Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-19T02:17:47.882Z Has data issue: false hasContentIssue false
  • English
  • Français

Biomarkers improve prediction of 30-day unplanned readmission or mortality after paediatric congenital heart surgery

Published online by Cambridge University Press:  10 July 2019

Jeremiah R. Brown ,
Meagan E. Stabler ,
Devin M. Parker ,
Luca Vricella ,
Sara Pasquali ,
JoAnna K. Leyenaar ,
Andrew R. Bohm ,
Todd MacKenzie ,
Chirag Parikh  and
Marshall L. Jacobs
...Show all authors
Jeremiah R. Brown*
Affiliation:
Department of Epidemiology, Geisel School of Medicine, Lebanon, NH, USA Department of Biomedical Data Science, Geisel School of Medicine, Hanover, NH, USA The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Hanover, NH, USA
Meagan E. Stabler
Affiliation:
Department of Epidemiology, Geisel School of Medicine, Lebanon, NH, USA
Devin M. Parker
Affiliation:
Department of Epidemiology, Geisel School of Medicine, Lebanon, NH, USA
Luca Vricella
Affiliation:
Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Sara Pasquali
Affiliation:
Division of Pediatric Cardiology, Department of Pediatrics, C.S. Mott Children’s Hospital, University of Michigan, Ann Arbor, MI, USA
JoAnna K. Leyenaar
Affiliation:
The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Hanover, NH, USA Department of Pediatrics, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
Andrew R. Bohm
Affiliation:
Department of Epidemiology, Geisel School of Medicine, Lebanon, NH, USA
Todd MacKenzie
Affiliation:
Department of Epidemiology, Geisel School of Medicine, Lebanon, NH, USA Department of Biomedical Data Science, Geisel School of Medicine, Hanover, NH, USA The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine, Hanover, NH, USA
Chirag Parikh
Affiliation:
Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
Marshall L. Jacobs
Affiliation:
Division of Cardiovascular Surgery, Department of Surgery, Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital and Florida Hospital for Children, Saint Petersburg, Tampa, and Orlando, FL, USA Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Jeffrey P. Jacobs
Affiliation:
Division of Cardiovascular Surgery, Department of Surgery, Johns Hopkins All Children’s Heart Institute, Johns Hopkins All Children’s Hospital and Florida Hospital for Children, Saint Petersburg, Tampa, and Orlando, FL, USA Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
Allen D. Everett
Affiliation:
Division of Pediatric Cardiology, Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
*
Author for correspondence: Jeremiah R. Brown, PhD, MS, Department of Epidemiology, Williamson Translational Research Building, HB 7505, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756, USA. Tel: 603 653 3576; Fax: 603 653 3554; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective:

To evaluate the association between novel pre- and post-operative biomarker levels and 30-day unplanned readmission or mortality after paediatric congenital heart surgery.

Methods:

Children aged 18 years or younger undergoing congenital heart surgery (n = 162) at Johns Hopkins Hospital from 2010 to 2014 were enrolled in the prospective cohort. Collected novel pre- and post-operative biomarkers include soluble suppression of tumorgenicity 2, galectin-3, N-terminal prohormone of brain natriuretic peptide, and glial fibrillary acidic protein. A model based on clinical variables from the Society of Thoracic Surgery database was developed and evaluated against two augmented models.

Results:

Unplanned readmission or mortality within 30 days of cardiac surgery occurred among 21 (13%) children. The clinical model augmented with pre-operative biomarkers demonstrated a statistically significant improvement over the clinical model alone with a receiver-operating characteristics curve of 0.754 (95% confidence interval: 0.65–0.86) compared to 0.617 (95% confidence interval: 0.47–0.76; p-value: 0.012). The clinical model augmented with pre- and post-operative biomarkers demonstrated a significant improvement over the clinical model alone, with a receiver-operating characteristics curve of 0.802 (95% confidence interval: 0.72–0.89; p-value: 0.003).

Conclusions:

Novel biomarkers add significant predictive value when assessing the likelihood of unplanned readmission or mortality after paediatric congenital heart surgery. Further exploration of the utility of these novel biomarkers during the pre- or post-operative period to identify early risk of mortality or readmission will aid in determining the clinical utility and application of these biomarkers into routine risk assessment.

