecystectomy [9].

The consequence in each category will form a range from

nearly inconsequential to very severe. For example, the

consequences of nausea and vomiting may range from

nuisance (frequent) to medical emergency in the form of

esophageal rupture (rare) [10]. Another example of how

visualizing a distribution can enhance the understanding

of risk comes from the relation between the heart rate

and myocardial oxygen demand as described by Slogoff

[11]. The graph below is an idealized relationship be-

tween the heart rate and the volume of ischemic left v en-

tricular wall. As the heart rate increases above certain

level, so may the volume of ischemic myocardium. It is

possible that two different individuals will experience

different magnitude of ischemia at the same heart rate,

thus calling for individualized control of the heart rate for

each patient [12]. In fact, there will be a whole distribu-

tion of the results for each heart rate, as shown on the

Figure 1. The graph shows the relation between cones-

quences and the frequency of adverse events. The largest

consequences are carried by infrequent events. Units are

arbitrary.

Risk and Uncertainty in Anesthesia

340

graph bel o w ( Figure 2).

The main determinant of the difficulty of risk analysis

is the underlying process and its distribution of outcomes.

As the complexity of the process increases, its results

become more uncertain. Indeed, the term uncertainty as it

applies to anesthesia and as it limits our ability to esti-

mate risk will be discussed below.

In terms more general than medicine, one has to real-

ize that the term “risk” encompasses questions related to

relatively simple events like the outcome of a die throw

to more complicated like risk of arrhythmia as a function

of p ot a ss iu m co n c entration in seru m, to very complic a te d ,

like Brownian motion. The statistical analysis of a proc-

ess may reveal that in some cases we may deal with the

easy problem that has known probability distribution an d

applicable tested mathematical methods. On the other

side of the spectrum we may encounter limits: the esti-

mation of the distribution parameters are only rough ap-

proximations carrying sizable error making risk analysis

exceedingly difficult [13]. Because different types of

distributions call for slightly different methods to calcu-

late parameters useful in assessing risk, it may be argued

that it would be prudent to determine what kind of prob-

ability distribution one is dealing with.

Expected, i.e. calculated, occurrence of adverse event

requires from us the knowledge (or assumption) of the

distribution. We don’t want to apply the methodology

developed for normal distribution if we are dealing with

another type of distribution because it may lead to un-

derestimation of the impact of rare events (contained in

the tails of a distribution).

There is, however, a subtle paradox in our ability to

determine what exactly is the distribution of outcomes

for any process [14]. In order to appropriately determine

the type of distribution we need to collect enough data.

How many? It depends on the type the distribution that

Figure 2. The graph represents idealized relationship be-

tween increased heart rate and the mean volume of

ischemic myocardium. The units are arbitrary. For every

heart rate value above “normal” there is a corresponding

distribution of the ischemic tissue volume.

will adequately model the studied process, i.e. the very

quality that we want to discover. To break out of such

circular argument we accept some necessary assumptions

supported by the collateral knowledge about the process.

The downside of such necessary method is a heightened

uncertainty (see below).

It is widely accepted that in biology and medicine the

majority of processes under investigation are normally

distributed. Most often used standard probability distri-

butions are: for infrequently occurring discrete events it

is assumed that the data fit Poisson distribution and for

frequently occurring events it is assumed that the data

approximate normal distribution. Alternatively, the data

may be mathematically transformed, most often in the

form of logarithms to “force” fit normal distribution [15].

The importance of the analysis of distribution is illus-

trated by the study published by Riou and coworkers [16].

The authors analyzed cases of a number of blunt trauma

victims. They modeled patient’s a priori probability dis-

tribution of survival showing that it is bimodal. Such

finding is important for the assumptions used in subse-

quent hypothesis testing, lik e inclu sion criteria for further

studies. The authors hypothesize that lumping all trauma

victims into one study cohort and disregarding the true

distribution of survival may have been responsible for

the negative results of several earlier studies and trials.

5. Risk and Uncertainty

What is simply denoted as risk of anesthesia is in reality

a composite of both risk and uncertainty. The meaning of

the term “uncertainty” may be a source of confu sion. For

the purpose of this discussion we will consider two dif-

ferent meanings of this term.

The most common use refers to stochastic uncertainty

as in “I am uncertain of a value or a measurement be-

cause of a small error”. It means that we can not assign

an exact value to a parameter in a statistical model that

was used for analysis, therefore the parameter is reported

with the measure of uncertainty, like confidence limits,

standard deviation, etc. In general, the uncertainty here

refers to the model and not to reality.

In the second case we restrict the taxonomy to epis-

temic uncertainty, i.e. the reality, its exact state.

The term refers to an unknown or undetermined part of

the risk, to our inability to determine “the state of the

world” or in other terms our lack of knowledge. While it

is possible to calculate risk, it is impossible to calculate

epistemic uncertainty [17]. While risk may be known

prior to each event, in the purely uncertain situation the

true risks are being discovered as that situation unfolds.

