biomarkers and risk models in cardiac surgery

DOI: 10.1161/CIRCULATIONAHA.114.011983

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Biomarkers and Risk Models in Cardiac Surgery

Running title: Shahian et al.; Biomarkers and Risk Models in Cardiac Surgery

David M. Shahian, MD1; Frederick L. Grover, MD2,3

1Massachusetts General Hospital and Harvard Medical School, Boston, MA; 2University of

Colorado School of Medicine-Anschutz Medical Campus, Aurora, CO; 3Denver Department of

Veterans Affairs Medical Center, Denver, CO

Address for Correspondence:

Frederick L. Grover, MD

University of Colorado School of Medicine

Division of Cardiothoracic Surgery

12613 E. 17th Avenue, C-310, Room 6602

Aurora, CO, 80045

Phone: 303-724-7428

Fax: 303-724-2806

Email: [email protected]

Journal Subject Code: Cardiovascular (CV) surgery:[39] CV surgery: other

Key words: Editorial, biomarker, risk model, cardiac surgery

1Massachusetts General Hospital and Harvard Medical School, Boston, MA; 22UnU iiversity of

Coololoraraadodod SSchchc ooooolll ofo Medicine-Anschutz Medicacall l CaCC mpus, Aurora, COCOO;;; 3Denver Department of

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Early risk models in cardiac surgery focused exclusively on coronary artery bypass grafting

surgery (CABG), using pre-procedural variables to estimate the likelihood of in-hospital or 30-

day mortality. These models were initially developed to assess provider performance, but they

have subsequently been used for patient counseling, shared decision-making, and a variety of

other applications. For provider profiling, the expected outcomes for all patients of a given

hospital or surgeon, estimated from regional or national benchmarks, are aggregated to calculate

the expected outcomes for their overall practice. These expected rates are compared with the

observed outcomes to calculate standardized mortality ratios or rates.

Cardiothoracic risk models have evolved to include procedures other than isolated CABG

and outcomes other than postoperative death, such as complications (e.g., reoperation, renal

failure, stroke), readmissions, reinterventions, functional recovery (patient reported outcomes),

costs, and long-term survival. These risk models may be helpful in identifying patients at higher

than average risk of early and late adverse postoperative outcomes, some of which may be

potentially prevented or at least mitigated with treatment modifications and enhanced follow-up.

These might include more extensive evaluation prior to discharge; post-discharge phone calls or

home visits by a nurse or PA; frequent communication between the patient’s primary care

physician and the hospital team; and early postoperative follow-up appointments.

As the science of cardiothoracic surgery has evolved, risk models have kept pace,

refining clinical predictor variables or adding new ones, allowing increasingly accurate risk

estimation. For example, the Society of Thoracic Surgeons Adult Cardiac Surgery Database

(STS ACSD) added both a qualitative variable for the presence of liver disease and a quantitative

measure of liver dysfunction, the MELD score, in its version 2.73 update. A frailty score was

also added, which substantially increases estimated risk when abnormal.1

and outcomes other than postoperative death, such as complications (e.g., reoperaraatiionoo , , rererenananal l l

failure, stroke), readmissions, reinterventions, functional recovery (patient reported outcomes),

cooststts,s,s, aaandndnd lllononong-teteermrmrm survival. These risk modelsss mmaay be helpful iin nn iddenenntititifyf ing patients at higher

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An even more recent trend in risk modeling has been the addition of various biomarkers

to supplement clinical risk predictors. Because of their exquisite sensitivity, these biomarkers

may provide incremental predictive value beyond that available from clinical data, thus

facilitating the detection of subclinical phenomena or providing objective confirmation of

clinical impressions. Two such biomarkers—Troponin T (TNT) and Beta-Type Natriuretic

Peptide (BNP)-- are the subject of a study by Lurati-Buse and colleagues in the current issue of

Circulation 2. The authors should be acknowledged for their leadership in this important new

area of investigation.

