Download Severe sepsis and septic shock are a leading cause of

Chapter I
Critically Ill Child
Critically ill child
Critically ill child means a child who is in a clinical state which may
result in cardiac arrest or severe neurologic complication, if not
recognized promptly. This term does not refer to any particular disease,
but many diseases can lead onto "critically ill state". Whether a child
presents with a primary cardiovascular, respiratory, neurologic, infectious
or metabolic disorder, the goal is early recognition of respiratory and
circulatory insufficiency (Kinsella.1993).
Acute life threatening condition can result from a serious illness
affecting one or more of the body systems necessary to maintain life or a
serious injury; trauma, burn, near drowning, poisoning which leads to
derangement in physiology with the potential to result in significant
morbidity and mortality without prompt and appropriate intervention
(Fletcher.1987).
Factors Potentially Affecting Outcomes of Critical Illness in Children
Baseline Illness
Children with chronic and complex diseases are at increased risk for
in-hospital clinical deterioration and are over-represented in the ICU
(Mestrovic, et al. 2007). Studies have shown prolonged ICU stay and
higher mortality rates in children with ventilator-associated pneumonia
(Srinivasan ,et al .2009), bone marrow transplant (Van Gestel, et
al.2008), hypoplastic left heart syndrome (HLHS) (Gordon, et al.2008),
hemophagocytic lymphohistiocytosis (HLH) (Karapinar, et al.2009),
hyperglycemia (Klein, et al. 2008) and acute kidney injury(Hayes,et
al.2009). The quality of life of patients with other chronic condition did
4
Chapter I
Critically Ill Child
not differ significantly from children who did not suffer from a chronic
condition (Mestrovic, et al.2007).
Acute Severity of Illness
Acute Severity of illness on ICU admission is highly correlated with
ICU survival in children (Pearson, et al.1997). In critically ill children,
the Severity of illness at PICU admission may be related to functional
outcomes as assessed by the PCPC (Pediatric Cerebral Performance
Category) and POPC (Pediatric overall performance category) (Fiser, et
al.2000). Increased Severity of illness may necessitate a longer PICU
stay. Longer PICU stay is associated with cognitive morbidity (Nelson, et
al. 2006) and increased mortality (Marcin, et al. 2001).
Development of the PRISM III scoring system has greatly enhanced
the paediatric intensivist’s ability to measure outcomes in the PICU
objectively. It permits the quantification of severity of illness through the
development of probability models predicting mortality risks (Lemeshow
and Le Gall. 1994).
Until recently, the Pediatric Risk of Mortality (PRISM) score (A
physiology based predictor for pediatric patients) was the only Severity of
illness assessment in the PICU. The Pediatric Index of Mortality (PIM)
score (which is more recent than PRISMIII score) was introduced later
which analyzes the condition of the patient directly upon arrival in the
PICU (Gemke and Van Vught. 2002). Some pediatric intensivists
considered that a good tool to estimate the severity of cases of MODS
observed in PICUs was needed to describe correctly the clinical course of
illnesses observed in critically ill children. Thus, it was thought
appropriate to undertake a research program to create and validate a score
5
Chapter I
Critically Ill Child
for MODS in children. That score was named the pediatric Logistic
Organ Dysfunction (PELOD) score (Lacroix and Cotting, 2005).
Unlike the PRISM system (developed to be a predictive marker), the
PELOD system was designed to be an outcome measure. Furthermore,
PRISM includes parameters related to different organ dysfunctions but
does not separate each dysfunction. The PELOD score describes the
number and severity of organ dysfunctions during the child’s stay in the
PICU, indepe ndent of cause (Leteurtre, et al. 2003).
Interventions
Less severe forms of critical illness require less intense forms of
resuscitation. These include urgent intubation, mechanical ventilation,
circulatory support with inotropes and administration of intravenous
fluids to restore circulation. Acquired morbidity in the ICU can follow
drug administration (Tobias. 2005), transfusion (Bateman, et al. 2008),
mechanical ventilation (Albuali, et al. 2007), monitoring, anticoagulation
and other therapies (Cengiz, et al. 2005).
The better survival outcomes observed in children could be due to
differences in patient characteristics, pre arrest conditions, earlier
recognition and treatment of the cardiac arrest, interventions during CPR,
and post resuscitation care. Children were monitored in an intensive care
unit before the arrest more frequently than adults, perhaps because of the
increased emphasis on early recognition and treatment of respiratory
failure and shock in pediatric advanced life-support resuscitation training
(Buist, et al. 2002).
In-ICU cardiac arrest is associated with high in-hospital mortality
and subsequent morbidity in survivors. Prearrest renal dysfunction and
6
Chapter I
Critically Ill Child
epinephrine infusion were associated with increased in-hospital mortality.
The use of post-arrest ECMO within 24 hrs was associated with reduced
mortality. Rigorous prospective evaluation of the role of ECMO
following cardiac arrest is needed (De Mos, et al. 2006).
Several studies showed that patients who received more than two
doses of standard-dose epinephrine did not survive (Young and Seidel.
1999). Termination of resuscitation has been recommended if there is no
Return of Spontaneous circulation (ROSC) after two doses of
epinephrine. These studies conflict with De Mos, et al. (2006) where
number of epinephrine boluses given during cardiac arrest was not
significantly associated with ICU or hospital survival. In studies where
there has been documented long-term survival after >2 doses of
epinephrine, most were severely neurologically impaired (Christiansen,
et al. 1997).
Timing of Treatment
Early applications of ICU interventions to treat patients with
evolving critical illness have been associated with improved outcome
(Buist, et al. 2002).
Increasing interest and application of early
interventions to improve patient outcome include earlier identification
and referral of children with evolving critical illness, Medical Emergency
Teams, and other ICU expertise (Pronovost, et al. 2002). This may
improve survival but does not necessarily equate to improved cognitive or
functional outcomes. These interventions may be improving timeliness of
admission to ICU and thus improving the outcomes of care, by permitting
intubation and other resuscitation to be delivered in the controlled ICU
environment (Parkhe, et al. 2002).
7
Chapter I
Critically Ill Child
Other Risk Factors
More than half of pediatric cardiac arrest cases occurred in patients
before age two (Engdahl, et al. 2003). More than 99% of all child deaths
occur in developing countries, and 10.1 million of the 10.9 million deaths
in children younger than five years are preventable (Fuhrman and
Zimmerman. 2005).
Other risk factors identified include: cause of the arrest, patient
location at the time of the arrest and length of stay in hospital prior to
ICU admission (Holleran. 2002).
Nature of the ICU
The nature of the PICU itself can affect patient outcomes. A study
evaluating changes of outcomes within a single ICU over a 10-year
period showed that increased ICU activity is significantly associated with
prolonged severity-adjusted survival (Kvale and Flaatten.2002). Another
study comparing the efficiency of open ICUs with closed ICUs showed
that ICU and hospital length of stay and days on mechanical ventilation
were lower in closed ICUs (Multz , et al. 1998). A study in 2007 showed
that patients with acute lung injury in a closed-model ICU have reduced
mortality (Treggiari , et al. 2007).
A systematic review by Kane, et al. (2007) examined nursing staff
patterns and their effects on patient outcomes and showed a significant
association between nurse staffing and hospital-related mortality. This
review suggests that higher quality of hospital care, effective nurse
retention strategies and implementation of collaborative evidence-based
medicine lead to better patient outcomes.
8
Chapter I
Critically Ill Child
Table (1): Criteria for PICU and PIICU admission and discharge.
Admission criteria
PICU
Discharge criteria
Patients
who
need
invasive Patient may be
monitoring: arterial and central venous PICU once the
catheters, pulmonary arterial lines, ICP reversed itself
catheters.
provided in
environment:
Patients with evidence of:
Respiratory impairment of failure.
discharged from the
disease process has
and care can be
a less intensive
Patient no longer requires invasive
monitoring.
Cardiovascular compromise: shock, Patient can protect his/her airway
(cough and gag reflexes) Patient is
hypotension, hypertensive crisis.
hemodynamically stable.
Acute neurologic deterioration: coma,
status epilepticus, increased ICP.
Acute renal failure requiring dialysis or
CVVH.
Bleeding disorders
massive transfusions.
PIICU
that
necessitate
Patients who do not require respiratory
assistance for acute respiratory failure but
may require continuous noninvasive
monitoring of vital signs, BP, SaO2,
Tco2, Tcco2.
Patient may be discharged from the
PIICU when it has been determined
that care can be provided in general
care areas.
Patients who require chronic respiratory
support
via
tracheostomies
or
noninvasive ventilation.
Patients who are in early cardiovascular
failure and require monitoring of vital
signs (noninvasive monitoring).
Patients with acute neurologic injury but
with a patent airway that they can protect
themselves.
Patients with MSOD who do not need a
PICU, but nursing care is not available
elsewhere (e.g., trauma victims, DKA).
BP = blood pressure; CVVH = continuous venovenous hemofiltration; DKA =
diabetic ketoacidosis; ICP = intracranial pressure; MSOD = multisystem organ
dysfunction; PICU = pediatric intensive care unit; PIICU = pediatric intermediate
9
Chapter I
Critically Ill Child
intensive care unit; SaO2 = arterial oxygen saturation; Tco2 = transcutaneous oxygen;
Tcco2 = transcutaneous carbon dioxide (Frankel. 2004).
Common problems in PICU:
 Malnutrition:
Malnutrition in the pediatric ICU population is a widely
acknowledged problem that may intensify underlying illnesses, increase
the risk of complications and affect growth and development (Pollack, et
al.1985).
Nutritional assessment upon admission to the ICU is necessary to
identify children at risk and to guide nutritional support during ICU stay.
