U.S. patent application number 16/948556 was filed with the patent office on 2021-01-14 for temporal pediatric sepsis biomarker risk model.
The applicant listed for this patent is CHILDREN'S HOSPITAL MEDICAL CENTER, University of Cincinnati. Invention is credited to Christopher John Lindsell, Hector R. Wong.
Application Number | 20210010083 16/948556 |
Document ID | / |
Family ID | 1000005117433 |
Filed Date | 2021-01-14 |
United States Patent
Application |
20210010083 |
Kind Code |
A1 |
Lindsell; Christopher John ;
et al. |
January 14, 2021 |
TEMPORAL PEDIATRIC SEPSIS BIOMARKER RISK MODEL
Abstract
Methods and compositions disclosed herein generally relate to
methods of identifying, validating, and measuring clinically
relevant, quantifiable biomarkers of diagnostic and therapeutic
responses for blood, vascular, cardiac, and respiratory tract
dysfunction, particularly as those responses relate to septic shock
in pediatric patients. In particular, the invention relates to
identifying one or more biomarkers associated with septic shock in
pediatric patients, obtaining a sample from a pediatric patient
having at least one indication of septic shock, then quantifying
from the sample an amount of one or more of said biomarkers,
wherein the level of said biomarker correlates with a predicted
outcome.
Inventors: |
Lindsell; Christopher John;
(Cincinnati, OH) ; Wong; Hector R.; (Cincinnati,
OH) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHILDREN'S HOSPITAL MEDICAL CENTER
University of Cincinnati |
Cincinnati
Cincinnati |
OH
OH |
US
US |
|
|
Family ID: |
1000005117433 |
Appl. No.: |
16/948556 |
Filed: |
September 23, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15038862 |
May 24, 2016 |
10815526 |
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PCT/US2014/067438 |
Nov 25, 2014 |
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16948556 |
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61908613 |
Nov 25, 2013 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
C12Q 2600/118 20130101;
C12Q 2600/106 20130101; C12Q 2600/158 20130101; C12Q 1/6883
20130101 |
International
Class: |
C12Q 1/6883 20060101
C12Q001/6883 |
Goverment Interests
STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH
[0002] This invention was made with government support under
HL100474, GM064619, GM099773, and TR000077 awarded by the National
Institutes of Health (NIH). The government has certain rights in
the invention.
Claims
1. A method of monitoring the therapeutic efficacy of a first
treatment administered to a patient with septic shock, and
administering to the patient a second treatment, the method
comprising: analyzing a first sample that has been obtained from
the patient at a first time point, which is during day 1 of
presentation with septic shock and before the first treatment has
been administered, to determine a first serum concentration level
of each of two biomarkers consisting of C-C chemokine ligand 3
(CCL3) and interleukin-8 (IL8); analyzing a second sample that has
been obtained from the patient at a second time point, which is
during day 3 of presentation with septic shock and after the first
treatment has been administered to the patient, to determine a
second serum concentration level of each of the biomarkers;
determining whether the level of each biomarker is elevated above a
cut-off level at each of the first and second time points,
identifying the patient as at high risk for a poor outcome where
any one of the following is true: a) a non-elevated level of CCL3
and an elevated level of IL8 at the first time point, and a highly
elevated level of IL8 at the second time point, or b) a highly
elevated level of CCL3 at the first time point, and a non-elevated
level of IL8 at the second time point, or c) an elevated level of
CCL3 and a highly elevated level of IL8 at the first time point,
and an elevated level of IL8 at the second time point; and
discontinuing administration of the first treatment and
administering to the patient identified as at high risk for a poor
outcome a second treatment selected from one or more of
extracorporeal membrane oxygenation/life support, plasmapheresis,
pulmonary artery catheterization, and high volume continuous
hemofiltration.
2. The method of claim 1, further comprising analyzing the second
sample for a third biomarker, heat shock protein 70 kDa (HSPA1B)
and identifying the patient as at high risk for a poor outcome
where the patient has an elevated level of CCL3 and a non-highly
elevated level of IL8 at the first time point, and elevated levels
of IL8 and HSPA1B at the second time point.
3. The method of claim 1, further comprising obtaining at least one
additional sample(s) from the patient after a treatment has been
administered to the patient and analyzing the at least one
additional sample for the level of CCL, IL8, or HSPA1B.
4. The method of claim 3, wherein the at least one additional
sample is obtained 12-36 hours after the second time point.
5. The method of claim 3, wherein the at least one additional
sample is obtained within the first 60 hours of presentation with
septic shock.
6. The method of claim 1, wherein a) an elevated level of CCL3 at
the first time point corresponds to a serum CCL3 concentration
greater than 130 pg/ml, b) a highly elevated level of CCL3 at the
first time point corresponds to a serum CCL3 concentration greater
than 216 pg/ml, c) an elevated level of IL8 at the first time point
corresponds to a serum IL8 concentration greater than 125 pg/ml, d)
a highly elevated level of IL8 at the first time point corresponds
to a serum IL8 concentration greater than 436 pg/ml, e) an elevated
level of IL8 at the second time point corresponds to a serum IL8
concentration greater than 33 pg/ml, and f) a highly elevated level
of IL8 at the second time point corresponds to a scrum IL8
concentration greater than 123 pg/ml.
7. The method of claim 2, wherein an elevated level of HSPA1B at
the second time point corresponds to a serum HSPA1B concentration
greater than 1.20 .mu.g/ml.
8. The method of claim 1, wherein the method further comprises
receiving one or more patient demographic data and/or clinical
characteristics and/or results from other tests or indicia of
septic shock.
9. The method of claim 8, wherein a) the patient demographic data
comprises the age of the patient, or b) wherein the patient
demographic data and/or clinical characteristics and/or results
from other tests or indicia of septic shock comprises the septic
shock causative organism, the presence or absence of chronic
disease, and/or the gender, race, and/or co-morbidities of the
patient.
10. The method of claim 1, wherein the determination of whether the
level(s) of the one or more biomarkers are elevated is combined
with one or more additional population-based risk scores.
11. The method of claim 10, wherein the one or more
population-based risk scores comprises pediatric risk of mortality
(PRISM) and/or pediatric index of mortality (PIM).
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 15/038,862, filed May 24, 2016, which is a
national stage entry, filed under 35 U.S.C. .sctn. 371, of
International Application No. PCT/US2014/067438, filed on Nov. 25,
2014, which claims priority to U.S. Provisional Application No.
61/908,613, filed on Nov. 25, 2013, the entire disclosures of which
are incorporated by reference in their entirety.
FIELD OF THE INVENTION
[0003] The invention disclosed herein generally relates to the
identification and validation of clinically relevant, quantifiable
biomarkers of diagnostic and therapeutic responses for blood,
vascular, cardiac, and respiratory tract dysfunction.
BACKGROUND
[0004] Septic shock and severe sepsis represent a major public
health problem in the United States, despite the development of
increasingly powerful antibiotics and advanced forms of intensive
care unit-based support modalities (see, e.g., Shanley, T. et al.
Sepsis, 3.sup.rd Ed., St. Louis, Mo., Mosby (2006)). Worldwide,
septic shock affects millions of adults, killing approximately one
in four (see, e.g., Dellinger, R. et al. Crit. Care Med. 36:296-327
(2008)). A recent study suggests that the incidence and the
mortality rates of septic shock in adults are increasing in the
United States (Dombrovskiy, V. et al. Crit. Care Med. 35:1244-50
(2007)).
[0005] Septic shock is also a major problem in the pediatric age
group, as there are .about.42,000 cases of pediatric septic shock
per year in the United States alone, with a mortality rate of
.about.10% (see, e.g., Watson, R. et al. Am. J. Respir. Crit. Care
Med. 167:695-701 (2003)). While the pediatric mortality rate is
lower than that of adults, it nonetheless translates to more than
4,000 childhood deaths per year and countless years of lost
productivity due to death at a young age. While this high number of
pediatric deaths per year from septic shock indicates that more
children die per year in the United States from septic shock as the
primary cause than those children who die from cancer, funding
specifically targeted toward pediatric septic shock is
substantially lower than that for pediatric cancer.
[0006] Reliable stratification of outcome risk is fundamental to
effective clinical practice and clinical research (Marshall J.
Leukoc. Biol. 83:471-82 (2008)). Risk stratification tools specific
for septic shock in pediatric patients would be beneficial at
several levels, including stratification for interventional
clinical trials, better-informed decision making for individual
patients (i.e. prognostication), and as a metric for quality
improvement efforts.
