Skip to main content

Validation of an electronic surveillance system for acute lung injury

Intensive Care Medicine volume 35, pages 1018–1023 (2009)Cite this article

Abstract

Objective

Early detection of acute lung injury (ALI) is essential for timely implementation of evidence-based therapies and enrollment into clinical trials. We aimed to determine the accuracy of computerized syndrome surveillance for detection of ALI in hospitalized patients and compare it with routine clinical assessment.

Design

Using a near-real time copy of the electronic medical records, we developed and validated a custom ALI electronic alert (ALI “sniffer”) based on the European-American Consensus Conference Definition and compared its performance against provider-derived documentation.

Patients and setting

A total of 3,795 consecutive critically ill patients admitted to nine multidisciplinary intensive care units (ICUs) of a tertiary care teaching institution were included.

Measurements and main results

ALI developed in 325 patients and was recognized by bedside clinicians in only 86 (26.5%). Under-recognition of ALI was associated with not implementing protective mechanical ventilation (median tidal volumes of 9.2 vs. 8.0 ml/kg predicted body weight, P < 0.001). ALI “sniffer” demonstrated excellent sensitivity of 96% (95% CI 94–98) and moderate specificity of 89% (95% CI 88–90) with a positive predictive value ranging from 24% (95% CI 13–40) in the heart–lung transplant ICU to 64% (95% CI 55–71) in the medical ICU.

Conclusions

The computerized surveillance system accurately identifies critically ill patients who develop ALI syndrome. Since the lack of ALI recognition is a barrier to the timely implementation of best practices and enrollment into research studies, computerized syndrome surveillance could be a useful tool to enhance patient safety and clinical research.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

Fig. 1

References

  1. Brun-Buisson C, Minelli C, Bertolini G, Brazzi L, Pimentel J, Lewandowski K, Bion J, Romand JA, Villar J, Thorsteinsson A, Damas P, Armaganidis A, Lemaire F (2004) Epidemiology and outcome of acute lung injury in European intensive care units. Results from the ALIVE study. Intensive Care Med 30:51–61

    PubMed  Article  Google Scholar 

  2. Rubenfeld GD, Caldwell E, Peabody E, Weaver J, Martin DP, Neff M, Stern EJ, Hudson LD (2005) Incidence and outcomes of acute lung injury. N Engl J Med 353:1685–1693

    PubMed  Article  CAS  Google Scholar 

  3. Ferguson ND, Frutos-Vivar F, Esteban A, Fernandez-Segoviano P, Aramburu JA, Najera L, Stewart TE (2005) Acute respiratory distress syndrome: underrecognition by clinicians and diagnostic accuracy of three clinical definitions. Crit Care Med 33:2228–2234

    PubMed  Article  Google Scholar 

  4. Rubenfeld GD, Cooper C, Carter G, Thompson BT, Hudson LD (2004) Barriers to providing lung-protective ventilation to patients with acute lung injury. Crit Care Med 32:1289–1293

    PubMed  Article  Google Scholar 

  5. Umoh NJ, Fan E, Mendez-Tellez PA, Sevransky JE, Dennison CR, Shanholtz C, Pronovost PJ, Needham DM (2008) Patient and intensive care unit organizational factors associated with low tidal volume ventilation in acute lung injury. Crit Care Med 36:1463–1468

    PubMed  Article  Google Scholar 

  6. Mullon J, Gajic O, Afessa B (2006) Compliance with evidence-proven mechanical ventilation strategy in acute lung injury. Proc Am Thorac Soc A570

  7. Chambrin MC (2001) Alarms in the intensive care unit: how can the number of false alarms be reduced? Crit Care 5:184–188

    PubMed  Article  CAS  Google Scholar 

  8. Chambrin MC, Ravaux P, Calvelo-Aros D, Jaborska A, Chopin C, Boniface B (1999) Multicentric study of monitoring alarms in the adult intensive care unit (ICU): a descriptive analysis. Intensive Care Med 25:1360–1366

    PubMed  Article  CAS  Google Scholar 

  9. Finlay HE, Cassorla L, Feiner J, Toy P (2005) Designing and testing a computer-based screening system for transfusion-related acute lung injury. Am J Clin Pathol 124:1–9

    Article  Google Scholar 

  10. Hripcsak G, Friedman C, Alderson PO, DuMouchel W, Johnson SB, Clayton PD (1995) Unlocking clinical data from narrative reports: a study of natural language processing. Ann Intern Med 122:681–688

    PubMed  CAS  Google Scholar 

  11. Herasevich V, Yilmaz M, Khan H, Chute CG, Gajic O (2007) Rule base system for identification of patients with specific critical care syndromes: the “sniffer” for acute lung injury. Proc AMIA Symp 972

