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Derivation and Validation of a General Predictive Model for Long Term Risks for Mortality and Invasive Interventions in Congenital and Acquired Cardiac Conditions Encountered in the Young

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Abstract

Accurate prognostic assessment is a key driver of clinical decision making in heart disease in the young (HDY). This investigation aims to derive, validate, and calibrate multivariable predictive models for time to surgical or catheter-mediated intervention (INT) and for time to death in HDY. 4108 unique subjects were prospectively and consecutively enrolled, and randomized to derivation and validation cohorts. Total follow-up was 26,578 patient-years, with 102 deaths and 868 INTs. Accelerated failure time multivariable predictive models for the outcomes, based on primary and secondary diagnoses, pathophysiologic severity, age, sex, genetic comorbidities, and prior interventional history, were derived using piecewise exponential methodology. Model predictions were validated, calibrated, and evaluated for sensitivity to changes in the independent variables. Model validity was excellent for predicting mortality and INT at 4 months, 1, 5, 10, and 22 years (areas under receiver operating characteristic curves 0.813–0.915). Model calibration was better for INT than for mortality. Age, sex, and genetic comorbidities were significant independent factors, but predicted outcomes were most sensitive to variations in composite predictors incorporating primary diagnosis, pathophysiologic severity, secondary diagnosis, and prior intervention. Despite 22 years of data acquisition, no significant cohort effects were identified in which predicted mortality and intervention varied by study entry date. A piecewise exponential model predicting survival and freedom from INT is derived which demonstrates excellent validity, and performs well on a clinical sample of HDY outpatients. Objective model-based predictions could educate both patient and provider, and inform clinical decision making in HDY.

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Abbreviations

DxPxint :

Diagnosis/Pathophysiology based score for intervention

DxPxmort :

Diagnosis/Pathophysiology based score for mortality

HDY:

Heart disease in the young

HIDxPxint :

High risk category of Diagnosis/Pathophysiology based score for intervention

INT:

Surgical or catheter-mediated intervention

OIG:

Other inborn genetic condition

PFFI:

Predicted freedom from intervention

PSURV:

Predicted survival

References

  1. Jacobs JP, Jacobs ML, Lacour-Gayet FG, Jenkins KJ, Gauvreau K, Bacha E, Maruszewski B, Clarke DR, Tchervenkov CI, Gaynor JW, Spray TL, Stellin G, O’Bien SM, Elliott MJ, Mavroudis C (2009) Stratification of complexity improves the utility and accuracy of outcomes analysis in a multi-institutional congenital heart surgery database: application of the risk adjustment in congenital heart surgery (RACHS-1) and aristotle systems in the society of thoracic surgeons (STS) congenital heart surgery database. Pediatr Cardiol 30(8):1117–1130. https://doi.org/10.1007/s00246-009-9496-0. (PMID: 19771463)

    Article  PubMed  Google Scholar 

  2. Jacobs ML, O’Brien SM, Jacobs JP, Mavroudis C, Lacour-Gayet F, Pasquali SK, Welke K, Pizarro C, Tsai F, Clarke DR (2013) An empirically based tool for analyzing morbidity associated with operations for congenital heart disease. J Thorac Cardiovasc Surg 145(4):1046–1057. https://doi.org/10.1016/j.jtcvs.2012.06.029

    Article  PubMed  Google Scholar 

  3. Karamlou T, McCrindle BW, Blackstone EH, Cai S, Jonas RA, Bradley SM, Ashburn DA, Caldarone CA, Williams WG (2010) Lesion-specific outcomes in neonates undergoing congenital heart surgery are related predominantly to patient and management factors rather than institution or surgeon experience: a congenital heart surgeons society study. J Thorac Cardiovasc Surg 139(3):569-577.e1. https://doi.org/10.1016/j.jtcvs.2008.11.073. (Epub 2009 Nov 11 PMID: 19909989)

    Article  PubMed  Google Scholar 

  4. Manes A, Palazzini M, Leci E, Bacchi Reggiani ML, Branzi A, Galiè N (2014) Current era survival of patients with pulmonary arterial hypertension associated with congenital heart disease: a comparison between clinical subgroups. Eur Heart J 35(11):716–724. https://doi.org/10.1093/eurheartj/eht072. (Epub 2013 Mar 1 PMID: 23455361)