SUMMARY
[0007] Embodiments of the invention encompass methods of monitoring
the therapeutic efficacy of a treatment being administered to a
patient with septic shock, the methods including: identifying a
pediatric patient with septic shock; obtaining a first sample from
the patient at a first time point; analyzing the first sample to
determine a first level of at least one biomarker associated with
septic shock in pediatric patients; determining whether the level
is elevated above a cut-off level, wherein the presence of an
elevated level indicates that the patient has an elevated
likelihood of being classified as high risk for a poor outcome and
the absence of an elevated level indicates that the patient has a
reduced likelihood of being classified as high risk for a poor
outcome; obtaining a second sample from the patient at a second
time point, wherein the second time point occurs after a treatment
has been administered to the patient; analyzing the second sample
to determine a second level of the at least one biomarker;
determining whether the second level is elevated above a cut-off
level, wherein the presence of an elevated level indicates that the
patient has an elevated likelihood of being classified as high risk
for a poor outcome and the absence of an elevated level indicates
that the patient has a reduced likelihood of being classified as
high risk for a poor outcome; and comparing the patient's risk for
a poor outcome at each of the time points to determine if the
patient's risk for a poor outcome increased or decreased between
the time points, where an increased risk for a poor outcome can
indicate that a therapy has had a poor efficacy, and a reduced risk
for a poor outcome can indicate that a therapy has had a good
efficacy.
[0008] Embodiments of the invention encompass the use of methods of
monitoring the therapeutic efficacy of a treatment being
administered to a patient with septic shock, the methods including:
identifying a pediatric patient with septic shock; obtaining a
first sample from the patient at a first time point; analyzing the
first sample to determine a first level of at least one biomarker
associated with septic shock in pediatric patients; determining
whether the level is elevated above a cut-off level, wherein the
presence of an elevated level indicates that the patient has an
elevated likelihood of being classified as high risk for a poor
outcome and the absence of an elevated level indicates that the
patient has a reduced likelihood of being classified as high risk
for a poor outcome; obtaining a second sample from the patient at a
second time point, wherein the second time point occurs after a
treatment has been administered to the patient; analyzing the
second sample to determine a second level of the at least one
biomarker; determining whether the second level is elevated above a
cut-off level, wherein the presence of an elevated level indicates
that the patient has an elevated likelihood of being classified as
high risk for a poor outcome and the absence of an elevated level
indicates that the patient has a reduced likelihood of being
classified as high risk for a poor outcome; and comparing the
patient's risk for a poor outcome at each of the time points to
determine if the patient's risk for a poor outcome increased or
decreased between the time points, where an increased risk for a
poor outcome can indicate that a therapy has had a poor efficacy,
and a reduced risk for a poor outcome can indicate that a therapy
has had a good efficacy in order to treat a pediatric patient with
septic shock.
[0009] In some embodiments, the first time point can be within the
first hour of presentation with septic shock. In some embodiments,
the first time point can be within the first 8 hours of
presentation with septic shock. In some embodiments, the first time
point can be within the first 24 hours of presentation with septic
shock. In some embodiments, the second time point can be 24-60
hours after the first time point.
[0010] In some embodiments, the method can further include:
obtaining a third sample from the patient at a third time point,
wherein the third time point occurs after a treatment has been
administered to the patient; analyzing the third sample to
determine a third level of the at least one biomarker; and
determining whether the third level is elevated above a cut-off
level. In some embodiments, the first time point can be on the
first day the patient presents with septic shock, the second time
point can be 12-36 hours after the first time point, and the third
time point can be 12-36 hours after the second time point.
[0011] In some embodiments, the method can further include:
obtaining at least one additional sample(s) from the patient at at
least one additional time point(s), wherein the at least one
additional time point(s) occur after a treatment has been
administered to the patient; analyzing the at least one additional
sample to determine at least one additional level of the at least
one biomarker; and determining whether the at least one additional
level is elevated above a cut-off level. In some embodiments, the
at least one additional time point occurs within the first 60 hours
of presentation with septic shock.
[0012] In some embodiments, the at least one biomarker can be
selected from the group consisting of CCL3, IL8, and HSPA1B. In
some embodiments, the at least one biomarker can be all of CCL3,
IL8, and HSPA1B.
[0013] In some embodiments, a classification of high risk includes:
a) a non-elevated level of CCL3 and an elevated level of IL8 at the
first time point, and a highly elevated level of IL8 at the second
time point, or b) a highly elevated level of CCL3 at the first time
point, and a non-elevated level of IL8 at the second time point, or
c) an elevated level of CCL3 and a highly elevated level of IL8 at
the first time point, and an elevated level of IL8 at the second
time point, or d) an elevated level of CCL3 and a non-highly
elevated level of IL8 at the first time point, and elevated levels
of IL8 and HSPA1B at the second time point, and a classification of
low risk includes: g) non-elevated levels of CCL3 and IL8 at the
first time point, or h) a non-elevated level of CCL3 and an
elevated level of IL8 at the first time point, and a non-highly
elevated level of IL8 at the second time point, or i) a non-highly
elevated level of CCL3 at the first time point, and a non-elevated
level of IL8 at the second time point, or j) an elevated level of
CCL3 and a non-highly elevated level of IL8 at the first time
point, and an elevated level of IL8 and a non-elevated level of
HSPA1B at the second time point. In some embodiments, a) an
elevated level of CCL3 at the first time point corresponds to a
serum CCL3 concentration greater than 130 pg/ml, b) a highly
elevated level of CCL3 at the first time point corresponds to a
serum CCL3 concentration greater than 216 pg/ml, c) an elevated
level of IL8 at the first time point corresponds to a serum IL8
concentration greater than 125 pg/ml, d) a highly elevated level of
IL8 at the first time point corresponds to a serum IL8
concentration greater than 436 pg/ml, e) an elevated level of IL8
at the second time point corresponds to a serum IL8 concentration
greater than 33 pg/ml, f) an elevated level of IL8 at the second
time point corresponds to a serum IL8 concentration greater than
123 pg/ml, and g) an elevated level of HSPA1B at the second time
point corresponds to a serum HSPA1B concentration greater than 1.20
.mu.g/ml.
[0014] In some embodiments, the determination of whether the
level(s) of the one or more biomarkers are elevated above a cut-off
level includes applying the patient to a decision tree including
the one or more biomarkers. In some embodiments, the patient can be
applied to the decision tree depicted in FIG. 2, with terminal
nodes 3, 5, 7, and 8 corresponding to a classification of high risk
and terminal nodes 11, 2, 4, and 6 corresponding to a
classification of low risk.
[0015] In some embodiments, the determination of whether the
level(s) of the one or more biomarkers are elevated can be combined
with one or more patient demographic data and/or clinical
characteristics and/or results from other tests or indicia of
septic shock. In some embodiments, the patient demographic data
comprises the age of the patient. In some embodiments, the patient
demographic data and/or clinical characteristics and/or results
from other tests or indicia of septic shock comprises the septic
shock causative organism, the presence or absence of chronic
disease, and/or the gender, race, and/or co-morbidities of the
patient.
[0016] In some embodiments, the determination of whether the
level(s) of the one or more biomarkers are elevated can be combined
with one or more additional population-based risk scores. In some
embodiments, the one or more population-based risk scores include
PRISM and/or PIM.
[0017] Embodiments of the invention also include methods of
providing individualized treatment for a pediatric patient with
septic shock, including identifying a pediatric patient with septic
shock; obtaining a first sample from the patient at a first time
point; analyzing the first sample to determine a first level of at
least one biomarker associated with septic shock in pediatric
patients; determining whether the level is elevated above a cut-off
level, wherein the presence of an elevated level indicates that the
patient has an elevated likelihood of being classified as high risk
for a poor outcome and the absence of an elevated level indicates
that the patient has a reduced likelihood of being classified as
high risk for a poor outcome; obtaining a second sample from the
patient at a second time point, wherein the second time point
occurs after a treatment has been administered to the patient;
analyzing the second sample to determine a second level of the at
least one biomarker; determining whether the second level is
elevated above a cut-off level, wherein the presence of an elevated
level indicates that the patient has an elevated likelihood of
being classified as high risk for a poor outcome and the absence of
an elevated level indicates that the patient has a reduced
likelihood of being classified as high risk for a poor outcome; and
comparing the patient's risk for a poor outcome at each of the time
points to determine if the patient's risk for a poor outcome
increased or decreased between the time points, where an increased
risk for a poor outcome can indicate that a therapy has had a poor
efficacy, and a reduced risk for a poor outcome can indicate that a
therapy has had a good efficacy, and further including
discontinuing administration of a treatment determined to have had
a poor efficacy, thereby providing individualized treatment.