Keywords

Paediatric CHDbiomarkerspredictionreadmission
Type
Original Article
Information
Cardiology in the Young , Volume 29 , Issue 8 , August 2019 , pp. 1051 - 1056
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Yang, Q, Chen, H, Correa, A, Devine, O, Mathews, TJ, Honein, MA. Racial differences in infant mortality attributable to birth defects in the United States, 1989–2002. Birth Defects Res A Clin Mol Teratology 2006; 76: 706–713.CrossRefGoogle ScholarPubMed
Kogon, B, Jain, A, Oster, M, Woodall, K, Kanter, K, Kirshbom, P. Risk factors associated with readmission after pediatric cardiothoracic surgery. Ann Thorac Surg 2012; 94: 865–873.CrossRefGoogle ScholarPubMed
Dabal, RJ, Kirklin, JK, Kukreja, M, et al. The modern Fontan operation shows no increase in mortality out to 20 years: a new paradigm. J Thorac Cardiovasc Surg 2014; 148: 2517–2523. e2511.CrossRefGoogle ScholarPubMed
Knowles, RL, Bull, C, Wren, C, Dezateux, C. Mortality with congenital heart defects in England and Wales, 1959–2009: exploring technological change through period and birth cohort analysis. Arch Dis Child 2012; 97: 861–865.CrossRefGoogle ScholarPubMed
Mackie, AS, Gauvreau, K, Newburger, JW, Mayer, JE, Erickson, LC. Risk factors for readmission after neonatal cardiac surgery. Ann Thorac Surg. 2004; 78: 1972–1978; discussion 1978.CrossRefGoogle ScholarPubMed
Kansagara, D, Englander, H, Salanitro, A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011; 306: 1688–1698.CrossRefGoogle ScholarPubMed
Tregay, J, Wray, J, Bull, C, et al. Unexpected deaths and unplanned re-admissions in infants discharged home after cardiac surgery: a systematic review of potential risk factors. Cardiol Young. 2015; 25: 839–852.CrossRefGoogle ScholarPubMed
Polineni, S, Parker, DM, Alam, SS, et al. Predictive Ability of Novel Cardiac Biomarkers ST2, Galectin-3, and N-terminal prohormone of brain natriuretic peptide before cardiac surgery. J Am Heart Assoc 2018; 7: 14.CrossRefGoogle Scholar
Gaggin, HK, Januzzi, JL. Biomarkers and diagnostics in heart failure. Biochim Biophys Acta Mol Basis Dis 2013; 1832: 2442–2450.CrossRefGoogle ScholarPubMed
Fernandes, BA, Maher, KO, Deshpande, SR. Cardiac biomarkers in pediatric heart disease: a state of art review. World J cardiol 2016; 8: 719–727.CrossRefGoogle ScholarPubMed
Mathews, LR, Lott, JM, Isse, K, et al. Elevated ST2 distinguishes incidences of pediatric heart and small bowel transplant rejection. American J Transplant 2016; 16: 938–950.CrossRefGoogle ScholarPubMed
Chida, A, Sato, H, Shintani, M, et al. Soluble ST2 and N-terminal pro-brain natriuretic peptide combination. Useful biomarker for predicting outcome of childhoodpulmonary arterial hypertension. Circ J 2014; 78: 436–442.CrossRefGoogle ScholarPubMed
Yang, Z, Wang, KKW. Glial Fibrillary acidic protein: from intermediate filament assembly and gliosis to neurobiomarker. Trends Neurosci 2015; 38: 364–374.CrossRefGoogle ScholarPubMed
Dumic, J, Dabelic, S, Flogel, M. Galectin-3: an open-ended story. Biochim Biophys Acta 2006; 1760: 616–635.CrossRefGoogle ScholarPubMed
Opotowsky, AR, Baraona, F, Owumi, J, et al. Galectin-3 is elevated and associated with adverse outcomes in patients with single-ventricle fontan circulation. J Am Heart Assoc 2016; 5: 1.CrossRefGoogle ScholarPubMed
Magruder, JT, Hibino, N, Collica, S, et al. Association of nadir oxygen delivery on cardiopulmonary bypass with serum glial fibrillary acid protein levels in paediatric heart surgery patients. Interact Cardiovasc Thorac Surg 2016; 23: 531–537.CrossRefGoogle ScholarPubMed