    Article  PubMed  Google Scholar 

  5. Oliver JM, Gallego P, Gonzalez AE, Garcia-Hamilton D, Avila P, Yotti R, Ferreira I, Fernandez-Aviles F (2017) Risk factors for excess mortality in adults with congenital heart diseases. Eur Heart J 38(16):1233–1241. https://doi.org/10.1093/eurheartj/ehw590. (PMID: 28077469)

    Article  PubMed  Google Scholar 

  6. Danford DA, Martin AB, Danford CJ, Kaul S, Marshall AM, Kutty S (2015) Clinical Implications of a Multivariate Stratification Model for the Estimation of Prognosis in Ventricular Septal Defect. J Pediatr 167(1):103–107. https://doi.org/10.1016/j.jpeds.2015.04.005. (Epub 2015 Apr 30 PMID: 25935817)

    Article  PubMed  Google Scholar 

  7. Evans JM, Dharmar M, Meierhenry E, Marcin JP, Raff GW (2014) Association between down syndrome and in-hospital death among children undergoing surgery for congenital heart disease: a US population-based study. Circ Cardiovasc Qual Outcomes 7(3):445–452. https://doi.org/10.1161/CIRCOUTCOMES.113.000764. (Epub 2014 Apr 22 PMID: 24755908)

    Article  PubMed  Google Scholar 

  8. Peterson JK, Setty SP, Knight JH, Thomas AS, Moller JH, Kochilas LK (2019) Postoperative and long-term outcomes in children with Trisomy 21 and single ventricle palliation. Congenit Heart Dis 14(5):854–863. https://doi.org/10.1111/chd.12823

    Article  PubMed  PubMed Central  Google Scholar 

  9. Meyer RE, Liu G, Gilboa SM, Ethen MK, Aylsworth AS, Powell CM, Flood TJ, Mai CT, Wang Y, Canfield MA, National Birth Defects Prevention Network (2016) Survival of children with trisomy 13 and trisomy 18: a multi-state population-based study. Am J Med Genet A 170A(4):825–837. https://doi.org/10.1002/ajmg.a.37495

    Article  CAS  PubMed  Google Scholar 

  10. Jernigan EG, Strassle PD, Stebbins RC, Meyer RE, Nelson JS (2017) Effect of concomitant birth defects and genetic anomalies on infant mortality in tetralogy of fallot. Birth Defects Res 109(14):1154–1165. https://doi.org/10.1002/bdr2.1057. (Epub 2017 Jun 19 PMID: 28627098)

    Article  CAS  PubMed  Google Scholar 

  11. van Mil S, Heung T, Malecki S, Van L, Chang J, Breetvelt E, Wald R, Oechslin E, Silversides C, Bassett AS (2020) Impact of a 22q11.2 microdeletion on adult all-cause mortality in tetralogy of fallot patients. Can J Cardiol 36(7):1091–1097. https://doi.org/10.1016/j.cjca.2020.04.019. (Epub 2020 Apr 26 PMID: 32348848)

    Article  PubMed  Google Scholar 

  12. Farina MA, Hook EB (1978) Apparent sex difference in spontaneous closure of ventricular septal defect. J Pediatr 93(6):1065–1066. https://doi.org/10.1016/s0022-3476(78)81268-2. (PMID: 722432)

    Article  CAS  PubMed  Google Scholar 

  13. Sheiner E, Wainstock T, Landau D, Walfisch A (2017) The association between sex and long-term pediatric cardiovascular morbidity. J Pediatr 180:68-73.e1. https://doi.org/10.1016/j.jpeds.2016.09.014. (Epub 2016 Oct 13 PMID: 27745861)

    Article  PubMed  Google Scholar 

  14. Tsuda E, Yamada O, Kitano M (2019) Improvement of the outcome in patients with infantile dilated cardiomyopathy over three decades - The usefulness of long-term gradually medical supportive care. J Cardiol 74(2):189–194. https://doi.org/10.1016/j.jjcc.2019.02.005. (Epub 2019 Mar 12 PMID: 30876708)