[0018] Embodiments of the invention also include the use of methods
of providing individualized treatment for a pediatric patient with
septic shock, including identifying a pediatric patient with septic
shock; obtaining a first sample from the patient at a first time
point; analyzing the first sample to determine a first level of at
least one biomarker associated with septic shock in pediatric
patients; determining whether the level is elevated above a cut-off
level, wherein the presence of an elevated level indicates that the
patient has an elevated likelihood of being classified as high risk
for a poor outcome and the absence of an elevated level indicates
that the patient has a reduced likelihood of being classified as
high risk for a poor outcome; obtaining a second sample from the
patient at a second time point, wherein the second time point
occurs after a treatment has been administered to the patient;
analyzing the second sample to determine a second level of the at
least one biomarker; determining whether the second level is
elevated above a cut-off level, wherein the presence of an elevated
level indicates that the patient has an elevated likelihood of
being classified as high risk for a poor outcome and the absence of
an elevated level indicates that the patient has a reduced
likelihood of being classified as high risk for a poor outcome; and
comparing the patient's risk for a poor outcome at each of the time
points to determine if the patient's risk for a poor outcome
increased or decreased between the time points, where an increased
risk for a poor outcome can indicate that a therapy has had a poor
efficacy, and a reduced risk for a poor outcome can indicate that a
therapy has had a good efficacy, and further including
discontinuing administration of a treatment determined to have had
a poor efficacy, thereby providing individualized treatment.
[0019] In some embodiments, a therapy determined to have had a poor
efficacy can be replaced with at least one high risk therapy. In
some embodiments, the at least one high risk therapy includes
extracorporeal membrane oxygenation/life support, plasmapheresis,
pulmonary artery catheterization, and/or high volume continuous
hemofiltration. Embodiments of the invention are also directed to
methods of improving an outcome in a pediatric patient with septic
shock via replacing a therapy determined to have had a poor
efficacy with at least one high risk therapy.
BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Those of skill in the art will understand that the drawings,
described below, are for illustrative purposes only. The drawings
are not intended to limit the scope of the present teachings in any
way.
[0021] FIG. 1 depicts the classification tree from the derivation
cohort (N=225). The classification tree consists of 7
biomarker-based decision rules and 14 daughter nodes. The
classification tree includes day 1 and day 3 data for interleukin-8
(IL8) and C-C chemokine ligand 3 (CCL3), and day 3 data heat shock
protein 70 kDa 1B (HSPA1B). Each node provides the biomarker serum
concentration-based decision rule and the number of subjects with
and without complicated course (CC), with the respective rates. For
consistency, the serum concentrations of all biomarkers are
provided in pg/ml. Terminal nodes (TNs) 1, 2, 4, and 6 are
considered low risk nodes, whereas terminal nodes 3, 5, 7, and 8
are considered high-risk terminal nodes. To calculate the
diagnostic test characteristics, all subjects in the low risk
terminal nodes (n=126) were classified as predicted to not have a
complicated course, whereas all subjects in the high risk terminal
nodes (n=99) were classified as predicted to have a complicated
course.
[0022] FIG. 2 depicts the classification tree from the updated
model based on the combined derivation and test cohorts (N=299).
The classification tree consists of 7 biomarker-based decision
rules and 14 daughter nodes. The classification tree includes day 1
and 3 interleukin-8 (IL8 data), day 1 C-C chemokine ligand 3 (CCL3)
data, and day 3 heat shock protein 70 kDa 1B (HSPA1B) data. Each
node provides the biomarker serum concentration-based decision rule
and the number of subjects with and without a complicated course
(CC), with the respective rates. For consistency, the serum
concentrations of all stratification biomarkers are provided in
pg/ml. Terminal nodes (TNs) 1, 2, 4, and 6 are considered low risk
nodes for a complicated course, whereas terminal nodes 3, 5, 7, and
8 are considered high-risk terminal nodes for a complicated course.
To calculate the diagnostic test characteristics, all subjects in
the low risk terminal nodes (n=168) were classified as predicted to
not have a complicated course, whereas all subjects in the high
risk terminal nodes (n=131) were classified as predicted to have a
complicated course.
DETAILED DESCRIPTION OF THE INVENTION
[0023] All references cited herein are incorporated by reference in
their entirety. Also incorporated herein by reference in their
entirety include: U.S. Patent Application No. 61/595,996,
BIOMARKERS OF SEPTIC SHOCK, filed on Feb. 7, 2012; U.S. Provisional
Application No. 61/721,705, A MULTI-BIOMARKER-BASED OUTCOME RISK
STRATIFICATION MODEL FOR ADULT SEPTIC SHOCK, filed on Nov. 2, 2012;
International Patent Application No. PCT/US13/25223, A
MULTI-BIOMARKER-BASED OUTCOME RISK STRATIFICATION MODEL FOR
PEDIATRIC SEPTIC SHOCK, filed on Feb. 7, 2013; and International
Patent Application No. PCT/US13/25221, A MULTI-BIOMARKER-BASED
OUTCOME RISK STRATIFICATION MODEL FOR ADULT SEPTIC SHOCK, filed on
Feb. 7, 2013.
[0024] Unless otherwise noted, terms are to be understood according
to conventional usage by those of ordinary skill in the relevant
art.
[0025] As used herein, the term "sample" encompasses a sample
obtained from a subject or patient. The sample can be of any
biological tissue or fluid. Such samples include, but are not
limited to, sputum, saliva, buccal sample, oral sample, blood,
serum, mucus, plasma, urine, blood cells (e.g., white cells),
circulating cells (e.g. stem cells or endothelial cells in the
blood), tissue, core or fine needle biopsy samples, cell-containing
body fluids, free floating nucleic acids, urine, stool, peritoneal
fluid, and pleural fluid, tear fluid, or cells therefrom. Samples
can also include sections of tissues such as frozen or fixed
sections taken for histological purposes or microdissected cells or
extracellular parts thereof. A sample to be analyzed can be tissue
material from a tissue biopsy obtained by aspiration or punch,
excision or by any other surgical method leading to biopsy or
resected cellular material. Such a sample can comprise cells
obtained from a subject or patient. In some embodiments, the sample
is a body fluid that include, for example, blood fluids, serum,
mucus, plasma, lymph, ascitic fluids, gynecological fluids, or
urine but not limited to these fluids. In some embodiments, the
sample can be a non-invasive sample, such as, for example, a saline
swish, a buccal scrape, a buccal swab, and the like.
[0026] As used herein, "blood" can include, for example, plasma,
serum, whole blood, blood lysates, and the like.
[0027] As used herein, the term "assessing" includes any form of
measurement, and includes determining if an element is present or
not. The terms "determining," "measuring," "evaluating,"
"assessing" and "assaying" can be used interchangeably and can
include quantitative and/or qualitative determinations.
[0028] As used herein, the term "monitoring" with reference to
septic shock refers to a method or process of determining the
severity or degree of septic shock or stratifying septic shock
based on risk and/or probability of mortality. In some embodiments,
monitoring relates to a method or process of determining the
therapeutic efficacy of a treatment being administered to a
patient.
[0029] As used herein, "outcome" can refer to the primary outcome
studied, typically 28-day survival/mortality. The importance of
survival/mortality in the context of pediatric septic shock is
readily evident. The common choice of 28 days was based on the fact
that 28-day mortality is a standard primary endpoint for
interventional clinical trials involving critically ill patients.
In some embodiments, an increased risk for a poor outcome indicates
that a therapy has had a poor efficacy, and a reduced risk for a
poor outcome indicates that a therapy has had a good efficacy.
[0030] As used herein, "outcome" can also refer to the secondary
outcome studied, namely resolution of organ failure after 14 days
or 28 days or limb loss. Although mortality/survival is obviously
an important outcome, survivors have clinically relevant short- and
long-term morbidities that impact quality of life, which are not
captured by the dichotomy of "alive" or "dead." In the absence of a
formal, validated quality of life measurement tool for survivors of
pediatric septic shock, resolution of organ failure was tracked as
a secondary outcome measure. Specifically, the presence or absence
of new organ failure over two timeframes was tracked: 14 days after
admission and 28 days after admission. Patients having organ
failure beyond 28 days are likely to survive with significant
morbidities having negative consequences for quality of life. Organ
failure was defined based on published and well-accepted criteria
for the pediatric population (Goldstein, B. et al. Pediatr. Crit.
Care Med. 6:208 (2005)). Specifically, cardiovascular, respiratory,
renal, hepatic, hematologic, and neurologic failure were tracked.
In addition, limb loss was tracked as a secondary outcome. Although
limb loss is not a true "organ failure," it is an important
consequence of pediatric septic shock with obvious impact on
quality of life.
[0031] As used herein, the terms "predicting outcome" and "outcome
risk stratification" with reference to septic shock refers to a
method or process of prognosticating a patient's risk of a certain
outcome. In some embodiments, predicting an outcome relates to
monitoring the therapeutic efficacy of a treatment being
administered to a patient. In some embodiments, predicting an
outcome relates to determining a relative risk of mortality. Such
mortality risk can be high risk, moderate risk, moderate-high risk,
moderate-low risk, or low risk. Alternatively, such mortality risk
can be described simply as high risk or low risk, corresponding to
high risk of death or high likelihood of survival, respectively. As
related to the terminal nodes of the decision trees described
herein, a "high risk terminal node" corresponds to a high mortality
probability, whereas a "low risk terminal node" corresponds to a
low mortality probability.