Vedovelli, L, Padalino, M, Simonato, M, et al. Cardiopulmonary bypass increases plasma glial fibrillary acidic protein only in first stage palliation of hypoplastic left heart syndrome. Can J Cardiol 2016; 32: 355–361.CrossRefGoogle ScholarPubMed
Vedovelli, L, Padalino, M, D’Aronco, S, et al. Glial fibrillary acidic protein plasma levels are correlated with degree of hypothermia during cardiopulmonary bypass in congenital heart disease surgery. Interact Cardiovasc Thorac Surg 2017; 24: 436–442.Google ScholarPubMed
Bembea, MM, Savage, W, Strouse, JJ, et al. Glial fibrillary acidic protein as a brain injury biomarker in children undergoing extracorporeal membrane oxygenation. Pediatr Crit Care Med 2011; 12: 572–579.CrossRefGoogle ScholarPubMed
Stewart, A, Tekes, A, Huisman, TA, et al. Glial fibrillary acidic protein as a biomarker for periventricular white matter injury. Am J Obstet Gynecol 2013; 209: 27.e21–27.CrossRefGoogle ScholarPubMed
Brunetti, MA, Jennings, JM, Easley, RB, et al. Glial fibrillary acidic protein in children with congenital heart disease undergoing cardiopulmonary bypass. Cardiol Young 2014; 24: 623–631.CrossRefGoogle ScholarPubMed
Hori, D, Ono, M, Rappold, TE, et al. Hypotension after cardiac operations based on autoregulation monitoring leads to brain cellular injury. Ann Thorac Surg 2015; 100: 487–493.CrossRefGoogle ScholarPubMed
Hori, D, Everett, AD, Lee, JK, et al. Rewarming rate during cardiopulmonary bypass is associated with release of glial fibrillary acidic protein. Ann Thorac Surg 2015; 100: 1353–1358.CrossRefGoogle ScholarPubMed
Ennen, CS, Huisman, TA, Savage, WJ, et al. Glial fibrillary acidic protein as a biomarker for neonatal hypoxic-ischemic encephalopathy treated with whole-body cooling. Am J Obstet Gynecol 2011; 205: 251.e251–257.CrossRefGoogle ScholarPubMed
Brown, JR, Jacobs, JP, Alam, SS, et al. Utility of biomarkers to improve prediction of readmission or mortality after cardiac surgery. Ann Thorac Surg 2018; 106: 1294–1301.CrossRefGoogle ScholarPubMed
O’Brien, SM, Jacobs, JP, Pasquali, SK, et al. The society of thoracic surgeons congenital heart surgery database mortality risk model: part 1-statistical methodology. Ann Thorac Surg 2015; 100: 1054–1062.CrossRefGoogle ScholarPubMed
Hajian-Tilaki, K. Receiver Operating Characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian J Intern Med 2013; 4: 627–635.Google ScholarPubMed
Cook, NR, Ridker, PM. The use and magnitude of reclassification measures for individual predictors of global cardiovascular risk. Ann Intern Med 2009; 150: 795–802.CrossRefGoogle Scholar
Pencina, MJ, D’Agostino, RB, Pencina, KM, Janssens, ACJW, Greenland, P. Interpreting incremental value of markers added to risk prediction models. Am J Epidemiol 2012; 176: 473–481.CrossRefGoogle ScholarPubMed
Cook, NR, Paynter, NP. Performance of reclassification statistics in comparing risk prediction models. Biom J 2011; 53: 237–258.CrossRefGoogle ScholarPubMed
Hanley, JA, McNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143: 29–36.CrossRefGoogle Scholar
Efron, B, Tibshirani, R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1986; 1: 54–75.CrossRefGoogle Scholar
Meeusen, JW, Johnson, JN, Gray, A, et al. Soluble ST2 and galectin-3 in pediatric patients without heart failure. Clin Biochem 2015; 48: 1337–1340.CrossRefGoogle ScholarPubMed
Tang, WH, Shrestha, K, Shao, Z, et al. Usefulness of plasma galectin-3 levels in systolic heart failure to predict renal insufficiency and survival. Am J Cardiol 2011; 108: 385–390.CrossRefGoogle Scholar
Rehman, SU, Mueller, T, Januzzi, JL , Jr. Characteristics of the novel interleukin family biomarker ST2 in patients with acute heart failure. J Am Coll Cardiol 2008; 52: 1458–1465.CrossRefGoogle ScholarPubMed
