Skip to main content
Log in

A retrospective evaluation of the risk of bias in perioperative temperature metrics

  • Original Research
  • Published:
Journal of Clinical Monitoring and Computing Aims and scope Submit manuscript

Abstract

The prevention and treatment of hypothermia is an important part of routine anesthesia care. Avoidance of perioperative hypothermia was introduced as a quality metric in 2010. We sought to assess the integrity of the perioperative hypothermia metric in routine care at a single large center. Perioperative temperatures from all anesthetics of at least 60 min duration between January 2012 and 2017 were eligible for inclusion in analysis. Temperatures were displayed graphically, assessed for normality, and analyzed using paired comparisons. Automatically-recorded temperatures were obtained from several monitoring sites. Provider-entered temperatures were non-normally distributed, exhibiting peaks at temperatures at multiples of 0.5 °C. Automatically-acquired temperatures, on the other hand, were more normally distributed, demonstrating smoother curves without peaks at multiples of 0.5 °C. Automatically-acquired median temperature was highest, 36.8 °C (SD = 0.8 °C), followed by the three manually acquired temperatures (nurse-documented postoperative temperature, 36.5 °C [SD = 0.6 °C]; intraoperative manual temperature, 36.5 °C [SD = 0.6 °C]; provider-documented postoperative temperature, 36.1 °C [SD = 0.6 °C]). Provider-entered temperatures exhibit values that are unlikely to represent a normal probability distribution around a central physiologic value. Manually-entered perioperative temperatures appear to cluster around salient anchoring values, either deliberately, or as an unintended result driven by cognitive bias. Automatically-acquired temperatures may be superior for quality metric purposes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. Hart SR, et al. Unintended perioperative hypothermia. Ochsner J. 2011;11(3):259–70.

    PubMed  PubMed Central  Google Scholar 

  2. Madrid E, et al. Active body surface warming systems for preventing complications caused by inadvertent perioperative hypothermia in adults. Cochrane Database Syst Rev. 2016;4:Cd009016.

    PubMed  Google Scholar 

  3. Kurz A, Sessler DI, Lenhardt R. Perioperative normothermia to reduce the incidence of surgical-wound infection and shorten hospitalization. Study of Wound Infection and Temperature Group. N Engl J Med. 1996;334(19):1209–15.

    Article  CAS  PubMed  Google Scholar 

  4. Poveda VB, Nascimento AS. The effect of intraoperative hypothermia upon blood transfusion needs and length of stay among gastrointestinal system cancer surgery. Eur J Cancer Care (Engl), 2017. 26(6).

  5. Yi J, et al. Maintaining intraoperative normothermia reduces blood loss in patients undergoing major operations: a pilot randomized controlled clinical trial. BMC Anesthesiol. 2018;18(1):126.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Wong PF, et al. Randomized clinical trial of perioperative systemic warming in major elective abdominal surgery. Br J Surg. 2007;94(4):421–6.

    Article  CAS  PubMed  Google Scholar 

  7. Alderson P, et al. Thermal insulation for preventing inadvertent perioperative hypothermia. Cochrane Database Syst Rev, 2014(6): Cd009908.

  8. Sun Z, et al. Intraoperative core temperature patterns, transfusion requirement, and hospital duration in patients warmed with forced air. J Am Soc Anesthesiol. 2015;122(2):276–85.

    Article  Google Scholar 

  9. PQRS Measure #193: Perioperative Temperature Management. 2015. https://www.pqrspro.com/cmsmeasures/2015/perioperative_temperature_management/. Accessed 25 Mar 2017.

  10. PQRS Measure #424: Perioperative Temperature Management. 2016. https://www.pqrspro.com/cmsmeasures/2016/perioperative_temperature_management-2/. Accessed 25 Mar 2017.

  11. Medicare CF, Services M. Physician quality reporting system. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/PQRS/index.html. Accessed 21 June 2012.

  12. Wax DB, et al. Manual editing of automatically recorded data in an anesthesia information management system. J Am Soc Anesthesiol. 2008;109(5):811–5.

    Article  Google Scholar 

  13. Thavarajah S, White WB, Mansoor GA. Terminal digit bias in a specialty hypertension faculty practice. J Hum Hypertens. 2003;17(12):819–22.

    Article  CAS  PubMed  Google Scholar 

  14. von Elm E, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7.

    Article  Google Scholar 

  15. Deal LG, et al. Are anesthesia start and end times randomly distributed? The influence of electronic records. J Clin Anesth. 2014;26(4):264–70.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Phelps M, et al. Comparison of minute distribution frequency for anesthesia start and end times from an anesthesia information management system and paper records. J Clin Monit Comput. 2016: 31: 1–6.

    Google Scholar 

  17. Slutsky AS, Ranieri VM. Mechanical ventilation: lessons from the ARDSNet trial. Respir Res. 2000;1(2):73–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Chatterjee P, Joynt KE. Do cardiology quality measures actually improve patient outcomes? J Am Heart Assoc. 2014;3(1):e000404.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Burack JH, et al. Public reporting of surgical mortality: a survey of New York State cardiothoracic surgeons. Ann Thor Surg. 1999;68(4):1195–200.

    Article  CAS  Google Scholar 

  20. Hua M, et al. Impact of public reporting of 30-day mortality on timing of death after coronary artery bypass graft surgery. Anesthesiology. 2017;127(6):953–60.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Van Schalkwyk J, et al. Does manual anaesthetic record capture remove clinically important data? Br J Anaesthesia. 2011;107(4):546–52.

    Article  Google Scholar 

  22. Block FE. Normal fluctuation of physiologic cardiovascular variables during anesthesia and the phenomenon of “smoothing”. J Clin Monit. 1991;7(2):141–5.

    Article  PubMed  Google Scholar 

  23. Hermanns H, et al. Assessment of skin temperature during regional anaesthesia-What the anaesthesiologist should know. Acta Anaesthesiol Scand. 2018;62(9):1280–9.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Patrick Jablonski, Ph.D., provided statistical recommendations to assist in the preparation and refinement of the statistical analysis.

Funding

Dr. Freundlich receives Grant support from an NIH-NCATS KL2 (KL2 TR002245).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Robert E. Freundlich.

Ethics declarations

Conflict of interest

Dr. Freundlich has received grant support and consulting fees from Medtronic for work unrelated to the content of this manuscript.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Freundlich, R.E., Nelson, S.E., Qiu, Y. et al. A retrospective evaluation of the risk of bias in perioperative temperature metrics. J Clin Monit Comput 33, 911–916 (2019). https://doi.org/10.1007/s10877-018-0233-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10877-018-0233-1

Keywords

Navigation