[0032] As used herein, the term "high risk clinical trial" refers
to one in which the test agent has "more than minimal risk" (as
defined by the terminology used by institutional review boards, or
IRBs). In some embodiments, a high risk clinical trial is a drug
trial.
[0033] As used herein, the term "low risk clinical trial" refers to
one in which the test agent has "minimal risk" (as defined by the
terminology used by IRBs). In some embodiments, a low risk clinical
trial is one that is not a drug trial. In some embodiments, a low
risk clinical trial is one that that involves the use of a monitor
or clinical practice process. In some embodiments, a low risk
clinical trial is an observational clinical trial.
[0034] As used herein, the terms "modulated" or "modulation," or
"regulated" or "regulation" and "differentially regulated" can
refer to both up regulation (i.e., activation or stimulation, e.g.,
by agonizing or potentiating) and down regulation (i.e., inhibition
or suppression, e.g., by antagonizing, decreasing or inhibiting),
unless otherwise specified or clear from the context of a specific
usage.
[0035] As used herein, the term "subject" refers to any member of
the animal kingdom. In some embodiments, a subject is a human
patient. In some embodiments, a subject is a pediatric patient. In
some embodiments, a pediatric patient is a patient under 18 years
of age, while an adult patient is 18 or older.
[0036] As used herein, the terms "treatment," "treating," "treat,"
and the like, refer to obtaining a desired pharmacologic and/or
physiologic effect. The effect can be prophylactic in terms of
completely or partially preventing a disease or symptom thereof
and/or can be therapeutic in terms of a partial or complete cure
for a disease and/or adverse effect attributable to the disease.
"Treatment," as used herein, covers any treatment of a disease in a
subject, particularly in a human, and includes: (a) preventing the
disease from occurring in a subject which may be predisposed to the
disease but has not yet been diagnosed as having it; (b) inhibiting
the disease, i.e., arresting its development; and (c) relieving the
disease, i.e., causing regression of the disease and/or relieving
one or more disease symptoms. "Treatment" can also encompass
delivery of an agent or administration of a therapy in order to
provide for a pharmacologic effect, even in the absence of a
disease or condition.
[0037] As used herein, the term "marker" or "biomarker" refers to a
biological molecule, such as, for example, a nucleic acid, peptide,
protein, hormone, and the like, whose presence or concentration can
be detected and correlated with a known condition, such as a
disease state. It can also be used to refer to a differentially
expressed gene whose expression pattern can be utilized as part of
a predictive, prognostic or diagnostic process in healthy
conditions or a disease state, or which, alternatively, can be used
in methods for identifying a useful treatment or prevention
therapy.
[0038] As used herein, the term "expression levels" refers, for
example, to a determined level of biomarker expression. The term
"pattern of expression levels" refers to a determined level of
biomarker expression compared either to a reference (e.g. a
housekeeping gene or inversely regulated genes, or other reference
biomarker) or to a computed average expression value (e.g. in
DNA-chip analyses). A pattern is not limited to the comparison of
two biomarkers but is more related to multiple comparisons of
biomarkers to reference biomarkers or samples. A certain "pattern
of expression levels" can also result and be determined by
comparison and measurement of several biomarkers as disclosed
herein and display the relative abundance of these transcripts to
each other.
[0039] As used herein, a "reference pattern of expression levels"
refers to any pattern of expression levels that can be used for the
comparison to another pattern of expression levels. In some
embodiments of the invention, a reference pattern of expression
levels is, for example, an average pattern of expression levels
observed in a group of healthy or diseased individuals, serving as
a reference group.
[0040] As used herein, the term "decision tree" refers to a
standard machine learning technique for multivariate data analysis
and classification. Decision trees can be used to derive easily
interpretable and intuitive rules for decision support systems.
[0041] A pediatric sepsis biomarker risk model, called PERSEVERE
(PEdiatRic SEpsis biomarkEr Risk modEl) has been previously derived
and validated (Wong H. et al., Crit. Care 16:R174 (2012)).
PERSEVERE assigns a 28-day mortality probability for children with
septic shock based on a panel of five biomarkers and age. The
biomarkers that were used to derive PERSEVERE were measured from
serum samples obtained during the first 24 hours of presentation to
the pediatric intensive care unit (PICU) with septic shock, which
is a clinically relevant time period for assigning mortality risk
in this heterogeneous population.
[0042] While the ability of PERSEVERE to assign a reliable
mortality probability during the initial stages of septic shock has
inherent utility at multiple levels, it fails to consider temporal
changes in biomarker levels and how these temporal changes may
further inform the estimation of risk for poor outcome. This is
important because the natural history of septic shock is
intrinsically dynamic and subject to change in response to therapy
(Hanna W. and Wong H., Crit. Care Clin. 29:203-222 (2013); Wong H.,
Pediatr. Res. 73:564-569 (2013); Wynn J. et al., Pediatrics
125:1031-1041 (2010)). Consequently, the risk for poor outcome also
changes over time, and it is biologically plausible that temporal
changes in the PERSEVERE biomarkers may reflect this change.
[0043] The results described herein relate to the derivation of a
temporal version of PERSEVERE (tPERSEVERE). This model incorporates
biomarker measurements at two time points, specifically the first
and third day following presentation, during the initial three days
of illness in order to estimate the probability of a "poor outcome"
or a "complicated course," defined as persistence of .gtoreq.2
organ failures at seven days after meeting criteria for septic
shock, or death within 28 days. The prognostic accuracy of
tPERSEVERE in an independent test cohort was subsequently
tested.
[0044] This model was developed from a prospective, multi-center
pediatric septic shock clinical and biological database, at
nineteen pediatric institutions, with a derivation cohort (n=225)
and a test cohort (n=72) of patients with septic shock. Biomarkers
were measured in the derivation cohort using serum samples obtained
during day 1 and day 3 of septic shock. Classification and
Regression Tree (CART) analysis was used to derive a model to
estimate the risk of a complicated course. The derived model was
tested in the test cohort and subsequently updated using the
combined derivation and test cohorts. The derived model had a
sensitivity for a complicated course of 90% (95% CI 78-96),
specificity was 70% (62-77), positive predictive value was 47%
(37-58), and negative predictive value was 96% (91-99). The area
under the receiver operating characteristic curve was 0.85
(0.79-0.90). Similar test characteristics were observed in the test
cohort. The updated model had a sensitivity of 91% (81-96), a
specificity of 70% (64-76), a positive predictive value of 47%
(39-56), and a negative predictive value of 96% (92-99).
[0045] tPERSEVERE was found to accurately and reliably estimate the
risk of a complicated course in a heterogeneous cohort of children
with septic shock. The study subjects were drawn from multiple
centers and pooled from four distinct databanks, thus adding
substantial variability with regard to pathology and therapeutic
interventions. Despite the concern that such heterogeneity might
diminish the accuracy of predictions, tPERSEVERE was found to
perform reliably, indicating that tPERSEVERE will be generalizable
upon further testing.
[0046] The positive and negative predictive values of a diagnostic
test are influenced by the prevalence of the outcome of interest
(Kaplan J. et al. Pediatr. Crit. Care Med. 12:165-73 (2011)). In
this study, the prevalence of a complicated course was about 23%,
so one would expect that the positive predictive value would be
lower than the negative predictive value. Further, if one assumes
that therapeutic interventions are beneficial and can ameliorate
the risk of a poor outcome, then some of the false positives (which
lower the positive predictive value and specificity) likely
represent patients in whom the predicted poor outcome was prevented
by therapeutic interventions.
[0047] The high sensitivity allows one to reliably identify
patients at risk for a poor outcome, while the high negative
predictive value allows one to identify those who are low risk. A
dichotomous interpretation of the model is that it can be used to
divide a heterogeneous cohort of children with septic shock into
two groups that differ by a factor of ten in the probability of a
poor outcome. An alternative interpretation of the model is to view
each terminal node individually, which allows for the assignment of
a range of probabilities for a complicated course.
[0048] The modeling procedures used in this derivation were focused
on a composite outcome variable, complicated course, whereas the
previous study focused on 28-day mortality (Wong H. et al., Crit.
Care 16:R174 (2012)). There are two primary reasons for this change
in focus. First, while 28-day mortality is an important outcome
variable, mortality alone does not fully capture all septic
shock-associated morbidity. Organ failure has been associated with
poor functional outcomes in septic shock survivors (Typpo K. et
al., Pediatr. Crit. Care Med. 10:562-570 (2009)); therefore, the
composite variable used in this study has been recently proposed as
a clinically relevant study endpoint (Mickiewicz B. et al., Am. J.
Respir. Crit. Care Med. 187:967-976 (2013); Abulebda A. et al.,
Crit. Care Med. In Press (2013)). Second, the incidence of
mortality in the study cohorts was too low for reliable modeling.
