PhD Defense

Introduction Database Description State of the Art AI Methods Applied An AI Tour of Sepsis Conclusions

On the Intelligent Management of Sepsis in the
Intensive Care Unit

Vicent J. Ribas

LSI - SOCO
Technical University of Catalonia (UPC)
Barcelona

January 28, 2013

Vicent J. Ribas LSI - SOCO Technical University of Catalonia (UPC) Barcelona
On the Intelligent Management of Sepsis in the Intensive Care Unit


Contents
1 Introduction
Introduction
Thesis Objectives

2 Database Description
Dataset
Available Data
3 State of the Art

4 AI Methods Applied

5 An AI Tour of Sepsis
Incidence of Sepsis
Protection against Sepsis
Mortality Prediction with a Latent Data Representation
Risk of Death Assessment from Observed Data
6 Conclusions
Incidence of Sepsis and Coadjuvant Factors
Protective Eﬀects of Statins
Mortality Predictors and Their Accuracy
Contributions
Outline for Future Work
Publications



Introduction



Introduction

Introduction

Sepsis is a clinical syndrome deﬁned by the presence of
infection and Systemic Inﬂammatory Response Syndrome
(SIRS).



Introduction

Introduction

(SIRS).
This can lead to severe sepsis or to septic shock (severe sepsis
with hypotension refractory to ﬂuid administration) and
multi-organ failure.



Introduction

Introduction

(SIRS).
In western countries, septic patients account for as much as
25% of ICU bed utilization and the pathology occurs in 1% -
2% of all hospitalizations.



Introduction

Introduction

(SIRS).
In western countries, septic patients account for as much as
25% of ICU bed utilization and the pathology occurs in 1% -
2% of all hospitalizations.
The mortality rates of sepsis range from 12.8% for sepsis and
20.7% for severe sepsis, and up to 45.7% for septic shock.


Introduction

The medical management of sepsis and the study of its
prognosis and outcome is a relevant medical research
challenge.



Introduction

The medical management of sepsis and the study of its
prognosis and outcome is a relevant medical research
challenge.
Provided that such methods are to be used in a clinical
environment (ICU), it requires prediction methods that are
robust, accurate and readily interpretable.



Thesis Objectives

Thesis Objectives

Improve our knowledge about the incidence of Sepsis.



Thesis Objectives

Thesis Objectives

Improve our understanding about Sepsis and its protective
factors.



Thesis Objectives

Thesis Objectives

factors.
Study of the evolution of Sepsis into more critical states with
respect to several management/measurement variables.



Thesis Objectives

Thesis Objectives

factors.
Study of the evolution of Sepsis into more critical states with
respect to several management/measurement variables.
Develop a system that could provide prognostic indicators of
mortality (RoD) that can be used in the ICU (acc., sens,
spec), if possible, at the onset of the pathology.



Dataset

Available Datasets

A prospective observational cohort study of adult patients with
severe sepsis was conducted at the Critical Care Department
of the Vall d’ Hebron University Hospital (Barcelona, Spain).



Dataset

Available Datasets

Data from 750 and 354 patients with severe sepsis was
collected in this ICU between June, 2007 and December, 2010.



Dataset

Available Datasets

55% of cases correspond to ‘medical’ sepsis.



Dataset

Available Datasets

The mean age of the patients in the database was 57.08 (with
standard deviation ±16.65) years.



Dataset

Available Datasets

The mean age of the patients in the database was 57.08 (with
standard deviation ±16.65) years.
40% of patients were female.



Available Data

Available Attributes
The collected data show the worst values for all variables
during the ﬁrst 24 hours of evolution of Severe Sepsis.



Available Data

Organ dysfunction was evaluated by means of the SOFA score
system, which objectively measures organ dysfunction for 6
organs/systems.
Cardiovascular (CV) 2.86 (1.62) Haematologic (HAEMATO) 0.78 (1.14)
Respiratory (RESP) 2.31 (1.15) Global SOFA score 7.94 (3.86)
Central Nerv. Sys. (CNS) 0.48 (1.00) Dysf. Organs 1.68 (1.09)
Hepatic (HEPA) 0.48 (0.92) Failure Organs 1.51 (1.02)
Renal (REN) 1.06 (1.20) Total Dysf. Organs 3.18 (1.32)



Available Data

organs/systems.

Severity was evaluated by means of the APACHE II score,
which was 23.03 ± 9.62 for the population under study.



Available Data

organs/systems.

