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Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol

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Abstract

Purpose

Computerized clinical decision support systems (CDSS) for intensive insulin therapy (IIT) generate recommendations using blood glucose (BG) values manually transcribed from testing devices to computers, a potential source of error. We quantified the frequency and effect of blood glucose transcription mismatches on IIT protocol performance.

Methods

We examined 38 months of retrospective data for patients treated with CDSS IIT in two intensive care units at one teaching hospital. A manually transcribed BG value not equal to a corresponding device value was deemed mismatched. For mismatches we recalculated CDSS recommendations using device BG values. We compared matched and mismatched data in terms of CDSS alerts, blood glucose variability, and dosing.

Results

Of 189,499 CDSS IIT instances, 5.3% contained mismatched BG values. Mismatched data triggered 93 false alerts and failed to issue 170 alerts for nurses to notify physicians. Four of six BG variability measures differed between matched and mismatched data. Overall insulin dose was greater for matched than mismatched [matched 3.8 (1.6–6.0), median (interquartile range, IQR), versus 3.6 (1.6–5.7); p < 0.001], but recalculated and actual dose were similar. In mismatches preceding hypoglycemia, recalculated insulin dose was significantly lower than actual dose [recalculated 2.7 (0.4–5.0), median (IQR), versus 3.5 (1.4–5.6)]. In mismatches preceding hyperglycemia, recalculated insulin dose was significantly greater than actual dose [recalculated 4.7 (3.3–6.2), median (IQR), versus 3.3 (2.4–4.3); p < 0.001]. Administration of recalculated doses might have prevented blood glucose excursions.

Conclusions

Mismatched blood glucose values can influence CDSS IIT protocol performance.

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Notes

  1. This includes description of CDSS IIT workflow, algorithm, and user interface.

  2. We defined hypoglycemia as BG <60 mg/dL in accordance with National Quality Forum “never event” specifications for reporting adverse events in US hospitals [12].

References

  1. Wiener RS, Wiener DC, Larson RJ (2008) Benefits and risks of tight glucose control in critically ill adults: a meta-analysis. JAMA 300:933–944

    Article  CAS  PubMed  Google Scholar 

  2. Boord JB, Sharifi M, Greevy RA, Griffin MR, Lee VK, Webb TA, May ME, Waitman LR, May AK, Miller RA (2007) Computer-based insulin infusion protocol improves glycemia control over manual protocol. J Am Med Inform Assoc 14:278–287

    Article  PubMed  Google Scholar 

  3. Dortch MJ, Mowery NT, Ozdas A, Dossett L, Cao H, Collier B, Holder G, Miller RA, May AK (2008) A computerized insulin infusion titration protocol improves glucose control with less hypoglycemia compared to a manual titration protocol in a trauma intensive care unit. J Parenter Enteral Nutr 32:18–27

    Article  CAS  Google Scholar 

  4. Dossett LA, Cao H, Mowery NT, Dortch MJ, Morris JM Jr, May AK (2008) Blood glucose variability is associated with mortality in the surgical intensive care unit. Am Surg 74:679–685 (discussion 685)

    PubMed  Google Scholar 

  5. Mowery NT, Dortch MJ, Dossett LA, Norris PR, Diaz JJ Jr, Morris JA Jr, May AK (2009) Insulin resistance despite tight glucose control is associated with mortality in critically ill surgical patients. J Intensive Care Med 24:242–251

    Article  PubMed  Google Scholar 

  6. Dossett LA, Collier B, Donahue R, Mowery NT, Dortch MJ, Guillamondegui O, Diaz JJ, May AK (2008) Intensive insulin therapy in practice: can we do it? J Parenter Enteral Nutr 33:14–20

    Article  Google Scholar 

  7. Vogelzang M, Loef BG, Regtien JG, van der Horst IC, van Assen H, Zijlstra F, Nijsten MW (2008) Computer-assisted glucose control in critically ill patients. Intensive Care Med 34:1421–1427

    Article  PubMed  Google Scholar 

  8. Eslami S, Abu-Hanna A, de Jonge E, de Keizer NF (2009) Tight glycemic control and computerized decision-support systems: a systematic review. Intensive Care Med 35:1505–1517

    Article  PubMed  Google Scholar 

  9. Campion TR Jr, Waitman LR, May AK, Ozdas A, Lorenzi NM, Gadd CS (2010) Social, organizational, and contextual characteristics of clinical decision support systems for intensive insulin therapy: a literature review and case study. Int J Med Inform 79:31–43

    Article  PubMed  Google Scholar 

  10. Bode BW, Braithwaite SS, Steed RD, Davidson PC (2004) Intravenous insulin infusion therapy: indications, methods, and transition to subcutaneous insulin therapy. Endocr Pract 10(Suppl 2):71–80