Importantly, the five false negative subjects in the derivation
cohort and the three false negatives in the test cohort all
survived. This indicates that tPERSEVERE has very high reliability
for predicting mortality, even though it was derived to estimate
the risk of a complicated course.
[0049] The performance of PERSEVERE was previously compared to that
of PRISM, and it was found that PERSEVERE outperformed PRISM (Wong
H. et al., Crit. Care 16:R174 (2012); Wong H. et al., PloS One in
press (2014)). tPERSEVERE has presently not been compared to PRISM
because the latter is not intended to be used as a temporal scoring
system.
[0050] tPERSEVERE can be used as an adjunct to traditional
physiological parameters for monitoring therapeutic interventions
in children with septic shock. Assuming that the risk of a
complicated course is modified by therapy, tPERSEVERE provides an
objective readout of therapeutic effectiveness by comparison to the
baseline risk predicted by PERSEVERE. A changing risk, reflected by
changing biomarkers, can even serve as a surrogate outcome variable
in Phase 1 or 2 interventional clinical trials.
[0051] In the initially derived tPERSEVERE, 49% of the derivation
cohort subjects and 63% of the test cohort subjects occupy terminal
nodes 1 and 8, which are dependent only on day 1 data. However, in
the updated model, there is only one terminal node that is
dependent exclusively on day 1 data (TN1), and only 28% of the
subjects occupy this node. The remaining terminal nodes are
informed by both day 1 and day 3 biomarker data.
[0052] In conclusion, a temporal version of PERSEVERE (tPERSEVERE)
has been derived, tested, and updated. tPERSEVERE can be used to
stratify patients or to monitor the therapeutic efficacy of a
treatment being administered to a patient with septic shock.
tPERSEVERE can be used as an adjunct to physiological assessments
for monitoring the efficacy of therapeutic interventions in
children with septic shock, or to serve as a surrogate outcome
variable in clinical trials.
Use of Multiple Time Points
[0053] The temporal biomarker-based risk model, as described
herein, uses data from 2 or more time points in order to monitor
the therapeutic efficacy of a treatment being administered to a
patient with septic shock. As will be appreciated by those skilled
in the art, such a temporal biomarker-based risk model can be
designed to use any number of time points greater than 1, with any
interval of time between additional time points. In practice, those
implementing the temporal biomarker-based risk model can select an
appropriate number of time points at which to acquire data, as well
as appropriate intervals of time between additional time
points.
[0054] In some embodiments, the temporal biomarker-based risk model
can use 2 or more time points. In some embodiments, the temporal
biomarker-based risk model can use 3 or more time points. In some
embodiments, the temporal biomarker-based risk model can use 4 or
more time points. In some embodiments, the temporal biomarker-based
risk model can use 5 or more time points. In some embodiments, the
temporal biomarker-based risk model can use 6 or more, 7 or more, 8
or more, 9 or more, or 10 or more time points. In some embodiments,
the temporal biomarker-based risk model can use more than 15 time
points.
[0055] In some embodiments, the first time point is within the
first hour of presentation with septic shock. In some embodiments,
the first time point is within the first 8 hours of presentation
with septic shock. In some embodiments, the first time point is
within the first 24 hours of presentation with septic shock. In
some embodiments, the first time point is within the first 36 hours
of presentation with septic shock. In some embodiments, the first
time point is within the first 48 hours of presentation with septic
shock. In some embodiments, the first time point is after the first
48 hours of presentation with septic shock.
[0056] The temporal biomarker-based risk model uses 2 or more time
points, wherein each additional time point occurs after the time
point that immediately precedes it. In some embodiments, the
additional time point occurs after a treatment has been
administered to the patient. In some embodiments, the additional
time point is within 1 hour of the time point that immediately
precedes it. In some embodiments, the additional time point is
between 1-12 hours after the time point that immediately precedes
it. In some embodiments, the additional time point is between 12-24
hours after the time point that immediately precedes it. In some
embodiments, the additional time point is between 24-60 hours after
the time point that immediately precedes it. In some embodiments,
the additional time point is more than 60 hours after the time
point that immediately precedes it.
[0057] The second time point occurs after the first time point. In
some embodiments, the second time point occurs after a treatment
has been administered to the patient. In some embodiments, the
second time point is within 1 hour of the first time point. In some
embodiments, the second time point is between 1-12 hours after the
first time point. In some embodiments, the second time point is
between 12-24 hours after the first time point. In some
embodiments, the second time point is between 24-60 hours after the
first time point. In some embodiments, the second time point is
more than 60 hours after the first time point.
[0058] In embodiments where the temporal biomarker-based risk model
uses 3 or more time points, the third time point occurs after the
second time point. In some embodiments, the third time point occurs
after a treatment has been administered to the patient. In some
embodiments, the third time point is within 1 hour of the second
time point. In some embodiments, the third time point is between
1-12 hours after the second time point. In some embodiments, the
third time point is between 12-24 hours after the second time
point. In some embodiments, the third time point is between 24-60
hours after the second time point. In some embodiments, the third
time point is more than 60 hours after the second time point.
[0059] In embodiments where the temporal biomarker-based risk model
uses 4 or more time points, the fourth time point occurs after the
third time point. In some embodiments, the fourth time point occurs
after a treatment has been administered to the patient. In some
embodiments, the fourth time point is within 1 hour of the third
time point. In some embodiments, the fourth time point is between
1-12 hours after the third time point. In some embodiments, the
fourth time point is between 12-24 hours after the third time
point. In some embodiments, the fourth time point is between 24-60
hours after the third time point. In some embodiments, the fourth
time point is more than 60 hours after the third time point.
[0060] In embodiments where the temporal biomarker-based risk model
uses 5 or more time points, the fifth time point occurs after the
fourth time point. In some embodiments, the fifth time point occurs
after a treatment has been administered to the patient. In some
embodiments, the fifth time point is within 1 hour of the fourth
time point. In some embodiments, the fifth time point is between
1-12 hours after the fourth time point. In some embodiments, the
fifth time point is between 12-24 hours after the fourth time
point. In some embodiments, the fifth time point is between 24-60
hours after the fourth time point. In some embodiments, the fifth
time point is more than 60 hours after the fourth time point.
Additional Patient Information
[0061] The demographic data, clinical characteristics, and/or
results from other tests or indicia of septic shock specific to a
pediatric patient with septic shock can affect the patient's
outcome risk. Accordingly, such demographic data, clinical
characteristics, and/or results from other tests or indicia of
septic shock can be incorporated into the methods described herein
which allow for stratification of individual pediatric patients in
order to determine the patient's outcome risk. Such demographic
data, clinical characteristics, and/or results from other tests or
indicia of septic shock can also be used in combination with the
methods described herein which allow for stratification of
individual pediatric patients in order to determine the patient's
outcome risk.
[0062] Such pediatric patient demographic data can include, for
example, the patient's age, race, gender, and the like.
[0063] In some embodiments, the temporal biomarker-based risk model
described herein can incorporate the patient's age to determine an
outcome risk. In some embodiments, the temporal biomarker-based
risk model described herein can incorporate the patient's race to
determine an outcome risk. In some embodiments, the temporal
biomarker-based risk model described herein can incorporate the
patient's gender to determine an outcome risk.
[0064] In some embodiments, the temporal biomarker-based risk model
described herein can be used in combination with the patient's age
to determine an outcome risk. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with the patient's race to determine an outcome risk.
In some embodiments, the temporal biomarker-based risk model
described herein can be used in combination with the patient's
gender to determine an outcome risk.
[0065] Such patient clinical characteristics and/or results from
other tests or indicia of septic shock can include, for example,
the patient's co-morbidities and/or septic shock causative
organism, and the like.
[0066] Patient co-morbidities can include, for example, acute
lymphocytic leukemia, acute myeloid leukemia, aplastic anemia,
atrial and ventricular septal defects, bone marrow transplantation,
caustic ingestion, chronic granulomatous disease, chronic hepatic
failure, chronic lung disease, chronic lymphopenia, chronic
obstructive pulmonary disease (COPD), congestive heart failure
(NYHA Class IV CHF), Cri du Chat syndrome, cyclic neutropenia,
developmental delay, diabetes, DiGeorge syndrome, Down syndrome,
drowning, end stage renal disease, glycogen storage disease type 1,
hematologic or metastatic solid organ malignancy, hemophagocytic
lymphohistiocytosis, hepatoblastoma, heterotaxy, hydrocephalus,
hypoplastic left heart syndrome, IPEX Syndrome, kidney transplant,
Langerhans cell histiocytosis, liver and bowel transplant, liver
failure, liver transplant, medulloblastoma, metaleukodystrophy,
mitochondrial disorder, multiple congenital anomalies,
multi-visceral transplant, nephrotic syndrome, neuroblastoma,
neuromuscular disorder, obstructed pulmonary veins, Pallister
Killian syndrome, Prader-Willi syndrome, requirement for chronic
dialysis, requirement for chronic steroids, retinoblastoma,
rhabdomyosarcoma, rhabdosarcoma, sarcoma, seizure disorder, severe
combined immune deficiency, short gut syndrome, sickle cell
disease, sleep apnea, small bowel transplant, subglottic stenosis,
tracheal stenosis, traumatic brain injury, trisomy 18, type 1
diabetes mellitus, unspecified brain tumor, unspecified congenital
heart disease, unspecified leukemia, VATER Syndrome, Wilms tumor,
and the like. Any one or more of the above patient co-morbidities
can be indicative of the presence or absence of chronic disease in
the patient.