Severity was evaluated by means of the APACHE II score,
which was 23.03 ± 9.62 for the population under study.
The mortality rate intra-ICU for our study population was
26.32%.


Available Data


List of attributes used in this study:
Age Haemocultures 6h
Dysfunctional Organs for
Gender SOFA 1-2 Antibiotics 6h

Sepsis Focus Dysfunctional Organs for Volume 6h
SOFA 3-4 O2 Central Venous Saturation
Germ Class
Total Number of 6h
Polimicrobial Infection
Dysfunctional Organs Haematocrit 6h
Base Pathology
Mechanical Ventilation Transfusions 6h
Cardiovascular SOFA Score
Oxygenation Index Dobutamine 6h
Respiratory SOFA Score PaO2 /FiO2
Surviving Sepsis Campaign
CNS SOFA score Vasoactive Drugs Bundles 24h
Hepatic SOFA Score Platelet Count Glycaemia 24h
Renal SOFA Score APACHE II Score PPlateau
Haematologic SOFA Score Surviving Sepsis Campaign Worst Lactate
Total SOFA Score Bundles 6h
O2 Central Venous Saturation



Quantitative Analysis of the Pathophysiology of Sepsis

Reference Objective Model Variables
Rackow et al. 1991 Severity ODE B. Flow
MAP
Art. Res
React. Hyperemia
Kimberly et al. 2000 Shock Autonomic HR
Coupling BP
Ross et al. 1998 Inﬂam. ODE Pathogen
Shock ANN (RBF) Cell Damage
Immuno. Resp.
Othman et al. 2003 Shock Gastric Impedance
and 2004 Mucosa



Quantitative Analysis of the Prognosis of Sepsis (Medical
Practice)

Reference Objective Model Variables
Knaus et al. 1985 Eval. LR SOFA
MODS APACHE II
LODS
Adler et al. 2008 Sepsis LR SAPS 3
Paetz et al. 2001 Shock RBF SBP
Thrombocit.
Lact.
Savkin et al. 2010 Sepsis SVM Culture
RR, HR
Lact.
White cell
Brause et al. 2002 Sepsis HMM Sep. States
Full Obs.



AI Methods Applied



AI Methods Applied

Graphical models from Algebraic Statistics chapter 6.
CART chapter 6.
Logistic regression over latent factors chapter 7.
Shrinkage methods chapter 8. [Hastie]
Support Vector Machines chapter 8 [Sch¨lkopf, Christianini].
o
Generative kernels chapter 8.
Simpliﬁed Fisher kernel.
Quotient Basis Kernel.
Kernels based on the JS metric.
Relevance Vector Machines chapter 8 [Tipping, Fletcher].



Shrinkage methods

M
Linear Regression: minw Lλ (w, y, x) = i=1 (yi − g (xi ))2 ,



Shrinkage methods

M
Ridge regression:
M
minw Lλ (w, y, x) = minw λ w 2 + i=1 (yi − g (xi ))2 ,



Shrinkage methods

M
Ridge regression:
M
Lasso (L1 loss function):
M
minw Lλ (w, y, x) = minw λ|w|1 + i=1 (yi − g (xi ))2 ,



Shrinkage methods

M
Ridge regression:
M
Lasso (L1 loss function):
M
minw Lλ (w, y, x) = minw λ|w|1 + i=1 (yi − g (xi ))2 ,
Relevance vector machine (SVM with priors) [Tipping,
Fletcher].



Why generative kernels?

Selection of kernel function for a given problem is not trivial.




One normally must have good insight about the problem at
hand.




hand.
Mapping over higher dimensions simpliﬁes the problem but
computational cost grows with ∼ d 3 .




hand.
Solution: exploit the statistical structure of the data to build
the kernel!




hand.
Solution: exploit the statistical structure of the data to build
the kernel!
Requirement: pdf must be a regular exponential family. This
requirement is fulﬁlled by the dataset of this Ph.D.!



Generative kernels

We propose three generative approaches:



Generative kernels

exploit the algebraic structure of the dataset (Quotient Basis).



Generative kernels

exploit the momentum generation properties of regular
exponential families (i.e. the second derivative of the
log-Laplace function G corresponds to the covariance) to
calculate the Fisher kernel.



Generative kernels

exploit the momentum generation properties of regular
exponential families (i.e. the second derivative of the
log-Laplace function G corresponds to the covariance) to
calculate the Fisher kernel.
use the dual of the log-Laplace function, which corresponds to
the negative entropy, and a metric (Jensen-Shannon) to build
the kernels through application of the properties for pd and nd
functions (i.e. centering, inversion and exponentiation).



