Transition of children with life-limiting conditions to adult care and healthcare use: a systematic review.

BACKGROUND: Improved survival has led to increasing numbers of children with life-limiting conditions transitioning to adult healthcare services. There are concerns that transition may lead to a reduction in care quality and increases in emergency care. This review explores evidence for differences in health or social care use post- versus pre-transition to adult services. METHODS: MEDLINE, EMBASE, CINAHL, PsychINFO and Social Science Citation Index were searched. Studies published in English since 1990 including individuals with any life-limiting condition post- and pre-transition and reporting a health or social care use outcome were included. Data were extracted and quality assessed by one reviewer with 30% checked by an independent reviewer. RESULTS: Nineteen papers (18 studies) met the inclusion criteria. There was evidence for both increases and decreases (post- versus pre-transition) in outpatient attendance, inpatient admissions, inpatient bed days and health service costs; for increases in Emergency Department visits and for decreases in individuals receiving physiotherapy. CONCLUSIONS: Evidence for changes in healthcare use post- versus pre-transition is mixed and conflicting, although there is evidence for an increase in Emergency Department visits and a reduction in access to physiotherapy. More high-quality research is needed to better link changes in care to the transition. IMPACT: Evidence for changes in healthcare use associated with transition to adult services is conflicting. Emergency Department visits increase and access to physiotherapy decreases at transition. There are marked differences between care patterns in the United States and Canada.

Are observation selection methods important when comparing early warning score performance?

INTRODUCTION: Sicker patients generally have more vital sign assessments, particularly immediately before an adverse outcome, and especially if the vital sign monitoring schedule is driven by an early warning score (EWS) value. This lack of independence could influence the measured discriminatory performance of an EWS. METHODS: We used a population of 1564,143 consecutive vital signs observation sets collected as a routine part of patients' care. We compared 35 published EWSs for their discrimination of the risk of death within 24h of an observation set using (1) all observations in our dataset, (2) one observation per patient care episode, chosen at random and (3) one observation per patient care episode, chosen as the closest to a randomly selected point in time in each episode. We compared the area under the ROC curve (AUROC) as a measure of discrimination for each of the 35 EWSs under each observation selection method and looked for changes in their rank order. RESULTS: There were no significant changes in rank order of the EWSs based on AUROC between the different observation selection methods, except for one EWS that included age among its components. Whichever method of observation selection was used, the National Early Warning Score (NEWS) showed the highest discrimination of risk of death within 24h. AUROCs were higher when only one observation set was used per episode of care (significantly higher for many EWSs, including NEWS). CONCLUSIONS: Vital sign measurements can be treated as if they are independent - multiple observations can be used from each episode of care--when comparing the performance and ranking of EWSs, provided no EWS includes age.

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.