Patient Safety Tip of the Week

September 11, 2012

In Search of the Ideal Early Warning Score



In multiple columns on rapid response teams (RRT’s) we’ve noted that the evidence for improved patient outcomes in response to RRT activation has been scant. And we’ve reiterated that the likely reason is not the performance of the RRT but rather the fact that we are recognizing the clinical deterioration of the patient too late. Multiple studies have demonstrated that most patients who suffer an in-hospital cardiac or respiratory arrest have had some deterioration of vital signs or other clinical signs at least 6-8 hours (and many longer) prior to the arrest. So for many years the search has been on for better ways to identify clinical deterioration earlier.


Intermittent vital sign recording is simply not good enough to identify such deterioration. All too often those vital signs are assessed at one point in time and potential trends not seen. Ironically, the switch from paper to electronic health records may have even further aggravated that problem since some of the EMR’s don’t provide easy access to a graphic flow chart similar to the paper vital signs flowchart that always sat on top of the patient chart. Add to that the fact that the way we take vital signs may alter the very vital signs we are assessing. In our numerous columns on opioid-related or oxygen-related respiratory depression we’ve pointed out that assessment of respiratory rate is particularly prone to error. That is the vital sign most often omitted. But also when you stimulate a person who is obtunded because of CO2 retention they may become alert and their respiratory rate actually increases. Yet respiratory rate abnormalities, when properly assessed, may be the best predictors of clinical deterioration.


And there are numerous problems with any threshold-based alarm systems (see our February 22, 2011 Patient Safety Tip of the Week “Rethinking Alarms”). In that column we highlighted a very insightful study by Lynn et al (Lynn 2011) that described many of the flaws in current patient monitoring systems, particularly those monitoring for respiratory complications. And we stressed the need for “smart” alarm systems that can monitor multiple parameters in an integrated fashion to detect deterioration earlier.


So many have attempted to develop “track and trigger” systems where physiological parameters are monitored in search of trends indicating clinical or physiological deterioration that merits assessment and intervention. Most recent research is focusing on using continuous monitoring of multiple parameters (physiological surveillance) but some older “early warning systems” (EWS) looked at multiple parameters collectively and tried to fit them into a “score” that would indicate deterioration and trigger a response.


Use of early warning scores (EWS) has never really caught on in the US. Yet we all agree that earlier recognition of clinical deterioration is critical and needs improvement (see our Patient Safety Tips of the Week for December 29, 2009 “Recognizing Deteriorating Patients”, March 15, 2011 Early Warnings for Sepsis” and February 22, 2011 Patient Safety Tip of the Week “Rethinking Alarms”).


The problem with most early warning systems is that they may not have been validated, particularly for the setting or patient population where being used (Kyriacos 2011) and a paucity of randomized controlled trials or other high quality studies evaluating use of tools for the identification of deteriorating patients (CADTH 2011, Gao 2007). The predictive utilities may vary depending upon whether your population is in the ICU, general ward, or emergency department or whether the patient is a “medical” or “surgical” patient, etc. Another problem is that the individual parameters used may differ from system to system. They usually include some combination of respiratory rate, heart rate, blood pressure, and temperature but some include level of arousal, oxygen saturation, urine output, or nurses’ general perception regarding the patient. Weighting may be assigned to various parameters but this weighting may differ from system to system. And the aggregate score that should trigger a response may vary from system to system and study to study.


The modified early warning score (MEWS) is probably the best known of these tools designed to alert staff to early clinical deterioration. In our March 2012 What’s New in the Patient Safety World column “Value of an Expanded Early Warning System Score” we noted a study (Smith 2012) from the Netherlands that showed the impact of that score in predicting clinical deterioration in patients admitted to general or trauma surgery wards. The tool included the basic parameters included in earlier versions of the MEWS (heart rate, systolic BP, respiratory rate, oxygen saturation, temperature, and level of consciousness) but added some new parameters. One was urinary output. The other was a more subjective parameter: the nurse’s level of concern about the patient’s condition. Of 592 consecutive patients admitted to the general and trauma surgery wards of a level I trauma center 8% of patients met their composite outcome of death, reanimation (resuscitation), unexpected ICU admission, emergency operation, or severe complication. Patients reaching a score of 3 or higher on the expanded tool were 11 times more likely to meet the composite endpoint, even after adjustment for the ASA grade. The negative predictive value of the score was 97%, indicating its use as a screening tool is quite valuable. The sensitivity was 74% and the positive predictive value 26%.


