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E. Rahbar et al.
25.5.3 Case-Control Study
Case-control studies are retrospective studies that
identify two groups of patients; one group with
the known disease (cases) and another group
without the disease (controls). The goal is to compare the proportion of a certain exposure between
case and control groups. These studies are often
susceptible to recall bias as patients with knowledge of their disease are likely to recall being subjected to a particular exposure (e.g., high-tension
power lines). However, case- control studies are
very useful for identifying risk factors of a rare
disease. Additionally, confounding factors (i.e.,
factors that are associated with both the disease
and exposure) may also introduce bias. In this
case, matched case-control studies are used to
minimize confounding. For example, when
attempting to identify risk factors for type II diabetes through a case-control study, it is important
to control for age because age is associated with
both type II diabetes and various exposures.
25.5.4 Cohort Study
Cohort studies are prospective studies that follow a predetermined disease-free group of
patients over a period of time. As the study progresses, some individuals develop the disease
and others do not. The development of the
disease is then related to the exposure variables
observed over the time period of the study.
These studies sometimes require a long span of
time, during which loss of patients is likely to
occur. Cohort studies are useful when examining the effect of various risk factors on the
development of disease.
25.5.5 Cross-Sectional Study
In cross-sectional studies, the patient population is asked about their current disease status
and current and/or past exposure status to various risk factors. Cross-sectional studies compare the prevalence of disease at one point in
time between exposed and unexposed
individuals. This is different than the prospec-
tive (cohort) study where the incidence of disease rather than prevalence of disease is
investigated.
25.5.6 Clinical Trials
Clinical trials are distinguished by several traits
that help make their findings more valid and reliable. Good clinical trials are randomized, which
helps to minimize selection bias. They could be
double- blinded, which minimizes measurement
bias by reducing confounding by investigators
and patients who may be aware of the therapy
they are giving or receiving. Multi-centered trials reduce confounding due to local or regional
differences and limited sample sizes. Placebo
controls help to ensure that the trial is doubleblind and helps to reduce measurement bias. A
crossover design ensures that a patient receives a
therapy for at least half of the trial and a placebo
for the remainder – it helps to serve as an internal control and reduces measurement bias. The
best clinical trials incorporate as many of these
traits as possible. They are designed in such a
way that their outcomes can typically be trusted
if all tenets of the study design are faithfully followed. The major determent to clinical trials is
their high cost. One note regarding clinical trials:
in order for a randomized study to be properly
evaluated, the sample size must be carefully
predetermined.
25.6 Measures of Associations
Between Two Binary
Variables
Depending on the study design, different measures of association can be used to display relationships between variables. As mentioned
earlier, the chi-square test of independence can
be used to test the null hypothesis that the
exposure and disease are not associated with
each other against the alternative that there is
an association. However, there are several
methods to measure associations between two
binary variables including odds ratio and
relative risk. In a case-control study, where a
()
()
ab
+
+
abccd
ab
+–
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365
Fig. 25.3 Illustration of the
four possible scenarios from
a “disease-exposure”
relationship
group of patients who have the disease are
compared to a group of patients who do not
have the disease with respect to their exposure,
the data can be organized in the form of a 2 × 2
contingency table, as shown in Fig. 25.3.
25.6.1 Odds Ratio
The odds ratio is a descriptive statistic that can be
thought of as determining the strength of an association between two binary variables. The odds
ratio is defined as the ratio of odds of exposure
among patients who have the disease relative to
the odds of exposure among patients who do not
have the disease. The odds of an event refers to
the probability of the event occurring over the
probability of the event not occurring. Simply, the
odds ratio is calculated by the formula given in
Eq. 25.14. It is often used in retrospective, casecontrol and cross-sectional studies to evaluate the
particular effect of a risk factor on disease.
Standard statistical packages provide 95
confidence intervals for odds ratios. If the 95 %
confidence intervals for odds ratios do not include
1, then one can conclude that there is an association between the disease and exposure. Also there
is a formula for calculating the 95 % confidence
intervals for odds ratios based on the information
in the contingency table. For additional information we refer the reader to Rosner’s textbook,
Fundamentals of Biostatistics.