The repertoire of routine laboratory parameters includes several markers
(e.g., albumin, urea, triglycerides, electrolytes) that can provide useful
and easily obtainable information regarding nutritional status and
requirements (Selberg and Sel.2001).
Abnormalities in these parameters reflect derangements in several
metabolic pathways and may represent the severity of depletions
occurring during critical illness. Previously, we have shown the
nutritional status of children admitted to an ICU to deteriorate during
admission (Hulst, et al. 2004).
Nutritional support is essential in the care of critically ill children
because inadequate feeding can increase morbidity and mortality rates
(Biolo, et al. 1997).
 Sepsis:
Sepsis remains an important health problem in children, as it is in
adults (Angus, et al. 2001). Systemic inflammatory response syndrome
(SIRS), sepsis, sever sepsis, and septic shock are frequently observed in
10
Chapter I
Critically Ill Child
the PICU. SIRS has been reported with an incidence up to 82% in the
PICU (Despond, et al. 2001). Moreover, infection may lead to multiple
organ dysfunction syndrome (MODS; defined as more than two organ
dysfunctions); this is particularly true in children with septic shock who
receive delayed treatment or have primary or acquired immunodeficiency
(Carcillo. 2003).
 Pain:
Pain is a complex phenomenon that received little attention in
pediatrics until the late 1970s. Until recently, many health care
professionals mistakenly believed that the child's immature nervous
system rendered him or her in capable of feeling pain or assumed that the
child was pain free. Several studies documented the inadequacy of
analgesia provided to pediatric patients (e.g. following cardiovascular
surgery). In addition, there was a lack of scientific information regarding
the safety and efficacy of analgesics in young children and difficulty in
identifying pain behaviors in infant and children. Clearly children can fed
pain, and adequate analgesia is required in critical care unit (Hazinski.
1999).
 Hyperglycemia:
Despite potential positive effects, prolonged hyperglycemia in
critically ill adults has been shown to be associated with a number of
deleterious consequences contributing to greater risks of morbidity and
mortality, even in the absence of preexisting diabetes mellitus
(Umpierrez, et al. 2002).
Elevated glucose concentrations have been associated with increased
risks of congestive heart failure, cardiogenic shock, and poor functional
11
Chapter I
Critically Ill Child
recovery after stroke as well as increased risks of dying after myocardial
infarction and ischemic stroke among nondiabetic patients. Even among
non-critically ill adult patients admitted to general patient care units,
patients with newly diagnosed hyperglycemia had a significantly higher
mortality rate and a lower functional outcome compared with known
diabetic patients or normoglycemic patients (Capes, et al. 2001).
Hyperglycemia may be less prevalent among children because
diabetes mellitus is much less common in this age group (Green, et al.
2003). Furthermore, hyperglycemia in the pediatric population may have
different effects on morbidity and mortality compared with adults as a
consequence of different metabolic demands, differences in comorbid
conditions, or age-dependent factors (Agus and Jaksic. 2002).
 Psychosocial problem:
The hospitalization of a child for even a minor illness is stressful for
both child and family. Life-threatening illness or injury magnifies the
stress. The sensitive, empathetic nurse can mitigate some of the most
frightening aspects of critical care, making the experience more positive
and perhaps even growth promoting. Intervention should be aimed at
enhancing individual coping skills sot that the child and family can apply
them to other stress full experiences and situations in the future. The
physicians and nurses are life lines controlling the life of the child and
access to the child at all times, the nurse must convey sensitivity,
compassion and support to the family and must support the family as the
family attempts to support the child (Hazinski. 1999).
Monitoring the critically ill or injured child:
12
Chapter I
Critically Ill Child
Hemodynamic and physiologic monitoring are basic elements in the
evaluation and care of any critically ill or injured patient. Standard
monitoring typically includes automated non-invasive blood pressure
measurement, electrocardiographic monitoring, pulse oximetry, and urine
output. However, these measures may fail to identify states of
"compensated" shock or other pathologic processes in a reliable and
timely manner (Martin and M.J. 2007).
Modern intraoperative and intensive care practices have incorporated
more advanced and invasive monitoring techniques, such as arterial
catheterization and blood gas analysis, central venous catheterization,
pulmonary artery catheterization, and intracranial pressure monitoring.
Although these have become standard and accepted techniques, there are
significant limitations and risks associated with their use in pediatric
patients, including the availability and proper sizing of equipment,
technical difficulties with vascular access, clinical expertise in
interpretation of the data, and procedural-related pain and distress. In
addition, the quality and clinical utility of the data provided by these
monitoring systems is highly variable (Martin and M.J. 2007).
A variety of newer noninvasive advanced monitoring systems have
been developed and there is a growing body of experience with the
clinical utility of these systems. The use and applications of ultrasound in
critical care is expanding exponentially. Thoracic ultrasound can quickly
and reliably assess for pneumothorax, hemothorax, cardiac contractility
and volume, pulmonary hypertension, and intravascular volume status
(Martin and M.J. 2007). Thoracic bioimpedance technology uses a
transcutaneous electrical current to noninvasively assess thoracic blood
volume, stroke volume, and cardiac output. These measurements have
13
Chapter I
Critically Ill Child
been shown to correlate well with invasive and echocardiographic
measures of cardiac output (Keren, et al. 2007).
Gastric perfusion measures (pH and PCO2) have been validated as an
early marker of hyperfusion and shock, but require a specially designed
nasogastric
tube
and
equipment.
New
sublingual
probes
can
noninvasively provide equivalent data to gastric tonometry (Creteur.
2006).
Transcutaneous tissue perfusion measures of oxygen and CO2
tension can reliably estimate arterial blood gas measurements under
uniform flow conditions. In pathologic flow states such as severe shock,
these measures can provide evidence of local tissue hypoperfusion and
can be used to guide therapy and the adequacy of resuscitation (Martin,
et al. 2005).
Near-infrared
noninvasively
spectroscopy
measure
tissue
technology
perfusion
is
and
being
guide
used
to
therapeutic
interventions in a variety of sites, including skeletal, muscle, and brain
(Lima and Bakker. 2005).
14
Chapter I I
Critically Ill Child
CHAPTER II
Scoring system in pediatric intensive care unit
Critically ill patients are typically characterized by disturbances of
body homeostasis. Both in adults and children, these disturbances can be
estimated by measuring how much apart one or many physiologic
variables are from the normal range. Scoring systems can be constructed
with such variables. Many types of scores have been developed (Lacroix
and Cotting .2005).
The practice of paediatric critical care is dynamic and evolving.
Paediatric population is a vulnerable group necessitating standard care for
medically and surgically ill children.(Gomke, et al.1994)
In context of intensive care, a rational and objective way to define
and quantify severity of illness is through the development of probability
models predicting mortality risks. Such predictive models or scoring
system have been developed for all age groups including paediatric.
(Lemeshow, et al.1993).
Scoring systems are arrived at evaluation of the patient’s mortality
risk in the ICU by assigning a score to patient and predicting the
outcome. However, patient’s mortality is not only affected by ICU
performance but also depends on many other factors such as demographic
and clinical characteristic of population,infra structure and non medical
factors (management and organization), case mix and admission practice.
(Bertolini ,et al.1988).
15
Chapter I I
Critically Ill Child
These scoring systems assign a relative value or indication of
severity to a clinical condition. The score itself can be used for various
purposes, such as measuring clinical conditions or states and severity of
illness for the purpose of assessing the risk of a disease or designated
outcome. The most commonly measured pediatric ICU outcomes are
mortality, length of stay, functional outcome, and organ dysfunction
(Marcin and Pollack. 2007).
Scoring system in paediatric intensive care units:
Past, present & future
Initially scoring systems were developed for trauma patients and
were either specific anatomical methods (abbreviated injury scale 1969,
burn score 1971, injury severity score 1974) or specific physiological
methods (trauma index 1971, Glasgow coma scale 1974, trauma score
1981 and sepsis score 1983).
Therapeutic Intervention Scoring System
 (TISS)
In 1974 Therapeutic intervention scoring system (TISS) was
introduced by Cullen D J et al to quantitate severity of illness according
to the therapeutic interventions received by the patients. Each
intervention had a value of 1-4 points based upon the complexity and
invasiveness of intervention with a total score of 70 interventions.TISS
has been utilized for many purposes which include:1- Determining the severity of illness
2- Establishing nurse patient ratio in ICU.
16
Chapter I I
Critically Ill Child
3- Assessing current utilization of hospital intensive care beds.
4- Establishing future need and numbers of ICU beds particularly in
response to request for certification of need.
TISS was found to be a useful tool for obtaining comparable data
which could be utilized for administrative, management and clinical
purposes,
within
and
between
hospital
settings.(
Keene
and
Culten.1983)
Unfortunately, the TISS score is heavily influenced by diagnosis,
indicating the TISS score depends on the monitoring and therapeutic
philosophies of the physicians and institutions using it (Pollack .1987).
Compared with other predictors, it cannot quantify mortality risk.
Efforts to evolve the TISS score by combining physiological dysfunction
with therapies has been relatively unsuccessful ( Cullen, et al.1984).
The Acute Physiology and Chronic Health
Evaluation (APACHE) system
The acute physiology and chronic health evaluation (APACHE)
system was introduced (for adult patients) in 1981 by an expert panel of
physicians who selected and weighed 34 laboratory and clinical
measurements based on perceived impact on mortality. It consisted of 2
parts: An acute physiology score that reflected the degree of physiologic
derangements and a chronic health evaluation that reflected patient’s
status before the acute illness. There are now three APACHE system i.e.
I, II,III. An increasing APACHE II score reflects increased severity of
disease and a higher risk of hospital death.But the system was neither
designed nor intended to predict for individual patients and it has an error
17
Chapter I I
Critically Ill Child
rate of approximately 15% for the prediction of hospital mortality using
0.50 decision point. APACHE III was introduced in 1991 to expand and
improve the prognostic estimate provided by APACHE II. (Knaus, et
al.1991).
