Prediction of in-hospital mortality and length of stay using an early warning scoring system: clinical audit

Abstract

-This aim of this study was to assess the impact of the introduction of a standardised early warning scoring system (SEWS) on physiological observations and patient outcomes in unselected acute admissions at point of entry to care. A sequential clinical audit was performed on 848 patients admitted to a combined medical and surgical assessment unit during two separate 11-day periods. Physiological parameters (respiratory rate, oxygen saturation, temperature, blood pressure, heart rate, and conscious level), in-hospital mortality, length of stay, transfer to critical care and staff satisfaction were documented. Documentation of these physiological parameters improved (P<0.001-0.005) with the exception of oxygen saturation (P=0.069). The admission early warning score correlated both with in-hospital mortality (P<0.001) and length of stay (P=0.001). Following the introduction of the scoring system, inpatient mortality decreased (P=0.046). Staff responding to a questionnaire indicated that the scoring system increased awareness of illness severity (80%) and prompted earlier interventions (60%). A standardised early warning scoring system improves documentation of physiological parameters, correlates with in-hospital mortality, and helps predict length of stay. KEY WORDS: acute admissions, early warning scoring system, length of stay, mortalityAt point of entry to care, emergency medical and surgical referrals are often at their least clinically stable. 1,2 Early indicators of serious illness may not be recognised or acted upon, leading to rapid subsequent clinical deterioration with important implications for critical care, morbidity, and mortality. 3,4 These concerns have been given fresh emphasis by the National Confidential Enquiry into Patient Outcome and Death (NCEPOD). 5 In response to recommendations from the Royal Colleges and the Department of Health, 6,7 the Emergency Medical Admissions Scoping Group of NHS Quality Improvement Scotland was convened. Thereafter, an Illness Severity Scoring Subgroup was given the remit to develop a patient observation chart incorporating a standardised early warning scoring system (SEWS). Physiological parameters contributing to SEWS are respiratory rate, oxygen saturation, temperature, systolic blood pressure, heart rate, and conscious level ( Table 1). The inclusion of oxygen saturation, shown to have a significant relationship with short-to medium-term mortality in emergency medical admissions, 8 distinguishes SEWS from a previously described modified early warning scoring system (MEWS). 9 The SEWS chart was designed to be visually striking and simple to complete. 10 It incorporates an escalation policy prompting more frequent observations and urgent medical assessment. On the reverse there are simple patient management guidelines for first responders. The early MEWS experience was that a score of ≥5 was associated with an increased risk of in-hospital death. Accordingly, the SEWS threshold for medical review was set at a score of 4, the aim being to intensify tr...

The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death

“…16 For the other three outcomes, the AUROCs (95% CI) for the other 33 EWSs ranged from 0.570 (0.553 to 0.568) 17 to 0.827 (0.814 to 0.840) 18 (unanticipated ICU admission); 0.813 (0.802 to 0.824) 15 to 0.858 (0.849 to 0.867) 16 (death); and 0.736 (0.727 to 0.745) 17 to 0.834 (0.826 to 0.842) 19 (any outcome) ( Table 2). The comparative performance of each of the 33 EWSs and NEWS, for each of the four outcomes, is shown in Figure 2.…”

“…The presentation of the efficiency curve for NEWS and the combined outcome ( Figure 3) provides a measure of the number of "triggers" that would be generated at different values of NEWS, thereby permitting hospitals to estimate the impact of choosing any particular NEWS value as the trigger for specific clinical intervention. Figure 4 demonstrates a significant reduction in the number of responses required to detect those who die, suffer a cardiac arrest or require unanticipated ICU admission within 24 hours of a given EWS value for NEWS compared to the EWS described by Paterson 19 -the best performing of the other 33 EWS evaluated.…”

Mortality in out-of-hours emergency medical admissions – more than just a weekend effect

“…Several studies have shown that hospital mortality can be predicted on the basis of illness severity scores calculated at the time of admission, either in the emergency department, 18,19 an assessment unit 20,21 or a medical ward. 22 None of these studies looked at whether the average early warning scores varied according to time or day of admission; although such scores are somewhat blunt tools, this analysis may be a useful next step to help our understanding of the variation in mortality we have observed.…”

Effect of implementation of a rapid response system on protocol adherence in a surgical ward

“…The AWTTS allocates points to the vital parameters in a weighted manner. Since higher scores are associated with worse outcomes [51] , the use of an aggregated system may convince ward nurses and ward physicians to call for help if the score increases.…”