Research Article | Open Access
Brian H. Nathanson, Thomas L. Higgins, "The Use of Scan Statistics and Control Charts in Assessing Ventilator-Associated Pneumonia Quality Control Programs", Journal of Healthcare Engineering, vol. 1, Article ID 764691, 0 page, 2010. https://doi.org/10.1260/2040-2295.1.4.579
The Use of Scan Statistics and Control Charts in Assessing Ventilator-Associated Pneumonia Quality Control Programs
Abstract
Scan statistics are concerned with clusters of events over time. In the realm of critical care medicine, such clusters might include the occurrence of ventilator-associated pneumonia (VAP). Given N patients over time, the number of observations in a “moving window” of fixed length can be counted and the maximum cluster value becomes a scan statistic for which both parametric and exact methods exist to calculate its rarity. A statistically unusual cluster may indicate a breakdown in quality. Another approach to monitoring rare events is a g-type statistical process control chart where prospectively observing unusually long periods of time between events can indicate a significant improvement in quality. Both methods are presented in detail and applied to a 24-bed medical/surgical ICU's experience with VAP during a 27-month period.
References
- T. L. Higgins, D. Teres, W. S. Copes, B. H. Nathanson, A. A. Kramer, and M. Stark, “Assessing contemporary ICU outcome: An updated mortality probability admission model (MPM0-III),” Crit Care Med, vol. 35, pp. 827–835, 2007. View at: Google Scholar
- J. F. Dasta, T. P. Mclaughlin, S. H. Moody et al., “Daily cost of an intensive care unit day: The contribution of mechanical ventilation,” Crit Care Med, vol. 33, pp. 1266–1271, 2005. View at: Google Scholar
- I. Porzecanski and D. L. Bowton, “Diagnosis and treatment of ventilator-associated pneumonia,” Chest, vol. 130, pp. 597–604, 2006. View at: Google Scholar
- S. M. Koenig and J. D. Truwitt, “Ventilator-associated pneumonia: diagnosis, treatment, and prevention,” Clinical Microbiology Reviews, pp. 637–657, 2006, Oct. View at: Google Scholar
- M. D. Zilberberg, A. F. Shorr, and M. H. Kollef, “Implementing quality improvements in the intensive care unit: Ventilator Bundle as an example,” Crit Care Med, vol. 37, pp. 305–309, 2009. View at: Google Scholar
- M. S. Niederman, D. E. Craven, M. J. Bonten et al., “Guidelines for the management of adults with hospital-acquired, ventilator-associated, and healthcare-associated pneumonia,” Am J Respir Crit Care Med, vol. 171, pp. 388–416, 2005. View at: Google Scholar
- N. Safdar, C. Dezfukian, H. R. Collard et al., “Clinical and economic consequences of ventilator-associated pneumonia: A systematic review,” Crit Care Med, vol. 33, pp. 2184–2193, 2005. View at: Google Scholar
- M. J. M. Bonten, M. H. Kollef, and J. B. Hall, “Risk factors for ventilator-associated pneumonia: from epidemiology to patient management,” Clinical Infectious Diseases, vol. 38, pp. 1141–9, 2004. View at: Google Scholar
- P. Caruso, S. Denari, S. A. L. Ruiz, S. E. Demarzo, and D. Deheinzelin, “Saline instillation before tracheal suctioning decreases the incidence of ventilator-association pneumonia,” Crit Care Med, vol. 37, pp. 32–38, 2009. View at: Google Scholar
- M. Klompas and R. Platt, “Ventilator-associated pneumonia—the wrong quality measure for benchmarking,” Ann Intern Med, vol. 147, pp. 803–805, 2007. View at: Google Scholar
- T. Lisboa, D. E. Craven, and J. Rello, “Safety in critical care and pulmonary medicine: Should ventilatorassociated pneumonia be a quality indicator for patient safety?” Clinical Pulmonary Medicine, vol. 16, pp. 28–32, 2009. View at: Google Scholar
- Canadian Critical Care Trials Group, “A randomized trial of diagnostic techniques for ventilatorassociated pneumonia,” N Engl J Med., vol. 355, pp. 2619–30, 2006. View at: Google Scholar
- P. R. Miller, J. C. Johnson III, T. Karchmer et al., “National nosocomial infection surveillance system: from benchmark to bedside in trauma patients,” J Trauma, vol. 60, pp. 98–103, 2006. View at: Google Scholar
- C. E. Luyt, J. Chastre, and J. Y. Fagon, “Value of the clinical pulmonary infection score for the identification and management of ventilator-associated pneumonia,” Intensive Care Med, vol. 30, pp. 844–852, 2004. View at: Google Scholar
- M. Fartoukh, B. Maitre, S. Honore et al., “Diagnosing pneumonia during mechanical ventilation: the clinical pulmonary infection score revisited,” Am J Respir Crit Care Med, vol. 168, pp. 173–179, 2003. View at: Google Scholar
- J. Glaz, J. Naus, and S. Wallenstein, Scan Statistics, Springer-Verlag, New York, NY, 2001.