25.6.2 Relative Risk
Relative risk is used to compare the chance of a
particular disease between the exposed and non-
+
Exposure
I
exposed groups. For example, in a cohort study,
the risk of a smoker developing lung cancer
would be compared to the group of nonsmokers
and the result given in terms of the relative risk of
lung cancer. Relative risk is calculated as
follows:
Standard statistical packages also provide
95 % confidence intervals for relative risk. If
the 95 % confidence intervals for relative risks
do not include 1, then one can conclude that
there is an association between the disease and
exposure. The formula for calculating the 95 %
confidence intervals for relative risks can be
found in Rosner’s textbook, Fundamentals of
Biostatistics. The relative risk must be used
with care as minor differences in risks between
the two groups can result in a large ratio. In
these cases, you must also report the absolute
risk for the disease, which is simply the probability of the disease.
Odds Ratio =
d
b
(25.14)
%
25.6.3 Attributable Risk
To compare risks of disease between exposed
and non-exposed groups in a cohort study, one
can calculate the attributable risk. It is calculated
as the difference between the incidence of disease in exposed group and incidence of disease
in non- exposed group (Eq. 25.16). Therefore, it
represents the additional incidence of disease
related to exposures and often called the risk
difference.
Attributable Risk =
Disease
c
RelativeRiska=
/
/
ccd
a
−
d
+
+
(25.15)
(25.16)
366
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25.6.4 Associations vs. Causal
Relationships
It is important to note that associations do not
imply causal relationships. In fact, in clinical
research it is very difficult to establish causal
relationships. Depending on the study design,
one can build evidence for or against a causal
relationship. For example, in randomized control
trials it is easier to establish causal relationships
than in retrospective studies. Randomized controlled trials with adequate sample size and blinding are usually the best evidence for a cause and
effect relationship.
When investigating whether an exposure has a
causal relationship with a disease, it is important
to evaluate if the association is an artifact of measurement bias or random variation (chance). If
the association is not due to bias and seems
unlikely, then one must consider if the association is occurring indirectly, potentially through
confounding factors. If one does not find confounding and the study is well designed, a causal
relationship is likely. For more detailed discussion on causality, we refer the reader to Fletcher’s
book, Clinical Epidemiology, and Rothman’s
book, Modern Epidemiology.
25.7 Diagnostic Tests
So far the focus of this chapter has been on the use
of statistics in the development of various clinical
studies to investigate the associations between
exposures and disease. However, clinicians are
also interested in assessing the predictive power of
diagnostic tests. In order to assess the accuracy of
a diagnostic test result, one must know the person’s true status of the disease. Results from a
diagnostic test can be classified as true positives,
true negatives, false positives, and false negatives,
as illustrated in Fig.
when a test designed to determine the presence of
a disease reports a correct answer. A true negative
occurs when a test correctly reports that a disease
is not present. False positives can be psychologically detrimental to a patient, such as when a test
reports positive HIV status when the patient actu-
25.4. A true positive occurs
E. Rahbar et al.
Disease
+
+
Test
–
Total
Fig. 25.4 The four results that can be obtained from a
test for a particular disease, along with the calculations for
sensitivity, specificity, positive predictive value, and negative predictive value
FN
TP +
FN
–
FPTP
TN
FP +
TN
Total
TP + FP
FN +
TN
ally does not have this disease. They can also result
in increased cost of care for unnecessary treatments since the patient has been falsely identified
as diseased. False negatives can prevent a patient
from receiving therapy when a test incorrectly
reports that a patient does not have a disease.
25.7.1 Sensitivity
Sensitivity is a measure of the proportion of true
positives, calculated as the number of people who
tested positive among all who have the disease
(Eq. 25.17). A highly sensitive test will have a
low rate of false negatives. Further, if the sensitivity is high enough and the test results negative,
one can trust that the patient does not have a disease. Sensitive tests are often valuable as screening tests for a population.
Sensitivity =
TP
(25.17)
25.7.2 Specificity
Specificity is a measure of the proportion of true
negatives, calculated as the number of people
who tested negative among all who do not have
the disease (Eq. 25.18). Specific tests have very
low rates of false positives, so a true-positive
result is considered to be trustworthy. If a patient
TP +FP
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367
obtains a positive result on a specific test, they are
effectively ruled in for a particular disease.
Specificity =
TN
TN +FP
(25.18)
25.7.3 Positive Predictive Value
Positive predictive values are used to determine
the chance of having a disease given a positive
test result. It is calculated as the number of true
positives divided by the total of positive test
results (Eq. 25.19). The positive predictive value
is used in conjunction with the pretest probability
to determine the chance the patient truly has a
disease. For example, doing a test for the Ebola
virus is unlikely to be meaningful, even with a
positive result, on a healthy American in
Nebraska who has never traveled to Africa.
