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A critical assessment of early warning score records in 168,000 patients

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Abstract

The national early warning score (NEWS) is recommended to detect deterioration in hospitalised patients. In 2013, a NEWS-based system was introduced in a hospital service with over 250,000 annual admissions, generating large amounts of NEWS data. The quality of such data has not been described. We critically assessed NEWS data recorded over 12 months. This observational study included NEWS records from adult inpatients hospitalized in the Capital Region of Denmark during 2014. Physiological variables and the use of supplementary oxygen (NEWS variables) were recorded. We identified implausible records and assessed the distributions of NEWS variable values. Of 2,835,331 NEWS records, 271,103 (10%) were incomplete with one or more variable missing and 0.2% of records containing implausible values. Digit preferences were identified for respiratory rate, supplementation oxygen flow, pulse rate, and systolic blood pressure. There was an accumulation of pulse rate records below 91 beats per minute. Among complete NEWS records, 64% had NEWS ≥ 1; 29% had NEWS ≥ 3; and 8% had NEWS ≥ 6. In a large set of NEWS data, 10% of the records were incomplete. In a system where data were manually entered into an electronic medical record, digit preferences and the accumulation of pulse rate records below 91 beats per minute, which is the limit for NEWS point generation, showed that staff practice influenced the recorded values. This indicates a potential limitation of transferability of research results obtained in such systems to fully automated systems.

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References

  1. Smith GB. In-hospital cardiac arrest: is it time for an in-hospital “chain of prevention”? Resuscitation. 2010;81:1209–11.

    Article  PubMed  Google Scholar 

  2. Morgan R, Williams F, Wright M. An early warning scoring system for detecting developing critical illness. Clin Intensive Care. 1997;8:100.

    Google Scholar 

  3. Subbe CP, Kruger M, Rutherford P, Gemmel L. Validation of a modified early warning score in medical admissions. QJM. 2001;94:521–6.

    Article  CAS  PubMed  Google Scholar 

  4. Prytherch DR, Smith GB, Schmidt PE, Featherstone PI. ViEWS—towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81:932–7.

    Article  PubMed  Google Scholar 

  5. Smith GB, Prytherch DR, Schmidt PE, Featherstone PI. Review and performance evaluation of aggregate weighted “track and trigger” systems. Resuscitation. 2008;77:170–9.

    Article  PubMed  Google Scholar 

  6. Tarassenko L, Clifton DA, Pinsky MR, Hravnak MT, Woods JR, Watkinson PJ. Centile-based early warning scores derived from statistical distributions of vital signs. Resuscitation. 2011;82:1013–8.

    Article  PubMed  Google Scholar 

  7. Churpek MM, Yuen TC, Winslow C, Robicsek AA, Meltzer DO, Gibbons RD, et al. Multicenter development and validation of a risk stratification tool for ward patients. Am J Respir Crit Care Med. 2014;190:649–55.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Xu M, Tam B, Thabane L, Fox-Robichaud A. A protocol for developing early warning score models from vital signs data in hospitals using ensembles of decision trees. BMJ Open. 2015;5:e008699.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Welch J, Kanter B, Skora B, McCombie S, Henry I, McCombie D, et al. Multi-parameter vital sign database to assist in alarm optimization for general care units. J Clin Monit Comput. 2016;30:895–900.

    Article  PubMed  Google Scholar 

  10. Royal College of Physicians of London. National early warning score (NEWS): standardising the assessment of acute-illness severity in the NHS. London: Royal College of Physicians; 2012.

    Google Scholar 

  11. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National early warning score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation. 2013;84:465–70.

    Article  PubMed  Google Scholar 

  12. Petersen JA, Mackel R, Antonsen K, Rasmussen LS. Serious adverse events in a hospital using early warning score – What went wrong? Resuscitation. 2014;85:1699–703.

    Article  PubMed  Google Scholar 

  13. Romero-Brufau S, Huddleston JM, Escobar GJ, Liebow M. Why the C-statistic is not informative to evaluate early warning scores and what metrics to use. Crit Care. 2015;19:285.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Pedersen NE, Oestergaard D, Lippert A. End points for validating early warning scores in the context of rapid response systems: a Delphi consensus study. Acta Anaesthesiol Scand. 2016;60:616–22.

    Article  CAS  PubMed  Google Scholar 

  15. Philips medizinsysteme. Intelluvue cl respiration pod premarket notification. United States Food and Drug Administration. https://www.accessdata.fda.gov/cdrh_docs/pdf12/K122223.pdf. (2014) Accessed 6 January 2017.