Quotient Basis Kernel

Let A be a set of n unique points A = {a1 , . . . , an } and τ a term
ordering. A Gr¨bner basis of A, G = g1 , . . . , gt , is a Gr¨bner basis
o o
of I (A). Therefore, the points in A can be presented as the set of
solutions of 
 g1 (a) = 0

g2 (a) = 0


 ···
gt (a) = 0





Let A, be a set of n × s unique support points A = {a1 , . . . , an }
and τ a term ordering. A monomial basis of the set of polynomial
functions over A is

ESTτ = {x α : x α ∈ LT(g ) : g ∈ I (A) }
/

This means that ESTτ comprises the elements x α that are not
divisible by any of the leading terms of the elements of the Gr¨bner
o
basis of I (A).




Let τ be a term ordering and let us consider an ordering over the
support points A = {a1 , . . . , an }. We call design matrix (i.e. ESTτ
evaluated in A) the following n × c matrix

Z = [ESTτ ] A
where c is the cardinality of ESTτ and n is the number of support
points. The covariance of the design matrix of ESTτ , which is a
kernel, is the QBK.



Simplified Fisher Kernel
Let P = (P|η ∈ N) be a regular exponential family with canonical
sufficient statistic T . Then the log likelihood function takes the
form
l(η|T ) = n(η t T − G (η))
The score function is the gradient

∂l(η|T ) ∂
U(T , η) = = nT − G (η)
∂η ∂η
So the simplified Fisher Kernel is:

k(x, z) = U(Tx , η)U(Tz , η)t



Kernels based on the Jensen Shannon Metric




Let P = (P|η ∈ N) be a regular exponential family with canonical
suﬃcient statistic T . Then the log likelihood function takes the
form
l(η|T ) = n(η t T − G (η))
This function accepts a convex - conjugate (Legendre Dual) of the
form
l(γ|T ) = n(γ t T − F (γ))
In our case, the dual F is the negative log-entropy function.




F (γ1 ) + F (γ2 ) γ1 + γ2
JS(γ1 , γ2 ) = −F .
2 2

Centred kernel:
φ(x, y ) = JS(x, x0 ) + JS(y , x0 ) − JS(x, y ) − JS(x0 , x0 ).




F (γ1 ) + F (γ2 ) γ1 + γ2
JS(γ1 , γ2 ) = −F .
2 2

Centred kernel:
Exponentiated kernel: φ(x, y ) = exp(−tJS(x, y )) ∀t > 0.




F (γ1 ) + F (γ2 ) γ1 + γ2
JS(γ1 , γ2 ) = −F .
2 2

Centred kernel:
Exponentiated kernel: φ(x, y ) = exp(−tJS(x, y )) ∀t > 0.
1
Inverse kernel: φ(x, y ) = t+JS(x,y ) ∀t > 0.



Route map



Incidence of Sepsis

SOFA Score and Sepsis

Study based on the basal SOFA scale.



Incidence of Sepsis


A SOFA≥2 is demonstrative of MODS while a SOFA-CV > 2
is demonstrative of Septic Shock.



Incidence of Sepsis


A SOFA≥2 is demonstrative of MODS while a SOFA-CV > 2
is demonstrative of Septic Shock.
By the deﬁnition of SOFA it is obvious that Severe Sepsis,
Shock and MODS are dependent on each other (i.e. all imply
organ dysfunction).



Incidence of Sepsis

Bayes Network for Sepsis

node X1 corresponds to the
X1 X2 unobserved number of
Severe Sepsis.
node X2 corresponds to
Septic Shock.
node X3 corresponds to
MODS.
X3 X4 node X4 corresponds to ICU
result.



Incidence of Sepsis

Incidence of Sepsis

Bayes Network yields an incidence of 164 cases / 100000 hab
(i.e. 164 vs 118 in our database).
The incidence reported in Madrid is 141 cases / 100000 hab.
and Castilla Le´n is 250 cases / 100000 hab.
o
The discrepancy between these ﬁgures lies mainly in the study
design (haemocultures at admission vs. people being treated
for Sepsis).




Statins
Statins is a common drug for the treatment of high
cholesterol levels.
Beyond their hypolipemic properties, they also exercise
anti-inflammatory, immunomodulator and antioxidant actions.
Statins modulate vasoreactivity in the coagulation system.
Recent studies suggest that they present beneficial effects for
infection prevention and treatment, impacting the ICU
outcome.
These results are still controversial in the medical community.
In our dataset with 750 patients, 106 (i.e. 14.13%) received
preadmission treatment with statins.



