A Comparison of the Quick Sequential (Sepsis-Related) Organ Failure Assessment Score and the National Early Warning Score in Non-ICU Patients With/Without Infection.

OBJECTIVES: The Sepsis-3 task force recommended the quick Sequential (Sepsis-Related) Organ Failure Assessment score for identifying patients with suspected infection who are at greater risk of poor outcomes, but many hospitals already use the National Early Warning Score to identify high-risk patients, irrespective of diagnosis. We sought to compare the performance of quick Sequential (Sepsis-Related) Organ Failure Assessment and National Early Warning Score in hospitalized, non-ICU patients with and without an infection. DESIGN: Retrospective cohort study. SETTING: Large U.K. General Hospital. PATIENTS: Adults hospitalized between January 1, 2010, and February 1, 2016. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We applied the quick Sequential (Sepsis-Related) Organ Failure Assessment score and National Early Warning Score to 5,435,344 vital signs sets (241,996 hospital admissions). Patients were categorized as having no infection, primary infection, or secondary infection using International Classification of Diseases, 10th Edition codes. National Early Warning Score was significantly better at discriminating in-hospital mortality, irrespective of infection status (no infection, National Early Warning Score 0.831 [0.825-0.838] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.688 [0.680-0.695]; primary infection, National Early Warning Score 0.805 [0.799-0.812] vs quick Sequential [Sepsis-Related] Organ Failure Assessment 0.677 [0.670-0.685]). Similarly, National Early Warning Score performed significantly better in all patient groups (all admissions, emergency medicine admissions, and emergency surgery admissions) for all outcomes studied. Overall, quick Sequential (Sepsis-Related) Organ Failure Assessment performed no better, and often worse, in admissions with infection than without. CONCLUSIONS: The National Early Warning Score outperforms the quick Sequential (Sepsis-Related) Organ Failure Assessment score, irrespective of infection status. These findings suggest that quick Sequential (Sepsis-Related) Organ Failure Assessment should be reevaluated as the system of choice for identifying non-ICU patients with suspected infection who are at greater risk of poor outcome.

Decision-tree early warning score (DTEWS) validates the design of the National Early Warning Score (NEWS).

AIM OF STUDY: To compare the performance of a human-generated, trial and error-optimised early warning score (EWS), i.e., National Early Warning Score (NEWS), with one generated entirely algorithmically using Decision Tree (DT) analysis. MATERIALS AND METHODS: We used DT analysis to construct a decision-tree EWS (DTEWS) from a database of 198,755 vital signs observation sets collected from 35,585 consecutive, completed acute medical admissions. We evaluated the ability of DTEWS to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit admission or death, each within 24h of a given vital signs observation. We compared the performance of DTEWS and NEWS using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: The structures of DTEWS and NEWS were very similar. The AUROC (95% CI) for DTEWS for cardiac arrest, unanticipated ICU admission, death, and any of the outcomes, all within 24h, were 0.708 (0.669-0.747), 0.862 (0.852-0.872), 0.899 (0.892-0.907), and 0.877 (0.870-0.883), respectively. Values for NEWS were 0.722 (0.685-0.759) [cardiac arrest], 0.857 (0.847-0.868) [unanticipated ICU admission}, 0.894 (0.887-0.902) [death], and 0.873 (0.866-0.879) [any outcome]. CONCLUSIONS: The decision-tree technique independently validates the composition and weightings of NEWS. The DT approach quickly provided an almost identical EWS to NEWS, although one that admittedly would benefit from fine-tuning using clinical knowledge. We believe that DT analysis could be used to quickly develop candidate models for disease-specific EWSs, which may be required in future.

Development and validation of a decision tree early warning score based on routine laboratory test results for the discrimination of hospital mortality in emergency medical admissions.

AIM OF STUDY: To build an early warning score (EWS) based exclusively on routinely undertaken laboratory tests that might provide early discrimination of in-hospital death and could be easily implemented on paper. MATERIALS AND METHODS: Using a database of combined haematology and biochemistry results for 86,472 discharged adult patients for whom the admission specialty was Medicine, we used decision tree (DT) analysis to generate a laboratory decision tree early warning score (LDT-EWS) for each gender. LDT-EWS was developed for a single set (n=3496) (Q1) and validated in 22 other discrete sets each of three months long (Q2, Q3…Q23) (total n=82,976; range of n=3428 to 4093) by testing its ability to discriminate in-hospital death using the area under the receiver-operating characteristic (AUROC) curve. RESULTS: The data generated slightly different models for male and female patients. The ranges of AUROC values (95% CI) for LDT-EWS with in-hospital death as the outcome for the validation sets Q2-Q23 were: 0.755 (0.727-0.783) (Q16) to 0.801 (0.776-0.826) [all patients combined, n=82,976]; 0.744 (0.704-0.784, Q16) to 0.824 (0.792-0.856, Q2) [39,591 males]; and 0.742 (0.707-0.777, Q10) to 0.826 (0.796-0.856, Q12) [43,385 females]. CONCLUSIONS: This study provides evidence that the results of commonly measured laboratory tests collected soon after hospital admission can be represented in a simple, paper-based EWS (LDT-EWS) to discriminate in-hospital mortality. We hypothesise that, with appropriate modification, it might be possible to extend the use of LDT-EWS throughout the patient's hospital stay.

Should age be included as a component of track and trigger systems used to identify sick adult patients?

AIM OF STUDY: Few published "track and trigger systems" used to identify sick adult patients incorporate patient age as a variable. We investigated the relationship between vital signs, patient age and in-hospital mortality and investigated the impact of patient age on the function as predictors of in-hospital mortality of the two most commonly used track and trigger systems. MATERIALS AND METHODS: Using a database of 9987 vital signs datasets, we studied the relationship between admission vital signs and in-hospital mortality for a range of selected vital signs, grouped by patient age. We also used the vital signs data set to study the impact of patient age on the relationship between patient triggers using the "MET criteria" and "MEWS", and in-hospital mortality. RESULTS: At hospital discharge, there were 9152 (91.6%) survivors and 835 (8.4%) non-survivors. As admission vital signs worsened, mortality increased for each age range. Where groups of patients had triggered a certain MET criterion, mortality was higher as patient age increased. Mortality varied significantly with age (p<0.05; Fishers exact test) for breathing rate >36breathsmin(-1), systolic BP<90mmHg and decreased conscious level. For each age group, mortality also increased as total MEWS score increased. As the number of simultaneously occurring MEWS abnormalities, or simultaneously occurring MET criteria, increased, mortality increased for each age range. CONCLUSIONS: Age has a significant impact on in-hospital mortality. Our data suggest that the inclusion of age as a component of these systems could be advantageous in improving their function.