Journal of Clinical Monitoring and Computing

, Volume 32, Issue 2, pp 215–219 | Cite as

Predicting fluid responsiveness in whom? A simulated example of patient spectrum influencing the receiver operating characteristics curve

  • Lars Øivind HøisethEmail author
  • Jostein S. Hagemo
Original Research


The influence of patient spectrum on the sensitivities and specificities of diagnostic methods has been termed spectrum bias or spectrum effect. Receiver operating characteristics curves are often used to assess the ability of diagnostic methods to predict fluid responsiveness. As a receiver operating characteristics curve is a presentation of sensitivity and specificity, the purpose of the present manuscript was to explore if patient spectrum could affect areas under receiver operating characteristics curves and their gray zones. Relationships between stroke volume variation and change in stroke volume in two different patient populations using simulated data. Simulated patient populations with stroke volume variation values between 5 and 15 or 3 and 25% had median (2.5th–97.5th percentiles) areas under receiver operating characteristics curves of 0.79 (0.65–0.90) and 0.93 (0.85–0.99), respectively. The gray zones indicating range of diagnostic uncertainty were also affected. The patient spectrum can affect common statistics from receiver operating characteristics curves, indicating the need for considering patient spectrum when evaluating the abilities of different methods to predict fluid responsiveness.


Diagnostic techniques and procedures Sensitivity and specificity Population characteristics 



This study was funded by departmental resources only.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Cecconi M, Parsons AK, Rhodes A. What is a fluid challenge? Curr Opin Crit Care. 2011;17(3):290–295. doi: 10.1097/MCC.0b013e32834699cd.CrossRefPubMedGoogle Scholar
  2. 2.
    Ransohoff DF, Feinstein AR. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests. N Engl J Med. 1978;299(17):926–930. doi: 10.1056/NEJM197810262991705.CrossRefPubMedGoogle Scholar
  3. 3.
    Mulherin SA. Spectrum bias or spectrum effect? subgroup variation in diagnostic test evaluation. Ann Intern Med. 2002;137(7):598. doi: 10.7326/0003-4819-137-7-200210010-00011.CrossRefPubMedGoogle Scholar
  4. 4.
    Usher-Smith JA, Sharp SJ, Griffin SJ. The spectrum effect in tests for risk prediction, screening, and diagnosis. BMJ. 2016;353:i3139. doi: 10.1136/bmj.i3139.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Søreide K, Kørner H, Søreide JA. Diagnostic accuracy and receiver-operating characteristics curve analysis in surgical research and decision making. Ann Surg. 2011;253(1):27–34. doi: 10.1097/SLA.0b013e318204a892.CrossRefPubMedGoogle Scholar
  6. 6.
    Ray P, Le Manach Y, Riou B, Houle TT. Statistical evaluation of a biomarker. Anesthesiology. 2010;112(4):1023–1040. doi: 10.1097/ALN.0b013e3181d47604.CrossRefPubMedGoogle Scholar
  7. 7.
    Cannesson M. The “grey zone” or how to avoid the binary constraint of decision-making. Can J Anaesth. 2015. doi: 10.1007/s12630-015-0465-1.PubMedGoogle Scholar
  8. 8.
    Bossuyt PM, Reitsma JB, Bruns DE, Gatsonis CA, Glasziou PP, Irwig L, Lijmer JG, Moher D, Rennie D, de Vet HC, Kressel HY, Rifai N, Golub RM, Altman DG, Hooft L, Korevaar DA, Cohen JF, Group S (2015) STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies. BMJ 351:h5527. doi: 10.1136/bmj.h5527.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Muller M. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform. 2011;12:77. doi: 10.1186/1471-2105-12-77.CrossRefGoogle Scholar
  10. 10.
    Cannesson M, Le Manach Y, Hofer CK, Goarin JP, Lehot JJ, Vallet B, Tavernier B. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a “gray zone” approach. Anesthesiology. 2011;115(2):231–241. doi: 10.1097/ALN.0b013e318225b80a.CrossRefPubMedGoogle Scholar
  11. 11.
    Bland JM, Altman DG. Correlation in restricted ranges of data. BMJ. 2011;342:d556. doi: 10.1136/bmj.d556.CrossRefPubMedGoogle Scholar
  12. 12.
    Kirkeby-Garstad I, Tronnes H, Stenseth R, Sellevold OF, Aadahl P, Skogvoll E. The precision of pulmonary artery catheter bolus thermodilution cardiac output measurements varies with the clinical situation. J Cardiothorac Vasc Anesth. 2015;29(4):881–888. doi: 10.1053/j.jvca.2014.12.016.CrossRefPubMedGoogle Scholar
  13. 13.
    Vos JJ, Poterman M, Salm PP, Van Amsterdam K, Struys MM, Scheeren TW, Kalmar AF. Noninvasive pulse pressure variation and stroke volume variation to predict fluid responsiveness at multiple thresholds: a prospective observational study. Can J Anaesth. 2015;62(11):1153–1160. doi: 10.1007/s12630-015-0464-2.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Division of Emergencies and Critical Care, Department of AnesthesiologyOslo University HospitalOsloNorway
  2. 2.Section of Vascular Investigations, Division of Cardiovascular and Pulmonary Diseases, Department of Vascular SurgeryOslo University HospitalOsloNorway
  3. 3.Norwegian Air Ambulance FoundationDrøbakNorway
  4. 4.Division of Prehospital Care, Air Ambulance DepartmentOslo University HospitalOsloNorway

Personalised recommendations