APACHE
III
system
consists
of
points
for
physiologic
abnormalities, age and chronic health status.
Scoring is based on a degree of abnormality in 17 physiologic
variables (APS), which reflects value for vital signs, laboratory tests and
neurological status. In addition, points are added based on age and 7 comorbid conditions shown to have a significant impact on short term
mortality. (Knaus, et al.1993)
The APACHE system is appropriate for adult ICUs. However the
changing physiology with growth and development within the wide
spectrum of ages of pediatric patients prevents its direct application to
PICUs.The limited number of patients and diverse conditions make
diagnostic subgroups difficult to study ( Pollack, et al.1984).
However in the recent past some scoring systems have been
developed for PICU mortality prediction like for example Physiologic
stability index ( PSI ).
Physiologic Stability Index (PSI).
Physiologic Stability index (PSI) was developed by a group of
paediatric intensivists in 1984 from TISS as TISS only indirectly reflects
the severity of illness by assessing therapeutic needs. PSI assesses the
severity of acute illness in the total population of paediatric intensive care
unit patients by quantitating the degree of derangement in 34 variables
from 7 major physiologic systems. Each variable was assigned a score of
18
Chapter I I
Critically Ill Child
1(abnormality worth concern but not to change therapy), 3 (need to
change therapy), and 5 (life threatening). (Table 1) This reflected the
clinical importance of derangements but not necessarily the amount of
deviation from the normal value. The most abnormal value of a variable
recorded within 24 hours was used ( Yeh, et al.1984).
Table (2)
Physiologic stability Index:
Physiologic Systems (7) and Variables (34)
(1)Cardiovascular: systolic blood pressure, diastolic blood pressure, heart
rate, cardiac index, C(a-v)O2, CVP, PCWP
(2)Respiratory: respiratory rate, PaO2, PaO2/FIO2, PaCO2
(3) Neurologic: Glasgow coma score, intracranial pressure, seizures, pupils
(4) Hematologic: hemoglobin, WBC count, platelet count, PT/PTT, FSP
(5) Renal: BUN, creatinine, urine output
(6) Gastrointestinal: AST/ALT, amylase, total bilirubin, albumin
(7) Metabolic: sodium, potassium, calcium, glucose, osmolality, pH,
HCO3
Points for each variable:
 0, 1, 3, 5
 reflect clinical importance of derangement, with more abnormal
having higher point value
19
Chapter I I
Critically Ill Child
 not intended to reflect magnitude of deviation from the normal value
PSI however, is time consuming; requiring the use of 34 variables
from 7 physiologic systems and also it is a subjective score. A total of
294 clinical classification system (CCS) class III and IV patients in a
PICU were evaluated by using PSI / TISS ratio. Non survivors had
significantly higher (p < 0.01) PSI and TISS scores than survivors.
Medical patients had the highest PSI / TISS ratio scores while,
cardiovascular patients had lowest PSI / TISS ratio scores. (Pollack, et
al.1984)
PIM II score:
The PIM II scoring system was developed on 20,787 patients from
14 ICUs and pediatric ICUs from Australia, New Zealand, and the United
Kingdom (Slater, et al. 2003).
PIM II requires 10 variables collected from the time of admission up
to the first hour after admission. Compared with PRISM III, PIM II is less
likely to be biased by the quality of treatment after admission to the
pediatric ICU because variables are collected within the first hour rather
than the first 12 hours after admission; however, PIM II does include
predictor variables that are pediatric-ICU interventions (i.e., mechanical
ventilation), which is also subject to bias from different intervention
thresholds (Marcin and Pollack. 2007).
PIM II has also been tested for reliability and has been shown that
some training is necessary to ensure accurate and reliable data abstraction
(Vankeulen, et al. 2005). Similar to PRISM III, most tests of
discrimination have shown PIM II to be accurate in discriminating
20
Chapter I I
Critically Ill Child
nonsurvivors from survivors (c-statistic range from 0.74 to 0.90) but have
inconsistent calibration statistics (Slater and Shann. 2004).
The main weakness of PIM II score is that is has not been tested in
North America and in many other countries around the world. On the
other hand, the PIM score can be used for the same purpose as the
PRISM score. More studies are required before one can conclude that the
PRISM III is better than the PIM II or vice versa (Lacroix and Cotting.,
2005).
PIM II is calculated from the information collected at the time a
child is admitted to your ICU. Because PIM II describes how ill the child
was at the time you started intensive care, the observations to be recorded
are those made at or about the time of first contact may be in your ICU,
your emergency department, a ward in your own hospital, or in another
hospital (e.g. on a retrieval). If information is missing (e.g. base excess is
not measured) record zero, except for systolic blood pressure, which
should be recorded as 120. include all children admitted to your ICU
(consecutive admissions) (Slater, et al. 2003).
21
Chapter I I
Critically Ill Child
Table (3): PIM II (Pediatric index of mortality)
Variables:
1. Systolic blood pressure, mmHg (unknown= 120)1
2. Pupillary reactions to bright light (3mm and both fixed = 1, other or
unknown= 0)2
3. PaO2, mmHg (unknown= 0) FIO2 at the time of PaO2 if oxygen via ETT or
headbox (unknown= 0).
4. Base excess in arterial or capillary blood, mmol/1 (unknown = 0).
5. Mechanical ventilation at any time during the first hour in ICU (no=0, yes =
1)3.
6. Elective admission to ICU (no=0, yes = 1)4
7. Recovery from surgery or procedure is the main reason for ICU admission
(no=0, yes = 1)5
8. Admitted following cardiac bypass (no=0, yes =1)6
9. High risk diagnosis. Record the number in brackets. If in doubt record 0.
[0] None.
[1] Cardiac arrest preceding ICU admission7.
[2] Severe combined immune deficiency.
[3] Leukemia or lymphoma after first induction
[4] Spontaneous cerebral hemorrhage 8
[5] Cardiomyopathy or myocarditis
[7] HIV infection
[8] Liver failure is the main reason for ICU admission 10
[9] Nero- degenerative disorder 11
10. Low risk diagnosis. Record the number in brackets. If in doubt record 0.
[0] None
[1] Asthma is the main reason for ICU admission.
[2] Bronchiolitis is the main reason for ICU admission. 12
22
Chapter I I
Critically Ill Child
[3] Croup is the main reason for ICU admission.
[4] Obstructive sleep apnea is the main reason for ICU admission 13
[5] Diabetic keto- acidosis is the main reason for ICU admission.
Logit = (-4.8841) + (values  Beta)+ (0.01395  (absolute (SBP-120) + (0.1040
 (absolute base excess) + (0.2888  (1000  Fio2/PaO2) predicted death rate = e Logit/
(1 + e Logit) (Slater, et al. 2003).
Coding rules. These rules must be followed carefully for PIM I to
perform reliably:
1. Record SBP as 0 if the patients are in cardiac arrest, record 30 if the
patient is shocked and the blood pressure is so low that it cannot be
measured.
2. Papillary reactions to bright light are used as an index or brain
function. Do no record an abnormal finding if this is due to drugs,
toxins or local eye injury.
3. Mechanical ventilation includes mask or nasal CPAP or BiPAP or
negative pressure ventilation.
4. Elective admission. Include admission after elective surgery or
admission for an elective monitoring, or review of home ventilation.
An ICU admission or an operation is considered elective if it could be
postponed for more than 6 h without adverse effect.
5. Recovery from surgery or procedure includes a radiology procedure or
cardiac catheter. Do not include patients admitted from the operating
theatre where recovery from surgery is not the main reason for ICU
admission (e.g. a patient with a head injury who is admitted from
theatre after insertion of an ICP monitor; in this patient the main
reason for ICU admission is the head injury).
23
Chapter I I
Critically Ill Child
6. cardiac bypass. These patients must also be coded as recovery from
surgery.
7. Cardiac arrest preceding ICU admission includes both in- hospital and
out- of hospital arrests. Requires either documented absent pulse or
the requirement for external cardiac compression. Do not include past
history of cardiac arrest.
8. Cerebral hemorrhage must be spontaneous (e.g. from aneurysm or AV
malformation). Do not include traumatic cerebral hemorrhage or
intracranial hemorrhage that is not intracerebral (e.g. subdural
hemorrhage).
9. Hypoplastic left heart syndrome. Any age, but include only cases
where a Norwood procedure or equivalent is or was required in the
neonatal period to sustain life.
10.liver failure acute or chronic must be the main reason for ICU
admission. Include patients admitted for recovery following liver
transplantation for acute or chronic liver failure.
11.Neuro- degenerative disorder. Requires a history of progressive loss of
milestones or a diagnosis where this will inevitably occur.
12.Bronchiolitis. Include children who present either with respiratory
distress or central apnea where the clinical diagnosis is bronchiloitis.
13.Obstructive sleep apnea. Include patients admitted following
adenoidectomy and /or tonsillectomy in whom obstructive sleep apnea
is the main reason for ICU admission (and code as recovery from
surgery) (Slater, et al. 2003).
24
Chapter I I
Critically Ill Child
To reduce the number of physiologic variables required for severity
of illness assessment and to obtain an objective weighting of remaining
variables, a second
generation
score called pediatric risk
of
mortality(PRISM) has been devised by Pollack MM et al in 1988
(Pollack, et al.1988).
Pediatric Risk Of Mortality (PRISM)
Pediatric risk of mortality (PRISM) score allows for mortality risk
assessment in the paediatric ICU. PRISM was developed from PSI to
reduce the number of variables from 34 to 14 and number of ranges from
75 to 23 without losing the predictive power (Table 4). It is institution
independent and can be used within limits to compare different intensive
care units. (Balakrishnan, et al.1992).
