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Definition

An early warning score (EWS) is a clinical decision support tool used to quickly assess the severity of illness in a patient. It is based on clinical physiologic data including blood pressure, heart rate, respiratory rate and temperature. It also may include observational data such as level of consciousness. The EWS is calculated based on the parameters measured; higher scores correlate with increasing severity of illness.

Impact of Respiratory Compromise

Changes in respiratory vital signs that accompany respiratory compromise often precede in-hospital deterioration and are associated with increased mortality due to the high likelihood of decompensation into respiratory insufficiency and failure, as well as respiratory arrest.1,2,3,4 Moreover, many in-hospital declines may be preventable with better respiratory monitoring and early intervention.5,6,7

Impact of Respiratory Insufficiency

Postoperative respiratory insufficiency is one of the most common and serious complications during postoperative period.8 Common respiratory insufficiency causes include are atelectasis, aspiration, pulmonary edema and pulmonary embolism. Typically, respiratory insufficiency treatment strategies are directed at addressing the cause of the insufficiency and restoration of pulmonary function. Therefore, it is necessary to assess patients for respiratory insufficiency risks prior to surgery, and monitored closely during and after the procedure.

Effectiveness of Early Warning Scores

In order to be effective, early warning scores, like single-parameter alarms, must monitor the status of the patient and have a defined activation criteria that prompts intervention. However, unlike single-parameter alarms, which have been demonstrated to have specificities as low as 15%, early warning scores have demonstrated improved sensitivity and specificity to adverse events.9,10 Several clinical studies have demonstrated the ability of EWS implementation to improve patient outcomes.11,12,13

Clinical Outcomes

Bellomo et al. reported on a study, involving close to 20,000 patients and 10 hospitals on three different continents, and found that the use of an automated clinical decision support system, when compared with hospitals’ previous practices for measuring vital signs and calculating EWS to activate a rapid response team (RRT), is associated with increased survival immediately after RRT treatment; shorter median hospital length of stay in patients in the U.S. hospitals included in the study; and shorter time to complete and record a set of vital signs.14

Economic Impact

An evaluation of the cost savings attributable to implementing a continuous monitoring system in a 316-bed medical-surgical unit revealed the average net benefit ranged from $224 to $710 per patient per year. The cost savings were attributed to decreases in length of hospital stay and ICU length of stay for patients transferred from general care floors.15

REAL-WORLD EVIDENCE FOR RESPIRATORY COMPROMISE PREVENTION IN PROCEDURAL SEDATION

Incidence of respiratory adverse events in moderate to deep procedural sedation is often underestimated, still reported in published clinical studies16 and its consequences may, even if rarely, lead to death.16

The outcomes pledge program by Medtronic will help you measure the incidence of adverse events in your own setting, with your own clinical team and your own protocols and assess the impact of capnography monitoring on the prevention of such events.

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VITAL SYNC™ EARLY WARNING SCORE APP

The Vital Sync™ early warning score (EWS) app helps hospitals realize the benefits of using an automated early warning system. The app continuously monitors patient information from multiple bedside and wearable devices, EMRs as well as data that has been manually entered by clinicians, to automatically calculate an EWS based on your facility’s chosen algorithm. Clinicians know when the first signs of patient deterioration appear so they can take the appropriate action.

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Automated Early Warning Scores

Clinical Outcomes

Economic Impact

Acute Care & Monitoring

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Resources

  • 1. Respiratory Compromise Insitute. http://www.respiratorycompromise.org/. 2017

  • 2. Barfod, C., Lauritzen, M. M., Danker, J. K., et al. Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department - a prospective cohort study. Scand J Trauma Resusc Emerg Med. 2012;20:28. 

  • 3. Buist, M., Bernard, S., Nguyen, T. V., Moore, G., & Anderson, J. Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. Resuscitation. 2004;62(2):137-141.

  • 4. Chaboyer, W., Thalib, L., Foster, M., Ball, C., & Richards, B. Predictors of adverse events in patients after discharge from the intensive care unit. Am J Crit Care. 2008;17(3):255-263; quiz 264.

  • 5. Lee, L. A., Caplan, R. A., Stephens, L. S., et al. Postoperative opioid-induced respiratory depression: a closed claims analysis. Anesthesiology. 2015;122(3):659-665. 

  • 6. Quach, J. L., Downey, A. W., Haase, M., Haase-Fielitz, A., Jones, D., & Bellomo, R. Characteristics and outcomes of patients receiving a medical emergency team review for respiratory distress or hypotension. J Crit Care. 2008;23(3):325-331.

  • 7. Taenzer, A. H., Pyke, J. B., McGrath, S. P., & Blike, G. T. Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study. Anesthesiology. 2010;112(2):282-287.

  • 8. Teba L., Omert, L.A. Postoperative respiratory insufficiency. Am Fam Physician. 1995 1;51(6):1473-80.

  • 9. Siebig, S., Kuhls, S., Imhoff, M., Gather, U., Scholmerich, J., & Wrede, C. E. Intensive care unit alarms--how many do we need?Crit Care Med. 2010;38(2):451-456. 

  • 10. Prytherch, D. R., Smith, G. B., Schmidt, P. E., & Featherstone, P. I. ViEWS--Towards a national early warning score for detecting adult inpatient deterioration. Resuscitation. 2010;81(8):932-937.

  • 11. Bellomo, R., Ackerman, M., Bailey, M., et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349-2361.

  • 12. DeVita, M. A., Braithwaite, R. S., Mahidhara, R., Stuart, S., Foraida, M., & Simmons, R. L. Use of medical emergency team responses to reduce hospital cardiopulmonary arrests. Qual Saf Health Care. 2004;13(4):251-254.

  • 13. Moon, A., Cosgrove, J. F., Lea, D., Fairs, A., & Cressey, D. M. An eight year audit before and after the introduction of modified early warning score (MEWS) charts, of patients admitted to a tertiary referral intensive care unit after CPR.Resuscitation. 2011;82(2):150-154.

  • 14. Bellomo, R., Ackerman, M., Bailey, M., et al. A controlled trial of electronic automated advisory vital signs monitoring in general hospital wards. Crit Care Med. 2012;40(8):2349-2361.

  • 15. Slight, S. P., Franz, C., Olugbile, M., Brown, H. V., Bates, D. W., & Zimlichman, E. The return on investment of implementing a continuous monitoring system in general medical-surgical units. Crit Care Med. 2014;42(8):1862-1868. 

  • 16. Leslie K, Allen ML, Hessian EC, Peyton PJ, Kasza J, Courtney A, et al. Safety of sedation for gastrointestinal endoscopy in a group of university-affiliated hospitals: A prospective cohort study. Br J Anaesth. 2017;118(1):90–9. https://pubmed.ncbi.nlm.nih.gov/28039246/

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