intraoperative fluid management€¦ · intraoperative fluid management david g hovord ba mb bchir...
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Intraoperative Fluid
Management
David G Hovord BA MB BChir FRCA
Clinical Assistant Professor
University of Michigan
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Objectives
• Examine impact of perioperative renal
failure, and discuss structure and
function of kidney
• Explore strategies for periop fluid
management
• Discuss possible future directions for
intra-operative decision making aids
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Renal failure
• Increased risk of CKD
• Increased mortality
• Independent risk factor for
cardiovascular complications
• Much higher cost of care and resource
utilization
• Risk adjusted $16,000 increase in cost
of care
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Question
Is a small bump in creatinine an issue?
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Bihorac et al - 2013
• Looked at various poor outcomes,
including death
• Attempt to find degree of renal failure
that matters
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Bihorac et al - 2013
• Found that rises in serum Creatinine of
0.2mg/dl or greater, or 10% changes
from baseline were associated with
increased mortality and morbidity
• Not causative
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Back to Basic(s)
Why are the kidneys so sensitive to
changes in circulating volume?
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Back to Basic(s)
The kidneys, unlike the lungs, do not
have a dual blood supply
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Renal blood supply
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Renal blood supply
• Primary function is to maintain
filtration fraction
• With decrease in incoming blood
efferent arteriole must constrict
• See ACE inhibitor and renal artery
stenosis
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Prediction
What kind of patients get significant renal
failure?
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Prediction of renal failure
• Kheterpal, Tremper et al 2009
• Used NSQIP definition of renal failure –
an increase of 2.0mg/dl creatinine
• Renal failure rate of 1%
• From NSQIP database
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Pre-operative predictors
• Age >56
• Male
• Emergent surgery
• High risk surgery
• Diabetes
• Acute heart failure
• Ascites
• Hypertension
• Pre-op mild/moderate renal failure
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Kheterpal et al 2007
• Single center, included intra-operative
data also
• Renal failure defined as drop below
50ml/min
• Rate of 0.8%
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Kheterpal et al 2007
Pre-op risk factors
• Age, emergent surgery, liver disease, BMI
high risk surgery, PVOD and COPD
Intra-op risk factors
• Total vasopressor dose, use of
vasopressor infusion, administration of
diuretic
• ARF associated with increased
mortality at 30, 60 and 365 days
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Strategies
Wet vs dry
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Shoemaker et al 1988
• Cardiac index >4.5L/min/m2
• DO2 >600ml/min/m2
• VO2 >170ml/min/m2
• ‘Supra-max’
• Achieved with fluids, blood,
vasodilators, inotropes
• Reduced mortality – 21% vs 38%
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Goal Directed Therapy
• Optimizing stroke volume and cardiac
output – ‘supramax lite’
• Requires a monitor and an intervention
• Initially PA Catheter, followed by EDM
• Then pulse contour analysis
(calibrated and un-calibrated)
• Includes PPV/SPV from art line
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Goal Directed Therapy
• Considerable heterogeneity in clinical
trials
• Mainly compared to standard therapy –
this has changed a lot over the years
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OPTIMISE trial – Pearse et al
2014 JAMA• Large UK based, multi-center
• 734 patients
• Major general surgery
• Usual care vs cardiac output guided
algorithm
• Primary outcome 30 day composite
mortality and morbidity
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OPTIMISE
• Used LiDCO pulse contour analysis
device
• Give 250cc bolus of colloid over 5
mins
• Stop when SV fails to rise by at least
10%
• Also ran infusion of dopexamine until
6 hours post op
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OPTIMISE
• Overall fluid volumes given similar
• No significant difference in primary
outcome
• Or outcomes for length of stay, ICU
days, 30 or 180 day mortality
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OPTIMISE - Meta-analysis
• Reduced post-op infection
• Reduced length of stay
• But not 30 day mortality
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GDT - conclusion
• Popular – easy to do
• Evidence inconclusive
• Doesn’t alter overall amount of fluid
given
• May reduce immediate complications
• No mortality effect
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GDT - conclusions
• Evidence weaker when used inside an
Enhanced Recovery After Surgery
program
• Patients optimized better priot to
surgery?
