Medical University of Vienna Jeroen S. de Bruin

Validation of Fuzzy Logic in Infection Surveillance

Jeroen S. de Bruin, Alexander Blacky, Walter Koller, Klaus-Peter Adlassnig

Medical University of Vienna Jeroen S. de Bruin

About me…

University Assistant at the Medical University of Vienna

Main research topicThe electronic detection of healthcare-associated infections

Medical University of Vienna Jeroen S. de Bruin

Healthcare-Associated Infections

Definition according to the ECDC:

An infection is considered as [healthcare]-associated if it occurs later than 48 hours after admittance to a [healthcare] facility.

Commonly abbreviated as either HAI, or HCAI.

Medical University of Vienna Jeroen S. de Bruin

HAI types

Various (main) types of HAI, depending on infection site:

• Blood stream infection (BSI)• Pneumonia (PN)• Urinary tract infection (UTI)• Central venous catheter-related infection (CRI)• Surgical site infection (SSI)

Medical University of Vienna Jeroen S. de Bruin

Electronic detection data

PDMS Biochemistry

Microbiology

Medical University of Vienna Jeroen S. de Bruin

Electronic detection system

Medical University of Vienna Jeroen S. de Bruin

Fuzzy set theory• Perform a qualitative abstraction on quantifiable

data.• Calculating the compatibility between the

patient’s measurable health status and an abstract linguistic clinical concept

Fuzzy logic• Inference mechanisms to reason about more

abstract clinical concepts using fuzzy sets.

Fuzzy set theory & Logic

Medical University of Vienna Jeroen S. de Bruin

Why use Fuzzy?

Fuzzy set theory and logic introduce graduality

• Infections and infection signs no longer simply appear, but the development process can be seen and tracked

Potential clinical uses:• Patterns & prediction• Early intervention• Correct classification of HAIs

Medical University of Vienna Jeroen S. de Bruin

Practical example

Make fixed (crisp) thresholds fuzzy!• Fuzzy region of fever between 37.5 and 38

degrees

Medical University of Vienna Jeroen S. de Bruin

Fever fuzzy set

Medical University of Vienna Jeroen S. de Bruin

Blood stream infection

Medical University of Vienna Jeroen S. de Bruin

Choice of fuzzy threshold• How to determine if the threshold was adequately

chosen?• Can it be wider? Is it too wide?

Hypothesis• Patients with a fuzzy indication of HAI tend to

have fuzzy values for infection indicators (e.g. fever, hypotension, leukopenia, etc) more often

Fuzzy threshold valid?

Medical University of Vienna Jeroen S. de Bruin

Validation experiment

• Period: January – December 2011• #Stations: 10 intensive care units• #Patients: 2,429• #Patient days: 24,487• Infection subset: CRI

Medical University of Vienna Jeroen S. de Bruin

Validation resultsInfection Parameter No infection signs Fuzzy CRI signs p

#Fuzzy values (%) #Fuzzy values (%)

Increased body temperature 14.1 20

0.003

Shock 27.2 38< 0.001

Increased C-reactive protein 24.0 77

< 0.001

Leukopenia 2.7 7< 0.001

Leukocytosis 6.3 90.032

Fever 61.8 90 < 0.001

Hypotension 65.4 68 0.297

Clinical signs of BSI 20.6 100< 0.001

Medical University of Vienna Jeroen S. de Bruin

Validation resultsInfection Parameter No infection signs Fuzzy CRI signs p

#Fuzzy values (%) #Fuzzy values (%)

Increased body temperature 14.1 20

0.003

Shock 27.2 38< 0.001

Increased C-reactive protein 24.0 77

< 0.001

Leukopenia 2.7 7< 0.001

Leukocytosis 6.3 90.032

Fever 61.8 90 < 0.001

Hypotension 65.4 68 0.297

Clinical signs of BSI 20.6 100< 0.001

Medical University of Vienna Jeroen S. de Bruin

Conclusions

• Fuzzy logic can be used effectively to detect patients with mild or partial signs of infection

• Potential clinical uses for this method include:– Prediction– Early intervention– Accurate classification of HAI

Medical University of Vienna Jeroen S. de Bruin

Thank you!

• Many thanks go out to:– Dr. Harald Mandl– The Clinical Institute of Hospital Hygiene