TNT is a sensitive marker of myocardial cell injury, often used in the evaluation and risk

stratification of patients with acute coronary syndromes. TNT has also been studied in patients

undergoing CABG, including previous investigations by the authors of the current study, and

elevated levels are associated with less favorable short and longer term outcomes3-6. In CABG

patients, TNT levels may be elevated preoperatively due to acute coronary syndromes of varying

severity and acuity. Even when preoperative levels are normal, as in many elective surgical

patients, postoperative elevations of this sensitive biomarker occur due to cardiac incisions or

manipulation, defibrillation, reperfusion, or myocardial injury from inadequate protection.

Substantially elevated postoperative levels in some patients may alert providers to the need for

enhanced follow-up in an attempt to mitigate short and long-term consequences, and they may

also suggest the need to re-evaluate surgical strategies (e.g., myocardial protection).

BNP is released in response to ventricular volume or pressure overload or ischemia 7.

Clinically, BNP elevations are most commonly associated with ventricular dysfunction or heart

failure and portend a worse prognosis. In CABG, Fox et al 7-10 showed that increased

preoperative and peak postoperative BNP levels (especially the former) were independently

tratification of patients with acute coronary syndromes. TNT has also been studidiiededd iinn papapatititienenentsts

undergoing CABG, including previous investigations by the authors of the current study, and

ellevevvatatatededed lllevevevelele s ararreee aassociated with less favorablelee shhhort and longeer r r termmm oooutu comes3-6. In CABG

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associated with early cardiac dysfunction, longer length of stay, and decreased long-term survival

and physical functioning. As with TNT, it is possible that we may be able to provide more than just

enhanced follow-up of patients with elevated perioperative BNP levels. For example, at least for

elective procedures, might there be an opportunity to use preoperative BNP as a marker of

increased risk and to modify perioperative management accordingly. In ambulatory heart failure,

for example, numerous studies have shown that BNP-guided medical management designed to

blunt BNP increases may improve outcomes and overall functioning 7, 11-14.

In the current study, Lurati-Buse and colleagues 2 report that addition of these two

biomarkers augments the performance of a cardiac surgery risk model containing preoperative

clinical risk factors (EuroSCORE), supplemented with clinical data on selected postoperative

complications. Among 1559 patients enrolled in this prospective single center study between

January 2007 and January 2010, follow up was 99.1% at one year; 11.3% of patients experienced

the composite endpoint (death or major adverse cardiac events [MACE: myocardial infarction,

cardiac arrest, need for subsequent surgical or percutaneous coronary intervention, and

congestive heart failure requiring hospitalization] within 1 year); and 6.6% of patients died.

Based on biomarker levels from 6 AM on postoperative days one and two, the adjusted hazard

ratio (HR) for a TNT level exceeding their threshold of 0.8 g/L was 2.13 (95% CI, 1.47-3.15),

and the corresponding HR for BNP exceeding their threshold of 790 ng/L was 2.44 (95% CI,

1.65-3.62). Compared to patients with no events during 12 month follow up, those who had an

event had a higher EuroSCORE, TNT, and BNP, and more postoperative complications.

Troponin and BNP were higher after procedures other than isolated CABG, although non-

isolated CABG procedures were not further subdivided. Patients with elevated biomarkers

experienced the composite outcome after a median of 22 days, compared with patients without

clinical risk factors (EuroSCORE), supplemented with clinical data on selected ppposostotot pepeperararatititivevev

complications. Among 1559 patients enrolled in this prospective single center study between

Jaanunuuararryy y 202020070707 andndd JJJaanuary 2010, follow up was 99999.11% at one yeaar;r;r 11.1.3%3%3% of patients experienced

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elevated biomarkers whose events occurred at a median of 87 days (p <0.001), suggesting to the

authors an opportunity for intervention such as “more stringent in-hospital monitoring in step-

down units or systematic telemetry monitoring on the regular ward, dedicated post-cardiac

surgery outpatients clinics, or virtual wards” 2.