Now the National Health Service in the UK, in partnership with the Royal College of Physicians and multiple other stakeholder organizations, has recently taken the unprecedented step of recommending all its hospitals use the national early warning score (NEWS) to assess patients and identify early clinical deterioration. The summary document describes all that went into development of the NEWS system. Those stakeholders recognized that there was great variation in the ways hospitals assessed patients for deterioration. They looked at multiple early warning scoring tools and determined that the NEWS was the best tool to standardize on. It was good at discriminating risk of acute mortality and more sensitive than most of the other tools. A great deal of training and education will go into implementation of the NEWS system and built in are plans for evaluation of its efficacy and refinement of its features.


The NEWS score is based on respiratory rate, temperature, heart rate, systolic blood pressure, oxygen saturation and level of consciousness. Each is scored from 0 to 3 and aggregated into a total score (2 additional points are added for those patients requiring oxygen). The recommended responses are stratified according to the score. A score of 5-6 (or a score of 3 on any of the individual items) should prompt an urgent review by a clinician and a score of 7 or higher should prompt an assessment by a team with critical care competencies. The NEWS resources downloadable from the Royal College of Physicians website are actually quite useful. To facilitate standardization and a national unified approach, NEWS uses a color-coded clinical observation chart to record and view all the variables and the total score.


This is really a grand experiment. They have basically taken away the great variation that had been present in and across hospitals and now have a standardized system on which they can base prospective outcome studies.



A clinical deterioration prediction tool for internal medicine patients has also recently been developed (Kirkland 2012). They did regression analysis on one population to derive a scoring system, then validated it retrospectively on another population. They found that the Braden Scale score, respiratory rate, oxygen saturation, and shock index were predictive of clinical deterioration 2 to 12 hours in the future. This makes good use of the Braden Scale, which most hospitals use to assess patient risk for decubiti. Interestingly, the Braden Scale score has also been recently shown to be predictive of complications in elderly patients undergoing surgery (Cohen 2012). These are good examples of using data that is already being collected to provide additional useful information.


Other studies looking at predicting which acute medical patients will need ICU care have found that tools such as PREEMPT-2 and PREAMBLE-2 outperformed other scoring systems (Carmichael 2011). Another recent study has shown it is feasible to use an EMR-based score to detect impending deterioration of patients who are not yet in intensive care (Escobar 2012).


Churpek and colleagues (Churpek 2012) looked at patients who had cardiac arrests on the wards and looked at predictability using both MEWS and other vital sign parameters. They found that on admission the scores were comparable to nested case controls. However, by 48 hours prior to cardiac arrest differences in these parameters compared to the controls were apparent. The maximum MEWS score was the best predictor, followed by the maximum respiratory rate, maximum heart rate, maximum pulse pressure index, and minimum diastolic blood pressure. They conclude that the MEWS does significantly differ between cardiac arrest patients and controls by 48 hours prior to the event but that the MEWS contains some poor predictors (temperature) and omits significant predictors such as diastolic blood pressure and pulse pressure index.


MEWS has also been used to predict mortality in some circumstances. But some studies have suggested that the Simple Clinical Score (SCS) or the Rapid Emergency Medicine Score (REMS) are better at predicting mortality in patients with sepsis in general internal medicine departments (Ghanem-Zoubi 2011).


In addition to being valid and reliable, a good physiological monitoring system should fit into workflows and notify healthcare workers of the need to respond with minimal interruptions and distractions for “false alarms”. Some of the newer physiological monitoring systems (Taenzer 2011) have addressed the alarm fatigue issue by developing a balance between sensitivity and specificity. But the other critical characteristic is including a short delay into the notification phase of the response system. These features are typically compromises that may lead to failure to recognize some cases of deterioration immediately but overall leads to fewer false alarms and fewer unnecessary interruptions that might detract from other nursing care and less alarm fatigue. Those authors had previously shown that a patient surveillance system based on continuous pulse oximetry with nursing notification of violation of alarm limits via wireless pager successfully reduced both rescue event rates and ICU transfers (Taenzer 2010). A built-in delay in the nurse notification eliminated many of the transient and motion artifact-generated false alarms.