• Less bowel prep, better hydrated at
presentation
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Zero balance
• Idea to keep patient ‘net zero’ at end of
surgery
• Change in mindset
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Brandstrup et al 2003
• Aiming at ‘unchanged body weight’ in
elective colorectal surgery
• Randomized, observer blinded
• 141 patients
• Average BMI 25
• 98% patients ASA 1 or 2
• Significant difference in fluid admin –
2740ml vs 5388
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Brandstrup et al 2003
• Reduced cardiopulmonary
complications – 7% vs 24%
• Reduced wound healing complications
– 16% vs 31%
• Renal failure not significantly different
in the two groups
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Conflicting approaches
• One where we measure every variable
possible and despatch the kitchen sink
to attain a goal
• Another where we don’t measure so
much and stick to plan A – zero
balance
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Myles et al NEJM 2018
• Multi-center, international, randomized
• 3000 high risk patients
• Restrictive vs liberal iv fluid regime
during and up to 24 hours following
surgery
• RELIEF trial
• Australia and Canada 75% total
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RELIEF trial
• 1490 vs 1493 patients
• Mainly ASA 3 and 4 (62% vs 62.4%)
• Criteria – Age >70, or presence of heart
disease, diabetes, renal impairment or
morbid obesity
• Major abdominal surgery, but liver
resection excluded
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RELIEF trial
• Liberal regime
• 10ml/kg crystalloid on induction
• Followed by 8ml/kg/hr through surgery
• 1.5ml/kg/hr following that
• At 24hrs – median 6146ml total fluid
given
• Median weight gain 1.6kg
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RELIEF trial
• Restrictive regime
• Max 5ml/kg at induction
• No other iv fluids to given unless
indicated by a goal-directed device
(EDM or pulse contour analysis)
• Crystalloid at 5ml/kg/hr through
surgery
• Followed by 0.8ml/kg/hr post-op
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RELIEF trial
• At 24 hours – median fluid 3671ml
• Weight gain 0.3kg
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RELIEF trial - outcomes
• 1 year disability free survival –
restrictive 81.9% vs 82.3%
• AKI: 8.6% vs 5.0% (P<0.001)
• Septic complications or death 21.8%
vs 19.8% (P=0.19)
• Surgical site infection 16.5% v 13.6%
and RRT 0.9% vs 0.3% were higher but
not significantly so
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RELIEF trial
• Problematic
• Did the pendulum swing too far
(again)?
• Editorial (Brandstrup) ‘…a modestly
liberal fluid is safer than a truly
restrictive regime’
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RELIEF trial
• Surgery performed is much different
• Minimally invasive
• Patient profile has changed
• More co-existing disease
• More likely to have renal perfusion at
the margin
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BJA 2006 – editorial
• Titled – Wet, dry or something else?
• ‘The great fluid debate continues to
rage’
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‘Wet, dry or something else’-
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BJA 2015 – Minto and Mythen
• Science, art or random chaos?
• Editorial accompanying study by Lilot
et al
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Lilot et al
• Retrospective analysis
• 5912 patients, UC Irvine and Vanderbilt
• Intra-abdominal surgery, minimal
blood loss
• Regression analysis favored strongly
personnel over patient factors
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Minimal effect
• Minimum or median MAP
• Median heart rate
• EBL
• Surgical approach
• A patient undergoing a 4h procedure,
weighing 75kg could receive between
700 and 4500ml crystalloid, depending
on their anesthesia provider
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Subgroup
• Prostatectomies removed due to
specific protocol at UC Irvine
• However when data analyzed
separately this group had lowest
infusion rate and smallest range of
variability
• Provider effect eliminated by a
protocol
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Minto and Mythen
Do you really know how
much fluid you give?
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Summary
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Summary
• Clear sense of incorrect approaches
• Evidence against for ‘one size fits all’
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Strategies – initial plan
• History and physical
• Assessment of fluid deficit prior to
induction of anesthesia
• Procedure specific goals
• Clear plan and goals
• Incorporate data gained at induction
into assessment
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Strategies
• Use of dynamic monitoring
• Careful assessment of EBL, insensible
loss
• SPV, PPV from art line
• EDM
• Understanding of limitations
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Strategies - data
• Individual data
• Process
• Outcome
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Strategies
• Decision support software
• AlertWatch is one example of this
• At least – ensuring attention directed
to fluid administration
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Future directions
Better analysis of available data
Better data (monitors and markers –
blood and urine)
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Markers
Gleeson et al - Feb 2019
Renin as a marker of Tissue-Perfusion
and Prognosis in Critically Ill Patients
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Gleeson et al 2019
• Outperformed lactate as a predictor of
ICU mortality
• Not affected by RRT
• Under investigation
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Other markers
• Cystatin C – a better creatinine?
• L-FABP – released by kidneys into
urine under oxidative stress
• No ‘ideal marker yet found’
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Future directions
Predicting Blood Pressure Response to
Fluid Bolus Therapy Using Attention-
Based Neural Networks for Clinical
Interpretability
Girkar et al Dec 2018 (pre-print)
MIT Computer Science Lab
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Machine learning
• Model developed for administration of
fluid bolus
• Then test model on remaining data and
assess its predictive value – in this
case it was 85%
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Machine learning
• You can then look into the algorithm
• Five most important features –
respiratory rate, diastolic BP,
temperature, bicarb and base excess
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Machine learning
• Not causative
• Data dependent
• Machine algorithm will cheat – fracture
prediction without using image
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Machine learning
• May be of great utility in future
• Simple things can be implemented
immediately
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Discussion
Questions?