PositivePredictiveValue =
TP
(25.19)
25.7.4 Negative Predictive Value
The negative predictive value determines the
chance of not having a particular disease given a
negative test result. It is calculated as the number of
true negatives divided by the total number of negative test results (Eq. 25.20). The negative predictive
value is also used in conjunction with pretest probability and clinical suspicion to determine whether
a patient is likely to have a particular disease.
NegativePredictiveValue =
TN
TN +FN
(25.20)
25.8 Summary and Conclusions
In this chapter we discussed the steps towards
developing a successful clinical study beginning with identifying one’s target population.
Once the population is identified, a research
question is formulated into a testing hypothesis,
when possible, and an appropriate study design
is implemented. Accordingly, data is collected in
a randomized non-bias fashion to improve data
quality and is analyzed using various significance
tests. Additionally, associations can be assessed
using odds ratio, relative risk, and attributable risk.
We hope that this chapter has provided a basic
understanding of clinical study design and testing hypotheses and illustrated the importance of
biostatistics in clinical and translational research.
However, we acknowledge that the material provided here may not be sufficient to independently
design a clinical study. Therefore, we strongly recommend that you consult biostatisticians and epidemiologists when designing complex clinical and
translational studies.
References
1. Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon H,
Walter S. Basic statistics for clinicians: 1. Hypothesis
testing. CMAJ. 1995;152(1):27–32. Review.
2. Guyatt G, Jaeschke R, Heddle N, Cook D, Shannon
H, Walter S. Basic statistics for clinicians: 2.
Interpreting study results: confidence intervals.
CMAJ. 1995;152(2):169–73.
3. Jaeschke R, Guyatt G, Shannon H, Walter S, Cook D,
Heddle N. Basic statistics for clinicians: 3. Assessing
the effects of treatment: measures of association.
CMAJ. 1995;152(3):351–7.
4. Guyatt G, Walter S, Shannon H, Cook D, Jaeschke R,
Heddle N. Basic statistics for clinicians: 4.
Correlation and regression. CMAJ. 1995;152(4):
497–504.
5. Hayward RS, Wilson MC, Tunis SR, Bass EB,
Guyatt G. Users’ Guides to the medical literature.
VIII. How to use clinical practice guidelines. A. Are
the recommendations valid? The Evidence-Based
Medicine Working Group. JAMA. 1995;274(7):
570–4.
6. Wilson MC, Hayward RS, Tunis SR, Bass EB,
Guyatt G. Users’ guides to the Medical Literature.
VIII. How to use clinical practice guidelines. B.
what are the recommendations and will they help
you in caring for your patients? The Evidence-Based
Medicine Working Group. JAMA. 1995;274(20):
1630–2.
7. Guyatt GH, Sackett DL, Sinclair JC, Hayward R,
Cook DJ, Cook RJ. Users’ guides to the medical literature. IX. A method for grading health care recommendations. Evidence-Based Medicine Working
Group. JAMA. 1995;274(22):1800–4.
8. Levin LA, Danesh-Meyer HV. Lost in translation:
bumps in the road between bench and bedside. JAMA.
2010;303:1533–4.
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9. Baggerly KA, Morris JS, Edmonson SR, Coombes
KR. Signal in Noise: Evaluating Reported
Reproducibility of Serum Proteomic Tests for Ovarian
Cancer. J Natl Cancer Inst. 2005;97:307–9.
10. Ransohoff DF, Gourlay ML. Sources of bias in specimens for research about molecular markers for cancer. J Clin Oncol. 2010;28:698–704.
11. Ioannidis JP. Why most published research findings
are false. PLoS Med. 2005;2:e124.
12. Rosner B. Fundamentals of biostatistics. 6th ed.
Stamford: Thomson Learning; 2006.
13. Fletcher R, Fletcher S. Clinical epidemiology: the
essentials. 4th ed. New York: Lippincott Williams and
Wilkins; 2005.
14. Rothman KJ, Greenland S, editors. Modern epidemiology. 2nd ed. Philadelphia: Lippincott Williams &
Wilkins; 1998.