[0067] Septic shock causative organisms can include, for example,
Acinetobacter baumannii, Adenovirus, Bacteroides species, Candida
species, Capnotyophaga jenuni, Cytomegalovirus, Enterobacter
cloacae, Enterococcus faecalis, Escherichia coli, Herpes simplex
virus, Human metapneumovirus, Influenza A, Klebsiella pneumonia,
Micrococcus species, mixed bacterial infection, Moraxella
catarrhalis, Neisseria meningitides, Parainfluenza, Pseudomonas
species, Serratia marcescens, Staphylococcus aureus, Streptococcus
agalactiae, Streptococcus milleri, Streptococcus pneumonia,
Streptococcus pyogenes, unspecified gram negative rods, unspecified
gram positive cocci, and the like.
[0068] In some embodiments, the temporal biomarker-based risk model
described herein can incorporate the patient's co-morbidities to
determine an outcome risk. In some embodiments, the temporal
biomarker-based risk model described herein can incorporate the
patient's septic shock causative organism to determine an outcome
risk.
[0069] In some embodiments, the temporal biomarker-based risk model
described herein can be used in combination with the patient's
co-morbidities to determine an outcome risk. In some embodiments,
the temporal biomarker-based risk model described herein can be
used in combination with the patient's septic shock causative
organism to determine an outcome risk.
Population-Based Risk Scores
[0070] A number of models that generate mortality prediction scores
based on physiological variables have been developed to date. These
can include the APACHE, PRISM, Pediatric Index of Mortality (PIM),
and/pediatric logistic organ dysfunction (PELOD) models, and the
like. The APACHE model considered can be APACHE I, APACHE II,
APACHE III, APACHE IV, or a subsequent iteration of APACHE.
[0071] Such models can be very effective for estimating
population-based outcome risks but are not intended for
stratification of individual patients. The methods described herein
which allow for stratification of individual patients can be used
alone or in combination with one or more existing population-based
risk scores.
[0072] In some embodiments, the temporal biomarker-based risk model
described herein can be used with one or more additional
population-based risk scores. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with APACHE. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with PRISM. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with PIM. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with PELOD. In some embodiments, the temporal
biomarker-based risk model described herein can be used in
combination with a population-based risk score other than APACHE,
PRISM, PELOD, and PRISM.
High Risk Therapies
[0073] High risk, invasive therapeutic and support modalities can
be used to treat septic shock. The methods described herein which
allow for the patient's outcome risk to be determined can help
inform clinical decisions regarding the application of high risk
therapies to specific pediatric patients, based on the patient's
outcome risk.
[0074] High risk therapies include, for example, extracorporeal
membrane oxygenation/life support, plasmapheresis, pulmonary artery
catheterization, high volume continuous hemofiltration, and the
like.
[0075] In some embodiments, individualized treatment can be
provided to a pediatric patient by selecting a pediatric patient
classified as high risk by the methods described herein for one or
more high risk therapies. In some embodiments, individualized
treatment can be provided to a pediatric patient by excluding a
pediatric patient classified as low risk from one or more high risk
therapies.
[0076] Certain embodiments of the invention include using
quantification data from a gene-expression analysis and/or from a
mRNA analysis, from a sample of blood, urine, saliva,
broncho-alveolar lavage fluid, or the like. Embodiments of the
invention include not only methods of conducting and interpreting
such tests but also include reagents, kits, assays, and the like,
for conducting the tests.
[0077] Diagnostic-testing procedure performance is commonly
described by evaluating control groups to obtain four critical test
characteristics, namely positive predictive value (PPV), negative
predictive value (NPV), sensitivity, and specificity, which provide
information regarding the effectiveness of the test. The PPV of a
particular diagnostic test represents the proportion of positive
tests in subjects with the condition of interest (i.e. proportion
of true positives); for tests with a high PPV, a positive test
indicates the presence of the condition in question. The NPV of a
particular diagnostic test represents the proportion of negative
tests in subjects without the condition of interest (i.e.
proportion of true negatives); for tests with a high NPV, a
negative test indicates the absence of the condition. Sensitivity
represents the proportion of subjects with the condition of
interest who will have a positive test; for tests with high
sensitivity, a positive test indicates the presence of the
condition in question. Specificity represents the proportion of
subjects without the condition of interest who will have a negative
test; for tests with high specificity, a negative test indicates
the absence of the condition.
[0078] The threshold for the disease state can alternatively be
defined as a 1-D quantitative score, or diagnostic cutoff, based
upon receiver operating characteristic (ROC) analysis. The
quantitative score based upon ROC analysis can be used to determine
the specificity and/or the sensitivity of a given diagnosis based
upon subjecting a patient to the decision tree described herein in
order to predict an outcome for a pediatric patient with septic
shock.
[0079] The correlations disclosed herein, between pediatric patient
septic shock biomarker levels and/or mRNA levels and/or gene
expression levels, provide a basis for conducting a diagnosis of
septic shock, or for conducting a stratification of patients with
septic shock, or for enhancing the reliability of a diagnosis of
septic shock by combining the results of a quantification of a
septic shock biomarker with results from other tests or indicia of
septic shock. For example, the results of a quantification of one
biomarker could be combined with the results of a quantification of
one or more additional biomarker, cytokine, mRNA, or the like.
Thus, even in situations in which a given biomarker correlates only
moderately or weakly with septic shock, providing only a relatively
small PPV, NPV, specificity, and/or sensitivity, the correlation
can be one indicium, combinable with one or more others that, in
combination, provide an enhanced clarity and certainty of
diagnosis. Accordingly, the methods and materials of the invention
are expressly contemplated to be used both alone and in combination
with other tests and indicia, whether quantitative or qualitative
in nature.
[0080] Having described the invention in detail, it will be
apparent that modifications, variations, and equivalent embodiments
are possible without departing the scope of the invention defined
in the appended claims. Furthermore, it should be appreciated that
all examples in the present disclosure are provided as non-limiting
examples.
EXAMPLES
[0081] The following non-limiting examples are provided to further
illustrate embodiments of the invention disclosed herein. It should
be appreciated by those of skill in the art that the techniques
disclosed in the examples that follow represent approaches that
have been found to function well in the practice of the invention,
and thus can be considered to constitute examples of modes for its
practice. However, those of skill in the art should, in light of
the present disclosure, appreciate that many changes can be made in
the specific embodiments that are disclosed and still obtain a like
or similar result without departing from the spirit and scope of
the invention.
Example 1
Derivation Cohort Study Subjects
[0082] Seventeen institutions contributed biological specimens and
clinical data to a central repository, with approval from the
Institutional Review Boards of each participating institution. Data
collection methods have been previously described (Wong H. et al.,
Crit. Care 16:R174 (2012)). Children <10 years of age admitted
to the PICU and meeting pediatric-specific criteria for septic
shock were eligible for enrollment. After informed consent from
parents or legal guardians, serum samples were obtained within 24
hours of initial presentation to the PICU with septic shock; these
are referred to as "day 1" samples. Forty-eight hours after
obtaining day 1 samples, a second serum sample was obtained; these
are referred to as "day 3" samples. Of the 355 subjects in the
original PERSEVERE derivation and validation cohorts, there were
225 with biomarker data available for both day 1 and day 3. The
current analysis included these 225 subjects, all of whom were
enrolled between May 2002 and August 2010.
Example 2
Test Cohort Study Subjects
[0083] The test cohort subjects were pooled from four sources, with
approval from the respective Institutional Review Boards.
Thirty-three subjects were included from an ongoing genomics study
in pediatric septic shock being conducted at 17 participating
institutions (Cvijanovich N. et al., Physiol. Genomics 34:127-134
(2008); Shanley T. et al., Mol. Med. 13:495-508 (2007); Wong H. et
al., Crit. Care Med. 37:1558-1566 (2009); Wong H. et al., BMC Med.
7:34 (2009); Wong H. et al., Physiol. Genomics 30:146-155 (2007);
Wynn J. et al., Mol. Med. 17:1146-1156 (2011); Basu R. et al.,
Crit. Care 15:R273 (2011); Wong H. et al., Crit. Care Med.
39:2511-2517 (2011); Wong H. et al., Pediatr. Crit. Care Med.