Although the current study adds some new information, such as adjustment for

complications, it also presents methodological concerns and opportunities for future

investigation. For example, the authors regard it as a strength of their study that they included all

consecutive cardiac surgery patients, ranging from isolated CABG to CABG plus valve and

multiple valve procedures. Heterogeneous study cohorts such as this are often chosen in order to

increase sample size and statistical power. The STS National Database has generally taken a

different tact in developing and applying risk models, believing it is preferable to create more

homogeneous patient cohorts (e.g., isolated CABG, isolated AVR) 15-18. This reduces extraneous

sources of variation--noise--resulting from the aggregation of highly disparate procedures, and

instead allows the analyses to focus on the source of variation that is of real interest, in this case

the association of biomarkers with clinical outcomes. We might expect, for example, that the

relative elevations of TNT and BNP would be quite different among the diverse group of

procedures included in the current study 2. In CABG patients with acute coronary syndromes,

TNT elevation might predominate, whereas in patients with chronic aortic regurgitation and

heart failure, BNP elevations might be more prominent. Using homogeneous procedure cohorts

would allow a more critical examination of these two biomarkers.

Use of the original Euroscore as the benchmark risk model in this study is also somewhat

problematic 19. If the goal is to assess the incremental benefit of biomarkers to the predictive

accuracy of existing clinical risk models, then it is only fair that the most contemporary and best

ncrease sample size and statistical power. The STS National Database has geneeraralllllyyy tataakekekennn a a a

different tact in developing and applying risk models, believing it is preferable to create more

hoommomogegegeneneeououo sss paaatititieenent cohorts (e.g., isolated CABBBG,G, isolated AVR)R)R 15--18188. . TThis reduces extraneous

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available risk models be used as the baseline for comparison. Although the authors supplemented

the original EuroSCORE with several new variables from EuroSCORE II, it would be interesting

to repeat this study using alternative risk models such as the STS ACSD, which is updated with

new and more granular data elements every three years. Version 2.81 of the ACSD includes

specific fields for 6 preoperative biomarkers (BNP, NTproBNP, hsTNT, hsCRP, and GDF-15) so

that their associations with outcomes may be explicitly studied.

Another major objective of the current study was to evaluate whether postoperative

biomarkers provide incremental predictive value to clinical risk models that were already

supplemented by data regarding postoperative complications. However, the only complications

included in this study (which were generally coded at the discretion of the surgeon and not

adjudicated) were sepsis, sternal infection without sepsis, respiratory infections (pneumonia,

ventilator associated pneumonia, or purulent tracheobronchitis without sepsis), and acute kidney

injury (AKI) classification. Stroke was not recorded, nor were hemodynamic complications such

as postoperative low cardiac output syndrome, requirement for inotropic support or IABP, or

potentially lethal ventricular arrhythmias, all of which might impact long-term MACE. If these

clinical complications had been included, the additive value of biomarkers might have been less.

Subsequent studies including these additional complications would be useful to more

convincingly validate the additive value of biomarkers.

The timing of biomarker collection in the current investigation2-- 6 AM on postoperative

days 1 and 2—may not be optimal, and this also provides fertile opportunities for additional

study. Previous reports by Fox and colleagues 7, 8, 10 demonstrated that postoperative BNP

increased significantly for the first 3 days postoperatively, then plateaus or decline by days 4

and 5, leading them to use peak rather than early postoperative BNP levels for their analyses.

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Collection of specimens only on the first two postoperative mornings would therefore potentially

miss the peak values.

