Assessment of MDR risk: Focus on ESBL - Gestão Eventos Symposium/Day7...RISK ASSESSMENT Several...

Assessment of MDR risk: Focus on ESBL

Nuno Rocha Pereira

➤ Travel, accomodation and registatrion in meetings, workshops and congress

• MSD, Pfizer, ViiV Healthcare, Janssen

➤ Invited talks

• Pfizer

CONFLICTS OF INTEREST

BACKGROUND

➤ Antimicrobial resistance is a worldwide concern

➤ Burden among gram negatives increasing

➤ Most common mechanism of resistance among Gram

negatives is the production of β-lactamases

• ESBLs among the most important and are mostly

expressed by Enterobacteriaceae.

• ESBLs confer resistance to all β-lactams with the

exception of carbapenems and, in some selected

cases, beta-lactam/beta-lactamase inhibitors

• Commonly other resistance mechanisms coexist

Bassetti M et al. Expert Review of Anti-Infective Therapy 2016

http://dx.doi.org/10.1080/14787210.2017.1251840

BACKGROUND

ESBL IMPACT

➤ Increased likelihood of inadequate empirical coverage

➤ Independent predictor of impaired survival (higher mortality)

➤ Higher LOS

➤ Increased carbapenem exposure

➤ ESBL-EB bacteremia associated with higher mortality

• Inadequate empirical therapy

Freeman JT et al. International Journal of Infectious Diseases 2012 ; 16: e371-e374

Peralta et al. BMC Infectious Diseases 2012; 12:245

Barbier et al. J Antimicrob Chemother 2016; 71: 1088–1097

A VICIOUS CYCLE

Increasing resistance

(ESBL)

Broad spectrum antibiotics)

(Carbapenems)

Increasing resistance

(Carbapenemases)

Limited therapeutic options

CARBAPENEM RESISTANCE

A CLINICAL DILEMMA

Effective

antimicrobial therapy

Good use of

antimicrobials

A CLINICAL DILEMMA

Effective antimicrobial

therapy

Good use of

antimicrobials

Risk assessment Better outcomes

ESBL RISK ASSESSMENT

➤ Prior colonization/infection and risk of infection

➤ “Traditional” risk factors

➤ Risk scores and prediction rules

PRIOR COLONIZATION/INFECTION

➤ Utility of prior cultures in predicting antibiotic resistance of bloodstream

infections due to Gram-negative pathogens: a multicentre observational

cohort study

• 04/2010 – 03/2015; USA and Canada

• Inpatients with monomicrobial gram negative bacteremia

• Predict resistance with prior isolates:

MacFadden DR et al. Clinical Microbiology and Infection 2017;

http://dx.doi.org/10.1016/j.cmi.2017.07.032

ESBL COLONIZATION AND INFECTION

➤ Significance of Prior Digestive Colonization With ESBL Enterobacteriaceae in

Patients With VAP

• Retrospective cohort study (January 2006 – October 2013)

• Included patients with suspected VAP (clinical judgement)

• ESBL-EB rectal swab at admission and every week thereafter

Bruyère R et al. Critical Care Medicine 2016; 44(4):699–706


➤ Significance of Prior Digestive Colonization With ESBL Enterobacteriaceae in

Patients With VAP

• ESBL-EB digestive colonization prior to VAP episode found in 40 cases (6,8%)

• ESBL-EB VAP: 40,0% (ESBL+) vs 0,7% (ESBL-) p < 0,01

• ESBL-EB rectal carriage can predict subsequent ESBL-EB infection

Sensitivity (%)

[95% CI]

Specificity (%)

[95% CI]

Positive

Predictive Value

(%)

[95% CI]

Negative

Predictive Value

(%)

[95% CI]

Positive LR

[95% CI]

Negative LR

[95% CI]

All VAP 85,0%

[62,1-96,8]

95,7%

[93,7-97,3]

41,5%

[26,3-57,9]

99,4%

[98,4-99,9]

19,8

[9,8-35,4]

0,15

[0,0-0,4]

Bruyère R et al. Critical Care Medicine 2016; 44(4):699–706


➤ ICU Acquisition Rate, Risk Factors, and Clinical Significance of Digestive Tract