Hypothesis

In our dataset with 750 patients, 106 (i.e. 14.13%) received
preadmission treatment with statins.
Do statins play a protective role in the prognosis of Sepsis?
Does this role depend on the Sepsis continuum (MODS and
Septic Shock)?




Models of Conditional Independence

We aim to ﬁnd the relation between the administration of statin
drugs prior to ICU admission and the mortality rate in Severe
Sepsis patients. Thus, we test the null hypothesis that the ICU
outcome is independent of the preadmission use of statins
for given APACHE II and SOFA scores. Ho :

Ho : {X1 } ⊥ {X4 }|{X2 }, {X3 }.
⊥ (1)

where {X1 } is the ICU outcome, {X4 } the preadmission use of
statins, {X3 } the SOFA score, and {X2 } the APACHE II score.




We have two options based on Algebraic Models to reject Ho :
study the rank of the minors of our observation matrix
(tedious for large datasets).
Algebraic Interpolation (more fun):
Assume that our observed dataset are the zeroes of polynomial
set of equations (and an order on the variables).




On a ﬁrst level of abstraction there is a Polynomial Ideal
associated to this set of equations (vanishing ideal).




On a second level of abstraction, this ideal is generated by a
ﬁnite basis (Hilbert basis theorem). This is a Gr¨bner basis.
o




On a second level of abstraction, this ideal is generated by a
ﬁnite basis (Hilbert basis theorem). This is a Gr¨bner basis.
o
On a third level of abstraction, the terms of the ordering that
are not divided by the leading terms of the Gr¨bner basis are
o
deﬁned as a Quotient Basis.



Algebraic Interpolation

The resulting products between
terms of the Quotient Basis
Statins SOFA APACHE II
1 1 1 show the
2 1 1 interaction/dependence
1 2 1 between terms.
2 2 1
1 1 2 By the Hammersley - Cliﬀord
2 1 2 theorem (factorisation of the
1 2 2 terms of the Quotient Basis)
2 2 2
show that there is a Graphical
Model associated to the
vanishing ideal.





Ordering: x1 x2 x3 x1 x2 x1 x3
x1 x2 x1 x2 x3 x2 x3 1
Ideal I =
2 2 2
x3 − 3x3 + 2, x2 − 3x2 + 2, x1 − 3x1 + 2 .
Gr¨bner G =
o
2 − 3x + 2, x 2 − 3x + 2, x 2 − 3x + 2 .
x3 3 2 2 1 1
Quotient Basis
x3 x4
B = {1, x3 , x2 , x2 x3 , x1 , x1 x3 , x1 x2 , x1 x2 x3 }.





P(x4 ) = η7 x1 x2 x3 + η6 x1 x2 + η5 x1 x3
− η4 x2 x3 − η3 x1 + η2 x2 + η1 x3 + η0

Solving for {x1 , x2 , x3 } by substitution and also knowing that
η0 = 1 − 7 ηi yields the interpolation polynomial
i=1

P(x4 ) = −1/50x1 x2 x3 + 3/100x1 x2 + 9/100x1 x3
− 3/25x2 x3 − 21/100x1 + 27/100x2 + 8/25x3 + 7/25




Statins SOFA APACHE II Result=1 Result=2
1 1 1 0.64 0.36
2 1 1 0.53 0.47
1 2 1 0.80 0.20
2 2 1 0.70 0.30
1 1 2 0.91 0.09
2 1 2 0.87 0.13
1 2 2 0.93 0.07
2 2 2 0.88 0.12




Analysis with Decision Trees




Factor Analysis




Factor Analysis

A Factor Analysis (FA) model concerns a Gaussian hidden
variable model with d observed variables Xi and k hidden
variables Yi .
FA assumes (X , Y ) follows a joint multivariate normal
distribution with positive deﬁnite covariance matrix.
What is sought is a model of the form x − µ = Λ + .




Factor Analysis

The FA model Fd,k is the family of multivariate normal
distributions Nd (µ, Σ) on Rd whose mean vector µ is an arbitrary
vector in Rd and whose covariance matrix Σ lies in the
(non-convex) cone

Fd,k = {Ω + ΛΛt ∈ Rd×d : Ω 0 diagonal, Λ ∈ Rd×k }

= {Ω + Ψ ∈ Rd×d : Ω 0 diagonal, Ψ 0 symmetric, rank (Ψ) ≤ k}.