In 1996 physiological variables and their ranges as well as diagnostic
and other risk variables reflective of mortality risk were re evaluated by
Pollack MM et al to update and improve the performance of second
generation PRISM score. Thus PRISM III was developed.( Pollack, et
al.1996)
Table (4)
PRISM
25
Chapter I I
Critically Ill Child
Parameters:
(1) Systolic blood pressure and age
(2) Diastolic blood pressure
(3) Heart rate
(4) Respiratory rate
(5) PaO2 to FIO2 ratio
(6) PaCO2
(7) Glasgow coma score
(8) Pupillary reactions to light
(9) PT and PTT
(10) Total serum bilirubin
(11) Serum potassium
(12) Serum total calcium
(13) Glucose
(14) Bicarbonate
PRISM III
The pediatric Risk of Mortality (PRISM) III score was first
developed in 1996. It is a commonly used mortality prediction model.
(Marcin and Pollack, 2007).
The PRISM III scoring system was originally developed on 11, 165
patients from 32 different pediatric ICUs in the United states and is
currently being updated on approximately 15,000 patients. Over 100
pediatric ICUs have software that enables the collection and computation
of PRISM III- related outcomes. With approximately 350 pediatric ICUs
in the United States (Randolph, et al. 2004). This represents a
participation rate of more than 25% of pediatric ICUs. Mortality risk
assessments are made using the first 12 hours of physiologic,
demographic, and diagnostic data. Twelve hours of data collection ensure
that over 905 of laboratory tests will be captured at least once of all the
tests collected in the first 24 hours after admission.(Pollack, et al. 1997)
PRISM-APS is a score to measure physiologic status on a more refined
26
Chapter I I
Critically Ill Child
scale to distinguish changes in physiologic stability that might not be
large enough to alter mortality risk (Marcin and Pollack, 2007).
The variables that were most predictive of mortality as indicated by
the highest PRISM scores were minimum systolic BP, abnormal pupillary
reflexes and stupor/coma were retained from PRISM score.
Variables in the original PRISM that were not included in PRISM III
are diastolic BP, respiratory rate, PaCO2/F1O2, serum bilirubin and
calcium concentration.
The PRISM III score is an improved version of the PRISM score
developed at the Children’s National Medical Center in Washington, DC
based on data collected at 32 pediatric intensive care units using 11,165
admissions. The PRISM III score contains 17 variables or signs of
cardiovascular, neurologic, or vital functions (systolic blood pressure,
heart rate, Glasgow com score, pupillary reflexes, temperature), acid base
status (pH, CO2, Pco2 and Pao2), chemistry tests (glucose, potassium,
creatinine, blood urea nitrogen), hematology tests (white blood cell count,
platelet count, prothrombin time, partial thromboplastin time), and other
factors like operative status and some types of diseases (Pollack, et al.
1996).
Subscores:
(1) cardiovascular and neurologic vital signs: 5 measures
(2) acid-base and blood gas: 5 measures
(3) chemistry tests: 4 measures
(4) hematology tests: 3 measures (with PT and PTT counted as one)
Grading variables:
27
Chapter I I
Critically Ill Child
 Use the highest and/or lowest values for scoring.
28
Chapter I I
Critically Ill Child
Table (5)
Cardiovascular and
Findings
Neurologic Vital Signs
Systolic blood pressure neonate AND > 55 mm Hg
neonate AND 40 -55 mmHg
neonate AND < 40 mm Hg
infant AND > 65 mm Hg
infant AND 45 -65 mm Hg
infant AND < 45 mm Hg
child AND > 75 mm Hg
child AND 55 -75 mm Hg
child AND < 55 mm Hg
adolescent AND > 85 mmHg
adolescent AND 65 -85 mm Hg
adolescent AND < 65 mm Hg
Heart rate
neonate AND < 215 beats/minute
neonate AND 215 - 225 bpm
neonate AND > 225 beats/minute
infant AND < 215 beats/minute
infant AND 215 - 225 bpm
infant AND > 225 beats/minute
child AND < 185 beats/minute
child AND 185 - 205 bpm
child AND > 205 beats/minute
adolescent AND < 145 beats/minute
adolescent AND 145 - 155 bpm
adolescent AND > 155 beats/minute
Points
0
3
7
0
3
7
0
3
7
0
3
7
temperature
< 33°C
33 - 40°C
> 40°C
0
3
4
0
3
4
0
3
4
0
3
4
3
0
3
mental status
Glasgow coma score >= 8
Glasgow coma score < 8
0
5
pupillary response
both reactive
1 reactive AND (1 fixed AND > 3
mm)
both fixed AND both > 3 mm
29
0
7
11
Chapter I I
Critically Ill Child
where:
 The heart rate should not be monitored during crying or iatrogenic
agitation.
 Pupillary size should not be assessed after iatrogenic dilatation.
 Body temperature may be rectal, oral, axillary or blood.
 Mental status should not be scored within 2 hours of sedation,
paralysis or anesthesia. If sedation, paralysis or anesthesia is
continuous, score based status prior to sedation, paralysis or
anesthesia.
Table (6)
Acid-Base and
Blood Gases
Acidosis
Ph
PCO2
total CO2
PaO2
Findings
points
pH > 7.28 AND total CO2 >= 17 mEq/L
(pH 7.0 - 7.28) OR (total CO2 5 - 16.9 mEq/L)
pH < 7.0 OR total CO2 < 5
0
2
6
< 7.48
7.48 - 7.55
> 7.55
< 50 mm Hg
50 - 75 mm Hg
> 75 mm Hg
<= 34 mEq/L
> 34 mEq/L
42.0 - 49.9 mm Hg
< 42 mm Hg
0
2
3
0
1
3
0
4
3
6
where:
 PaO2 requires arterial blood.
 PCO2 can be measured from arterial, venous or
specimens.
30
capillary
Chapter I I
Critically Ill Child
Table (7)
Chemistry Tests
glucose
<= 200 mg/dL
> 200 mg/dL
points
0
2
Potassium
<= 6.9 mEq/L
> 6.9 mEq/L
0
3
neonate AND <= 0.85 mg/dL
neonate AND > 0.85 mg/dL
infant AND <= 0.90 mg/dL
infant AND > 0.90 mg/dL
child AND <= 0.90 mg/dL
child AND > 0.90 mg/dL
adolescent AND <= 1.30mg/dL adolescent AND
> 1.30 mg/dL
neonate AND <= 11.9 mg/dL
neonate AND > 11.9 mg/dL
not neonate AND <= 14.9 mg/dL not neonate
AND > 14.9 mg/dL
0
2
0
2
0
2
0
2
0
3
0
3
Creatinine
BUN
Findings
where:
• Whole blood measurements for glucose are increased 10% over
serum; for potassium 0.4 mEq/L.
Table (8)
Hematologic Tests
white blood cell count
platelet count
PT and PTT
Findings
>= 3,000 per μL
< 3,000 per μL
> 200,000 per μL
100,000 - 200,000 per μL
50,000 - 99,999 per μL
< 50,000 per μL
neonate AND PT <= 22 seconds AND
PTT <= 85 seconds
neonate AND (PT > 22 seconds OR
neonate and PTT > 85 seconds)
not neonate AND PT <= 22 seconds
AND PTT <= 57 seconds
not neonate AND (PT > 22 seconds
OR PTT > 57 seconds)
where:
31
Points
0
3
0
2
4
5
0
3
0
3
Chapter I I
Critically Ill Child
 The upper limit of the normal reference ranges for PT and PTT
are not given.
Other factors to document:
(1) nonoperative cardiovascular disease
(2) chromosomal anomaly
(3) cancer
(4) previous ICU admission during current admission
(5) pre-ICU CPR during current admission
(6) post-operative (not including catheterizations) during past 24
hours
(7) acute diabetes with ketoacidosis or other severe complication
(8) admission from inpatient unit (do not count if in ICU for < 2
hours or if transferred from surgical recovery room)
Cardiovascular and neurologic subscore = (points for systolic
pressure) + (points for temperature) + (points for mental status)+ (points
for heart rate) + (points for pupillary reflex)
Acid-base and blood gas subscore = (points for acidosis) + (points
for pH) + (points for PaCO2)+ (points for total CO2) + (points for PaO2)
Chemistry subscore = (points for glucose) + (points for potassium) +
(points for creatinine)+ (points for blood urea nitrogen)
Hematology subscore = (points for WBC count) + (points for
platelet count) + (points for PT and PTT testing)
Total PRISM III score = (cardiovascular and neurologic
subscore) + (acid base and blood gas subscore) + (chemistry subscore)+
(hematology subscore)
32
Chapter I I
Critically Ill Child
Interpretation:
 Minimum subscore and total score: 0
 Maximum cardiovascular and neurologic subscore: 30
 Maximum acid-base and blood gas subscore: 22
 Maximum chemistry subscore: 10
 Maximum hematology subscore: 12
 Maximum total PRISM III score: 74
 The higher the total score, the worse the prognosis.
 A rising score indicates deterioration.
 If performed during the first 12 hours in the ICU, the score is
designated PRISM-12.
 If performed during the first 24 hours in the ICU, it is
designated PRISM-24.
Predictive equations:
• Predictive equations for prognosis are available for the 12 hour and
24 hour scores
PRISM III is a widely accepted and is a standard against which other
scores are compared.
However there some problems with the use of PRISM III:
- A lot of information is needed to calculate it and many
units do not calculate it routinely.Worst reading of 12/ 24 hours is
used and a lot of deaths occur (in one study over 40%) with in first 24
hours, so the score may be diagnosing death rather predicting it.