Colonization With ESBL Enterobacteriaceae: A Systematic Review and Meta-

Analysis

• 13 studies included >> 15 045 ICU patients

Detsis M et al. Critical Care Medicine 2017; 45(4):705–714


➤ ICU Acquisition Rate, Risk Factors, and Clinical Significance of Digestive Tract

Colonization With ESBL Enterobacteriaceae: A Systematic Review and Meta-

Analysis

Detsis M et al. Critical Care Medicine 2017; 45(4):705–714

Sensitivity (%)

[95% CI]

Specificity (%)

[95% CI]

Positive LR

[95% CI]

Negative LR

[95% CI]

Studies

combined

95,1%

[54,7,1-99,7]

89,2%

[77,2-95,3]

8,80

[4,15-18,67]

0,055

[0,004-0,75]


➤ Colonisation with ESBL Enterobacteriaceae and risk for infection among

patients with solid or haematological malignancy: a systematic review and

meta-analysis

• 10 studies included >> 2211 patients

Alevizakos M et al. International Journal of Antimicrobial Agents 2016; 48: 647–654

RISK FACTORS

Bassetti M et al. Expert Review of Anti-Infective Therapy 2016

http://dx.doi.org/10.1080/14787210.2017.1251840

RISK FACTORS

➤ Bloodstream infection with ESBL Enterobacteriaceae at a tertiary care hospital

in New Zealand: risk factors and outcome

• 01/05/2003 – 31/03/2007

• ESBL-EB bacteremia cases (n=44) / EB bacteremia controls (n=44)

• Risk factors (multivariate analysis):

o Known colonization with an ESBL-EB (OR 46,2)

o Exposure to 1st-generation cephalosporins (OR 12,3)

o Exposure to fluoroquinolones (OR 6,56

o Total inpatient days (OR 1,033 per admission day)

Freeman JT et al. International Journal of Infectious Diseases 2012 ; 16: e371-e374

RISK FACTORS ➤ Risk factors for bloodstream infection caused by ESBL producing Escherichia

coli and Klebsiella pneumoniae: A focus on antimicrobials including cefepime

➤ Retrospective case control study

➤ 12/2004 – 08/2009

• ESBL-E.coli or K.pneumoniae bacteremia cases (n=103) / E.coli or K.pneumoniae

bacteremia controls (n=103)

• Risk factors (multivariate analysis):

o Any tumor in the prior 5 years (OR 3,6); History of a cerebrovascular accident (OR

2,4)

o Dementia (OR 3,4); Low albumin (OR 9,5); Prior hospitalization (OR 3,2)

o Presence of an indwelling CVC (OR 8,0) or urinary catheter (OR 4,4)

o Exposure to antimicrobials (cefepime OR 15,3; Pip/taz OR 5,3; Linezolid OR 5,5)

Chopra T et al. American Journal of Infection Control 2015 ; 1-5

RISK SCORES

➤ Identifying Patients Harboring ESBL Enterobacteriaceae on Hospital

Admission: Derivation and Validation of a Scoring System

• Derivation cohort:

o 01/01/2008 – 31/12/2008; Catholic University Hospital, Rome

o Cases: Inpatients with at least one culture with ESBL-EB within 48h of

admission

o Two controls for each case (without culture positive for EB; matching by hospital

ward and admission month)

o 113 cases included (total number of patients included 339)

Tumbarello M et al. Antimicrobial Agents and Chemotherapy 2011; 55(7):3485–3490

RISK SCORES



• Validation cohort:

o 01/06/2009 – 31/12/2009; San Martino University Hospital (Genoa) or San

Giovanni Battista-Molinette Hospital (Turin)

o Four controls for each case

o 102 cases included (total number of patients included 510)


RISK SCORES




RISK SCORES



• Limitations:

o Not applicable to other locations

o Needs validation against a different type of control population, i.e., hospitalized

patients suspected of infection

Combined

cohort Sensitivity (%) Specificity (%)

Positive

preditive value

(%)

Negative

preditive value

(%)

Score > 3 93% 62% 45% 97%

Score > 6 63% 95% 80% 88%


RISK SCORES

➤ Utility of a Clinical Risk Factor Scoring Model in Predicting Infection with ESBL

Enterobacteriaceae on Hospital Admission

• Duke University Hospital

• 01/01/2008 – 31/12/2010

• Cases: Inpatients with at least one culture with ESBL-EB within 48h of admission

and clinical signs of active infection

• Three controls for each case (without culture positive for EB; matching by hospital

ward and admission month)