Vein Anesthesia
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David O. Joseph , Jessica L. Myers ,
and Eugene W. Moretti
2 6
Contents
26.1 Introduction .............................................. 369
26.2 Local Anesthesia ....................................... 369
26.2.1 Discovery and Development ........................ 369
26.2.2 Pharmacology .............................................. 370
26.2.3 Onset and Duration of Action ...................... 372
26.2.4 Calculating Dosage and Drug
Administration ............................................. 372
26.2.5 Toxicity and Treatment of Toxicity .............. 373
26.3 Tumescent Anesthesia .............................. 374
References ............................................................... 374
Abstract
Local anesthesia and tumescent anesthesia
play a key role in the success of phlebology
procedures. This chapter reviews key principles of these procedures. Local anesthetics
have been used for decades by physicians to
provide pain relief during simple surgical procedures. The utility of this is twofold: fi rst,
they provide a degree of anesthesia at the
operative site that decreases or even eliminates the need for general anesthesia; second,
they can provide several hours of analgesia
that decreases the amount of narcotic given
both intra- and postoperatively.
26.1 Introduction
Local anesthesia and tumescent anesthesia play a
key role in the success of phlebology procedures.
This chapter reviews key principles of these
procedures.
D. O. Joseph , MD, MS
Department of Anesthesia , University of Texas
at Houston Medical School , Houston , TX , USA
e-mail: david.o.joseph@uth.tmc.edu
J. L. Myers , MD • E. W. Moretti , MD, MHsc (*)
Department of Anesthesiology , Duke University
Medical Center , Durham , NC , USA
eugene.moretti@dm.duke.edu
E. Mowatt-Larssen et al. (eds.), Phlebology, Vein Surgery and Ultrasonography,
DOI 10.1007/978-3-319-01812-6_26, © Springer International Publishing Switzerland 2014
26.2 Local Anesthesia
26.2.1 Discovery and Development
Local anesthetics have been used for decades by
physicians to provide pain relief during simple
surgical procedures. The utility of this is twofold:
fi rst, they provide a degree of anesthesia at the
operative site that decreases or even eliminates
the need for general anesthesia; second, they can
369
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D.O. Joseph et al.
provide several hours of analgesia that decreases
the amount of narcotic given both intra- and
postoperatively.
Cocaine was the fi rst local anesthetic discovered. South American Indians would chew the
leaves of the plant Erythroxylum coca to increase
their physical endurance when working. They
also found that chewing coca leaves would make
their mouths and tongues numb. This fi nding
prompted a German graduate student by the name
of Niemann to isolate cocaine from the leaves of
the coca plant in 1860. Eventually, due to its ability to anesthetize the cornea, it was used as a local
anesthetic for glaucoma surgery [ 1 , 2 ]. Subsequent
to this, cocaine use for regional and local anesthesia became widespread in Europe and the USA.
However, the toxic effects of the drug were soon
elucidated after deaths were reported of both
patients and medical staff whom had become
addicted. It was not until advances in organic
chemistry in 1891 that newer local anesthetics
could be synthesized, specifi cally the amino esters
(e.g., tropocaine, eucaine, holocaine, orthoform,
benzocaine, and tetracaine). The amino amides
were developed between 1898 and 1972 (e.g., nirvaquine, procaine, chloroprocaine, cinchocaine,
lidocaine, mepivacaine, prilocaine, efocaine,
bupivacaine, etidocaine, and articaine). Both the
amino esters and amino amides demonstrated signifi cantly less toxicity than cocaine, and many are
still in mainstream use today (Table 26.1 ).
Bupivacaine was fi rst synthesized in 1957 and
was of particular interest because of its long duration of action. Major side effects of bupivacaine
include central nervous system and cardiovascular
toxicity [ 3 – 6 ]. Ropivacaine is an alternative local
anesthetic with fewer toxicities and is derived from
the optically active isomers of mepivacaine [ 3 , 7 ].
Table 26.1 Commonly used local anesthetics
Esters Amides
Procaine (Novocain
Chloroprocaine
(Nesacaine
®
) Lidocaine (Xylocaine®)
®
)
Mepivacaine (Polocaine
Carbocaine
Bupivacaine (Marcaine
Prilocaine (Citanest
Ropivacaine (Naropin
®
)
®
or
®
)
®
)
®
)
26.2.2 Pharmacology
The mechanism of action for local anesthetics is
an alteration of sodium conduction across the
neuronal cell membrane. The resting membrane
potential is established across a neuronal membrane by the sodium/potassium ATPase pump.
This results in a cell membrane with a negative
electrical potential of about −70 mV. When a
stimulus is applied to a neuron, there is an initial
opening of the sodium channels and a positive
change in membrane potential. When a certain
threshold is met (approximately −55 mV), a
larger opening of voltage-gated sodium channels
produces an action potential which is propagated
as an impulse along the neuronal cell (Fig. 26.1 ).