33
Chapter I I
Critically Ill Child
There may be blurring of differences of 2 units as patient in a
good unit may recover rapidly and score may be lower and the same
patient in a bad unit might have had higher score due to poor
management and high mortality of bad unit may be interpreted as due to
sicker patients.
The time spent in the hospital before coming to ICU could improve
the PRISM score and predict lower than actual mortality ( lead time bias).
( Shann, et al.1997)
Uses of models of mortality prediction including
PRISM III: These models including PRISM III are most applicable to groups of
patients (e.g. to assess institutional performance). These models help us
to investigate best ways of organizing PICU by comparing different
units.(pollock, et al.1996)They also help us to monitor effect of change
in practice by observing trends within the unit over a time.(shann, et
al.1997) They can also be used for controlling severity of illness for
various clinical trials. ( Pollack, et al.1996)
They can be applied for resource utilization (rationing intensive care).
PRISM III takes 24 hours to complete and can’t be used in
regulating admission to PICU. (Teres and Lemeshow.1994)
They have been used to assess relation between severity of illness
and length of stay or cost.
In 1997 Pollack et al, developed a physiology based measure of
physiologic instability for use in pediatric patients that has an expanded
scale compare with the prism III score and called as the Pediatric
34
Chapter I I
Critically Ill Child
risk of mortality Ill-acute physiology score (prism Ill-APS) .
It has 59 ranges of 21 physiologic variables .It was designed to have
a broad severity scale from 0-356, with the higher values indicating
higher instability.Data were collected from consecutive admissions to 32
Pediatric ICU’s (11165 admissions, 543 deaths). (Pollack, et al.1997)
Most patients who had PRISM Ill-APS score of greater than 80 had
mortality greater than 97%. It concluded that the PRISM Ill-APS score is
an expanded measure of physiologic instability that has been validated
against mortality. Compared with prism III, prism Ill-APS should be
more sensitive to small changes in physiologic status even those that may
not contribute significantly to mortality risk. Patient assessment for future
studies for issues e.g. effectiveness of drugs or for other purposes might
be more concerned with the physiologic status.
However even this should not be used for quality assessments or
calculating risk of individual patients. (Pollack, et al.1997)
Multiple Organ Dysfunction score (MODS ) scores:
The diagnosis of MODS is supported by the observation in a
critically ill patient of the simultaneous dysfunction of at least two organ
systems. Up to seven organs have been considered.
Respiratory, cardiovascular, neurologic, hematologic, renal, hepatic,
and gastrointestinal. The diagnostic criteria of these dysfunctions were
defined by Wilkinson, et al. (1986) and Proulx, et al. (1996). These
definitions were updated by experts who met in San Antonio in 2002
(Goldstein, et al.2005) and in Boston in 2004 (Brilli and Goldstein.
2005). It must be underlined that the diagnostic accuracy of the variables
considered in these definitions have never been scientifically validated,
35
Chapter I I
Critically Ill Child
but these diagnostic criteria of pediatric MODS are extensively used by
practitioners and investigators (Lacroix and Cotting, 2005).
In critically ill adults, at least three quantitative scoring systems
estimating the severity of cases of MODS have been developed and
validated: the Multiple Organ Dysfunction score (Marshall, et al. 1995),
the Logistic Organ Dysfunction score (Le Gall, et al. 1995), and the
Sepsis Organ Failure Assessment (SOFA) score (Vincent, et al. 1996).
In children, the number of dysfunctional organs is frequently used to
describe the severity of cases of Pediatric MODS, the rationale being that
MODS with more organ dysfunctions should be more severe. This
ordinal scale has been named the pediatric MODS score by some
physicians. There can be zero to six or seven organ dysfunctions (the
gastrointestinal dysfunction is not retained by most experts). MODS score
is not without interest. There is indeed a relationship between the number
of organ dysfunctions and the mortality rate
(Lacroix and Cotting,
2005).
Such scoring systems can be used to measure sequentially organ
dysfunction among a cohort of patients receiving different therapies for a
similar disease process of different cohorts of patients at different
hospitals. These scores can then allow comparisons to be made between
these cohorts to allow inferences to be made between the effects of
different therapies of different hospitals on rates of organ dysfunctions,
respectively. Such scores may also allow one to draw inferences about
pathophysiology and disease progress (Marcin and Pollack. 2007). The
most commonly used score that estimates severity of multiple organ
dysfunction syndrome in pediatrics is the Paediatric Logistic Organ
Dysfunction score (Marcin and Pollack. 2007).
36
Chapter I I
Critically Ill Child
PELOD score:
Development and validation:
Some pediatric intensivists considered that a good tool to estimate
the severity of cases of MODS observed in PICUs was needed to describe
correctly the clinical course of illnesses observed in critically ill children.
Thus, it was thought appropriate to undertake a research program to
create and validate a score for MODS in children. That score was named
the pediatric Logistic Organ Dysfunction (PELOD) score (Lacroix and
Cotting. 2005).
It is initially described in 1999 (Leteurtre, et al. 1999). Expert
opinion and data analyses on 594 patients admitted to 3 academic
pediatric ICUs and contains 12 variables. The paediatric Logistic Organ
Dysfunction score was validated on a sample of 1806 patients from
Canada, France, and Switzerland as a score associated with mortality
(Leteurtre, et al. 2003). It was then validated on a sample of 209 patients
from north India (Thukral, et al. 2007). The score can also be used
sequentially on a daily basis to measure changes in organ dysfunction
with time thus adding to the utility of the score as a measurement of
changes in organ function status over time (Thukral, et al. 2007).
Variables were only measured if clinical status of patients justified
their knowledge. If a variable was not measured, it was assumed to be
within the normal range. The most abnormal values were retained for the
statistical analysis. All physiologic data accumulated during the
preterminal period in dying patients or the last 2 hrs of life were not
considered for analysis. Physiologic variables for which values are age
dependent were stratified into four age groups: neonates (7 days or 1
month), infants (1-12 months) children (12-14 months), and adolescents
37
Chapter I I
Critically Ill Child
( 144 months). Moreover, the weight of each variable to predict death
was estimated independently. Four levels of increasing severity were
found by cluster analysis, and values of 0,1,10 or 20 points were
attributed to these levels. Then, the severity and the weight of each organ
dysfunction were integrated into the PELOD score using multivariate
logistic regression. Six systems contributed to the PELOD score, and 12
variables were retained . (Lacroix and Cotting. 2005).
A daily PELOD score was also studied during the first 5 days in the
PICU. When a variable was not measured, it was assumed to be either
identical to the last measurement (if the physician considered that the
value of the variable did not change) or normal (if the physician
considered that the value of the variable was normal). The most abnormal
values during the day under evaluation were retained for analysis. The
predictive value of the daily PELOD score was quite good (Lacroix and
Cotting. 2005).
38
Chapter I I
Critically Ill Child
Table (9): An example of a composite score, the Pediatric Logistic Organ
Dysfunction (PELOD) score
Points by level of severity for each system
Organ systems and variables
0
Respiratory system
Pao2 in mmHg/FIO2 ratio (kPa/%)a
> 70 (9.3) and
b
Paco2 (mmHg or kPa)
≤ 90 (11.7) and
c
Mechanical ventilation
No ventilation
Cardiovascular systemd
Heart rate (beats/min)
< 12 yrs
≥ 12 yrs
Systolic blood pressure, mmHg
< 1 month
1 month – 1 yr
1-12 yrs
≥ 12 yrs
Neurologic system
Glasgow coma scoree
Pupillary reactionf
Hepatic system
ALT or SGOT, IU/L
Prothrombin time (% of standard),
INR
Renal system: creatinine, µmol/L
(mg/dL):
< 7 days
7 days – 1yr
1-12 yrs
≥ 12yrs
Hematologic system
White blood cell count (109/L)
Platelet count (109/L)
1
10
20
------Ventilation
≤ 70 (9.3) ---or
---> 90 (11.7) -------
≤ 195
≤ 150
and
> 65
> 75
> 85
> 95
-------
-------
-------------
> 195
> 150
or
35-65
35-75
45-85
55-95
12-15 and
Both reactive
7-11
----
4-6 or
Both fixed
3
----
< 950 and
> 60 or < 1.4
≥ 950 or
≤60 or <1.4
-------
-------
< 140 (1.59)
< 55 (0.62)
< 100 (1.13)
< 140 (1.59)
-------------
≥
140
(1.59)
≥ 55 (0.62)
≥
100
(1.13)
≥
140
(1.59)
-------------
> 4.5 and
≥ 35
1.5 – 4.4 or
< 35
< 1.5
----
-------
ALT, alanine aminotransferase; SGOT, serum glutamic-oxaloacetic
transaminase; INR, international normalized ratio. (Leteurtre, et al. 2003)
39
< 35
< 35
< 45
< 55
Chapter I I
Critically Ill Child
Use arterial measurement only for Pao2; bPaco2 may be measured
from arterial, capillary or venous samples; cthe use of the mask
ventilation is not considered as mechanical ventilation; ddo not assess
heart rate and systolic blood pressure during crying or iatrogenic
agitation; euse the lowest value (if the patient is sedated, record the
estimated Glasgow coma score before sedation; assess only patients with
known or suspected acute central nervous system disease); fpupillary
reactions: nonreactive pupils must be > 3mm and do not assess after
iatrogenic papillary dilatation (Leteurtre, et al. 2003).
All variables must be measured at least one. If they are not
measured, they are assumed to be within the normal range for scoring
purposes. If they are measured more than once in 24 hrs, the most severe
value is used in calculating the score. To calculate the PELOD score,
each organ dysfunction receives points for the single variable associated
with the most points for example, if the worst heart rate of the day is 200
beats/min (10 PELOD points) and the systolic blood pressure remains at
30mmHg (20 PELOD points), then 20 PELOD points are assigned.
