Predicting Unplanned Intensive Care Unit Admission for Trauma Patients: The CRASH Score

INTRODUCTION

Unplanned transfer of trauma patients to the intensive care unit (ICU) carries an associated increase in mortality, hospital length of stay, and cost. Trauma teams need to determine which patients necessitate ICU admission on presentation rather than waiting to intervene on deteriorating patients. This study sought to develop a novel Clinical Risk of Acute ICU Status during Hospitalization (CRASH) score to predict the risk of unplanned ICU admission.

METHODS

The 2017 Trauma Quality Improvement Program database was queried for patients admitted to nonICU locations. The group was randomly divided into two equal sets (derivation and validation). Multiple logistic regression models were created to determine the risk of unplanned ICU admission using patient demographics, comorbidities, and injuries. The weighted average and relative impact of each independent predictor were used to derive a CRASH score. The score was validated using area under the curve.

RESULTS

A total of 624,786 trauma patients were admitted to nonICU locations. From 312,393 patients in the derivation-set, 3769 (1.2%) had an unplanned ICU admission. A total of 24 independent predictors of unplanned ICU admission were identified and the CRASH score was derived with scores ranging from 0 to 32. The unplanned ICU admission rate increased steadily from 0.1% to 3.9% then 12.9% at scores of 0, 6, and 14, respectively. The area under the curve for was 0.78.

CONCLUSIONS

The CRASH score is a novel and validated tool to predict unplanned ICU admission for trauma patients. This tool may help providers admit patients to the appropriate level of care or identify patients at-risk for decompensation.

Defining Risk and Risk Factors for Unplanned ICU Admission of Trauma Patients

BACKGROUND

Unplanned ICU admissions (up-ICUad) are associated with poor outcomes. It is difficult to identify who is at risk for up-ICUad in trauma patients. This study aimed to identify injury patterns and comorbidities associated with up-ICUad and develop a predictive tool for who is at risk.

METHODS

A retrospective study compared trauma patients admitted to the floor who experienced an up-ICUad to similar patients without an up-ICUad. Univariate analysis and multivariate logistic regression identified independent risk factors associated with up-ICUad. Based on those factors, a Risk Score (RS) was created and compared between the two groups.

RESULTS

2.15% of the 7206 patients experienced an up-ICUad. The up-ICUad group was older, experienced longer length of stay, and had higher mortality. Age, congestive heart failure, COPD, peptic ulcer disease, mild liver disease, CKD, and significant injuries to the thorax, spine, and lower extremities were independently associated with up-ICUad. A RS equation was created and was used for each patient.

CONCLUSIONS

Trauma patients are at increased risk for up-ICUad based on specific factors. These factors can be used to calculate a RS to determine who is at greatest risk for an up-ICUad which may be helpful for preventing up-ICUad.

Scoping review: The use of early warning systems for the identification of in-hospital patients at risk of deterioration

INTRODUCTION

Early warning systems (EWS) were developed as a means of alerting medical staff to patient clinical decline. Since 85% of severe adverse events are preceded by abnormal physiological signs, the patient bed-side vital signs observation chart has emerged as an EWS tool to help staff identify and quantify deteriorating patients. There are three broad categories of patient observation chart EWS: single or multiple parameter systems; aggregated weighted scoring systems; or combinations of single or multiple parameter and aggregated weighted scoring systems.

OBJECTIVE

This scoping review is an overview of quantitative studies and systematic reviews examining the efficiency of the adult EWS charts in the recognition of in-hospital patient deterioration.

METHOD

A broad search was undertaken of peer-reviewed publications, official government websites and databases housing research theses, using combinations of keywords and phrases.

DATA SOURCES

CINAHL with full text; MedLine, PsycINFO, MasterFILE Premier, GreenFILE and ScienceDirect. Also, the Cochrane Library database, Department of Health government websites and Ethos, ProQuest and Trove databases were searched.

EXCLUSIONS

Paediatric, obstetric and intensive care studies, studies undertaken at the point of hospital admission or pre-admission, non-English publications and editorials.

RESULTS

Five hundred and sixty five publications, government documents, reports and theses were located of which 91 were considered and 21 were included in the scoping review. Of the 21 publications eight studies compared the efficacy of various EWS and 13 publications validated specific EWS.

CONCLUSIONS

There is low level quantitative evidence that EWS improve patient outcomes and strong anecdotal evidence that they augment the ability of the clinical staff to recognise and respond to patient decline, thus reducing the incidence of severe adverse events. Although aggregated weighted scoring systems are most frequently used, the efficiency of the specific EWS appears to be dependent on the patient cohort, facilities available and staff training and attitude. While the review demonstrates support for EWS, researchers caution that given the contribution of human factors to the EWS decision-making process, patient EWS charts alone cannot replace good clinical judgment.

Standardized measurement of the Modified Early Warning Score results in enhanced implementation of a Rapid Response System: a quasi-experimental study

PURPOSE

To study the effect of protocolized measurement (three times daily) of the Modified Early Warning Score (MEWS) versus measurement on indication on the degree of implementation of the Rapid Response System (RRS).

METHODS

A quasi-experimental study was conducted in a University Hospital in Amsterdam between September and November 2011. Patients who were admitted for at least one overnight stay were included. Wards were randomized to measure the MEWS three times daily ("protocolized") versus measuring the MEWS "when clinically indicated" in the control group. At the end of each month, for an entire seven-day week, all vital signs recorded for patients were registered. The outcomes were categorized into process measures including the degree of implementation and compliance to set monitoring standards and secondly, outcomes such as the degree of delay in physician notification and Rapid Response Team (RRT) activation in patients with raised MEWS (MEWS≥3).

RESULTS

MEWS calculations from vital signs occurred in 70% (2513/3585) on the protocolized wards versus 2% (65/3013) in the control group. Compliance with the protocolized regime was presents in 68% (819/1205), compliance in the control group was present in 4% (47/1232) of the measurements. There were 90 calls to primary physicians on the protocolized and 9 calls on the control wards. Additionally on protocolized wards, there were twice as much RRT calls per admission.

CONCLUSIONS

Vital signs and MEWS determination three times daily, results in better detection of physiological abnormalities and more reliable activations of the RRT.