    Article  PubMed  Google Scholar 

  15. Oster ME, Lee KA, Honein MA, Riehle-Colarusso T, Shin M, Correa A (2013) Temporal trends in survival among infants with critical congenital heart defects. Pediatrics 131(5):e1502–e1508. https://doi.org/10.1542/peds.2012-3435

    Article  PubMed  Google Scholar 

  16. Kang Y, Kwak JG, Min J, Lim JH, Kim WH (2021) Twenty-year experience with truncus arteriosus repair: changes in risk factors in the current era. Pediatr Cardiol 42(1):123–130. https://doi.org/10.1007/s00246-020-02461-5. (Epub 2020 Sep 29 PMID: 32995903)

    Article  PubMed  Google Scholar 

  17. Khairy P, Ionescu-Ittu R, Mackie AS, Abrahamowicz M, Pilote L, Marelli AJ (2010) Changing mortality in congenital heart disease. J Am Coll Cardiol 56(14):1149–1157. https://doi.org/10.1016/j.jacc.2010.03.085. (PMID: 20863956)

    Article  PubMed  Google Scholar 

  18. Anderson BR, Stevens KN, Nicolson SC, Gruber SB, Spray TL, Wernovsky G, Gruber PJ (2013) Contemporary outcomes of surgical ventricular septal defect closure. J Thorac Cardiovasc Surg 145(3):641–647. https://doi.org/10.1016/j.jtcvs.2012.11.032. (PMID: 23414985)

    Article  PubMed  Google Scholar 

  19. Kaplan EL, Meier P (1958) Nonparametric estimation from incomplete observations. J Am Stat Assoc 53(282):457–481

    Article  Google Scholar 

  20. Cox DR (1972) Regression models and life-tables. J Roy Stat Soc: Ser B (Methodol) 34(2):187–202

    Google Scholar 

  21. Demler OV, Paynter NP, Cook NR (2018) Reclassification calibration test for censored survival data: performance and comparison to goodness-of-fit criteria. Diagn Progn Res. 2:16. https://doi.org/10.1186/s41512-018-0034-5

    Article  PubMed  PubMed Central  Google Scholar 

  22. Ying T, Shi B, Kelly PJ, Pilmore H, Clayton PA, Chadban SJ (2020) Death after kidney transplantation: an analysis by era and time post-transplant. J Am Soc Nephrol 31(12):2887–2899. https://doi.org/10.1681/ASN.2020050566

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Miller J, Wey A, Musgrove D, Son Ahn Y, Hart A, Kasiske BL, Hirose R, Israni AK, Snyder JJ (2021) Mortality among solid organ waitlist candidates during COVID-19 in the United States. Am J Transplant. https://doi.org/10.1111/ajt.16550

    Article  PubMed  Google Scholar 

  24. Ji SY, Lee J, Lee JH, Lee ST, Won JK, Kim JW, Kim YH, Kim TM, Choi SH, Park SH, Kim Y, Park CK (2020) Radiological assessment schedule for high-grade glioma patients during the surveillance period using parametric modeling. Neuro Oncol. https://doi.org/10.1093/neuonc/noaa250

    Article  PubMed  PubMed Central  Google Scholar 

  25. Heser K, Fink A, Reinke C, Wagner M, Doblhammer G (2020) The temporal association between incident late-life depression and incident dementia. Acta Psychiatr Scand 142(5):402–412. https://doi.org/10.1111/acps.13220. (Epub 2020 Aug 7 PMID: 32712956)

    Article  CAS  PubMed  Google Scholar 

  26. Graff-Radford J, Lesnick T, Rabinstein AA, Gunter J, Aakre J, Przybelski SA, Spychalla AJ, Huston J 3rd, Brown RD Jr, Mielke MM, Lowe VJ, Knopman DS, Petersen RC, Jack CR Jr, Vemuri P, Kremers W, Kantarci K (2020) Cerebral microbleed incidence, relationship to amyloid burden: The Mayo Clinic Study of Aging. Neurology 94(2):e190–e199. https://doi.org/10.1212/WNL.0000000000008735

    Article  PubMed  PubMed Central  Google Scholar 

  27. Bradley SEK, Polis CB, Bankole A, Croft T (2019) Global contraceptive failure rates: who is most at risk? Stud Fam Plann 50(1):3–24. https://doi.org/10.1111/sifp.12085