The difference between the risk and epistemic uncer-

tainty can be summarized as follows:

1) Risk: The outcome of the process is governed by a

known probability distrib ution and there are known tools

Risk and Uncertainty in Anesthesia 341

to analyze it (moments of a distribution: mean, variance,

skewness, etc.)

2) Uncertainty: there is not enough knowledge about

the process, or the distribution of outcome is unknown,

or, in the extreme case, the distribution is probably

known but the tools to determine the risk are very limited

(as in fat tails distributions).

Both the risk and uncertainty mesh in almost every-

thing we do as physicians.

Uncertainty, as applied to medicine and bracketing

both categories, can be appreciated in light of very

widespread errors in reported research and a low overall

probability of any results being actually correct [18].

Such findings should increase our skepticism, they

should serve as a remainder that uncertainty is a big part

of what we think we know.

A suitable example of uncertainty includes a burning

of the corner of the mouth during tonsillectomy. It may

happen when the surgeon uses long non-insulated cautery.

Since there is no reliable method to determine the true

probability of such event, it is relegated to uncertainty.

Similarly, the possibility of explosion in the contempo-

rary anesthesia machine either due to chemical reaction

[19,20], or a malfunction of an electronic part [8]. An

instructive example of uncertainty in anesthesia is a brief

history of perioperative use of beta blocking agents. In

the early 1970s the prevailing opinion was that beta

blocking agents should be avoided in perioperative pe-

riod due to their negative inotropism. That point of view

changed in the late 1970s and early 1980s when it was

observed that sudden cessation of beta blocking agents

may lead to increased perioperative incidents of ischemia

and heart attacks. It culminated in a series of guidelines

formulated by ACC/AHA advocating the use of beta

blocking agents [21], and was later incorporated by Cen-

ter for Medicare Services in its Surgical Care Improve-

ment Project (SCIP). In 2008 the results of a large pe-

rioperative ischemic evaluation study (POISE) revealed

that indeed the incidence of myocardial infarction is

lower in the group treated with metoprolol, but the mor-

tality in treated group was higher: 3.1% versus 2.3%

[22,23]. Metoprolol prevented myocardial infarction in

1.5% but at the same time it caused excess deaths in

0.8% and stroke in 0.5% of the patients. Since POISE

studied only acute perioperative treatment with meto-

prolol, the conclusions do not necessarily apply to chro-

nic treatment.

Similar discussion, curren tly taking place in anesthesia

literature, refers to long term outcomes as a function of

cumulative duration of deep hypnotic time as measured

by BIS. A number of stud ies seem to suppor t the asso cia-

tion between the cumulative time of deep anesthesia and

mortality up to 2 years later. At the same time a number

of studies failed to demonstrate that association (for brief

reviews see [24,25].

Other examples of considerable uncertainty in anes-

thesia include regional anesthesia in presence of neuro-

logical disease [26], diseases linked to malignant hyper-

thermia [27], prediction of difficult intubation [28,29],

the long term effect of anesthesia on the immune system

[30], the platelet count as a restrictive factor for neu-

roaxial block in obstetrics [31,32], or the hemoglobin

levels that would trigger a decision to transfuse [33,34].

Uncertainty about the information or a measure also

plays a role in anesthesia practice but under different

circumstances. Consider a case of 45 years old male who

is being prepared for cholecystectomy. It is reasonable to

suspect that he has some degree of coronary atheroscle-

rosis, since the lifetime risk of coronary artery disease

events at that age are approximately 40% [35]. Because

the presence of the disease carries a potential impact on

the outcome, an anesthesio logist may not be content with

the known probability but may wish to increase his con-

fidence of the absence or presence of the disease in this

particular case 1. The frequency of an adverse event may

be approximated from a priori large scale epidemiologi-

cal studies and from personal experience. An anesthesi-

ologist has to determine the probability of an adverse

event for a given patient, i.e. assign the numeric value or

a linguistic equivalen t. It is usually a gu ess and thus there

is a degree of uncertainty about its value. Based on the

interview, tests and his own experience the anesthesiolo-

gist adjusts the confidence that the probability has a cer-

tain value. In other words, as the information is gathered

the uncertainty decreases. As the uncertainty decreases,

the confidence about a probability of an event changes.

The above does not change the true probability of any

given event occurring. We often rely on a published

mean occurrence or magnitude of an event of interest.

We may apply it as a risk measure in a particular situa-

tion involving our patient. The fact about such practice

worth remembering is that our patient may not be a typi-

cal member of the cohort used for the original study.

Depending on how atypical he is, the usefulness of the

mean value varies. During the evolution of the time se-

ries (progression of the case) the initial conclusions are

constantly reassessed. The perceived probability may

suddenly change during the case. This may occur due to

an unknown factor that revealed itself during the case

and it constitutes the epistemic uncertainty discussed

above. Alternatively, it may be attributed to the process

of anesthesia and progression of the surgical procedure.