    PubMed  Google Scholar 

  11. White NH, Skor D, Santiago JV (1982) Practical closed-loop insulin delivery. A system for the maintenance of overnight euglycemia and the calculation of basal insulin requirements in insulin-dependent diabetics. Ann Intern Med 97:210–213

    CAS  PubMed  Google Scholar 

  12. Kizer KW (2002) Serious reportable adverse events in healthcare: a consensus report. The National Quality Forum, Washington DC. February 13, 2010. http://www.qualityforum.org/WorkArea/linkit.aspx?LinkIdentifier=id&ItemID=1221

  13. Carayon P, Gurses AP (2005) A human factors engineering conceptual framework of nursing workload and patient safety in intensive care units. Intensive Crit Care Nurs 21:284–301

    Article  PubMed  Google Scholar 

  14. Anger KE, Szumita PM (2006) Barriers to glucose control in the intensive care unit. Pharmacotherapy 26:214–228

    Article  PubMed  Google Scholar 

  15. De Block C, Manuel-y-Keenoy B, Van Gaal L, Rogiers P (2006) Intensive insulin therapy in the intensive care unit: assessment by continuous glucose monitoring. Diabetes Care 29:1750–1756

    Article  PubMed  Google Scholar 

  16. Yamashita K, Okabayashi T, Yokoyama T, Yatabe T, Maeda H, Manabe M, Hanazaki K (2009) Accuracy and reliability of continuous blood glucose monitor in post-surgical patients. Acta Anaesthesiol Scand 53:66–71

    Article  CAS  PubMed  Google Scholar 

  17. Frankel HL, Crede WB, Topal JE, Roumanis SA, Devlin MW, Foley AB (2005) Use of corporate Six Sigma performance-improvement strategies to reduce incidence of catheter-related bloodstream infections in a surgical ICU. J Am Coll Surg 201:349–358

    Article  PubMed  Google Scholar 

  18. Wagner MM, Hogan WR (1996) The accuracy of medication data in an outpatient electronic medical record. J Am Med Inform Assoc 3:234–244

    CAS  PubMed  Google Scholar 

  19. Berner ES, Kasiraman RK, Yu F, Ray MN, Houston TK (2005) Data quality in the outpatient setting: impact on clinical decision support systems. AMIA Annu Symp Proc 2005:41–45

  20. Vawdrey DK, Gardner RM, Evans RS, Orme JF Jr, Clemmer TP, Greenway L, Drews FA (2007) Assessing data quality in manual entry of ventilator settings. J Am Med Inform Assoc 14:295–303

    Article  PubMed  Google Scholar 

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Acknowledgments

Mr. Campion received support from National Library of Medicine Training Grant NLM T15 007450-07. The authors acknowledge Rondi M. Kauffmann, MD, Nancy M. Lorenzi, PhD, and Patrick Norris, PhD for their involvement in this project.

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Authors and Affiliations

  1. Department of Biomedical Informatics, Vanderbilt University School of Medicine, 400 Eskind Biomedical Library, 2209 Garland Avenue, Nashville, TN, 37232, USA

    Thomas R. Campion Jr.

  2. Division of Trauma and Surgical Critical Care, Vanderbilt University School of Medicine, 404 Medical Arts Building, 1211 21st Avenue South, Nashville, TN, 37212, USA

    Addison K. May

  3. Department of Biomedical Informatics, Vanderbilt University School of Medicine, LL Eskind Biomedical Library, 2209 Garland Avenue, Nashville, TN, 37232, USA

    Lemuel R. Waitman

  4. Department of Biomedical Informatics, Vanderbilt University School of Medicine, Suite 680, 3401 West End Avenue, Nashville, TN, 37212, USA

    Asli Ozdas

  5. Department of Biomedical Informatics, Vanderbilt University School of Medicine, 444 Eskind Biomedical Library, 2209 Garland Avenue, Nashville, TN, 37232, USA

    Cynthia S. Gadd

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  1. Thomas R. Campion Jr.
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  2. Addison K. May
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  3. Lemuel R. Waitman
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  4. Asli Ozdas
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  5. Cynthia S. Gadd
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Correspondence to Thomas R. Campion Jr..

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Campion, T.R., May, A.K., Waitman, L.R. et al. Effects of blood glucose transcription mismatches on a computer-based intensive insulin therapy protocol. Intensive Care Med 36, 1566–1570 (2010). https://doi.org/10.1007/s00134-010-1868-7

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  • DOI: https://doi.org/10.1007/s00134-010-1868-7

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