    Article  PubMed  PubMed Central  Google Scholar 

  28. PRINCIPLE Trial Collaborative Group (2021) Azithromycin for community treatment of suspected COVID-19 in people at increased risk of an adverse clinical course in the UK (PRINCIPLE): a randomised, controlled, open-label, adaptive platform trial. Lancet. https://doi.org/10.1016/S0140-6736(21)00461-X

    Article  PubMed Central  Google Scholar 

  29. Morris CD, Menashe VD (1991) 25-year mortality after surgical repair of congenital heart defect in childhood: a population based cohort study. JAMA 266(24):3447–3452 (PMID: 1744959)

    Article  CAS  PubMed  Google Scholar 

  30. Samánek M, Vorísková M (1999) Congenital heart disease among 815,569 children born between 1980 and 1990 and their 15-year survival: a prospective Bohemia survival study. Pediatr Cardiol 20(6):411–417. https://doi.org/10.1007/s002469900502. (PMID: 10556387)

    Article  PubMed  Google Scholar 

  31. Mat Bah MN, Sapian MH, Jamil MT, Alias A, Zahari N (2018) Survival and associated risk factors for mortality among infants with critical congenital heart disease in a developing country. Pediatr Cardiol 39(7):1389–1396. https://doi.org/10.1007/s00246-018-1908-6. (Epub 2018 May 14 PMID: 29756159)

    Article  PubMed  Google Scholar 

  32. Larsen SH, Olsen M, Emmertsen K, Hjortdal VE (2017) Interventional treatment of patients with congenital heart disease: nationwide danish experience over 39 years. J Am Coll Cardiol 69(22):2725–2732. https://doi.org/10.1016/j.jacc.2017.03.587. (PMID: 28571637)

    Article  PubMed  Google Scholar 

  33. Jenkins KJ, Gauvreau K, Newburger JW, Spray TL, Moller JH, Iezzoni LI (2002) Consensus-based method for risk adjustment for surgery for congenital heart disease. J Thorac Cardiovasc Surg 123(1):110–118. https://doi.org/10.1067/mtc.2002.119064. (PMID: 11782764)

    Article  PubMed  Google Scholar 

  34. Larsen SH, Pedersen J, Jacobsen J, Johnsen SP, Hansen OK, Hjortdal V (2005) The RACHS-1 risk categories reflect mortality and length of stay in a Danish population of children operated for congenital heart disease. Eur J Cardiothorac Surg 28(6):877–881. https://doi.org/10.1016/j.ejcts.2005.09.008. (Epub 2005 Oct 19 PMID: 16242940)

    Article  PubMed  Google Scholar 

  35. Nakayama Y, Shibasaki M, Shime N, Nakajima Y, Mizobe T, Sawa T (2013) The RACHS-1 risk category can be a predictor of perioperative recovery in Asian pediatric cardiac surgery patients. J Anesth 27(6):850–854. https://doi.org/10.1007/s00540-013-1645-1. (Epub 2013 Jun 6 PMID: 23740139)

    Article  PubMed  Google Scholar 

  36. Kang N, Tsang VT, Elliott MJ, de Leval MR, Cole TJ (2006) Does the Aristotle Score predict outcome in congenital heart surgery? Eur J Cardiothorac Surg 29(6):986–988. https://doi.org/10.1016/j.ejcts.2006.01.066. (Epub 2006 May 4 PMID: 16677819)

    Article  PubMed  Google Scholar 

  37. Faraoni D, Vo D, Nasr VG, DiNardo JA (2016) Development and validation of a risk stratification score for children with congenital heart disease undergoing noncardiac surgery. Anesth Analg 123(4):824–830. https://doi.org/10.1213/ANE.0000000000001500. (PMID: 27529321)

    Article  PubMed  Google Scholar 

  38. Larsen SH, Emmertsen K, Johnsen SP, Pedersen J, Hjortholm K, Hjortdal VE (2011) Survival and morbidity following congenital heart surgery in a population-based cohort of children–up to 12 years of follow-up. Congenit Heart Dis 6(4):322–329. https://doi.org/10.1111/j.1747-0803.2011.00495.x. (Epub 2011 Mar 21 PMID: 21418533)