6. Compounding Problems

Multiple co-morbidities and other variables attributed to

patient (age, weight, sex, and other genetic factors, etc.)

significantly complicate the task to gauge the risk. The

assessment of a probability of a single event is an over-

Risk and Uncertainty in Anesthesia

342

simplified problem. The reality challenges us to consider

more complicated situations in the form of conditional

probabilities. Such empirical data are available in the

form of different indices.

A conditional probability can be defined as follows:

what is the probability of A given th e presence (absence)

of condition B. There is a lot of epidemiological data

accumulated over time based on the above question.

Given the dynamic nature of medicine where practice is

undergoing a slow but continuous change, and the dy-

namic nature of societal factors relevant to health, the

accumulated data “age” over time and will serve as the

estimates only. One may say that the probability estima-

tion is thus condition al on accumulated knowledge at the

time of the study. Real life situations pose even more

challenging tasks in the form of multiple nested condi-

tional and joint probab ilities.

Other factors complicating the assessment of risk in-

clude the fact that the more information we seek, the

more likely it is that we will include erroneous one. Each

test has the inherent limitations summarized as its sensi-

tivity and specificity. As the information is being gath-

ered, its predictive value reaches a plateau. The individ-

ual gathering the information will not necessarily gain

any new insight or, more importantly, more information

will not help him to correct possibly wrong initial con-

clusion. However, as numerous psychological experi-

ments show, more information improves only self confi-

dence even if the conclusion is incorrect [36,37]. Finally,

the process of obtaining additional information intended

to reduce risk may have the opposite results: periopera-

tive consultations seem to increase mortality [38].

Variability encountered between individuals adminis-

tering anesthesia, surgeons, nurses, implemented systems

like infection control, medication checks, frequency with

which any given case is done in a given hospital, and so

on, all add further layers of nested probabilities. All of it

impacts the risk.

The psychological constrains on assessing risk and de-

cision making warrant a little more attention. There are

several innate mechanisms severely limiting our ability

to make rational decisions, to recognize, to address, and

to react properly to the issues related to risk. Those limi-

tations have been studied by cognitive psychologists and

apply to all of us. Indeed, the recent study estimated the

frequency of cognitive errors among anesthesiologists

[39] and reported 14 types of cognitive errors such as

anchoring ( focusing on one issue at the expense of un-

derstanding the whole situation) or premature closure

(accepting a diagnosis prematurely). Seven out of 14

errors were made with a frequency higher than 50%.

Cognitive and decision errors are made even when the

outlining probabilities of some event are known. Kah-

neman determined the decision weights when people

have to make a decision in a situation with an upfront

known probability of ou tcome. There is a strong propen-

sity to overweight small and underweight high probabili-

ties. When the probability of an event is 1%, the corre-

sponding decision weight is 5.5, when the probability is

5% the corresponding weight is 13.2. The opposite is true

at the high probability of an event: when it is 80% the

corresponding decision weight is 60.1, when it is 90%

the weight is only 71.2, and when it is 99% the weight is

only 91.2. It shows that there is a tendency to deliberate

over and emphasize unlikely outcomes but hope for the

best when the outcome is most likely unfavorable [40].

Such innate psychological constrains that may or may

not be modified by training should be recognized as an

additional risk gen erating factor.

7. Conclusions

A very complicated system of interdependencies that we

sketched here implicates that risk assessment contains

“elements of craft-like judgment”. Those craft-like ele-

ments are heuristics and professional judgment. In the

somewhat narrow meaning, the term professional judg-

ment refers to one’s ability to make app ropriate decisions

under mixed conditions of risk and uncertainty. We ac-

quire that ability by repetitive performance of the same

task and by continuous intellectual challenge encoun-

tered during postgrad uate training . Su ch repetitio n allo ws

us to gather the empirical and theoretical evidence about

our work, and to develop some ability to implement ac-

cumulated experience with a good outcome. It also de-

creases the variance of outcome, that is, it allows us to

develop techniques to assure as uniform outcomes as

possible.

The professional judgment embodies the intuitive un-

derstanding of possible outcomes without underestima-

tion of the uncertainty. It is in essence our ability to de-

velop a good sense of posterior probabilities related to

each individual case. In that sense it is what is defined as

subjective probability: our educated guess about how

likely is an occurrence of a particular event. There even

may be a stark difference between on one hand the abil-

ity of an individual to correctly solve an exercise based

on Bayes theorem and on the other a correct implementa-

tion of that theorem in every day practice. We can rarely

or never grasp all the elements relevant to risk estimation

in an individual who is about to undergo a particular sur-

gical procedure. Open and honest dialog with our pa-

tients based on published literature, accepted practice

standards, and our own clinical accumen remains the

foundation of our ability to communicate risk to our pa-

tients.

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