In addition to obtaining additional postoperative biomarker samples beyond day 2, it

would also be interesting to repeat these studies with adjustment for preoperative TNT or BNP,

or to design analyses that compare the added predictive value of preoperative and postoperative

determinations. Early postoperative biomarker elevations may reflect residually elevated

preoperative levels due to myocardial injury or heart failure; elevations due to intraoperative

management or events; or a combination of both. Studies by Fox and colleagues 8 suggest that

preoperative BNP may be a better predictor of length of stay and longer term mortality after

CABG than peak postoperative BNP, although postoperative BNP may be useful if preoperative

values are not available. From a pathophysiologic perspective, it would certainly be more

satisfying to know the temporal pattern of biomarker elevations, which may help to better

understand both causality and potential mitigation.

Finally, the authors use Net Reclassification Improvement (NRI) as one method to assess

the added predictive value of the two biomarkers, a widely used approach since the pioneering

report of Pencina and colleagues 20 . NRI addresses the observation that even seemingly

important biomarkers often add little to the area under the curve (AUC, or c-index) of predictive

models, and thus perhaps underestimate their true importance. Reclassification takes a different

approach, assessing the impact of a new predictor on correctly moving patients to a different risk

category. Quoting from the original article: “The reclassification of people who develop and who

do not develop events should be considered separately. Any ‘upward’ movement in categories

for event subjects (i.e. those with the event) implies improved classification, and any ‘downward

movement’ indicates worse reclassification. The interpretation is opposite for people who do not

CABG than peak postoperative BNP, although postoperative BNP may be usefulull if ff prpreoeopepeperararatitivev

values are not available. From a pathophysiologic perspective, it would certainly be more

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develop events. The improvement in reclassification can be quantified as a sum of differences in

proportions of individuals moving up minus the proportion moving down for people who

develop events, and the proportion of individuals moving down minus the proportion moving up

for people who do not develop events. We call this sum the NRI.”20 The theoretical range of each

of these two components of the overall NRI is – 1 (or -100%) to + 1 (or +100%), and the

theoretical range of the overall NRI is thus -2 to +2 21.

Despite its growing popularity, NRI has not always been applied or interpreted correctly,

and some even question its theoretical foundation 21-23. The current study illustrates one

important feature of this method--the importance of always considering the two components of

the overall NRI (NRI for events and non-events) separately. The overall NRI associated with

addition of TNT and BNP to the EuroSCORE was 0.276. Decomposing the two components of

the overall NRI, as described above, there was reasonably strong performance of the new

biomarker in detecting increased risk in patients who actually sustained an event (NRIevents 0.696

) but very poor performance in correctly reclassifying non-event patients (NRInon-events -0.420). In

other words, among 100 patients not suffering MACE, a net of 42 would be misclassified “up”

by the biomarker-augmented model, which is not reassuring performance.

In conclusion, the study of Lurati Buse and colleagues 2 is an interesting addition to the

growing literature on the utility of biomarkers in CABG risk prediction, and the authors are to be

congratulated for their pioneering work in this area. Subsequent studies should address the

methodological issues discussed in this review, which would add even more to our understanding

of this complex issue. Finally, although identification of patients at higher risk for short and long

term problems, and increased attention to such patients, are laudable goals, perhaps we now need

to move to a more proactive approach. Future research should be aimed at identifying when and

he overall NRI (NRI for events and non-events) separately. The overall NRI assosoociciiaatededed wwwititith hh

addition of TNT and BNP to the EuroSCORE was 0.276. Decomposing the two components of

hhe e ovovoveereralalallll NRNRNRI,, aaasss ded scribed above, there was rreaeae ssoonably strongg ppperfofoormrmrmance of the new

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why these molecules are released in cardiac surgery patients (the contributions of a patient’s

presenting disease state versus the effects of surgery and anesthesia). Ultimately, the goal is to

mitigate, when possible, the factors that lead to biomarker elevations, thereby potentially

improving outcomes.