The points are not added to obtain 30 PELOD points for the organ
dysfunction (the maximum number of points for an organ is 20, and the
maximum PELOD score is 71). For the daily PELOD, when a variable is
not measured, it is assumed to be either identical to the last measurement
(if the physician considers that the value of the variable did not change)
or normal if the physician considers that the value of the variable is
normal). The equation to estimate probability of death is: probability
death = 1 ÷ (1 + exp [7.64 – 0.30 x PELOD]) (Leteurtre, et al. 2003).
40
Chapter I I
Critically Ill Child
Limitations of Scoring Systems:
There are limitations to acuity scoring systems in assessing quality
improvement. For example, if a hospital is using a mortality scoring
system, it may be able to demonstrate that its pediatric ICU has good
outcomes (effective care), but its pediatric ICU care may not be patient
centered or equitable. Furthermore, many of the acuity scoring systems
do not account for factors known to affect outcomes.
Measures of patient length of stay do not account for patient and/or
physician preference, patients' social situations, and bed availability. To
this end, a pediatric ICU may be inappropriately identified as "not
efficient" (long length of stay) if other unmeasured factors are not
considered; therefore, although acuity scoring systems are important
research tools and improve the accuracy of benchmarking, there are
limitations to the systems' ability to themselves achieve quality
improvement (Marcin and Pollack. 2007).
Limitations of the PELOD score:
First, treatment bias may be a problem because the PELOD score
includes data that can be modulated by the care provided during PICU
stay. Thus, the PELOD score cannot differentiate therapy and severity of
disease, but this bias is unavoidable unless one is ready to give no
treatment to critically ill children. Second, the PELOD score is not
validated to predict post-ICU morbidity and mortality and mortality;
further studies are required before the PELOD score can be used as a
surrogate outcome of post ICU morbidity and mortality (Lacroix and
Cotting. 2005). Third, the points given to each criterion of the score
(0,1,10 or 20) can not add up to a sum with predicted risk of mortality
between 5% and 15% (Garncia, et al. 2006).
41
Chapter I I
Critically Ill Child
PELOD score and sepsis:
Recently, further evaluation of the PELOD score was done in the
context of sepsis. Four "septic states" have been defined in critically ill
patients: systemic inflammatory response syndrome (SIRS), sepsis,
severe sepsis, and septic shock (Hayden. 1994).
One can ask if knowing both the PELOD score and the worst septic
state observed during PICU stay improves our capacity to predict the risk
of death. To address this question, we tested the hypothesis that the risk
of death increases with the PELOD score and with the severity of the
worst septic state and that this increase is higher if one takes into account
both the PELOD score and the worst septic state (Leclerc, et al. 2005).
Septic states were prospectively recorded during the development
study of the PELOD score. The hazard ratios of deaths were 7.43 for
SIRS and sepsis (we combined hazard rations of SIRS and sepsis because
they were similar), 27.40 for sever sepsis, and 61.40 for septic shock.
If we took into account the hazard ratio of the PELOD score (1.096
PELOD point
), the adjusted hazard ratios became 9.04 for SIRS/sepsis, 18.8
for severe sepsis, and 32.6 for septic shock. The combined hazard ratio
(HR) of death was calculated with the following equation: (HR
PELOD point
x HRseptic state).
For example, a patient in severe sepsis presents with a PELOD
score of 24. Its combined hazard ratio would be 169.6 (1.096 24 x 18.8).
This is significantly higher than the hazard ratio of 27.40 that we got for
severe sepsis alone, without any reference to the PELOD score.
Therefore, we can conclude that there is some accrual in the information
collected if one takes into account both the PELOD score and the worst
42
Chapter I I
Critically Ill Child
septic state of a group of critically ill children (Lacroix and Cotting,
2005).
PELOD score as surrogate outcome:
Presently, it is the incidence rate of death in ICUs that is considered
the standard outcome measure for clinical trials run in critically ill adults.
The main justification for such a choice is that death is hard data and that
it is quite difficult to bias such outcome. The feasibility of a randomized
clinical trial run in PICUs may be a problem if one chooses mortality rate
as the primary outcome measure because death is a rare event in PICUs:
death rate is between 20% and 40% in adult ICUs, whereas it is 4% to 6%
in PICUs (Leteurtre, et al. 2003).
Such a low rate of death should increase significantly the sample
size required to complete pediatric trials. Indeed, the number of patients
required may be so huge that it could be impossible to collect them. The
incidence rate of MODS in PICUs is about 18% to 25% (Proulx, et al.
1996); this is significantly higher than the death rate. Thus, it makes
sense to ask the question: can the PELOD score be a good surrogate
outcome of death for clinical studies run in PICUs? (Lacroix and
Cotting. 2005).
By definition, a surrogate outcome is an outcome measure used
instead of the gold standard. Its relationship to the gold standard must be
good; this is the case for MODS and PELOD scores because MODS is
present in almost all dying critically ill children. There must be a
meaningful cause effect relationship between the surrogate outcome and
the gold standard. MODS is the cause of death of most critically ill
children who die in the PICU (Proulx, et al. 1996).
43
Chapter I I
Critically Ill Child
The best way to check if there is really a cause effect relationship
between MODS or PELOD score and death would be to test the
hypothesis that improving MODS or PELOD score decreased death rate;
this remains to be done. The prevalence or incidence rate of the surrogate
outcome should be significantly greater than it is for the gold standard;
the incidence rate of MODS is at least three to four times the death rate in
PICUs. The scientific value of surrogate outcome must be well estimated;
this is the case of the PELOD score (Lacroix and Cotting. 2005).
Surrogate outcomes are frequently misused (Fleming and DeMets.
1996). For example, it might look appropriate to use as a surrogate
outcome the blood level of protein C in a randomized clinical trial on the
efficacy of activated protein C. This is inappropriate because the link
between the blood level and clinically relevant outcome like survival or
severity of MODS is not well established.
Actually, the blood level of activated protein C would be a good
measure of the compliance to the research protocol, but it can not be
considered as a reliable surrogate outcome to death. On the other hand, a
surrogate outcome can be better than the gold standard in some
circumstances. As stated by Zygun, et al. (2005), "death cannot predict
the neurologic outcome of severe brain injury, while there are data
suggesting that a MOD [Multiple Organ Dysfunction] score can be in
critically ill adults with severe brain injury".
That there is such relationship between the PELOD score and
neurologic outcome or the quality of life of critically ill children who
survive a PICU stay remains to be determined. On the other hand, it is
clear that using a severity scale like the PELOD score as a surrogate
outcome of death should have indeed a great impact on the sample size of
44
Chapter I I
Critically Ill Child
clinical trials undertaken in PICUs. Thus, it makes sense to use the
PELOD score as a surrogate outcome in some randomized clinical trial
run in PICUs (Lacroix and Cotting. 2005).
The PELOD and the daily PELOD scores seem to be valid measures
of the severity of illness in the PICU. The delta PELOD score (
PELOD) equals the worst PELOD after randomization minus daily
PELOD score at any of randomization) may also be useful to chart the
course critical illness (Marshall. 1999).
P-MODs:
Dr. Graciano and colleagues (Graciano, et al. 2005), report the
development and validation an additional pediatric organ dysfunction
score, the pediatric Multiple organ Dysfunction Score (P-MODS). This
score was developed using electronic medical records from a large
database of patients at a single institution over a 3.5-yr time period
(n=6456). Twenty- two candidate variables were analyzed from five
organ systems (cardiovascular, respiratory, hepatic, hematologic, and
renal).
Although neurologic, dysfunction has been recognized to be a
feature of multiple organ dysfunction syndrome, the authors omitted the
variable from their analysis due to concerns about confounding by the use
of sedative or paralytic agents in their patients. The severity of organ
dysfunction was graded on a scale from 0 to 4 for the variables
representing each organ system.
The variables used for the final score included the most abnormal
values recorded for arterial lactate, PaO2/FIO2 ratio, total bilirubin,
fibrinogen, and blood urea nitrogen during the PICU stay. When used to
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predict mortality in the validation set, the calibration across mortality risk
groups was adequate (Dominguez and Huh. 2005).
The P-MODS appears to have face validity in that the organ systems
contained in the score have been recognized to represent organ
dysfunction and the score is scaled such that worsening values in the
organ system domains represent increasing organ dysfunction. The score
appears to have content validity in that the variables in each domain of
the score represent organ dysfunction in the relevant organ system.
Finally, the authors have sought to demonstrate validity by using the
score to predict mortality (since multiple organ system dysfunction is
often associated with mortality in ICU patients and there is no goldstandard criterion measure of organ dysfunction) (Dominguez and Huh.
2005).
Score
Lactic acid, mmol/L
Pao2/FIO2
Bilirubin a
mol/L
mg/dL
Fibrinogen b
mol/L
mg/dL
BUNc
mol/L
mg/dL
0
1
 150
1
1-2
150-100
2
2-5
100-75
3
5-7.5
75-50
 8.5
 0.5
8.5-34.2
0.5-2.0
34.285.5
2.0-5.0
85.5-171 171
5.0-10.0  10
 4.40 4.40-3.70
 150 150-125
7.10-14.3
 7.10
20-40
 20
3.70-3.0
125-100
14.321.4
40-60
4
7.5
 50
3.0-2.20
100-75
 2.20
 75
21.428.5
60-80
 28.5
 80
Table (10): Pediatric Multiple Organ Dysfunction Score (P-MODS)
(Graciano, et al.2005).
The pediatric cerebral Performance category (PCPC) and the
Pediatric Overall Performance Category (POPC) are modified functional
outcome scores developed from the Glasgow Outcome Scales to assess
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the short- term functional outcome of pediatric intensive care (Fiser, et
al. 2000).
