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.