11:349-355 (2010); Wong H. et al., Am. J. Resp. Crit. Care Med.
178:276-282 (2008)). The enrollment criteria were identical to
those for the derivation cohort. The current analysis included
subjects enrolled between September 2011 and May 2013.
[0084] Eleven subjects were included from among those enrolled in a
quality improvement program at one institution. The institution
uses PERSEVERE to benchmark septic shock outcomes for all patients
admitted to the PICU with septic shock. Enrollment procedures were
identical to those described above, except that there was no age
restriction, and the Institutional Review Board granted permission
for waiver of informed consent. Serum samples were collected from
residual blood samples in the clinical laboratory. Subjects from
this source were enrolled between September 2012 and May 2013.
[0085] Nineteen subjects (age range: 8 days to 18 years) were
participants in a prospective, observational study at Ann &
Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill.,
evaluating nitric oxide metabolism and mitochondrial function in
children with septic shock (Weiss S. et al., Pediatr. Crit. Care
Med. 13:e210-218 (2012)). Of the 30 subjects with septic shock
enrolled in that study, 19 had serum samples available for
analysis. The current analysis included subjects enrolled between
May 2009 and June 2010.
[0086] Eleven subjects (age range: 2 to 20 years old) were
participants in a prospective, observational study at Yale-New
Haven Children's Hospital, New Haven, Conn., evaluating
angiopoietin levels in children with septic shock (Giuliano J. et
al., Pediatr. Crit. Care Med. In Press (2013)). Of the 17 subjects
with septic shock enrolled in that study, 11 had serum samples
available for analysis. The current analysis included subjects
enrolled between September 2009 and December 2011.
Example 3
Study Procedures
[0087] For all studies, annotated clinical and laboratory data were
collected daily while the participant was in the PICU. Illness
severity was calculated prospectively using the Pediatric Risk of
Mortality (PRISM) score (Pollack M. et al., J. Pediatr. 131:575-581
(1997)). The number of organ failures during the initial 7 days of
PICU admission was recorded using pediatric-specific criteria
(Goldstein B. et al., Pediatr. Crit. Care Med. 6:2-8 (2005)).
All-cause mortality was tracked for 28 days after meeting criteria
for septic shock. The composite endpoint used herein termed
"complicated course", was defined as persistence of two or more
organ failures at seven days after meeting criteria for septic
shock, or death within 28 days of presentation (Mickiewicz B. et
al., Am. J. Respir. Crit. Care Med. 187:967-976 (2013); Abulebda A.
et al., Crit. Care Med. In Press (2013); Xiao W. et al., J. Exp.
Med. 208:2581-2590 (2011)).
Example 4
Biomarkers
[0088] PERSEVERE includes C-C chemokine ligand 3 (CCL3),
interleukin 8 (IL8), heat shock protein 70 kDa 1B (HSPA1B),
granzyme B (GZMB), and matrix metallopeptidase 8 (MMP8). Serum
concentrations of these biomarkers were measured using a multi-plex
magnetic bead platform (MILLIPLEX.TM. MAP) designed for this
project by the EMD Millipore Corporation (Billerica, Mass.).
Biomarker concentrations were measured in a Luminex.RTM. 100/200
System (Luminex Corporation, Austin, Tex.), according the
manufacturers' specifications. Assay performance data were
previously published (Wong H. et al., Crit. Care 16:R174
(2012)).
Example 5
Statistical Analysis
[0089] Initially, data were described using medians, interquartile
ranges, frequencies, and percentages. Comparisons between groups
used the Mann-Whitney U-test, Chi-square, or Fisher's Exact tests
as appropriate. Descriptive statistics and comparisons used
SigmaStat Software (Systat Software, Inc., San Jose, Calif.).
[0090] CART analysis was used to derive tPERSEVERE (Salford
Predictive Modeler v6.6, Salford Systems, San Diego, Calif.) (Wong
H. et al., Crit. Care 16:R174 (2012); Che D. et al., Adv. Exp. Med.
Biol. 696:191-199 (2011); Muller R. et al., Clin. Chim. Acta
394:1-6 (2008)). The primary outcome variable for the modeling
procedures was complicated course. The absolute day 1 and day 3
biomarker values, the percentage change in biomarker values from
day 1 to day 3, and age were considered in the modeling procedures.
Performance of the derived model was reported using diagnostic test
statistics with 95% confidence intervals computed using the score
method as implemented by the VassarStats Website for Statistical
Computation (Computation VWfS, found at http <colon slash
slash>faculty <dot>vassar <dot>edu
<slash>lowry <slash>VassarStats <dot>html).
Example 6
[0091] Deriving tPERSEVERE
[0092] Table 1 shows the demographic and clinical characteristics
of the derivation cohort (n=225). The 52 (23%) subjects with a
complicated course had a higher median PRISM score and were less
likely to have a causative organism isolated compared to the 173
subjects with a non-complicated course. No other differences were
observed.
TABLE-US-00001 TABLE 1 Demographics and clinical characteristics of
the derivation and test cohorts. Derivation Cohort Test Cohort Non-
Non- Complicated Complicated Complicated Complicated All Course
Course All Course Course N 225 173 52 74 58 16 Mortality (%) 7 n/a
n/a 5 n/a n/a Median age 2.3 (0.8-5.6) 2.4 (1.0-6.0) 1.5 (0.7-4.4)
5.7 (1.7-12.2).sup.3 5.7 (1.7-12.2) 5.8 (1.1-14.1) years (IQR)
Median PRISM 14 (9-21) 12 (8-18) 21 (12-26).sup.2 11 (9-19) 11
(7-19) 14 (11-20) score (IQR).sup.1 # of males (%) 141 (63) 105
(61) 36 (69) 37 (50) 31 (53) 6 (38).sup.3 # of females (%) 84 (37)
68 (39) 16 (31) 37 (50) 27 (47) 10 (62) # for race (%) Caucasian
160 (71) 126 (73) 34 (65) 50 (68) 38 (66) 12 (75) African 37 (16)
28 (16) 9 (17) 7 (9) 5 (9) 2 (13) American Other 13 (6) 9 (5) 4 (8)
1 (1) 1 (2) 0 (0) Unreported 15 (7) 10 (6) 5 (10) 16 (22).sup.3 14
(24) 2 (13) # with gram (+) 61 (27) 43 (25) 18 (35) 20 (27) 14 (24)
6 (38) bacteria (%) # with gram (-) 64 (28) 45 (26) 19 (37) 14 (19)
10 (17) 4 (25) bacteria (%) # with viral 23 (10) 15 (9) 8 (15) 3
(4) 3 (5) 0 (0) infection (%) # with fungal 3 (1) 2 (1) 1 (2) 3 (4)
3 (5) 0 (0) infection (%) # with no 82 (36) 71 (41) 11 (21).sup.2
37 (50).sup.3 31 (53) 6 (38) organism isolated (%) # with any co-
98 (44) 78 (45) 20 (38) 12 (16).sup.3 10 (17) 2 (13) morbidity (%)
# with 16 (7) 14 (8) 2 (4) 0 (0).sup.3 0 (0) 0 (0) malignancy (%) #
with immune 32 (14) 28 (16) 4 (8) 0 (0).sup.3 0 (0) 0 (0)
suppression (%).sup.4 .sup.1Nineteen subjects (15 with a
non-complicated course and 4 with a complicated course) in the test
cohort did not have available PRISM scores. .sup.2p < 0.05 vs.
respective subjects with a non-complicated course. .sup.3p <
0.05 vs. derivation cohort. .sup.4Refers to patients with immune
suppression not related to cancer (for example, those receiving
immune suppressive medication for solid organ or bone marrow
transplantation, or those with a primary immune deficiency).
[0093] FIG. 1 depicts the derived model. Maximum accuracy was
achieved with five biomarker variables, namely absolute day 1 IL8
and CCL3 values and absolute day 3 IL8, CCL3, and HSPA1B values.
None of the other biomarker variables or age contributed to
predictive accuracy. There were four low probability terminal nodes
for a complicated course (0.0 to 7.9% probability; terminal nodes
TN1, TN2, TN4, and TN6) and four high probability terminal nodes
(35 to 58% probability; TN3, TNS, TN7, and TN8). Among the 126
subjects classified as low probability, 121 (96%) had a
non-complicated course, and five (4%) had a complicated course.
Among the 99 subjects classified as high probability, 47 (47%) had
a complicated course. Table 2 shows the diagnostic test
characteristics of the derived decision tree.
TABLE-US-00002 TABLE 2 Test characteristics of the decision tree.
