Biomarkers and Risk Models in Cardiac Surgery
2014; Lippincott Williams & Wilkins; Volume: 130; Issue: 12 Linguagem: Inglês
10.1161/circulationaha.114.011983
ISSN1524-4539
AutoresDavid M. Shahian, Frederick L. Grover,
Tópico(s)Cardiac Valve Diseases and Treatments
ResumoHomeCirculationVol. 130, No. 12Biomarkers and Risk Models in Cardiac Surgery Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBBiomarkers and Risk Models in Cardiac Surgery David M. Shahian, MD and Frederick L. Grover, MD David M. ShahianDavid M. Shahian From Massachusetts General Hospital and Harvard Medical School, Boston (D.M.S.); University of Colorado School of Medicine-Anschutz Medical Campus, Aurora (F.L.G.); and Denver Department of Veterans Affairs Medical Center, Denver, CO (F.L.G.). and Frederick L. GroverFrederick L. Grover From Massachusetts General Hospital and Harvard Medical School, Boston (D.M.S.); University of Colorado School of Medicine-Anschutz Medical Campus, Aurora (F.L.G.); and Denver Department of Veterans Affairs Medical Center, Denver, CO (F.L.G.). Originally published6 Aug 2014https://doi.org/10.1161/CIRCULATIONAHA.114.011983Circulation. 2014;130:932–935Other version(s) of this articleYou are viewing the most recent version of this article. Previous versions: September 16, 2014: Previous Version 1 Early risk models in cardiac surgery focused exclusively on coronary artery bypass grafting surgery (CABG), using preprocedural 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.Article see p 948Cardiothoracic risk models have evolved to include procedures other than isolated CABG and outcomes other than postoperative death, such as complications (eg, 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 before discharge, postdischarge 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.1An 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 B-Type Natriuretic Peptide (BNP)—are the subject of a study by Lurati Buse and colleagues2 in the current issue of Circulation. 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 outcomes.3–6 In CABG patients, TNT levels may be elevated preoperatively as a result of 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 as a result of 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 (eg, 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 al7–10 showed that increased preoperative and peak postoperative BNP levels (especially the former) were independently 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.7,11–14In the current study, Lurati Buse and colleagues2 report that addition of these 2 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 1 year; 11.3% of patients experienced the composite end point (death or major adverse cardiac events [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 1 and 2, the adjusted hazard ratio (HR) for a TNT level exceeding their threshold of 0.8 μg/L was 2.13 (95% confidence interval [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 with 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 nonisolated CABG procedures were not further subdivided. Patients with elevated biomarkers experienced the composite outcome after a median of 22 days, compared with patients without 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 postcardiac surgery outpatients clinics, or virtual wards."2Although 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 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 (eg, isolated CABG, isolated aortic valve replacement).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 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 classification. Stroke was not recorded, nor were hemodynamic complications such as postoperative low cardiac output syndrome, requirement for inotropic support or intraaortic balloon pump, or potentially lethal ventricular arrhythmias, all of which might impact long-term major adverse cardiac events. 