• 110 cases included (338 controls)

Johnson M et al. Infect Control Hosp Epidemiol 2013; 34(4): 385–392

RISK SCORES



• Using Italian model: AUC ROC 0,88

• Using Duke model: AUC ROC 0,89


RISK SCORES




Duke Score Sensitivity (%) Specificity (%)

Positive

preditive value

(%)

Negative

preditive value

(%)

Score > 4 87% 69% 48% 94%

Score > 8 58% 95% 79% 87%

RISK SCORES

➤ Clinical risk scoring system for predicting extended-spectrum β-lactamase-

producing Escherichia coli infection in hospitalized patients

• 01/2011 – 12/2014, Thailand

• Inpatients with positive cultures for E. coli

• Cases ESBL-EC (n=443); Controls non-ESBL-EC (n=367)

Kengkla K et al. Journal of Hospital Infection 2016, doi: 10.1016/j.jhin.2016.01.0072013

RISK SCORES

➤ Clinical risk scoring system for predicting extended-spectrum β-lactamase-

producing Escherichia coli infection in hospitalized patients

• AUC ROC 0,77

• Limitations: Only for E.coli, single centre.

Kengkla K et al. Journal of Hospital Infection 2016, doi: 10.1016/j.jhin.2016.01.0072013

Thailand Score Sensitivity (%) Specificity (%)

Positive

preditive value

(%)

Negative

preditive value

(%)

Score > 9 74% 66% 73% 68%

Score > 12 53% 90% 86% 61%

RISK SCORES ➤ Development and validation of the INCREMENT-ESBL predictive score for

mortality in patients with bloodstream infections due to ESBL

Enterobacteriaceae

• Large multinational cohort (37 hospitals in 11 countries)

• 01/2004 – 12/2013

• Included patients with ESBL-EB bacteremia

Palacios-Baena ZR et al. J Antimicrob Chemother 2017; doi:10.1093/jac/dkw513

RISK SCORES

➤ Development and validation of the INCREMENT-ESBL predictive score for

mortality in patients with bloodstream infections due to ESBL

Enterobacteriaceae

Palacios-Baena ZR et al. J Antimicrob Chemother 2017; doi:10.1093/jac/dkw513

RISK SCORES

➤ PREDICT

• Ongoing multinational study (aiming 20 000 participants)

• Validation of prediction rules to detect 3G cephalosporins resistance in bacteremic

patients

• Prediction rule to use at the beginning of empirical therapy

• Separate prediction rules for hospital and community acquired bacteremia

• Preliminary results in ECCMID 2018

Deelens JWT et al. PREDICT study protocol

DECISION TREE

➤ A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected

With an ESBL–Producing Organism

• 10/2008 – 03/2015

• Included 1288 patiens with bacteremia due to E.coli, K. pneumoniae or K. oxytoca

• 194 (15%) patients with ESBL + bacteremia

• 5 predictors for decision tree:

• Prior history of ESBL colonization or infection

• Presence of chronic indwelling vascular hardware

• Age (model-derived dichotomization at 43 years)

• Recent hospitalization in an ESBL high-burden region

• Total antibiotic exposure in the prior 6 months (model-derived dichotomization at 6 days)

Goodman KE et al. Clinical Infectious Diseases 2016;63(7):896–903

DECISION TREE

➤ A Clinical Decision Tree to Predict Whether a Bacteremic Patient Is Infected

With an ESBL–Producing Organism

Sensitivity (%) Specificity (%)

Positive

preditive value

(%)

Negative

preditive value

(%)

Decision

Tree 51% 99,1% 90,8% 91,9%

Goodman KE et al. Clinical Infectious Diseases 2016;63(7):896–903

RISK ASSESSMENT

➤ Several strategies available:

• Probabilities related to patient characteristics (comorbidities)

• Past culture results / information about colonizations

• Known risk factors for different bacteria and resistance

• Risk scores

• Decision support systems

➤ All strategies have limitations and pitfalls

CLINICAL JUDGEMENT

Clinical judgement

Comorbidities

Local epidemiology

“Individual epidemiology”

Illness severity

Risk factors

Risk scores

Decision support systems

Guidelines

Medicine is a science of

uncertainty and an art of

probability

Sir William Osler (1849-1919)

Thank you.

Nuno Rocha Pereira

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