The scales can be used to assess changes in cognitive (PCPC) and
physical disabilities (POPC). These scales were internally validated on
1469 subjects admitted to a single institution's ICU from 1989 to 1990.
The potential utility of these measures lies in their ability to compare the
effects of different process of care and their resulting effects on
functional outcomes. Recently, PCPC and POPC were shown to correlate
with a higher severity of illness (higher PRISM mortality predictions) and
longer length of stays on a sample of 11, 104 patients from 16 Pediatric
ICUs (Fiser, et al. 2000)
. More importantly, the scores correlate with the Stanford- Binet
intelligence Scale IV (measured at hospital discharge), the Bayley Scales
of infant Development II (measured at hospital discharge), and the
Vineleand Adaptive Behavior Scales (measured at 1 month and 6 months
after discharge) (Fiser, et al. 2000). However, in general, functional
outcome scores are difficult to use as a measure of quality of care because
they do not discriminate between the effects of the disease and its
treatment (Marcin and Pollack. 2007).
Other Scores Used in the ICU:
In addition to mortality, functional outcome, and organ dysfunction
scores, there are more diagnostic specific scores that have been used in
the pediatric ICU for research and quality assessments. Most scores have
been developed on adult patients.
General severity of illness scores include the Acute Physiology and
Chronic
Health
Evaluation
(APACHE
47
III
and
APACHE
IV)
Chapter I I
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(Zimmernem, et al. 2006). Score and the Mortality Prediction Model
(MPM III) (lemeshow, et al. 1993). Adult trauma scores include the
Revised Trauma score (RTS) (Eichelberger, et al. 1993). The injury
severity Score (ISS) (Baker and O'Neill. 1976). And the Trauma score
and injury severity score (TRISS), which is the combination of the
physiologic RTS and anatomic ISS (Champion, et al. 1990). A severity
characterization of Trauma (ASCOT). (Champion, et al.1990). And the
international Classification of Disease Injury Severity Score (ICISS)
(Osler, et al. 2002). Although there exists a pediatric Trauma score, it is
more complicated and only mildly improved performance compared with
the adult RTS (Kaufmann, et al. 1990).
There is also a plethora of scoring systems developed for specific
pediatric diagnoses that have a variety of applications in the pediatric
ICU. These include, but are not limited to, CROUP scores (Leipzig, et al.
1979), asthma scores (Carroll, et al. 2005), pediatric respiratory failure
(Timmons, et al. 1995), and meningococcemia (Flaegstad, et al. 1995).
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CHAPTER III
Lactate
Many variables measured in critically ill patients have been used to
estimate severity of disease, prognosticate morbidity and mortality,
evaluate costs of treatment, and finally indicate specific treatment and
monitor the adequacy of treatment and its timing. Although in our mind
strongly linked to tissue hypoxia, lactate levels follow many more
metabolic processes not related to tissue hypoxia and, therefore, subject
to many disturbances found in various clinical situations. (Bakker, et al.
2013).
History of lactate
The first description of lactate originates from 1780 when Karl
Scheele found lactate in sour milk. It took almost 70 years before the
German physician-chemist Joseph Scherer demonstrated the presence of
lactate in human blood. Where Scherer analysed blood drawn from a
young woman who had just died from what we now call septic shock, it
was Carl Folwarczny in 1858 who demonstrated the presence of lactate
in the blood . (Kompanje, et al. 2007).
Metabolism of lactate
Lactate is a crucial metabolite in the two main energy (ATP)producing processes that power life: glycolysis and oxidative
phosphorylation (OxPhos). Glycolysis, process that occurred very early in
evolution (approximately 3 billion years ago), converts glucose into two
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molecules of pyruvate with the concomitant generation of 2 ATP. When
atmospheric oxygen levels rose (1 billion years ago), mitochondria
developed to generate far more energy from glucose (36 ATP molecules
for 1 glucose molecule), although following a much more complicated
process (Krebs cycle and OxPhos). Glycolysis and OxPhos steadily
metabolize glucose when conditions are stable (Figure 1).
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Figure (1) Lactate at the cellular level. Usually not oxygen shortage
per se, but acute energy requirements is a key determinant of lactate
levels.
a-Under stable conditions, glucose is converted to pyruvate,
generating 2 ATP, and pyruvate is then subsequently fully oxidized to
CO2 generating ~36 ATP.
b-Under stress, glycolysis can increase by a factor 100 to 1,000,
provided that glucose is present and pyruvate is converted to lactate.
Irrespective of optimal mitochondrial function and oxygenation, such a
rate of pyruvate production will saturate the mitochondrial tricarboxylic
acid cycle and oxidative phosphorylation (OxPhos).
c-During recovery, lactate is converted back to pyruvate and fully
oxidized.
Pyruvate is the molecule that links these two reactions. Because the
rate of glycolysis can increase two to three orders of a magnitude faster
than OxPhos, glycolysis can briefly provide far more ATP. Excess
pyruvate will rapidly accumulate and is diverted to lactate in order for
glycolysis to proceed (Figure 1b).
During recovery (Figure 1c) lactate is converted into pyruvate. In
both directions this is catalyzed by the enzyme lactate dehydrogenase
(LDH).
Thus, when rapidly large amounts of energy are required, such as
under circumstances of cellular stress, lactate serves as a critical buffer
that allows glycolysis to accelerate. Also, at the level of the organism
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(Figure 2), lactate has a similar role as an intermediate fuel that is readily
exchanged between various tissues, facilitated bya family of membranebound mono-carboxylate transporters (MCT). Over the past two decades,
lactate shuttles between astrocytes, neurons, striated muscle, cardiac
muscle, as well as the liver and kidneys have been demonstrated
(Leverve. 1999).
Figure (2) Lactate at the physiological level. The flexible use of
glucose and lactate as fuels on the cellular level is mirrored at the
organism level. All living tissues can consume glucose. From the
glucose/lactate point of view, three sorts of tissues/cells exist:
1) cells that must produce lactate because they lack mitochondria, e.g.,
red blood cells;
2) tissues or cells that either produce or consume lactate depending on
circumstances, i.e., all mitochondria-containing cells;
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3) tissues that can perform gluconeogenesis and export glucose that is
resynthesized from lactate. The liver and the kidneys can only
perform gluconeogenesis and export glucose. Only this so-called
Cori cycle carries an energy penalty, whereas the other shuttles do
not lead to “waste” of energy.
It should be noted that whereas the Cori cycle involves energy
consuming hepatic or renal gluconeogenesis to convert lactate into
glucose, direct interorgan exchange of lactate itself does not carry an
energy penalty and even exogenous lactate may serve as a suitable
substrate . (Leverve.1999).
Lactate and acidosis
The metabolism of glucose during tissue hypoxia results in
production of lactate, ATP, and water (Zilva.1978).
The production of H+ originates from the hydrolysis of ATP to
ADP. In the presence of oxygen and provided that OxPhos can keep up
with glycolysis, these H+ ions can be used together with lactate in the
OxPhos in the mitochondria and acidosis is thus less likely to develop.
Stewart challenged the classic Henderson-Hasselbalch approach where
the acidosis in his approach is the result of the dissociation of water to
maintain acid–base equilibrium by the addition of the strong ion lactateto the circulation (Stewart.1983).
There is however not a strong relationship between arterial pH and
lactate levels. Even at higher lactate levels, only a weak, although
significant, correlation exists. When evaluating the significance for
patient outcome and the origin of the metabolic acidosis, it is probably
more realistic to use the term: lactate associated metabolic acidosis, a
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combination that also carries the highest risk of mortality . (Gunnerson
,et al.2006).
Lactate and tissue hypoxia
Many experimental studies have confirmed the relationship between
tissue hypoxia and the generation of lactate by reducing the components
of systemic oxygen delivery (haemoglobin level, oxygen saturation, and
cardiac output) until the extraction of oxygen can no longer maintain
oxygen availability to the cells to meet their demands (Zhang and
Vincent.1993).
At a critical level of oxygen delivery, oxygen consumption becomes
limited by oxygen delivery, and this coincides with a sharp increase in
lactate levels. Also, clinical data indicate the relationship between the
presence of this supply dependent state of oxygen consumption and
increased lactate levels similar to animal studies (Bakker and Vincent.
1991).
In a landmark study, Ronco et al. showed that this phenomenon also
was present in patients when oxygen delivery decreased until circulatory
arrest during end-of-life care (Ronco, et al.1993).
This has been confirmed in an experimental study of cardiac
tamponade by Zhang et al. which showed that resolution of the supplydependent state of oxygen consumption by resolving the tamponade was
associated with an increase in oxygen consumption to baseline levels and
normalisation of lactate levels.(Zhang, et al.1993)
Because the exchange of oxygen takes place in the microcirculation,
alterations in microcirculatory perfusion also can result in limited oxygen
availability. Particularly in sepsis, microcirculatory derangement or
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shunting may lead to insufficient oxygen that is delivered to the cell,
thereby increasing lactate levels (Zhang, et al. 1993).
This is indirectly illustrated by the observation that improving
capillary perfusion has been correlated to a reduction in lactate levels in
patients with septic shock, independent of changes in systemic
haemodynamic variables (De Backer, et al.2006).
Given the near equilibrium reaction between lactate and pyruvate
and its connection with the cellular oxidoreduction state, the lactate-topyruvate ratio (L:P) provides additional information as L:P is coupled to
the cytoplasmic NADH:NAD+ (Levy, et al.1997).
However, it must be noted that in contrast to lactate, pyruvate is far
from trivial to reliably measure in clinical practice and therefore its use is
limited in critically ill patients (Weil and Tang.2009).
Lactate production in aerobic metabolism
Aerobic glucose metabolism to lactate may be a preferred way to
rapidly produce significant energy amounts. Therefore, stimulating
increased aerobic glucose metabolism has been shown to increase lactate
levels in the absence of tissue hypoxia. Most notably, the administration
of epinephrine has long been shown to result in a dose-dependent
increase in lactate levels (Griffith, et al.1939).