Conflict of Interest Disclosures: David Shahian reports the following uncompensated positions:

Chair, STS National Database Workforce, Chair, STS Quality Measurement Task Force,

Steering Committee, STS-ACC TVT Registry, Executive Committee, AMA PCPI, Board of

Directors, National Quality Forum, and Co-Chair, National Quality Registry Network. Frederick

L. Grover is a consultant without pay for Somahlution. He is also a Past Chair of STS Council of

Quality, Research and Patient Safety, Past member of ACC NCDR Board, Vice Chair of

STS/ACC TVT Steering Committee and is on the National Quality Forum Surgery Standing

Committee.

References: 1. Afilalo J, Mottillo S, Eisenberg MJ, Alexander KP, Noiseux N, Perrault LP, Morin JF, Langlois Y, Ohayon SM, Monette J, Boivin JF, Shahian DM, Bergman H. Addition of Frailty and Disability to Cardiac Surgery Risk Scores Identifies Elderly Patients at High Risk of Mortality or Major Morbidity. Circ Cardiovasc Qual Outcomes. 2012;5:222-228. 2. Lurati Buse GA. Troponin T and Brain Natriuretic Peptide after on-pump cardiac surgery: prognostic impact on 12-month mortality and major cardiac events after adjustment for postoperative complications. Circulation. 2014;130:XX-XXX. 3. Lurati Buse GA, Koller MT, Grapow M, Bruni CM, Kasper J, Seeberger MD, Filipovic M. 12-month outcome after cardiac surgery: prediction by troponin T in combination with the European system for cardiac operative risk evaluation. Ann Thorac Surg. 2009;88:1806-1812. 4. Nesher N, Alghamdi AA, Singh SK, Sever JY, Christakis GT, Goldman BS, Cohen GN, Moussa F, Fremes SE. Troponin after cardiac surgery: a predictor or a phenomenon? Ann Thorac Surg. 2008;85:1348-1354. 5. Paparella D, Cappabianca G, Visicchio G, Galeone A, Marzovillo A, Gallo N, Memmola C, Schinosa LL. Cardiac troponin I release after coronary artery bypass grafting operation: effects on operative and midterm survival. Ann Thorac Surg. 2005;80:1758-1764.

Quality, Research and Patient Safety, Past member of ACC NCDR Board, Vice e ChChChaiaiair r r ofofof

STS/ACC TVT Steering Committee and is on the National Quality Forum Surgeeryryy SSStatandndndinininggg

Committee.

RReffeferences:

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Body SC. Increased peak postoperative B-type natriuretic peptide predicts decreassededed llonongegeg r-r-tetermphysical function after primary coronary artery bypass graft surgery. Anesthesioololoogygyy.2011;114:807-816.

10. Fox AA, , Shernan SK, Collard CD, Liu KY, Aranki SF, DeSantis SMS , Jarolim P, Body SC. Prreoeoopepeperraratititivevev BB-ttypypypee natriuretic peptide is as indedeepepep nndent predictooor rr off vvvenenentricular dysfunction andmommortrttallity afafteteer prprprimimimarary y y cocoorororonanaryryry aaartrtereryy y bybypapap ssss ggraaafftting.g. JJJ ThThThorracacac CCarardidiiovovo asscc SuSuSurgrgrg.220000808;136:4522-4-4461616 .

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ER, Shewan CM, Dokholyan RS, Peterson ED, Edwards FH, Anderson RP. The SSocococieietytyy oof f Thoracic Surgeons 2008 cardiac surgery risk models: part 3--valve plus coronaryryy artrtr errry y y bybybypapapassss grafting surgery. Ann Thorac Surg. 2009;88:S43-S62.

19. SiS regag r S,S, Groenwold RH, de Heer F, Bots ML,, van der Graaf Y, van Herwerden LA. Peerfrfrfororormmamancncncee of ttthehehe original EuroSCORE. Eur J JJ CaCaCardiothorac Sururrg.g 2020011212;41:746-754.

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David M. Shahian and Frederick L. GroverBiomarkers and Risk Models in Cardiac Surgery

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biomarkers and risk models in cardiac surgery

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