- N. Neff and J. Naus, Selected Tables in Mathematical Statistics, Vol 6: The Distribution of the Size of the Maximum Cluster of Points on a Line, American Mathematical Society, Providence, RI, 1980.
- J. Rello, D. A. Ollendorf, G. Oster et al., “Epidemiology and outcomes of ventilator-associated pneumonia in a large U.S. database,” Chest, vol. 122, pp. 2115–2121, 2002. View at: Google Scholar
- R. J. Fantus and J. Fildes, “Trauma season,” Bull Am Coll Surg, vol. 91, pp. 58–59, 2006. View at: Google Scholar
- J. H. Edwards, “The recognition and estimation of cyclic trends,” Annals of Human Genetics, vol. 25, pp. 83–87, 1961. View at: Google Scholar
- S. D. Walter and J. M. Elwood, “A test for seasonality of events with a variable population at risk,” British Journal of Preventive and Social Medicine, vol. 29, pp. 18–21, 1975. View at: Google Scholar
- P. A. Rogerson, “A generalization of Hewitt's test for seasonality,” International Journal of Epidemiology, vol. 25, pp. 644–648, 1996. View at: Google Scholar
- F. Gao, K. S. Chia, I. Krantz et al., “On the application of the von Mises distribution and angular regression methods to investigate seasonality of disease onset,” Statistics in Medicine, vol. 25, pp. 1593–1618, 2006. View at: Google Scholar
- S. Wallenstein, C. R. Weinberg, and M. Gould, “Testing for a pulse in seasonal event data,” Biometrics, vol. 45, pp. 817–830, 1989. View at: Google Scholar
- F. K. Hwang, “A generalization of the Karlin-McGregor theorem on coincident probabilities and an application to clustering,” Annals of Probability, vol. 5, pp. 814–817, 1977. View at: Google Scholar
- J. C. Benneyan, “Number-between g-type statistical quality control charts for monitoring adverse events,” Health Care Management Science, vol. 4, pp. 305–318, 2001. View at: Google Scholar
- J. C. Benneyan, “Performance of number-between g-type statistical control charts for monitoring adverse events,” Health Care Management Science, vol. 4, pp. 319–336, 2001. View at: Google Scholar
- J. C. Benneyan, R. C. Lloyd, and P. E. Plesk, “Statistical process control as a tool for research and healthcare improvement,” Quality and Safety in Healthcare, vol. 12, pp. 458–464, 2003. View at: Google Scholar
- R. J. Wall, E. W. Ely, T. A. Elasy et al., “Using real time process measurements to reduce catheter related bloodstream infections in the intensive care unit,” Quality and Safety in Healthcare, vol. 14, pp. 295–302, 2005. View at: Google Scholar
- R. S. Kennet and S. Zacks, Modern Industrial Statistics, Brooks/Cole Publishing, Pacific Grove, CA, 1998.
- D. C. Montgomery, Introduction to Statistical Quality Control, J. Wiley & Sons, New York, NY, 5th edition, 2004.
- M. R. Reynolds Jr. and Z. G. Stoumbos, “A CUSUM chart for monitoring a proportion when inspecting continuously,” Journal of Quality Technology, vol. 31, no. 1, pp. 87–108, 1999. View at: Google Scholar
- L. H. Sego, W. H. Woodall, and M. R. Reynolds Jr., “A comparison of surveillance methods for small incidence rates,” Statistics in Medicine, vol. 27, pp. 1225–1247, 2008. View at: Google Scholar
- M. D. Joner, W. H. William, and M. R. Reynolds, “Detecting a rate increase using a Bernoulli scan statistic,” Statistics in Medicine, vol. 27, pp. 2555–2575, 2008. View at: Google Scholar
- J. Naus and S. Wallenstein, “Temporal surveillance using scan statistics,” Statistics in Medicine, pp. 311–324, 2006. View at: Google Scholar
- D. Speigelhalter, O. Grigg, R. Kinsman, and T. Treasure, “Risk-adjusted probability ratio tests: applications to Bristol, Shipman and adult cardiac surgery,” Int J Qual Health Care, vol. 15, pp. 7–13, 2003. View at: Google Scholar
- J. E. Zimmerman, A. A. Kramer, D. S. McNair, and F. M. Malila, “Acute Physiology and Chronic Health Evaluation (APACHE) IV: Hospital mortality assessment for today's critically ill patients,” Crit Care Med, vol. 34, no. 5, pp. 1297–1310, 2006. View at: Google Scholar
- T. L. Higgins, D. Teres, W. S. Copes, B. H. Nathanson, M. Stark, and A. A. Kramer, “Assessing contemporary ICU outcome: An updated mortality probability admission model (MPM0-III),” Crit Care Med, vol. 35, pp. 827–835, 2007. View at: Google Scholar
- M. H. Kollef, “Prevention of hospital-associated pneumonia and ventilator-associated pneumonia,” Crit Care Med, vol. 32, pp. 396–1405, 2004. View at: Google Scholar
- P. Gastmeier and C. Geffers, “Prevention of ventilator-associated pneumonia: analysis of studies published since 2004,” Journal of Hospital Infection, vol. 67, pp. 1–8, 2007. View at: Google Scholar
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