Derivation Test Updated Cohort Cohort Model Number of Subjects 225
74 299 Number of True 47 13 62 Positives Number of True 121 47 162
Negatives Number of False 52 11 69 Positives Number of False 5 3 6
Negatives Sensitivity 90% (78-96) 81% (54-95) 91% (81-96)
Specificity 70% (62-77) 81% (68-90) 70% (64-76) Positive Predictive
47% (37-58) 54% (33-74) 47% (39-56) Value Negative Predictive 96%
(91-99) 94% (82-98) 96% (92-99) Value +Likelihood Ratio 3.0
(2.4-3.8) 4.3 (2.4-7.7) 3.1% (2.5-3.8) -Likelihood Ratio 0.1
(0.1-0.3) 0.2 (0.1-0.6) 0.1 (0.1-0.3) Area Under the Curve 0.85
(0.79-0.90) 0.83 (0.74-0.93) 0.84 (0.79-0.89)
Example 7
[0094] Testing tPERSEVERE
[0095] The independent test cohort consisted of 74 subjects with
septic shock, of whom 16 (22%) had a complicated course. Table 1
shows the demographics and clinical characteristics of the test
cohort. Compared to the derivation cohort, the test cohort subjects
had a higher median age, a higher proportion had no race reported,
a higher proportion had no causative organism isolated, and a lower
proportion had malignancy, immune suppression, or any other
co-morbidity. Within the test cohort, the subjects with a
complicated course had a lower proportion of males, compared to the
subjects with a non-complicated course. No other differences were
observed.
[0096] The test cohort subjects were classified according to the
derived model. Among the 50 subjects classified as low probability
for a complicated course, 47 (94%) had a non-complicated course,
and three (6%) had a complicated course. Among the 24 subjects
classified as high probability, 13 (54%) had a complicated course.
Table 2 shows the diagnostic test characteristics of tPERSEVERE in
the test cohort.
Example 8
[0097] Updating tPERSEVERE
[0098] tPERSEVERE was updated using all 299 subjects in the
combined derivation and test cohorts. All potential biomarker
variables and age were considered in the updating process. FIG. 2
depicts the updated version of tPERSEVERE. Maximum accuracy was
achieved with the same biomarker variables as the originally
derived decision tree, except that day 3 CCL3 data no longer added
to the predictive accuracy. In addition, a day 1 CCL3-based
decision rule replaced the day 1 IL8-based, first-level decision
rule in the originally derived decision tree.
[0099] The updated version of tPERSEVERE contains four low
probability terminal nodes for a complicated course (0.0 to 6.1%
probability; TN1, TN2, TN4, and TN6) and four high probability
terminal nodes (35.3 to 57.9% probability; TN3, TNS, TN7, and TN8).
Among the 168 subjects classified as low probability, 162 (96%) had
a non-complicated course, and six (4%) had a complicated course.
Among the 131 subjects classified as high probability, 62 (47%) had
a complicated course. Table 2 shows the diagnostic test
characteristics of the updated version of tPERSEVERE.
Example 9
[0100] Using tPERSEVERE to Monitor Therapeutic Efficacy
[0101] tPERSEVERE is used to monitor the therapeutic efficacy of a
treatment being administered to a patient with septic shock. First,
a pediatric patient with septic shock is identified. A first sample
from the patient at a first time point and analyzed to determine a
first level of at least one biomarker associated with septic shock
in pediatric patients in order to determine whether the level is
elevated above a cut-off level, wherein the presence of an elevated
level indicates that the patient has an elevated likelihood of
being classified as high risk for a poor outcome and the absence of
an elevated level indicates that the patient has a reduced
likelihood of being classified as high risk for a poor outcome.
Then, a second sample is obtained from the patient at a second time
point, wherein the second time point occurs after a treatment has
been administered to the patient. The second sample is then
analyzed to determine a second level of the at least one biomarker
in order to determine whether the second level is elevated above a
cut-off level, wherein the presence of an elevated level indicates
that the patient has an elevated likelihood of being classified as
high risk for a poor outcome and the absence of an elevated level
indicates that the patient has a reduced likelihood of being
classified as high risk for a poor outcome. The patient's risk for
a poor outcome at each of the time points is then compared in order
to determine if the patient's risk for a poor outcome increased or
decreased between the time points, where an increased risk for a
poor outcome indicates that a therapy has had a poor efficacy, and
a reduced risk for a poor outcome indicates that a therapy has had
a good efficacy.
Example 10
[0102] Using tPERSEVERE to Provide Individualized Treatment
[0103] tPERSEVERE is used to provide individualized treatment for a
pediatric patient with septic shock. First, the therapeutic
efficacy of a treatment being administered to a pediatric patient
with septic shock is determined as described in Example 9. If a
treatment is determined to have had a poor efficacy, that treatment
is discontinued, thereby providing individualized treatment.
Example 9
[0104] Using tPERSEVERE to Improve Outcome
[0105] tPERSEVERE is used to improve an outcome for a pediatric
patient with septic shock. First, the therapeutic efficacy of a
treatment being administered to a pediatric patient with septic
shock is determined as described in Example 9. If a treatment is
determined to have had a poor efficacy, that treatment is
discontinued. The discontinued treatment which has had a poor
efficacy is then replaced with a different treatment, in order to
achieve an improved outcome. The alternative treatment can be a
high risk therapy.
[0106] The various methods and techniques described above provide a
number of ways to carry out the application. Of course, it is to be
understood that not necessarily all objectives or advantages
described can be achieved in accordance with any particular
embodiment described herein. Thus, for example, those skilled in
the art will recognize that the methods can be performed in a
manner that achieves or optimizes one advantage or group of
advantages as taught herein without necessarily achieving other
objectives or advantages as taught or suggested herein. A variety
of alternatives are mentioned herein. It is to be understood that
some preferred embodiments specifically include one, another, or
several features, while others specifically exclude one, another,
or several features, while still others mitigate a particular
feature by inclusion of one, another, or several advantageous
features.
[0107] Furthermore, the skilled artisan will recognize the
applicability of various features from different embodiments.
Similarly, the various elements, features and steps discussed
above, as well as other known equivalents for each such element,
feature or step, can be employed in various combinations by one of
ordinary skill in this art to perform methods in accordance with
the principles described herein. Among the various elements,
features, and steps some will be specifically included and others
specifically excluded in diverse embodiments.
[0108] Although the application has been disclosed in the context
of certain embodiments and examples, it will be understood by those
skilled in the art that the embodiments of the application extend
beyond the specifically disclosed embodiments to other alternative
embodiments and/or uses and modifications and equivalents
thereof.
[0109] In some embodiments, the numbers expressing quantities of
ingredients, properties such as molecular weight, reaction
conditions, and so forth, used to describe and claim certain
embodiments of the application are to be understood as being
modified in some instances by the term "about." Accordingly, in
some embodiments, the numerical parameters set forth in the written
description and attached claims are approximations that can vary
depending upon the desired properties sought to be obtained by a
particular embodiment. In some embodiments, the numerical
parameters should be construed in light of the number of reported
significant digits and by applying ordinary rounding techniques.
Notwithstanding that the numerical ranges and parameters setting
forth the broad scope of some embodiments of the application are
approximations, the numerical values set forth in the specific
examples are reported as precisely as practicable.
[0110] In some embodiments, the terms "a" and "an" and "the" and
similar references used in the context of describing a particular
embodiment of the application (especially in the context of certain
of the following claims) can be construed to cover both the
singular and the plural. The recitation of ranges of values herein
is merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range.
Unless otherwise indicated herein, each individual value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (for example, "such as") provided with
respect to certain embodiments herein is intended merely to better
illuminate the application and does not pose a limitation on the
scope of the application otherwise claimed. No language in the
specification should be construed as indicating any non-claimed
element essential to the practice of the application.
[0111] Preferred embodiments of this application are described
herein, including the best mode known to the inventors for carrying
out the application. Variations on those preferred embodiments will
become apparent to those of ordinary skill in the art upon reading
the foregoing description. It is contemplated that skilled artisans
can employ such variations as appropriate, and the application can
be practiced otherwise than specifically described herein.
Accordingly, many embodiments of this application include all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the application unless
otherwise indicated herein or otherwise clearly contradicted by
context.
[0112] All patents, patent applications, publications of patent
applications, and other material, such as articles, books,
specifications, publications, documents, things, and/or the like,
referenced herein are hereby incorporated herein by this reference
in their entirety for all purposes, excepting any prosecution file
history associated with same, any of same that is inconsistent with
or in conflict with the present document, or any of same that may
have a limiting affect as to the broadest scope of the claims now
or later associated with the present document. By way of example,
should there be any inconsistency or conflict between the
description, definition, and/or the use of a term associated with
any of the incorporated material and that associated with the
present document, the description, definition, and/or the use of
the term in the present document shall prevail.
[0113] In closing, it is to be understood that the embodiments of
the application disclosed herein are illustrative of the principles
of the embodiments of the application. Other modifications that can
be employed can be within the scope of the application. Thus, by
way of example, but not of limitation, alternative configurations
of the embodiments of the application can be utilized in accordance
with the teachings herein. Accordingly, embodiments of the present
application are not limited to that precisely as shown and
described.
* * * * *