Skip to main content Accessibility help
×
Home

Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data

  • Sanjukta N. Bose (a1) (a2), Adam Verigan (a3), Jade Hanson (a3) (a4), Luis M. Ahumada (a5), Sharon R. Ghazarian (a5), Neil A. Goldenberg (a3) (a4) (a6), Arabela Stock (a7) and Jeffrey P. Jacobs (a1) (a2) (a3) (a4) (a5) (a6) (a7)...
  • Please note a correction has been issued for this article.

Abstract

Objective:

To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU.

Methods:

We performed a single-institution retrospective cohort study (11 January 2013–16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children’s hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model’s discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest.

Results:

The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%.

Conclusions:

Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.

Copyright

Corresponding author

Author for correspondence: J. P. Jacobs, MD, 2021 Brightwaters Blvd., Saint Petersburg, Florida 33704. Tel: (727) 235-3100; Fax: (727) 551-0404; E-mail: JeffJacobs@msn.com

Footnotes

Hide All

The original version of this article was published with one incorrect author name. A notice detailing this has been published and the error rectified in the online and print PDF and HTML copies.

Footnotes

References

Hide All
1. Meyer, L, Stubbs, B, Fahrenbruch, C, et al. Incidence, causes, and survival trends from cardiovascular-related sudden cardiac arrest in children and young adults 0 to 35 years of age: a 30-year review. Circulation 2012: 126: 13631372.
2. Brooten, D, Youngblut, JM, Caicedo, C, et al. Cause of death of infants and children in the intensive care unit: parents’ recall vs chart review. Am J Crit Care 2016; 25: 235242.
3. Atkins, DL, Everson-Stewart, S, Sears, GK, et al. Epidemiology and outcomes from out-of-hospital cardiac arrest in children. Circulation 2009; 119: 14841491.
4. Eisenberg, M, Bergner, L, Hallstrom, A. Epidemiology of cardiac arrest and resuscitation in children. Ann Emergency Med 1983; 12: 672674.
5. Young, KD, Gausche-Hill, M, McClung, CD, et al. A prospective, population-based study of the epidemiology and outcome of out-of-hospital pediatric cardiopulmonary arrest. Pediatrics 2004; 114: 157164.
6. Schindler, MB, Bohn, D, Cox, PN, et al. Outcome of out-of-hospital cardiac or respiratory arrest in children. N Engl J Med 1996; 335: 14731479.
7. Meert, KL, Donaldson, A, Nadkarni, V, et al. Multicenter cohort study of in-hospital pediatric cardiac arrest. Pediatr Crit Care Med 2009; 10: 544.
8. Tress, EE, Kochanek, PM, Saladino, RA, et al. Cardiac arrest in children. J Emerg Trauma Shock 2010; 3: 267.10.4103/0974-2700.66528
9. Pollack, MM, Patel, KM, Ruttimann, UE. PRISM III: an updated pediatric risk of mortality score. Crit Care Med 1996; 24: 743752.
10. Pollack, MM, Holubkov, R, Funai, T, et al. The pediatric risk of mortality score: update 2015. Pediatr Crit Care Med 2016; 17: 2.
11. Czaja, AS, Scanlon, MC, Kuhn, EM, et al. Performance of the pediatric index of mortality 2 for pediatric cardiac surgery patients. Pediatr Crit Care Med 2011; 12: 184189.
12. McLellan, MC, Gauvreau, K, Connor, JA. Validation of the cardiac children’s hospital early warning score: an early warning scoring tool to prevent cardiopulmonary arrests in children with heart disease. Congenital Heart Dis 2014; 9: 194202.
13. McLellan, MC, Connor, JA. The cardiac children’s hospital early warning score (C-CHEWS). J Pediatr Nurs 2013; 28: 171178.
14. Jeffries, HE, Soto-Campos, G, Katch, A, et al. Pediatric index of cardiac surgical intensive care mortality risk score for pediatric cardiac critical care. Pediatr Crit Care Med 2015; 16: 846852.
15. O’brien, SM, Clarke, DR, Jacobs, JP, et al. An empirically based tool for analyzing mortality associated with congenital heart surgery. J Thoracic Cardiovasc Surg 2009; 138: 11391153.
16. Rogers, L, Ray, S, Johnson, M, et al. The inadequate oxygen delivery index and low cardiac output syndrome score as predictors of adverse events associated with low cardiac output syndrome early after cardiac bypass. Pediatr Crit Care Med 2019; 20: 737743.
17. Siberry, G, Iannone, R. The Harriet Lane handbook: a manual for pediatric house officers (15th edn). Consultant 2000; 40: 248248.
18. Version M. 9.0. 0 (R2016a). MathWorks Inc., Natick, MA, USA, 2016.
