Predicting in-hospital mortality and unanticipated admissions to the intensive care unit using routinely collected blood tests and vital signs: development and validation of a multivariable model.

AIM: The National Early Warning System (NEWS) is based on vital signs; the Laboratory Decision Tree Early Warning Score (LDT-EWS) on laboratory test results. We aimed to develop and validate a new EWS (the LDTEWS:NEWS risk index) by combining the two and evaluating the discrimination of the primary outcome of unanticipated intensive care unit (ICU) admission or in-hospital mortality, within 24 hours. METHODS: We studied emergency medical admissions, aged 16 years or over, admitted to Oxford University Hospitals (OUH) and Portsmouth Hospitals (PH). Each admission had vital signs and laboratory tests measured within their hospital stay. We combined LDT-EWS and NEWS values using a linear time-decay weighting function imposed on the most recent blood tests. The LDTEWS:NEWS risk index was developed using data from 5 years of admissions to PH, and validated on a year of data from both PH and OUH. We tested the risk index's ability to discriminate the primary outcome using the c-statistic. RESULTS: The development cohort contained 97,933 admissions (median age = 73 years) of which 4,723 (4.8%) resulted in in-hospital death and 1,078 (1.1%) in unanticipated ICU admission. We validated the risk index using data from PH (n = 21,028) and OUH (n = 16,383). The risk index showed a higher discrimination in the validation sets (c-statistic value (95% CI)) (PH, 0.901 (0.898-0.905); OUH, 0.916 (0.911-0.921)), than NEWS alone (PH, 0.877 (0.873-0.882); OUH, 0.898 (0.893-0.904)). CONCLUSIONS: The LDTEWS:NEWS risk index increases the ability to identify patients at risk of deterioration, compared to NEWS alone

A comparison of the ability of the National Early Warning Score and the National Early Warning Score 2 to identify patients at risk of in-hospital mortality: A multi-centre database study.

AIMS: To compare the ability of the National Early Warning Score (NEWS) and the National Early Warning Score 2 (NEWS2) to identify patients at risk of in-hospital mortality and other adverse outcomes. METHODS: We undertook a multi-centre retrospective observational study at five acute hospitals from two UK NHS Trusts. Data were obtained from completed adult admissions who were not fit enough to be discharged alive on the day of admission. Diagnostic coding and oxygen prescriptions were used to identify patients with type II respiratory failure (T2RF). The primary outcome was in-hospital mortality within 24 h of a vital signs observation. Secondary outcomes included unanticipated intensive care unit admission or cardiac arrest within 24 h of a vital signs observation. Discrimination was assessed using the c-statistic. RESULTS: Among 251,266 adult admissions, 48,898 were identified to be at risk of T2RF by diagnostic coding. In this group, NEWS2 showed statistically significant lower discrimination (c-statistic, 95% CI) for identifying in-hospital mortality within 24 h (0.860, 0.857-0.864) than NEWS (0.881, 0.878-0.884). For 1394 admissions with documented T2RF, discrimination was similar for both systems: NEWS2 (0.841, 0.827-0.855), NEWS (0.862, 0.848-0.875). For all secondary endpoints, NEWS2 showed no improvements in discrimination. CONCLUSIONS: NEWS2 modifications to NEWS do not improve discrimination of adverse outcomes in patients with documented T2RF and decrease discrimination in patients at risk of T2RF. Further evaluation of the relationship between SpO2 values, oxygen therapy and risk should be investigated further before wide-scale adoption of NEWS2

Effect of Digital Early Warning Scores on Hospital Vital Sign Observation Protocol Adherence: Stepped-Wedge Evaluation

Background: Early warning scores (EWS) are routinely used in hospitals to assess a patient’s risk of deterioration. EWS are traditionally recorded on paper observation charts but are increasingly recorded digitally. In either case, evidence for the clinical effectiveness of such scores is mixed, and previous studies have not considered whether EWS leads to changes in how deteriorating patients are managed. Objective: This study aims to examine whether the introduction of a digital EWS system was associated with more frequent observation of patients with abnormal vital signs, a precursor to earlier clinical intervention. Methods: We conducted a 2-armed stepped-wedge study from February 2015 to December 2016, over 4 hospitals in 1 UK hospital trust. In the control arm, vital signs were recorded using paper observation charts. In the intervention arm, a digital EWS system was used. The primary outcome measure was time to next observation (TTNO), defined as the time between a patient’s first elevated EWS (EWS ≥3) and subsequent observations set. Secondary outcomes were time to death in the hospital, length of stay, and time to unplanned intensive care unit admission. Differences between the 2 arms were analyzed using a mixed-effects Cox model. The usability of the system was assessed using the system usability score survey. Results: We included 12,802 admissions, 1084 in the paper (control) arm and 11,718 in the digital EWS (intervention) arm. The system usability score was 77.6, indicating good usability. The median TTNO in the control and intervention arms were 128 (IQR 73-218) minutes and 131 (IQR 73-223) minutes, respectively. The corresponding hazard ratio for TTNO was 0.99 (95% CI 0.91-1.07; P=.73). Conclusions: We demonstrated strong clinical engagement with the system. We found no difference in any of the predefined patient outcomes, suggesting that the introduction of a highly usable electronic system can be achieved without impacting clinical care. Our findings contrast with previous claims that digital EWS systems are associated with improvement in clinical outcomes. Future research should investigate how digital EWS systems can be integrated with new clinical pathways adjusting staff behaviors to improve patient outcomes

Patient centred variables with univariate associations with unplanned ICU admission: a systematic review

Background: Multiple predictive scores using Electronic Patient Record data have been developed for hospitalised patients at risk of clinical deterioration. Methods used to select patient centred variables for inclusion in these scores varies. We performed a systematic review to describe univariate associations with unplanned Intensive Care Unit (ICU) admission with the aim of assisting model development for future scores that predict clinical deterioration. Methods: Data sources were MEDLINE, EMBASE, CINAHL, CENTRAL and the Cochrane Database of Systematic Reviews. Included studies were published since 2000 describing an association between patient centred variables and unplanned ICU admission determined using univariate analysis. Two authors independently screened titles, abstracts and full texts against inclusion and exclusion criteria. DistillerSR (Evidence Partners, Canada, Ottawa, Ontario) software was used to manage the data and identify duplicate search results. All screening and data extraction forms were implemented within DistillerSR. Study quality was assessed using an adapted version of the Newcastle-Ottawa Scale. Variables were analysed for strength of association with unplanned ICU admission. Results: The database search yielded 1520 unique studies; 1462 were removed after title and abstract review; 57 underwent full text screening; 16 studies were included. One hundred and eighty nine variables with an evaluated univariate association with unplanned ICU admission were described. Discussion: Being male, increasing age, a history of congestive cardiac failure or diabetes, a diagnosis of hepatic disease or having abnormal vital signs were all strongly associated with ICU admission. Conclusion: These findings will assist variable selection during the development of future models predicting unplanned ICU admission.James Malycha, Timothy Bonnici, David A. Clifton, Guy Ludbrook, J. Duncan Young, and Peter J. Watkinso