  12. Afessa B, Keegan MT, Hubmayr RD, Naessens JM, Gajic O, Long KH, Peters SG (2005) Evaluating the performance of an institution using an intensive care unit benchmark. Mayo Clin Proc 80:174–180

    PubMed  Article  Google Scholar 

  13. Herasevich V, Keegan MT, Tines D, Malinchoc MM, Hanson AC, Katyal P, Hubmayr RD, Afessa B, Gajic O (2007) “ICU of the future”: informatics infrastructure for syndrome surveillance, decision support, data mining, and modeling of critical illness. J Crit Care 22:340

    Article  Google Scholar 

  14. Bernard GR, Artigas A, Brigham KL, Carlet J, Falke K, Hudson L, Lamy M, Legall JR, Morris A, Spragg R (1994) The American-European Consensus Conference on ARDS. Definitions, mechanisms, relevant outcomes, and clinical trial coordination. Am J Respir Crit Care Med 149:818–824

    PubMed  CAS  Google Scholar 

  15. Iapichino G, Albicini M, Umbrello M, Sacconi F, Fermo I, Pavlovich R, Paroni R, Bellani G, Mistraletti G, Cugno M, Pesenti A, Gattinoni L (2008) Tight glycemic control does not affect asymmetric-dimethylarginine in septic patients. Intensive Care Med 34:1843–1850

    PubMed  Article  Google Scholar 

  16. Moran JL, Bersten AD, Solomon PJ (2005) Meta-analysis of controlled trials of ventilator therapy in acute lung injury and acute respiratory distress syndrome: an alternative perspective. Intensive Care Med 31:227–235

    PubMed  Article  Google Scholar 

  17. Miller GA (1956) The magical number seven plus or minus two: some limits on our capacity for processing information. Psychol Rev 63:81–97

    PubMed  Article  CAS  Google Scholar 

  18. Cowan N (2001) The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav Brain Sci 24:87–114 discussion 114–185

    PubMed  Article  CAS  Google Scholar 

  19. Wolthuis EK, Korevaar JC, Spronk P, Kuiper MA, Dzoljic M, Vroom MB, Schultz MJ (2005) Feedback and education improve physician compliance in use of lung-protective mechanical ventilation. Intensive Care Med 31:540–546

    PubMed  Article  Google Scholar 

  20. Evans RS, Pestotnik SL, Classen DC, Bass SB, Menlove RL, Gardner RM, Burke JP (1991) Development of a computerized adverse drug event monitor. Proc Annu Symp Comput Appl Med Care 23–27

  21. Kilbridge PM, Campbell UC, Cozart HB, Mojarrad MG (2006) Automated surveillance for adverse drug events at a community hospital and an academic medical center. J Am Med Inform Assoc 13:372–377

    PubMed  Article  Google Scholar 

  22. Schoenberg R, Sands DZ, Safran C (1999) Making ICU alarms meaningful: a comparison of traditional vs. trend-based algorithms. Proc AMIA Symp 379–383

  23. Tsien CL, Fackler JC (1997) Poor prognosis for existing monitors in the intensive care unit. Crit Care Med 25:614–619

    PubMed  Article  CAS  Google Scholar 

Download references

Acknowledgments

This publication was made possible by grant no. 1 KL2 RR024151 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), the NIH Roadmap for Medical Research and the Mayo Foundation. Its contents are solely the responsibility of the authors and do not necessarily represent the official view of the NCRR or NIH. Information on NCRR is available at http://www.ncrr.nih.gov/. Information on Reengineering the Clinical Research Enterprise can be obtained from http://nihroadmap.nih.gov/clinicalresearch/overviewtranslational.asp. This study was supported in part by NHLBI K23 HL78743-01A1 and NIH KL2 RR024151.

Author information

Affiliations

  1. Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic College of Medicine, 200 First Street SW, Rochester, MN, 55905, USA

    Vitaly Herasevich, Murat Yilmaz, Hasrat Khan, Rolf D. Hubmayr & Ognjen Gajic

  2. Department of Critical Care Services, MeritCare Medical Center, 801 Broadway N, Fargo, ND, 58122, USA

    Hasrat Khan

  3. Department of Anesthesiology and Intensive Care, Medical Faculty, Akdeniz University (MY), Antalya, Turkey

    Murat Yilmaz

Authors
  1. Vitaly Herasevich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Murat Yilmaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hasrat Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rolf D. Hubmayr
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ognjen Gajic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ognjen Gajic.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Herasevich, V., Yilmaz, M., Khan, H. et al. Validation of an electronic surveillance system for acute lung injury. Intensive Care Med 35, 1018–1023 (2009). https://doi.org/10.1007/s00134-009-1460-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00134-009-1460-1

Keywords

  • Respiratory distress syndrome
  • Adult
  • Syndrome surveillance
  • Database
  • Datamart
  • Diagnosis