Factor Analysis
Factor 1: cardiovascular function, cardiovascular SOFA score
and vasoactive drugs.
Factor 2: haematologic function (haematologic SOFA score
and platelet count).
Factor 3: respiratory function, Respiratory SOFA score and
PaO2 /FiO2 ratio.
Factor 4: use of mechanical ventilation and PPlateau.
Factor 5: 24h SSC bundles and glycaemic indices.
Factor 6: micro-organism producing the Sepsis and whether
this sepsis polimicrobial or not.
Factor 7: renal function measured by the SOFA score and
total SOFA score.



Factor Analysis

Factor 8: antibiotics and haemocultures during the ﬁrst 6h of
ICU stay.
Factor 9: number of organs in dysfunction.
Factor 10: hepatic function measured by the SOFA score.
Factor 11: CNS function and number of organs in dysfunction.
Factor 12: loci of Sepsis and poly-microbial.
Factor 13: APACHE II score and worst lactate levels.
Factor 14: Total number of organs in dysfunction.




Logistic Regression

Log-odd ratio of a Binomial distribution is

p
log = β0 + βX. (2)
1−p

Where β0 is the intercept and β is vector of logistic regression
coeﬃcients estimated through ML with a generalised linear model.




Experiment setting

10-Fold cross-validation.
LR over Latent Factors (backward feature selection).
Subset selection of the Original Variables (backward feature
selection).
Comparison with RoD formula based on the APACHE II score
ROD
ln 1−ROD = −3.517 + 0.146 · A + .




Logistic Regression over Latent Factors

β Coeﬀ MAX MIN Z-score
Intercept 1.22 1.53 .87 7.11
F4 -0.54 -0.23 -0.86 -3.38
F10 -0.69 -0.38 -1.05 -4.26
F9 -0.51 -0.21 -0.81 -3.36
F13 -0.49 -0.24 -0.74 -3.80




Logistic Regression over Latent Factors

β Coeﬀ MAX MIN Z-score
Intercept 4.20 3.11 5.29 7.56
APACHE II -0.08 -0.13 -0.04 -3.77
Worst Lact. -0.25 -0.38 -0.11 -3.63




Results

Method AUC Error Rate Sens. Spec. Dataset
LR-FA 0.78 0.24 0.65 0.80 FA
LR 0.75 0.30 0.64 0.72 LR
APACHE II 0.70 0.28 0.82 0.55 N/A




RoD with RVM

The model performance was evaluated by means of 10-Fold
Cross-Validation.




RoD with RVM

Cross-Validation.
The RVM yielded an accuracy of mortality prediction of 0.86;
a prediction error of 0.18; a sensitivity of 0.67; and a
speciﬁcity of 0.87.




RoD with RVM

Cross-Validation.
The RVM yielded an accuracy of mortality prediction of 0.86;
a prediction error of 0.18; a sensitivity of 0.67; and a
speciﬁcity of 0.87.
RVM selected the following attributes (corresponding to
weights):
Number of dysfunctional organs (w1 = −0.039)
Mechanical Ventilation (w2 = −0.101)
APACHE II (w3 = −0.337)
Resuscitation Bundles (6h) (w4 = 0.037)




The coeﬃcients corresponding to the rest of attributes were
set to values close to zero as part of the training process. This
reduces the number of attributes (34 to just 4) and improves
its interpretability.




The negative weights (number of dysfunctional organs,
mechanical ventilation, APACHE II) are related to a higher
mortality risk.




The negative weights (number of dysfunctional organs,
mechanical ventilation, APACHE II) are related to a higher
mortality risk.
The SSC bundles (resuscitation bundles) are associated to a
protective eﬀect (i.e. antibiotics administration, performance
of haemocultures, administration of volume and vasoactive
drugs and so on).




Comparison with other shrinkage methods

The predictive ability of the RVM has also been compared to
that of other well established shrinkage methods for regression.




Comparison with other shrinkage methods

The predictive ability of the RVM has also been compared to
that of other well established shrinkage methods for regression.
The selected attributes and coeﬃcients for each method were:
Lasso:
Age (w1 = 0.007)
Ridge Regression: Germ Class (w2 = 0.005) Logistic Regression with backward
feature selection:
Number of dysfunctional PaO2 /FiO2 (w3 = 0.001)
organs for SOFA 3-4 APACHE II (w4 = −0.006) Intercept (w1 = 4.20)
(w1 = −0.021) Number of Dysfunctional
SvcO2 6h (w5 = −0.001)
APACHE II (w2 = −0.127) Organs (w1 = −0.12)
Haematocrit 6h
Worst Lactate (w6 = 0.009) APACHE II (w2 = −0.08)
(w3 = −0.126). Worst Lactate (w3 = −0.25)
Worst Lactate
(w7 = −0.023)
SvcO2 (w8 = −0.006).