This impulse also conducts pain signals from a
peripheral nerve to the spinal cord and subsequently to the brain. Local anesthetics exert their
effect mainly by blocking the sodium conduction
necessary for the initiation and propagation of the
action potential. Sodium channels are membrane
proteins that consist of a large alpha subunit and
one or two smaller beta subunits. The alpha subunit allows the passage of sodium ions [ 8 , 9 ].
Local anesthetics bind to a specifi c site on the
alpha subunit from inside the cell. The voltagegated sodium channels exist in three states: the
resting, activated, and inactivated states. Local
anesthetics have a greater affi nity for the sodium
channel when in the inactivated and activated
states as compared to the resting state; therefore,
local anesthetics exert their greatest effect on
nerves that are fi ring rapidly [ 10 ].
Local anesthetics are compounds that exist in
solution. The pKa of a compound in solution is the
pH at which 50 % of the compound exists in ionic
form and 50 % exists in nonionic form. The tendency to release hydrogen ion determines a compound’s strength as an acid, and the tendency to
bind hydrogen ion determines its strength as a base.
As the pH decreases, there are more hydrogen ions
in solution and therefore a greater tendency for the
compound to hold on to hydrogen. As the pH
increases, there are fewer hydrogen ions in solution, and therefore it increases the tendency of the
compound to release hydrogen into solution. Local
anesthetics are weak bases. Structurally they exist
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Action potential
+
NA
+
NA
371
+
K
+
K
Fig. 26.1 Propagation of an action potential
Action potential
+
NA
+
NA
+
K
+
K
Action potential
+
NA
+
NA
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D.O. Joseph et al.
as amino esters or amino amides. The amino group
when bound to a hydrogen ion forms a charged
species. It should be noted also that the amino
group on local anesthetics has a pKa that is higher
than physiological pH.
Therefore, if a local anesthetic has a low pKa
or one that is close to physiological pH, it has a
higher proportion of non-ionized species and can
gain access to a neuronal cell better compared to
a compound that has a high pKa; this is the case
as non-ionized compounds tend to be more lipophilic and thereby penetrate the cell membrane
more readily. An alternative explanation for the
function of local anesthetics involves altering the
fl uidity of the neuronal cell membrane in such a
way that the conformation of the sodium channel
changes, thereby changing its conductance
[ 2 , 11 ]. When a local anesthetic has a pKa close
to physiological pH, it has a higher concentration
of its non-ionized, lipophilic form that can pass
through the neuronal cell membrane; this translates into a faster onset [ 7 ]. Table 26.2 shows the
physical properties (including pKa values) for the
more commonly used local anesthetics [ 10 – 13 ].
Local anesthetics in the form of esters are
eliminated via plasma esterases, while the amides
are absorbed into the circulation and eventually
metabolized by the liver and excreted by the kidneys [ 1 , 14 ]. When choosing which anesthetic to
use for a fi eld block, it is important to consider
onset and duration of action and the suitability of
tissue for a block. In addition, the use of a
Table 26.2 Physical properties of the commonly used
local anesthetics
Concentration
(%) pKa pH Onset
Esters
Procaine 0.25–0.5 8.9 3.5–5 Fast
Chloroprocaine 1–2 9 4.5 Fast
Amides
Lidocaine 1–2 7.7 5.0–7.0 Fast
Prilocaine 1 7.7 4.5 Fast
Mepivacaine 1 7.6 4.5–6.8 Fast
Bupivacaine 0.25 8.1 4–6.5 Fast
Ropivacaine
a
Onset is listed with regard to local infi ltration.
Ropivacaine has a slower onset time when used for
peripheral nerve blocks
a
0.5 8.2 5.5–6.0 Fast
vasoconstrictor and the maximum dose of local
anesthetic are of equal importance.
26.2.3 Onset and Duration of Action
Lipid solubility can affect onset of action for a
local anesthetic (Table 26.2 ). Increased concen-
tration of the drug (even if it is known to be a
drug of slow onset) can speed up the initiation of
the block; such is the case with chloroprocaine
(pKa 9 and ionized proportion of 97 %). The
onset and duration of action are also affected by
the target tissue: highly vascular tissues may take
the drug away from the site and limit its maximum effect. Uptake is slower for highly lipidsoluble drugs and those that avidly bind to
protein. Generally, amides tend to have a longer
duration of action than esters (Table 26.3 ).
Most local anesthetics are vasodilators which
increase removal of the drug from the operative site;
ropivacaine is an exception to this with its intrinsic
vasoconstrictive properties. Epinephrine can be
added to most local anesthetics to cause vasoconstriction and improve its availability to the tissue.