  16. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria: R Foundation for Statistical Computing; 2015. https://www.R-project.org/.

  17. Dowle M, Srinivasan A, Short T, Saporta SL with contributions from R, Antonyan E. data.table: Extension of Data.frame. 2015. https://CRAN.R-project.org/package=data.table.

  18. Clifton DA, Clifton L, Sandu D-M, Smith GB, Tarassenko L, Vollam SA, et al. “Errors” and omissions in paper-based early warning scores: the association with changes in vital signs—a database analysis. BMJ Open. 2015;5:e007376.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kellett J, Kim A. Validation of an abbreviated Vitalpac™ early warning score (ViEWS) in 75,419 consecutive admissions to a canadian regional hospital. Resuscitation. 2012;83:297–302.

    Article  PubMed  Google Scholar 

  20. Goldberg SI, Shubina M, Niemierko A, Turchin A. A weighty problem: identification, characteristics and risk factors for errors in EMR data. AMIA Annu Symp Proc. 2010;2010:251–5.

    PubMed  PubMed Central  Google Scholar 

  21. Ljunggren M, Castrén M, Nordberg M, Kurland L. The association between vital signs and mortality in a retrospective cohort study of an unselected emergency department population. Scand J Trauma Resusc Emerg Med. 2016;24:21.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Wen SW, Kramer MS, Hoey J, Hanley JA, Usher RH. Terminal digit preference, random error, and bias in routine clinical measurement of blood pressure. J Clin Epidemiol. 1993;46:1187–93.

    Article  CAS  PubMed  Google Scholar 

  23. Granholm A, Pedersen NE, Lippert A, Petersen LF, Rasmussen LS. Respiratory rates measured by a standardised clinical approach, ward staff, and a wireless device. Acta Anaesthesiol Scand. 2016;60:1444–52.

    Article  CAS  PubMed  Google Scholar 

  24. Wager KA, Schaffner MJ, Foulois B, Swanson Kazley A, Parker C, Walo H. Comparison of the quality and timeliness of vital signs data using three different data-entry devices: CIN. Comput Inform Nurs. 2010;28:205–12.

    Article  PubMed  Google Scholar 

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Acknowledgements

The study was funded by the Capital Region of Denmark. LSR and JAP received funding from the TrygFonden.

Funding

The study was funded by the Capital Region of Denmark. LSR and JAP received funding from the TrygFonden.

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Authors and Affiliations

  1. Herlev Hospital, Copenhagen Academy for Medical Education and Simulation, Capital Region of Denmark, University of Copenhagen, 25th floor, Herlev Ringvej 75, 2730, Herlev, Denmark

    Niels Egholm Pedersen & Doris Østergaard

  2. Copenhagen Academy for Medical Education and Simulation, Capital Region of Denmark, University of Copenhagen, Herlev Ringvej 75, 2730, Herlev, Denmark

    Anne Lippert

  3. Department of Anaesthesia, Centre of Head and Orthopaedics, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark

    Lars Simon Rasmussen

  4. Department of Anaesthesia and Intensive Care, Bispebjerg-Frederiksberg Hospital, University of Copenhagen, Bispebjerg Bakke 23, 2400, Copenhagen NV, Denmark

    John Asger Petersen

  5. Department of Public Health, University of Copenhagen, Øster Farimagsgade 5, 1014, Copenhagen K, Denmark

    Thomas Alexander Gerds

Authors
  1. Niels Egholm Pedersen
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  2. Lars Simon Rasmussen
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  3. John Asger Petersen
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  4. Thomas Alexander Gerds
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  5. Doris Østergaard
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  6. Anne Lippert
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Corresponding author

Correspondence to Niels Egholm Pedersen.

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The authors state that they have no conflicts of interest.

Ethical approval

The study was approved by the Danish Health and Medicines Authority (Journal No. 3-3013-531/11) and the Danish Data Protection Agency (Journal No. CHR-2014-002/02636). The ethics committee deemed that no formal approval was needed under Danish law (Journal No. H-3-2013-FSP52).

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Pedersen, N.E., Rasmussen, L.S., Petersen, J.A. et al. A critical assessment of early warning score records in 168,000 patients. J Clin Monit Comput 32, 109–116 (2018). https://doi.org/10.1007/s10877-017-0003-5

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  • DOI: https://doi.org/10.1007/s10877-017-0003-5

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