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 colleagues7,8,10 demonstrated that postoperative BNP increased significantly for the first 3 days postoperatively, then plateaus or declines by days 4 and 5, leading them to use peak rather than early postoperative BNP levels for their analyses. Collection of specimens only on the first 2 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 resulting from myocardial injury or heart failure, elevations attributable to intraoperative management or events, or a combination of both. Studies by Fox and colleagues8 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 1 method to assess the added predictive value of the 2 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 (ie, those with the event) implies improved classification, and any 'downward movement' indicates worse reclassification. The interpretation is opposite for people who do not 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 2 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 +2.21Despite 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 1 important feature of this method—the importance of always considering the 2 components of the overall NRI (NRI for events and nonevents) separately. The overall NRI associated with addition of TNT and BNP to the EuroSCORE was 0.276. Decomposing the 2 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 nonevent patients (NRInon-events −0.420). In other words, among 100 patients not suffering major adverse cardiac events, 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 colleagues2 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 better characterizing when and 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.DisclosuresDr 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. Dr Grover is a consultant without pay for Somahlution. He is also a Past President of the STS, 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.FootnotesCorrespondence to Frederick L. Grover, MD, University of Colorado School of Medicine, Division of Cardiothoracic Surgery, 12631 E. 17th Avenue, C-310, Room 6602, Aurora, CO 80045. E-mail [email protected]References1. 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; 5:222–8.LinkGoogle Scholar2. Lurati Buse GAL, Bollinger D, Seeberger E, Kasper J, Grapow M, Koller MT, Seeberger MD, Filipovic M. Troponin T and B-type natriuretic peptide after on-pump cardiac surgery: prognostic impact on 12-month mortality and major cardiac events after adjustment for postoperative complications.Circulation. 2014; 130:948–957.LinkGoogle Scholar3. Lurati Buse GA, Koller MT, Grapow M, Brüni 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.CrossrefMedlineGoogle Scholar4. 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.CrossrefMedlineGoogle Scholar5. Paparella D, Cappabianca G, Visicchio G, Galeone A, Marzovillo A, Gallo N, Memmola C, Schinosa Lde L. Cardiac troponin I release after coronary artery bypass grafting operation: effects on operative and midterm survival.Ann Thorac Surg. 2005; 80:1758–1764.CrossrefMedlineGoogle Scholar6. Provenchère S, Berroeta C, Reynaud C, Baron G, Poirier I, Desmonts JM, Iung B, Dehoux M, Philip I, Bénessiano J. Plasma brain natriuretic peptide and cardiac troponin I concentrations after adult cardiac surgery: association with postoperative cardiac dysfunction and 1-year mortality.Crit Care Med. 2006; 34:995–1000.CrossrefMedlineGoogle Scholar7. Fox AA, Nascimben L, Body SC, Collard CD, Mitani AA, Liu KY, Muehlschlegel JD, Shernan SK, Marcantonio ER. Increased perioperative B-type natriuretic peptide associates with heart failure hospitalization or heart failure death after coronary artery bypass graft surgery.Anesthesiology. 2013; 119:284–294.CrossrefMedlineGoogle Scholar8. Fox AA, Muehlschlegel JD, Body SC, Shernan SK, Liu KY, Perry TE, Aranki SF, Cook EF, Marcantonio ER, Collard CD. Comparison of the utility of preoperative versus postoperative B-type natriuretic peptide for predicting hospital length of stay and mortality after primary coronary artery bypass grafting.Anesthesiology. 