    Article  PubMed  Google Scholar 

  39. Stefanescu A, Macklin EA, Lin E, Dudzinski DM, Johnson J, Kennedy KF, Jacoby D, DeFaria YD, Lewis GD, Yeh RW, Liberthson R, Lui G, Bhatt AB (2014) Usefulness of the Seattle heart failure model to identify adults with congenital heart disease at high risk of poor outcome. Am J Cardiol 113(5):865–870. https://doi.org/10.1016/j.amjcard.2013.11.043. (Epub 2013 Dec 12 PMID: 24411285)

    Article  PubMed  Google Scholar 

  40. Ombelet F, Goossens E, Apers S, Budts W, Gewillig M, Moons P (2019) Predicting 15-year mortality in adults with congenital heart disease using disease severity and functional indices. Can J Cardiol 35(7):907–913. https://doi.org/10.1016/j.cjca.2019.04.018. (Epub 2019 Apr 26 PMID: 31292090)

    Article  PubMed  Google Scholar 

  41. Knowles RL, Bull C, Wren C, Wade A, Goldstein H, Dezateux C et al (2014) Modelling survival and mortality risk to 15 years of age for a national cohort of children with serious congenital heart defects diagnosed in infancy. PLoS ONE 9(8):e106806. https://doi.org/10.1371/journal.pone.0106806

    Article  PubMed  PubMed Central  Google Scholar 

  42. Stout KK, Daniels CJ, Aboulhosn JA, Bozkurt B, Broberg CS, Colman JM, Crumb SR, Dearani JA, Fuller S, Gurvitz M, Khairy P, Landzberg MJ, Saidi A, Valente AM, Van Hare GF (2019) 2018 AHA/ACC guideline for the management of adults with congenital heart disease: executive summary: a report of the American college of cardiology/American heart association task force on clinical practice guidelines. Circulation 139(14):e637–e697. https://doi.org/10.1161/CIR.0000000000000602. (PMID: 30586768)

    Article  PubMed  Google Scholar 

  43. Ferencz C, Rubin JD, McCarter RJ, Brenner JI, Neill CA, Perry LW, Hepner SI, Downing JW (1985) Congenital heart disease: prevalence at livebirth: the Baltimore-Washington infant study. Am J Epidemiol 121(1):31–36. https://doi.org/10.1093/oxfordjournals.aje.a113979. (PMID: 3964990)

    Article  CAS  PubMed  Google Scholar 

  44. Stephensen SS, Sigfusson G, Eiriksson H, Sverrisson JT, Torfason B, Haraldsson A, Helgason H (2004) Congenital cardiac malformations in Iceland from 1990 through 1999. Cardiol Young 14(4):396–401. https://doi.org/10.1017/S1047951104004081. (PMID: 15680046)

    Article  PubMed  Google Scholar 

  45. Moons P, Sluysmans T, De Wolf D, Massin M, Suys B, Benatar A, Gewillig M (2009) Congenital heart disease in 111 225 births in Belgium: birth prevalence, treatment and survival in the 21st century. Acta Paediatr 98(3):472–477. https://doi.org/10.1111/j.1651-2227.2008.01152.x. (Epub 2008 Nov 30 PMID: 19046347)

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. University of Nebraska Medical Center, Omaha, NE, USA

    David A. Danford & Anji T. Yetman

  2. Criss Heart Center at Children’s Hospital and Medical Center, Omaha, NE, USA

    David A. Danford & Anji T. Yetman

  3. School of Public Health, University of Nebraska Medical Center, Omaha, NE, USA

    Gleb Haynatzki

  4. 804 S. 129th Ave, Omaha, NE, 68154, USA

    David A. Danford

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DD contributed conceptualization and design, data collection and curation, and drafted the initial manuscript. AY and GH provided supervision and oversight. GH and DD developed the methodology and participated in the formal analysis. All authors reviewed and edited the manuscript and gave final approval.

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Danford, D.A., Yetman, A.T. & Haynatzki, G. Derivation and Validation of a General Predictive Model for Long Term Risks for Mortality and Invasive Interventions in Congenital and Acquired Cardiac Conditions Encountered in the Young. Pediatr Cardiol 44, 1763–1777 (2023). https://doi.org/10.1007/s00246-023-03154-5

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