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A Service Evaluation of Procalcitonin after PRORATA Daniel Cottle DICM John Butler, Tony Dunne, Sanchia Pickering Manchester Royal Infirmary 2011.

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Presentation on theme: "A Service Evaluation of Procalcitonin after PRORATA Daniel Cottle DICM John Butler, Tony Dunne, Sanchia Pickering Manchester Royal Infirmary 2011."— Presentation transcript:

1 A Service Evaluation of Procalcitonin after PRORATA Daniel Cottle DICM John Butler, Tony Dunne, Sanchia Pickering Manchester Royal Infirmary 2011

2 Antimicrobial resistance in ICU CPC MRSA

3 Antimicrobial resistance in ICU Major factor affecting patient outcome and resources. Insufficient infection control measures. Selective antibiotic pressure. Reducing antibiotic use may contain the emergence of multi-drug resistance bacteria in ITUs. Stop inappropriate prescribing. Shorten duration of treatment of antibiotics.

4 Procalcitonin Normally produced by the C-cells of the thyroid Normally undetectable Unknown role in sepsis Multiple sources in sepsis Analgesic

5 Procalcitonin – in relevant bacterial infection produced and released into circulation from the whole body Calcitonin in healthy personsPCT in bacterial infection Calcitonin PCT Müller B. et al., JCEM 2001 www.procalcitonin.com

6 Procalcitonin- Kinetics Fast increase of PCT after bacterial challenge Fast increase (after 3-4 hours), high dynamic range Wide concentration range < 0.05 ng/ml - 1000 ng/ml Short half-life time (~ 24 h) independent of renal function Easy to measure in serum and plasma - stable in vivo and in vitro Brunkhorst FM et alIntens Care Med 1998; 24:888-892

7 PRORATA Lancet 2010 Multicentre, randomised, controlled trial. 311 procalcitonin, 319 control. Days without antibiotics: – 14.3 days PCT : 11.6 control. The mean duration of the first episode of antibiotics was reduced from 9.9 days to 6.1 days, AR 3.8 days; 95% CI 2.7-4.8, p<0.0001 No increase in mortality

8 Figure 3 Source: The Lancet 2010; 375:463-474 (DOI:10.1016/S0140-6736(09)61879-1)The Lancet 2010; 375:463-474 Terms and Conditions

9 Methods Baseline data collection Protocol Introduction of protocol Promote protocol Reinforce protocol Data collection Analysis Mean (standard deviation) Student’s t-test

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11 Exclusions Post bone marrow transplant Pregnancy TB, PCJ, toxoplasmosis Neutropenia Expected to die

12 Example Patient 38 DateClinical eventsAntibioticsCRPWBCPCTMicrobiology culture Temp Highest or Lowest LactateVentilated Y/N Antibiotic Changes 8/12/2011 coamoxyclav, clari33114.215 y 8/13/2011 coamoxyclav, clari31020.811 y 8/14/2011 coamoxyclav, clari22519.56.5 y 8/15/2011 coamoxyclav, clari 13.43.2 y 8/16/2011 coamoxyclav, clari33111.4 yy 8/17/2011?chest sepsisTazocin5113.1 yn 8/18/2011 Tazocin4912.10.53>80% Reduction yn

13 Results 60 antibiotic episodes 8 to the ward 4 died 6 exclusions 42 analysed

14 29 stopped early 8 no difference 1 escalated because 4 escalated despite

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20 Chest sepsis Mean course length 5.5 days PRORATA: CAP 5.5 days, VAP 7.7 days 8 had a starting PCT <0.5 3 could have stopped earlier

21 Abdo sepsis Mean course length 7.9 days PRORATA: 8.1 days 4 escalated despite PCT 1 could have stopped earlier

22 Conclusions PCT reduced antibiotic use on our unit Definite end-point More structured approach Could this be reduced further? PCT <0.5 as a rule out?

23 QUESTIONS?


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