All these studies demonstrate that it should be possible through physiological monitoring and track and trigger systems to identify patients at greatest risk for deterioration considerably earlier than we have in the past (and hopefully tailor interventions to prevent further deterioration). Given the increasing sophistication of physiologic monitoring systems and integration with the electronic medical record we have little doubt that in the near future we will have systems in place, often running in the background, that solve the elusive problem of early detection of clinical deterioration.




Some of our other columns on MEWS or recognition of clinical deterioration:








Lynn LA, Curry JP. Patterns of unexpected in-hospital deaths: a root cause analysis. Patient Safety in Surgery 2011, 5:3 (11 February 2011)



Kyriacos U, Jelsma J, Jordan S. Monitoring vital signs using early warning scoring systems: a review of the literature. Journal of Nursing Management 2011; 19(3): 311–330



CADTH (Canadian Agency for Drugs and Technology in Health). Tools for the Early Identification of Adult Inpatients at Risk for Deterioration: Clinical Evidence and Guidelines. 22 November 2011



Gao H, McDonnell A, Harrison DA, et al. Systematic review and evaluation of physiological track and trigger warning systems for identifying at-risk patients on the ward. Intensive Care Medicine 2007; 33(4): 667 – 679



Smith T, Den Hartog D, Moerman T, et al. Accuracy of an expanded early warning score for patients in general and trauma surgery wards. British Journal of Surgery 2012; 99: 192-197



Royal College of Physicians (UK). National Early Warning Score (NEWS). Standardising the assessment of acute-illness severity in the NHS. July 2012



Royal College of Physicians. National Early Warning Score (NEWS): Standardising the assessment of acuteillness severity in the NHS. Report of a working party. London: RCP, 2012



color coded chart

Observation chart for the National Early Warning Score (NEWS)


National Early Warning Score (NEWS)

The scoring system


The National Early Warning Score (NEWS) thresholds and triggers


Clinical response to NEWS triggers



Kirkland LL, Malinchoc M, O'Byrne M, et al. A Clinical Deterioration Prediction Tool for Internal Medicine Patients. American Journal of Medical Quality 2012; 1062860612450459, first published on July 19, 2012 as doi:10.1177/1062860612450459



Cohen R-R, Lagoo-Deenadayalan SA, Heflin MT, et al. Exploring Predictors of Complication in Older Surgical Patients: A Deficit Accumulation Index and the Braden Scale. J Am Geriatr Soc 2012;  Early View Article first published online: 20 Aug 2012



Carmichael HA, Robertson E, Austin  J, et al. A new approach to scoring systems to improve identification of acute medical admissions that will require critical care. Scott Med J 2011; 56: 195-202



Escobar GJ, LaGuardia JC, Turk BJ, et al. Early detection of impending physiologic deterioration among patients who are not in intensive care: Development of predictive models using data from an automated electronic medical record. J Hosp Med 2012; 7(5): 388–395

Article first published online: 22 MAR 2012



Churpek MM, Yuen TC, Huber MT, Park SY, Hall JB, Edelson DP. Predicting Cardiac Arrest on the Wards: A Nested Case-Control Study. Chest 2012; 141(5): 1170-1176



Ghanem-Zoubi NO, Vardi M, Laor A, Weber G, Bitterman H.  Assessment of disease-severity scoring systems for patients with sepsis in general internal medicine departments. Critical Care 2011, 15: R95 (14 March 2011)



Taenzer, Andreas H.; Pyke, Joshua B.; McGrath, Susan P. A Review of Current and Emerging Approaches to Address Failure-to-Rescue. Anesthesiology 2011; 115(2): 421-431



Taenzer AH, Pyke JB, McGrath SP, Blike GT: Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: A before-and-after concurrence study. Anesthesiology 2010; 112: 282–7













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