2010; 112:842–851.CrossrefMedlineGoogle Scholar9. Fox AA, Marcantonio ER, Collard CD, Thoma M, Perry TE, Shernan SK, Muehlschlegel JD, Body SC. Increased peak postoperative B-type natriuretic peptide predicts decreased longer-term physical function after primary coronary artery bypass graft surgery.Anesthesiology. 2011; 114:807–816.CrossrefMedlineGoogle Scholar10. Fox AA, Shernan SK, Collard CD, Liu KY, Aranki SF, DeSantis SM, Jarolim P, Body SC. Preoperative B-type natriuretic peptide is as independent predictor of ventricular dysfunction and mortality after primary coronary artery bypass grafting.J Thorac Cardiovasc Surg. 2008; 136:452–461.CrossrefMedlineGoogle Scholar11. Januzzi JL, Rehman SU, Mohammed AA, Bhardwaj A, Barajas L, Barajas J, Kim HN, Baggish AL, Weiner RB, Chen-Tournoux A, Marshall JE, Moore SA, Carlson WD, Lewis GD, Shin J, Sullivan D, Parks K, Wang TJ, Gregory SA, Uthamalingam S, Semigran MJ. Use of amino-terminal pro-B-type natriuretic peptide to guide outpatient therapy of patients with chronic left ventricular systolic dysfunction.J Am Coll Cardiol. 2011; 58:1881–1889.CrossrefMedlineGoogle Scholar12. Berger R, Moertl D, Peter S, Ahmadi R, Huelsmann M, Yamuti S, Wagner B, Pacher R. N-terminal pro-B-type natriuretic peptide-guided, intensive patient management in addition to multidisciplinary care in chronic heart failure a 3-arm, prospective, randomized pilot study.J Am Coll Cardiol. 2010; 55:645–653.CrossrefMedlineGoogle Scholar13. Jourdain P, Jondeau G, Funck F, Gueffet P, Le Helloco A, Donal E, Aupetit JF, Aumont MC, Galinier M, Eicher JC, Cohen-Solal A, Juillière Y. Plasma brain natriuretic peptide-guided therapy to improve outcome in heart failure: the STARS-BNP Multicenter Study.J Am Coll Cardiol. 2007; 49:1733–1739.CrossrefMedlineGoogle Scholar14. Pfisterer M, Buser P, Rickli H, Gutmann M, Erne P, Rickenbacher P, Vuillomenet A, Jeker U, Dubach P, Beer H, Yoon SI, Suter T, Osterhues HH, Schieber MM, Hilti P, Schindler R, Brunner-La Rocca HP; TIME-CHF Investigators. BNP-guided vs symptom-guided heart failure therapy: the Trial of Intensified vs Standard Medical Therapy in Elderly Patients With Congestive Heart Failure (TIME-CHF) randomized trial.JAMA. 2009; 301:383–392.CrossrefMedlineGoogle Scholar15. Shahian DM, He X, Jacobs JP, Rankin JS, Peterson ED, Welke KF, Filardo G, Shewan CM, O'Brien SM. Issues in quality measurement: target population, risk adjustment, and ratings.Ann Thorac Surg. 2013; 96:718–726.CrossrefMedlineGoogle Scholar16. Shahian DM, O'Brien SM, Filardo G, Ferraris VA, Haan CK, Rich JB, Normand SL, DeLong ER, Shewan CM, Dokholyan RS, Peterson ED, Edwards FH, Anderson RP; Society of Thoracic Surgeons Quality Measurement Task Force. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 1–coronary artery bypass grafting surgery.Ann Thorac Surg. 2009; 88(1 Suppl):S2–22.CrossrefMedlineGoogle Scholar17. O'Brien SM, Shahian DM, Filardo G, Ferraris VA, Haan CK, Rich JB, Normand SL, Delong ER, Shewan CM, Dokholyan RS, Peterson ED, Edwards FH, Anderson RP. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 2–isolated valve surgery.Ann Thorac Surg. 2009; 88:S23–S42.CrossrefMedlineGoogle Scholar18. Shahian DM, O'Brien SM, Filardo G, Ferraris VA, Haan CK, Rich JB, Normand SL, DeLong ER, Shewan CM, Dokholyan RS, Peterson ED, Edwards FH, Anderson RP; Society of Thoracic Surgeons Quality Measurement Task Force. The Society of Thoracic Surgeons 2008 cardiac surgery risk models: part 3–valve plus coronary artery bypass grafting surgery.Ann Thorac Surg. 2009; 88(1 Suppl):S43–S62.CrossrefMedlineGoogle Scholar19. Siregar S, Groenwold RH, de Heer F, Bots ML, van der Graaf Y, van Herwerden LA. Performance of the original EuroSCORE.Eur J Cardiothorac Surg. 2012; 41:746–754.CrossrefMedlineGoogle Scholar20. Pencina MJ, D'Agostino RB, D'Agostino RB, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.Stat Med. 2008; 27:157–72; discussion 207.CrossrefMedlineGoogle Scholar21. Leening MJ, Vedder MM, Witteman JC, Pencina MJ, Steyerberg EW. Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide.Ann Intern Med. 2014; 160:122–131.CrossrefMedlineGoogle Scholar22. Kerr KF, Wang Z, Janes H, McClelland RL, Psaty BM, Pepe MS. Net reclassification indices for evaluating risk prediction instruments: a critical review.Epidemiology. 2014; 25:114–121.CrossrefMedlineGoogle Scholar23. Vickers AJ, Pepe M. Does the net reclassification improvement help us evaluate models and markers?Ann Intern Med. 2014; 160:136–137.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsCited By Wozolek A, Jaquet O, Donneau A, Lancellotti P, Legoff C, Cavalier E, Radermecker M, Lavigne J, Durieux R, Roediger L, Senard M, Hubert M, Brichant J, Amabili P and Hans G (2022) Cardiac Biomarkers and Prediction of Early Outcome After Heart Valve Surgery: A Prospective Observational Study, Journal of Cardiothoracic and Vascular Anesthesia, 10.1053/j.jvca.2021.06.028, 36:3, (862-869), Online publication date: 1-Mar-2022. Pittams A, Iddawela S, Zaidi S, Tyson N and Harky A (2022) Scoring Systems for Risk Stratification in Patients Undergoing Cardiac Surgery, Journal of Cardiothoracic and Vascular Anesthesia, 10.1053/j.jvca.2021.03.005, 36:4, (1148-1156), Online publication date: 1-Apr-2022. Ladha K and Wijeysundera D (2021) Prognostic Risks and Preoperative Assessment Evidence-Based Practice in Perioperative Cardiac Anesthesia and Surgery, 10.1007/978-3-030-47887-2_2, (5-15), . Holm J, Cederholm I, Alehagen U, Lindahl T and Szabó Z (2020) Biomarker dynamics in cardiac surgery: a prospective observational study on MR-proADM, MR-proANP, hs-CRP and sP-selectin plasma levels in the perioperative period, Biomarkers, 10.1080/1354750X.2020.1748716, 25:3, (296-304), Online publication date: 2-Apr-2020. Provenchère S, Guglielminotti J, Gouel-Chéron A, Bresson E, Desplanque L, Bouleti C, Iung B, Montravers P, Dehoux M and Longrois D (2019) Postoperative Cardiac Troponin I Thresholds Associated With 1-Year Cardiac Mortality After Adult Cardiac Surgery: An Attempt to Link Risk Stratification With Management Stratification in an Observational Study, Journal of Cardiothoracic and Vascular Anesthesia, 10.1053/j.jvca.2019.06.039, 33:12, (3320-3330), Online publication date: 1-Dec-2019. Stabler M, Rezaee M, Parker D, MacKenzie T, Bohm A, DiScipio A, Malenka D and Brown J (2019) sST2 as a novel biomarker for the prediction of in-hospital mortality after coronary artery bypass grafting, Biomarkers, 10.1080/1354750X.2018.1556338, 24:3, (268-276), Online publication date: 3-Apr-2019. Brown J, Thiessen-Philbrook H, Goodrich C, Bohm A, Alam S, Coca S, McArthur E, Garg A and Parikh C (2019) Are Urinary Biomarkers Better Than Acute Kidney Injury Duration for Predicting Readmission?, The Annals of Thoracic Surgery, 10.1016/j.athoracsur.2019.02.005, 107:6, (1699-1705), Online publication date: 1-Jun-2019. Brown J, Jacobs J, Alam S, Thiessen-Philbrook H, Everett A, Likosky D, Lobdell K, Wyler von Ballmoos M, Parker D, Garg A, Mackenzie T, Jacobs M and Parikh C (2018) Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery, The Annals of Thoracic Surgery, 10.1016/j.athoracsur.2018.06.052, 106:5, (1294-1301), Online publication date: 1-Nov-2018. Jacobs J, Alam S, Owens S, Parker D, Rezaee M, Likosky D, Shahian D, Jacobs M, Thiessen-Philbrook H, Wyler von Ballmoos M, Lobdell K, MacKenzie T, Everett A, Parikh C and Brown J (2018) The Association Between Novel Biomarkers and 1-Year Readmission or Mortality After Cardiac Surgery, The Annals of Thoracic Surgery, 10.1016/j.athoracsur.2018.04.084, 106:4, (1122-1128), Online publication date: 1-Oct-2018. September 16, 2014Vol 130, Issue 12 Advertisement Article InformationMetrics © 2014 American Heart Association, Inc.https://doi.org/10.1161/CIRCULATIONAHA.114.011983PMID: 25098241 Originally publishedAugust 6, 2014 KeywordsbiomarkersEditorialscardiac surgeryrisk assessmentPDF download Advertisement SubjectsCardiovascular Surgery
Referência(s)