Yancy, CW, Jessup, M, Bozkurt, B, et al. 2013 ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 2013; 128: 1810–1852.CrossRefGoogle Scholar
Mohammed, LA, Gafar, H.S., Hussien, N.R. Galectin-3 as early detector of heart failure in children with congenital acyanotic heart disease. Clin Med Diagn 2014; 4: 90–98.Google Scholar
Goldman, J, Becker, ML, Jones, B, Clements, M, Leeder, JS. Development of biomarkers to optimize pediatric patient management: what makes children different?. Biomark Med 2011; 5: 781–794.CrossRefGoogle ScholarPubMed
Cantinotti, M, Passino, C, Storti, S, Ripoli, A, Zyw, L, Clerico, A. Clinical relevance of time course of BNP levels in neonates with congenital heart diseases. Clin Chim Acta 2011; 412: 2300–2304.CrossRefGoogle ScholarPubMed
Holmgren, D, Westerlind, A, Lundberg, PA, Wahlander, H. Increased plasma levels of natriuretic peptide type B and A in children with congenital heart defects with left compared with right ventricular volume overload or pressure overload. Clin physiol Funct Imaging 2005; 25: 263–269.CrossRefGoogle ScholarPubMed
Chave, M, Marques-Vidal, P. Factors associated with readmission of patients with congenital heart disease in a Swiss University Hospital. Pediatr cardiol 2017; 38: 650–655.CrossRefGoogle Scholar
Crowe, S, Ridout, DA, Knowles, R, et al. Death and emergency readmission of infants discharged after interventions for congenital heart disease: a national study of 7643 infants to inform service improvement. J Am Heart Assoc 2016; 5: 5.CrossRefGoogle ScholarPubMed
Kogon, B, Woodall, K, Kanter, K, Alsoufi, B, Oster, M. Reducing readmissions following paediatric cardiothoracic surgery: a quality improvement initiative. Cardiol Young 2015; 25: 935–940.CrossRefGoogle ScholarPubMed
Vo, D, Zurakowski, D, Faraoni, D. Incidence and predictors of 30-day postoperative readmission in children. Paediatr Anaesth 2018; 28: 63–70.CrossRefGoogle ScholarPubMed
Lawley, CM, Lain, SJ, Figtree, GA, Sholler, GF, Winlaw, DS, Roberts, CL. Mortality, rehospitalizations and costs in children undergoing a cardiac procedure in their first year of life in New South Wales, Australia. Int J Cardiol 2017; 241: 156–162.CrossRefGoogle ScholarPubMed
Parker, DM, Everett, AD, Stabler, ME, et al. Biomarkers associated with 30-day readmission and mortality after pediatric congenital heart surgery. J Card Surg 2019; 34: 329–336.CrossRefGoogle ScholarPubMed
Parker, DM, Everett, AD, Stabler, ME, et al. The Association between Cardiac Biomarker NT-proBNP and 30-day Readmission or Mortality after Pediatric Congenital Heart Surgery. World J Pediatr Congenit Heart Surg 2019; Accepted March 6, 2019.CrossRefGoogle Scholar
Parker, DM, Everett, AD, Stabler, ME, et al. Novel Biomarkers Improves Prediction of 365-day Readmission after Pediatric Congenital Heart Surgery. ATS 2019; Accepted May 21, 2019.CrossRefGoogle Scholar
Turley, K, Tyndall, M, Roge, C, et al. Critical pathway methodology: effectiveness in congenital heart surgery. Ann Thorac Surg 1994; 58: 57–63; discussion 63–55.CrossRefGoogle ScholarPubMed
Williams, DL, Gelijns, AC, Moskowitz, AJ, et al. Hypoplastic left heart syndrome: valuing the survival. J Thorac Cardiovasc Surg 2000; 119: 720–731.CrossRefGoogle ScholarPubMed
Jacobs, JP, Jacobs, ML, Maruszewski, B, et al. Initial application in the EACTS and STS Congenital Heart Surgery Databases of an empirically derived methodology of complexity adjustment to evaluate surgical case mix and results. Eur J cardiothorac Surg 2012; 42: 775–779; discussion 779–780.CrossRefGoogle ScholarPubMed
Supplementary material: File

Brown et al. supplementary material

Online Supplement

Download Brown et al. supplementary material(File)
File 32.4 KB