19. Pangerc, U, Jager, F. Robust detection of heart beats in multimodal data using integer multiplier digital filters and morphological algorithms. Computing in Cardiology Conference (CinC), 2014, 2014. IEEE, Cambridge, MA, USA.
20. Moody, G, Moody, B, Silva, I. Robust detection of heart beats in multimodal data: the physionet/computing in cardiology challenge 2014. Computing in Cardiology Conference (CinC), 2014, 2014. IEEE, Cambridge, MA, USA.
21. Blough, DK, Madden, CW, Hornbrook, MC. Modeling risk using generalized linear models. J Health Econ 1999; 18: 153171.
22. Dobson, AJ, Barnett, A. An Introduction to Generalized Linear Models. CRC press, Boca Raton, FL, 2008.
23. Aitkin, M. A general maximum likelihood analysis of variance components in generalized linear models. Biometrics 1999; 55: 117128.10.1111/j.0006-341X.1999.00117.x
24. McCullagh, P. Generalized linear models. Eur J Oper Res 1984; 16: 285292.10.1016/0377-2217(84)90282-0
25. Gallop, RJ, Crits-Christoph, P, Muenz, LR, et al. Determination and interpretation of the optimal operating point for ROC curves derived through generalized linear models. Understanding Stat 2003; 2: 219242.
26. Fawcett, T. An introduction to ROC analysis. Pattern Recogn Lett 2006; 27: 861874.
27. Greiner, M, Pfeiffer, D, Smith, R. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev Vet Med 2000; 45: 2341.
28. Hodgetts, TJ, Kenward, G, Vlachonikolis, IG, et al. The identification of risk factors for cardiac arrest and formulation of activation criteria to alert a medical emergency team. Resuscitation 2002; 54: 125131.
29. Fieselmann, JF, Hendryx, MS, Helms, CM, et al. Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients. J Gen Int Med 1993; 8: 354360.
30. Ghuran, A, Reid, F, La Rovere, MT, et al. Heart rate turbulence-based predictors of fatal and nonfatal cardiac arrest (The autonomic tone and reflexes after myocardial infarction substudy). Am J Cardiol 2002; 89: 184190.
31. Kleiger, RE, Stein, PK, Bigger, JT. Heart rate variability: measurement and clinical utility. Ann Noninvas Electro 2005; 10: 88101.
32. La Rovere, MT, Bigger, JT, Marcus, FI, et al. Baroreflex sensitivity and heart-rate variability in prediction of total cardiac mortality after myocardial infarction. Lancet 1998; 351: 478484.
33. Malik, M. Heart rate variability. Circulation 1996; 93: 10431065.
34. Martin, GJ, Magid, NM, Myers, G, et al. Heart rate variability and sudden death secondary to coronary artery disease during ambulatory electrocardiographic monitoring. Am J Cardiol 1987; 60: 8689.
35. Nolan, J, Batin, PD, Andrews, R, et al. Prospective study of heart rate variability and mortality in chronic heart failure. Circulation 1998; 98: 15101516.
36. Singer, DH, Martin, GJ, Magid, N, et al. Low heart rate variability and sudden cardiac death. J Electro 1988; 21: S46S55.
37. Galinier, M, Pathak, A, Fourcade, J, et al. Depressed low frequency power of heart rate variability as an independent predictor of sudden death in chronic heart failure. Eur Heart J 2000; 21: 475482.
38. Lahiri, MK, Kannankeril, PJ, Goldberger, JJ. Assessment of autonomic function in cardiovascular disease. J Am Coll Cardiol 2008; 51: 17251733.
39. Kennedy, CE, Aoki, N, Mariscalco, M, et al. Using time series analysis to predict cardiac arrest in a pediatric intensive care unit. Pediatr Crit Care Med 2015; 16: e332.
40. Kennedy, CE, Turley, JP. Time series analysis as input for clinical predictive modeling: modeling cardiac arrest in a pediatric ICU. Theor Biol Med Model 2011; 8: 40.10.1186/1742-4682-8-40
41. McCullagh, P, Nelder, JA. Generalized Linear Models, Vol. 37. CRC Press, Boca Raton, FL, 1989.
42. Nelder, JA, Baker, RJ. Generalized Linear Models. Wiley, Hoboken, NJ, 1972.
43. McCullagh, P, Nelder, J. Generalized Linear Models. Chapman and Hall, New York, 1983.
44. Deeks, JJ, Altman, DG. Diagnostic tests 4: likelihood ratios. BMJ 2004; 329: 168169.
45. Akobeng, AK. Understanding diagnostic tests 2: likelihood ratios, pre-and post-test probabilities and their use in clinical practice. Acta Paediatr 2007; 96: 487491.

Keywords

Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data

  • Sanjukta N. Bose (a1) (a2), Adam Verigan (a3), Jade Hanson (a3) (a4), Luis M. Ahumada (a5), Sharon R. Ghazarian (a5), Neil A. Goldenberg (a3) (a4) (a6), Arabela Stock (a7) and Jeffrey P. Jacobs (a1) (a2) (a3) (a4) (a5) (a6) (a7)...
  • Please note a correction has been issued for this article.

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed

A correction has been issued for this article: