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Heart rate time series characteristics for early detection of infections in critically ill patients

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Abstract

It is difficult to make a distinction between inflammation and infection. Therefore, new strategies are required to allow accurate detection of infection. Here, we hypothesize that we can distinguish infected from non-infected ICU patients based on dynamic features of serum cytokine concentrations and heart rate time series. Serum cytokine profiles and heart rate time series of 39 patients were available for this study. The serum concentration of ten cytokines were measured using blood sampled every 10 min between 2100 and 0600 hours. Heart rate was recorded every minute. Ten metrics were used to extract features from these time series to obtain an accurate classification of infected patients. The predictive power of the metrics derived from the heart rate time series was investigated using decision tree analysis. Finally, logistic regression methods were used to examine whether classification performance improved with inclusion of features derived from the cytokine time series. The AUC of a decision tree based on two heart rate features was 0.88. The model had good calibration with 0.09 Hosmer–Lemeshow p value. There was no significant additional value of adding static cytokine levels or cytokine time series information to the generated decision tree model. The results suggest that heart rate is a better marker for infection than information captured by cytokine time series when the exact stage of infection is not known. The predictive value of (expensive) biomarkers should always be weighed against the routinely monitored data, and such biomarkers have to demonstrate added value.

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References

  1. Weber DJ, Raasch R, Rutala WA. Nosocomial infections in the ICU: the growing importance of antibiotic-resistant pathogens. Chest. 1999;115(Suppl. 1):34S–41S. doi:10.1378/chest.115.suppl_1.34S.

    Article  CAS  PubMed  Google Scholar 

  2. Sax H, Clack L, Touveneau S, da Liberdade JantaradaF, Pittet D, Zingg W. Implementation of infection control best practice in intensive care units throughout Europe: a mixed-method evaluation study. Implement Sci. 2013;8:24. doi:10.1186/1748-5908-8-24.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hotchkiss RS, Coopersmith CM, McDunn JE, Ferguson TA. The sepsis seesaw: tilting toward immunosuppression. Nat Med. 2009;15(5):496–7. doi:10.1038/nm0509-496.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Hotchkiss RS, Opal SM. Immunotherapy for sepsis: a new approach against an ancient foe. N Engl J Med. 2010;363(1):87. doi:10.1056/NEJMcibr1004371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Suprin E, Camus C, Gacouin A, Le Tulzo Y, Lavoue S, Feuillu A, Thomas R. Procalcitonin: a valuable indicator of infection in a medical ICU? Intens Care Med. 2000;26:1232–8. doi:10.1007/s001340000580.

    Article  CAS  Google Scholar 

  6. Mitaka C. Clinical laboratory differentiation of infectious versus non-infectious systemic inflammatory response syndrome. Clin Chim Acta. 2005;351(1):17–29. doi:10.1016/j.cccn.2004.08.018.

    Article  CAS  PubMed  Google Scholar 

  7. Ahmad S, Tejuja A, Newman KD, Zarychanski R, Seely AJ. Clinical review: a review and analysis of heart rate variability and the diagnosis and prognosis of infection. Crit Care. 2009;13:232. doi:10.1186/cc8132.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lake DE, Richman JS, Griffin MP, Moorman JR. Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol. 2002;283:R789–97. doi:10.1152/ajpregu.00069.2002.

    Article  CAS  PubMed  Google Scholar 

  9. Kovatchev BP, Farhy LS, Cao H, Griffin MP, Lake DE, Moorman JR. Sample asymmetry analysis of heart rate characteristics with application to neonatal sepsis and systemic inflammatory response syndrome. Pediatr Res. 2003;54:892–8. doi:10.1203/01.PDR.0000088074.97781.4F.

    Article  PubMed  Google Scholar 

  10. Griffin MP, Lake DE, Bissonette EA, Harrell FE Jr, O’Shea TM, Moorman JR. Heart rate characteristics: novel physiomarkers to predict neonatal infection and death. Pediatrics. 2005;116:1070–4. doi:10.1542/peds.2004-2461.

    Article  PubMed  Google Scholar 

  11. Castelli GP, Pognani C, Meisner M, Stuani A, Bellomi D, Sgarbi L. Procalcitonin and C-reactive protein during systemic inflammatory response syndrome, sepsis and organ dysfunction. Crit Care. 2004;8:R234. doi:10.1186/cc2877.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Assicot M, Bohuon C, Gendrel D, Raymond J, Carsin H, Guilbaud J. High serum procalcitonin concentrations in patients with sepsis and infection. Lancet. 1993;341:515–8. doi:10.1016/0140-6736(93)90277-N.

    Article  CAS  PubMed  Google Scholar 

  13. Fu Q, Zhu J, Van Eyk JE. Comparison of multiplex immunoassay platforms. Clin Chem. 2010;56:314–8. doi:10.1373/clinchem.2009.135087.

    Article  CAS  PubMed  Google Scholar 

  14. Horan TC, Andrus M, Dudeck MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control. 2008;36:309–32. doi:10.1016/j.ajic.2008.03.002.

    Article  PubMed  Google Scholar 

  15. Tambuyzer T, De Waele T, Chiers K, Berckmans D, Goddeeris BM, Aerts JM. Interleukin-6 dynamics as a basis for an early-warning monitor for sepsis and inflammation in individual pigs. Res Vet Sci. 2014;96:460–3. doi:10.1016/j.rvsc.2014.03.014.

    Article  CAS  PubMed  Google Scholar 

  16. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278:H2039–49.

    CAS  PubMed  Google Scholar 

  17. Dakos V, Carpenter SR, Brock WA, Ellison AM, Guttal V, Ives AR, et al. Methods for detecting early warnings of critical transitions in time series illustrated using simulated ecological data. PLoS One. 2012;7:e41010. doi:10.1371/journal.pone.0041010.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Basseville ME, Nikiforov IV. Detection of abrupt changes: theory and application. Englewood Cliffs: Prentice Hall; 1993.