Also, stimulation of the phosphofructokinase enzyme by alkalosis
(respiratory and metabolic) has been shown to increase lactate levels
(Zborowska-Sluis AND Dossetor.1967).
Clinically often used therapeutic interventions also have been shown
to increase aerobic lactate production (McMahon, et al.1988).
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The aerobic production of lactate as an energy source is related to
the very high lactate levels found in patients with lymphoma, a
phenomenon referred to as the Warburg effect .When treating the
lymphoma, both lactate levels and LDH respond to chemotherapy .
(Warburg .1956).
Recently, it has been shown that the activity of the Na+/K+ pump
system, which requires significant amounts of ATP for its function, is
related to increased lactate levels in both experimental and clinical
conditions (Levy, et al.2005). unrelated to the presence of tissue hypoxia.
Such enhanced glycolysis can be triggered by cytokine-mediated uptake
of glucose (Taylor, et al.1992) or catecholamine-stimulated increased NaK-pump activity (McCarter, et al.2000) supported by both experimental
and clinical studies (Haji-Michael, et al.1999).
Still unresolved discussion, has focused on the presence of
mitochondrial dysfunction in critically ill that could limit pyruvate
metabolism (and thus increase lactate levels) in the absence of limited
oxygen availability (Brealey, et al.2002).
Infusion of Ringer’s lactate does not hamper the accuracy of lactate
measurement (Didwania, et al.1997).
Mechanism of hyperlactemia in sepsis and septic shock
Traditionally, lactic acidosis in sepsis is attributed to anaerobic
glycolysis due to inadequate oxygen delivery.
However, it has become clear that the mechanism of hyperlactemia
in sepsis is multifactorial and due to factors beyond hypoxic tissue injury
alone (Bolton .2007) .
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James et al. proposed that lactic acidosis refractory to standard
resuscitation is frequently due to increased aerobic glycolysis in skeletal
muscle secondary to epinephrine-stimulated Na+, K+-ATPase activity
and not to anaerobic glycolysis from hypoperfusion , and warned that
continued attempts at resuscitation targeting lactate levels could lead to
unnecessary blood transfusion and use of inotropic agents (James, et
al.1999).
Furthermore, Gutierrez et al. emphasized that the etiology of
prolonged lactic acidosis in sepsis is often multifactorial, making it an
unreliable marker of oxygen debt and inadequate resuscitation (Gutierrez
and Wulf.1996 ).
Interestingly, it has been demonstrated that septic patients with
hyperlactemia after 24 h of resuscitation had similar lactate production
but lower lactate clearance than septic patients with normal blood lactate
(Levraut, et al.1998).
This finding raises doubts about the reliability of hyperlactemia as an
indicator of the intensity of anaerobic metabolism in septic patients,
suggesting instead that persistence of hyperlactemia during sepsis may be
more representative of inadequate lactate clearance as opposed to pure
lactate overproduction (Levraut, et al.1998).
These findings questioned the notion that hyperlactemia in sepsis
and septic shock is solely the result of hypoperfusion. Further support for
the role of inadequate lactate clearance stems from Gibot et al. who used
an endotoxemia model to demonstrate that lactate levels in sepsis can be
elevated despite adequate systemic perfusion,blood pressure, and oxygen
delivery. (Gibot.2012)
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Similarly, in a pig model, Ven Genderen et al. showed that septic
shock behaves differently from obstructive/circulatory shock that even
when cardiac output and other systemic parameters are optimized, there
continues to be regional microvascular oxygen mismatch in septic shock,
as compared to obstructive/circulatory shock. The authors postulate that
this regional microvascular oxygen mismatch leads to elevations in
lactate that may be partially unresponsive to traditional resuscitative
interventions that only target systemic parameters (Ven Genderen, et
al.2014) .
Likewise, Hernandez et al. showed lactate in septic shock to have a
biphasic response, with an initial rapid improvement followed by a much
slower normalization many hours later, and hypothesized that this slow
and delayed response might be attributed to the microvascular oxygen
mismatch as described by Ven Genderen et al., making further systemic
resuscitation through traditional sepsis resuscitation bundles ineffective
and perhaps detrimental (Hernandez, et al.2014).
In fact, Marik attribute hyperlactemia in the later phase of sepsis to
increased aerobic glycolysis due to a stress response and also label any
attempts at using traditional sepsis therapies to normalize lactate during
this later stage as flawed and potentially harmful (Marik.2013).
With these findings, Rivers et al. warn against using only lactate
clearance as a marker of sepsis recovery and state that lactate clearance,
central venous oxygen saturation (ScvO2), and other markers are
complementary and not mutually exclusive end points. (Rivers, et al.
2011)
Thus, new insight regarding the mechanism for hyperlactemia in
sepsis following adequate initial resuscitation and infection control
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demonstrates that tissue hypoxia is not the sole etiology of hyperlactemia
during late sepsis.Nevertheless, data supporting the clinical utility of
lactate as a marker of early sepsis recovery is robust while the role of
continued lactate monitoring beyond the initial resuscitation period into
the stage of late sepsis and its potential to guide treatment during this
later stage remains uncertain . (Bakker.2015)
Lactate as a prognosticator in early sepsis management
In their 2004 and 2008 sepsis guidelines, Dellinger et al.
recommended measurement of lactate on initial presentation,with an
elevated value signifying tissue hypoperfusion and necessitating
aggressive resuscitation (Dellinger, et al .2004)
Although their guidelines suggested measuring lactate only upon
presentation, many clinicians and researchers have attempted to capitalize
on the test’s theoretical diagnostic and predictive value by including
additional measurements during the resuscitation process.(Nguyen, et al.
2010).
For example, it was shown that lactate clearance greater than 10 %
from initial measurement during the first 2 to 6 h of resuscitation
predicted survival from septic shock and that protocols targeting lactate
clearance of at least 10 % produced similar short-term survival rates to
protocols
using
Central
venous
oxygen
saturation
(ScvO2)
monitoring .Moreover, it was demonstrated that for every 10 % increase
in lactate clearance, there was a corresponding 11 % decrease in
inhospital mortality. (Nguyen, et al.2004)
Similarly, septic patients with lactate clearance of greater than 20
% during the initial 8 h of resuscitation had a 22 % decline in the relative
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risk of mortality, compared with patients having lactate clearances of less
than 20 % (Jansen, et al.2010) .
Since these initial studies are evaluating lactate as a marker of
recovery in sepsis and septic shock, further research has evaluated the
role of lactate monitoring during the early resuscitative period. For
example, Puskarich et al. studied resuscitation during the initial 6 h of
treatment and demonstrated that achieving an ScvO2 goal ≥70 % without
obtaining a lactate clearance goal ≥10 % was associated with higher
mortality than reaching the lactate clearance goal without the ScvO2 goal
(Puskarich, et al.2012).
Furthermore, these same authors showed that early lactate
normalization (within 6 h) was a predictor of survival in patients being
treated for sepsis and septic shock. ( Puskarich, et al.2013)
Nguyen et al. investigated the addition of lactate clearance within
the first 12 h of resuscitation to the severe sepsis resuscitation bundle and
showed that including lactate clearance leads to an almost twofold
increase in relative risk reduction of death. (Nguyen, et al. 2011)
In response to the convincing literature supporting the utility of
lactate clearance in early sepsis, the newest surviving sepsis guidelines
for early goal-directed therapy (EGDT) now includes lactate clearance
during the first 6 h of resuscitation as a goal of early resuscitation .
(Jansen, et al.2010)
Thus, research regarding lactate monitoring as a marker of recovery
in severe sepsis and septic shock has proven fruitful but has primarily
focused on the early resuscitation period . (Arnold, et al. 2009)
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Lactate as a late prognosticator in sepsis management
Literature evaluating the clinical and predictive value of lactate
measurements beyond the initial 6-h resuscitation period in the medical
management of sepsis is significantly less robust (Bakker, et al. 1996).
In a study of 137 surgical intensive care unit (SICU) patients,
Husain et al. showed elevated initial and 24-h lactate levels to be
significant predictors of mortality, with mortality ranging from 10 to 67
% depending on whether lactate levels normalized or failed to normalize
at 24 h, respectively (Husain, et al. 2003).
In another study investigating SICU patients, Bakker et al.
showed that lactate clearance measured 24 h after admission was a
significant predictor of in-hospital mortality and that the duration of
persistent lactic acidosis was more predictive of mortality than the initial
lactate value (Bakker, et al. 1996).
Similarly, Friedman et al. showed in a 35-patient study that
survivors of severe sepsis admitted to the medical intensive care unit
(MICU) or SICU had significantly lower lactate values at 24 h of
resuscitation than nonsurvivors (Friedman, et al. 1995)
Finally, Manikis et al. followed lactate measurements every 8 h for
>72 h in 129 trauma patients and demonstrated serial lactate
measurements and the duration of hyperlactemia to be reliable indicators
of morbidity and mortality following trauma (Manikis, et al. 1995)
In a 94-patient SICU sepsis study, Marty et al. measured lactate at
time0 (T), T6, T12, and T24 and showed that the best predictor of death
was the T24 clearance. These authors concluded that during the first 24 h
in the ICU, hyperlactemia, even after the “golden hours,” is associated
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with increased mortality, and lactate clearance-directed therapy should be
considered for the first 24 h of treatment (Marty, et al. 2013)
Similarly, in an 81-patient study, Herwanto et al. investigated the
role of 6-, 12-, and 24-h lactate clearance in patients with sepsis and
septic shock and found only the 24-h lactate clearance measurements to
be associated with mortality (Herwanto, et al. 2014) .
62