The three shrinkage methods evaluated in this section agreed
in detecting as prognostic factors the Severity measured by
the APACHE II score and acidosis.




Organ dysfunction and mechanical ventilation or other
parameters related to it like PaO2 /FiO2 also play a role in the
prognosis of Sepsis.




Organ dysfunction and mechanical ventilation or other
parameters related to it like PaO2 /FiO2 also play a role in the
prognosis of Sepsis.
The accuracy of each method was the following:

Method AUC Error Rate Sens. Spec.
RVM 0.86 0.18 0.67 0.87
Logistic 0.75 0.30 0.64 0.72
Ridge 0.70 0.25 0.63 0.79
Lasso 0.70 0.32 0.67 0.68




RoD with Generative Kernels - QBK -

x1 is the Number of Dysfunctional Organs as measured by the
SOFA Score.
x2 corresponds to Mechanical Ventilation (yes/no).
x3 corresponds to Severity as Measured by the APACHE II
Score.
x4 corresponds to the SSC Resuscitation Bundles (i.e.
administration of antibiotics, performance of haemocultures
and so on). This is also a binary variable.




RoD with Generative Kernels - QBK -
 

 1, x4 , x3 , x3 x4 , 

 


 x2 , x2 x4 , x2 x3 , x2 x3 x4 , 




 2 2 2 2
x2 , x2 x4 , x2 x3 , x2 x3 x4 ,



 
3 3 3 3
 


 x2 , x2 x4 , x2 x3 , x2 x3 x4 , 


4, x 4x , x 4x , x 4x x ,
 
x2 2 4 2 3 2 3 4

 

 
ESTτ = 5 5 6
x2 , x2 x4 , x2 , x1 ,
x1 x4 , x1 x3 , x1 x3 x4 , x1 x2 ,

 


 



 2
x1 x2 x4 , x1 x2 x3 , x1 x2 x3 x4 , x1 x2 ,



 


 2 2 2 3
x1 x2 x4 , x1 x2 x3 , x1 x2 x3 x4 , x1 x2 ,



 


 3x , x x 3x , x x 3x x , x x 4,
x1 x2 4 1 2 3 1 2 3 4 1 2



 
4 4 5
 
 x1 x2 x4 , x1 x2 x3 , x1 x2 




RoD with Generative Kernels

We have used Matlab’s Support Vector Machine QP solver
implemented in the BioInformatics and Optimization Toolboxes.
We have also used 10-fold cross validation to evaluate the
classiﬁcation performance for the diﬀerent kernels. A grid search
yielded the appropriate values for C parameters for each Kernel.
More particularly,
Quotient Basis and Fisher C = 1.
Generative Kernels C = 10. Also the parameter t for the
Exponential and Inverse Kernels was set to 2.
Gaussian, Linear and Polynomial Kernels C = 10.




RoD with Generative Kernels

Kernel AUC Error Rate Sens. Spec. CPU time [s]
Quotient 0.89 0.18 0.70 0.86 1.45
Fisher 0.76 0.18 0.68 0.86 1.39
Exponential 0.75 0.21 0.70 0.82 1.64
Inverse 0.62 0.22 0.70 0.82 1.68
Centred 0.75 0.21 0.70 0.82 1.99
Gaussian 0.83 0.24 0.65 0.81 1.56
Poly (order 2) 0.69 0.28 0.71 0.76 1.59
Linear 0.62 0.26 0.62 0.78 1.35



Incidence of Sepsis and Coadjutant Factors
SIRS pathology has proven to be a very sensitive indicator of
Sepsis but also one of poor speciﬁcity.




Castilla y Le´n report an incidence of 250 cases /100.000 hab.
o
and Madrid 141 cases/100.000 hab.




o
Our Bayes Network yielded an estimation of 164 cases /
100.000 hab (i.e. 164 vs 118 in our database).




o
Our Bayes Network yielded an estimation of 164 cases /
100.000 hab (i.e. 164 vs 118 in our database).
There are diﬀerent comorbidities and coadjuvant factors that
clearly play a role in Sepsis.


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