    Google Scholar 

  19. Aboy M, Cuesta-Frau D, Austin D, Mico-Tormos P. Characterization of sample entropy in the context of biomedical signal analysis. In: 29th annual international conference of the IEEE Engineering in Medicine and Biology Society. 2007; p. 5942–5.

  20. Taylor CJ, Pedregal DJ, Young PC, Tych W. Environmental time series analysis and forecasting with the captain toolbox. Environ Model Softw. 2006;22:797–814. doi:10.1016/j.envsoft.2006.03.002.

    Article  Google Scholar 

  21. Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2008.

    Google Scholar 

  22. Fan J, Upadhye S, Worster A. Understanding receiver operating characteristic (ROC) curves. Can J Emerg Med. 2006;8:19–20. doi:10.1017/S1481803500013336.

    Google Scholar 

  23. Karmakar CK, Khandoker AH, Gubbi J, Palaniswami M. Defining asymmetry in heart rate variability signals using a Poincaré plot. Physiol Meas. 2009;30:1227. doi:10.1088/0967-3334/30/11/007.

    Article  CAS  PubMed  Google Scholar 

  24. Cinel I, Opal SM. Molecular biology of inflammation and sepsis: a primer*. Crit Care Med. 2009;37:291–304. doi:10.1097/CCM.0b013e31819267fb.

    Article  CAS  PubMed  Google Scholar 

  25. Turnbull AV, Rivier CL. Regulation of the hypothalamic–pituitary–adrenal axis by cytokines: actions and mechanisms of action. Physiol Rev. 1999;79(1):1–71.

    CAS  PubMed  Google Scholar 

  26. Opp MR. Cytokines and sleep. Sleep Med Rev. 2005;9:355–64. doi:10.1016/j.smrv.2005.01.002.

    Article  PubMed  Google Scholar 

  27. Dimopoulou I, Orfanos S, Kotanidou A, Livaditi O, Giamarellos-Bourboulis E, Athanasiou C, et al. Plasma pro-and anti-inflammatory cytokine levels and outcome prediction in unselected critically ill patients. Cytokine. 2008;41:263–7. doi:10.1016/j.cyto.2007.11.019.

    Article  CAS  PubMed  Google Scholar 

  28. Reinhart K, Bauer M, Riedemann NC, Hartog CS. New approaches to sepsis: molecular diagnostics and biomarkers. Clin Microbiol Rev. 2012;25:609–34. doi:10.1128/CMR.00016-12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Imanishi J. Expression of cytokines in bacterial and viral infections and their biochemical aspects. J Biochem. 2000;2000(127):525–30. doi:10.1093/oxfordjournals.jbchem.a022636.

    Article  Google Scholar 

  30. Brown KL, Cosseau C, Gardy JL, Hancock RE. Complexities of targeting innate immunity to treat infection. Trends Immunol. 2007;28:260–6. doi:10.1016/j.it.2007.04.005.

    Article  CAS  PubMed  Google Scholar 

  31. Namas R, Zamora R, Namas R, An G, Doyle J, Dick TE, et al. Sepsis: something old, something new, and a systems view. J Crit Care. 2012;27:314-e1. doi:10.1016/j.jcrc.2011.05.025.

    Article  Google Scholar 

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Acknowledgments

This study was funded by the Flemish government agency for Innovation by Science and Technology (IWT, Grant SBO-080040).

Author contributions

TT, GVdB and GM designed the study and interpreted the results. EB and HV performed the blood sampling in patients and healthy volunteers. TH and MB quantified the serum cytokine concentrations. PM and GM performed the infection scoring, and obtained the informed consents from the patients. FG and GM supervised the machine learning analyses. JMA and DB supervised the time series analyses. TT did the data analyses. All authors have read and approved the final manuscript.

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Authors and Affiliations

  1. Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001, Louvain, Belgium

    T. Tambuyzer, D. Berckmans & J. M. Aerts

  2. Laboratory of Intensive Care Medicine, Division Cellular and Molecular Medicine, Clinical Department, KU Leuven, Herestraat 49, 3000, Louvain, Belgium

    F. Guiza, E. Boonen, P. Meersseman, H. Vervenne, G. Van den Berghe & G. Meyfroidt

  3. Medical Intensive Care Unit, Department of Internal Medicine, KU Leuven, Herestraat 49, 3000, Louvain, Belgium

    P. Meersseman

  4. Immunoendocrine Research Unit, Medical Department M, Aarhus University Hospital, Norrebrogade 44, 8000, Aarhus C, Denmark

    T. K. Hansen & M. Bjerre

Authors
  1. T. Tambuyzer
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  2. F. Guiza
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  3. E. Boonen
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  4. P. Meersseman
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  5. H. Vervenne
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  6. T. K. Hansen
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  7. M. Bjerre
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  8. G. Van den Berghe
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  9. D. Berckmans
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  10. J. M. Aerts
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  11. G. Meyfroidt
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Corresponding author

Correspondence to J. M. Aerts.

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Conflict of interest

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Ethical standards

The study was approved by the Institutional Ethical Review Board of the University Hospitals Leuven (ML6625).

Informed consent

Written informed consent was obtained from the patients’ next of kin and from the healthy volunteers.

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Tambuyzer, T., Guiza, F., Boonen, E. et al. Heart rate time series characteristics for early detection of infections in critically ill patients. J Clin Monit Comput 31, 407–415 (2017). https://doi.org/10.1007/s10877-016-9870-4

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  • DOI: https://doi.org/10.1007/s10877-016-9870-4

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