Epinephrine also signifi cantly increases duration of
action for infi ltration anesthesia and peripheral nerve
blocks when used with the shorter-duration local
anesthetics. The use of a vasoconstrictor and local
anesthetics with intrinsic vasoconstrictive properties
should be avoided in an operative fi eld that has either
a compromised or an end arterial blood supply due
to the risk of tissue necrosis [ 14 , 15 ].
Suitability of the operative site is an important
consideration; infected tissue is a poor target site
for an infi ltrative local anesthetic block. Infection
causes a lowering of the pH, leading to a greater
amount of ionized molecule that poorly penetrates the cell membrane. In these situations,
moderate sedation with adjunctive intravenous
pain control may be necessary.
26.2.4 Calculating Dosage and Drug
Administration
When administering local anesthetics, the maximum dosage of the drug that can be safely given
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373
Table 26.3 Maximum dosages
allowable for the administration of
infi ltrative local anesthesia
Esters
Procaine 5 20–30 min 7 mg/kg 30 min
Chloroprocaine 11 15–30 min 14 mg/kg 30 min
Lidocaine 4 30 min to 2 h 7 mg/kg Up to 3 h
Mepivacaine
Prilocaine 7 30 min to
Bupivacaine
Ropivacaine 5 2–6 h N/A N/A
a
Avoid in pregnancy
b
Avoid in pregnancy until term
Maximum
dose
(plain)
(mg/kg)
a
4 1.5–3 h 7 mg/kg Approximately
b
2 2–4 h 3 mg/kg 3–4 h
is of great importance. Due to its vasoconstrictive
properties, epinephrine often allows greater
amounts of local anesthesia to be used as it limits
systemic toxicity by decreasing absorption from
the operative site. The maximum dose is
expressed in milligrams per kilogram of the
patient’s body weight. Concentration of local
anesthetics is expressed as a percentage (e.g.,
0.25 % bupivacaine or 1 % lidocaine). The conversion is as follows: 1 % = 10 mg/mL. The relative contraindication for the use of epinephrine
is in patients at risk of myocardial ischemia, as
accidental intravenous injection can lead to
tachyarrhythmias and myocardial ischemia. Other
patients at increased risk are those with hyperthyroidism or on medications that alter the effects of
catecholamines (e.g., monoamine oxidase inhibitors and tricyclic antidepressants) [ 1 ].
26.2.5 Toxicity and Treatment
of Toxicity
Toxicity to local anesthetics can be either local
or systemic; local adverse effects can manifest as
paresthesias, while systemic toxicity manifests
as cardiovascular (CV) or central nervous system (CNS) problems such as hypotension, tinnitus, confusion, and respiratory depression. Toxic
reactions such as anaphylaxis or methemoglobinemia can also occasionally occur, especially
with benzocaine, lidocaine, and prilocaine.
Duration of
action (plain)
1.5 h
Maximum
dose (with
epinephrine)
8 mg/kg Up to 2 h
Duration of
action (with
epinephrine)
20–30 %
longer
Higher levels of methemoglobin (20–45 %) may
cause headache, lethargy, tachycardia, or dizziness. Shortness of breath, arrhythmias, cardiac
failure, and seizures occur at levels >45 %.
Above 70 %, there is a high risk of mortality.
Methylene blue 1 % can be given at a dose of
1–2 mL/kg for treatment. If methemoglobinemia
persists, this dose can be repeated 30–60 min
later [ 2 , 12 ].
Comorbidities like renal or hepatic failure,
respiratory acidosis, heart block, or other cardiac
problems can worsen the severity of these reactions. Pregnancy and extremes of age are also
conditions that warrant caution with the use of
local anesthetics [ 7 ]. In fact, mepivacaine is con-
traindicated in pregnancy because of poor fetal
metabolism (hepatic immaturity). Bupivacaine
is also contraindicated in the parturient due to
the physiological changes associated with pregnancy as well as direct effects of progesterone
[ 12 ]. It is important to remember, however, that
the primary cause for systemic toxicity is an
unintentional intravascular injection of local
anesthetic [ 7 ].
Allergies to local anesthetics can manifest as
rash or urticaria. Anaphylaxis is extremely
uncommon, but it should never be overlooked.
Acute allergic reactions associated with the
amino esters are usually caused by a hypersensitivity reaction to para-aminobenzoic acid
(PABA). Some preparations of amino amides
contain methylparaben, a compound chemically
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